.\\n I also add label|START counts.\\n \\\"\\\"\\\"\\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\\n for instance in instance_list: # Set of <(w, pos), l>\\n index = 0\\n for t in instance.data: # Tuple of (w, pos)\\n index = instance.data.index(t)\\n # print t[0] # word\\n # print instance.label[index] # label\\n if t in self.V:\\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\\n else:\\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\\n if index > 0:\\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\\n else:\\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1\",\n \"def _format_data(self):\\n formatted_data = []\\n\\n for row in self._data_agg_by_mean_value.iterrows():\\n \\n car_make = row[0]\\n mean_car_value = round(row[1][0], 2)\\n formatted_data.append({'car_make': car_make, 'mean_car_value': mean_car_value})\\n\\n return formatted_data\",\n \"def tabulate(image_path):\\n img = cv.imread(image_path, cv.IMREAD_ANYDEPTH)\\n unique, counts = np.unique(img, return_counts=True)\\n dummy = [print(a[0], a[1]) for a in zip(unique, counts)]\",\n \"def tabulate(self):\\n for test_name, test in self.test_types.items():\\n for ivs_name, ivs in self.ivs.items():\\n if self.verbose:\\n print(\\\"{0}: {1}\\\".format(ivs_name, test_name))\\n tree = test(ivs)\\n if not tree:\\n continue\\n score = tree.score(True)\\n if self.verbose > 1:\\n tree.print_structure()\\n\\n self.result_matrix['ivs name'][ivs_name][test_name] = score\\n self.result_matrix['test type'][test_name][ivs_name] = score\",\n \"def make_summary_tables( res ):\\n\\n # transform second table to csv and read this as a dataFrame\\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\\\",\\\",index_col=0)\\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\\n\\n # first table is trickier because the data is spread on to columns, and there is title line\\n L = res.tables[0].as_html().split('\\\\n')\\n L.pop(1) # get rid of the title\\n tmp = pd.read_html('\\\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \\n\\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\\n # NB : I exclude the last 2 elements which are empty \\n \\n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\\n \\n return result_general_df , result_fit_df\",\n \"def get_table_data(data):\\n logger.info('Generating summary table')\\n\\n table_dict = data.T.to_dict()\\n table = []\\n for date in sorted(data.index):\\n table.append(table_dict[date])\\n table.sort(key=lambda x: x['DATE'], reverse=True)\\n for ii in range(len(table)):\\n for k, v in table[ii].items():\\n if pd.isnull(v):\\n table[ii][k] = None\\n return table\",\n \"def from_multicsv(self,input_data):\\n reformatted_data = []\\n for (i,row) in enumerate(input_data):\\n if i==0:\\n headers = row\\n else:\\n data_row = {}\\n for (j,h) in enumerate(headers):\\n if j<len(row):\\n data_row.update({h : row[j]})\\n else:\\n data_row.update({h : 0})\\n reformatted_data.append(data_row)\\n return reformatted_data\",\n \"def prepare_table(table):\\n n = len(table)\\n for i, row in enumerate(table):\\n assert len(row) == n, f\\\"len(row) = {len(row)} != {n} = n\\\"\\n for j, _ in enumerate(row):\\n if i == j:\\n table[i][i] = 0.0\\n elif i > j:\\n table[i][j] = 1 - table[j][i]\\n return table\",\n \"def tabler(subcorpus_names, list_of_dicts, num_rows):\\n import pandas as pd\\n cols = []\\n for subcorp, data in zip(subcorpus_names, list_of_dicts):\\n col = pd.Series([w for w, v in data.most_common(num_rows)], name = subcorp)\\n cols.append(col)\\n word_table = pd.concat(cols, axis = 1)\\n return word_table\",\n \"def create_table(categories:list)->str:\\n\\n total_spent = get_total_spent(categories)\\n\\n table = str()\\n\\n for row_num in range(11):\\n row_label = 100 - row_num*10\\n\\n # Row label creation - ie 100| .. 90| ... 80| ...etc\\n row = f\\\"{row_label:>3}|\\\"\\n\\n for category in categories:\\n percentage = math.floor(category.total_spent/total_spent * 10) * 10\\n if percentage >= row_label:\\n row += ' o '\\n else:\\n row += ' '\\n \\n table += row + ' \\\\n'\\n return table\",\n \"def create_table(values, col_labels, act, s, max_=False, att=0):\\n sort_by = ['combinator', 'normaliz', 'drop'] if att != 0 else ['combinator', 'normaliz', 'init', 'drop']\\n table = pd.DataFrame(values, columns=col_labels).sort_values(by=sort_by)\\n cm_green = sns.light_palette(\\\"green\\\", as_cmap=True)\\n cm_green_inv = sns.light_palette(\\\"green\\\", n_colors=10, reverse=True, as_cmap=True)\\n subset_to_color = ['act', 'combinator', 'normaliz', 'init', 'drop']\\n if max_ is False:\\n subset_to_color = subset_to_color[1:]\\n if att != 0:\\n subset_to_color = ['combinator']\\n # TODO: set back to col_labels[-10:] when epochs are 200\\n styled_table = table.style.apply(apply_color, subset=subset_to_color, axis=0). \\\\\\n apply(highlight_max, axis=None, subset=col_labels[-3:], mode=s). \\\\\\n apply(highlight_max, axis=None, subset=['min acc'], mode=s). \\\\\\n apply(highlight_max, axis=None, subset=['max acc'], mode=s). \\\\\\n hide_index(). \\\\\\n background_gradient(subset='d(a₀,aₙ)', cmap=cm_green). \\\\\\n background_gradient(subset=['zeroed neurons'], cmap=cm_green). \\\\\\n background_gradient(subset=['params'], cmap=cm_green_inv). \\\\\\n set_properties(**{'width': '250px'}, **{'text-align': 'center'}). \\\\\\n set_caption(act + ' [{}]'.format(s.upper())). \\\\\\n set_table_styles([{'selector': 'caption', 'props': [('color', 'black'), ('font-size', '25px')]},\\n {'selector': 'tr:nth-of-type(odd)', 'props': [('background', '#eee')]},\\n {'selector': 'tr:nth-of-type(even)', 'props': [('background', 'white')]},\\n {'selector': 'th', 'props': [('background', '#606060'), ('color', 'white')]}])\\n return styled_table\",\n \"def _finalize(self, sampled_data):\\n final_data = dict()\\n for table_name, table_rows in sampled_data.items():\\n parents = self.metadata.get_parents(table_name)\\n if parents:\\n for parent_name in parents:\\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\\n if foreign_key not in table_rows:\\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\\n table_rows[foreign_key] = parent_ids\\n\\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\\n\\n fields = self.metadata.get_fields(table_name)\\n\\n final_data[table_name] = reversed_data[list(fields.keys())]\\n\\n return final_data\",\n \"def table_summary():\\n \\n t = dict()\\n t['name'] = get_names()\\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\\n N = len(t['name'])\\n \\n # host\\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\\\'itoi']\\n \\n # progenitor\\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\\n \\n # progenitor type\\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\\n \\n # metallicity\\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\\n \\n # associations\\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\\n \\n tout = Table(t)\\n tout.pprint()\\n tout.write('../data/stream_origin.fits', overwrite=True)\",\n \"def reformat_data(self, df, ids):\\n data = np.zeros((len(ids), self.n_sample_rows + 1, self.n_features))\\n idx = 0\\n for i in ids:\\n sample = df.loc[i]\\n data[idx, 0:89, :] = sample.values\\n data[idx, 89, :] = np.mean(sample.values)\\n idx += 1\\n return data\",\n \"def writeTotalIndex(map_data):\\n ids = map_data['id']\\n index = [0.0] * len(ids)\\n\\n colnames = ['cars', 'bikes', 'ages', 'parking', 'male_singles',\\n 'female_singles', 'digging', 'freeparking']\\n weights = [-0.5, 0.5, 0.1, -0.5, 1.0, 1.0, -1.0, 0.25]\\n for colname, weight in zip(colnames, weights):\\n values = map_data[colname]\\n\\n index += values * (weight / values.max())\\n\\n toJson('final', pd.DataFrame({'id': ids, 'counts': index}))\",\n \"def build_fisher_contingency_table(overlap_count, user_count, gene_count, count):\\n table = np.zeros(shape=(2, 2))\\n table[0, 0] = overlap_count\\n table[0, 1] = user_count - overlap_count\\n table[1, 0] = gene_count - overlap_count\\n table[1, 1] = count - user_count - gene_count + overlap_count\\n\\n return table\",\n \"def per_sample_taxa_summaries(open_table, output_format):\\n t = parse_biom_table(open_table)\\n header = \\\"#taxon\\\\trelative_abundance\\\\n\\\"\\n\\n for v, id_, md in t.iter():\\n with open(output_format % id_, 'w') as f:\\n f.write(header)\\n\\n for sorted_v, taxa in \\\\\\n sorted(zip(v, t.ids(axis='observation')))[::-1]:\\n if sorted_v:\\n f.write(\\\"%s\\\\t%f\\\\n\\\" % (taxa, sorted_v))\",\n \"def make_table(ranked_means):\\n fp = open(\\\"table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c|}\\n \\\\\\\\hline\\n \\\\\\\\multicolumn{2}{|c||}{Slowest} & \\\\\\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n Feature & Rate & Feature & Rate \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\\"\\\"\\\")\\n top_10 = ranked_means[0:10]\\n bottom_10 = ranked_means[-10:]\\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\\n f_name = f_name.split(\\\":\\\")[-1]\\n f_name = f_name.rsplit(\\\" \\\", 1)[0] if f_name.endswith(\\\"(V)\\\") else f_name\\n s_name = s_name.split(\\\":\\\")[-1]\\n s_name = s_name.rsplit(\\\" \\\", 1)[0] if s_name.endswith(\\\"(V)\\\") else s_name\\n fp.write(\\\" %s & %.2f & %s & %.2f \\\\\\\\\\\\\\\\ \\\\n\\\" % \\\\\\n (f_name, f_rate, s_name, s_rate))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\\n\\n fp = open(\\\"supp_meaning_table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\\n \\\\\\\\hline\\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\n\\\"\\\"\\\")\\n feature_names = [f.split(\\\":\\\")[-1] for (r,f) in ranked_means]\\n feature_names.sort(key=lambda s: s.lower())\\n col1 = feature_names[0:25]\\n col2 = feature_names[25:50]\\n col3 = feature_names[50:75]\\n col4 = feature_names[75:]\\n for a,b,c,d in zip(col1,col2,col3,col4):\\n x,y,z,w = [get_meaning_category(i) or \\\"Excluded\\\" for i in (a,b,c,d)]\\n # Lop off (V)s (we needed them above for get_meaning_category to work)\\n a,b,c,d = [f.rsplit(\\\" \\\", 1)[0] if f.endswith(\\\"(V)\\\") else f for f in (a,b,c,d)]\\n fp.write(\\\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\\\\\\\\\ \\\\n\\\" % (a, x, b, y, c, z, d, w))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\",\n \"def build_table(type_, test_type, device_name, thresholds):\\n x = PrettyTable() \\n x.field_names = [device_name] + thresholds\\n \\n \\\"Chrome,\\\" + test_type + \\\",\\\" + str(notAfter_date) + \\\",\\\" + thresholds[index], \\\",fail\\\"\\n \\n ##read all Chromep entries\\n ##get all test_type rows\\n ##loop rows\\n ##show table\",\n \"def summarize_as_table(self):\\n h = human_readable_size\\n h_throughput = human_readable_throughput\\n table = [\\n ['Total Time (seconds)', '%.3f' % self.total_time,\\n self.std_dev_total_time],\\n ['Maximum Memory', h(self.max_memory), h(self.std_dev_max_memory)],\\n ['Maximum CPU (percent)', '%.1f' % self.max_cpu,\\n self.std_dev_max_cpu],\\n ['Maximum Sent Throughput', h_throughput(self.max_sent_throughput),\\n h_throughput(self.max_sent_throughput)],\\n ['Maximum Recv Throughput', h_throughput(self.max_recv_throughput),\\n h_throughput(self.max_recv_throughput)],\\n ['Average Memory', h(self.average_memory),\\n h(self.std_dev_average_memory)],\\n ['Average CPU (percent)', '%.1f' % self.average_cpu,\\n self.std_dev_average_cpu],\\n ['Average Sent Throughput',\\n h_throughput(self.average_sent_throughput),\\n h_throughput(self.average_sent_throughput)],\\n ['Average Recv Throughput',\\n h_throughput(self.average_recv_throughput),\\n h_throughput(self.average_recv_throughput)],\\n ]\\n return tabulate(\\n table,\\n headers=[\\n 'Metric over %s run(s)' % (self.total_files),\\n 'Mean',\\n 'Standard Deviation'\\n ],\\n tablefmt=\\\"grid\\\"\\n )\",\n \"def fill_table(table, keep_unknowns=False):\\n if not keep_unknowns:\\n table.drop(table.index[table.phylum == \\\"unknown\\\"], inplace=True)\\n table.drop(table.index[table.genus == \\\"unknown\\\"], inplace=True)\\n table.drop(table.index[table.eggNOG == \\\"unknown\\\"], inplace=True)\\n for cohort in table[\\\"cohort_origin\\\"].unique():\\n cohort_table = table.loc[table[\\\"cohort_origin\\\"] == cohort]\\n\\n if cohort not in table_summary:\\n table_summary[cohort] = {}\\n\\n if \\\"N\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"N\\\"] = 1\\n else:\\n table_summary[cohort][\\\"N\\\"] += 1\\n\\n if \\\"phylum\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"phylum\\\"] = pd.DataFrame(columns=[\\\"phylum\\\"])\\n\\n #count the number of unique values in the phylum column\\n table_phylum = cohort_table.loc[table['genus'] != \\\"unknown\\\",['phylum']].apply(pd.Series.value_counts,axis=0)\\n table_summary[cohort][\\\"phylum\\\"] = table_summary[cohort][\\\"phylum\\\"].add(table_phylum,fill_value=0)\\n \\n if \\\"genus\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"genus\\\"] = pd.DataFrame(columns=[\\\"genus\\\"])\\n\\n #count the number of unique values in the genus column\\n table_genus = cohort_table.loc[table['genus'] != \\\"unknown\\\",['genus']].apply(pd.Series.value_counts,axis=0)\\n table_summary[cohort][\\\"genus\\\"] = table_summary[cohort][\\\"genus\\\"].add(table_genus,fill_value=0)\\n \\n if \\\"eggNOG\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"eggNOG\\\"] = pd.DataFrame(columns=[\\\"eggNOG\\\"])\\n\\n index = 0\\n cogs_table = {}\\n\\n #forced to do this by each row because one gene belongs to multiple cogs\\n for row in cohort_table[\\\"eggNOG\\\"]:\\n if \\\"gene_cog\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"gene_cog\\\"] = 0\\n table_summary[cohort][\\\"gene_cog\\\"] += 1\\n if \\\";\\\" in row:\\n cogs = row.split(\\\";\\\")\\n for cog in cogs:\\n if cog not in cogs_table:\\n cogs_table[cog] = 0\\n cogs_table[cog] += 1\\n else:\\n if row not in cogs_table:\\n cogs_table[row] = 0\\n cogs_table[row] += 1\\n\\n #create data frame and create the table\\n cog_pd = pd.DataFrame.from_dict(cogs_table, orient=\\\"index\\\")\\n cog_pd.columns = [\\\"eggNOG\\\"]\\n table_summary[cohort][\\\"eggNOG\\\"] = table_summary[cohort][\\\"eggNOG\\\"].add(cog_pd,fill_value=0)\",\n \"def get_summary_of_records(self):\\n ids = self.get_saleman_ids()\\n table = [\\n [\\\"Seller name\\\",\\\"Number of sales\\\",\\\"Total Value ($)\\\"]\\n ]\\n for id in ids:\\n table_id = [self.get_seller_name(id),self.get_number_of_sales(id),\\n self.get_total_of_saleman(id)]\\n table.append(table_id)\\n data_table = AsciiTable(table)\\n print(data_table.table)\",\n \"def build_table():\\n with contextlib.ExitStack() as stack:\\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\\n iters = [(line.split() for line in f) for f in files]\\n for it in iters:\\n next(it)\\n key = operator.itemgetter(0)\\n table = []\\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\\n props = list(g)\\n if len(props) == len(iters):\\n table.append([k] + [x[1] for x in props])\\n for snp in table:\\n print(*snp)\",\n \"def count_instances(tbl, col2count, colcounted):\\n counted_ser = tbl[col2count].value_counts()\\n counted_df = pd.DataFrame(counted_ser, columns=[colcounted]).reset_index()\\n counted_df.rename(columns={'index':col2count},inplace=True)\\n tbl = tbl.merge(counted_df,on=col2count)\\n return tbl\",\n \"def flatten(counts):\\n single_names = {}\\n long_names = {}\\n for i in range(len(counts.items())):\\n if(len(counts.items()[i][0].split(\\\" \\\")) <= 1):\\n single_names[str(counts.items()[i][0])] = counts.items()[i][1]\\n else:\\n long_names[str(counts.items()[i][0])] = counts.items()[i][1]\\n \\n starter_list = [[[x[0]],x[1]] for x in long_names.items()]\\n for i in range(len(single_names.items())):\\n matched = False\\n for j in range(len(starter_list)):\\n if(single_names.items()[i][0] in starter_list[j][0][0].split(\\\" \\\")):\\n starter_list[j][0].append(single_names.items()[i][0])\\n starter_list[j][1] += single_names.items()[i][1]\\n matched = True\\n break\\n \\n if(matched == False):\\n starter_list.append([[single_names.items()[i][0]], single_names.items()[i][1]]) \\n \\n \\n return starter_list\",\n \"def count_prdctr_freqs(results, column_out):\\n \\n from collections import Counter\\n \\n c = Counter()\\n for itm in results: \\n item = itm\\n if(isnumber(item)): \\n item = [item]\\n \\n c.update(Counter(item))\\n \\n df_out = pd.DataFrame(list(dict(c).items()), columns=[column_out, 'Frequency (%)'])\\n df_out = df_out.sort_values(by=['Frequency (%)'], ascending=False)\\n df_out['Frequency (%)'] = (df_out['Frequency (%)']/results.shape[0])*100.\\n \\n return df_out\",\n \"def _load_sample_table(self):\\n self.sampleTable = pd.read_table(self.config['sampletable'], sep='\\\\t', dtype=str)\\n self.sampleTable.set_index('sampleID', inplace=True)\\n self.samples = self.sampleTable.reset_index().to_dict('records')\",\n \"def column_creator(path):\\n if not os.path.exists(path+'tables'):\\n os.makedirs(path+'tables')\\n\\n\\n # Sequences\\n if os.path.exists(path+'SEQ.txt'):\\n with open(os.path.join(path+'SEQ.txt')) as f1, open(os.path.join(path+'tables/sequences_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n # Modifications\\n if os.path.exists(path + 'modifications.txt'):\\n\\n with open(os.path.join(path+'modifications.txt')) as f1, open(os.path.join(path+'tables/modifications_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n # Spectrum identify:\\n if os.path.exists(path + 'spectrum_identify.txt'):\\n\\n with open(os.path.join(path+'spectrum_identify.txt')) as f1, open(path+'tables/spectrum_ide_table.txt', 'a') as f3:\\n lines1 = f1.read().count('\\\\n')\\n f3.write(\\\"%s\\\\n%s\\\\n\\\" % (\\\"Spectrum Number\\\",lines1))\\n f1.close()\\n f3.close()\\n\\n if os.path.exists(path + 'spectrum_unidentify.txt'):\\n with open(os.path.join(path+'spectrum_unidentify.txt')) as f2, open(path+'tables/spectrum_unide_table.txt', 'a') as f3:\\n lines2 = f2.read().count('\\\\n')\\n f3.write(\\\"%s\\\\n%s\\\\n\\\" % (\\\"Spectrum Number\\\",lines2))\\n f2.close()\\n f3.close()\\n\\n if os.path.exists(path+'taxonomy_identify.txt'):\\n # Taxonomy ide:\\n with open(os.path.join(path+'taxonomy_identify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_ide_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n\\n if os.path.exists(path + 'taxonomy_unidentify.txt'):\\n # Taxonomy unide:\\n with open(os.path.join(path+'taxonomy_unidentify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_unide_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\",\n \"def stack_table(A: pd.DataFrame) -> pd.DataFrame:\\r\\n A = pd.DataFrame(A.stack(dropna=False))\\r\\n A.columns = ['factor']\\r\\n return A\",\n \"def tabulate(table):\\n cw = {} # column widths\\n\\n # Trim leading and trailing whitespace from each element.\\n for i, row in enumerate(table):\\n for j, element in enumerate(row):\\n table[i][j] = element.strip()\\n\\n # Find the max element width for each column.\\n for row in table:\\n for j, element in enumerate(row):\\n cw[j] = max(cw.get(j, 0), len(element))\\n\\n # Reformat elements to align columns.\\n for i, row in enumerate(table):\\n for j, element in enumerate(row):\\n table[i][j] = ' ' + element.ljust(cw[j]) + ' '\",\n \"def format(self, table):\\n #return table.data.to_json()\\n m = table.as_array()\\n rank = len(m.shape)\\n is_table = len(table.headers)<=5 or (len(table.headers)>5 and (table.headers[0] != '0' or table.headers[1] != '1' or table.headers[2] != '2' ))\\n\\n if rank<3 and is_table:\\n v = []\\n for i in range(len(table.headers)):\\n vv = {\\n 'offset': table.offset,\\n 'header': table.headers[i],\\n 'type': table.types[i],\\n 'data': _replace_nans(m[:,i].tolist()) if rank>1 else _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n vv[\\\"size\\\"] = table.sizes[0]\\n v.append(vv)\\n else:\\n # if hasattr(data, \\\"strip\\\") or \\\\\\n # (not hasattr(data, \\\"__getitem__\\\") and \\\\\\n # not hasattr(data, \\\"__iter__\\\")):\\n # # data is not a list/tuple => wrap it\\n # data = [ data ]\\n v = {\\n 'offset': table.offset,\\n #'headers': table.headers,\\n 'type': table.types[0],\\n 'data': _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n v[\\\"size\\\"] = table.sizes\\n\\n return json.dumps(v, cls=ExtEncoder)\",\n \"def _formatsensorinfo():\\n df = pd.DataFrame(columns=['Sensor', 'Resolution (m)', 'Band Count'])\\n for i, (image, key) in enumerate(_sensor_info().items()):\\n df.loc[i] = [image, key['resolution'], key['band_count']]\\n\\n return df\",\n \"def column_stats(self, df, prefix='', ignore=None):\\n if ignore is None:\\n ignore = {}\\n\\n ignore = ignore.union({'uuid', 'kf_id', 'created_at', 'modified_at',\\n 'external_id'})\\n TABLES = {}\\n FIGURES = {}\\n\\n for col in set(df.columns) - ignore:\\n counts = (df.groupby(col)[col]\\n .count()\\n .sort_values(ascending=False))\\n if len(counts)> 0:\\n f = plt.figure()\\n counts.plot(kind='bar')\\n plt.title(col)\\n plt.tight_layout()\\n f.savefig(self.output+'figures/{}.png'.format(col))\\n plt.close(f)\\n FIGURES[col] = self.output+'figures/{}.png'.format(col)\\n\\n TABLES[col] = pd.DataFrame(counts.values, counts.index, columns=['count'])\\n TABLES[col].to_csv(self.output+'tables/{}{}.csv'\\n .format(prefix+'_' if prefix else '', col))\\n TABLES[col] = TABLES[col].reset_index().to_html(index=False)\\n\\n return FIGURES, TABLES\",\n \"def result_to_dataframe(data):\\n letters, statistics = zip(*data)\\n dataframe = pd.DataFrame(data=list(statistics), index=letters, columns=['SUM', 'SUM_OF_SQUARES', 'MAX', 'MIN', 'COUNT']).sort_index()\\n dataframe['MEAN'] = dataframe['SUM'] / dataframe['COUNT']\\n dataframe['VARIANCE'] = dataframe['SUM_OF_SQUARES'] / dataframe['COUNT'] - dataframe['MEAN']**2\\n dataframe['STANDARD_DEVIATION'] = dataframe['VARIANCE']**0.5\\n logging.info(\\\"Total datapoints read: {}.\\\".format(dataframe['COUNT'].sum()))\\n return dataframe\",\n \"def pretty(self):\\n #table = [\\\"\\\".join([\\\"%8s \\\" % s for s in self.alpha.getSymbols()])]\\n table = []\\n for row in PWM.getFreq(self):\\n table.append(\\\"\\\".join([\\\"%8.6f \\\" % y for y in row]))\\n return table\",\n \"def to_countvectors(self):\\n if hasattr(self, \\\"ifp\\\"):\\n df = self.to_dataframe()\\n return to_countvectors(df)\\n raise AttributeError(\\\"Please use the `run` method before\\\")\",\n \"def obj_value_counts(df):\\n df_obj = obj_df(df)\\n for col in df_obj.columns:\\n print(df_obj[col].value_counts())\\n print('-'*100)\",\n \"def expand_counts(counts):\\n result = []\\n for i, c in enumerate(counts):\\n result.append(zeros(c, int) + i)\\n return concatenate(result)\",\n \"def _prepare_stats_table(self, pinfos):\\n\\n stats_tbl = OrderedDict()\\n stats_tbl[\\\"Title\\\"] = OrderedDict()\\n for res in self.rsts:\\n stats_tbl[res.reportid] = OrderedDict()\\n\\n for pinfo in pinfos:\\n for colname in (pinfo[\\\"colname\\\"], pinfo[\\\"xcolname\\\"]):\\n if colname in stats_tbl[\\\"Title\\\"]:\\n continue\\n\\n # Each column name is represented by a row in the statistics table. Fill the \\\"Title\\\"\\n # column.\\n title_dict = stats_tbl[\\\"Title\\\"][colname] = OrderedDict()\\n defs = self._refdefs.info[colname]\\n\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\":\\n # Convert nanoseconds to microseconds.\\n unit = \\\"us\\\"\\n else:\\n unit = defs.get(\\\"short_unit\\\", \\\"\\\")\\n\\n title_dict[\\\"colname\\\"] = colname\\n if unit:\\n title_dict[\\\"colname\\\"] += f\\\", {unit}\\\"\\n title_dict[\\\"coldescr\\\"] = defs[\\\"descr\\\"]\\n\\n title_dict[\\\"funcs\\\"] = OrderedDict()\\n for funcname in self._stats_funcs:\\n if funcname in self.rsts[0].cstats[colname]:\\n title_dict[\\\"funcs\\\"][funcname] = RORawResult.get_stat_func_descr(funcname)\\n\\n # Now fill the values for each result.\\n for res in self.rsts:\\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\\n res_dict[\\\"funcs\\\"] = OrderedDict()\\n\\n for funcname in title_dict[\\\"funcs\\\"]:\\n val = res.cstats[colname][funcname]\\n fmt = \\\"{}\\\"\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\" and \\\"index\\\" not in funcname:\\n val /= 1000\\n fmt = \\\"{:.2f}\\\"\\n if defs[\\\"type\\\"] == \\\"float\\\":\\n fmt = \\\"{:.2f}\\\"\\n\\n fdict = res_dict[\\\"funcs\\\"][funcname] = OrderedDict()\\n fdict[\\\"val\\\"] = fmt.format(val)\\n fdict[\\\"raw_val\\\"] = val\\n\\n if self._refres.reportid == res.reportid:\\n fdict[\\\"hovertext\\\"] = \\\"This is the reference result, other results \\\" \\\\\\n \\\"are compared to this one.\\\"\\n continue\\n\\n ref_fdict = stats_tbl[self._refres.reportid][colname][\\\"funcs\\\"][funcname]\\n change = val - ref_fdict[\\\"raw_val\\\"]\\n if ref_fdict[\\\"raw_val\\\"]:\\n percent = (change / ref_fdict[\\\"raw_val\\\"]) * 100\\n else:\\n percent = change\\n change = fmt.format(change) + unit\\n percent = \\\"{:.1f}%\\\".format(percent)\\n fdict[\\\"hovertext\\\"] = f\\\"Change: {change} ({percent})\\\"\\n\\n return stats_tbl\",\n \"def parameter_count_table(model: nn.Module, max_depth: int = 3) -> str:\\n count: typing.DefaultDict[str, int] = parameter_count(model)\\n # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.\\n param_shape: typing.Dict[str, typing.Tuple] = {\\n k: tuple(v.shape) for k, v in model.named_parameters()\\n }\\n\\n # pyre-fixme[24]: Generic type `tuple` expects at least 1 type parameter.\\n table: typing.List[typing.Tuple] = []\\n\\n def format_size(x: int) -> str:\\n if x > 1e8:\\n return \\\"{:.1f}G\\\".format(x / 1e9)\\n if x > 1e5:\\n return \\\"{:.1f}M\\\".format(x / 1e6)\\n if x > 1e2:\\n return \\\"{:.1f}K\\\".format(x / 1e3)\\n return str(x)\\n\\n def fill(lvl: int, prefix: str) -> None:\\n if lvl >= max_depth:\\n return\\n for name, v in count.items():\\n if name.count(\\\".\\\") == lvl and name.startswith(prefix):\\n indent = \\\" \\\" * (lvl + 1)\\n if name in param_shape:\\n table.append((indent + name, indent + str(param_shape[name])))\\n else:\\n table.append((indent + name, indent + format_size(v)))\\n fill(lvl + 1, name + \\\".\\\")\\n\\n table.append((\\\"model\\\", format_size(count.pop(\\\"\\\"))))\\n fill(0, \\\"\\\")\\n\\n old_ws = tabulate.PRESERVE_WHITESPACE\\n tabulate.PRESERVE_WHITESPACE = True\\n tab = tabulate.tabulate(\\n table, headers=[\\\"name\\\", \\\"#elements or shape\\\"], tablefmt=\\\"pipe\\\"\\n )\\n tabulate.PRESERVE_WHITESPACE = old_ws\\n return tab\",\n \"def make_lof_table(data_table, my_genes, my_samples, summary_func):\\n table_header = [\\\"Gene\\\"] + my_samples + [\\n \\\"Missense:Benign\\\", \\\"Missense:Possibly\\\", \\\"Missense:Probably\\\",\\n \\\"MissenseNA\\\", \\\"Indel\\\", \\\"Nonsense\\\", \\\"Frameshift\\\", \\\"Splice-site\\\",\\n \\\"Synonymous\\\"]\\n table_records = []\\n\\n gs_lookup = group_data_by_gs(data_table)\\n for gene in my_genes:\\n synonymous = missense_benign = missense_possibly = missense_probably = \\\\\\n missense_na = frameshift = nonsense = splice = indel = 0\\n\\n out_row = [gene]\\n for sample in my_samples:\\n normalized = [0]\\n # Count mutations of each type for this gene and sample\\n for entry in gs_lookup[gene][sample]:\\n if entry['muttype'] == 'Silent':\\n synonymous += 1\\n continue\\n if entry['muttype'] == 'Intron':\\n # Shouldn't be here; ignore\\n continue\\n\\n if entry['muttype'] == 'Missense_Mutation':\\n if entry['consequence'] == 'benign':\\n missense_benign += 1\\n elif entry['consequence'] == 'possibly':\\n missense_possibly += 1\\n elif entry['consequence'] == 'probably':\\n missense_probably += 1\\n elif entry['consequence'] == 'NA':\\n missense_na += 1\\n else:\\n print(\\\"Unhandled missense consequence level:\\\",\\n entry['consequence'], file=sys.stderr)\\n elif entry['muttype'] == 'Nonsense_Mutation':\\n nonsense += 1\\n elif entry['muttype'] == 'Splice_Site':\\n splice += 1\\n elif entry['muttype'] in ('Frame_Shift_Ins', 'Frame_Shift_Del'):\\n frameshift += 1\\n elif entry['muttype'] in ('In_Frame_Ins', 'In_Frame_Del'):\\n indel += 1\\n else:\\n print(\\\"Unhandled mutation type:\\\", entry['muttype'],\\n file=sys.stderr)\\n continue\\n\\n normalized.append(entry['normalized'])\\n # Summarize the normalized mutation counts for this gene and sample\\n out_row.append(summary_func(normalized))\\n out_row.extend((missense_benign, missense_possibly, missense_probably,\\n missense_na, indel, nonsense, frameshift, splice,\\n synonymous))\\n table_records.append(out_row)\\n\\n return pandas.DataFrame.from_records(table_records, columns=table_header)\",\n \"def simple_format_table(table):\\n s = [[str(e) for e in row] for row in table]\\n lens = [max(map(len, col)) for col in zip(*s)]\\n fmt = '\\\\t'.join('{{:{}}}'.format(x) for x in lens)\\n table = [fmt.format(*row) for row in s]\\n return '\\\\n'.join(table)\",\n \"def print_statistics(samples, values=None, sample_labels=None, value_labels=None):\\n num_vars, nsamples = samples.shape\\n if values is None:\\n values = np.empty((nsamples, 0))\\n if values.ndim == 1:\\n values = values[:, np.newaxis]\\n num_qoi = values.shape[1]\\n assert nsamples == values.shape[0]\\n if sample_labels is None:\\n sample_labels = ['z%d' % ii for ii in range(num_vars)]\\n if value_labels is None:\\n value_labels = ['y%d' % ii for ii in range(num_qoi)]\\n data = [(label, s) for s, label in zip(samples, sample_labels)]\\n data += [(label, s) for s, label in zip(values.T, value_labels)]\\n\\n # data = [(label, s) for s, label in zip(samples, sample_labels)]\\n # data += [(label, s) for s, label in zip(values.T, value_labels)]\\n # data = dict(data)\\n # df = DataFrame(index=np.arange(nsamples), data=data)\\n # print(df.describe())\\n\\n str_format = ' '.join([\\\"{:<6}\\\"]+[\\\"{:^10}\\\"]*(len(data)))\\n print(str_format.format(*([\\\" \\\"]+[dat[0] for dat in data])))\\n stat_funs = [lambda x: x.shape[0], lambda x: x.mean(), lambda x: x.std(),\\n lambda x: x.min(), lambda x: x.max()]\\n stat_labels = [\\\"count\\\", \\\"mean\\\", \\\"std\\\", \\\"min\\\", \\\"max\\\"]\\n str_format = ' '.join([\\\"{:<6}\\\"]+[\\\"{:10.6f}\\\"]*(len(data)))\\n for stat_fun, stat_label in zip(stat_funs, stat_labels):\\n print(str_format.format(\\n *([stat_label]+[stat_fun(dat[1]) for dat in data])))\",\n \"def start_table(self):\\n self.col_widths = []\\n self.result = \\\"\\\"\",\n \"def test_format_histograms(self):\\r\\n self.assertEqual(format_histograms(array([0, 1, 0, 2, 2, 3]),\\r\\n array(\\r\\n [2, 1, 0, 2, 0, 0]), array(\\r\\n [0, 0, 0, 2, 0, 1]),\\r\\n array(\\r\\n [100, 110, 120, 130, 140, 150, 160])),\\r\\n \\\"\\\"\\\"# bins raw sequence lengths, length of sequences that pass quality filters before processing, and lengths of sequences that pass quality filters post processing.\\\\nLength\\\\tRaw\\\\tBefore\\\\tAfter\\\\n100\\\\t0\\\\t2\\\\t0\\\\n110\\\\t1\\\\t1\\\\t0\\\\n120\\\\t0\\\\t0\\\\t0\\\\n130\\\\t2\\\\t2\\\\t2\\\\n140\\\\t2\\\\t0\\\\t0\\\\n150\\\\t3\\\\t0\\\\t1\\\"\\\"\\\")\",\n \"def summary_table(countries: List[str]):\\n \\n df_list = []\\n \\n for country in countries:\\n acceleration_figures = acceleration(country)\\n pop = COUNTRY_DATA[country]['population']\\n df_list.append(\\n [\\n country,\\n COUNTRY_DATA[country]['data'].confirmed[-1],\\n int(acceleration_figures[0] * pop),\\n COUNTRY_DATA[country]['data'].deaths[-1],\\n int(acceleration_figures[1] * pop),\\n ]\\n )\\n\\n return df_list\",\n \"def get_counts(label, colorby, num_categories, dir_path, level, color_data,\\r\\n prefs, pref_colors, background_color, label_color, chart_type,\\r\\n generate_image_type, plot_width, plot_height,\\r\\n bar_width, dpi, raw_fpath, resize_nth_label, label_type,\\r\\n include_html_legend, include_html_counts):\\r\\n\\r\\n img_data = []\\r\\n labels = []\\r\\n level_counts = []\\r\\n\\r\\n sample_ids, otu_ids, otu_table = color_data\\r\\n labels = sample_ids\\r\\n\\r\\n # iterate over the counts table and cleanup taxa labels\\r\\n for idx, counts in enumerate(otu_table):\\r\\n taxonomy = otu_ids[idx]\\r\\n split_label = [i for i in taxonomy.strip().split(\\\";\\\")]\\r\\n taxonomy = ';'.join(split_label)\\r\\n level_counts.append((sum(map(float, counts)), taxonomy,\\r\\n '<br>'.join(split_label)))\\r\\n all_sum = sum([c_over[0] for c_over in level_counts])\\r\\n\\r\\n # get the fractions for all samples\\r\\n fracs_labels_other, fracs_labels, all_counts, other_cat, red, other_frac = \\\\\\r\\n get_fracs(level_counts, num_categories, all_sum,\\r\\n chart_type, True)\\r\\n\\r\\n # if making pie charts we perform a couple extra steps, such as making a\\r\\n # total pie chart\\r\\n if chart_type == 'pie':\\r\\n # make the total pie chart\\r\\n img_data.extend(\\r\\n make_HTML_table(label, other_frac, all_sum, red, other_cat,\\r\\n fracs_labels_other, fracs_labels, dir_path,\\r\\n all_counts, level, prefs, pref_colors,\\r\\n background_color, label_color, chart_type,\\r\\n label, generate_image_type, plot_width,\\r\\n plot_height, bar_width, dpi, resize_nth_label,\\r\\n label_type, include_html_legend,\\r\\n include_html_counts))\\r\\n\\r\\n if colorby is not None:\\r\\n # in the case the user specifies only certain samples we need to\\r\\n # handle that case\\r\\n for i, l in enumerate(sample_ids):\\r\\n if l not in colorby:\\r\\n continue\\r\\n total = 0\\r\\n sample_counts = []\\r\\n for idx, counts in enumerate(otu_table):\\r\\n taxonomy = otu_ids[idx]\\r\\n split_label = [j for j in taxonomy.strip().split(\\\";\\\")]\\r\\n taxonomy = ';'.join(split_label)\\r\\n c = float(counts[i])\\r\\n if c > 0:\\r\\n total += c\\r\\n sample_counts.append((c, taxonomy,\\r\\n '<br>'.join(split_label)))\\r\\n\\r\\n # get fractions for specific samples\\r\\n fracs_labels_other, fracs_labels, all_counts,\\\\\\r\\n other_cat, red, other_frac = get_fracs(sample_counts,\\r\\n num_categories,\\r\\n total, chart_type, True)\\r\\n\\r\\n # make the per sample pie charts\\r\\n img_data.extend(make_HTML_table('_'.join([label, l.strip()]),\\r\\n other_frac, total, red, other_cat, fracs_labels_other,\\r\\n fracs_labels, dir_path, all_counts, level,\\r\\n prefs, pref_colors, background_color, label_color,\\r\\n chart_type, l.strip(\\r\\n ), generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi,\\r\\n resize_nth_label, label_type, include_html_legend,\\r\\n include_html_counts))\\r\\n\\r\\n # if making an area/bar chart we do not make per sample images, instead\\r\\n # we make a total chart only\\r\\n elif chart_type == 'area' or chart_type == 'bar':\\r\\n area_plot_arr = []\\r\\n area_plot_sample_ids = []\\r\\n area_plot_taxa_arr = []\\r\\n taxa_html = []\\r\\n total_area_table_out = []\\r\\n total_sums = []\\r\\n\\r\\n if colorby is not None:\\r\\n # in the case the user specifies only certain samples we need to\\r\\n # handle that case\\r\\n for i, l in enumerate(sample_ids):\\r\\n if l not in colorby:\\r\\n continue\\r\\n total = 0\\r\\n area_plot_sample_ids.append(l)\\r\\n sample_counts = []\\r\\n\\r\\n # iterate over the counts and cleanup taxa for this particular\\r\\n # fxn\\r\\n\\r\\n #add in zip\\r\\n for idx, counts in enumerate(otu_table):\\r\\n taxonomy = otu_ids[idx]\\r\\n split_label = [j for j in taxonomy.strip().split(\\\";\\\")]\\r\\n taxonomy = ';'.join(split_label)\\r\\n\\r\\n c = float(counts[i])\\r\\n total += c\\r\\n sample_counts.append(\\r\\n (c, taxonomy, '<br>'.join(split_label)))\\r\\n\\r\\n # get fractions for specific samples\\r\\n fracs_labels_other, fracs_labels, all_counts,\\\\\\r\\n other_cat, red, other_frac = get_fracs(sample_counts,\\r\\n len(sample_counts), total,\\r\\n chart_type, False)\\r\\n\\r\\n total_area_table_out.append(all_counts)\\r\\n total_sums.append(sum([float(i) for i in all_counts]))\\r\\n\\r\\n # get the percents for each taxa and sample\\r\\n area_plot_per = []\\r\\n area_plot_taxa = []\\r\\n for i in fracs_labels_other:\\r\\n area_plot_per.append(i[1])\\r\\n area_plot_taxa.append(i[0])\\r\\n\\r\\n area_plot_arr.append(area_plot_per)\\r\\n area_plot_taxa_arr.append(area_plot_taxa)\\r\\n\\r\\n # write out the data table html, since it is different than pie chart\\r\\n # data table\\r\\n taxa_html.append('<tr><th>' + l.strip() +\\r\\n '</th></tr>' + ''.join(all_counts) + '')\\r\\n\\r\\n data_table = zip(*total_area_table_out)\\r\\n\\r\\n # create link for raw data file\\r\\n data_html_str = '<table><tr class=ntitle><td><a href=\\\"%s\\\" target=\\\"_blank\\\">View Table (%s)</a></td></tr></table>' % \\\\\\r\\n (os.path.join('raw_data', os.path.split(raw_fpath)[-1]),\\r\\n os.path.splitext(raw_fpath)[-1])\\r\\n\\r\\n # create the output table\\r\\n data_html_str += '<table cellpadding=1 cellspacing=1 border=1 ' + \\\\\\r\\n 'style=\\\\\\\"text-align:center;border-color:white;' +\\\\\\r\\n 'border-style:groove;\\\\\\\">' + \\\\\\r\\n '<tr class=\\\\\\\"ntitle\\\\\\\"><td class=\\\\\\\"header\\\\\\\" colspan=\\\"2\\\"></td><td' +\\\\\\r\\n ' valign=\\\\\\\"bottom\\\\\\\" class=\\\\\\\"header\\\\\\\" colspan=\\\"2\\\">Total</td>'\\r\\n\\r\\n ct_head_row = '<tr class=ntitle>' + \\\\\\r\\n '<td valign=\\\\\\\"bottom\\\\\\\" ' + \\\\\\r\\n 'class=\\\\\\\"header\\\\\\\">Legend</td><td ' + \\\\\\r\\n 'valign=\\\\\\\"bottom\\\\\\\" class=\\\\\\\"header\\\\\\\">Taxonomy</td>' + \\\\\\r\\n '<td class=\\\\\\\"header\\\\\\\">count</td><td class=\\\\\\\"header\\\\\\\">%</td>'\\r\\n\\r\\n if not include_html_counts:\\r\\n # list all samples in the header\\r\\n for i in area_plot_sample_ids:\\r\\n data_html_str += '<td valign=bottom class=header>%s</td>' % (i)\\r\\n ct_head_row += '<td class=\\\\\\\"header\\\\\\\">%</td>'\\r\\n else:\\r\\n # list all samples in the header\\r\\n for i in area_plot_sample_ids:\\r\\n data_html_str += '<td colspan=\\\\\\\"2\\\\\\\" valign=\\\\\\\"bottom\\\\\\\" class=\\\\\\\"header\\\\\\\">%s</td>'\\\\\\r\\n % (i)\\r\\n ct_head_row += '<td class=\\\\\\\"header\\\\\\\">count</td><td class=\\\\\\\"header\\\\\\\">%</td>'\\r\\n\\r\\n data_html_str += '</tr>'\\r\\n ct_head_row += '</tr>'\\r\\n data_html_str += ct_head_row\\r\\n table_sum = sum(total_sums)\\r\\n\\r\\n # list taxa in first row\\r\\n for ct, dat in enumerate(otu_ids):\\r\\n tax = dat\\r\\n split_label = [i for i in tax.strip().split(\\\";\\\")]\\r\\n split_label[-1] = \\\"<a href=javascript:gg(\\\\'%s\\\\');>%s</a>\\\" % \\\\\\r\\n (split_label[-1].replace(' ', '+'),\\r\\n split_label[-1].replace(' ', ' '))\\r\\n joined_label = ';'.join(split_label).replace('\\\"', '')\\r\\n row_sum = sum([float(i) for i in data_table[ct]])\\r\\n data_html_str += \\\"<tr><td class=\\\\\\\"normal\\\\\\\" bgcolor=\\\\\\\"%s\\\\\\\"> </td><td style=\\\\\\\"text-align:left;\\\\\\\" class=\\\\\\\"normal\\\\\\\">%s</td><td class=\\\\\\\"normal\\\\\\\">%5.0f</td><td class=\\\\\\\"normal\\\\\\\">%5.1f%</td>\\\"\\\\\\r\\n % (data_colors[pref_colors[tax]].toHex(), joined_label,\\r\\n row_sum, row_sum / table_sum * 100)\\r\\n\\r\\n # add the percent taxa for each sample\\r\\n for i, per_tax in enumerate(data_table[ct]):\\r\\n if float(per_tax) > 0:\\r\\n if not include_html_counts:\\r\\n data_html_str += '<td class=\\\\\\\"normal\\\\\\\" style=\\\\\\\"border-color:%s;\\\\\\\">%5.1f%</td>' %\\\\\\r\\n (data_colors[pref_colors[tax]].toHex(),\\r\\n (float(per_tax) / total_sums[i] * 100))\\r\\n else:\\r\\n data_html_str += '<td class=\\\\\\\"normal\\\\\\\" style=\\\\\\\"border-color:%s;\\\\\\\">%5.0f</td><td class=\\\\\\\"normal\\\\\\\" style=\\\\\\\"border-color:%s;\\\\\\\">%5.1f%</td>' %\\\\\\r\\n (data_colors[\\r\\n pref_colors[tax]].toHex(), float(per_tax),\\r\\n data_colors[pref_colors[tax]].toHex(),\\r\\n (float(per_tax) / total_sums[i] * 100))\\r\\n else:\\r\\n if not include_html_counts:\\r\\n data_html_str += '<td class=\\\\\\\"normal\\\\\\\">%5.1f%</td>' % \\\\\\r\\n (float(per_tax) / total_sums[i] * 100)\\r\\n else:\\r\\n data_html_str += '<td class=\\\\\\\"normal\\\\\\\">%5.0f</td><td class=\\\\\\\"normal\\\\\\\">%5.1f%</td>' % \\\\\\r\\n (float(per_tax), float(per_tax)\\r\\n / total_sums[i] * 100)\\r\\n\\r\\n data_html_str += '</tr>'\\r\\n\\r\\n data_html_str += '</table>'\\r\\n\\r\\n if include_html_counts:\\r\\n # add a note on the counts since they can be relative or absolute\\r\\n # values\\r\\n data_html_str += '<p><em>NOTE: the counts displayed pertain to either relative or absolute values depending on your selection from summarize_taxa.py. For relative values, the numbers are converted to integer, so counts below 0.5 appear as 0. Also, if you chose to display numeric data, the table headers may not be in the same order as the plot.</em></p>'\\r\\n\\r\\n # make sure that the taxa array is in the proper order\\r\\n for i in range(len(area_plot_taxa_arr) - 1):\\r\\n if area_plot_taxa_arr[i] != area_plot_taxa_arr[i + 1]:\\r\\n raise ValueError('The taxonomies are out of order!')\\r\\n\\r\\n # add data to the html output\\r\\n img_data.extend(make_HTML_table(area_plot_sample_ids,\\r\\n other_frac, all_sum, red, otu_ids, area_plot_arr,\\r\\n fracs_labels, dir_path, [' '.join(\\r\\n taxa_html)], level,\\r\\n prefs, pref_colors, background_color, label_color, chart_type,\\r\\n label, generate_image_type,\\r\\n plot_width, plot_height, bar_width, dpi, resize_nth_label,\\r\\n label_type, include_html_legend, include_html_counts))\\r\\n img_data.append(data_html_str)\\r\\n\\r\\n return img_data\",\n \"def tree2OTU_table(mvp_tree):\\n series = []\\n for terminal in mvp_tree.feature_tree.get_terminals():\\n try:\\n series.append(terminal.sample_series)\\n except:\\n print('there is no sample series in tree2OTU ')\\n df = pd.dataframe(series)\\n return df\",\n \"def prepare_class_freqs(cls_counts, n_classes):\\n\\n if None in cls_counts:\\n return None\\n\\n lst_cls_counts = []\\n\\n for party_cls_counts in cls_counts:\\n temp = [0] * n_classes\\n for label, count in party_cls_counts.items():\\n temp[int(label)] = int(count)\\n\\n lst_cls_counts.append(np.array(temp))\\n\\n return lst_cls_counts\",\n \"def makeTableNamesList(n, ):\",\n \"def fastqc_stats_table(self):\\n \\n headers = OrderedDict()\\n headers['percent_duplicates'] = {\\n 'title': '% Dups',\\n 'description': '% Duplicate Reads',\\n 'max': 100,\\n 'min': 0,\\n 'scale': 'RdYlGn-rev',\\n 'format': '{:.1f}%'\\n }\\n headers['percent_gc'] = {\\n 'title': '% GC',\\n 'description': 'Average % GC Content',\\n 'max': 80,\\n 'min': 20,\\n 'scale': 'PRGn',\\n 'format': '{:.0f}%'\\n }\\n headers['avg_sequence_length'] = {\\n 'title': 'Length',\\n 'description': 'Average Sequence Length (bp)',\\n 'min': 0,\\n 'scale': 'RdYlGn',\\n 'format': '{:.0f}'\\n }\\n headers['total_sequences'] = {\\n 'title': 'M Seqs',\\n 'description': 'Total Sequences (millions)',\\n 'min': 0,\\n 'scale': 'Blues',\\n 'modify': lambda x: x / 1000000,\\n 'shared_key': 'read_count'\\n }\\n self.general_stats_addcols(self.fastqc_stats, headers)\",\n \"def _generate_expected_summary_table():\\n expected_summary = SummaryTable()\\n # 1 pub/send per default emit period\\n expected_summary.increment_pub()\\n expected_summary.increment_send()\\n return expected_summary\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57405555","0.56753397","0.56595165","0.56088334","0.558755","0.5580088","0.55345875","0.55330217","0.5509548","0.543421","0.5379833","0.5359173","0.53470004","0.52842575","0.52725303","0.5265805","0.5256941","0.52491903","0.52491903","0.52352023","0.52184176","0.5210343","0.5186094","0.5181094","0.5177044","0.51626515","0.5162241","0.516095","0.516095","0.51574534","0.51536584","0.5152442","0.51489735","0.51471347","0.51438355","0.51140195","0.51136845","0.51062876","0.5095472","0.5079911","0.50647867","0.50595415","0.50550973","0.5050915","0.5038118","0.50340736","0.50127065","0.50121963","0.5000586","0.49998978","0.49988917","0.49951482","0.49846387","0.4984482","0.49806964","0.4972534","0.49673676","0.4966203","0.4961195","0.4961049","0.49516347","0.49494374","0.49492437","0.49413514","0.49373916","0.4936244","0.49353832","0.49303764","0.49238023","0.49222514","0.49116588","0.49094725","0.49001428","0.4883092","0.48812306","0.48653036","0.48583058","0.48567784","0.4851121","0.48385784","0.48343903","0.48337904","0.4830861","0.48270935","0.48222396","0.4817311","0.4812682","0.481219","0.4809277","0.48054144","0.4802383","0.48004514","0.4791193","0.47888666","0.47797054","0.4772948","0.4772133","0.47721258","0.47707912","0.4770469"],"string":"[\n \"0.57405555\",\n \"0.56753397\",\n \"0.56595165\",\n \"0.56088334\",\n \"0.558755\",\n \"0.5580088\",\n \"0.55345875\",\n \"0.55330217\",\n \"0.5509548\",\n \"0.543421\",\n \"0.5379833\",\n \"0.5359173\",\n \"0.53470004\",\n \"0.52842575\",\n \"0.52725303\",\n \"0.5265805\",\n \"0.5256941\",\n \"0.52491903\",\n \"0.52491903\",\n \"0.52352023\",\n \"0.52184176\",\n \"0.5210343\",\n \"0.5186094\",\n \"0.5181094\",\n \"0.5177044\",\n \"0.51626515\",\n \"0.5162241\",\n \"0.516095\",\n \"0.516095\",\n \"0.51574534\",\n \"0.51536584\",\n \"0.5152442\",\n \"0.51489735\",\n \"0.51471347\",\n \"0.51438355\",\n \"0.51140195\",\n \"0.51136845\",\n \"0.51062876\",\n \"0.5095472\",\n \"0.5079911\",\n \"0.50647867\",\n \"0.50595415\",\n \"0.50550973\",\n \"0.5050915\",\n \"0.5038118\",\n \"0.50340736\",\n \"0.50127065\",\n \"0.50121963\",\n \"0.5000586\",\n \"0.49998978\",\n \"0.49988917\",\n \"0.49951482\",\n \"0.49846387\",\n \"0.4984482\",\n \"0.49806964\",\n \"0.4972534\",\n \"0.49673676\",\n \"0.4966203\",\n \"0.4961195\",\n \"0.4961049\",\n \"0.49516347\",\n \"0.49494374\",\n \"0.49492437\",\n \"0.49413514\",\n \"0.49373916\",\n \"0.4936244\",\n \"0.49353832\",\n \"0.49303764\",\n \"0.49238023\",\n \"0.49222514\",\n \"0.49116588\",\n \"0.49094725\",\n \"0.49001428\",\n \"0.4883092\",\n \"0.48812306\",\n \"0.48653036\",\n \"0.48583058\",\n \"0.48567784\",\n \"0.4851121\",\n \"0.48385784\",\n \"0.48343903\",\n \"0.48337904\",\n \"0.4830861\",\n \"0.48270935\",\n \"0.48222396\",\n \"0.4817311\",\n \"0.4812682\",\n \"0.481219\",\n \"0.4809277\",\n \"0.48054144\",\n \"0.4802383\",\n \"0.48004514\",\n \"0.4791193\",\n \"0.47888666\",\n \"0.47797054\",\n \"0.4772948\",\n \"0.4772133\",\n \"0.47721258\",\n \"0.47707912\",\n \"0.4770469\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.627885"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7491,"cells":{"query":{"kind":"string","value":"reformat fpkm table into flattened table of sample_names/values"},"document":{"kind":"string","value":"def reformat_fpkmTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.fpkmTable: fpkmTable = self.fpkmTable[:];\n else: fpkmTable = [];\n\n fpkmTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=fpkmTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'fpkm');\n return fpkmTable_flat;"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def format_unifrac_sample_mapping(sample_ids, otu_ids, otu_table_array):\r\n out = []\r\n for i, row in enumerate(otu_table_array):\r\n for j, val in enumerate(row):\r\n if val > 0:\r\n line = [otu_ids[i], sample_ids[j], str(val)]\r\n out.append('\\t'.join(line))\r\n return out","def format_df(df):\n df = df.transpose()\n df.rename(columns=df.iloc[0], inplace=True)\n df.drop('gene', inplace=True)\n df.index.rename('sample_id', inplace=True)\n return df","def format_df(df):\n df = df.transpose()\n df.rename(columns=df.iloc[0], inplace=True)\n df.drop('gene', inplace=True)\n df.index.rename('sample_id', inplace=True)\n return df","def from_multicsv(self,input_data):\n reformatted_data = []\n for (i,row) in enumerate(input_data):\n if i==0:\n headers = row\n else:\n data_row = {}\n for (j,h) in enumerate(headers):\n if j<len(row):\n data_row.update({h : row[j]})\n else:\n data_row.update({h : 0})\n reformatted_data.append(data_row)\n return reformatted_data","def convert_to_table_format(package):\n tables = list()\n for primary_table_id in package.primary_table_ids:\n tables.append(StachExtensions.__generate_table(package, primary_table_id))\n return tables","def print_tables(hash_table, f_output, l_samples):\n\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\n 'ExAC_SAS',\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\n 'Polyphen2_HVAR_score',\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\n l_fields = l_fields + l_samples\n \n l_chr = set([item[0] for item in hash_table.keys()])\n\n fo = open(f_output, 'w')\n fo.write(','.join(l_fields) + '\\n')\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\n')\n fo.close()","def tree2OTU_table(mvp_tree):\n series = []\n for terminal in mvp_tree.feature_tree.get_terminals():\n try:\n series.append(terminal.sample_series)\n except:\n print('there is no sample series in tree2OTU ')\n df = pd.dataframe(series)\n return df","def to_frame(self):\n # Create a set of dictionaries/lists for each column\n data = dict([(i_var.name, []) for i_var in self.inputs])\n data.update({self.OUTPUT_LABEL: [], self.INPUT_LABEL: [], self.name: []})\n\n # A very ugly loop to produce all the probabilities in a nice way.\n # Note that this just reproduces what is already in `self.lookup`.\n # Honestly, I just haven't thought of a better way to get nice output.\n for i_index, i_state in enumerate(self.input_states):\n for o_var, results in zip(self.outputs, self.per_state_results):\n for o_state, o_p in enumerate(results[i_index]):\n for i_var, s in zip(self.inputs, i_state):\n data[i_var.name].append(s)\n data[self.OUTPUT_LABEL].append(o_var.name)\n data[self.INPUT_LABEL].append(o_state)\n data[self.name].append(o_p)\n all_data = pd.DataFrame(data=data)\n\n # The magnificent pivot table function does all the work\n return pd.pivot_table(data=all_data, values=[self.name],\n index=[i_var.name for i_var in self.inputs],\n columns=[self.OUTPUT_LABEL, self.INPUT_LABEL])","def test_toTable(self):\r\n # Empty results.\r\n out_f = StringIO()\r\n self.res1.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(),\r\n \"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\\n\")\r\n out_f.close()\r\n\r\n # Results with multiple samples.\r\n exp = \"\"\"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\r\nS1\\t5\\t21\\t1.5\\t2.5\\t3.5\r\nS1\\t10\\t20\\t2.5\\t2.5\\t3.5\r\nS1\\t20\\t30\\t3.5\\t2.5\\t3.5\r\nS2\\t1\\t3\\t0.4\\t2.5\\t3.5\r\n\"\"\"\r\n out_f = StringIO()\r\n self.res2.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()\r\n\r\n # Custom header.\r\n exp = \"\"\"foo\\tbar\\tbaz\\tbazaar\\tbazaaar\\tbazaaaar\r\nS1\\t5\\t21\\t1.5\\t2.5\\t3.5\r\n\"\"\"\r\n out_f = StringIO()\r\n self.res1.addSample('S1', 42)\r\n self.res1.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)\r\n self.res1.toTable(out_f,\r\n header=['foo', 'bar', 'baz', 'bazaar', 'bazaaar', 'bazaaaar'])\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()\r\n\r\n # Invalid header.\r\n with self.assertRaises(ValueError):\r\n out_f = StringIO()\r\n self.res1.toTable(out_f, header=['foo'])\r\n\r\n # Cells with None as their value.\r\n exp = \"\"\"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\r\nS1\\t43\\tN/A\\tN/A\\tN/A\\tN/A\r\n\"\"\"\r\n out_f = StringIO()\r\n res = RichnessEstimatesResults()\r\n res.addSample('S1', 42)\r\n res.addSampleEstimate('S1', 43, None, None, None, None)\r\n res.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def _finalize(self, sampled_data):\n final_data = dict()\n for table_name, table_rows in sampled_data.items():\n parents = self.metadata.get_parents(table_name)\n if parents:\n for parent_name in parents:\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\n if foreign_key not in table_rows:\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\n table_rows[foreign_key] = parent_ids\n\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\n\n fields = self.metadata.get_fields(table_name)\n\n final_data[table_name] = reversed_data[list(fields.keys())]\n\n return final_data","def reformat_data(self, df, ids):\n data = np.zeros((len(ids), self.n_sample_rows + 1, self.n_features))\n idx = 0\n for i in ids:\n sample = df.loc[i]\n data[idx, 0:89, :] = sample.values\n data[idx, 89, :] = np.mean(sample.values)\n idx += 1\n return data","def preprocessKNN(self):\n\n feature_list = []\n\n for index, row in self.all_data.iterrows():\n chans = cv2.split(row['image'])\n\n features = []\n for chan in chans:\n hist = cv2.calcHist(chan, [0], None, [64], [0,256])\n features.extend(hist)\n\n features = np.array(features).flatten()\n feature_list.append(features)\n\n df = self.all_data[['name', 'genre']].copy()\n\n feature_df = pd.DataFrame(feature_list)\n\n df = df.join(feature_df)\n\n return df","def get_normalized_data_table(table_metadata, debug=False):\n suffix = table_metadata.get('suffix', '')\n data_table = table_metadata['table_class'](\n file_path=table_metadata['csv_filename'], suffix=suffix)\n drop_headers(table_metadata['document_label'], data_table.data)\n rename_headers(table_metadata['document_label'], data_table.data)\n print_data_table_length(table_metadata['document_label'],\n data_table.data,\n debug=debug)\n return data_table","def __profile_to_table(data):\n output = [[\"condition\", \"mean\", \"min\", \"max\"]]\n order = data[\"order\"]\n\n for o in order:\n try:\n values = data[\"data\"][o]\n output.append(\n [o, str(mean(values)), str(min(values)), str(max(values))]\n )\n except Exception as e:\n print(e)\n\n return \"\\n\".join([\"\\t\".join(l) for l in output])","def _get_table_from_samples(self, index):\n df = pd.DataFrame()\n for sample in self.samples:\n sd = sample.to_dict()\n ser = pd.Series(\n {k: v for (k, v) in list(sd.items()) if not k.startswith(\"_\")}\n )\n df = df.append(ser, ignore_index=True)\n index = [index] if isinstance(index, str) else index\n if not all([i in df.columns for i in index]):\n _LOGGER.debug(\n \"Could not set {} index. At least one of the \"\n \"requested columns does not exist: {}\".\n format(CFG_SAMPLE_TABLE_KEY, index))\n return df\n _LOGGER.debug(\"Setting sample_table index to: {}\".format(index))\n df.set_index(keys=index, drop=False, inplace=True)\n return df","def test_format_unifrac_sample_mapping(self):\r\n a = [[1, 0, 0], [0, 2, 4], [7, 0, 9.0]]\r\n otu_ids = ['OTUa', 'OTUb', 'OTUc']\r\n sample_ids = ['Sa', 'Sb', 'Sc']\r\n result = format_unifrac_sample_mapping(sample_ids, otu_ids, a)\r\n self.assertEqual(\r\n result,\r\n ['OTUa\\tSa\\t1',\r\n 'OTUb\\tSb\\t2',\r\n 'OTUb\\tSc\\t4',\r\n 'OTUc\\tSa\\t7',\r\n 'OTUc\\tSc\\t9.0'])","def _format_data(self):\n formatted_data = []\n\n for row in self._data_agg_by_mean_value.iterrows():\n \n car_make = row[0]\n mean_car_value = round(row[1][0], 2)\n formatted_data.append({'car_make': car_make, 'mean_car_value': mean_car_value})\n\n return formatted_data","def reformat_countOrFPKMTable(\n self,\n countOrFPKMTable_I=None,\n analysis_id_I=None,\n sna2experimentID_I=None,\n sna2sns_I=None,\n count_or_FPKM = 'count'):\n #format into a dictionary of rows for quick aligning with the tracking_id\n countOrFPKMTable_flat = [];\n for row in countOrFPKMTable_I:\n for k,v in row.items():\n if k=='tracking_id':continue;\n tmp = {};\n tmp['analysis_id'] = analysis_id_I;\n tmp['tracking_id'] = row['tracking_id'];\n\n sample_name_lst = k.split('_');\n sample_name_base = '_'.join(sample_name_lst[:-1]);\n sample_name_rep = eval(sample_name_lst[-1]);\n if sna2experimentID_I: \n experiment_id = sna2experimentID_I[sample_name_base];\n else:\n experiment_id=None;\n tmp['experiment_id'] = experiment_id;\n if sna2sns_I: \n sample_name = sna2sns_I[sample_name_base][sample_name_rep];\n else:\n sample_name=k;\n tmp['sample_name'] = sample_name;\n\n tmp['value'] = v;\n tmp['value_units'] = count_or_FPKM;\n tmp['used_'] = True;\n tmp['comment_'] = None;\n countOrFPKMTable_flat.append(tmp);\n return countOrFPKMTable_flat;","def tsv_samples_to_names(self, name='samples_to_names.tsv'):\n with open(self.a.out_dir + name, 'w') as handle:\n content = self.df_sample_names.to_csv(None, sep=self.sep, float_format=self.float_format)\n handle.writelines(content)","def transform(filtered_list):\n out_put = {}\n out_list = []\n # loop to get the required columns, random ordered\n for item in filtered_list:\n for val in item._fields:\n if val in type_dict:\n out_put[val] = type_dict.get(val)(getattr(item, val))\n out_list.append(out_put)\n out_put = {}\n\n # loop to the ordered columns data as per output\n all_rows = []\n for item in out_list:\n tmp_row = []\n for key in type_dict.keys():\n out_put[key] = item[key]\n tmp_row.append(item[key])\n all_rows.append(tmp_row)\n\n col_row = [col.replace('_', '-') for col in type_dict.keys()]\n all_rows.insert(0, col_row)\n return all_rows","def per_sample_taxa_summaries(open_table, output_format):\n t = parse_biom_table(open_table)\n header = \"#taxon\\trelative_abundance\\n\"\n\n for v, id_, md in t.iter():\n with open(output_format % id_, 'w') as f:\n f.write(header)\n\n for sorted_v, taxa in \\\n sorted(zip(v, t.ids(axis='observation')))[::-1]:\n if sorted_v:\n f.write(\"%s\\t%f\\n\" % (taxa, sorted_v))","def generate_table(results):\n keyslist = list(results[0].keys())\n table = PrettyTable(keyslist)\n for dct in results:\n table.add_row([dct.get(c, \"\") for c in keyslist])\n return table","def _load_sample_table(self):\n self.sampleTable = pd.read_table(self.config['sampletable'], sep='\\t', dtype=str)\n self.sampleTable.set_index('sampleID', inplace=True)\n self.samples = self.sampleTable.reset_index().to_dict('records')","def transform(input):\n transformed_file = []\n\n for row in input:\n names = row['name'].split()\n row['fname'] = names[0]\n row['lname'] = names[1]\n del row['name']\n transformed_file.append(row)\n return transformed_file","def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\n datafile = open(output_table, \"w\")\n datafile.write(\n \"Feature_1\" + '\\t' + \"Feature_2\" + \"\\t\" + \"plus_plus\" + '\\t' + \"minus_minus\" + '\\t' + \"plus_minus\" + '\\t' + \"minus_plus\" + '\\t' + \"p_value_same_opposite\" + '\\t' + \"p-value_same_opposite_Bonferoni_corrected\" + '\\t' + \"Ratio_same_opposite\" + '\\t' + \"p_value_divergent_convergent\" + '\\t' + \"p_value_divergent_convergent Bonferoni corrected\" + '\\t' + \"Ratio divergent convergent\" + '\\n')\n for i in range(len(NamesL_pairs)):\n datafile.write(\n NamesL_pairs[i][0] + '\\t' + NamesL_pairs[i][1] + '\\t' + str(p_pL[i]) + '\\t' + str(m_mL[i]) + '\\t' + str(\n p_mL[i]) + '\\t' + str(m_pL[i]) + '\\t' + str(p_valsL[i]) + '\\t' + str(p_vals_BonferoniL[i]) + '\\t' + str(\n RatiosL[i]) + '\\t' + str(p_valsL_divergent_convergent[i]) + '\\t' + str(\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\t' + str(RatiosL_divergent_convergent[i]) + '\\n')\n datafile.close()\n return","def split_otu_table_on_sample_metadata(otu_table_f, mapping_f, mapping_field):\r\n mapping_f = list(mapping_f)\r\n mapping_values = get_mapping_values(mapping_f, mapping_field)\r\n otu_table = parse_biom_table(otu_table_f)\r\n\r\n for v in mapping_values:\r\n v_fp_str = v.replace(' ', '_')\r\n sample_ids_to_keep = sample_ids_from_metadata_description(\r\n mapping_f, valid_states_str=\"%s:%s\" % (mapping_field, v))\r\n\r\n try:\r\n filtered_otu_table = otu_table.filterSamples(\r\n lambda values, id_, metadata: id_ in sample_ids_to_keep)\r\n except TableException:\r\n # all samples are filtered out, so no otu table to write\r\n continue\r\n yield v_fp_str, format_biom_table(filtered_otu_table)","def formatOutput(tranisitionTable):\n \n # TODO: Make all the processing done in this function, rather than main\n for key in sorted(transitionTable.iterkeys()):\n transitionTable[key].sort(key = operator.itemgetter(1)) #.sort is in-place\n \n return transitionTable","def make_table(ranked_means):\n fp = open(\"table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c|}\n \\\\hline\n \\\\multicolumn{2}{|c||}{Slowest} & \\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\ \\\\hline\n Feature & Rate & Feature & Rate \\\\\\\\ \\\\hline\n\"\"\")\n top_10 = ranked_means[0:10]\n bottom_10 = ranked_means[-10:]\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\n f_name = f_name.split(\":\")[-1]\n f_name = f_name.rsplit(\" \", 1)[0] if f_name.endswith(\"(V)\") else f_name\n s_name = s_name.split(\":\")[-1]\n s_name = s_name.rsplit(\" \", 1)[0] if s_name.endswith(\"(V)\") else s_name\n fp.write(\" %s & %.2f & %s & %.2f \\\\\\\\ \\n\" % \\\n (f_name, f_rate, s_name, s_rate))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()\n\n fp = open(\"supp_meaning_table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\n \\\\hline\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\ \\\\hline\n\n\"\"\")\n feature_names = [f.split(\":\")[-1] for (r,f) in ranked_means]\n feature_names.sort(key=lambda s: s.lower())\n col1 = feature_names[0:25]\n col2 = feature_names[25:50]\n col3 = feature_names[50:75]\n col4 = feature_names[75:]\n for a,b,c,d in zip(col1,col2,col3,col4):\n x,y,z,w = [get_meaning_category(i) or \"Excluded\" for i in (a,b,c,d)]\n # Lop off (V)s (we needed them above for get_meaning_category to work)\n a,b,c,d = [f.rsplit(\" \", 1)[0] if f.endswith(\"(V)\") else f for f in (a,b,c,d)]\n fp.write(\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\ \\n\" % (a, x, b, y, c, z, d, w))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()","def print_tsv(data, filename):\n with open(filename, 'wt') as fout:\n writefile = partial(print, sep='\\t', file=fout)\n writefile('Sample', *expected_header)\n for sample in data:\n for entry in data[sample]:\n writefile(sample, *(entry[field] for field in expected_header))","def matrix2Table(self, matrix): \n M = TableModel()\n M.addColumn('Mutations')\n\n fields = matrix.columnHeaders()\n for f in fields:\n M.addColumn(f)\n i = matrix.indexOfColumnWithHeader('Mutations')\n for row in matrix:\n mutationSet = Core.Data.MutationSet(row[i])\n code = '+'.join(mutationSet.mutationCodes(reduced=True))\n M.addRow(code)\n for f in fields:\n j = matrix.indexOfColumnWithHeader(f)\n if f == 'Mutations':\n M.data[code]['Mutations'] = code\n else: \n M.data[code][f] = str(row[j])\n return M","def format(self, table):\n #return table.data.to_json()\n m = table.as_array()\n rank = len(m.shape)\n is_table = len(table.headers)<=5 or (len(table.headers)>5 and (table.headers[0] != '0' or table.headers[1] != '1' or table.headers[2] != '2' ))\n\n if rank<3 and is_table:\n v = []\n for i in range(len(table.headers)):\n vv = {\n 'offset': table.offset,\n 'header': table.headers[i],\n 'type': table.types[i],\n 'data': _replace_nans(m[:,i].tolist()) if rank>1 else _replace_nans(m.tolist()),\n }\n if table.sizes is not None:\n vv[\"size\"] = table.sizes[0]\n v.append(vv)\n else:\n # if hasattr(data, \"strip\") or \\\n # (not hasattr(data, \"__getitem__\") and \\\n # not hasattr(data, \"__iter__\")):\n # # data is not a list/tuple => wrap it\n # data = [ data ]\n v = {\n 'offset': table.offset,\n #'headers': table.headers,\n 'type': table.types[0],\n 'data': _replace_nans(m.tolist()),\n }\n if table.sizes is not None:\n v[\"size\"] = table.sizes\n\n return json.dumps(v, cls=ExtEncoder)","def preprocess_feature(df):","def to_tsv(self, out_dir, sep=\"\\t\", prefix=None, **kwargs):\n os.makedirs(out_dir, exist_ok=True) # create dirs if non-existent\n prefix = f\"{prefix}_\" if prefix else \"\"\n fpaths = [\n os.path.join(out_dir, f\"{prefix}{suf}.tsv\")\n for suf in [\"data\", \"sample_meta\"]\n ]\n self.data.to_csv(fpaths[0], sep=\"\\t\", **kwargs)\n self.sample_meta.to_csv(fpaths[1], sep=\"\\t\", **kwargs)","def prepare_batch_sample_set_for_metadata_export(path, tsca_id):\n raw = pd.read_table(path)\n print( \"%d Samples in this batch\" % raw.shape[0] )\n\n # Create dfs to upload\n all_samples = pd.concat([pd.DataFrame(index=raw.index, columns=['membership:sample_set_id'], data=tsca_id), \\\n raw[ ['sample_id', 'sample_type'] ]], axis=1)\n\n\n tumors = all_samples.loc[ all_samples['sample_type'] == \"Tumor\", ['membership:sample_set_id', 'sample_id'] ]\n tumors.loc[: , 'membership:sample_set_id'] = \"%s_T\"%tsca_id\n \n normals = all_samples.loc[ all_samples['sample_type'] == \"Normal\", ['membership:sample_set_id', 'sample_id'] ]\n normals.loc[: , 'membership:sample_set_id'] = \"%s_N\"%tsca_id\n\n all_samples = all_samples.drop('sample_type', axis=1)\n return (all_samples, tumors, normals)","def transform_instances_table_output(result):\n\n table_output = []\n for item in result:\n table_output.append(_transform_instance_row(item))\n\n return table_output","def build_table():\n with contextlib.ExitStack() as stack:\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\n iters = [(line.split() for line in f) for f in files]\n for it in iters:\n next(it)\n key = operator.itemgetter(0)\n table = []\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\n props = list(g)\n if len(props) == len(iters):\n table.append([k] + [x[1] for x in props])\n for snp in table:\n print(*snp)","def normalize_table(input_fp, output_fp, relative_abund, presence_absence,\n axis):\n table = load_table(input_fp)\n result = _normalize_table(table, relative_abund, presence_absence, axis)\n\n write_biom_table(result, 'hdf5' if HAVE_H5PY else 'json', output_fp)","def reverse_transform(self, data):\n table = []\n\n for i in range(self.metadata['num_features']):\n column_data = data['f%02d' % i]\n column_metadata = self.metadata['details'][i]\n\n if column_metadata['type'] == 'value':\n column = self.continous_transformer.inverse_transform(column_data, column_metadata)\n\n if column_metadata['type'] == 'category':\n self.categorical_transformer.classes_ = column_metadata['mapping']\n column = self.categorical_transformer.inverse_transform(\n column_data.ravel().astype(np.int32))\n\n table.append(column)\n\n result = pd.DataFrame(dict(enumerate(table)))\n result.columns = self.columns\n return result","def summarize_sample_props(psd_list, sample_list):\n prop_list = [psd.sample_props for psd in psd_list]\n cols = ['amplicon median', 'mean size', 'lower size', 'upper size']\n\n return pd.DataFrame(prop_list, columns=cols, index=sample_list)","def pivot(self):\n\t\tpivoted = Dataset()\n\t\tfor (index, header) in enumerate(self.headers):\n\t\t\tfor row in self.rows:\n\t\t\t\tif index == 0:\n\t\t\t\t\tpivoted.headers.append(row[header])\n\t\t\t\telse:\n\t\t\t\t\tif len(pivoted.rows) < index:\n\t\t\t\t\t\tpivoted.rows.extend([{} for x in range(index - len(pivoted.rows))])\n\t\t\t\t\tpivoted.rows[index - 1][row[self.headers[0]]] = row[header]\n\t\treturn pivoted","def tabulate(d, transpose=False, thousands=True, key_fun=None, sep=\",\", align=True):\n pairs = d.keys()\n rows, cols = zip(*pairs)\n if transpose:\n rows, cols = cols, rows\n\n rows = sorted(set(rows))\n cols = sorted(set(cols))\n header = [\"o\"] + list(cols)\n table = []\n for r in rows:\n combo = [(r, c) for c in cols]\n if transpose:\n combo = [(c, r) for (r, c) in combo]\n data = [d.get(x, \"n/a\") for x in combo]\n data = [\"{0:.1f}\".format(x) if isinstance(x, float) else x for x in data]\n if key_fun:\n data = [key_fun(x) for x in data]\n table.append([str(r)] + data)\n\n if not align:\n formatted = load_csv(header, table, sep=sep)\n return \"\\n\".join(formatted)\n\n return loadtable(header, table, thousands=thousands)","def converttable(tablecode):\n table = etree.XML(tablecode)\n rows = iter(table)\n headers = [col.text for col in next(rows)]\n data = []\n for row in rows:\n values = [col.text for col in row]\n debugprint(dict(zip(headers, values)), \"RAW JSON\")\n data.append(dict(zip(headers, values)))\n return data","def normalize_to_flat(classifier, df, col):\n name = str(classifier) + '_df'\n new_df= json_normalize(df.loc[classifier][col])\n new_df['classifier'] = [classifier]\n return new_df","def flatten_data(self, data):\n for row in data:\n flat_row = {k: self._flatten_value(v) for k, v in row.items()}\n yield flat_row","def generate_table(self, rows):\n ...","def convert_quick_table(result):\n headline = result.split('\\n',1)[0]\n names, converters = MastCasJobs.get_converters(headline, delimiter=',')\n tab = ascii.read(MastCasJobs.replacenull(result,delimiter=','),\n guess=False,fast_reader=False,format='csv',\n names=names,converters=converters)\n return tab","def tab_delim_table(self):\n self.generate()\n\n header = ' \\t '.join([r'{: ^7}'.format(col) for col in self.columns])\n lines = []\n for row in self.rows:\n bits = []\n for col in self.columns:\n if col in self.formatters:\n bits.append(self.formatters[col].format(row[col]))\n else:\n bits.append(self.formatters.get(col, '{: ^7}').format(row[col] if row[col] else ''))\n lines.append(' \\t '.join(bits))\n\n return \"{}\\n{}\".format(header, '\\n'.join(lines))","def normalize_values(self, data):\n\n df = pd.DataFrame(data[1:], columns = data[0]).astype(str)\n\n df = df.replace(ami_md_constants.NAS)\n\n df = df.replace(ami_md_constants.REGEX_REPLACE_DICT, regex=True)\n df = df.replace(ami_md_constants.STRING_REPLACE_DICT)\n df['source.object.format_type'] = df['source.object.format'].map(ami_md_constants.FORMAT_TYPE)\n\n for key in ami_md_constants.MEASURE_UNIT_MAPS.keys():\n value_map = ami_md_constants.MEASURE_UNIT_MAPS[key]\n df = self.map_value(df,\n value_map['from_column'],\n value_map['to_column'],\n value_map['constant_value'],\n value_map['values_map_column'],\n value_map['values_map'])\n\n #force all the numerics back to numeric, and drop all empty columns\n df = df.apply(pd.to_numeric, errors='ignore').dropna(axis=1, how = \"all\")\n\n vals = df.values.tolist()\n cols = df.columns.tolist()\n vals.insert(0, cols)\n\n return vals","def make_summary_tables( res ):\n\n # transform second table to csv and read this as a dataFrame\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\",\",index_col=0)\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\n\n # first table is trickier because the data is spread on to columns, and there is title line\n L = res.tables[0].as_html().split('\\n')\n L.pop(1) # get rid of the title\n tmp = pd.read_html('\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \n\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\n # NB : I exclude the last 2 elements which are empty \n \n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\n \n return result_general_df , result_fit_df","def make_pivot_table(data, index_name, columns, values, afffunc):\n\n pvt = data.pivot_table(index=[index_name], columns=[columns], values=values, aggfunc=afffunc)\n return pvt","def make_table_from_exploded_df(exploded_df: pd.DataFrame, row_heading_cols, column_heading_cols, dfs_dict=None,\n value_col: str = \"text\", concat_with: str = \" | \",\n convert_numeric_items=False, sort_headers=True) -> pd.DataFrame:\n for heading_col in (row_heading_cols + column_heading_cols):\n exploded_df[heading_col].fillna(\"\", inplace=True)\n table = exploded_df.pivot_table(index=row_heading_cols, columns=column_heading_cols, values=value_col,\n aggfunc=(lambda a: concat_with.join(a)))\n\n if type(table) == pd.Series:\n table = table.to_frame()\n row_nones = [None for _ in range((table.index.nlevels))]\n col_nones = [None for _ in range((table.columns.nlevels))]\n\n table = table.rename_axis(index=row_nones, columns=col_nones)\n\n if convert_numeric_items:\n num_rows, num_cols = _infer_numeric_rows_cols(exploded_df, row_heading_cols, column_heading_cols)\n table = convert_cols_to_numeric(table, num_cols, num_rows)\n\n if sort_headers and column_heading_cols != [\"column_index\"] and len(\n column_heading_cols) == 1 and dfs_dict is not None:\n col_headings = table.columns.to_list()\n cols = dfs_dict[\"col_headers\"][dfs_dict[\"col_headers\"][\"text\"].isin(col_headings)]\n cols = cols.sort_values(\"column_index_begin\")\n col_headings_sorted = cols[\"text\"].to_list()\n\n not_inc_headings = [x for x in col_headings if x not in col_headings_sorted]\n col_headings_sorted = col_headings_sorted + not_inc_headings\n table = table[col_headings_sorted]\n\n elif sort_headers and column_heading_cols != [\"column_index\"] and len(\n column_heading_cols) > 1 and dfs_dict is not None:\n headers = table.columns.to_frame()\n sorted_headers_arr = _order_multiindex(headers, column_heading_cols, 0, dfs_dict[\"col_headers\"])\n sorted_headers = [tuple(header) for header in sorted_headers_arr]\n not_inc_headings = [x for x in table.columns.to_list() if x not in sorted_headers]\n sorted_headers = sorted_headers + not_inc_headings\n table = table[sorted_headers]\n\n if sort_headers and row_heading_cols != [\"row_index\"] and len(row_heading_cols) == 1 and dfs_dict is not None:\n row_headings = table.index.to_list()\n rows = dfs_dict[\"row_headers\"][dfs_dict[\"row_headers\"][\"text\"].isin(row_headings)]\n rows = rows.sort_values(\"row_index_begin\")\n row_headings_sorted = rows[\"text\"].to_list()\n\n not_inc_headings = [x for x in row_headings if x not in row_headings_sorted]\n row_headings_sorted = row_headings_sorted + not_inc_headings\n\n table = table.reindex(row_headings_sorted)\n\n elif sort_headers and row_heading_cols != [\"column_index\"] and len(row_heading_cols) > 1 and dfs_dict is not None:\n headers = table.index.to_frame()\n sorted_headers_arr = _order_multiindex(headers, row_heading_cols, 0, dfs_dict[\"row_headers\"])\n sorted_headers = [tuple(header) for header in sorted_headers_arr]\n\n not_inc_headings = [x for x in table.index.to_list() if x not in sorted_headers]\n sorted_headers = sorted_headers + not_inc_headings\n table = table.reindex(sorted_headers)\n\n return table","def _buildtable(self):\n\n tabrows = []\n\n for i, (expid, exfiles) in enumerate(self._exposure_files.items()):\n specflux_b, specflux_r, specflux_z = [], [], []\n tab = None\n\n if len(exfiles) == 0:\n continue\n\n print(expid)\n for exfile in exfiles:\n print(exfile)\n hdu = fits.open(exfile)\n\n # The following tables are present in the redux sframes and the\n # nightwatch qcframes.\n wave = hdu['WAVELENGTH'].data\n\n # However, in the nightwatch files the wavelength data are a\n # table of size nfiber x nwavelength.\n if self._filetype == 'nightwatch':\n if wave.ndim > 1:\n wave = wave[0]\n\n fluxhead = hdu['FLUX'].header\n fluxdata = hdu['FLUX'].data\n ivardata = hdu['IVAR'].data\n fibermap = hdu['FIBERMAP'].data\n exptime = fluxhead['EXPTIME']\n if not np.all(self._unditherfa['FIBER'] ==\n np.arange(len(self._unditherfa))):\n raise ValueError('weird fiberassign file format!')\n fibermap = self._unditherfa[fibermap['FIBER']]\n\n target_id = fibermap['TARGETID']\n target_ra = fibermap['TARGET_RA']\n target_dec = fibermap['TARGET_DEC']\n fiber = fibermap['FIBER']\n objtype = fibermap['OBJTYPE']\n flux_g = fibermap['FLUX_G']\n flux_r = fibermap['FLUX_R']\n flux_z = fibermap['FLUX_Z']\n x, y = [fibermap['FIBERASSIGN_{}'.format(val)] for val in ('X', 'Y')]\n\n camera = fluxhead['CAMERA'][0].upper()\n\n if getattr(self, '_deltara', None) is not None:\n dra = self._deltara[i]*np.ones(len(fiber))\n ddec = self._deltadec[i]*np.ones(len(fiber))\n elif self._dithertype == 'telescope':\n dithra = self._ditherfa['target_ra']\n dithdec = self._ditherfa['target_dec']\n udithra = self._unditherfa['target_ra']\n udithdec = self._unditherfa['target_dec']\n ontarget = ((self._ditherfa['targetid'] ==\n self._unditherfa['targetid']) &\n (self._ditherfa['objtype'] == 'TGT'))\n dfiberra = (dithra-udithra)*np.cos(np.radians(udithdec))*60*60\n dfiberdec = (dithdec-udithdec)*60*60\n if not np.all(self._ditherfa['FIBER'] ==\n np.arange(len(self._ditherfa))):\n raise ValueError('unexpected shape of dither file')\n dfiberra[~ontarget] = np.nan\n dfiberdec[~ontarget] = np.nan\n dfiberra = dfiberra[fiber]\n dfiberdec = dfiberdec[fiber]\n wcs = self.lookup_wcs(fluxhead['MJD-OBS'])\n centralwcs = self._central_wcs\n if (~np.isfinite(centralwcs['cenra'][1]) or\n ~np.isfinite(centralwcs['cendec'][1])):\n raise ValueError('central pointing ra/dec is NaN!')\n dtelra = (wcs['cenra'][1]-centralwcs['cenra'][1])\n dtelra *= np.cos(np.radians(centralwcs['cendec'][1]))\n dteldec = wcs['cendec'][1]-centralwcs['cendec'][1]\n dra = dfiberra + dtelra*60*60\n ddec = dfiberdec + dteldec*60*60\n if np.all(~np.isfinite(dra)):\n print('warning: no good telescope offset for %s' %\n exfile)\n else:\n raise ValueError('not implemented')\n \n for j, fiber_id in enumerate(fiber):\n flux = fluxdata[j]\n ivar = ivardata[j]\n if not np.any(ivar > 0):\n specflux = 0\n specflux_ivar = 0\n else:\n meanivar = np.mean(ivar[ivar > 0])\n mask = ivar > meanivar / 100\n specflux = np.trapz(flux*mask, wave)\n specflux_ivar = 1./np.sum(ivar[mask]**-1)\n # Schlegel: sum over correct wavelengths, all three\n # filters, plus 11 pixel median filter to reject\n # cosmics.\n # will require being better about reading in\n # the spectrographs together.\n tabrows.append((expid, exptime,\n target_id[j], target_ra[j], target_dec[j],\n fiber[j], objtype[j],\n flux_g[j], flux_r[j], flux_z[j],\n specflux, specflux_ivar, camera,\n dra[j], ddec[j],\n x[j], y[j]))\n\n tab = Table(rows=tabrows,\n names=('EXPID', 'EXPTIME',\n 'TARGETID', 'TARGET_RA', 'TARGET_DEC',\n 'FIBER', 'OBJTYPE',\n 'FLUX_G', 'FLUX_R', 'FLUX_Z',\n 'SPECTROFLUX', 'SPECTROFLUX_IVAR', 'CAMERA',\n 'DELTA_X_ARCSEC', 'DELTA_Y_ARCSEC',\n 'XFOCAL', 'YFOCAL'),\n meta={'EXTNAME' : 'DITHER',\n 'TILEID' : '{}'.format(self._tileid)})\n\n return tab","def reformat_attrTable(\n self):\n #format into a dictionary of rows for quick aligning with the tracking_id\n if self.attrTable: attrTable = self.attrTable[:];\n else: attrTable = [];\n\n attrTable_dict = {};\n for row in attrTable:\n attrTable_dict[row['tracking_id']] = row;\n return attrTable_dict;","def join_gene_tables(gene_tables,output,verbose=None):\n \n gene_table_data={}\n start_column_id=\"\"\n samples=[]\n file_basenames=[]\n index=0\n for gene_table in gene_tables:\n \n if verbose:\n print(\"Reading file: \" + gene_table)\n \n lines=util.process_gene_table_with_header(gene_table, allow_for_missing_header=True)\n header=next(lines)\n \n # get the basename of the file\n file_basename='.'.join(os.path.basename(gene_table).split('.')[:-1])\n file_basenames.append(file_basename)\n \n if header:\n header_info=header.split(GENE_TABLE_DELIMITER)\n if not start_column_id:\n start_column_id=header_info[0]\n # allow for multiple samples\n sample_names=header_info[1:]\n else:\n # if there is no header in the file then use the file name as the sample name\n sample_names=[file_basename]\n \n for line in lines:\n data=line.split(GENE_TABLE_DELIMITER)\n try:\n gene=data[0]\n # if the header names multiple samples, merge all samples\n # this prevents extra columns from being included in some rows\n # this requires files containing multiple samples to include a header\n data_points=data[1:len(sample_names)+1]\n except IndexError:\n gene=\"\"\n\n if gene:\n current_data=gene_table_data.get(gene,\"\")\n fill = index - current_data.count(GENE_TABLE_DELIMITER)\n if fill > 0:\n # fill in zeros for samples without data then add data point\n gene_table_data[gene]=current_data + GENE_TABLE_DELIMITER.join([\"0\"]*fill) + GENE_TABLE_DELIMITER + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\n elif fill < 0:\n # add data point to other data point from the same sample\n current_data_points=current_data.split(GENE_TABLE_DELIMITER)\n for i,point in enumerate(data_points):\n store_index=len(data_points)*-1-1+i\n current_data_points[store_index]=str(float(current_data_points[store_index])+float(point))\n gene_table_data[gene] = GENE_TABLE_DELIMITER.join(current_data_points)\n else:\n # add data point to end of list\n gene_table_data[gene] = current_data + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\n \n samples+=sample_names\n index+=len(sample_names)\n # if all of the header names for the files are the same\n # then use the file names as headers\n if samples.count(samples[0]) == len(samples):\n samples=file_basenames\n \n # write the joined gene table\n if not start_column_id:\n start_column_id=\"# header \"\n sample_header=[start_column_id]+samples\n total_gene_tables=len(samples)\n sorted_gene_list=util.fsort(list(gene_table_data))\n try:\n file_handle=open(output,\"w\")\n file_handle.write(GENE_TABLE_DELIMITER.join(sample_header)+\"\\n\")\n except EnvironmentError:\n sys.exit(\"Unable to write file: \" + output) \n \n for gene in sorted_gene_list:\n # extend gene data for any gene that is not included in all samples\n current_data=gene_table_data[gene]\n fill = total_gene_tables - current_data.count(GENE_TABLE_DELIMITER)\n if fill:\n current_data=current_data + GENE_TABLE_DELIMITER.join([\"0\"]*fill) + GENE_TABLE_DELIMITER\n file_handle.write(gene+GENE_TABLE_DELIMITER+current_data.rstrip(GENE_TABLE_DELIMITER)+\"\\n\")\n \n file_handle.close()","def getAllSampleFields(self, sample_id, study_id):\n sample_tables = []\n sample_tables.append('sample')\n sample_tables.append('common_fields')\n sample_tables.append('extra_sample_')\n sample_tables.append('air')\n sample_tables.append('other_environment')\n sample_tables.append('sediment')\n sample_tables.append('soil')\n sample_tables.append('wastewater_sludge')\n sample_tables.append('water')\n sample_tables.append('host_assoc_vertibrate')\n sample_tables.append('host_associated_plant')\n sample_tables.append('human_associated')\n sample_tables.append('host_sample')\n sample_tables.append('host')\n \n filled_fields = {}\n \n con = self.getMetadataDatabaseConnection()\n cursor = con.cursor()\n \n for table in sample_tables:\n if 'extra_sample_' in table:\n statement = 'select * from %s%s where sample_id = %s' % (table, study_id, sample_id)\n elif table == 'host':\n statement = 'select * from host h inner join host_sample hs on h.host_id = hs.host_id where sample_id = %s' % sample_id\n else:\n statement = 'select * from %s where sample_id = %s' % (table, sample_id)\n \n try:\n cursor.execute(statement)\n except Exception, e:\n print str(e)\n print 'Error running query:\\n'\n print statement\n print 'Running next query...\\n'\n \n continue\n \n data = cursor.fetchall()\n\n # Get the column names\n col_names = []\n for i in range(0, len(cursor.description)):\n col_names.append(cursor.description[i][0])\n \n # Find the rows with data\n for row in data:\n i = 0\n for field in row:\n if field != None and field != '':\n filled_fields[col_names[i]] = field\n i += 1\n \n return filled_fields","def columnar(table: list[dict[str, str]]) -> dict[str, list[str]]:\n result: dict[str, list[str]] = {}\n keys = table[0].keys()\n for key in keys:\n result[key] = column_values(table, key)\n return result","def gen_tab(cat):\n\n col = ['FLUX_APER2','FLUX_APER4','FLUX_APER5','FLUX_APER8','FLUX_APER10','FLUX_APER14',\n 'MAG_APER2','MAG_APER4','MAG_APER5','MAG_APER8','MAG_APER10','MAG_APER14',\n 'MAG_AUTO','MAG_PETRO','KRON_RADIUS',\n 'PETRO_RADIUS','FLUX_MAX','ISOAREAF_IMAGE','x',\n 'y','ra','dec','X2_IMAGE','Y2_IMAGE','XY_IMAGE',\n 'THETA_IMAGE','X2WIN_IMAGE','Y2WIN_IMAGE','XYWIN_IMAGE','AWIN_IMAGE','BWIN_IMAGE',\n 'THETAWIN_IMAGE','AWIN_WORLD','BWIN_WORLD','THETAWIN_WORLD',\n 'MU_MAX','FLAGS','FWHM_IMAGE','ELONGATION','SEX_CLASS','FLUX_RADIUS25',\n 'FLUX_RADIUS50','FLUX_RADIUS85','FLUX_RADIUS95','FLUX_RADIUS99']\n print('generating features table: {}'.format(cat))\n tab = pd.read_table(cat,skiprows=41,sep=r'\\s+',header=None, names=col)\n\n # crop the image for just using the central part of the image\n tab = crop(tab)\n\n # add concentration column by subtracting mag10 by mag5, rejecting the detections with negative concentration\n tab['CONCENT'] = tab.MAG_APER5 - tab.MAG_APER10\n tab = tab[tab.CONCENT > 0]\n\n # normalizing the columns\n print('normalizing features...')\n seesq_norm = ['X2_IMAGE','Y2_IMAGE','X2WIN_IMAGE',\n 'Y2WIN_IMAGE','XY_IMAGE','XYWIN_IMAGE',\n 'ISOAREAF_IMAGE']\n see_norm = ['AWIN_WORLD','AWIN_WORLD','FWHM_IMAGE',\n 'KRON_RADIUS','PETRO_RADIUS','FLUX_RADIUS25',\n 'FLUX_RADIUS50','FLUX_RADIUS85',\n 'FLUX_RADIUS95','FLUX_RADIUS99']\n mag_norm = ['MAG_APER4','MAG_APER5','MAG_APER8',\n 'MAG_APER10','MAG_APER14','MAG_AUTO',\n 'MAG_PETRO','MU_MAX','CONCENT']\n flux_norm = ['FLUX_APER2','FLUX_APER4','FLUX_APER5',\n 'FLUX_APER8','FLUX_APER10','FLUX_APER14']\n fwhm_mean = tab.FWHM_IMAGE.mean()\n for seesq_col in seesq_norm:\n tab[seesq_col] = tab[seesq_col] / (fwhm_mean**2)\n for see_col in see_norm:\n tab[see_col] = tab[see_col] / fwhm_mean\n for mag_col in mag_norm:\n tab[mag_col] = tab[mag_col] * tab['MAG_APER2']\n for flux_col in flux_norm:\n tab[flux_col] = tab[flux_col] * tab['FLUX_MAX']\n tab['CONCENT'] = -1 * tab['CONCENT']\n\n # add column for galactic latitude\n print('calculating galactic latitude...')\n ra = np.array(tab['ra'].values)\n dec = np.array(tab['dec'].values)\n pos = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')\n tab['b'] = list(pos.galactic.b.deg)\n\n tab.drop(['MAG_APER2','FLUX_MAX','x','y'], axis=1, inplace=True)\n tab.to_csv(cat[:-4]+'.csv', index=False, header=True)","def make_lof_table(data_table, my_genes, my_samples, summary_func):\n table_header = [\"Gene\"] + my_samples + [\n \"Missense:Benign\", \"Missense:Possibly\", \"Missense:Probably\",\n \"MissenseNA\", \"Indel\", \"Nonsense\", \"Frameshift\", \"Splice-site\",\n \"Synonymous\"]\n table_records = []\n\n gs_lookup = group_data_by_gs(data_table)\n for gene in my_genes:\n synonymous = missense_benign = missense_possibly = missense_probably = \\\n missense_na = frameshift = nonsense = splice = indel = 0\n\n out_row = [gene]\n for sample in my_samples:\n normalized = [0]\n # Count mutations of each type for this gene and sample\n for entry in gs_lookup[gene][sample]:\n if entry['muttype'] == 'Silent':\n synonymous += 1\n continue\n if entry['muttype'] == 'Intron':\n # Shouldn't be here; ignore\n continue\n\n if entry['muttype'] == 'Missense_Mutation':\n if entry['consequence'] == 'benign':\n missense_benign += 1\n elif entry['consequence'] == 'possibly':\n missense_possibly += 1\n elif entry['consequence'] == 'probably':\n missense_probably += 1\n elif entry['consequence'] == 'NA':\n missense_na += 1\n else:\n print(\"Unhandled missense consequence level:\",\n entry['consequence'], file=sys.stderr)\n elif entry['muttype'] == 'Nonsense_Mutation':\n nonsense += 1\n elif entry['muttype'] == 'Splice_Site':\n splice += 1\n elif entry['muttype'] in ('Frame_Shift_Ins', 'Frame_Shift_Del'):\n frameshift += 1\n elif entry['muttype'] in ('In_Frame_Ins', 'In_Frame_Del'):\n indel += 1\n else:\n print(\"Unhandled mutation type:\", entry['muttype'],\n file=sys.stderr)\n continue\n\n normalized.append(entry['normalized'])\n # Summarize the normalized mutation counts for this gene and sample\n out_row.append(summary_func(normalized))\n out_row.extend((missense_benign, missense_possibly, missense_probably,\n missense_na, indel, nonsense, frameshift, splice,\n synonymous))\n table_records.append(out_row)\n\n return pandas.DataFrame.from_records(table_records, columns=table_header)","def construct_table(self):\n table_str = self.header_row\n row_lbls, col_lbls = self.get_idxvals()\n for r,rlbl in enumerate(row_lbls):\n row_data = [self.data[rlbl,clbl] for clbl in col_lbls]\n table_str += self.construct_row(r, row_data)\n \n return table_str","def format_bgc_metadata(df,float_id):\n mdf = df[bgc_metadata_columns]\n bgc_metadata_dict = {}\n for col in list(mdf):\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\"'\",'\"'))\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \n bgc_metadata_df = pd.DataFrame({\"float_id\": [float_id], \"Metadata_Dict\": [bgc_metadata_dict]})\n return bgc_metadata_df","def parse_distmat_to_dict(table):\r\n\r\n col_headers, row_headers, data = parse_matrix(table)\r\n assert(col_headers == row_headers)\r\n\r\n result = defaultdict(dict)\r\n for (sample_id_x, row) in zip(col_headers, data):\r\n for (sample_id_y, value) in zip(row_headers, row):\r\n result[sample_id_x][sample_id_y] = value\r\n return result","def df_sample_names(self):\n return self.abundance_mat_mult(True)","def pivot_scenarios(df, prefix, scen_map, df_type=\"pop\"):\n df[\"scenario\"] = prefix + \"_\" + df_type + \"_\" +\\\n df[\"scenario\"].map(scen_map)\n df = df.pivot_table(values=[\"lower\", \"mean\", \"upper\" ,\"value\"],\n index=\"location_id\",\n columns=\"scenario\",\n aggfunc=\"first\").reset_index()\n # This flattens the column levels\n df.columns = ['_'.join(col) for col in df.columns.values if col]\n df.rename(columns={\"location_id_\":\"location_id\"}, inplace=True)\n\n return df","def get_sample_colnames(ms_df: DF) -> List[str]:\n\n sample_numbers = get_sample_numbers(ms_df)\n\n target_sample_cols = list()\n for sample in sample_numbers:\n for col in SAMPLE_COLS:\n target_sample_cols.append('{attr}_{sample}'.format(attr=col, sample=sample))\n return target_sample_cols","def pretty(self):\n #table = [\"\".join([\"%8s \" % s for s in self.alpha.getSymbols()])]\n table = []\n for row in PWM.getFreq(self):\n table.append(\"\".join([\"%8.6f \" % y for y in row]))\n return table","def _dump_table(table: Model, directory: Path, format_: str):\n try:\n table.select().tuples()\n table.fields()\n dataset = tablib.Dataset(*table.select().tuples(), headers=table.fields())\n except:\n print(table._meta.database.get_columns(table.table_name()))\n\n if directory is not None:\n print(f\" Dumping {table.table_name()}...\")\n out_file = Path(directory) / f\"{table.table_name()}.{format_}\"\n out_file.write_text(dataset.export(format_))\n print(\" Done.\")\n print(\"=====================\")\n else:\n print(dataset.export(\"csv\"))","def permute_table(dtable):\n shuffle_field(dtable, 'gene')\n shuffle_field(dtable, 'sample')\n shuffle_field(dtable, 'Normalized')\n if 'Filler' in dtable:\n del dtable['Filler']","def experiment_list_table_format(result):\n table = []\n for item in result:\n table.append(experiment_show_table_format(item))\n return table","def get_split_summary_table(all_df, train_df, test_df):\n table = PrettyTable()\n table.field_names = ['set', 'N total', 'N non-ICH', 'N ICH', 'frac non-ICH', 'frac ICH']\n for df, name in zip([all_df, train_df, test_df],['All', 'Train', 'Test']):\n table.add_row([name, len(df), len(df[df.Hemorrhage == 0]), len(df[df.Hemorrhage == 1]),\n f'{len(df[df.Hemorrhage == 0])/len(df):.3%}', f'{len(df[df.Hemorrhage == 1])/len(df):.3%}'])\n return table","def convert_data(test_data,params,list_dict,rational_present=True,topk=2):\n \"\"\"input: params -- input dict, list_dict -- previous predictions containing rationals\n rational_present -- whether to keep rational only or remove them only\n topk -- how many words to select\"\"\"\n \n temp_dict={}\n for ele in list_dict:\n temp_dict[ele['annotation_id']]=ele['rationales'][0]['soft_rationale_predictions']\n \n test_data_modified=[]\n \n for index,row in tqdm(test_data.iterrows(),total=len(test_data)):\n try:\n attention=temp_dict[row['Post_id']]\n except KeyError:\n continue\n topk_indices = sorted(range(len(attention)), key=lambda i: attention[i])[-topk:]\n new_text =[]\n new_attention =[]\n if(rational_present):\n if(params['bert_tokens']):\n new_attention =[0]\n new_text = [101]\n for i in range(len(row['Text'])):\n if(i in topk_indices):\n new_text.append(row['Text'][i])\n new_attention.append(row['Attention'][i])\n if(params['bert_tokens']):\n new_attention.append(0)\n new_text.append(102)\n else:\n for i in range(len(row['Text'])):\n if(i not in topk_indices):\n new_text.append(row['Text'][i])\n new_attention.append(row['Attention'][i])\n test_data_modified.append([row['Post_id'],new_text,new_attention,row['Label']])\n\n df=pd.DataFrame(test_data_modified,columns=test_data.columns)\n return df","def export_data(fp, app_name):\n from otree.views.admin import get_display_table_rows\n colnames, rows = get_display_table_rows(\n app_name, for_export=True, subsession_pk=None)\n colnames = ['{}.{}'.format(k, v) for k, v in colnames]\n writer = csv.writer(fp)\n writer.writerows([colnames])\n writer.writerows(rows)","def print_table(source, count=False):\n table_value = []\n table_header = []\n for source_key, source_value in source.items():\n for item in source_value:\n table_value.append([v for v in item.values()])\n table_header.append([k for k in item.keys()])\n if not count:\n print(tabulate(table_value,\n headers=table_header[0],\n tablefmt='orgtbl'))\n else:\n print(tabulate([[len(source_value)]],\n headers=[source_key],\n tablefmt='orgtbl'))","def read_table(table_name, hf, df_fmt, ilo, ihi):\n dfs = []\n for dt, block in df_fmt.groupby(\"dtype\"):\n \n # check if this dtype contains waveform data\n if 'waveform' in block['name'].values:\n wf_group = f\"/{table_name}/waveform\"\n wf_block = read_waveforms(wf_group, hf, df_fmt, ilo, ihi)\n wf_rows, wf_cols = wf_block.shape\n nrows = wf_rows\n \n # get number of additional columns\n new_cols = [c for c in list(block[\"name\"].values) if c != 'waveform']\n newcols = len(new_cols)\n \n # allocate the full numpy array for this dtype\n np_block = np.empty((nrows, newcols + wf_cols), dtype=dt)\n np_block[:, newcols:] = wf_block\n \n cols = []\n for i, col in enumerate(new_cols):\n ds = hf[f\"{table_name}/{col}\"] \n \n if ihi is None:\n ihi = ds.shape[0]\n nwfs = ihi - ilo + 1 # inclusive\n \n np_block[:, i] = ds[ilo:ihi]\n cols.append(col)\n cols.extend(np.arange(wf_cols)) \n\n dfs.append(pd.DataFrame(np_block, columns=cols))\n \n # read normal 'array<1>{real}' columns\n else:\n ncols = len(block)\n nrows = block[\"size\"].unique()\n if len(nrows) > 1:\n print('Error, columns are different lengths')\n exit()\n nrows = nrows[0]\n np_block = np.empty((nrows, ncols), dtype=dt)\n \n for i, col in enumerate(block[\"name\"]):\n ds = hf[f\"{table_name}/{col}\"]\n np_block[:,i] = ds[...]\n \n dfs.append(pd.DataFrame(np_block, columns=block[\"name\"])) \n \n # concat final DF after grouping dtypes and avoiding copies\n return pd.concat(dfs, axis=1, copy=False)","def change_food_names_in_final_table(df):\n # df = pd.read_excel(\"final_dataset_with_median_all.xlsx\")\n df.reset_index(drop=True, inplace=True)\n\n foods = df['food_names']\n food_examples = []\n indices = list(range(0, len(foods)))\n for i in indices:\n print(i)\n food_examples.append(str(foods[i]) + str(i))\n food_examples = pd.Series(food_examples)\n\n df.drop(labels=['food_names'], axis=\"columns\", inplace=True)\n df['food_names'] = food_examples\n writer = pd.ExcelWriter('final_dataset_with_median.xlsx', engine='xlsxwriter')\n df.to_excel(writer, sheet_name='Sheet1')\n writer.save()","def _gen_table_rows(self):\n row_labels = self._get_padded_row_labels()\n column_labels = self._get_padded_column_labels()\n for row in zip(*column_labels):\n yield ''.join('<td>%s</td>' % c for c in row)\n for label, row_string in zip(row_labels, HeatMap._gen_table_rows(self)):\n yield ''.join('<td>%s</td>' % c for c in label) + row_string","def basic_table_details():\n tbl: pa.table = pa.Table.from_pylist([\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\n ])\n\n results = {\n 'column_names': tbl.column_names,\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\n 'nbytes': tbl.nbytes,\n 'num_columns': tbl.num_columns,\n 'num_rows': tbl.num_rows,\n 'schema': tbl.schema,\n 'shape': tbl.shape,\n }\n\n print(results)","def tranform_data(args):\n data_dir = args.data_dir\n save_dir = args.save_dir\n os.makedirs(save_dir)\n maxsize = args.maxsize\n maxlen = args.maxlen \n ext = args.ext\n datetime = args.datetime\n classes = set()\n nb_files = 0\n time_col = 'time'\n event_col = 'event'\n gt_ids = None\n if args.ext == \"pkl\":\n with open(Path(args.data_dir, \"fx_labels\"), \"rb\") as fp:\n gt_ids = pickle.load(fp)[:maxsize]\n labels = np.unique(gt_ids)\n gt_data = []\n for i in range (len(gt_ids)):\n gt_data.append(int(np.nonzero(gt_ids[i] == labels)[0]))\n gt = {'cluster_id': gt_data}\n print(gt_data)\n gt_table = pd.DataFrame(data=gt)\n gt_table.to_csv(Path(save_dir, 'clusters.csv'))\n if Path(args.data_dir, 'clusters.csv').exists():\n gt_ids = pd.read_csv(Path(args.data_dir, 'clusters.csv'))[:(maxsize)]\n gt_ids.to_csv(Path(save_dir, 'clusters.csv'))","def get_table_data(data):\n logger.info('Generating summary table')\n\n table_dict = data.T.to_dict()\n table = []\n for date in sorted(data.index):\n table.append(table_dict[date])\n table.sort(key=lambda x: x['DATE'], reverse=True)\n for ii in range(len(table)):\n for k, v in table[ii].items():\n if pd.isnull(v):\n table[ii][k] = None\n return table","def create_table(build_list):\n # extract list of files:\n all_files = build_list.split(',')\n\n output_file = os.getcwd() + \"/fpkm_table.tbl\"\n verbalise(\"B\", \"saving built table to file \", output_file)\n\n awk_cmd = \"\"\"awk '!($10~/FPKM/){\\\n gene_sample[$1,FILENAME]=$9;\\\n samples[FILENAME]=1;genes[$1]=1}\\\n END{printf \"%s\\t\", \"genes\";\\\n for(g in genes){printf \"%s\\t\", g};print \"\";\\\n for(s in samples){printf \"%s\\t\",s;\\\n for(g in genes){printf \"%s\\t\", gene_sample[g,s]};\\\n print \"\"}}' \"\"\"\n\n full_cmd = awk_cmd + \" \".join(all_files) + \">\" + str(output_file)\n\n # build table:\n os.system(full_cmd)\n\n # shorten filenames within table for easier reading!\n #output\n\n return output_file","def tabular_data(self):\n path = CFG.GRAPHS_DIR\n chdir(path)\n\n if self.experiment_count == 1:\n f = open(self.tablefile, 'w')\n f.write(self.print_border_line())\n f.write(self.table_header())\n f.write(self.print_border_line())\n f.write(self.pretty_string(\"Functions\"))\n f.write(self.pretty_string(\"Batch Size\"))\n f.write(self.pretty_string(\"Training (%)\"))\n f.write(self.pretty_string(\"Testing (%)\", True))\n f.write('\\n')\n f.write(self.print_border_line())\n f.close()\n\n f = open(self.tablefile, 'a')\n f.write(self.pretty_string(self.function_name))\n f.write(self.pretty_string(str(self.batch_size)))\n f.write(self.pretty_string(self.tr_mean_str))\n f.write(self.pretty_string(self.test_mean_str, True))\n f.write('\\n')\n f.close()","def parse_trflp(lines):\r\n\r\n sample_ids = []\r\n otu_ids = []\r\n data = []\r\n non_alphanum_mask = re.compile('[^\\w|^\\t]')\r\n # not sure why the above regex doesn't cover the following regex...\r\n dash_space_mask = re.compile('[_ -]')\r\n\r\n for i, line in enumerate(lines):\r\n elements = line.strip('\\n').split('\\t')\r\n\r\n # special handling for the first line only\r\n if i == 0:\r\n # validating if the file has a header\r\n if elements[0] == '':\r\n for otu_id in elements[1:]:\r\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\r\n continue\r\n else:\r\n for j, otu_id in enumerate(elements[1:]):\r\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n\r\n # handling of all other lines\r\n current_row = []\r\n\r\n # converting each value in the row to int\r\n for count in elements[1:]:\r\n try:\r\n current_row.append(int(round(float(count), 0)))\r\n except ValueError:\r\n current_row.append(0)\r\n\r\n # if the sum of all the values is equial to 0 ignore line\r\n if sum(current_row) == 0:\r\n continue\r\n\r\n # adding sample header to list\r\n sample_ids.append(non_alphanum_mask.sub('.',\r\n dash_space_mask.sub('.', elements[0])))\r\n\r\n # validating the size of the headers to add missing columns\r\n # this is only valid when there is no header\r\n if len(current_row) > len(otu_ids):\r\n # modify header data\r\n extra_cols = []\r\n for j in range(len(otu_ids), len(current_row)):\r\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n # modify data\r\n for j in range(len(data)):\r\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\r\n\r\n otu_ids.extend(extra_cols)\r\n elif len(current_row) < len(otu_ids):\r\n # modify data\r\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\r\n\r\n data.append(current_row)\r\n\r\n return sample_ids, otu_ids, asarray(data).transpose()","def data_in(input_filename, sample_id):\n \n print('Processing sample {}'.format(sample_id))\n # Encoding set to latin1 due to presence of degree symbol\n # newlines in CC2 logger details fields will cause issues\n header_rows = 30\n df_param = pd.read_table(input_filename, \n nrows=header_rows, \n encoding=\"latin1\", \n header=None)\n df_val = pd.read_table(input_filename, skiprows=header_rows)\n df_param_indexed = df_param.set_index(0)\n# redundant due to parsing sample id from filename\n# sample_id = df_param_indexed.loc['Sample ID', 1]\n \n d1 = {1: pd.Series(['', ''], index=['Sample ID', 'Label'])}\n df_val_params = pd.DataFrame(d1)\n df_val_params.loc['Sample ID', 1] = sample_id\n df_val_params.loc['Label', 1] = sample_id\n\n return df_param_indexed, df_val, df_val_params","def convert_list_of_freqs_to_dataframe(referanse):\n res = []\n for x in referanse:\n res.append( dict(x))\n result = pd.DataFrame(res).transpose()\n normalize_corpus_dataframe(result)\n return result","def get_sample_ids(maptable):\n return [line[0] for line in maptable[1:]]","def generate_table(self, outtablename,\n cols=['A', 'B', 'AB'],\n generateTable=True):\n if generateTable:\n new_indices = ['time (s)', 'mean counts']\n for idx in self.data[cols].describe().index[2:]:\n new_indices.append(idx)\n outTable = self.data[cols].describe()\\\n .set_index(pd.Index(new_indices))\n outTable.to_latex(\n self.tables_dir + outtablename + \".tex\", float_format=\"%d\")\n print(\"Outtable: \", outTable)","def flatten_df(df):\n flat_array = df.values.flatten()\n flat_df = pd.DataFrame(flat_array)\n flat_df.columns = [\"loan\"]\n flat_df[\"row_no\"] = flat_df.reset_index().index\n flat_df = flat_df[[\"row_no\", \"loan\"]]\n flat_df.row_no = flat_df.row_no // 100\n return flat_df","def format_predictions(self, predictions):\n output = predictions.copy()\n\n column_names = predictions.columns\n\n for col in column_names:\n if col not in {'forecast_time', 'identifier'}:\n output[col] = predictions[col]\n\n return output","def columnar(list_table_rows: list[dict[str, str]]) -> dict[str, list[str]]:\n column_oriented_table: dict[str, list[str]] = {}\n first_row: dict[str, str] = list_table_rows[0]\n for column in first_row:\n column_oriented_table[column] = column_values(list_table_rows, column)\n return column_oriented_table","def _normalize_dataset(self):\n\n new_data = []\n columns = ['user_id', 'movie_id', 'rating']\n for line in self.data_file['users']:\n movies_by_user = [\n {'user_id': line['user_id'], 'movie_id': movie_id, 'rating': 5}\n for movie_id in line['movies']\n ]\n new_data.extend(movies_by_user)\n return pd.DataFrame(new_data, columns=columns)","def tabulate(self):\n for test_name, test in self.test_types.items():\n for ivs_name, ivs in self.ivs.items():\n if self.verbose:\n print(\"{0}: {1}\".format(ivs_name, test_name))\n tree = test(ivs)\n if not tree:\n continue\n score = tree.score(True)\n if self.verbose > 1:\n tree.print_structure()\n\n self.result_matrix['ivs name'][ivs_name][test_name] = score\n self.result_matrix['test type'][test_name][ivs_name] = score","def summary_scores(fold_scores_list): \n d = {}\n nulls = {}\n for idx, score in enumerate(fold_scores_list):\n d.update({f\"{idx+1}0 samples:\": score[0].T})\n nulls.update({f\"{idx+1}0 samples:\": score[1].T})\n \n summary_scores = pd.concat(d.values(), axis=1, keys=d.keys()).T\n summary_nulls = pd.concat(nulls.values(), axis=1, keys=nulls.keys()).T\n return summary_scores, summary_nulls","def to_table(self):\n table = Table()\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\n table['counts'] = self.counts.to_table()['data']\n if self.counts.data_err is not None:\n table['counts_err'] = self.counts.to_table()['data_err']\n table['livetime'] = self.livetime.to_table()['data']\n table['bkg'] = self.bg_rate.to_table()['data']\n if self.bg_rate.data_err is not None:\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\n table.meta['HDUNAME'] = \"bkg_2d\"\n return table","def flatten(tbl, tpcol, key, val):\n tblflat = tbl[tbl[tpcol].isin(val)]\n tblflatnm = '%s_flat' %key\n return tblflat, tblflatnm","def filter_mapping_file(map_data, map_header, good_sample_ids,\r\n include_repeat_cols=False, column_rename_ids=None):\r\n # keeping samples\r\n to_keep = []\r\n to_keep.extend([i for i in map_data if i[0] in good_sample_ids])\r\n\r\n # keeping columns\r\n headers = []\r\n to_keep = zip(*to_keep)\r\n headers.append(map_header[0])\r\n result = [to_keep[0]]\r\n\r\n if column_rename_ids:\r\n # reduce in 1 as we are not using the first colum (SampleID)\r\n column_rename_ids = column_rename_ids - 1\r\n for i, l in enumerate(to_keep[1:-1]):\r\n if i == column_rename_ids:\r\n if len(set(l)) != len(result[0]):\r\n raise ValueError(\r\n \"The column to rename the samples is not unique.\")\r\n result.append(result[0])\r\n result[0] = l\r\n headers.append('SampleID_was_' + map_header[i + 1])\r\n elif include_repeat_cols or len(set(l)) > 1:\r\n headers.append(map_header[i + 1])\r\n result.append(l)\r\n else:\r\n for i, l in enumerate(to_keep[1:-1]):\r\n if include_repeat_cols or len(set(l)) > 1:\r\n headers.append(map_header[i + 1])\r\n result.append(l)\r\n headers.append(map_header[-1])\r\n result.append(to_keep[-1])\r\n\r\n result = map(list, zip(*result))\r\n\r\n return headers, result","def convert_table(mkd):\n\t\n\tmd_table_codes = re.findall(r\".*\\|.*\\n.*\\-.*(?:\\n.*\\|.*)*\", mkd, re.M)\n\tfor md_code in md_table_codes:\n\t\t\n\t\tmd_rows = re.findall(r\"(.*\\|.*)\", md_code, re.M)\n\t\theader = md_rows.pop(0)\n\t\tcolumn_count = md_rows.pop(0).count(\"-\")\n\n\t\ttex_code = \"\\\\begin{tabular}{|\"+\"l|\"*column_count+\"}\\n\\hline\\n\"\n\t\ttex_code += header.strip(\" |\").replace(\"|\", \"&\")+\" \\\\\\\\\\n\\hline\\n\"\n\t\tfor row in md_rows:\n\t\t\ttex_code += row.strip(\" |\").replace(\"|\", \"&\")+\" \\\\\\\\\\n\"\n\t\ttex_code += \"\\hline\\n\\end{tabular}\"\n\n\t\tmkd = mkd.replace(md_code, tex_code)\n\n\treturn mkd","def normalize_dataset(self):","def revise_report_df(report_df):\n # quick check to see whether report_df column structure is as expected\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\n print(\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\")\n\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\n\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\n keys = ['precision', 'recall', 'f1-score', 'support']\n\n for col in dict_columns:\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\n\n # iterate row wise through dataframe to normalize dictionary values into flat tables\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\n\n # concat all classifier flat tables into one dataframe\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\n\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\n\n # select only created columns\n report_df = report_df.iloc[:,5:]\n # sort columns and filter out 'support' related columns\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\n\n return report_df","def print_table2(df, eval_dir):\n\n out_file = os.path.join(eval_dir, 'table2.txt')\n\n\n with open(out_file, \"w\") as text_file:\n\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\n # new line\n header_string = ' & '\n line_string = '({}) '.format(struc_name)\n\n for p_idx, phase in enumerate(['ED', 'ES']):\n for measure in ['dice', 'assd', 'hd']:\n\n header_string += ' & {} ({}) '.format(phase, measure)\n\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\n\n if measure == 'dice':\n\n line_string += ' & {:.3f}\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n else:\n line_string += ' & {:.2f}\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\n\n if p_idx == 0:\n header_string += ' & '\n line_string += ' & '\n\n header_string += ' \\\\\\\\ \\n'\n line_string += ' \\\\\\\\ \\n'\n\n if idx == 0:\n text_file.write(header_string)\n\n text_file.write(line_string)\n\n return 0","def __generate_table(package_response, primary_table_id):\n if isinstance(package_response, Package):\n primary_table = package_response.tables[primary_table_id]\n header_id = primary_table.definition.header_table_id\n header_table = package_response.tables[header_id]\n dimension_columns = list(filter(lambda column_obj: column_obj.is_dimension, primary_table.definition.columns))\n dimension_columns_count = len(dimension_columns)\n row_count = len(primary_table.data.rows)\n header_row_count = len(header_table.data.rows)\n\n headers = list(list())\n # Constructs the column headers by considering dimension columns and header rows\n for series_definition_column in header_table.definition.columns:\n header_row = list()\n for i in range(0, dimension_columns_count, 1):\n if dimension_columns[i].description is \"\":\n header_row.append(\" \")\n else:\n header_row.append(dimension_columns[i].description)\n\n for i in range(0, header_row_count, 1):\n header_row.append(str(_SeriesDataHelper.get_value_helper(header_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\n headers.append(header_row)\n\n data = list(list())\n # Constructs the column data\n for i in range(0, row_count, 1):\n data_row = list()\n for series_definition_column in primary_table.definition.columns:\n data_row.append(str(_SeriesDataHelper.get_value_helper(primary_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\n data.append(data_row)\n\n if len(header_table.definition.columns) > 1:\n data_frame = pd.DataFrame(data=data)\n data_frame.columns = pd.MultiIndex.from_arrays(headers)\n else:\n data_frame = pd.DataFrame(data=data, columns = headers[0])\n\n return data_frame\n\n else:\n ValueError(\"Response data passed should be of package type.\")"],"string":"[\n \"def format_unifrac_sample_mapping(sample_ids, otu_ids, otu_table_array):\\r\\n out = []\\r\\n for i, row in enumerate(otu_table_array):\\r\\n for j, val in enumerate(row):\\r\\n if val > 0:\\r\\n line = [otu_ids[i], sample_ids[j], str(val)]\\r\\n out.append('\\\\t'.join(line))\\r\\n return out\",\n \"def format_df(df):\\n df = df.transpose()\\n df.rename(columns=df.iloc[0], inplace=True)\\n df.drop('gene', inplace=True)\\n df.index.rename('sample_id', inplace=True)\\n return df\",\n \"def format_df(df):\\n df = df.transpose()\\n df.rename(columns=df.iloc[0], inplace=True)\\n df.drop('gene', inplace=True)\\n df.index.rename('sample_id', inplace=True)\\n return df\",\n \"def from_multicsv(self,input_data):\\n reformatted_data = []\\n for (i,row) in enumerate(input_data):\\n if i==0:\\n headers = row\\n else:\\n data_row = {}\\n for (j,h) in enumerate(headers):\\n if j<len(row):\\n data_row.update({h : row[j]})\\n else:\\n data_row.update({h : 0})\\n reformatted_data.append(data_row)\\n return reformatted_data\",\n \"def convert_to_table_format(package):\\n tables = list()\\n for primary_table_id in package.primary_table_ids:\\n tables.append(StachExtensions.__generate_table(package, primary_table_id))\\n return tables\",\n \"def print_tables(hash_table, f_output, l_samples):\\n\\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\\n 'ExAC_SAS',\\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\\n 'Polyphen2_HVAR_score',\\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\\n l_fields = l_fields + l_samples\\n \\n l_chr = set([item[0] for item in hash_table.keys()])\\n\\n fo = open(f_output, 'w')\\n fo.write(','.join(l_fields) + '\\\\n')\\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\\\n')\\n fo.close()\",\n \"def tree2OTU_table(mvp_tree):\\n series = []\\n for terminal in mvp_tree.feature_tree.get_terminals():\\n try:\\n series.append(terminal.sample_series)\\n except:\\n print('there is no sample series in tree2OTU ')\\n df = pd.dataframe(series)\\n return df\",\n \"def to_frame(self):\\n # Create a set of dictionaries/lists for each column\\n data = dict([(i_var.name, []) for i_var in self.inputs])\\n data.update({self.OUTPUT_LABEL: [], self.INPUT_LABEL: [], self.name: []})\\n\\n # A very ugly loop to produce all the probabilities in a nice way.\\n # Note that this just reproduces what is already in `self.lookup`.\\n # Honestly, I just haven't thought of a better way to get nice output.\\n for i_index, i_state in enumerate(self.input_states):\\n for o_var, results in zip(self.outputs, self.per_state_results):\\n for o_state, o_p in enumerate(results[i_index]):\\n for i_var, s in zip(self.inputs, i_state):\\n data[i_var.name].append(s)\\n data[self.OUTPUT_LABEL].append(o_var.name)\\n data[self.INPUT_LABEL].append(o_state)\\n data[self.name].append(o_p)\\n all_data = pd.DataFrame(data=data)\\n\\n # The magnificent pivot table function does all the work\\n return pd.pivot_table(data=all_data, values=[self.name],\\n index=[i_var.name for i_var in self.inputs],\\n columns=[self.OUTPUT_LABEL, self.INPUT_LABEL])\",\n \"def test_toTable(self):\\r\\n # Empty results.\\r\\n out_f = StringIO()\\r\\n self.res1.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(),\\r\\n \\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\\\n\\\")\\r\\n out_f.close()\\r\\n\\r\\n # Results with multiple samples.\\r\\n exp = \\\"\\\"\\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\r\\nS1\\\\t5\\\\t21\\\\t1.5\\\\t2.5\\\\t3.5\\r\\nS1\\\\t10\\\\t20\\\\t2.5\\\\t2.5\\\\t3.5\\r\\nS1\\\\t20\\\\t30\\\\t3.5\\\\t2.5\\\\t3.5\\r\\nS2\\\\t1\\\\t3\\\\t0.4\\\\t2.5\\\\t3.5\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n self.res2.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\\r\\n\\r\\n # Custom header.\\r\\n exp = \\\"\\\"\\\"foo\\\\tbar\\\\tbaz\\\\tbazaar\\\\tbazaaar\\\\tbazaaaar\\r\\nS1\\\\t5\\\\t21\\\\t1.5\\\\t2.5\\\\t3.5\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n self.res1.addSample('S1', 42)\\r\\n self.res1.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)\\r\\n self.res1.toTable(out_f,\\r\\n header=['foo', 'bar', 'baz', 'bazaar', 'bazaaar', 'bazaaaar'])\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\\r\\n\\r\\n # Invalid header.\\r\\n with self.assertRaises(ValueError):\\r\\n out_f = StringIO()\\r\\n self.res1.toTable(out_f, header=['foo'])\\r\\n\\r\\n # Cells with None as their value.\\r\\n exp = \\\"\\\"\\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\r\\nS1\\\\t43\\\\tN/A\\\\tN/A\\\\tN/A\\\\tN/A\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n res = RichnessEstimatesResults()\\r\\n res.addSample('S1', 42)\\r\\n res.addSampleEstimate('S1', 43, None, None, None, None)\\r\\n res.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def _finalize(self, sampled_data):\\n final_data = dict()\\n for table_name, table_rows in sampled_data.items():\\n parents = self.metadata.get_parents(table_name)\\n if parents:\\n for parent_name in parents:\\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\\n if foreign_key not in table_rows:\\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\\n table_rows[foreign_key] = parent_ids\\n\\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\\n\\n fields = self.metadata.get_fields(table_name)\\n\\n final_data[table_name] = reversed_data[list(fields.keys())]\\n\\n return final_data\",\n \"def reformat_data(self, df, ids):\\n data = np.zeros((len(ids), self.n_sample_rows + 1, self.n_features))\\n idx = 0\\n for i in ids:\\n sample = df.loc[i]\\n data[idx, 0:89, :] = sample.values\\n data[idx, 89, :] = np.mean(sample.values)\\n idx += 1\\n return data\",\n \"def preprocessKNN(self):\\n\\n feature_list = []\\n\\n for index, row in self.all_data.iterrows():\\n chans = cv2.split(row['image'])\\n\\n features = []\\n for chan in chans:\\n hist = cv2.calcHist(chan, [0], None, [64], [0,256])\\n features.extend(hist)\\n\\n features = np.array(features).flatten()\\n feature_list.append(features)\\n\\n df = self.all_data[['name', 'genre']].copy()\\n\\n feature_df = pd.DataFrame(feature_list)\\n\\n df = df.join(feature_df)\\n\\n return df\",\n \"def get_normalized_data_table(table_metadata, debug=False):\\n suffix = table_metadata.get('suffix', '')\\n data_table = table_metadata['table_class'](\\n file_path=table_metadata['csv_filename'], suffix=suffix)\\n drop_headers(table_metadata['document_label'], data_table.data)\\n rename_headers(table_metadata['document_label'], data_table.data)\\n print_data_table_length(table_metadata['document_label'],\\n data_table.data,\\n debug=debug)\\n return data_table\",\n \"def __profile_to_table(data):\\n output = [[\\\"condition\\\", \\\"mean\\\", \\\"min\\\", \\\"max\\\"]]\\n order = data[\\\"order\\\"]\\n\\n for o in order:\\n try:\\n values = data[\\\"data\\\"][o]\\n output.append(\\n [o, str(mean(values)), str(min(values)), str(max(values))]\\n )\\n except Exception as e:\\n print(e)\\n\\n return \\\"\\\\n\\\".join([\\\"\\\\t\\\".join(l) for l in output])\",\n \"def _get_table_from_samples(self, index):\\n df = pd.DataFrame()\\n for sample in self.samples:\\n sd = sample.to_dict()\\n ser = pd.Series(\\n {k: v for (k, v) in list(sd.items()) if not k.startswith(\\\"_\\\")}\\n )\\n df = df.append(ser, ignore_index=True)\\n index = [index] if isinstance(index, str) else index\\n if not all([i in df.columns for i in index]):\\n _LOGGER.debug(\\n \\\"Could not set {} index. At least one of the \\\"\\n \\\"requested columns does not exist: {}\\\".\\n format(CFG_SAMPLE_TABLE_KEY, index))\\n return df\\n _LOGGER.debug(\\\"Setting sample_table index to: {}\\\".format(index))\\n df.set_index(keys=index, drop=False, inplace=True)\\n return df\",\n \"def test_format_unifrac_sample_mapping(self):\\r\\n a = [[1, 0, 0], [0, 2, 4], [7, 0, 9.0]]\\r\\n otu_ids = ['OTUa', 'OTUb', 'OTUc']\\r\\n sample_ids = ['Sa', 'Sb', 'Sc']\\r\\n result = format_unifrac_sample_mapping(sample_ids, otu_ids, a)\\r\\n self.assertEqual(\\r\\n result,\\r\\n ['OTUa\\\\tSa\\\\t1',\\r\\n 'OTUb\\\\tSb\\\\t2',\\r\\n 'OTUb\\\\tSc\\\\t4',\\r\\n 'OTUc\\\\tSa\\\\t7',\\r\\n 'OTUc\\\\tSc\\\\t9.0'])\",\n \"def _format_data(self):\\n formatted_data = []\\n\\n for row in self._data_agg_by_mean_value.iterrows():\\n \\n car_make = row[0]\\n mean_car_value = round(row[1][0], 2)\\n formatted_data.append({'car_make': car_make, 'mean_car_value': mean_car_value})\\n\\n return formatted_data\",\n \"def reformat_countOrFPKMTable(\\n self,\\n countOrFPKMTable_I=None,\\n analysis_id_I=None,\\n sna2experimentID_I=None,\\n sna2sns_I=None,\\n count_or_FPKM = 'count'):\\n #format into a dictionary of rows for quick aligning with the tracking_id\\n countOrFPKMTable_flat = [];\\n for row in countOrFPKMTable_I:\\n for k,v in row.items():\\n if k=='tracking_id':continue;\\n tmp = {};\\n tmp['analysis_id'] = analysis_id_I;\\n tmp['tracking_id'] = row['tracking_id'];\\n\\n sample_name_lst = k.split('_');\\n sample_name_base = '_'.join(sample_name_lst[:-1]);\\n sample_name_rep = eval(sample_name_lst[-1]);\\n if sna2experimentID_I: \\n experiment_id = sna2experimentID_I[sample_name_base];\\n else:\\n experiment_id=None;\\n tmp['experiment_id'] = experiment_id;\\n if sna2sns_I: \\n sample_name = sna2sns_I[sample_name_base][sample_name_rep];\\n else:\\n sample_name=k;\\n tmp['sample_name'] = sample_name;\\n\\n tmp['value'] = v;\\n tmp['value_units'] = count_or_FPKM;\\n tmp['used_'] = True;\\n tmp['comment_'] = None;\\n countOrFPKMTable_flat.append(tmp);\\n return countOrFPKMTable_flat;\",\n \"def tsv_samples_to_names(self, name='samples_to_names.tsv'):\\n with open(self.a.out_dir + name, 'w') as handle:\\n content = self.df_sample_names.to_csv(None, sep=self.sep, float_format=self.float_format)\\n handle.writelines(content)\",\n \"def transform(filtered_list):\\n out_put = {}\\n out_list = []\\n # loop to get the required columns, random ordered\\n for item in filtered_list:\\n for val in item._fields:\\n if val in type_dict:\\n out_put[val] = type_dict.get(val)(getattr(item, val))\\n out_list.append(out_put)\\n out_put = {}\\n\\n # loop to the ordered columns data as per output\\n all_rows = []\\n for item in out_list:\\n tmp_row = []\\n for key in type_dict.keys():\\n out_put[key] = item[key]\\n tmp_row.append(item[key])\\n all_rows.append(tmp_row)\\n\\n col_row = [col.replace('_', '-') for col in type_dict.keys()]\\n all_rows.insert(0, col_row)\\n return all_rows\",\n \"def per_sample_taxa_summaries(open_table, output_format):\\n t = parse_biom_table(open_table)\\n header = \\\"#taxon\\\\trelative_abundance\\\\n\\\"\\n\\n for v, id_, md in t.iter():\\n with open(output_format % id_, 'w') as f:\\n f.write(header)\\n\\n for sorted_v, taxa in \\\\\\n sorted(zip(v, t.ids(axis='observation')))[::-1]:\\n if sorted_v:\\n f.write(\\\"%s\\\\t%f\\\\n\\\" % (taxa, sorted_v))\",\n \"def generate_table(results):\\n keyslist = list(results[0].keys())\\n table = PrettyTable(keyslist)\\n for dct in results:\\n table.add_row([dct.get(c, \\\"\\\") for c in keyslist])\\n return table\",\n \"def _load_sample_table(self):\\n self.sampleTable = pd.read_table(self.config['sampletable'], sep='\\\\t', dtype=str)\\n self.sampleTable.set_index('sampleID', inplace=True)\\n self.samples = self.sampleTable.reset_index().to_dict('records')\",\n \"def transform(input):\\n transformed_file = []\\n\\n for row in input:\\n names = row['name'].split()\\n row['fname'] = names[0]\\n row['lname'] = names[1]\\n del row['name']\\n transformed_file.append(row)\\n return transformed_file\",\n \"def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\\n datafile = open(output_table, \\\"w\\\")\\n datafile.write(\\n \\\"Feature_1\\\" + '\\\\t' + \\\"Feature_2\\\" + \\\"\\\\t\\\" + \\\"plus_plus\\\" + '\\\\t' + \\\"minus_minus\\\" + '\\\\t' + \\\"plus_minus\\\" + '\\\\t' + \\\"minus_plus\\\" + '\\\\t' + \\\"p_value_same_opposite\\\" + '\\\\t' + \\\"p-value_same_opposite_Bonferoni_corrected\\\" + '\\\\t' + \\\"Ratio_same_opposite\\\" + '\\\\t' + \\\"p_value_divergent_convergent\\\" + '\\\\t' + \\\"p_value_divergent_convergent Bonferoni corrected\\\" + '\\\\t' + \\\"Ratio divergent convergent\\\" + '\\\\n')\\n for i in range(len(NamesL_pairs)):\\n datafile.write(\\n NamesL_pairs[i][0] + '\\\\t' + NamesL_pairs[i][1] + '\\\\t' + str(p_pL[i]) + '\\\\t' + str(m_mL[i]) + '\\\\t' + str(\\n p_mL[i]) + '\\\\t' + str(m_pL[i]) + '\\\\t' + str(p_valsL[i]) + '\\\\t' + str(p_vals_BonferoniL[i]) + '\\\\t' + str(\\n RatiosL[i]) + '\\\\t' + str(p_valsL_divergent_convergent[i]) + '\\\\t' + str(\\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\\\t' + str(RatiosL_divergent_convergent[i]) + '\\\\n')\\n datafile.close()\\n return\",\n \"def split_otu_table_on_sample_metadata(otu_table_f, mapping_f, mapping_field):\\r\\n mapping_f = list(mapping_f)\\r\\n mapping_values = get_mapping_values(mapping_f, mapping_field)\\r\\n otu_table = parse_biom_table(otu_table_f)\\r\\n\\r\\n for v in mapping_values:\\r\\n v_fp_str = v.replace(' ', '_')\\r\\n sample_ids_to_keep = sample_ids_from_metadata_description(\\r\\n mapping_f, valid_states_str=\\\"%s:%s\\\" % (mapping_field, v))\\r\\n\\r\\n try:\\r\\n filtered_otu_table = otu_table.filterSamples(\\r\\n lambda values, id_, metadata: id_ in sample_ids_to_keep)\\r\\n except TableException:\\r\\n # all samples are filtered out, so no otu table to write\\r\\n continue\\r\\n yield v_fp_str, format_biom_table(filtered_otu_table)\",\n \"def formatOutput(tranisitionTable):\\n \\n # TODO: Make all the processing done in this function, rather than main\\n for key in sorted(transitionTable.iterkeys()):\\n transitionTable[key].sort(key = operator.itemgetter(1)) #.sort is in-place\\n \\n return transitionTable\",\n \"def make_table(ranked_means):\\n fp = open(\\\"table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c|}\\n \\\\\\\\hline\\n \\\\\\\\multicolumn{2}{|c||}{Slowest} & \\\\\\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n Feature & Rate & Feature & Rate \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\\"\\\"\\\")\\n top_10 = ranked_means[0:10]\\n bottom_10 = ranked_means[-10:]\\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\\n f_name = f_name.split(\\\":\\\")[-1]\\n f_name = f_name.rsplit(\\\" \\\", 1)[0] if f_name.endswith(\\\"(V)\\\") else f_name\\n s_name = s_name.split(\\\":\\\")[-1]\\n s_name = s_name.rsplit(\\\" \\\", 1)[0] if s_name.endswith(\\\"(V)\\\") else s_name\\n fp.write(\\\" %s & %.2f & %s & %.2f \\\\\\\\\\\\\\\\ \\\\n\\\" % \\\\\\n (f_name, f_rate, s_name, s_rate))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\\n\\n fp = open(\\\"supp_meaning_table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\\n \\\\\\\\hline\\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\n\\\"\\\"\\\")\\n feature_names = [f.split(\\\":\\\")[-1] for (r,f) in ranked_means]\\n feature_names.sort(key=lambda s: s.lower())\\n col1 = feature_names[0:25]\\n col2 = feature_names[25:50]\\n col3 = feature_names[50:75]\\n col4 = feature_names[75:]\\n for a,b,c,d in zip(col1,col2,col3,col4):\\n x,y,z,w = [get_meaning_category(i) or \\\"Excluded\\\" for i in (a,b,c,d)]\\n # Lop off (V)s (we needed them above for get_meaning_category to work)\\n a,b,c,d = [f.rsplit(\\\" \\\", 1)[0] if f.endswith(\\\"(V)\\\") else f for f in (a,b,c,d)]\\n fp.write(\\\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\\\\\\\\\ \\\\n\\\" % (a, x, b, y, c, z, d, w))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\",\n \"def print_tsv(data, filename):\\n with open(filename, 'wt') as fout:\\n writefile = partial(print, sep='\\\\t', file=fout)\\n writefile('Sample', *expected_header)\\n for sample in data:\\n for entry in data[sample]:\\n writefile(sample, *(entry[field] for field in expected_header))\",\n \"def matrix2Table(self, matrix): \\n M = TableModel()\\n M.addColumn('Mutations')\\n\\n fields = matrix.columnHeaders()\\n for f in fields:\\n M.addColumn(f)\\n i = matrix.indexOfColumnWithHeader('Mutations')\\n for row in matrix:\\n mutationSet = Core.Data.MutationSet(row[i])\\n code = '+'.join(mutationSet.mutationCodes(reduced=True))\\n M.addRow(code)\\n for f in fields:\\n j = matrix.indexOfColumnWithHeader(f)\\n if f == 'Mutations':\\n M.data[code]['Mutations'] = code\\n else: \\n M.data[code][f] = str(row[j])\\n return M\",\n \"def format(self, table):\\n #return table.data.to_json()\\n m = table.as_array()\\n rank = len(m.shape)\\n is_table = len(table.headers)<=5 or (len(table.headers)>5 and (table.headers[0] != '0' or table.headers[1] != '1' or table.headers[2] != '2' ))\\n\\n if rank<3 and is_table:\\n v = []\\n for i in range(len(table.headers)):\\n vv = {\\n 'offset': table.offset,\\n 'header': table.headers[i],\\n 'type': table.types[i],\\n 'data': _replace_nans(m[:,i].tolist()) if rank>1 else _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n vv[\\\"size\\\"] = table.sizes[0]\\n v.append(vv)\\n else:\\n # if hasattr(data, \\\"strip\\\") or \\\\\\n # (not hasattr(data, \\\"__getitem__\\\") and \\\\\\n # not hasattr(data, \\\"__iter__\\\")):\\n # # data is not a list/tuple => wrap it\\n # data = [ data ]\\n v = {\\n 'offset': table.offset,\\n #'headers': table.headers,\\n 'type': table.types[0],\\n 'data': _replace_nans(m.tolist()),\\n }\\n if table.sizes is not None:\\n v[\\\"size\\\"] = table.sizes\\n\\n return json.dumps(v, cls=ExtEncoder)\",\n \"def preprocess_feature(df):\",\n \"def to_tsv(self, out_dir, sep=\\\"\\\\t\\\", prefix=None, **kwargs):\\n os.makedirs(out_dir, exist_ok=True) # create dirs if non-existent\\n prefix = f\\\"{prefix}_\\\" if prefix else \\\"\\\"\\n fpaths = [\\n os.path.join(out_dir, f\\\"{prefix}{suf}.tsv\\\")\\n for suf in [\\\"data\\\", \\\"sample_meta\\\"]\\n ]\\n self.data.to_csv(fpaths[0], sep=\\\"\\\\t\\\", **kwargs)\\n self.sample_meta.to_csv(fpaths[1], sep=\\\"\\\\t\\\", **kwargs)\",\n \"def prepare_batch_sample_set_for_metadata_export(path, tsca_id):\\n raw = pd.read_table(path)\\n print( \\\"%d Samples in this batch\\\" % raw.shape[0] )\\n\\n # Create dfs to upload\\n all_samples = pd.concat([pd.DataFrame(index=raw.index, columns=['membership:sample_set_id'], data=tsca_id), \\\\\\n raw[ ['sample_id', 'sample_type'] ]], axis=1)\\n\\n\\n tumors = all_samples.loc[ all_samples['sample_type'] == \\\"Tumor\\\", ['membership:sample_set_id', 'sample_id'] ]\\n tumors.loc[: , 'membership:sample_set_id'] = \\\"%s_T\\\"%tsca_id\\n \\n normals = all_samples.loc[ all_samples['sample_type'] == \\\"Normal\\\", ['membership:sample_set_id', 'sample_id'] ]\\n normals.loc[: , 'membership:sample_set_id'] = \\\"%s_N\\\"%tsca_id\\n\\n all_samples = all_samples.drop('sample_type', axis=1)\\n return (all_samples, tumors, normals)\",\n \"def transform_instances_table_output(result):\\n\\n table_output = []\\n for item in result:\\n table_output.append(_transform_instance_row(item))\\n\\n return table_output\",\n \"def build_table():\\n with contextlib.ExitStack() as stack:\\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\\n iters = [(line.split() for line in f) for f in files]\\n for it in iters:\\n next(it)\\n key = operator.itemgetter(0)\\n table = []\\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\\n props = list(g)\\n if len(props) == len(iters):\\n table.append([k] + [x[1] for x in props])\\n for snp in table:\\n print(*snp)\",\n \"def normalize_table(input_fp, output_fp, relative_abund, presence_absence,\\n axis):\\n table = load_table(input_fp)\\n result = _normalize_table(table, relative_abund, presence_absence, axis)\\n\\n write_biom_table(result, 'hdf5' if HAVE_H5PY else 'json', output_fp)\",\n \"def reverse_transform(self, data):\\n table = []\\n\\n for i in range(self.metadata['num_features']):\\n column_data = data['f%02d' % i]\\n column_metadata = self.metadata['details'][i]\\n\\n if column_metadata['type'] == 'value':\\n column = self.continous_transformer.inverse_transform(column_data, column_metadata)\\n\\n if column_metadata['type'] == 'category':\\n self.categorical_transformer.classes_ = column_metadata['mapping']\\n column = self.categorical_transformer.inverse_transform(\\n column_data.ravel().astype(np.int32))\\n\\n table.append(column)\\n\\n result = pd.DataFrame(dict(enumerate(table)))\\n result.columns = self.columns\\n return result\",\n \"def summarize_sample_props(psd_list, sample_list):\\n prop_list = [psd.sample_props for psd in psd_list]\\n cols = ['amplicon median', 'mean size', 'lower size', 'upper size']\\n\\n return pd.DataFrame(prop_list, columns=cols, index=sample_list)\",\n \"def pivot(self):\\n\\t\\tpivoted = Dataset()\\n\\t\\tfor (index, header) in enumerate(self.headers):\\n\\t\\t\\tfor row in self.rows:\\n\\t\\t\\t\\tif index == 0:\\n\\t\\t\\t\\t\\tpivoted.headers.append(row[header])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif len(pivoted.rows) < index:\\n\\t\\t\\t\\t\\t\\tpivoted.rows.extend([{} for x in range(index - len(pivoted.rows))])\\n\\t\\t\\t\\t\\tpivoted.rows[index - 1][row[self.headers[0]]] = row[header]\\n\\t\\treturn pivoted\",\n \"def tabulate(d, transpose=False, thousands=True, key_fun=None, sep=\\\",\\\", align=True):\\n pairs = d.keys()\\n rows, cols = zip(*pairs)\\n if transpose:\\n rows, cols = cols, rows\\n\\n rows = sorted(set(rows))\\n cols = sorted(set(cols))\\n header = [\\\"o\\\"] + list(cols)\\n table = []\\n for r in rows:\\n combo = [(r, c) for c in cols]\\n if transpose:\\n combo = [(c, r) for (r, c) in combo]\\n data = [d.get(x, \\\"n/a\\\") for x in combo]\\n data = [\\\"{0:.1f}\\\".format(x) if isinstance(x, float) else x for x in data]\\n if key_fun:\\n data = [key_fun(x) for x in data]\\n table.append([str(r)] + data)\\n\\n if not align:\\n formatted = load_csv(header, table, sep=sep)\\n return \\\"\\\\n\\\".join(formatted)\\n\\n return loadtable(header, table, thousands=thousands)\",\n \"def converttable(tablecode):\\n table = etree.XML(tablecode)\\n rows = iter(table)\\n headers = [col.text for col in next(rows)]\\n data = []\\n for row in rows:\\n values = [col.text for col in row]\\n debugprint(dict(zip(headers, values)), \\\"RAW JSON\\\")\\n data.append(dict(zip(headers, values)))\\n return data\",\n \"def normalize_to_flat(classifier, df, col):\\n name = str(classifier) + '_df'\\n new_df= json_normalize(df.loc[classifier][col])\\n new_df['classifier'] = [classifier]\\n return new_df\",\n \"def flatten_data(self, data):\\n for row in data:\\n flat_row = {k: self._flatten_value(v) for k, v in row.items()}\\n yield flat_row\",\n \"def generate_table(self, rows):\\n ...\",\n \"def convert_quick_table(result):\\n headline = result.split('\\\\n',1)[0]\\n names, converters = MastCasJobs.get_converters(headline, delimiter=',')\\n tab = ascii.read(MastCasJobs.replacenull(result,delimiter=','),\\n guess=False,fast_reader=False,format='csv',\\n names=names,converters=converters)\\n return tab\",\n \"def tab_delim_table(self):\\n self.generate()\\n\\n header = ' \\\\t '.join([r'{: ^7}'.format(col) for col in self.columns])\\n lines = []\\n for row in self.rows:\\n bits = []\\n for col in self.columns:\\n if col in self.formatters:\\n bits.append(self.formatters[col].format(row[col]))\\n else:\\n bits.append(self.formatters.get(col, '{: ^7}').format(row[col] if row[col] else ''))\\n lines.append(' \\\\t '.join(bits))\\n\\n return \\\"{}\\\\n{}\\\".format(header, '\\\\n'.join(lines))\",\n \"def normalize_values(self, data):\\n\\n df = pd.DataFrame(data[1:], columns = data[0]).astype(str)\\n\\n df = df.replace(ami_md_constants.NAS)\\n\\n df = df.replace(ami_md_constants.REGEX_REPLACE_DICT, regex=True)\\n df = df.replace(ami_md_constants.STRING_REPLACE_DICT)\\n df['source.object.format_type'] = df['source.object.format'].map(ami_md_constants.FORMAT_TYPE)\\n\\n for key in ami_md_constants.MEASURE_UNIT_MAPS.keys():\\n value_map = ami_md_constants.MEASURE_UNIT_MAPS[key]\\n df = self.map_value(df,\\n value_map['from_column'],\\n value_map['to_column'],\\n value_map['constant_value'],\\n value_map['values_map_column'],\\n value_map['values_map'])\\n\\n #force all the numerics back to numeric, and drop all empty columns\\n df = df.apply(pd.to_numeric, errors='ignore').dropna(axis=1, how = \\\"all\\\")\\n\\n vals = df.values.tolist()\\n cols = df.columns.tolist()\\n vals.insert(0, cols)\\n\\n return vals\",\n \"def make_summary_tables( res ):\\n\\n # transform second table to csv and read this as a dataFrame\\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\\\",\\\",index_col=0)\\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\\n\\n # first table is trickier because the data is spread on to columns, and there is title line\\n L = res.tables[0].as_html().split('\\\\n')\\n L.pop(1) # get rid of the title\\n tmp = pd.read_html('\\\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \\n\\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\\n # NB : I exclude the last 2 elements which are empty \\n \\n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\\n \\n return result_general_df , result_fit_df\",\n \"def make_pivot_table(data, index_name, columns, values, afffunc):\\n\\n pvt = data.pivot_table(index=[index_name], columns=[columns], values=values, aggfunc=afffunc)\\n return pvt\",\n \"def make_table_from_exploded_df(exploded_df: pd.DataFrame, row_heading_cols, column_heading_cols, dfs_dict=None,\\n value_col: str = \\\"text\\\", concat_with: str = \\\" | \\\",\\n convert_numeric_items=False, sort_headers=True) -> pd.DataFrame:\\n for heading_col in (row_heading_cols + column_heading_cols):\\n exploded_df[heading_col].fillna(\\\"\\\", inplace=True)\\n table = exploded_df.pivot_table(index=row_heading_cols, columns=column_heading_cols, values=value_col,\\n aggfunc=(lambda a: concat_with.join(a)))\\n\\n if type(table) == pd.Series:\\n table = table.to_frame()\\n row_nones = [None for _ in range((table.index.nlevels))]\\n col_nones = [None for _ in range((table.columns.nlevels))]\\n\\n table = table.rename_axis(index=row_nones, columns=col_nones)\\n\\n if convert_numeric_items:\\n num_rows, num_cols = _infer_numeric_rows_cols(exploded_df, row_heading_cols, column_heading_cols)\\n table = convert_cols_to_numeric(table, num_cols, num_rows)\\n\\n if sort_headers and column_heading_cols != [\\\"column_index\\\"] and len(\\n column_heading_cols) == 1 and dfs_dict is not None:\\n col_headings = table.columns.to_list()\\n cols = dfs_dict[\\\"col_headers\\\"][dfs_dict[\\\"col_headers\\\"][\\\"text\\\"].isin(col_headings)]\\n cols = cols.sort_values(\\\"column_index_begin\\\")\\n col_headings_sorted = cols[\\\"text\\\"].to_list()\\n\\n not_inc_headings = [x for x in col_headings if x not in col_headings_sorted]\\n col_headings_sorted = col_headings_sorted + not_inc_headings\\n table = table[col_headings_sorted]\\n\\n elif sort_headers and column_heading_cols != [\\\"column_index\\\"] and len(\\n column_heading_cols) > 1 and dfs_dict is not None:\\n headers = table.columns.to_frame()\\n sorted_headers_arr = _order_multiindex(headers, column_heading_cols, 0, dfs_dict[\\\"col_headers\\\"])\\n sorted_headers = [tuple(header) for header in sorted_headers_arr]\\n not_inc_headings = [x for x in table.columns.to_list() if x not in sorted_headers]\\n sorted_headers = sorted_headers + not_inc_headings\\n table = table[sorted_headers]\\n\\n if sort_headers and row_heading_cols != [\\\"row_index\\\"] and len(row_heading_cols) == 1 and dfs_dict is not None:\\n row_headings = table.index.to_list()\\n rows = dfs_dict[\\\"row_headers\\\"][dfs_dict[\\\"row_headers\\\"][\\\"text\\\"].isin(row_headings)]\\n rows = rows.sort_values(\\\"row_index_begin\\\")\\n row_headings_sorted = rows[\\\"text\\\"].to_list()\\n\\n not_inc_headings = [x for x in row_headings if x not in row_headings_sorted]\\n row_headings_sorted = row_headings_sorted + not_inc_headings\\n\\n table = table.reindex(row_headings_sorted)\\n\\n elif sort_headers and row_heading_cols != [\\\"column_index\\\"] and len(row_heading_cols) > 1 and dfs_dict is not None:\\n headers = table.index.to_frame()\\n sorted_headers_arr = _order_multiindex(headers, row_heading_cols, 0, dfs_dict[\\\"row_headers\\\"])\\n sorted_headers = [tuple(header) for header in sorted_headers_arr]\\n\\n not_inc_headings = [x for x in table.index.to_list() if x not in sorted_headers]\\n sorted_headers = sorted_headers + not_inc_headings\\n table = table.reindex(sorted_headers)\\n\\n return table\",\n \"def _buildtable(self):\\n\\n tabrows = []\\n\\n for i, (expid, exfiles) in enumerate(self._exposure_files.items()):\\n specflux_b, specflux_r, specflux_z = [], [], []\\n tab = None\\n\\n if len(exfiles) == 0:\\n continue\\n\\n print(expid)\\n for exfile in exfiles:\\n print(exfile)\\n hdu = fits.open(exfile)\\n\\n # The following tables are present in the redux sframes and the\\n # nightwatch qcframes.\\n wave = hdu['WAVELENGTH'].data\\n\\n # However, in the nightwatch files the wavelength data are a\\n # table of size nfiber x nwavelength.\\n if self._filetype == 'nightwatch':\\n if wave.ndim > 1:\\n wave = wave[0]\\n\\n fluxhead = hdu['FLUX'].header\\n fluxdata = hdu['FLUX'].data\\n ivardata = hdu['IVAR'].data\\n fibermap = hdu['FIBERMAP'].data\\n exptime = fluxhead['EXPTIME']\\n if not np.all(self._unditherfa['FIBER'] ==\\n np.arange(len(self._unditherfa))):\\n raise ValueError('weird fiberassign file format!')\\n fibermap = self._unditherfa[fibermap['FIBER']]\\n\\n target_id = fibermap['TARGETID']\\n target_ra = fibermap['TARGET_RA']\\n target_dec = fibermap['TARGET_DEC']\\n fiber = fibermap['FIBER']\\n objtype = fibermap['OBJTYPE']\\n flux_g = fibermap['FLUX_G']\\n flux_r = fibermap['FLUX_R']\\n flux_z = fibermap['FLUX_Z']\\n x, y = [fibermap['FIBERASSIGN_{}'.format(val)] for val in ('X', 'Y')]\\n\\n camera = fluxhead['CAMERA'][0].upper()\\n\\n if getattr(self, '_deltara', None) is not None:\\n dra = self._deltara[i]*np.ones(len(fiber))\\n ddec = self._deltadec[i]*np.ones(len(fiber))\\n elif self._dithertype == 'telescope':\\n dithra = self._ditherfa['target_ra']\\n dithdec = self._ditherfa['target_dec']\\n udithra = self._unditherfa['target_ra']\\n udithdec = self._unditherfa['target_dec']\\n ontarget = ((self._ditherfa['targetid'] ==\\n self._unditherfa['targetid']) &\\n (self._ditherfa['objtype'] == 'TGT'))\\n dfiberra = (dithra-udithra)*np.cos(np.radians(udithdec))*60*60\\n dfiberdec = (dithdec-udithdec)*60*60\\n if not np.all(self._ditherfa['FIBER'] ==\\n np.arange(len(self._ditherfa))):\\n raise ValueError('unexpected shape of dither file')\\n dfiberra[~ontarget] = np.nan\\n dfiberdec[~ontarget] = np.nan\\n dfiberra = dfiberra[fiber]\\n dfiberdec = dfiberdec[fiber]\\n wcs = self.lookup_wcs(fluxhead['MJD-OBS'])\\n centralwcs = self._central_wcs\\n if (~np.isfinite(centralwcs['cenra'][1]) or\\n ~np.isfinite(centralwcs['cendec'][1])):\\n raise ValueError('central pointing ra/dec is NaN!')\\n dtelra = (wcs['cenra'][1]-centralwcs['cenra'][1])\\n dtelra *= np.cos(np.radians(centralwcs['cendec'][1]))\\n dteldec = wcs['cendec'][1]-centralwcs['cendec'][1]\\n dra = dfiberra + dtelra*60*60\\n ddec = dfiberdec + dteldec*60*60\\n if np.all(~np.isfinite(dra)):\\n print('warning: no good telescope offset for %s' %\\n exfile)\\n else:\\n raise ValueError('not implemented')\\n \\n for j, fiber_id in enumerate(fiber):\\n flux = fluxdata[j]\\n ivar = ivardata[j]\\n if not np.any(ivar > 0):\\n specflux = 0\\n specflux_ivar = 0\\n else:\\n meanivar = np.mean(ivar[ivar > 0])\\n mask = ivar > meanivar / 100\\n specflux = np.trapz(flux*mask, wave)\\n specflux_ivar = 1./np.sum(ivar[mask]**-1)\\n # Schlegel: sum over correct wavelengths, all three\\n # filters, plus 11 pixel median filter to reject\\n # cosmics.\\n # will require being better about reading in\\n # the spectrographs together.\\n tabrows.append((expid, exptime,\\n target_id[j], target_ra[j], target_dec[j],\\n fiber[j], objtype[j],\\n flux_g[j], flux_r[j], flux_z[j],\\n specflux, specflux_ivar, camera,\\n dra[j], ddec[j],\\n x[j], y[j]))\\n\\n tab = Table(rows=tabrows,\\n names=('EXPID', 'EXPTIME',\\n 'TARGETID', 'TARGET_RA', 'TARGET_DEC',\\n 'FIBER', 'OBJTYPE',\\n 'FLUX_G', 'FLUX_R', 'FLUX_Z',\\n 'SPECTROFLUX', 'SPECTROFLUX_IVAR', 'CAMERA',\\n 'DELTA_X_ARCSEC', 'DELTA_Y_ARCSEC',\\n 'XFOCAL', 'YFOCAL'),\\n meta={'EXTNAME' : 'DITHER',\\n 'TILEID' : '{}'.format(self._tileid)})\\n\\n return tab\",\n \"def reformat_attrTable(\\n self):\\n #format into a dictionary of rows for quick aligning with the tracking_id\\n if self.attrTable: attrTable = self.attrTable[:];\\n else: attrTable = [];\\n\\n attrTable_dict = {};\\n for row in attrTable:\\n attrTable_dict[row['tracking_id']] = row;\\n return attrTable_dict;\",\n \"def join_gene_tables(gene_tables,output,verbose=None):\\n \\n gene_table_data={}\\n start_column_id=\\\"\\\"\\n samples=[]\\n file_basenames=[]\\n index=0\\n for gene_table in gene_tables:\\n \\n if verbose:\\n print(\\\"Reading file: \\\" + gene_table)\\n \\n lines=util.process_gene_table_with_header(gene_table, allow_for_missing_header=True)\\n header=next(lines)\\n \\n # get the basename of the file\\n file_basename='.'.join(os.path.basename(gene_table).split('.')[:-1])\\n file_basenames.append(file_basename)\\n \\n if header:\\n header_info=header.split(GENE_TABLE_DELIMITER)\\n if not start_column_id:\\n start_column_id=header_info[0]\\n # allow for multiple samples\\n sample_names=header_info[1:]\\n else:\\n # if there is no header in the file then use the file name as the sample name\\n sample_names=[file_basename]\\n \\n for line in lines:\\n data=line.split(GENE_TABLE_DELIMITER)\\n try:\\n gene=data[0]\\n # if the header names multiple samples, merge all samples\\n # this prevents extra columns from being included in some rows\\n # this requires files containing multiple samples to include a header\\n data_points=data[1:len(sample_names)+1]\\n except IndexError:\\n gene=\\\"\\\"\\n\\n if gene:\\n current_data=gene_table_data.get(gene,\\\"\\\")\\n fill = index - current_data.count(GENE_TABLE_DELIMITER)\\n if fill > 0:\\n # fill in zeros for samples without data then add data point\\n gene_table_data[gene]=current_data + GENE_TABLE_DELIMITER.join([\\\"0\\\"]*fill) + GENE_TABLE_DELIMITER + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\\n elif fill < 0:\\n # add data point to other data point from the same sample\\n current_data_points=current_data.split(GENE_TABLE_DELIMITER)\\n for i,point in enumerate(data_points):\\n store_index=len(data_points)*-1-1+i\\n current_data_points[store_index]=str(float(current_data_points[store_index])+float(point))\\n gene_table_data[gene] = GENE_TABLE_DELIMITER.join(current_data_points)\\n else:\\n # add data point to end of list\\n gene_table_data[gene] = current_data + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\\n \\n samples+=sample_names\\n index+=len(sample_names)\\n # if all of the header names for the files are the same\\n # then use the file names as headers\\n if samples.count(samples[0]) == len(samples):\\n samples=file_basenames\\n \\n # write the joined gene table\\n if not start_column_id:\\n start_column_id=\\\"# header \\\"\\n sample_header=[start_column_id]+samples\\n total_gene_tables=len(samples)\\n sorted_gene_list=util.fsort(list(gene_table_data))\\n try:\\n file_handle=open(output,\\\"w\\\")\\n file_handle.write(GENE_TABLE_DELIMITER.join(sample_header)+\\\"\\\\n\\\")\\n except EnvironmentError:\\n sys.exit(\\\"Unable to write file: \\\" + output) \\n \\n for gene in sorted_gene_list:\\n # extend gene data for any gene that is not included in all samples\\n current_data=gene_table_data[gene]\\n fill = total_gene_tables - current_data.count(GENE_TABLE_DELIMITER)\\n if fill:\\n current_data=current_data + GENE_TABLE_DELIMITER.join([\\\"0\\\"]*fill) + GENE_TABLE_DELIMITER\\n file_handle.write(gene+GENE_TABLE_DELIMITER+current_data.rstrip(GENE_TABLE_DELIMITER)+\\\"\\\\n\\\")\\n \\n file_handle.close()\",\n \"def getAllSampleFields(self, sample_id, study_id):\\n sample_tables = []\\n sample_tables.append('sample')\\n sample_tables.append('common_fields')\\n sample_tables.append('extra_sample_')\\n sample_tables.append('air')\\n sample_tables.append('other_environment')\\n sample_tables.append('sediment')\\n sample_tables.append('soil')\\n sample_tables.append('wastewater_sludge')\\n sample_tables.append('water')\\n sample_tables.append('host_assoc_vertibrate')\\n sample_tables.append('host_associated_plant')\\n sample_tables.append('human_associated')\\n sample_tables.append('host_sample')\\n sample_tables.append('host')\\n \\n filled_fields = {}\\n \\n con = self.getMetadataDatabaseConnection()\\n cursor = con.cursor()\\n \\n for table in sample_tables:\\n if 'extra_sample_' in table:\\n statement = 'select * from %s%s where sample_id = %s' % (table, study_id, sample_id)\\n elif table == 'host':\\n statement = 'select * from host h inner join host_sample hs on h.host_id = hs.host_id where sample_id = %s' % sample_id\\n else:\\n statement = 'select * from %s where sample_id = %s' % (table, sample_id)\\n \\n try:\\n cursor.execute(statement)\\n except Exception, e:\\n print str(e)\\n print 'Error running query:\\\\n'\\n print statement\\n print 'Running next query...\\\\n'\\n \\n continue\\n \\n data = cursor.fetchall()\\n\\n # Get the column names\\n col_names = []\\n for i in range(0, len(cursor.description)):\\n col_names.append(cursor.description[i][0])\\n \\n # Find the rows with data\\n for row in data:\\n i = 0\\n for field in row:\\n if field != None and field != '':\\n filled_fields[col_names[i]] = field\\n i += 1\\n \\n return filled_fields\",\n \"def columnar(table: list[dict[str, str]]) -> dict[str, list[str]]:\\n result: dict[str, list[str]] = {}\\n keys = table[0].keys()\\n for key in keys:\\n result[key] = column_values(table, key)\\n return result\",\n \"def gen_tab(cat):\\n\\n col = ['FLUX_APER2','FLUX_APER4','FLUX_APER5','FLUX_APER8','FLUX_APER10','FLUX_APER14',\\n 'MAG_APER2','MAG_APER4','MAG_APER5','MAG_APER8','MAG_APER10','MAG_APER14',\\n 'MAG_AUTO','MAG_PETRO','KRON_RADIUS',\\n 'PETRO_RADIUS','FLUX_MAX','ISOAREAF_IMAGE','x',\\n 'y','ra','dec','X2_IMAGE','Y2_IMAGE','XY_IMAGE',\\n 'THETA_IMAGE','X2WIN_IMAGE','Y2WIN_IMAGE','XYWIN_IMAGE','AWIN_IMAGE','BWIN_IMAGE',\\n 'THETAWIN_IMAGE','AWIN_WORLD','BWIN_WORLD','THETAWIN_WORLD',\\n 'MU_MAX','FLAGS','FWHM_IMAGE','ELONGATION','SEX_CLASS','FLUX_RADIUS25',\\n 'FLUX_RADIUS50','FLUX_RADIUS85','FLUX_RADIUS95','FLUX_RADIUS99']\\n print('generating features table: {}'.format(cat))\\n tab = pd.read_table(cat,skiprows=41,sep=r'\\\\s+',header=None, names=col)\\n\\n # crop the image for just using the central part of the image\\n tab = crop(tab)\\n\\n # add concentration column by subtracting mag10 by mag5, rejecting the detections with negative concentration\\n tab['CONCENT'] = tab.MAG_APER5 - tab.MAG_APER10\\n tab = tab[tab.CONCENT > 0]\\n\\n # normalizing the columns\\n print('normalizing features...')\\n seesq_norm = ['X2_IMAGE','Y2_IMAGE','X2WIN_IMAGE',\\n 'Y2WIN_IMAGE','XY_IMAGE','XYWIN_IMAGE',\\n 'ISOAREAF_IMAGE']\\n see_norm = ['AWIN_WORLD','AWIN_WORLD','FWHM_IMAGE',\\n 'KRON_RADIUS','PETRO_RADIUS','FLUX_RADIUS25',\\n 'FLUX_RADIUS50','FLUX_RADIUS85',\\n 'FLUX_RADIUS95','FLUX_RADIUS99']\\n mag_norm = ['MAG_APER4','MAG_APER5','MAG_APER8',\\n 'MAG_APER10','MAG_APER14','MAG_AUTO',\\n 'MAG_PETRO','MU_MAX','CONCENT']\\n flux_norm = ['FLUX_APER2','FLUX_APER4','FLUX_APER5',\\n 'FLUX_APER8','FLUX_APER10','FLUX_APER14']\\n fwhm_mean = tab.FWHM_IMAGE.mean()\\n for seesq_col in seesq_norm:\\n tab[seesq_col] = tab[seesq_col] / (fwhm_mean**2)\\n for see_col in see_norm:\\n tab[see_col] = tab[see_col] / fwhm_mean\\n for mag_col in mag_norm:\\n tab[mag_col] = tab[mag_col] * tab['MAG_APER2']\\n for flux_col in flux_norm:\\n tab[flux_col] = tab[flux_col] * tab['FLUX_MAX']\\n tab['CONCENT'] = -1 * tab['CONCENT']\\n\\n # add column for galactic latitude\\n print('calculating galactic latitude...')\\n ra = np.array(tab['ra'].values)\\n dec = np.array(tab['dec'].values)\\n pos = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')\\n tab['b'] = list(pos.galactic.b.deg)\\n\\n tab.drop(['MAG_APER2','FLUX_MAX','x','y'], axis=1, inplace=True)\\n tab.to_csv(cat[:-4]+'.csv', index=False, header=True)\",\n \"def make_lof_table(data_table, my_genes, my_samples, summary_func):\\n table_header = [\\\"Gene\\\"] + my_samples + [\\n \\\"Missense:Benign\\\", \\\"Missense:Possibly\\\", \\\"Missense:Probably\\\",\\n \\\"MissenseNA\\\", \\\"Indel\\\", \\\"Nonsense\\\", \\\"Frameshift\\\", \\\"Splice-site\\\",\\n \\\"Synonymous\\\"]\\n table_records = []\\n\\n gs_lookup = group_data_by_gs(data_table)\\n for gene in my_genes:\\n synonymous = missense_benign = missense_possibly = missense_probably = \\\\\\n missense_na = frameshift = nonsense = splice = indel = 0\\n\\n out_row = [gene]\\n for sample in my_samples:\\n normalized = [0]\\n # Count mutations of each type for this gene and sample\\n for entry in gs_lookup[gene][sample]:\\n if entry['muttype'] == 'Silent':\\n synonymous += 1\\n continue\\n if entry['muttype'] == 'Intron':\\n # Shouldn't be here; ignore\\n continue\\n\\n if entry['muttype'] == 'Missense_Mutation':\\n if entry['consequence'] == 'benign':\\n missense_benign += 1\\n elif entry['consequence'] == 'possibly':\\n missense_possibly += 1\\n elif entry['consequence'] == 'probably':\\n missense_probably += 1\\n elif entry['consequence'] == 'NA':\\n missense_na += 1\\n else:\\n print(\\\"Unhandled missense consequence level:\\\",\\n entry['consequence'], file=sys.stderr)\\n elif entry['muttype'] == 'Nonsense_Mutation':\\n nonsense += 1\\n elif entry['muttype'] == 'Splice_Site':\\n splice += 1\\n elif entry['muttype'] in ('Frame_Shift_Ins', 'Frame_Shift_Del'):\\n frameshift += 1\\n elif entry['muttype'] in ('In_Frame_Ins', 'In_Frame_Del'):\\n indel += 1\\n else:\\n print(\\\"Unhandled mutation type:\\\", entry['muttype'],\\n file=sys.stderr)\\n continue\\n\\n normalized.append(entry['normalized'])\\n # Summarize the normalized mutation counts for this gene and sample\\n out_row.append(summary_func(normalized))\\n out_row.extend((missense_benign, missense_possibly, missense_probably,\\n missense_na, indel, nonsense, frameshift, splice,\\n synonymous))\\n table_records.append(out_row)\\n\\n return pandas.DataFrame.from_records(table_records, columns=table_header)\",\n \"def construct_table(self):\\n table_str = self.header_row\\n row_lbls, col_lbls = self.get_idxvals()\\n for r,rlbl in enumerate(row_lbls):\\n row_data = [self.data[rlbl,clbl] for clbl in col_lbls]\\n table_str += self.construct_row(r, row_data)\\n \\n return table_str\",\n \"def format_bgc_metadata(df,float_id):\\n mdf = df[bgc_metadata_columns]\\n bgc_metadata_dict = {}\\n for col in list(mdf):\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\\\"'\\\",'\\\"'))\\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \\n bgc_metadata_df = pd.DataFrame({\\\"float_id\\\": [float_id], \\\"Metadata_Dict\\\": [bgc_metadata_dict]})\\n return bgc_metadata_df\",\n \"def parse_distmat_to_dict(table):\\r\\n\\r\\n col_headers, row_headers, data = parse_matrix(table)\\r\\n assert(col_headers == row_headers)\\r\\n\\r\\n result = defaultdict(dict)\\r\\n for (sample_id_x, row) in zip(col_headers, data):\\r\\n for (sample_id_y, value) in zip(row_headers, row):\\r\\n result[sample_id_x][sample_id_y] = value\\r\\n return result\",\n \"def df_sample_names(self):\\n return self.abundance_mat_mult(True)\",\n \"def pivot_scenarios(df, prefix, scen_map, df_type=\\\"pop\\\"):\\n df[\\\"scenario\\\"] = prefix + \\\"_\\\" + df_type + \\\"_\\\" +\\\\\\n df[\\\"scenario\\\"].map(scen_map)\\n df = df.pivot_table(values=[\\\"lower\\\", \\\"mean\\\", \\\"upper\\\" ,\\\"value\\\"],\\n index=\\\"location_id\\\",\\n columns=\\\"scenario\\\",\\n aggfunc=\\\"first\\\").reset_index()\\n # This flattens the column levels\\n df.columns = ['_'.join(col) for col in df.columns.values if col]\\n df.rename(columns={\\\"location_id_\\\":\\\"location_id\\\"}, inplace=True)\\n\\n return df\",\n \"def get_sample_colnames(ms_df: DF) -> List[str]:\\n\\n sample_numbers = get_sample_numbers(ms_df)\\n\\n target_sample_cols = list()\\n for sample in sample_numbers:\\n for col in SAMPLE_COLS:\\n target_sample_cols.append('{attr}_{sample}'.format(attr=col, sample=sample))\\n return target_sample_cols\",\n \"def pretty(self):\\n #table = [\\\"\\\".join([\\\"%8s \\\" % s for s in self.alpha.getSymbols()])]\\n table = []\\n for row in PWM.getFreq(self):\\n table.append(\\\"\\\".join([\\\"%8.6f \\\" % y for y in row]))\\n return table\",\n \"def _dump_table(table: Model, directory: Path, format_: str):\\n try:\\n table.select().tuples()\\n table.fields()\\n dataset = tablib.Dataset(*table.select().tuples(), headers=table.fields())\\n except:\\n print(table._meta.database.get_columns(table.table_name()))\\n\\n if directory is not None:\\n print(f\\\" Dumping {table.table_name()}...\\\")\\n out_file = Path(directory) / f\\\"{table.table_name()}.{format_}\\\"\\n out_file.write_text(dataset.export(format_))\\n print(\\\" Done.\\\")\\n print(\\\"=====================\\\")\\n else:\\n print(dataset.export(\\\"csv\\\"))\",\n \"def permute_table(dtable):\\n shuffle_field(dtable, 'gene')\\n shuffle_field(dtable, 'sample')\\n shuffle_field(dtable, 'Normalized')\\n if 'Filler' in dtable:\\n del dtable['Filler']\",\n \"def experiment_list_table_format(result):\\n table = []\\n for item in result:\\n table.append(experiment_show_table_format(item))\\n return table\",\n \"def get_split_summary_table(all_df, train_df, test_df):\\n table = PrettyTable()\\n table.field_names = ['set', 'N total', 'N non-ICH', 'N ICH', 'frac non-ICH', 'frac ICH']\\n for df, name in zip([all_df, train_df, test_df],['All', 'Train', 'Test']):\\n table.add_row([name, len(df), len(df[df.Hemorrhage == 0]), len(df[df.Hemorrhage == 1]),\\n f'{len(df[df.Hemorrhage == 0])/len(df):.3%}', f'{len(df[df.Hemorrhage == 1])/len(df):.3%}'])\\n return table\",\n \"def convert_data(test_data,params,list_dict,rational_present=True,topk=2):\\n \\\"\\\"\\\"input: params -- input dict, list_dict -- previous predictions containing rationals\\n rational_present -- whether to keep rational only or remove them only\\n topk -- how many words to select\\\"\\\"\\\"\\n \\n temp_dict={}\\n for ele in list_dict:\\n temp_dict[ele['annotation_id']]=ele['rationales'][0]['soft_rationale_predictions']\\n \\n test_data_modified=[]\\n \\n for index,row in tqdm(test_data.iterrows(),total=len(test_data)):\\n try:\\n attention=temp_dict[row['Post_id']]\\n except KeyError:\\n continue\\n topk_indices = sorted(range(len(attention)), key=lambda i: attention[i])[-topk:]\\n new_text =[]\\n new_attention =[]\\n if(rational_present):\\n if(params['bert_tokens']):\\n new_attention =[0]\\n new_text = [101]\\n for i in range(len(row['Text'])):\\n if(i in topk_indices):\\n new_text.append(row['Text'][i])\\n new_attention.append(row['Attention'][i])\\n if(params['bert_tokens']):\\n new_attention.append(0)\\n new_text.append(102)\\n else:\\n for i in range(len(row['Text'])):\\n if(i not in topk_indices):\\n new_text.append(row['Text'][i])\\n new_attention.append(row['Attention'][i])\\n test_data_modified.append([row['Post_id'],new_text,new_attention,row['Label']])\\n\\n df=pd.DataFrame(test_data_modified,columns=test_data.columns)\\n return df\",\n \"def export_data(fp, app_name):\\n from otree.views.admin import get_display_table_rows\\n colnames, rows = get_display_table_rows(\\n app_name, for_export=True, subsession_pk=None)\\n colnames = ['{}.{}'.format(k, v) for k, v in colnames]\\n writer = csv.writer(fp)\\n writer.writerows([colnames])\\n writer.writerows(rows)\",\n \"def print_table(source, count=False):\\n table_value = []\\n table_header = []\\n for source_key, source_value in source.items():\\n for item in source_value:\\n table_value.append([v for v in item.values()])\\n table_header.append([k for k in item.keys()])\\n if not count:\\n print(tabulate(table_value,\\n headers=table_header[0],\\n tablefmt='orgtbl'))\\n else:\\n print(tabulate([[len(source_value)]],\\n headers=[source_key],\\n tablefmt='orgtbl'))\",\n \"def read_table(table_name, hf, df_fmt, ilo, ihi):\\n dfs = []\\n for dt, block in df_fmt.groupby(\\\"dtype\\\"):\\n \\n # check if this dtype contains waveform data\\n if 'waveform' in block['name'].values:\\n wf_group = f\\\"/{table_name}/waveform\\\"\\n wf_block = read_waveforms(wf_group, hf, df_fmt, ilo, ihi)\\n wf_rows, wf_cols = wf_block.shape\\n nrows = wf_rows\\n \\n # get number of additional columns\\n new_cols = [c for c in list(block[\\\"name\\\"].values) if c != 'waveform']\\n newcols = len(new_cols)\\n \\n # allocate the full numpy array for this dtype\\n np_block = np.empty((nrows, newcols + wf_cols), dtype=dt)\\n np_block[:, newcols:] = wf_block\\n \\n cols = []\\n for i, col in enumerate(new_cols):\\n ds = hf[f\\\"{table_name}/{col}\\\"] \\n \\n if ihi is None:\\n ihi = ds.shape[0]\\n nwfs = ihi - ilo + 1 # inclusive\\n \\n np_block[:, i] = ds[ilo:ihi]\\n cols.append(col)\\n cols.extend(np.arange(wf_cols)) \\n\\n dfs.append(pd.DataFrame(np_block, columns=cols))\\n \\n # read normal 'array<1>{real}' columns\\n else:\\n ncols = len(block)\\n nrows = block[\\\"size\\\"].unique()\\n if len(nrows) > 1:\\n print('Error, columns are different lengths')\\n exit()\\n nrows = nrows[0]\\n np_block = np.empty((nrows, ncols), dtype=dt)\\n \\n for i, col in enumerate(block[\\\"name\\\"]):\\n ds = hf[f\\\"{table_name}/{col}\\\"]\\n np_block[:,i] = ds[...]\\n \\n dfs.append(pd.DataFrame(np_block, columns=block[\\\"name\\\"])) \\n \\n # concat final DF after grouping dtypes and avoiding copies\\n return pd.concat(dfs, axis=1, copy=False)\",\n \"def change_food_names_in_final_table(df):\\n # df = pd.read_excel(\\\"final_dataset_with_median_all.xlsx\\\")\\n df.reset_index(drop=True, inplace=True)\\n\\n foods = df['food_names']\\n food_examples = []\\n indices = list(range(0, len(foods)))\\n for i in indices:\\n print(i)\\n food_examples.append(str(foods[i]) + str(i))\\n food_examples = pd.Series(food_examples)\\n\\n df.drop(labels=['food_names'], axis=\\\"columns\\\", inplace=True)\\n df['food_names'] = food_examples\\n writer = pd.ExcelWriter('final_dataset_with_median.xlsx', engine='xlsxwriter')\\n df.to_excel(writer, sheet_name='Sheet1')\\n writer.save()\",\n \"def _gen_table_rows(self):\\n row_labels = self._get_padded_row_labels()\\n column_labels = self._get_padded_column_labels()\\n for row in zip(*column_labels):\\n yield ''.join('<td>%s</td>' % c for c in row)\\n for label, row_string in zip(row_labels, HeatMap._gen_table_rows(self)):\\n yield ''.join('<td>%s</td>' % c for c in label) + row_string\",\n \"def basic_table_details():\\n tbl: pa.table = pa.Table.from_pylist([\\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\\n ])\\n\\n results = {\\n 'column_names': tbl.column_names,\\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\\n 'nbytes': tbl.nbytes,\\n 'num_columns': tbl.num_columns,\\n 'num_rows': tbl.num_rows,\\n 'schema': tbl.schema,\\n 'shape': tbl.shape,\\n }\\n\\n print(results)\",\n \"def tranform_data(args):\\n data_dir = args.data_dir\\n save_dir = args.save_dir\\n os.makedirs(save_dir)\\n maxsize = args.maxsize\\n maxlen = args.maxlen \\n ext = args.ext\\n datetime = args.datetime\\n classes = set()\\n nb_files = 0\\n time_col = 'time'\\n event_col = 'event'\\n gt_ids = None\\n if args.ext == \\\"pkl\\\":\\n with open(Path(args.data_dir, \\\"fx_labels\\\"), \\\"rb\\\") as fp:\\n gt_ids = pickle.load(fp)[:maxsize]\\n labels = np.unique(gt_ids)\\n gt_data = []\\n for i in range (len(gt_ids)):\\n gt_data.append(int(np.nonzero(gt_ids[i] == labels)[0]))\\n gt = {'cluster_id': gt_data}\\n print(gt_data)\\n gt_table = pd.DataFrame(data=gt)\\n gt_table.to_csv(Path(save_dir, 'clusters.csv'))\\n if Path(args.data_dir, 'clusters.csv').exists():\\n gt_ids = pd.read_csv(Path(args.data_dir, 'clusters.csv'))[:(maxsize)]\\n gt_ids.to_csv(Path(save_dir, 'clusters.csv'))\",\n \"def get_table_data(data):\\n logger.info('Generating summary table')\\n\\n table_dict = data.T.to_dict()\\n table = []\\n for date in sorted(data.index):\\n table.append(table_dict[date])\\n table.sort(key=lambda x: x['DATE'], reverse=True)\\n for ii in range(len(table)):\\n for k, v in table[ii].items():\\n if pd.isnull(v):\\n table[ii][k] = None\\n return table\",\n \"def create_table(build_list):\\n # extract list of files:\\n all_files = build_list.split(',')\\n\\n output_file = os.getcwd() + \\\"/fpkm_table.tbl\\\"\\n verbalise(\\\"B\\\", \\\"saving built table to file \\\", output_file)\\n\\n awk_cmd = \\\"\\\"\\\"awk '!($10~/FPKM/){\\\\\\n gene_sample[$1,FILENAME]=$9;\\\\\\n samples[FILENAME]=1;genes[$1]=1}\\\\\\n END{printf \\\"%s\\\\t\\\", \\\"genes\\\";\\\\\\n for(g in genes){printf \\\"%s\\\\t\\\", g};print \\\"\\\";\\\\\\n for(s in samples){printf \\\"%s\\\\t\\\",s;\\\\\\n for(g in genes){printf \\\"%s\\\\t\\\", gene_sample[g,s]};\\\\\\n print \\\"\\\"}}' \\\"\\\"\\\"\\n\\n full_cmd = awk_cmd + \\\" \\\".join(all_files) + \\\">\\\" + str(output_file)\\n\\n # build table:\\n os.system(full_cmd)\\n\\n # shorten filenames within table for easier reading!\\n #output\\n\\n return output_file\",\n \"def tabular_data(self):\\n path = CFG.GRAPHS_DIR\\n chdir(path)\\n\\n if self.experiment_count == 1:\\n f = open(self.tablefile, 'w')\\n f.write(self.print_border_line())\\n f.write(self.table_header())\\n f.write(self.print_border_line())\\n f.write(self.pretty_string(\\\"Functions\\\"))\\n f.write(self.pretty_string(\\\"Batch Size\\\"))\\n f.write(self.pretty_string(\\\"Training (%)\\\"))\\n f.write(self.pretty_string(\\\"Testing (%)\\\", True))\\n f.write('\\\\n')\\n f.write(self.print_border_line())\\n f.close()\\n\\n f = open(self.tablefile, 'a')\\n f.write(self.pretty_string(self.function_name))\\n f.write(self.pretty_string(str(self.batch_size)))\\n f.write(self.pretty_string(self.tr_mean_str))\\n f.write(self.pretty_string(self.test_mean_str, True))\\n f.write('\\\\n')\\n f.close()\",\n \"def parse_trflp(lines):\\r\\n\\r\\n sample_ids = []\\r\\n otu_ids = []\\r\\n data = []\\r\\n non_alphanum_mask = re.compile('[^\\\\w|^\\\\t]')\\r\\n # not sure why the above regex doesn't cover the following regex...\\r\\n dash_space_mask = re.compile('[_ -]')\\r\\n\\r\\n for i, line in enumerate(lines):\\r\\n elements = line.strip('\\\\n').split('\\\\t')\\r\\n\\r\\n # special handling for the first line only\\r\\n if i == 0:\\r\\n # validating if the file has a header\\r\\n if elements[0] == '':\\r\\n for otu_id in elements[1:]:\\r\\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\\r\\n continue\\r\\n else:\\r\\n for j, otu_id in enumerate(elements[1:]):\\r\\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n\\r\\n # handling of all other lines\\r\\n current_row = []\\r\\n\\r\\n # converting each value in the row to int\\r\\n for count in elements[1:]:\\r\\n try:\\r\\n current_row.append(int(round(float(count), 0)))\\r\\n except ValueError:\\r\\n current_row.append(0)\\r\\n\\r\\n # if the sum of all the values is equial to 0 ignore line\\r\\n if sum(current_row) == 0:\\r\\n continue\\r\\n\\r\\n # adding sample header to list\\r\\n sample_ids.append(non_alphanum_mask.sub('.',\\r\\n dash_space_mask.sub('.', elements[0])))\\r\\n\\r\\n # validating the size of the headers to add missing columns\\r\\n # this is only valid when there is no header\\r\\n if len(current_row) > len(otu_ids):\\r\\n # modify header data\\r\\n extra_cols = []\\r\\n for j in range(len(otu_ids), len(current_row)):\\r\\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n # modify data\\r\\n for j in range(len(data)):\\r\\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\\r\\n\\r\\n otu_ids.extend(extra_cols)\\r\\n elif len(current_row) < len(otu_ids):\\r\\n # modify data\\r\\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\\r\\n\\r\\n data.append(current_row)\\r\\n\\r\\n return sample_ids, otu_ids, asarray(data).transpose()\",\n \"def data_in(input_filename, sample_id):\\n \\n print('Processing sample {}'.format(sample_id))\\n # Encoding set to latin1 due to presence of degree symbol\\n # newlines in CC2 logger details fields will cause issues\\n header_rows = 30\\n df_param = pd.read_table(input_filename, \\n nrows=header_rows, \\n encoding=\\\"latin1\\\", \\n header=None)\\n df_val = pd.read_table(input_filename, skiprows=header_rows)\\n df_param_indexed = df_param.set_index(0)\\n# redundant due to parsing sample id from filename\\n# sample_id = df_param_indexed.loc['Sample ID', 1]\\n \\n d1 = {1: pd.Series(['', ''], index=['Sample ID', 'Label'])}\\n df_val_params = pd.DataFrame(d1)\\n df_val_params.loc['Sample ID', 1] = sample_id\\n df_val_params.loc['Label', 1] = sample_id\\n\\n return df_param_indexed, df_val, df_val_params\",\n \"def convert_list_of_freqs_to_dataframe(referanse):\\n res = []\\n for x in referanse:\\n res.append( dict(x))\\n result = pd.DataFrame(res).transpose()\\n normalize_corpus_dataframe(result)\\n return result\",\n \"def get_sample_ids(maptable):\\n return [line[0] for line in maptable[1:]]\",\n \"def generate_table(self, outtablename,\\n cols=['A', 'B', 'AB'],\\n generateTable=True):\\n if generateTable:\\n new_indices = ['time (s)', 'mean counts']\\n for idx in self.data[cols].describe().index[2:]:\\n new_indices.append(idx)\\n outTable = self.data[cols].describe()\\\\\\n .set_index(pd.Index(new_indices))\\n outTable.to_latex(\\n self.tables_dir + outtablename + \\\".tex\\\", float_format=\\\"%d\\\")\\n print(\\\"Outtable: \\\", outTable)\",\n \"def flatten_df(df):\\n flat_array = df.values.flatten()\\n flat_df = pd.DataFrame(flat_array)\\n flat_df.columns = [\\\"loan\\\"]\\n flat_df[\\\"row_no\\\"] = flat_df.reset_index().index\\n flat_df = flat_df[[\\\"row_no\\\", \\\"loan\\\"]]\\n flat_df.row_no = flat_df.row_no // 100\\n return flat_df\",\n \"def format_predictions(self, predictions):\\n output = predictions.copy()\\n\\n column_names = predictions.columns\\n\\n for col in column_names:\\n if col not in {'forecast_time', 'identifier'}:\\n output[col] = predictions[col]\\n\\n return output\",\n \"def columnar(list_table_rows: list[dict[str, str]]) -> dict[str, list[str]]:\\n column_oriented_table: dict[str, list[str]] = {}\\n first_row: dict[str, str] = list_table_rows[0]\\n for column in first_row:\\n column_oriented_table[column] = column_values(list_table_rows, column)\\n return column_oriented_table\",\n \"def _normalize_dataset(self):\\n\\n new_data = []\\n columns = ['user_id', 'movie_id', 'rating']\\n for line in self.data_file['users']:\\n movies_by_user = [\\n {'user_id': line['user_id'], 'movie_id': movie_id, 'rating': 5}\\n for movie_id in line['movies']\\n ]\\n new_data.extend(movies_by_user)\\n return pd.DataFrame(new_data, columns=columns)\",\n \"def tabulate(self):\\n for test_name, test in self.test_types.items():\\n for ivs_name, ivs in self.ivs.items():\\n if self.verbose:\\n print(\\\"{0}: {1}\\\".format(ivs_name, test_name))\\n tree = test(ivs)\\n if not tree:\\n continue\\n score = tree.score(True)\\n if self.verbose > 1:\\n tree.print_structure()\\n\\n self.result_matrix['ivs name'][ivs_name][test_name] = score\\n self.result_matrix['test type'][test_name][ivs_name] = score\",\n \"def summary_scores(fold_scores_list): \\n d = {}\\n nulls = {}\\n for idx, score in enumerate(fold_scores_list):\\n d.update({f\\\"{idx+1}0 samples:\\\": score[0].T})\\n nulls.update({f\\\"{idx+1}0 samples:\\\": score[1].T})\\n \\n summary_scores = pd.concat(d.values(), axis=1, keys=d.keys()).T\\n summary_nulls = pd.concat(nulls.values(), axis=1, keys=nulls.keys()).T\\n return summary_scores, summary_nulls\",\n \"def to_table(self):\\n table = Table()\\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\\n table['counts'] = self.counts.to_table()['data']\\n if self.counts.data_err is not None:\\n table['counts_err'] = self.counts.to_table()['data_err']\\n table['livetime'] = self.livetime.to_table()['data']\\n table['bkg'] = self.bg_rate.to_table()['data']\\n if self.bg_rate.data_err is not None:\\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\\n table.meta['HDUNAME'] = \\\"bkg_2d\\\"\\n return table\",\n \"def flatten(tbl, tpcol, key, val):\\n tblflat = tbl[tbl[tpcol].isin(val)]\\n tblflatnm = '%s_flat' %key\\n return tblflat, tblflatnm\",\n \"def filter_mapping_file(map_data, map_header, good_sample_ids,\\r\\n include_repeat_cols=False, column_rename_ids=None):\\r\\n # keeping samples\\r\\n to_keep = []\\r\\n to_keep.extend([i for i in map_data if i[0] in good_sample_ids])\\r\\n\\r\\n # keeping columns\\r\\n headers = []\\r\\n to_keep = zip(*to_keep)\\r\\n headers.append(map_header[0])\\r\\n result = [to_keep[0]]\\r\\n\\r\\n if column_rename_ids:\\r\\n # reduce in 1 as we are not using the first colum (SampleID)\\r\\n column_rename_ids = column_rename_ids - 1\\r\\n for i, l in enumerate(to_keep[1:-1]):\\r\\n if i == column_rename_ids:\\r\\n if len(set(l)) != len(result[0]):\\r\\n raise ValueError(\\r\\n \\\"The column to rename the samples is not unique.\\\")\\r\\n result.append(result[0])\\r\\n result[0] = l\\r\\n headers.append('SampleID_was_' + map_header[i + 1])\\r\\n elif include_repeat_cols or len(set(l)) > 1:\\r\\n headers.append(map_header[i + 1])\\r\\n result.append(l)\\r\\n else:\\r\\n for i, l in enumerate(to_keep[1:-1]):\\r\\n if include_repeat_cols or len(set(l)) > 1:\\r\\n headers.append(map_header[i + 1])\\r\\n result.append(l)\\r\\n headers.append(map_header[-1])\\r\\n result.append(to_keep[-1])\\r\\n\\r\\n result = map(list, zip(*result))\\r\\n\\r\\n return headers, result\",\n \"def convert_table(mkd):\\n\\t\\n\\tmd_table_codes = re.findall(r\\\".*\\\\|.*\\\\n.*\\\\-.*(?:\\\\n.*\\\\|.*)*\\\", mkd, re.M)\\n\\tfor md_code in md_table_codes:\\n\\t\\t\\n\\t\\tmd_rows = re.findall(r\\\"(.*\\\\|.*)\\\", md_code, re.M)\\n\\t\\theader = md_rows.pop(0)\\n\\t\\tcolumn_count = md_rows.pop(0).count(\\\"-\\\")\\n\\n\\t\\ttex_code = \\\"\\\\\\\\begin{tabular}{|\\\"+\\\"l|\\\"*column_count+\\\"}\\\\n\\\\hline\\\\n\\\"\\n\\t\\ttex_code += header.strip(\\\" |\\\").replace(\\\"|\\\", \\\"&\\\")+\\\" \\\\\\\\\\\\\\\\\\\\n\\\\hline\\\\n\\\"\\n\\t\\tfor row in md_rows:\\n\\t\\t\\ttex_code += row.strip(\\\" |\\\").replace(\\\"|\\\", \\\"&\\\")+\\\" \\\\\\\\\\\\\\\\\\\\n\\\"\\n\\t\\ttex_code += \\\"\\\\hline\\\\n\\\\end{tabular}\\\"\\n\\n\\t\\tmkd = mkd.replace(md_code, tex_code)\\n\\n\\treturn mkd\",\n \"def normalize_dataset(self):\",\n \"def revise_report_df(report_df):\\n # quick check to see whether report_df column structure is as expected\\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\\n print(\\\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\\\")\\n\\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\\n\\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\\n keys = ['precision', 'recall', 'f1-score', 'support']\\n\\n for col in dict_columns:\\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\\n\\n # iterate row wise through dataframe to normalize dictionary values into flat tables\\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\\n\\n # concat all classifier flat tables into one dataframe\\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\\n\\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\\n\\n # select only created columns\\n report_df = report_df.iloc[:,5:]\\n # sort columns and filter out 'support' related columns\\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\\n\\n return report_df\",\n \"def print_table2(df, eval_dir):\\n\\n out_file = os.path.join(eval_dir, 'table2.txt')\\n\\n\\n with open(out_file, \\\"w\\\") as text_file:\\n\\n for idx, struc_name in enumerate(['LV', 'RV', 'Myo']):\\n # new line\\n header_string = ' & '\\n line_string = '({}) '.format(struc_name)\\n\\n for p_idx, phase in enumerate(['ED', 'ES']):\\n for measure in ['dice', 'assd', 'hd']:\\n\\n header_string += ' & {} ({}) '.format(phase, measure)\\n\\n dat = df.loc[(df['phase'] == phase) & (df['struc'] == struc_name)]\\n\\n if measure == 'dice':\\n\\n line_string += ' & {:.3f}\\\\,({:.3f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n else:\\n line_string += ' & {:.2f}\\\\,({:.2f}) '.format(np.mean(dat[measure]), np.std(dat[measure]))\\n\\n if p_idx == 0:\\n header_string += ' & '\\n line_string += ' & '\\n\\n header_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n line_string += ' \\\\\\\\\\\\\\\\ \\\\n'\\n\\n if idx == 0:\\n text_file.write(header_string)\\n\\n text_file.write(line_string)\\n\\n return 0\",\n \"def __generate_table(package_response, primary_table_id):\\n if isinstance(package_response, Package):\\n primary_table = package_response.tables[primary_table_id]\\n header_id = primary_table.definition.header_table_id\\n header_table = package_response.tables[header_id]\\n dimension_columns = list(filter(lambda column_obj: column_obj.is_dimension, primary_table.definition.columns))\\n dimension_columns_count = len(dimension_columns)\\n row_count = len(primary_table.data.rows)\\n header_row_count = len(header_table.data.rows)\\n\\n headers = list(list())\\n # Constructs the column headers by considering dimension columns and header rows\\n for series_definition_column in header_table.definition.columns:\\n header_row = list()\\n for i in range(0, dimension_columns_count, 1):\\n if dimension_columns[i].description is \\\"\\\":\\n header_row.append(\\\" \\\")\\n else:\\n header_row.append(dimension_columns[i].description)\\n\\n for i in range(0, header_row_count, 1):\\n header_row.append(str(_SeriesDataHelper.get_value_helper(header_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\\n headers.append(header_row)\\n\\n data = list(list())\\n # Constructs the column data\\n for i in range(0, row_count, 1):\\n data_row = list()\\n for series_definition_column in primary_table.definition.columns:\\n data_row.append(str(_SeriesDataHelper.get_value_helper(primary_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\\n data.append(data_row)\\n\\n if len(header_table.definition.columns) > 1:\\n data_frame = pd.DataFrame(data=data)\\n data_frame.columns = pd.MultiIndex.from_arrays(headers)\\n else:\\n data_frame = pd.DataFrame(data=data, columns = headers[0])\\n\\n return data_frame\\n\\n else:\\n ValueError(\\\"Response data passed should be of package type.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60229045","0.5637049","0.5637049","0.53095335","0.53014034","0.52432746","0.5227828","0.51772594","0.51730895","0.51567525","0.51420665","0.51343006","0.511792","0.511474","0.5104135","0.5101256","0.50933254","0.5086502","0.5055283","0.5043328","0.50231004","0.50162965","0.500538","0.500419","0.49976316","0.49925217","0.4971346","0.49679187","0.49610928","0.4948993","0.49481377","0.49259666","0.49022692","0.48986858","0.48923644","0.48905692","0.48836884","0.48819518","0.48752242","0.48751757","0.4848846","0.4848253","0.4837781","0.48353788","0.483378","0.48268655","0.48264754","0.4818533","0.4791805","0.47870106","0.47868672","0.47828642","0.47821832","0.47803053","0.47778654","0.4775855","0.47668943","0.47658768","0.47639257","0.4759768","0.47527832","0.47493765","0.47413456","0.47404423","0.4739769","0.47287786","0.4717945","0.4713454","0.47050968","0.47026664","0.4701778","0.4699514","0.46983793","0.4691696","0.46911815","0.4687741","0.4683702","0.46740445","0.4655381","0.46490005","0.46482182","0.46474797","0.4641092","0.46394774","0.46380594","0.46298012","0.46293324","0.46255845","0.4624531","0.46211013","0.46200767","0.46178576","0.4612732","0.46009672","0.45995468","0.45978776","0.45969847","0.4594769","0.45947105","0.4587888"],"string":"[\n \"0.60229045\",\n \"0.5637049\",\n \"0.5637049\",\n \"0.53095335\",\n \"0.53014034\",\n \"0.52432746\",\n \"0.5227828\",\n \"0.51772594\",\n \"0.51730895\",\n \"0.51567525\",\n \"0.51420665\",\n \"0.51343006\",\n \"0.511792\",\n \"0.511474\",\n \"0.5104135\",\n \"0.5101256\",\n \"0.50933254\",\n \"0.5086502\",\n \"0.5055283\",\n \"0.5043328\",\n \"0.50231004\",\n \"0.50162965\",\n \"0.500538\",\n \"0.500419\",\n \"0.49976316\",\n \"0.49925217\",\n \"0.4971346\",\n \"0.49679187\",\n \"0.49610928\",\n \"0.4948993\",\n \"0.49481377\",\n \"0.49259666\",\n \"0.49022692\",\n \"0.48986858\",\n \"0.48923644\",\n \"0.48905692\",\n \"0.48836884\",\n \"0.48819518\",\n \"0.48752242\",\n \"0.48751757\",\n \"0.4848846\",\n \"0.4848253\",\n \"0.4837781\",\n \"0.48353788\",\n \"0.483378\",\n \"0.48268655\",\n \"0.48264754\",\n \"0.4818533\",\n \"0.4791805\",\n \"0.47870106\",\n \"0.47868672\",\n \"0.47828642\",\n \"0.47821832\",\n \"0.47803053\",\n \"0.47778654\",\n \"0.4775855\",\n \"0.47668943\",\n \"0.47658768\",\n \"0.47639257\",\n \"0.4759768\",\n \"0.47527832\",\n \"0.47493765\",\n \"0.47413456\",\n \"0.47404423\",\n \"0.4739769\",\n \"0.47287786\",\n \"0.4717945\",\n \"0.4713454\",\n \"0.47050968\",\n \"0.47026664\",\n \"0.4701778\",\n \"0.4699514\",\n \"0.46983793\",\n \"0.4691696\",\n \"0.46911815\",\n \"0.4687741\",\n \"0.4683702\",\n \"0.46740445\",\n \"0.4655381\",\n \"0.46490005\",\n \"0.46482182\",\n \"0.46474797\",\n \"0.4641092\",\n \"0.46394774\",\n \"0.46380594\",\n \"0.46298012\",\n \"0.46293324\",\n \"0.46255845\",\n \"0.4624531\",\n \"0.46211013\",\n \"0.46200767\",\n \"0.46178576\",\n \"0.4612732\",\n \"0.46009672\",\n \"0.45995468\",\n \"0.45978776\",\n \"0.45969847\",\n \"0.4594769\",\n \"0.45947105\",\n \"0.4587888\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5961496"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":7492,"cells":{"query":{"kind":"string","value":"reformat count or FPKM tables into flattened table of sample_names/values for rapid alignment of attr table with tracking_id"},"document":{"kind":"string","value":"def reformat_countOrFPKMTable(\n self,\n countOrFPKMTable_I=None,\n analysis_id_I=None,\n sna2experimentID_I=None,\n sna2sns_I=None,\n count_or_FPKM = 'count'):\n #format into a dictionary of rows for quick aligning with the tracking_id\n countOrFPKMTable_flat = [];\n for row in countOrFPKMTable_I:\n for k,v in row.items():\n if k=='tracking_id':continue;\n tmp = {};\n tmp['analysis_id'] = analysis_id_I;\n tmp['tracking_id'] = row['tracking_id'];\n\n sample_name_lst = k.split('_');\n sample_name_base = '_'.join(sample_name_lst[:-1]);\n sample_name_rep = eval(sample_name_lst[-1]);\n if sna2experimentID_I: \n experiment_id = sna2experimentID_I[sample_name_base];\n else:\n experiment_id=None;\n tmp['experiment_id'] = experiment_id;\n if sna2sns_I: \n sample_name = sna2sns_I[sample_name_base][sample_name_rep];\n else:\n sample_name=k;\n tmp['sample_name'] = sample_name;\n\n tmp['value'] = v;\n tmp['value_units'] = count_or_FPKM;\n tmp['used_'] = True;\n tmp['comment_'] = None;\n countOrFPKMTable_flat.append(tmp);\n return countOrFPKMTable_flat;"},"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 alignAndReformat_countFPKMattrTables(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n #reformat\n countTable_flat = self.reformat_countTable(\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,);\n fpkmTable_flat = self.reformat_fpkmTable(\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,);\n attrTable_dict = self.reformat_attrTable();\n #align\n countAndFpkmTable_aligned = [];\n for row in countTable_flat[:]:\n row.update(attrTable_dict[row['tracking_id']]);\n countAndFpkmTable_aligned.append(row);\n for row in fpkmTable_flat[:]:\n row.update(attrTable_dict[row['tracking_id']]);\n countAndFpkmTable_aligned.append(row);\n return countAndFpkmTable_aligned;","def reformat_countTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.countTable: countTable = self.countTable[:];\n else: countTable = [];\n\n countTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=countTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'count');\n return countTable_flat;","def reformat_fpkmTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.fpkmTable: fpkmTable = self.fpkmTable[:];\n else: fpkmTable = [];\n\n fpkmTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=fpkmTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'fpkm');\n return fpkmTable_flat;","def reformat_attrTable(\n self):\n #format into a dictionary of rows for quick aligning with the tracking_id\n if self.attrTable: attrTable = self.attrTable[:];\n else: attrTable = [];\n\n attrTable_dict = {};\n for row in attrTable:\n attrTable_dict[row['tracking_id']] = row;\n return attrTable_dict;","def freq_table(a):\n Detail_freq = a.loc[:, (a.dtypes == object) | (a.dtypes == long) ].columns.get_values().tolist()\n print(Detail_freq)\n for freq in Detail_freq:\n df1 = pd.DataFrame(a[freq].value_counts(dropna=False).astype(float).map('{:20,.0f}'.format).sort_index()).rename(columns={freq:'Count'})\n df2 = pd.DataFrame(a[freq].value_counts(normalize = True, dropna=False).map('{:,.2%}'.format).sort_index()).rename(columns={freq:'Percentage'})\n df = pd.concat([df1, df2], axis = 1)\n print(df)","def _finalize(self, sampled_data):\n final_data = dict()\n for table_name, table_rows in sampled_data.items():\n parents = self.metadata.get_parents(table_name)\n if parents:\n for parent_name in parents:\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\n if foreign_key not in table_rows:\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\n table_rows[foreign_key] = parent_ids\n\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\n\n fields = self.metadata.get_fields(table_name)\n\n final_data[table_name] = reversed_data[list(fields.keys())]\n\n return final_data","def format_unifrac_sample_mapping(sample_ids, otu_ids, otu_table_array):\r\n out = []\r\n for i, row in enumerate(otu_table_array):\r\n for j, val in enumerate(row):\r\n if val > 0:\r\n line = [otu_ids[i], sample_ids[j], str(val)]\r\n out.append('\\t'.join(line))\r\n return out","def per_sample_taxa_summaries(open_table, output_format):\n t = parse_biom_table(open_table)\n header = \"#taxon\\trelative_abundance\\n\"\n\n for v, id_, md in t.iter():\n with open(output_format % id_, 'w') as f:\n f.write(header)\n\n for sorted_v, taxa in \\\n sorted(zip(v, t.ids(axis='observation')))[::-1]:\n if sorted_v:\n f.write(\"%s\\t%f\\n\" % (taxa, sorted_v))","def reformat_data(self, df, ids):\n data = np.zeros((len(ids), self.n_sample_rows + 1, self.n_features))\n idx = 0\n for i in ids:\n sample = df.loc[i]\n data[idx, 0:89, :] = sample.values\n data[idx, 89, :] = np.mean(sample.values)\n idx += 1\n return data","def produce_mirna_single_counts_table(count_files, table_file):\n merge_count_tables(count_files, table_file, \".dedup.single.bam.mirbase_counts.txt\")","def make_summary_tables( res ):\n\n # transform second table to csv and read this as a dataFrame\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\",\",index_col=0)\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\n\n # first table is trickier because the data is spread on to columns, and there is title line\n L = res.tables[0].as_html().split('\\n')\n L.pop(1) # get rid of the title\n tmp = pd.read_html('\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \n\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\n # NB : I exclude the last 2 elements which are empty \n \n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\n \n return result_general_df , result_fit_df","def normalize_counts_to_tpm(counts_dir, gff_dir, out_dir, feat='CDS', id_sym='gene_id='):\n count_files = [os.path.join(counts_dir, f) for f in os.listdir(counts_dir)]\n all_tpms = {}\n for cf in count_files:\n if \"_counts\" not in cf:\n continue\n tpm = normalize_counts_to_tpm_one_file(cf, gff_dir, feat, id_sym)\n #out_file = \"{}_tpm.csv\".format(os.path.basename(cf))\n #out_path = os.path.join(out_dir, out_file)\n #with open(out_path, \"w\") as fo:\n # for gene, t in tpm.items():\n # fo.write(\"{},{}\\n\".format(gene, t))\n prefix = os.path.basename(cf).split(\"_trimmed\")[0] # this would be specific to my naming convention\n all_tpms[prefix] = tpm\n return all_tpms","def _create_counts(out_dts, out_dir):\n ma, ma_mirna = _merge(out_dts)\n out_ma = op.join(out_dir, \"counts.tsv\")\n out_ma_mirna = op.join(out_dir, \"counts_mirna.tsv\")\n ma.to_csv(out_ma, sep=\"\\t\")\n ma_mirna.to_csv(out_ma_mirna, sep=\"\\t\")\n return out_ma_mirna, out_ma","def print_tables(hash_table, f_output, l_samples):\n\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\n 'ExAC_SAS',\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\n 'Polyphen2_HVAR_score',\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\n l_fields = l_fields + l_samples\n \n l_chr = set([item[0] for item in hash_table.keys()])\n\n fo = open(f_output, 'w')\n fo.write(','.join(l_fields) + '\\n')\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\n')\n fo.close()","def get_split_summary_table(all_df, train_df, test_df):\n table = PrettyTable()\n table.field_names = ['set', 'N total', 'N non-ICH', 'N ICH', 'frac non-ICH', 'frac ICH']\n for df, name in zip([all_df, train_df, test_df],['All', 'Train', 'Test']):\n table.add_row([name, len(df), len(df[df.Hemorrhage == 0]), len(df[df.Hemorrhage == 1]),\n f'{len(df[df.Hemorrhage == 0])/len(df):.3%}', f'{len(df[df.Hemorrhage == 1])/len(df):.3%}'])\n return table","def prepare_batch_sample_set_for_metadata_export(path, tsca_id):\n raw = pd.read_table(path)\n print( \"%d Samples in this batch\" % raw.shape[0] )\n\n # Create dfs to upload\n all_samples = pd.concat([pd.DataFrame(index=raw.index, columns=['membership:sample_set_id'], data=tsca_id), \\\n raw[ ['sample_id', 'sample_type'] ]], axis=1)\n\n\n tumors = all_samples.loc[ all_samples['sample_type'] == \"Tumor\", ['membership:sample_set_id', 'sample_id'] ]\n tumors.loc[: , 'membership:sample_set_id'] = \"%s_T\"%tsca_id\n \n normals = all_samples.loc[ all_samples['sample_type'] == \"Normal\", ['membership:sample_set_id', 'sample_id'] ]\n normals.loc[: , 'membership:sample_set_id'] = \"%s_N\"%tsca_id\n\n all_samples = all_samples.drop('sample_type', axis=1)\n return (all_samples, tumors, normals)","def produce_mirna_allbest_counts_table(count_files, table_file):\n merge_count_tables(count_files, table_file, \".dedup.bam.mirbase_counts.txt\")","def write_count_table(filtered_counts, gene_names, ids_liver_header):\n with open(\"filtered_tcga_counts.tsv\", \"w\") as file:\n file.write(\"gene_id\\tgene_name\\t\" + \"\\t\".join(ids_liver_header) + \"\\n\")\n for gene_name, counts_line in zip(gene_names, filtered_counts):\n file.write(gene_name + \"\\t\" + \"\\t\" + \"\\t\".join(counts_line) + \"\\n\")","def make_lof_table(data_table, my_genes, my_samples, summary_func):\n table_header = [\"Gene\"] + my_samples + [\n \"Missense:Benign\", \"Missense:Possibly\", \"Missense:Probably\",\n \"MissenseNA\", \"Indel\", \"Nonsense\", \"Frameshift\", \"Splice-site\",\n \"Synonymous\"]\n table_records = []\n\n gs_lookup = group_data_by_gs(data_table)\n for gene in my_genes:\n synonymous = missense_benign = missense_possibly = missense_probably = \\\n missense_na = frameshift = nonsense = splice = indel = 0\n\n out_row = [gene]\n for sample in my_samples:\n normalized = [0]\n # Count mutations of each type for this gene and sample\n for entry in gs_lookup[gene][sample]:\n if entry['muttype'] == 'Silent':\n synonymous += 1\n continue\n if entry['muttype'] == 'Intron':\n # Shouldn't be here; ignore\n continue\n\n if entry['muttype'] == 'Missense_Mutation':\n if entry['consequence'] == 'benign':\n missense_benign += 1\n elif entry['consequence'] == 'possibly':\n missense_possibly += 1\n elif entry['consequence'] == 'probably':\n missense_probably += 1\n elif entry['consequence'] == 'NA':\n missense_na += 1\n else:\n print(\"Unhandled missense consequence level:\",\n entry['consequence'], file=sys.stderr)\n elif entry['muttype'] == 'Nonsense_Mutation':\n nonsense += 1\n elif entry['muttype'] == 'Splice_Site':\n splice += 1\n elif entry['muttype'] in ('Frame_Shift_Ins', 'Frame_Shift_Del'):\n frameshift += 1\n elif entry['muttype'] in ('In_Frame_Ins', 'In_Frame_Del'):\n indel += 1\n else:\n print(\"Unhandled mutation type:\", entry['muttype'],\n file=sys.stderr)\n continue\n\n normalized.append(entry['normalized'])\n # Summarize the normalized mutation counts for this gene and sample\n out_row.append(summary_func(normalized))\n out_row.extend((missense_benign, missense_possibly, missense_probably,\n missense_na, indel, nonsense, frameshift, splice,\n synonymous))\n table_records.append(out_row)\n\n return pandas.DataFrame.from_records(table_records, columns=table_header)","def join_gene_tables(gene_tables,output,verbose=None):\n \n gene_table_data={}\n start_column_id=\"\"\n samples=[]\n file_basenames=[]\n index=0\n for gene_table in gene_tables:\n \n if verbose:\n print(\"Reading file: \" + gene_table)\n \n lines=util.process_gene_table_with_header(gene_table, allow_for_missing_header=True)\n header=next(lines)\n \n # get the basename of the file\n file_basename='.'.join(os.path.basename(gene_table).split('.')[:-1])\n file_basenames.append(file_basename)\n \n if header:\n header_info=header.split(GENE_TABLE_DELIMITER)\n if not start_column_id:\n start_column_id=header_info[0]\n # allow for multiple samples\n sample_names=header_info[1:]\n else:\n # if there is no header in the file then use the file name as the sample name\n sample_names=[file_basename]\n \n for line in lines:\n data=line.split(GENE_TABLE_DELIMITER)\n try:\n gene=data[0]\n # if the header names multiple samples, merge all samples\n # this prevents extra columns from being included in some rows\n # this requires files containing multiple samples to include a header\n data_points=data[1:len(sample_names)+1]\n except IndexError:\n gene=\"\"\n\n if gene:\n current_data=gene_table_data.get(gene,\"\")\n fill = index - current_data.count(GENE_TABLE_DELIMITER)\n if fill > 0:\n # fill in zeros for samples without data then add data point\n gene_table_data[gene]=current_data + GENE_TABLE_DELIMITER.join([\"0\"]*fill) + GENE_TABLE_DELIMITER + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\n elif fill < 0:\n # add data point to other data point from the same sample\n current_data_points=current_data.split(GENE_TABLE_DELIMITER)\n for i,point in enumerate(data_points):\n store_index=len(data_points)*-1-1+i\n current_data_points[store_index]=str(float(current_data_points[store_index])+float(point))\n gene_table_data[gene] = GENE_TABLE_DELIMITER.join(current_data_points)\n else:\n # add data point to end of list\n gene_table_data[gene] = current_data + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\n \n samples+=sample_names\n index+=len(sample_names)\n # if all of the header names for the files are the same\n # then use the file names as headers\n if samples.count(samples[0]) == len(samples):\n samples=file_basenames\n \n # write the joined gene table\n if not start_column_id:\n start_column_id=\"# header \"\n sample_header=[start_column_id]+samples\n total_gene_tables=len(samples)\n sorted_gene_list=util.fsort(list(gene_table_data))\n try:\n file_handle=open(output,\"w\")\n file_handle.write(GENE_TABLE_DELIMITER.join(sample_header)+\"\\n\")\n except EnvironmentError:\n sys.exit(\"Unable to write file: \" + output) \n \n for gene in sorted_gene_list:\n # extend gene data for any gene that is not included in all samples\n current_data=gene_table_data[gene]\n fill = total_gene_tables - current_data.count(GENE_TABLE_DELIMITER)\n if fill:\n current_data=current_data + GENE_TABLE_DELIMITER.join([\"0\"]*fill) + GENE_TABLE_DELIMITER\n file_handle.write(gene+GENE_TABLE_DELIMITER+current_data.rstrip(GENE_TABLE_DELIMITER)+\"\\n\")\n \n file_handle.close()","def samples(app, args):\n engine = create_engine(args.datafile)\n meta = MetaData()\n meta.reflect(engine)\n print(\"\\t\".join([str(x).replace('counts.', '')\n for x in meta.tables['counts'].columns\n if not x == 'counts.index']))","def format_df(df):\n df = df.transpose()\n df.rename(columns=df.iloc[0], inplace=True)\n df.drop('gene', inplace=True)\n df.index.rename('sample_id', inplace=True)\n return df","def format_df(df):\n df = df.transpose()\n df.rename(columns=df.iloc[0], inplace=True)\n df.drop('gene', inplace=True)\n df.index.rename('sample_id', inplace=True)\n return df","def tranform_data(args):\n data_dir = args.data_dir\n save_dir = args.save_dir\n os.makedirs(save_dir)\n maxsize = args.maxsize\n maxlen = args.maxlen \n ext = args.ext\n datetime = args.datetime\n classes = set()\n nb_files = 0\n time_col = 'time'\n event_col = 'event'\n gt_ids = None\n if args.ext == \"pkl\":\n with open(Path(args.data_dir, \"fx_labels\"), \"rb\") as fp:\n gt_ids = pickle.load(fp)[:maxsize]\n labels = np.unique(gt_ids)\n gt_data = []\n for i in range (len(gt_ids)):\n gt_data.append(int(np.nonzero(gt_ids[i] == labels)[0]))\n gt = {'cluster_id': gt_data}\n print(gt_data)\n gt_table = pd.DataFrame(data=gt)\n gt_table.to_csv(Path(save_dir, 'clusters.csv'))\n if Path(args.data_dir, 'clusters.csv').exists():\n gt_ids = pd.read_csv(Path(args.data_dir, 'clusters.csv'))[:(maxsize)]\n gt_ids.to_csv(Path(save_dir, 'clusters.csv'))","def preprocess(df):\n \n # drop the following columns - irrelevant now\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\n , 'tmdbId', 'popularity', 'year']\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n # drop all of the language columns\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\"lan\"]\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n\n # loop through the columns we want to aggregate\n for col_type in [\n \"original_language_\"\n , \"prod_comp_cntry_\"\n , \"prod_comp_names_\"\n , \"writers_\"\n , \"actors_\"\n , \"genres_\"\n , \"director_\"\n ]:\n # create a dictionary of each unique value and its frequency\n val_freq = {}\n for col in df.columns:\n if col.startswith(col_type):\n val_freq[col] = df[col].sum()\n\n # create a dataframe from this dictionary; sort by count\n counts = pd.DataFrame.from_dict(\n val_freq\n , orient='index'\n , columns=['count']\n ).sort_values('count', ascending=False)\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\n\n # handle special case of production company country\n if col_type == \"prod_comp_cntry_\":\n DROP_COLUMNS = [col for col in counts.index][3:]\n\n # handle special case of directors\n elif col_type == \"director_\":\n DIRECTOR_COLS = [col for col in df.columns\n if col.startswith(\"director_\")\n and col!=\"director_pop\"]\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\n DROP_COLUMNS = DIRECTOR_COLS\n\n # handle special case of actors\n elif col_type == \"actors_\":\n ACTORS_COLS = [col for col in df.columns if \"actors\" in col]\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\n DROP_COLUMNS = ACTORS_COLS\n\n # handle all the other cases\n else:\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\n\n\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n ##########################################################################\n # adjust the data for inflation\n CPI_tf = df['CPIAUCSL'].max()\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n # no longer need CPI data\n df.drop('CPIAUCSL', axis=1, inplace=True)\n \n ########################################################################## \n # add in useful features about the cast and crew \n df['cast_crew_sum_pop'] = (\n df['director_pop']\n + df['avg_actor_pop']\n + df['avg_writer_pop']\n )\n df['cast_crew_product_pop'] = (\n df['director_pop']\n * df['avg_actor_pop']\n * df['avg_writer_pop']\n )\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\n df = df.query('10000 <= revenue').copy()\n df = df.query('100000 <= budget').copy()\n df.drop('sum_actor_pop', axis=1, inplace=True)\n df.drop('min_writer_pop', axis=1, inplace=True)\n\n # code to transform columns\n for col in [\n \"budget\", \"director_pop\", \"avg_writer_pop\"\n , \"max_writer_pop\", \"avg_actor_pop\", \"max_actor_pop\"\n , \"min_actor_pop\", 'cast_crew_sum_pop'\n , 'cast_crew_product_pop'\n ]:\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\n df.drop(col, axis=1, inplace=True)\n \n return df","def gen_tab(cat):\n\n col = ['FLUX_APER2','FLUX_APER4','FLUX_APER5','FLUX_APER8','FLUX_APER10','FLUX_APER14',\n 'MAG_APER2','MAG_APER4','MAG_APER5','MAG_APER8','MAG_APER10','MAG_APER14',\n 'MAG_AUTO','MAG_PETRO','KRON_RADIUS',\n 'PETRO_RADIUS','FLUX_MAX','ISOAREAF_IMAGE','x',\n 'y','ra','dec','X2_IMAGE','Y2_IMAGE','XY_IMAGE',\n 'THETA_IMAGE','X2WIN_IMAGE','Y2WIN_IMAGE','XYWIN_IMAGE','AWIN_IMAGE','BWIN_IMAGE',\n 'THETAWIN_IMAGE','AWIN_WORLD','BWIN_WORLD','THETAWIN_WORLD',\n 'MU_MAX','FLAGS','FWHM_IMAGE','ELONGATION','SEX_CLASS','FLUX_RADIUS25',\n 'FLUX_RADIUS50','FLUX_RADIUS85','FLUX_RADIUS95','FLUX_RADIUS99']\n print('generating features table: {}'.format(cat))\n tab = pd.read_table(cat,skiprows=41,sep=r'\\s+',header=None, names=col)\n\n # crop the image for just using the central part of the image\n tab = crop(tab)\n\n # add concentration column by subtracting mag10 by mag5, rejecting the detections with negative concentration\n tab['CONCENT'] = tab.MAG_APER5 - tab.MAG_APER10\n tab = tab[tab.CONCENT > 0]\n\n # normalizing the columns\n print('normalizing features...')\n seesq_norm = ['X2_IMAGE','Y2_IMAGE','X2WIN_IMAGE',\n 'Y2WIN_IMAGE','XY_IMAGE','XYWIN_IMAGE',\n 'ISOAREAF_IMAGE']\n see_norm = ['AWIN_WORLD','AWIN_WORLD','FWHM_IMAGE',\n 'KRON_RADIUS','PETRO_RADIUS','FLUX_RADIUS25',\n 'FLUX_RADIUS50','FLUX_RADIUS85',\n 'FLUX_RADIUS95','FLUX_RADIUS99']\n mag_norm = ['MAG_APER4','MAG_APER5','MAG_APER8',\n 'MAG_APER10','MAG_APER14','MAG_AUTO',\n 'MAG_PETRO','MU_MAX','CONCENT']\n flux_norm = ['FLUX_APER2','FLUX_APER4','FLUX_APER5',\n 'FLUX_APER8','FLUX_APER10','FLUX_APER14']\n fwhm_mean = tab.FWHM_IMAGE.mean()\n for seesq_col in seesq_norm:\n tab[seesq_col] = tab[seesq_col] / (fwhm_mean**2)\n for see_col in see_norm:\n tab[see_col] = tab[see_col] / fwhm_mean\n for mag_col in mag_norm:\n tab[mag_col] = tab[mag_col] * tab['MAG_APER2']\n for flux_col in flux_norm:\n tab[flux_col] = tab[flux_col] * tab['FLUX_MAX']\n tab['CONCENT'] = -1 * tab['CONCENT']\n\n # add column for galactic latitude\n print('calculating galactic latitude...')\n ra = np.array(tab['ra'].values)\n dec = np.array(tab['dec'].values)\n pos = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')\n tab['b'] = list(pos.galactic.b.deg)\n\n tab.drop(['MAG_APER2','FLUX_MAX','x','y'], axis=1, inplace=True)\n tab.to_csv(cat[:-4]+'.csv', index=False, header=True)","def build_table():\n with contextlib.ExitStack() as stack:\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\n iters = [(line.split() for line in f) for f in files]\n for it in iters:\n next(it)\n key = operator.itemgetter(0)\n table = []\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\n props = list(g)\n if len(props) == len(iters):\n table.append([k] + [x[1] for x in props])\n for snp in table:\n print(*snp)","def print_table(source, count=False):\n table_value = []\n table_header = []\n for source_key, source_value in source.items():\n for item in source_value:\n table_value.append([v for v in item.values()])\n table_header.append([k for k in item.keys()])\n if not count:\n print(tabulate(table_value,\n headers=table_header[0],\n tablefmt='orgtbl'))\n else:\n print(tabulate([[len(source_value)]],\n headers=[source_key],\n tablefmt='orgtbl'))","def make_table(ranked_means):\n fp = open(\"table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c|}\n \\\\hline\n \\\\multicolumn{2}{|c||}{Slowest} & \\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\ \\\\hline\n Feature & Rate & Feature & Rate \\\\\\\\ \\\\hline\n\"\"\")\n top_10 = ranked_means[0:10]\n bottom_10 = ranked_means[-10:]\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\n f_name = f_name.split(\":\")[-1]\n f_name = f_name.rsplit(\" \", 1)[0] if f_name.endswith(\"(V)\") else f_name\n s_name = s_name.split(\":\")[-1]\n s_name = s_name.rsplit(\" \", 1)[0] if s_name.endswith(\"(V)\") else s_name\n fp.write(\" %s & %.2f & %s & %.2f \\\\\\\\ \\n\" % \\\n (f_name, f_rate, s_name, s_rate))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()\n\n fp = open(\"supp_meaning_table.tex\", \"w\")\n fp.write(\"\"\"\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\n \\\\hline\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\ \\\\hline\n\n\"\"\")\n feature_names = [f.split(\":\")[-1] for (r,f) in ranked_means]\n feature_names.sort(key=lambda s: s.lower())\n col1 = feature_names[0:25]\n col2 = feature_names[25:50]\n col3 = feature_names[50:75]\n col4 = feature_names[75:]\n for a,b,c,d in zip(col1,col2,col3,col4):\n x,y,z,w = [get_meaning_category(i) or \"Excluded\" for i in (a,b,c,d)]\n # Lop off (V)s (we needed them above for get_meaning_category to work)\n a,b,c,d = [f.rsplit(\" \", 1)[0] if f.endswith(\"(V)\") else f for f in (a,b,c,d)]\n fp.write(\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\ \\n\" % (a, x, b, y, c, z, d, w))\n fp.write(\"\\\\hline\\n\")\n fp.write(\"\\\\end{tabular}\\n\")\n fp.close()","def _transform_idoc(df):\n global _SIMPLECOUNT_COLUMNS\n\n try:\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\n\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\n \n c_nc = df['admtypo3'] == 1\n c_tv = df['admtypo3'] == 2\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\n c_viol = df['offtype'] == 1\n c_male = df['sex'] == 'M'\n c_female = ~c_male\n\n c_first2 = [c_nc, c_tv]\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\n \n def helper(c, indicator_id, first2):\n df['fk_simplecount_indicator'] = indicator_id\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\n if first2:\n return df[c].groupby(g).size().reset_index(name='value')\n else:\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\n\n out = pd.DataFrame()\n for i in range(2):\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\n \n for i in range(len(c_others)):\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\n\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\n return out[_SIMPLECOUNT_COLUMNS]\n except:\n raise","def tabular_report(sample_dictionary, blast_dictionary):\n print(\"Writing the report...\")\n sample_dict = sample_dictionary.copy()\n blast_dict = blast_dictionary.copy()\n\n #creating quick dictionary to pull out trimmed sequence\n trimmed_data_dict={}\n for key in sample_dict.keys():\n try:\n trimmed_sequence=(sample_dict[key]['trimmed_sequence'])\n key = key.strip('@')\n trimmed_data_dict.update({key:trimmed_sequence})\n except KeyError:\n continue\n\n samples = []\n for sequenceID in sample_dict:\n samples.append(sequenceID[1:])\n \n records = blast_dict.keys()\n \n columns = [\"SeqID\", \"Trimmed Sequence\", \"Trimmed Sequence Length\",\"BLAST Sequence\", \n \"BLAST SeqLength\", \"Description\", \"Accession\", \"Db\", \n \"Score\", \"E_value\", \"Percent_Identity\", \"Organism\", \n \"Source\", \"Domain\", \"Taxonomy\"]\n \n #Writing\n OUT = open(\"blast_hits_report.txt\", \"w\")\n OUT.write('\\t'.join(columns) + '\\n')\n \n NO_HITS_OUT = open(\"blast_no_hits_report.txt\", \"w\")\n NO_HITS_OUT.write(\"SeqID\\tOriginal Seq\\tOriginal Seq Length\"\n \"\\tTrimmed Seq\\tTrimmed Seq Length\\tResult\\n\")\n\n for record in blast_dict.keys():\n\n trimmed_sequence = trimmed_data_dict[record]\n length_trimmed_sequence=len(trimmed_data_dict[record])\n \n #Used Brute force coding to be able to manipulate and add new columns to output\n try: \n OUT.write(str(record)\n +'\\t'+str(trimmed_sequence)\n +'\\t'+str(length_trimmed_sequence)\n +'\\t'+str(blast_dict[record]['Sequence'])\n +'\\t'+str(blast_dict[record]['SeqLength'])\n +'\\t'+str(blast_dict[record]['Description'])\n +'\\t'+str(blast_dict[record]['Accession'])\n +'\\t'+str(blast_dict[record]['Db'])\n +'\\t'+str(blast_dict[record]['Score'])\n +'\\t'+str(blast_dict[record]['E_value'])\n +'\\t'+str(blast_dict[record]['Percent_Identity'])\n +'\\t'+str(blast_dict[record]['Organism'])\n +'\\t'+str(blast_dict[record]['Source'])\n +'\\t'+str(blast_dict[record]['Domain'])\n +'\\t'+str(blast_dict[record]['Taxonomy'])+'\\n')\n except KeyError:\n continue\n\n for sample in samples:\n\n if sample not in records:\n sample_stripped = sample.split(\"\\t\")[0]\n\n #Get original trimmed sequence for reference\n try:\n trimmed_sequence = trimmed_data_dict[sample_stripped]\n length_trimmed_sequence=len(trimmed_sequence)\n except KeyError:\n trimmed_sequence = '-'\n length_trimmed_sequence=0\n\n NO_HITS_OUT.write(sample_stripped\n #Commend out original data being reported\n + '\\t' + sample_dict['@'+sample]['sequence'] \n + '\\t' + str(len(sample_dict['@'+sample]['sequence'])) \n + '\\t' + str(trimmed_sequence)\n + '\\t' + str(length_trimmed_sequence)\n\n + '\\t' + 'NO HIT OR SEQUENCE QUALITY BELOW THRESHOLD\\n')\n OUT.close()\n NO_HITS_OUT.close()","def reformat_file(inFile, outFile):\n \n with open(outFile, \"w\") as outHandle:\n \n\t\t# write header line\n\t\toutLine = [\"g1\", \"g2\", \"raw_count\", \"log(obs/exp)\"]\n\t\t\n\t\toutHandle.write(\"\\t\".join(outLine) + \"\\n\")\n\n\n\t\tfor i, line in enumerate(open(inFile)):\n\t\t\t\n\t\t\tif not i == 0:\n\t\t\t\t\n\t\t\t\tsp = line.strip().split(\"\\t\")\n\t\t\t\t\n\t\t\t\t# get row interaction counts and normalized obs/exp values\n\t\t\t\trawCount = sp[12]\n\t\t\t\tobsExp = sp[13]\n\t\t\t\t\n\t\t\t\tgenes1 = sp[4].split(\"|\")\n\t\t\t\tgenes2 = sp[10].split(\"|\")\n\t\t\t\t\n\t\t\t\t#~ print(g1, g2, rawCount)\n\t\t\t\t\n\t\t\t\t# iterate over all pairs\n\t\t\t\tfor g1 in genes1:\n\n\t\t\t\t\tfor g2 in genes2:\n\t\t\t\t\t\t\n\t\t\t\t\t\toutLine = [g1, g2, rawCount, obsExp]\n\t\t\t\t\t\t\n\t\t\t\t\t\toutHandle.write(\"\\t\".join(outLine) + \"\\n\")","def to_table(self):\n table = Table()\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\n table['counts'] = self.counts.to_table()['data']\n if self.counts.data_err is not None:\n table['counts_err'] = self.counts.to_table()['data_err']\n table['livetime'] = self.livetime.to_table()['data']\n table['bkg'] = self.bg_rate.to_table()['data']\n if self.bg_rate.data_err is not None:\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\n table.meta['HDUNAME'] = \"bkg_2d\"\n return table","def get_normalized_data_table(table_metadata, debug=False):\n suffix = table_metadata.get('suffix', '')\n data_table = table_metadata['table_class'](\n file_path=table_metadata['csv_filename'], suffix=suffix)\n drop_headers(table_metadata['document_label'], data_table.data)\n rename_headers(table_metadata['document_label'], data_table.data)\n print_data_table_length(table_metadata['document_label'],\n data_table.data,\n debug=debug)\n return data_table","def _collect_counts(self, instance_list):\n \"\"\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\n All \"rare words\" (those words that appear less than 3 times) are replaced by <UNK>.\n I also add label|START counts.\n \"\"\"\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\n for instance in instance_list: # Set of <(w, pos), l>\n index = 0\n for t in instance.data: # Tuple of (w, pos)\n index = instance.data.index(t)\n # print t[0] # word\n # print instance.label[index] # label\n if t in self.V:\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\n else:\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\n if index > 0:\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\n else:\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1","def fill_table(table, keep_unknowns=False):\n if not keep_unknowns:\n table.drop(table.index[table.phylum == \"unknown\"], inplace=True)\n table.drop(table.index[table.genus == \"unknown\"], inplace=True)\n table.drop(table.index[table.eggNOG == \"unknown\"], inplace=True)\n for cohort in table[\"cohort_origin\"].unique():\n cohort_table = table.loc[table[\"cohort_origin\"] == cohort]\n\n if cohort not in table_summary:\n table_summary[cohort] = {}\n\n if \"N\" not in table_summary[cohort]:\n table_summary[cohort][\"N\"] = 1\n else:\n table_summary[cohort][\"N\"] += 1\n\n if \"phylum\" not in table_summary[cohort]:\n table_summary[cohort][\"phylum\"] = pd.DataFrame(columns=[\"phylum\"])\n\n #count the number of unique values in the phylum column\n table_phylum = cohort_table.loc[table['genus'] != \"unknown\",['phylum']].apply(pd.Series.value_counts,axis=0)\n table_summary[cohort][\"phylum\"] = table_summary[cohort][\"phylum\"].add(table_phylum,fill_value=0)\n \n if \"genus\" not in table_summary[cohort]:\n table_summary[cohort][\"genus\"] = pd.DataFrame(columns=[\"genus\"])\n\n #count the number of unique values in the genus column\n table_genus = cohort_table.loc[table['genus'] != \"unknown\",['genus']].apply(pd.Series.value_counts,axis=0)\n table_summary[cohort][\"genus\"] = table_summary[cohort][\"genus\"].add(table_genus,fill_value=0)\n \n if \"eggNOG\" not in table_summary[cohort]:\n table_summary[cohort][\"eggNOG\"] = pd.DataFrame(columns=[\"eggNOG\"])\n\n index = 0\n cogs_table = {}\n\n #forced to do this by each row because one gene belongs to multiple cogs\n for row in cohort_table[\"eggNOG\"]:\n if \"gene_cog\" not in table_summary[cohort]:\n table_summary[cohort][\"gene_cog\"] = 0\n table_summary[cohort][\"gene_cog\"] += 1\n if \";\" in row:\n cogs = row.split(\";\")\n for cog in cogs:\n if cog not in cogs_table:\n cogs_table[cog] = 0\n cogs_table[cog] += 1\n else:\n if row not in cogs_table:\n cogs_table[row] = 0\n cogs_table[row] += 1\n\n #create data frame and create the table\n cog_pd = pd.DataFrame.from_dict(cogs_table, orient=\"index\")\n cog_pd.columns = [\"eggNOG\"]\n table_summary[cohort][\"eggNOG\"] = table_summary[cohort][\"eggNOG\"].add(cog_pd,fill_value=0)","def revise_report_df(report_df):\n # quick check to see whether report_df column structure is as expected\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\n print(\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\")\n\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\n\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\n keys = ['precision', 'recall', 'f1-score', 'support']\n\n for col in dict_columns:\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\n\n # iterate row wise through dataframe to normalize dictionary values into flat tables\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\n\n # concat all classifier flat tables into one dataframe\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\n\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\n\n # select only created columns\n report_df = report_df.iloc[:,5:]\n # sort columns and filter out 'support' related columns\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\n\n return report_df","def _get_table_from_samples(self, index):\n df = pd.DataFrame()\n for sample in self.samples:\n sd = sample.to_dict()\n ser = pd.Series(\n {k: v for (k, v) in list(sd.items()) if not k.startswith(\"_\")}\n )\n df = df.append(ser, ignore_index=True)\n index = [index] if isinstance(index, str) else index\n if not all([i in df.columns for i in index]):\n _LOGGER.debug(\n \"Could not set {} index. At least one of the \"\n \"requested columns does not exist: {}\".\n format(CFG_SAMPLE_TABLE_KEY, index))\n return df\n _LOGGER.debug(\"Setting sample_table index to: {}\".format(index))\n df.set_index(keys=index, drop=False, inplace=True)\n return df","def produce_mirna_unique_counts_table(count_files, table_file):\n merge_count_tables(count_files, table_file, \".dedup.uniq.bam.mirbase_counts.txt\")","def prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id):\n tn_pairs = pairs[(pairs['match_type'] == \"tumor_normal\") & (pairs['case_sample_tsca_id']==tsca_id)]\n tp_pairs = pairs[(pairs['match_type'] == \"tumor_primary\") & (pairs['case_sample_tsca_id']==tsca_id)]\n\n tn_pairsets = pd.merge(tn_pairs, all_samples[['entity:sample_id', 'tsca_id']], \\\n left_on='case_sample_id', right_on='entity:sample_id', \\\n how='inner')[['tsca_id', 'entity:pair_id']] \\\n .rename(columns={'tsca_id': 'membership:pair_set_id', 'entity:pair_id': 'pair_id'})\n\n tp_pairsets = pd.merge(tp_pairs, all_samples[['entity:sample_id', 'tsca_id']], \\\n left_on='case_sample_id', right_on='entity:sample_id', \\\n how='inner')[['tsca_id', 'entity:pair_id']] \\\n .rename(columns={'tsca_id': 'membership:pair_set_id', 'entity:pair_id': 'pair_id'})\n\n # Append _TN/_TP to the end of the tumor-normal/tumor-primary pair set ids\n tn_pairsets['membership:pair_set_id'] = tn_pairsets['membership:pair_set_id'].apply(lambda x: \"%s_TN\"%x)\n tp_pairsets['membership:pair_set_id'] = tp_pairsets['membership:pair_set_id'].apply(lambda x: \"%s_TP\"%x)\n \n return (tn_pairsets, tp_pairsets)","def getAllSampleFields(self, sample_id, study_id):\n sample_tables = []\n sample_tables.append('sample')\n sample_tables.append('common_fields')\n sample_tables.append('extra_sample_')\n sample_tables.append('air')\n sample_tables.append('other_environment')\n sample_tables.append('sediment')\n sample_tables.append('soil')\n sample_tables.append('wastewater_sludge')\n sample_tables.append('water')\n sample_tables.append('host_assoc_vertibrate')\n sample_tables.append('host_associated_plant')\n sample_tables.append('human_associated')\n sample_tables.append('host_sample')\n sample_tables.append('host')\n \n filled_fields = {}\n \n con = self.getMetadataDatabaseConnection()\n cursor = con.cursor()\n \n for table in sample_tables:\n if 'extra_sample_' in table:\n statement = 'select * from %s%s where sample_id = %s' % (table, study_id, sample_id)\n elif table == 'host':\n statement = 'select * from host h inner join host_sample hs on h.host_id = hs.host_id where sample_id = %s' % sample_id\n else:\n statement = 'select * from %s where sample_id = %s' % (table, sample_id)\n \n try:\n cursor.execute(statement)\n except Exception, e:\n print str(e)\n print 'Error running query:\\n'\n print statement\n print 'Running next query...\\n'\n \n continue\n \n data = cursor.fetchall()\n\n # Get the column names\n col_names = []\n for i in range(0, len(cursor.description)):\n col_names.append(cursor.description[i][0])\n \n # Find the rows with data\n for row in data:\n i = 0\n for field in row:\n if field != None and field != '':\n filled_fields[col_names[i]] = field\n i += 1\n \n return filled_fields","def summarize_sample_props(psd_list, sample_list):\n prop_list = [psd.sample_props for psd in psd_list]\n cols = ['amplicon median', 'mean size', 'lower size', 'upper size']\n\n return pd.DataFrame(prop_list, columns=cols, index=sample_list)","def column_creator(path):\n if not os.path.exists(path+'tables'):\n os.makedirs(path+'tables')\n\n\n # Sequences\n if os.path.exists(path+'SEQ.txt'):\n with open(os.path.join(path+'SEQ.txt')) as f1, open(os.path.join(path+'tables/sequences_table.txt'), 'a') as f2:\n c = Counter(x.strip() for x in f1)\n for x in c:\n f2.write(\"%s\\t%s\\n\" % (x, str(c[x])))\n f1.close()\n f2.close()\n\n # Modifications\n if os.path.exists(path + 'modifications.txt'):\n\n with open(os.path.join(path+'modifications.txt')) as f1, open(os.path.join(path+'tables/modifications_table.txt'), 'a') as f2:\n c = Counter(x.strip() for x in f1)\n for x in c:\n f2.write(\"%s\\t%s\\n\" % (x, str(c[x])))\n f1.close()\n f2.close()\n\n # Spectrum identify:\n if os.path.exists(path + 'spectrum_identify.txt'):\n\n with open(os.path.join(path+'spectrum_identify.txt')) as f1, open(path+'tables/spectrum_ide_table.txt', 'a') as f3:\n lines1 = f1.read().count('\\n')\n f3.write(\"%s\\n%s\\n\" % (\"Spectrum Number\",lines1))\n f1.close()\n f3.close()\n\n if os.path.exists(path + 'spectrum_unidentify.txt'):\n with open(os.path.join(path+'spectrum_unidentify.txt')) as f2, open(path+'tables/spectrum_unide_table.txt', 'a') as f3:\n lines2 = f2.read().count('\\n')\n f3.write(\"%s\\n%s\\n\" % (\"Spectrum Number\",lines2))\n f2.close()\n f3.close()\n\n if os.path.exists(path+'taxonomy_identify.txt'):\n # Taxonomy ide:\n with open(os.path.join(path+'taxonomy_identify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_ide_table.txt'), 'a') as f2:\n c = Counter(x.strip() for x in f1)\n for x in c:\n f2.write(\"%s\\t%s\\n\" % (x, str(c[x])))\n f1.close()\n f2.close()\n\n\n if os.path.exists(path + 'taxonomy_unidentify.txt'):\n # Taxonomy unide:\n with open(os.path.join(path+'taxonomy_unidentify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_unide_table.txt'), 'a') as f2:\n c = Counter(x.strip() for x in f1)\n for x in c:\n f2.write(\"%s\\t%s\\n\" % (x, str(c[x])))\n f1.close()\n f2.close()","def finalize(param, input_files='count_files'):\n\n import csv\n HELPER.writeLog('Collecting featureCount raw counts ... \\n', param)\n\n #check which of these files are actually available\n working_files = [iFile for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get feature ID using the first column in the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #For featureCount output, we want to skip the first two lines as they\n #include the featureCount call and the headers which we don't want\n next(csv_reader, None)\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n counts = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the expression values\n header = 'ID'\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n csv_file = open(param[input_files][idx])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n\n #Here too we want to skip the first two lines, before getting the counts\n next(csv_reader, None)\n next(csv_reader, None)\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\n idx = 0\n for row in csv_reader:\n counts[idx] = counts[idx]+'\\t'+row[6]\n idx += 1\n csv_file.close()\n\n #output the file\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\n out_handle = open(out_file, 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(counts)):\n out_handle.write(counts[i]+'\\n')\n out_handle.close()\n\n #output_phenotype_file\n HELPER.writeLog('Writing phenotype data ... \\n', param)\n MODULE_HELPER.output_sample_info(param)\n\n #write summary stats\n #featureCount does this on its own so we can just fetch each summary file\n #check which of these files are actually available\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\n\n if len(working_files) > 0:\n #get Status column from summary file using the first column in\n #the first file in the list of working files\n csv_file = open(working_files[0])\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Here, we want to skip the first line, as it simply points to the\n #alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start by taking the list of identifier,\n #which is the first column in the file\n entry = [row[0] for row in csv_reader]\n csv_file.close()\n\n #get all the summary stats for each sample\n header = 'Status'\n\n for idx in range(param['num_samples']):\n if param[input_files] != '':\n header = header+'\\t'+param['stub'][idx]\n #Fetch the corresponding sample's summary file\n csv_file = open(param[input_files][idx]+'.summary')\n csv_reader = csv.reader(csv_file, delimiter='\\t')\n #Again, we want to skip the first line, as it simply points\n #to the alignment file used when running featureCount\n next(csv_reader, None)\n\n #Now start getting the stats (row[1]) and add in the Status\n # (counts[i]) before it\n i = 0\n for row in csv_reader:\n entry[i] = entry[i]+'\\t'+row[1]\n i += 1\n csv_file.close()\n #output the file\n out_handle = open(param['working_dir']+\n 'results/featureCount/featureCount_stats.txt',\n 'w')\n out_handle.write(header+'\\n')\n\n for i in range(len(entry)):\n out_handle.write(entry[i]+'\\n')\n out_handle.close()\n else:\n print 'featureCount was not run successfully on any of the files..\\n'","def test_toTable(self):\r\n # Empty results.\r\n out_f = StringIO()\r\n self.res1.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(),\r\n \"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\\n\")\r\n out_f.close()\r\n\r\n # Results with multiple samples.\r\n exp = \"\"\"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\r\nS1\\t5\\t21\\t1.5\\t2.5\\t3.5\r\nS1\\t10\\t20\\t2.5\\t2.5\\t3.5\r\nS1\\t20\\t30\\t3.5\\t2.5\\t3.5\r\nS2\\t1\\t3\\t0.4\\t2.5\\t3.5\r\n\"\"\"\r\n out_f = StringIO()\r\n self.res2.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()\r\n\r\n # Custom header.\r\n exp = \"\"\"foo\\tbar\\tbaz\\tbazaar\\tbazaaar\\tbazaaaar\r\nS1\\t5\\t21\\t1.5\\t2.5\\t3.5\r\n\"\"\"\r\n out_f = StringIO()\r\n self.res1.addSample('S1', 42)\r\n self.res1.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)\r\n self.res1.toTable(out_f,\r\n header=['foo', 'bar', 'baz', 'bazaar', 'bazaaar', 'bazaaaar'])\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()\r\n\r\n # Invalid header.\r\n with self.assertRaises(ValueError):\r\n out_f = StringIO()\r\n self.res1.toTable(out_f, header=['foo'])\r\n\r\n # Cells with None as their value.\r\n exp = \"\"\"SampleID\\tSize\\tEstimate\\tStd Err\\tCI (lower)\\tCI (upper)\r\nS1\\t43\\tN/A\\tN/A\\tN/A\\tN/A\r\n\"\"\"\r\n out_f = StringIO()\r\n res = RichnessEstimatesResults()\r\n res.addSample('S1', 42)\r\n res.addSampleEstimate('S1', 43, None, None, None, None)\r\n res.toTable(out_f)\r\n self.assertEqual(out_f.getvalue(), exp)\r\n out_f.close()","def split_otu_table_on_sample_metadata(otu_table_f, mapping_f, mapping_field):\r\n mapping_f = list(mapping_f)\r\n mapping_values = get_mapping_values(mapping_f, mapping_field)\r\n otu_table = parse_biom_table(otu_table_f)\r\n\r\n for v in mapping_values:\r\n v_fp_str = v.replace(' ', '_')\r\n sample_ids_to_keep = sample_ids_from_metadata_description(\r\n mapping_f, valid_states_str=\"%s:%s\" % (mapping_field, v))\r\n\r\n try:\r\n filtered_otu_table = otu_table.filterSamples(\r\n lambda values, id_, metadata: id_ in sample_ids_to_keep)\r\n except TableException:\r\n # all samples are filtered out, so no otu table to write\r\n continue\r\n yield v_fp_str, format_biom_table(filtered_otu_table)","def _prepare_stats_table(self, pinfos):\n\n stats_tbl = OrderedDict()\n stats_tbl[\"Title\"] = OrderedDict()\n for res in self.rsts:\n stats_tbl[res.reportid] = OrderedDict()\n\n for pinfo in pinfos:\n for colname in (pinfo[\"colname\"], pinfo[\"xcolname\"]):\n if colname in stats_tbl[\"Title\"]:\n continue\n\n # Each column name is represented by a row in the statistics table. Fill the \"Title\"\n # column.\n title_dict = stats_tbl[\"Title\"][colname] = OrderedDict()\n defs = self._refdefs.info[colname]\n\n if defs.get(\"unit\") == \"nanosecond\":\n # Convert nanoseconds to microseconds.\n unit = \"us\"\n else:\n unit = defs.get(\"short_unit\", \"\")\n\n title_dict[\"colname\"] = colname\n if unit:\n title_dict[\"colname\"] += f\", {unit}\"\n title_dict[\"coldescr\"] = defs[\"descr\"]\n\n title_dict[\"funcs\"] = OrderedDict()\n for funcname in self._stats_funcs:\n if funcname in self.rsts[0].cstats[colname]:\n title_dict[\"funcs\"][funcname] = RORawResult.get_stat_func_descr(funcname)\n\n # Now fill the values for each result.\n for res in self.rsts:\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\n res_dict[\"funcs\"] = OrderedDict()\n\n for funcname in title_dict[\"funcs\"]:\n val = res.cstats[colname][funcname]\n fmt = \"{}\"\n if defs.get(\"unit\") == \"nanosecond\" and \"index\" not in funcname:\n val /= 1000\n fmt = \"{:.2f}\"\n if defs[\"type\"] == \"float\":\n fmt = \"{:.2f}\"\n\n fdict = res_dict[\"funcs\"][funcname] = OrderedDict()\n fdict[\"val\"] = fmt.format(val)\n fdict[\"raw_val\"] = val\n\n if self._refres.reportid == res.reportid:\n fdict[\"hovertext\"] = \"This is the reference result, other results \" \\\n \"are compared to this one.\"\n continue\n\n ref_fdict = stats_tbl[self._refres.reportid][colname][\"funcs\"][funcname]\n change = val - ref_fdict[\"raw_val\"]\n if ref_fdict[\"raw_val\"]:\n percent = (change / ref_fdict[\"raw_val\"]) * 100\n else:\n percent = change\n change = fmt.format(change) + unit\n percent = \"{:.1f}%\".format(percent)\n fdict[\"hovertext\"] = f\"Change: {change} ({percent})\"\n\n return stats_tbl","def summarize(crosswalk, incidence_table, control_spec):\n\n include_integer_colums = not setting('NO_INTEGERIZATION_EVER', False)\n\n crosswalk_df = crosswalk.to_frame()\n incidence_df = incidence_table.to_frame()\n\n geographies = setting('geographies')\n seed_geography = setting('seed_geography')\n meta_geography = geographies[0]\n sub_geographies = geographies[geographies.index(seed_geography) + 1:]\n hh_id_col = setting('household_id_col')\n\n meta_ids = crosswalk_df[meta_geography].unique()\n for meta_id in meta_ids:\n meta_summary_df = \\\n meta_summary(incidence_df, control_spec, meta_geography,\n meta_id, sub_geographies, hh_id_col)\n out_table('%s_%s' % (meta_geography, meta_id), meta_summary_df)\n\n hh_weights_summary = pd.DataFrame(index=incidence_df.index)\n\n # add seed level summaries\n seed_weights_df = get_weight_table(seed_geography)\n hh_weights_summary['%s_balanced_weight' % seed_geography] = seed_weights_df['balanced_weight']\n if include_integer_colums:\n hh_weights_summary['%s_integer_weight' % seed_geography] = seed_weights_df['integer_weight']\n\n for geography in sub_geographies:\n\n weights_df = get_weight_table(geography)\n\n if weights_df is None:\n continue\n\n if include_integer_colums:\n hh_weight_cols = [hh_id_col, 'balanced_weight', 'integer_weight']\n else:\n hh_weight_cols = [hh_id_col, 'balanced_weight']\n\n hh_weights = weights_df[hh_weight_cols].groupby([hh_id_col]).sum()\n hh_weights_summary['%s_balanced_weight' % geography] = hh_weights['balanced_weight']\n if include_integer_colums:\n hh_weights_summary['%s_integer_weight' % geography] = hh_weights['integer_weight']\n\n # aggregate to seed level\n aggegrate_weights = weights_df.groupby([seed_geography, hh_id_col], as_index=False).sum()\n aggegrate_weights.set_index(hh_id_col, inplace=True)\n\n if include_integer_colums:\n aggegrate_weight_cols = [seed_geography, 'balanced_weight', 'integer_weight']\n else:\n aggegrate_weight_cols = [seed_geography, 'balanced_weight']\n\n aggegrate_weights = aggegrate_weights[aggegrate_weight_cols]\n aggegrate_weights['sample_weight'] = incidence_df['sample_weight']\n aggegrate_weights['%s_preliminary_balanced_weight' % seed_geography] = \\\n seed_weights_df['preliminary_balanced_weight']\n aggegrate_weights['%s_balanced_weight' % seed_geography] = \\\n seed_weights_df['balanced_weight']\n if include_integer_colums:\n aggegrate_weights['%s_integer_weight' % seed_geography] = \\\n seed_weights_df['integer_weight']\n\n out_table('%s_aggregate' % (geography,), aggegrate_weights)\n\n summary_col = 'integer_weight' if include_integer_colums else 'balanced_weight'\n df = summarize_geography(seed_geography, summary_col, hh_id_col,\n crosswalk_df, weights_df, incidence_df)\n out_table('%s_%s' % (geography, seed_geography,), df)\n\n df = summarize_geography(geography, summary_col, hh_id_col,\n crosswalk_df, weights_df, incidence_df)\n out_table('%s' % (geography,), df)\n\n out_table('hh_weights', hh_weights_summary)","def accumulate_entries_as_tables(entries):\n name_table = {}\n num_table = {}\n for number, name in entries:\n name_table[name] = number\n num_table[number] = name\n\n return name_table, num_table","def table_summary():\n \n t = dict()\n t['name'] = get_names()\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\n N = len(t['name'])\n \n # host\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\'itoi']\n \n # progenitor\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\n \n # progenitor type\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\n \n # metallicity\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\n \n # associations\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\n \n tout = Table(t)\n tout.pprint()\n tout.write('../data/stream_origin.fits', overwrite=True)","def squash_records(key, sample_lookup, sample_ids, sample_indexes, normal_id):\n chrom, pos, ref, alt = key\n ids = []\n quals = []\n filters = []\n infos = []\n formats = []\n gtypes = {sid: [] for sid in sample_ids}\n caller_flags = {sid: collections.OrderedDict([(key, None)\n for key in CALLER_CODES])\n for sid in sample_ids}\n for sample_id, caller_lookup in sample_lookup.iteritems():\n caller_ids = []\n caller_quals = []\n caller_filters = []\n caller_infos = []\n for caller, records in caller_lookup.iteritems():\n # Squash repeat calls on a sample (e.g. FreeBayes single/somatic)\n caller_ids.append(consense_values([rec.ID for rec in records]))\n caller_quals.append(consense_values([rec.QUAL for rec in records]))\n caller_filters.extend(chain(*[rec.FILTER\n for rec in records if rec.FILTER]))\n caller_infos.append(consense_dicts([rec.INFO for rec in records]))\n formats.extend(chain(*[rec.FORMAT.split(':') for rec in records]))\n calls = [rec.genotype(sample_id) for rec in records]\n gtypes[sample_id].extend(calls) # ENH: consense\n caller_code = CALLER_NAMES[caller]\n caller_val = max(call.is_variant for call in calls)\n if caller_val is not None:\n caller_val = int(caller_val)\n caller_flags[sample_id][caller_code] = caller_val\n ids.extend(caller_ids)\n quals.extend(caller_quals)\n filters.extend(caller_filters)\n infos.extend(caller_infos)\n formats = list(unique(formats))\n out_record = Record(chrom, pos, consense_values(ids),\n ref, [alt], consense_values(quals),\n list(unique(filters)),\n consense_dicts(infos),\n \":\".join(formats + CALLER_CODES),\n sample_indexes)\n out_record.samples = [Call(out_record, sid,\n make_call_data(formats, gtypes[sid],\n caller_flags[sid]))\n for sid in sample_ids]\n\n # Post-processing\n # Fix AN and AC, which are defined specially in the VCF spec\n all_alleles = list(chain(*[gt.gt_alleles for gt in out_record.samples\n if gt.gt_nums]))\n # AN (1): \"Total number of alleles in called genotypes\"\n out_record.INFO[\"AN\"] = sum(a != '.' for a in all_alleles)\n # AC (A): \"Total number of alternate alleles in called genotypes\"\n # ENH: don't crash on multiallelics?\n out_record.INFO[\"AC\"] = sum(sum(gta not in ('.', '0') for gta in gt.gt_alleles)\n for gt in out_record.samples\n if gt.gt_nums)\n\n # NB: Last 2 validation errors:\n # CGP-1240 FORMAT tag [GL] expected different number of values (expected 2, found 3)\n # chr2\t209113113\t.\tG\tA\t2714.98\tPASS\tSOMATIC;NS=1;DP=653;DPB=716.75;AC=1;AN=3;AF=0.375;RO=590;AO=123;PRO=0.0;PAO=0.0;QR=21870;QA=4741;PQR=0.0;PQA=0.0;SRF=386;SRR=203;SAF=90;SAR=33;SRP=127.1;SAP=60.3689;AB=0.233397;ABP=328.364;RUN=1;RPP=24.6368;RPPR=13.5915625;RPL=44.0;RPR=79.0;EPP=4.44029;EPPR=8.549435;DPRA=1.56225;ODDS=272.57225;GTI=0;TYPE=snp;CIGAR=1X;NUMALT=1;MEANALT=2.0;LEN=1;MQM=60.0;MQMR=60.0177;PAIRED=0.95122;PAIREDR=0.958916;technology.ILLUMINA=1.0;MQ0=0;VT=SNP;BaseQRankSum=3.369;Dels=0.0;FS=2.338;HaplotypeScore=31.1133;MLEAC=1;MLEAF=0.5;MQ=60.02;MQRankSum=0.461;QD=7.05;ReadPosRankSum=-0.95;SOR=0.953;SF=0\tGT:AD:BQ:DP:FA:SS:GMIMUT:GMIMAF:GMICOV:GQ:RO:QR:AO:QA:GL:PL:CallHC:CallUG:CallFB:CallPI:CallSID:CallMU:LR\t0/1:387,117:38.0:506:0.239:2:117:23.0:504:120.456:401:14917:123:4741:-285.03,0.0,-1199.66:3594,0,14408:.:1:1:.:.:1:0.0723\t0:200,0:.:227:0.0:0:0:0.0:226:141.912:252:9271:0:0:0.0,-66.9517,-832.671:.:.:.:0:.:.:0:0.0607\n # chr5\t176715884\t.\tT\tG\t2652.62\tPASS\tSOMATIC;NS=1;DP=718;DPB=806.5;AC=1;AN=3;AF=0.375;RO=684;AO=122;PRO=0.0;PAO=0.0;QR=26587;QA=4642;PQR=0.0;PQA=0.0;SRF=334;SRR=350;SAF=62;SAR=60;SRP=3.803265;SAP=3.0815;AB=0.225508;ABP=357.065;RUN=1;RPP=5.57335;RPPR=3.3676575;RPL=67.0;RPR=55.0;EPP=4.14943;EPPR=9.662735;DPRA=1.1461875;ODDS=318.98275;GTI=0;TYPE=snp;CIGAR=1X;NUMALT=1;MEANALT=1.0;LEN=1;MQM=60.0;MQMR=60.0;PAIRED=0.991803;PAIREDR=0.97694275;technology.ILLUMINA=1.0;MQ0=0;VT=SNP;BaseQRankSum=-2.525;Dels=0.0;FS=0.0;HaplotypeScore=15.7349;MLEAC=1;MLEAF=0.5;MQ=60.0;MQRankSum=2.816;QD=6.74;ReadPosRankSum=1.308;SOR=0.691;SF=0\tGT:AD:BQ:DP:FA:SS:GMIMUT:GMIMAF:GMICOV:GQ:RO:QR:AO:QA:GL:PL:CallHC:CallUG:CallFB:CallPI:CallSID:CallMU:LR\t0/1:408,121:37.0:529:0.22:2:121:23.0:529:117.5855:419:16330:122:4642:-270.938,0.0,-1322.0:3592,0,16251:.:1:1:.:.:1:0.022\t0:284,0:.:319:0.0:0:0:0.0:319:136.171:354:13676:0:0:0.0,-95.905,-1230.06:.:.:.:0:.:.:0:0.0006\n # * FreeBayes somatic produces the 3 GL values like this, w/e\n # chr2\t209113113\t.\tG\tA\t2714.98\t.\tAB=0.233397;ABP=328.364;AC=1;AF=0.25;AN=4;AO=123;CIGAR=1X;DP=780;DPB=780;DPRA=2.083;EPP=4.44029;EPPR=9.74419;GTI=0;LEN=1;MEANALT=2;MQM=60;MQMR=60.0153;NS=2;NUMALT=1;ODDS=155.143;PAIRED=0.95122;PAIREDR=0.960184;PAO=0;PQA=0;PQR=0;PRO=0;QA=4741;QR=24188;RO=653;RPL=44;RPP=24.6368;RPPR=15.384;RPR=79;RUN=1;SAF=90;SAP=60.3689;SAR=33;SRF=424;SRP=129.458;SRR=229;TYPE=snp;technology.ILLUMINA=1;SOMATIC\tGT:GQ:DP:RO:QR:AO:QA:GL:GMIMUT:GMIMAF:GMICOV\t0/1:141.912:527:401:14917:123:4741:-285.03,0,-1199.66:123:23:524\t0/0:141.912:253:252:9271:0:0:0,-66.9517,-832.671:0:0:252\n # chr5\t176715884\t.\tT\tG\t2652.62\t.\tAB=0.225508;ABP=357.065;AC=1;AF=0.25;AN=4;AO=122;CIGAR=1X;DP=895;DPB=895;DPRA=1.52825;EPP=4.14943;EPPR=7.73248;GTI=0;LEN=1;MEANALT=1;MQM=60;MQMR=60;NS=2;NUMALT=1;ODDS=221.81;PAIRED=0.991803;PAIREDR=0.978008;PAO=0;PQA=0;PQR=0;PRO=0;QA=4642;QR=30006;RO=773;RPL=67;RPP=5.57335;RPPR=3.48505;RPR=55;RUN=1;SAF=62;SAP=3.0815;SAR=60;SRF=377;SRP=4.0244;SRR=396;TYPE=snp;technology.ILLUMINA=1;SOMATIC\tGT:GQ:DP:RO:QR:AO:QA:GL:GMIMUT:GMIMAF:GMICOV\t0/1:136.171:541:419:16330:122:4642:-270.938,0,-1322:122:23:541\t0/0:136.171:354:354:13676:0:0:0,-95.905,-1230.06:0:0:354\n\n # Set SOMATIC flag (only add, don't remove existing flags)\n global CNT_ATYPICAL\n global CNT_TYPICAL\n if normal_id:\n normal_gt = out_record.genotype(normal_id)\n if ((not normal_gt.is_variant) and\n (not out_record.INFO.get(\"SOMATIC\")) and\n any(gt.is_variant for gt in out_record.samples)):\n out_record.INFO[\"SOMATIC\"] = True\n if normal_gt.gt_nums is None:\n CNT_TYPICAL += 1\n else:\n CNT_ATYPICAL += 1\n\n return out_record","def summarize(data, verbal=False, using_files=True):\n\n if using_files:\n for file_name in tqdm(data):\n fill_table(pd.read_csv(file_name))\n else:\n for table in tqdm(data):\n fill_table(table)\n\n for cluster in table_summary:\n #total_genes = sum(table_summary[cluster][\"phylum\"].values) # number of genes\n #total_genes = table_summary[cluster][\"N\"] # number of samples\n total_genes = table_summary[cluster][\"eggNOG\"].eggNOG.sum() # number of genes in COGs with duplicates\n \n phylum_percent = table_summary[cluster][\"phylum\"].apply(lambda x: x/total_genes * 100)\n phylum_percent.columns = [\"percent\"]\n table_summary[cluster][\"phylum\"] = pd.concat([table_summary[cluster][\"phylum\"],phylum_percent],axis=1)\n\n #Read above for fix\n genus_percent = table_summary[cluster][\"genus\"].apply(lambda x: x/total_genes * 100)\n genus_percent.columns = [\"percent\"]\n table_summary[cluster][\"genus\"] = pd.concat([table_summary[cluster][\"genus\"],genus_percent],axis=1)\n\n #read above for fix\n cog_percent = table_summary[cluster][\"eggNOG\"].apply(lambda x: x/table_summary[cluster][\"gene_cog\"] * 100)\n cog_percent.columns = [\"percent\"]\n table_summary[cluster][\"eggNOG\"] = pd.concat([table_summary[cluster][\"eggNOG\"],cog_percent],axis=1)\n\n #Print the data\n if verbal:\n print \"Cluster %s:\\n\" % cluster\n print \"Number of Samples: %d\\n\" % table_summary[cluster][\"N\"]\n print \"Taxonomy:\"\n print table_summary[cluster][\"phylum\"].sort(\"percent\", ascending=False)\n print \"----------------------------------\"\n print table_summary[cluster][\"genus\"].sort(\"percent\", ascending=False)\n print \"-----------------------------------\"\n print \"COGS:\"\n print table_summary[cluster][\"eggNOG\"].sort(\"percent\", ascending=False)\n print \"------------------------------------\"\n print \"End Summary\"","def table_stats(self, db, dest, kvargs, lines):\n if 'table' in kvargs:\n tables = [db.get_table(kvargs['table'])]\n else:\n tables = db.tables()\n options = kvargs.get('options','')\n done = False\n for table in db.tables():\n print(\"======================= {} =======================\".format(table.name))\n if 'dump' in options:\n print(\"schema dump:\")\n table.dump()\n print(\"\")\n if 'head' in options:\n print(\"First 5 records:\")\n for source_record in db.read_records_as_dicts(tablename=table.name, limit=5):\n print(source_record)\n print(\"\")\n # Compute single-variable stats on each of the variables\n sw = stopwatch().start()\n print(\"Computing statistics...\")\n stats = {}\n census_checksum = 0\n \n if self.spark_context:\n print(\"Using spark to read {} ... assuming first line has headings\".format(table.filename))\n sc = self.spark_context\n data = sc.textFile(table.filename)\n header = data.first() # extract the header\n stats = data.filter(lambda row:row!=header).map(table.parse_line_to_dict).reduce(stats_reducer)\n else:\n try:\n for source_record in db.read_records_as_dicts(tablename=table.name,limit=self.limit):\n if source_record['RECTYPE']=='P':\n census_checksum += census_person_polynominal(source_record)\n stats = stats_reducer(source_record, stats)\n except KeyboardInterrupt as e:\n print(\"*** KeyboardInterrupt at count: {}\".format(stats[':count']))\n done = True\n if stats:\n print(\"total records: {} speed: {:8.0f} records/sec\".format( stats[':count'], stats[':count']/sw.elapsed()))\n tt = tytable.ttable()\n tt.add_head(['variable','min','avg','max'])\n tt.set_col_alignment(1,tytable.ttable.RIGHT)\n tt.set_col_alignment(2,tytable.ttable.RIGHT)\n tt.set_col_alignment(3,tytable.ttable.RIGHT)\n for key in stats_variable_names(stats):\n try:\n tt.add_data([key, stats[key+\":min\"], stats[key+\":sum\"] / stats[':count'], stats[key+\":max\"]])\n except TypeError:\n tt.add_data([key, stats[key+\":min\"], \"\", stats[key+\":max\"]])\n print(tt.typeset(mode=tytable.TEXT))\n if census_checksum:\n print(\"Census checksum: {}\".format(census_checksum))\n print(\"\")\n if done:\n return True # had the keyboard abort\n return True","def print_all_counts_as_shasta_matrix(all_counts, max_count=50, pseudocount=1):\n a_t_counts = all_counts[\"A\"] + all_counts[\"T\"]\n g_c_counts = all_counts[\"G\"] + all_counts[\"C\"]\n\n total = 0\n for i in range(max_count + 1):\n total += max(pseudocount, a_t_counts[i])\n\n line = list()\n for i in range(max_count + 1):\n count = max(pseudocount, a_t_counts[i])\n line.append(\"%.9f\" % math.log((count/total),10))\n\n print(\">AT prior\")\n print(\",\".join(line))\n print()\n\n total = 0\n for i in range(max_count + 1):\n total += max(pseudocount, g_c_counts[i])\n\n line = list()\n for i in range(max_count + 1):\n count = max(pseudocount, g_c_counts[i])\n line.append(\"%.9f\" % math.log((count/total),10))\n\n print(\">GC prior\")\n print(\",\".join(line))\n print()","def _create_metrics_table(font, format, base, create_glyph_metrics):\n # we don't set PCF_COMPRESSED_METRICS\n metrics = tuple(create_glyph_metrics(_g, base) for _g in font.glyphs)\n table_bytes = (\n bytes(le.uint32(format))\n + bytes(base.int32(len(metrics)))\n + b''.join(bytes(_t) for _t in metrics)\n )\n return table_bytes, format","def annotate_tsv_freq(in_tsv_gz,annotation_tsv):\n sys.stderr.write(\"Reading TSV file ...\\n\")\n nicollo = pd.read_csv(BOLLI, sep=\"\\t\")\n nicollo = nicollo.iloc[:,[1,2,4,5,23]]\n nicollo_counts = nicollo.groupby(['CHR','START'])['MT'].count()\n nol_var = nicollo.drop(['WT','MT'], axis = 1) \n nol_var = nol_var.set_index(['CHR', 'START'])\n\n #nicollo_counts = nicollo.groupby([\"CHR\",\"START\",\"WT\",\"MT\"]).size().reset_index(name=\"count\")\n #nicollo_counts = nicollo_counts[[\"CHR\", \"START\",\"count\"]].set_index(['CHR','START'])\n\n mmrf = pd.read_csv('/ifs/res/leukgen/home/yellapav/MMRF/MMRF_CoMMpass_IA9_All_Canonical_Variants.txt', sep=\"\\t\")\n mmrf=mmrf.iloc[:,[0,1,2,4,5,19,23]]\n mmrf=mmrf.drop_duplicates()\n\n mmrfM=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].median()\n mmrfC=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].count()\n mmrfQ25=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].quantile(q=0.25)\n mmrfQ75=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].quantile(q=0.75)\n \n\n #anno_tsv = pd.read_csv(annotation_tsv, comment='#',sep=\"\\t\")\n anno_tsv = pd.read_csv(annotation_tsv, comment='#',sep=\"\\t\", low_memory=False)\n #anno_tsv[anno_tsv['FILTER'] == \"PASS\"]\n counts_tsv=anno_tsv.groupby([\"CHR\",\"START\",\"REF\",\"ALT\"]).size().reset_index(name=\"count\")\n counts_tsv=counts_tsv[[\"CHR\", \"START\",\"count\"]].set_index(['CHR','START'])\n counts_median=anno_tsv.groupby(['CHR','START'])['TARGET_VAF'].median()\n\n\n\n inFile = gzip.open(in_tsv_gz,'r')\n \n sys.stderr.write(\"Annotating ...\\n\")\n for record in inFile:\n record=record.decode(\"utf-8\")\n record=record.rstrip()\n recArr=record.split(\"\\t\")\n \n cl = [] \n freq = [] \n medVAF = [] \n Q25 = [] \n Q75 = [] \n positions = [] \n normal = \"0\" \n normalVAF = \"0\" \n bolli_cl = [] \n bolli_freq = [] \n bolli_positions = [] \n bolli_anno = [] \n flag = 0\n bolli_flag = 0\n if record.startswith(\"#\"):\n continue\n\n if recArr[0] == \"ID_VARIANT\":\n cl = \"MMRF_Class\"\n freq = \"MMRF_Frequency\"\n medVAF = \"MMRF_VAF\"\n Q25 = \"MMRF_Q25\"\n Q75 = \"MMRF_Q75\"\n positions = \"MMRF_Positions\"\n normal = \"Normals_Frequency\"\n normalVAF = \"Normals_median_VAF\"\n bolli_cl = \"Bolli_Class\"\n bolli_freq = \"Bolli_Frequency\"\n bolli_positions = \"Bolli_Positions\"\n bolli_anno = \"Bolli_Annotation\"\n record = [ record, cl, freq, medVAF, Q25, Q75, positions, bolli_cl, bolli_freq, bolli_anno, bolli_positions, normal, normalVAF ]\n record = (\"\\t\".join(record))\n print(record)\n continue\n\n try:\n chrom = str(recArr[3])\n pos = int(recArr[4])\n start = int(recArr[4]) - 9\n end = int(recArr[4]) + 9\n if (chrom, pos) in mmrfC.index:\n cl = \"genomic_exact\"\n freq = str(mmrfC.loc[(chrom,pos)])\n medVAF = str(mmrfM.loc[(chrom,pos)])\n Q25 = str(mmrfQ25.loc[(chrom,pos)])\n Q75 = str(mmrfQ75.loc[(chrom,pos)])\n positions = str(pos)\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\n record = (\"\\t\".join(record))\n flag = 1\n if flag == 0:\n mmrfCsub=mmrfC.loc[chrom]\n if not mmrfCsub[(mmrfCsub.index >= start) & (mmrfCsub.index <= end)].empty:\n for i in mmrfCsub[(mmrfCsub.index >= start) & (mmrfCsub.index <= end)].index.values:\n cl = \"genomic_close\"\n freq.append(str(mmrfC.loc[(chrom,i)]))\n medVAF.append(str(mmrfM.loc[(chrom,i)]))\n Q25.append(str(mmrfQ25.loc[(chrom,i)]))\n Q75.append(str(mmrfQ75.loc[(chrom,i)]))\n positions.append(str(i))\n freq = (\":\".join(freq))\n medVAF = (\":\".join(medVAF))\n Q25 = (\":\".join(Q25))\n Q75 = (\":\".join(Q75))\n positions = (\":\".join(positions))\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\n record = (\"\\t\".join(record))\n else:\n cl = \"NA\"\n freq = \"NA\"\n medVAF = \"NA\"\n Q25 = \"NA\"\n Q75 = \"NA\"\n positions = \"NA\"\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\n record = (\"\\t\".join(record))\n\n\n except:\n cl = \"NA\"\n freq = \"NA\"\n medVAF = \"NA\"\n Q25 = \"NA\"\n Q75 = \"NA\"\n positions = \"NA\"\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\n record = (\"\\t\".join(record))\n\n\n\n try:\n chrom = str(recArr[3])\n pos = int(recArr[4])\n start = int(recArr[4]) - 9\n end = int(recArr[4]) + 9\n if (chrom, pos) in nicollo_counts.index:\n bolli_cl = \"genomic_exact\"\n bolli_freq = str(nicollo_counts.loc[(chrom,pos)]) \n bolli_positions = str(pos)\n bolli_anno = str(nol_var.loc[chrom, pos]['Variant_class'].values[0])\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\n record = (\"\\t\".join(record))\n bolli_flag = 1\n\n\n if bolli_flag == 0: \n nicollo_counts_sub=nicollo_counts.loc[chrom]\n if not nicollo_counts_sub[(nicollo_counts_sub.index >= start) & (nicollo_counts_sub.index <= end)].empty:\n for i in nicollo_counts_sub[(nicollo_counts_sub.index >= start) & (nicollo_counts_sub.index <= end)].index.values:\n #if not nicollo_counts_sub.ix[start:end].empty:\n # for i in nicollo_counts_sub.ix[start:end].index.values:\n #print(\"XXXXXXX\",i, nicollo_counts_sub.loc[(chrom,i)], start, end)\n bolli_cl = \"genomic_close\"\n bolli_freq.append(str(nicollo_counts.loc[(chrom,i)]))\n bolli_anno.append(str(nol_var.loc[(chrom,i)]['Variant_class'].values[0]))\n bolli_positions.append(str(i))\n bolli_freq = (\":\".join(bolli_freq))\n bolli_positions = (\":\".join(bolli_positions))\n bolli_anno = (\":\".join(bolli_anno))\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\n record = (\"\\t\".join(record))\n else:\n bolli_cl = \"NA\"\n bolli_freq = \"NA\"\n bolli_positions = \"NA\"\n bolli_anno = \"NA\"\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\n record = (\"\\t\".join(record))\n\n\n except:\n bolli_cl = \"NA\"\n bolli_freq = \"NA\"\n bolli_anno = \"NA\"\n bolli_positions = \"NA\"\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\n record = (\"\\t\".join(record))\n\n\n normal = \"0\"\n normalVAF = \"0\"\n try:\n chrom=str(recArr[3])\n pos=int(recArr[4])\n normal = counts_tsv.loc[(chrom,pos),\"count\"]\n normal = normal.ix[0]\n normal = str(normal)\n\n normalVAF = str(counts_median.loc[(chrom,pos)])\n\n record = [ record, normal, normalVAF ]\n record = (\"\\t\".join(record))\n print(record)\n\n except:\n normal = \"0\"\n normalVAF = \"0\"\n record = [ record, str(normal), str(normalVAF) ]\n record = (\"\\t\".join(record))\n print(record)","def format_used_features(model_dir):\n feature_keys = {\n \"indel_complexity\": \"ICP\",\n \"dissimilarity\": \"DSM\",\n \"indel_size\": \"ISZ\",\n \"repeat\": \"REP\",\n \"is_uniq_mapped\": \"UQM\",\n \"is_near_boundary\": \"NEB\",\n \"equivalence_exists\": \"EQX\",\n \"is_bidirectional\": \"BID\",\n \"is_multiallelic\": \"MTA\",\n \"is_inframe\": \"FRM\",\n \"is_splice\": \"SPL\",\n \"is_truncating\": \"TRN\",\n \"is_in_cdd\": \"CDD\",\n \"indel_location\": \"LOC\",\n \"is_nmd_insensitive\": \"NMD\",\n \"ipg\": \"IPG\",\n \"cds_length\": \"LEN\",\n \"lc\": \"LC\",\n \"local_lc\": \"LLC\",\n \"gc\": \"GC\",\n \"local_gc\": \"LGC\",\n \"strength\": \"SG\",\n \"local_strength\": \"LSG\",\n \"is_ins\": \"INS\",\n \"is_at_ins\": \"ATI\",\n \"is_at_del\": \"ATD\",\n \"is_gc_ins\": \"GCI\",\n \"is_gc_del\": \"GCD\",\n \"ref_count\": \"REFC\",\n \"alt_count\": \"ALTC\",\n \"is_on_db\": \"SNP\",\n }\n\n feature_dict = make_feature_dict(model_dir)\n\n features_used_for_sni = [\n feature_keys[f] for f in feature_dict[\"single_nucleotide_indels\"]\n ]\n features_used_for_mni = [\n feature_keys[f] for f in feature_dict[\"multi_nucleotide_indels\"]\n ]\n features_used_for_sni.sort()\n features_used_for_mni.sort()\n\n d = {}\n d[\"##features_used_for_single_nucleotide_indels\"] = \";\".join(features_used_for_sni)\n d[\"##features_used_for_multi_nucleotide_indels\"] = \";\".join(features_used_for_mni)\n\n return d","def transpose_labels(df, sort=False):\n label = 'gtruth'\n temp_gtruth = df[df['label'] == 'gtruth']\n for sample_method_to_test in ['lab', 'pier']:\n temp_gtruth = temp_gtruth \\\n .rename({\n f'{sample_method_to_test} total abundance': f'{sample_method_to_test} {label} total abundance',\n f'{sample_method_to_test} raw count': f'{sample_method_to_test} {label} raw count',\n f'{sample_method_to_test} nrmlzd raw count': f'{sample_method_to_test} {label} nrmlzd raw count',\n f'{sample_method_to_test} relative abundance': f'{sample_method_to_test} {label} relative abundance',\n f'{sample_method_to_test} cells/mL': f'{sample_method_to_test} {label} cells/mL'},\n axis=1)\n\n temp_gtruth = temp_gtruth.drop('label', axis=1)\n\n label = 'predicted'\n temp_pred = df[df['label'] == 'prediction']\n for sample_method_to_test in ['lab', 'pier']:\n temp_pred = temp_pred \\\n .rename({\n f'{sample_method_to_test} total abundance': f'{sample_method_to_test} {label} total abundance',\n f'{sample_method_to_test} raw count': f'{sample_method_to_test} {label} raw count',\n f'{sample_method_to_test} nrmlzd raw count': f'{sample_method_to_test} {label} nrmlzd raw count',\n f'{sample_method_to_test} relative abundance': f'{sample_method_to_test} {label} relative abundance',\n f'{sample_method_to_test} cells/mL': f'{sample_method_to_test} {label} cells/mL'},\n axis=1)\n temp_pred = temp_pred.drop('label', axis=1)\n\n merge_col = ['class', 'datetime', 'sampling time']\n micro_col = [col for col in df.columns if col.startswith('micro')]\n if all(mc in df.columns for mc in micro_col):\n merge_col += micro_col\n\n concat = temp_pred.merge(temp_gtruth, on=merge_col)\n\n # sort dataframe\n if sort:\n col = sorted(concat.columns)\n concat = concat[col[:2] + [col[-1]] + col[2:-1]]\n\n return concat","def convert_to_table_format(package):\n tables = list()\n for primary_table_id in package.primary_table_ids:\n tables.append(StachExtensions.__generate_table(package, primary_table_id))\n return tables","def collate_samples(tool_name, fields, samples):\n sample_dict = {}\n for sample in samples:\n sample_name = sample['name']\n sample_dict[sample_name] = {}\n tool_result = sample[tool_name]\n for field in fields:\n sample_dict[sample_name][field] = tool_result[field]\n\n return sample_dict","def data_prep(df, params, if_resample=False):\n\n if if_resample and (params['balanced'] in ['Bootstrap', 'Handsample']):\n if params['balanced'] == 'Bootstrap':\n df = resample(df=df, balance=params['balanced'], nclass=params['classnum'])\n elif params['balanced'] == 'Handsample':\n df = resample(df=df, balance=params['balanced'], nclass=params['classnum'])\n\n if params['classnum'] == 6:\n df.drop(df[df['label']=='PTSD'].index, axis=0, inplace=True)\n\n data = list(df.dialog)\n label_encode = LabelEncoder()\n output = dict()\n output['data'] = data\n output['encoded_label'] = label_encode.fit_transform(df.label)\n output['binary_label'] = label_binarize(y=output['encoded_label'], classes=np.arange(params['classnum']))\n return output, label_encode","def get_results(self):\n d = {}\n# r = {}\n for analyser in self.xml_tree.getroot():\n for child in analyser:\n if child.tag == 'all-records':\n for record in child:\n attributes = record.attrib\n sample = attributes['sampleId']\n assay_id = attributes['assayId']\n genotype = attributes['genotypeId']\n quality = attributes['description'].split('.')[0]\n if re.match(r'rs\\d+', assay_id):\n if sample in d:\n if assay_id in d[sample]:\n for allele in list(genotype):\n if allele not in d[sample][assay_id]['genotype']:\n d[sample][assay_id]['genotype'] += allele\n if quality not in d[sample][assay_id]['quality']:\n d[sample][assay_id]['quality'].append(quality)\n else:\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\n else:\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\n# if sample in r:\n# if assay_id in r[sample]:\n# for allele in list(genotype):\n# if allele not in r[sample][assay_id]:\n# r[sample][assay_id] += allele\n# else:\n# r[sample][assay_id] = genotype\n# else:\n# r[sample] = {assay_id: genotype}\n# for k, v in r.items():\n# for k1, v1, in v.items():\n# if len(v1) == 1:\n# v[k1] += v1\n# pprint.pprint(r)\n# df = pd.DataFrame.from_dict(r).transpose()\n# print(df)\n# df.to_excel('snpcheck.xlsx')\n return d","def _convert_tracks_to_tabular_format(tracks: List[Track]) -> pd.DataFrame:\n track_dfs: List[pd.DataFrame] = []\n\n for track in tracks:\n track_df = pd.DataFrame()\n\n observed_states: List[bool] = []\n timesteps: List[int] = []\n positions_x: List[float] = []\n positions_y: List[float] = []\n headings: List[float] = []\n velocities_x: List[float] = []\n velocities_y: List[float] = []\n\n for object_state in track.object_states:\n observed_states.append(object_state.observed)\n timesteps.append(object_state.timestep)\n positions_x.append(object_state.position[0])\n positions_y.append(object_state.position[1])\n headings.append(object_state.heading)\n velocities_x.append(object_state.velocity[0])\n velocities_y.append(object_state.velocity[1])\n\n track_df[\"observed\"] = observed_states\n track_df[\"track_id\"] = track.track_id\n track_df[\"object_type\"] = track.object_type.value\n track_df[\"object_category\"] = track.category.value\n track_df[\"timestep\"] = timesteps\n track_df[\"position_x\"] = positions_x\n track_df[\"position_y\"] = positions_y\n track_df[\"heading\"] = headings\n track_df[\"velocity_x\"] = velocities_x\n track_df[\"velocity_y\"] = velocities_y\n\n track_dfs.append(track_df)\n\n return pd.concat(track_dfs, ignore_index=True)","def encoding_labelcount(df, target=None):\n if not target:\n target = ['user_id', 'title']\n\n norm = round(\n df.shape[0] / 10000) # normalize the count by /per 100000 entries\n for col in target:\n df[col + '_labelcount'] = df[col].map(df[col].value_counts()) / norm\n df.drop([col], axis=1, inplace=True)\n return None","def _prepare_summary_table(self, raw_stats_paths, descr_paths):\n\n sum_tbl = OrderedDict()\n sum_tbl[\"Title\"] = OrderedDict()\n for res in self.rsts:\n sum_tbl[res.reportid] = OrderedDict()\n\n # Add tool information.\n key = \"tool_info\"\n sum_tbl[\"Title\"][key] = \"Data collection tool\"\n for res in self.rsts:\n sum_tbl[res.reportid][key] = f\"{res.info['toolname'].capitalize()} version \" \\\n f\"{res.info['toolver']}\"\n\n # Add datapoint counts.\n key = \"datapoints_cnt\"\n sum_tbl[\"Title\"][key] = \"Datapoints Count\"\n for res in self.rsts:\n sum_tbl[res.reportid][key] = len(res.df.index)\n\n # Add measurement resolution.\n for res in self.rsts:\n key = \"device_resolution\"\n resolution = res.info.get(\"resolution\")\n if resolution:\n sum_tbl[\"Title\"][key] = \"Device Resolution\"\n sum_tbl[res.reportid][key] = f\"{resolution}ns\"\n\n # Add links to the raw statistics directories.\n if raw_stats_paths:\n key = \"raw_stats\"\n sum_tbl[\"Title\"][key] = \"Raw statistics\"\n for res in self.rsts:\n path = raw_stats_paths.get(res.reportid, \"Not available\")\n sum_tbl[res.reportid][key] = path\n\n # Add links to the descriptions.\n if descr_paths:\n key = \"descr\"\n sum_tbl[\"Title\"][key] = \"Test description\"\n for res in self.rsts:\n path = descr_paths.get(res.reportid, \"Not available\")\n sum_tbl[res.reportid][key] = path\n\n return sum_tbl","def tabulate(store: ObservationStore) -> \\\n Generator[LabelledObservation, None, None]:\n for k in store:\n for ob in store[k]:\n yield when(ob), measured(ob), k","def flatten(counts):\n single_names = {}\n long_names = {}\n for i in range(len(counts.items())):\n if(len(counts.items()[i][0].split(\" \")) <= 1):\n single_names[str(counts.items()[i][0])] = counts.items()[i][1]\n else:\n long_names[str(counts.items()[i][0])] = counts.items()[i][1]\n \n starter_list = [[[x[0]],x[1]] for x in long_names.items()]\n for i in range(len(single_names.items())):\n matched = False\n for j in range(len(starter_list)):\n if(single_names.items()[i][0] in starter_list[j][0][0].split(\" \")):\n starter_list[j][0].append(single_names.items()[i][0])\n starter_list[j][1] += single_names.items()[i][1]\n matched = True\n break\n \n if(matched == False):\n starter_list.append([[single_names.items()[i][0]], single_names.items()[i][1]]) \n \n \n return starter_list","def test_format_unifrac_sample_mapping(self):\r\n a = [[1, 0, 0], [0, 2, 4], [7, 0, 9.0]]\r\n otu_ids = ['OTUa', 'OTUb', 'OTUc']\r\n sample_ids = ['Sa', 'Sb', 'Sc']\r\n result = format_unifrac_sample_mapping(sample_ids, otu_ids, a)\r\n self.assertEqual(\r\n result,\r\n ['OTUa\\tSa\\t1',\r\n 'OTUb\\tSb\\t2',\r\n 'OTUb\\tSc\\t4',\r\n 'OTUc\\tSa\\t7',\r\n 'OTUc\\tSc\\t9.0'])","def prepare_participants_for_metadata_export(path_to_samples_info, tsca_id): \n raw = pd.read_table(path_to_samples_info)\n print( \"%d Participants in this batch\" % raw['individual_id'].unique().shape[0] )\n # Data to upload\n data = pd.DataFrame(raw.individual_id.drop_duplicates()).rename(columns={'individual_id':'entity:participant_id'})\n return data","def collate_fn_pad_text_only(batch):\n output = {\n 'id': [],\n 'label': {\n 'intent': [],\n 'semiotic': [],\n 'contextual': [],\n },\n 'caption': [],\n }\n\n for sample in batch:\n output['id'].append(sample['id'])\n output['label']['intent'].append(sample['label']['intent'])\n output['label']['semiotic'].append(sample['label']['semiotic'])\n output['label']['contextual'].append(sample['label']['contextual'])\n output['caption'].append(sample['caption'])\n\n output['label']['intent'] = torch.LongTensor(output['label']['intent'])\n output['label']['semiotic'] = torch.LongTensor(output['label']['semiotic'])\n output['label']['contextual'] = torch.LongTensor(output['label']['contextual'])\n output['caption'] = torch.nn.utils.rnn.pad_sequence(output['caption']).t() # (batch_size, sequence_length)\n return output","def format_bgc_metadata(df,float_id):\n mdf = df[bgc_metadata_columns]\n bgc_metadata_dict = {}\n for col in list(mdf):\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\"'\",'\"'))\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \n bgc_metadata_df = pd.DataFrame({\"float_id\": [float_id], \"Metadata_Dict\": [bgc_metadata_dict]})\n return bgc_metadata_df","def count_variants(vcf_list, sample_list):\n\n df_lst = []\n\n sample_vcf_dct = dict(zip(sample_list,vcf_list))\n\n for s in sample_vcf_dct.keys():\n\n vcf_in = sample_vcf_dct[s]\n vcf = VariantFile(vcf_in)\n\n snv = 0\n indel = 0\n\n for rec in vcf:\n\n ref_len = len(rec.ref)\n\n for a in rec.alts:\n if len(a) > 1 or ref_len > 1:\n indel +=1\n else:\n snv +=1\n\n df_lst.append([s,snv,indel])\n\n out_df = pd.DataFrame(df_lst, columns=['sample','snvs','indels'])\n\n return out_df","def _compute_columns(log: EventLog, prefix_length: int, padding: bool) -> list:\n return [\"trace_id\"] + \\\n sorted(list({\n event['concept:name']\n for trace in log\n for event in trace[:prefix_length]\n })) + \\\n ['0'] if padding else [] + \\\n ['label']","def feature_table(chr_id, source, orient, genes, transcripts, cds, exons, unk):\n for gname, ginfo in genes.items():\n line = [str(chr_id), \n 'gbk_to_gff',\n ginfo[3],\n str(ginfo[0]),\n str(ginfo[1]),\n '.',\n ginfo[2],\n '.',\n 'ID='+str(gname)+';Name='+str(gname)+';Note='+ginfo[-1]]\n print '\\t'.join(line) \n ## construct the transcript line is not defined in the original file \n t_line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', ginfo[2], '.'] \n\n if not transcripts:\n t_line.append('ID=Transcript:'+str(gname)+';Parent='+str(gname))\n\n if exons: ## get the entire transcript region from the defined feature\n t_line[3] = str(exons[gname][0][0])\n t_line[4] = str(exons[gname][0][-1])\n elif cds:\n t_line[3] = str(cds[gname][0][0])\n t_line[4] = str(cds[gname][0][-1])\n print '\\t'.join(t_line) \n\n if exons:\n exon_line_print(t_line, exons[gname], 'Transcript:'+str(gname), 'exon')\n\n if cds:\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'CDS')\n if not exons:\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'exon')\n\n else: ## transcript is defined \n for idx in transcripts[gname]: \n t_line[2] = idx[3]\n t_line[3] = str(idx[0])\n t_line[4] = str(idx[1])\n t_line.append('ID='+str(idx[2])+';Parent='+str(gname))\n print '\\t'.join(t_line) \n \n ## feature line print call \n if exons:\n exon_line_print(t_line, exons[gname], str(idx[2]), 'exon')\n if cds:\n exon_line_print(t_line, cds[gname], str(idx[2]), 'CDS')\n if not exons:\n exon_line_print(t_line, cds[gname], str(idx[2]), 'exon')\n\n if len(genes) == 0: ## feature entry with fragment information \n \n line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', orient, '.'] \n fStart = fStop = None \n\n for eid, ex in cds.items(): \n fStart = ex[0][0] \n fStop = ex[0][-1]\n\n for eid, ex in exons.items(): \n fStart = ex[0][0] \n fStop = ex[0][-1]\n\n if fStart or fStart:\n\n line[2] = 'gene'\n line[3] = str(fStart)\n line[4] = str(fStop)\n line.append('ID=Unknown_Gene_' + str(unk) + ';Name=Unknown_Gene_' + str(unk))\n print \"\\t\".join(line)\n\n if not cds:\n line[2] = 'transcript'\n else:\n line[2] = 'mRNA'\n line[8] = 'ID=Unknown_Transcript_' + str(unk) + ';Parent=Unknown_Gene_' + str(unk)\n print \"\\t\".join(line)\n \n if exons:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\n \n if cds:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'CDS')\n if not exons:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\n \n unk +=1 \n\n return unk","def usage_table_format(result):\n table = []\n for item in result:\n row = OrderedDict()\n row['Value'] = item['name']['localizedValue']\n row['Usage'] = item['currentValue'] or \"0\"\n row['Limit'] = item['limit'] or \"0\"\n table.append(row)\n return table","def distinct_cols_multi_table(table_list=[], remove_columns=['PNR_LOC','PNR_CRT_DTE'], output_format='sql'):\n alfa = \"abcdefghijklmnopqrstuvwxyz\"\n \n all_cols=set()\n all_cols_str=''\n \n for i, table in enumerate(table_list):\n cols=table.columns\n\n #find new cols (cols in B but not in A)\n different_cols = set(table.columns)-all_cols\n\n #remove additional removal cols\n different_cols = different_cols-set(remove_columns)\n\n #append table references\n if output_format == 'sql':\n ref_cols = ',{0}.'.format(alfa[i])+', {0}.'.join(different_cols).format(alfa[i])\n #append to full column list & str\n all_cols = all_cols.union(different_cols)\n all_cols_str = all_cols_str +\"\"+ ref_cols\n \n elif output_format=='pyspark':\n ref_cols = ','.join('{1}.{0}'.format(w,alfa[i]) for w in different_cols)\n #append to full column list & str\n all_cols = all_cols.union(different_cols)\n all_cols_str = all_cols_str +\",\"+ ref_cols\n else:\n print(\"please select output_format = ['pyspark','sql']\")\n \n\n # remove first comma\n if output_format == 'sql':\n return all_cols_str[1:]\n elif output_format=='pyspark':\n return all_cols_str[1:].split(',')","def legacy_reporter(self):\n logging.info('Creating database-friendly summary report')\n header = '{}\\n'.format(','.join(self.legacy_headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SequencingDate\n data += GenObject.returnattr(sample.run, 'Date')\n # Analyst\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\n # Legacy ConFindr clean/contaminated call\n data += 'ND,'\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # NumClustersPF\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\n # Percentage of reads mapping to PhiX control\n data += GenObject.returnattr(sample.run, 'phix_aligned')\n # Error rate calculated from PhiX control\n data += GenObject.returnattr(sample.run, 'error_rate')\n # LengthForwardRead\n data += GenObject.returnattr(sample.run, 'forwardlength',\n number=True)\n # LengthReverseRead\n data += GenObject.returnattr(sample.run, 'reverselength',\n number=True)\n # Real time strain\n data += GenObject.returnattr(sample.run, 'Description')\n # Flowcell\n data += GenObject.returnattr(sample.run, 'flowcell')\n # MachineName\n data += GenObject.returnattr(sample.run, 'instrument')\n # PipelineVersion\n data += self.commit + ','\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # cgMLST\n try:\n if type(sample.cgmlst.sequencetype) is list:\n if sample.cgmlst.sequencetype:\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\n else:\n cgmlst_seq_type = 'ND,'\n else:\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\n data += cgmlst_seq_type\n except AttributeError:\n data += 'ND,'\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def generate_aggregates(self) -> None:\n self.create_count_map()\n self.create_total_count()\n self.create_n_1_gram_map()","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def summary_scores(fold_scores_list): \n d = {}\n nulls = {}\n for idx, score in enumerate(fold_scores_list):\n d.update({f\"{idx+1}0 samples:\": score[0].T})\n nulls.update({f\"{idx+1}0 samples:\": score[1].T})\n \n summary_scores = pd.concat(d.values(), axis=1, keys=d.keys()).T\n summary_nulls = pd.concat(nulls.values(), axis=1, keys=nulls.keys()).T\n return summary_scores, summary_nulls","def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\n datafile = open(output_table, \"w\")\n datafile.write(\n \"Feature_1\" + '\\t' + \"Feature_2\" + \"\\t\" + \"plus_plus\" + '\\t' + \"minus_minus\" + '\\t' + \"plus_minus\" + '\\t' + \"minus_plus\" + '\\t' + \"p_value_same_opposite\" + '\\t' + \"p-value_same_opposite_Bonferoni_corrected\" + '\\t' + \"Ratio_same_opposite\" + '\\t' + \"p_value_divergent_convergent\" + '\\t' + \"p_value_divergent_convergent Bonferoni corrected\" + '\\t' + \"Ratio divergent convergent\" + '\\n')\n for i in range(len(NamesL_pairs)):\n datafile.write(\n NamesL_pairs[i][0] + '\\t' + NamesL_pairs[i][1] + '\\t' + str(p_pL[i]) + '\\t' + str(m_mL[i]) + '\\t' + str(\n p_mL[i]) + '\\t' + str(m_pL[i]) + '\\t' + str(p_valsL[i]) + '\\t' + str(p_vals_BonferoniL[i]) + '\\t' + str(\n RatiosL[i]) + '\\t' + str(p_valsL_divergent_convergent[i]) + '\\t' + str(\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\t' + str(RatiosL_divergent_convergent[i]) + '\\n')\n datafile.close()\n return","def collate_fn(data):\n\toutput = dict()\n\n\tfor name in ['answer_ID','query_ID']:\n\t\toutput[name] = [ _[name] for _ in data]\n\n\n\tfor name in ['query_len','answer_len']:\n\t\ttemp = [ _[name] for _ in data]\t \n\t\toutput[name] = torch.stack(temp, dim=0) \n\t\n\t#deal with source and target\n\tfor name in ['answer','query']:\n\t\tlength = output['{0}_len'.format(name)]\n\t\tl = length.max().item()\n\n\t\tfor i in range(len(data)):\n\t\t\tif(l-length[i].item()>0):\n\t\t\t\tdata[i][name] = torch.cat([data[i][name],torch.zeros(l-length[i].item(),dtype=torch.long)],dim=-1)\n\n\t\ttemp = [ _[name] for _ in data]\n\t\t\n\t\toutput[name] = torch.stack(temp, dim=0).long()\n\t\t\n\n\treturn output","def reduce_data_to_necessary_columns(filtered_df):\n hist_df = filtered_df[\n [\n \"UniqueName\",\n \"Joins\",\n \"Projection_Attributes\",\n \"Selection_Attributes\",\n \"GroupBy\",\n \"OrderBy\",\n \"Strings\",\n \"Tables\",\n ]\n ].set_index(\"UniqueName\")\n return hist_df","def buildAdsTable_v1(output_file = None):\r\n ads_table = []\r\n text_props = ['readability_text', '_all']\r\n onto_props_with_mapping = {'phone':['telephone.name', 'telephone.name.raw'], 'email': ['email.name', 'email.name.raw'],\r\n 'posting_date':['inferlink_date', 'readability_date', 'high_recall_readability_date'],\r\n 'price':['price'], 'location':['addressLocality'],\r\n 'name':['name'],\r\n 'ethnicity':['ethnicity'],\r\n 'eye_color':['eyeColor'], 'title':['title'],\r\n 'hair_color':['hairColor'], 'nationality':['nationality'],\r\n 'business_type':['business_type'],\r\n 'business_name':['streetAddress'], 'services':['serviceType'],\r\n 'business': ['streetAddress'],\r\n 'physical_address': ['streetAddress'],\r\n 'gender':['gender'], 'top_level_domain':['top_level_domain'],\r\n 'obfuscation':['telephone.isObfuscated', 'email.isObfuscated'],\r\n 'age':['age'], 'hyperlink:':['relatedLink'], 'drug_use':['drug_use'],\r\n 'review_site':['review_site'], 'review_id':['review_id'],\r\n 'number_of_individuals':['name_count'],\r\n 'ad': ['identifier'],\r\n 'multiple_phone': ['telephone_count'],\r\n 'cluster': ['seller.uri'],\r\n 'seed': ['seller.telephone.name', 'seller.email.name']\r\n }\r\n non_readability_props = ['number_of_individuals', 'ad', 'multiple_phone', 'cluster', 'phone', 'posting_date', 'email']\r\n onto_props_without_mapping = ['image_with_email', 'image_with_phone']\r\n for property, value_list in onto_props_with_mapping.iteritems():\r\n dict = {}\r\n dict['onto_prop'] = property\r\n mappings = []\r\n tmp = {}\r\n for v in value_list:\r\n if property == 'phone' or v == 'seller.telephone.name':\r\n tmp[v] = 'build_phone_match_clause'\r\n tmp['_all'] = 'build_phone_match_clause'\r\n tmp['url'] = 'build_phone_regexp_clause'\r\n elif v == 'email.name':\r\n tmp[v] = 'build_email_match_clause'\r\n tmp['_all'] = 'build_match_phrase_clause'\r\n elif property == 'ad':\r\n tmp[v] = 'build_term_clause'\r\n elif '_count' in v:\r\n tmp[v] = 'build_count_match_clause'\r\n elif property == 'gender':\r\n tmp[v] = 'build_gender_match_clause'\r\n elif property == 'posting_date':\r\n tmp[v] = 'build_match_phrase_clause'\r\n else:\r\n tmp[v] = 'build_match_clause'\r\n if property not in non_readability_props:\r\n for v in text_props: # will overwrite for seller.telephone.name\r\n tmp[v] = 'build_match_clause_inner'\r\n mappings.append(tmp)\r\n dict['mappings'] = mappings\r\n ads_table.append(dict)\r\n\r\n for property in onto_props_without_mapping:\r\n dict = {}\r\n dict['onto_prop'] = property\r\n mappings = []\r\n tmp = {}\r\n for v in text_props:\r\n tmp[v] = 'build_match_clause_inner'\r\n mappings.append(tmp)\r\n dict['mappings'] = mappings\r\n ads_table.append(dict)\r\n\r\n if output_file:\r\n file = codecs.open(output_file, 'w', 'utf-8')\r\n for entry in ads_table:\r\n json.dump(entry, file)\r\n file.write('\\n')\r\n file.close()","def get_summary_of_records(self):\n ids = self.get_saleman_ids()\n table = [\n [\"Seller name\",\"Number of sales\",\"Total Value ($)\"]\n ]\n for id in ids:\n table_id = [self.get_seller_name(id),self.get_number_of_sales(id),\n self.get_total_of_saleman(id)]\n table.append(table_id)\n data_table = AsciiTable(table)\n print(data_table.table)","def convert_data(test_data,params,list_dict,rational_present=True,topk=2):\n \"\"\"input: params -- input dict, list_dict -- previous predictions containing rationals\n rational_present -- whether to keep rational only or remove them only\n topk -- how many words to select\"\"\"\n \n temp_dict={}\n for ele in list_dict:\n temp_dict[ele['annotation_id']]=ele['rationales'][0]['soft_rationale_predictions']\n \n test_data_modified=[]\n \n for index,row in tqdm(test_data.iterrows(),total=len(test_data)):\n try:\n attention=temp_dict[row['Post_id']]\n except KeyError:\n continue\n topk_indices = sorted(range(len(attention)), key=lambda i: attention[i])[-topk:]\n new_text =[]\n new_attention =[]\n if(rational_present):\n if(params['bert_tokens']):\n new_attention =[0]\n new_text = [101]\n for i in range(len(row['Text'])):\n if(i in topk_indices):\n new_text.append(row['Text'][i])\n new_attention.append(row['Attention'][i])\n if(params['bert_tokens']):\n new_attention.append(0)\n new_text.append(102)\n else:\n for i in range(len(row['Text'])):\n if(i not in topk_indices):\n new_text.append(row['Text'][i])\n new_attention.append(row['Attention'][i])\n test_data_modified.append([row['Post_id'],new_text,new_attention,row['Label']])\n\n df=pd.DataFrame(test_data_modified,columns=test_data.columns)\n return df","def parse_trflp(lines):\r\n\r\n sample_ids = []\r\n otu_ids = []\r\n data = []\r\n non_alphanum_mask = re.compile('[^\\w|^\\t]')\r\n # not sure why the above regex doesn't cover the following regex...\r\n dash_space_mask = re.compile('[_ -]')\r\n\r\n for i, line in enumerate(lines):\r\n elements = line.strip('\\n').split('\\t')\r\n\r\n # special handling for the first line only\r\n if i == 0:\r\n # validating if the file has a header\r\n if elements[0] == '':\r\n for otu_id in elements[1:]:\r\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\r\n continue\r\n else:\r\n for j, otu_id in enumerate(elements[1:]):\r\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n\r\n # handling of all other lines\r\n current_row = []\r\n\r\n # converting each value in the row to int\r\n for count in elements[1:]:\r\n try:\r\n current_row.append(int(round(float(count), 0)))\r\n except ValueError:\r\n current_row.append(0)\r\n\r\n # if the sum of all the values is equial to 0 ignore line\r\n if sum(current_row) == 0:\r\n continue\r\n\r\n # adding sample header to list\r\n sample_ids.append(non_alphanum_mask.sub('.',\r\n dash_space_mask.sub('.', elements[0])))\r\n\r\n # validating the size of the headers to add missing columns\r\n # this is only valid when there is no header\r\n if len(current_row) > len(otu_ids):\r\n # modify header data\r\n extra_cols = []\r\n for j in range(len(otu_ids), len(current_row)):\r\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n # modify data\r\n for j in range(len(data)):\r\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\r\n\r\n otu_ids.extend(extra_cols)\r\n elif len(current_row) < len(otu_ids):\r\n # modify data\r\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\r\n\r\n data.append(current_row)\r\n\r\n return sample_ids, otu_ids, asarray(data).transpose()","def reformat_analogs(analogs, prefix = 'modified-analogfile.tsv'):\n # import numpy as np\n import pandas as pd\n\n try:\n num_cols = 3 # dateAnlg, Dis, Corr\n\n # Create dataframe and read in output csv file of analogs process\n dfS = pd.DataFrame()\n dfS = pd.read_csv(analogs, delimiter=r\"\\s+\", index_col=0)\n\n # Find number of analogues\n num_analogues = (dfS.shape[1]) / 3\n # LOGGER.debug('num_analogues: %s', num_analogues)\n\n # Define temporary df\n df_anlg = dfS.iloc[:, 0:num_analogues] # store only anlg dates\n df_dis = dfS.iloc[:, num_analogues:2 * num_analogues] # store only dis\n df_corr = dfS.iloc[:, 2 * num_analogues:3 *\n num_analogues] # store only corr\n\n # remove index name before stacking\n df_anlg.index.name = \"\"\n df_dis.index.name = \"\"\n df_corr.index.name = \"\"\n\n dateStack = df_anlg.stack()\n disStack = df_dis.stack().abs() # raw values < 0 so take abs\n corrStack = df_corr.stack()\n\n # Create df of correct dimensions (n x num_cols) using dfS\n df_all = dfS.iloc[:, 0:num_cols] # NB data are placeholders\n # Rename cols\n df_all.columns = ['dateAnlg', 'Dis', 'Corr']\n # Replicate each row 20 times (for dcjs format)\n df_all = df_all.loc[np.repeat(df_all.index.values, num_analogues)]\n # Replace data placeholders with correct values\n df_all['dateAnlg'] = list(dateStack)\n df_all['Dis'] = list(disStack)\n df_all['Corr'] = list(corrStack)\n # Name index col\n df_all.index.name = 'dateRef'\n\n # save to tsv file\n analogs_mod = prefix\n df_all.to_csv(analogs_mod, sep='\\t')\n LOGGER.info('successfully reformatted analog file')\n except Exception:\n msg = 'failed to reformat analog file'\n LOGGER.exception(msg)\n raise Exception(msg)\n return analogs_mod","def data_in(input_filename, sample_id):\n \n print('Processing sample {}'.format(sample_id))\n # Encoding set to latin1 due to presence of degree symbol\n # newlines in CC2 logger details fields will cause issues\n header_rows = 30\n df_param = pd.read_table(input_filename, \n nrows=header_rows, \n encoding=\"latin1\", \n header=None)\n df_val = pd.read_table(input_filename, skiprows=header_rows)\n df_param_indexed = df_param.set_index(0)\n# redundant due to parsing sample id from filename\n# sample_id = df_param_indexed.loc['Sample ID', 1]\n \n d1 = {1: pd.Series(['', ''], index=['Sample ID', 'Label'])}\n df_val_params = pd.DataFrame(d1)\n df_val_params.loc['Sample ID', 1] = sample_id\n df_val_params.loc['Label', 1] = sample_id\n\n return df_param_indexed, df_val, df_val_params","def prepare_data(base_df, n_seconds_min=3):\n # Remove too short samples\n source_df = base_df.loc[base_df['seconds'] > n_seconds_min]\n # Group speakers duplicated by id\n df = source_df.loc[:, ['speaker_id', 'dataset_name']]\n df = df.set_index('speaker_id')\n df = df.loc[~df.index.duplicated(keep='first')]\n dfGrouped = source_df.groupby(['speaker_id']).sum()\n # Count the number of samples for each speaker\n dfCountAudio = source_df.groupby(['speaker_id']).count().filepath\n speakers_duration = dfGrouped.join(df)\n speakers_duration = speakers_duration.join(dfCountAudio)\n speakers_duration = speakers_duration.rename(columns={'filepath': 'n_samples'})\n return source_df, speakers_duration","def aggregate_counts(counts_files,\n output_file = '/dev/stdout', \n sample_names=None, \n sep=\"\\t\", \n header=0, \n comment=\"#\"):\n sample_pos = -1\n \n if sample_names is not None:\n if len(sample_names)!=len(counts_files):\n logging.error(\"Number of sample names is not the same length as \",\n \"the number of counts files.\")\n raise RuntimeError(\"\")\n\n # read in all counts files\n counts_df = [pd.read_csv(file, sep=sep, header=header, comment=comment) \n for file in counts_files]\n\n # overwrite the sample names if provided\n if sample_names:\n for i, df in enumerate(counts_df):\n #counts_df[i].columns[sample_pos] = sample_names[i]\n new_columns = df.columns.tolist()\n new_columns[sample_pos] = sample_names[i]\n df.columns = new_columns\n else:\n # check sample names are all different\n sample_names_from_files = [df.columns[sample_pos] for df in counts_df]\n\n if (len(set(sample_names_from_files))<len(counts_files)):\n logging.error(\"Sample names in counts files are not unique. Fix \",\n \"or provide a list of sample names to use.\")\n raise RunTimeError()\n\n\n # merge the dataframes together\n merged_df = reduce(lambda x, y: pd.merge(x,y), counts_df)\n\n\n # output\n if header is not None:\n out_header = True\n\n with open(output_file, 'w') as handle:\n merged_df.to_csv(handle, sep=sep, header=out_header, index=False)\n\n return 0","def make_case_metadata_table_query():\n case_aliquot_table_id, study_table_id, case_external_mapping_table_id = get_mapping_table_ids()\n\n file_count_table_name = construct_table_name(API_PARAMS, prefix=BQ_PARAMS['FILE_COUNT_TABLE'])\n file_count_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{file_count_table_name}\"\n\n return f\"\"\"\n WITH case_project_file_count AS (\n SELECT DISTINCT c.case_id, c.case_submitter_id, c.project_submitter_id, \n s.project_name, s.program_name, s.project_id,\n fc.file_id_count as file_count\n FROM `{case_external_mapping_table_id}` c\n JOIN `{study_table_id}` s\n ON c.project_id = s.project_id\n JOIN `{file_count_table_id}` fc\n ON c.case_id = fc.case_id\n )\n\n SELECT ca.case_id, ca.case_submitter_id, ca.primary_site, ca.disease_type,\n cp.project_name, cp.program_name, cp.project_id, cp.file_count\n FROM `{case_aliquot_table_id}` AS ca\n JOIN case_project_file_count AS cp\n ON cp.case_id = ca.case_id\n \"\"\"","def generate_table(self, outtablename,\n cols=['A', 'B', 'AB'],\n generateTable=True):\n if generateTable:\n new_indices = ['time (s)', 'mean counts']\n for idx in self.data[cols].describe().index[2:]:\n new_indices.append(idx)\n outTable = self.data[cols].describe()\\\n .set_index(pd.Index(new_indices))\n outTable.to_latex(\n self.tables_dir + outtablename + \".tex\", float_format=\"%d\")\n print(\"Outtable: \", outTable)","def prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id):\n # export raw data\n data = pd.read_table(path_to_samples_info)\n # Rename columns to match firecloud requirements\n data = data.rename(columns={'sample_id':'entity:sample_id', 'individual_id':'participant_id'})\n # Locations of BAM files in google bucket\n path_in_bucket_full = \"gs://%s/seq_data/%s\" % (google_bucket_id, tsca_id)\n # Extract bam filename\n data['bam_filename'] = data.apply(lambda row: row['clean_bam_file_capture'].split('/')[-1], axis=1)\n # Create bai filename (change extension on .bam file)\n data['bai_filename'] = data.apply(lambda row: \"%s%s\" %(row['bam_filename'][:-3], 'bai'), axis=1)\n # Change BAM path from xchip to Google cloud\n data['clean_bam_file_capture'] = \\\n data.apply( lambda row: \"%s/%s/%s\" \\\n %(path_in_bucket_full, row['external_id_validation'], row['bam_filename']), axis=1)\n # Add location of .bai file \n data['clean_bai_file_capture'] = \\\n data.apply( lambda row: \"%s/%s/%s\" \\\n %(path_in_bucket_full, row['external_id_validation'], row['bai_filename']), axis=1)\n # Add TSCA ID\n data['tsca_id'] = tsca_id\n\n # Reorganize columns (entity:sample_id at the beginning)\n columns = ['entity:sample_id'] + [col for col in data if col != 'entity:sample_id']\n data = data[columns]\n return data","def transform(filtered_list):\n out_put = {}\n out_list = []\n # loop to get the required columns, random ordered\n for item in filtered_list:\n for val in item._fields:\n if val in type_dict:\n out_put[val] = type_dict.get(val)(getattr(item, val))\n out_list.append(out_put)\n out_put = {}\n\n # loop to the ordered columns data as per output\n all_rows = []\n for item in out_list:\n tmp_row = []\n for key in type_dict.keys():\n out_put[key] = item[key]\n tmp_row.append(item[key])\n all_rows.append(tmp_row)\n\n col_row = [col.replace('_', '-') for col in type_dict.keys()]\n all_rows.insert(0, col_row)\n return all_rows","def expand_affiliation(df):\n from pandas import Series\n res = df[[\"source_id\", \"author_ids\", \"afid\"]].copy()\n res['afid'] = res[\"afid\"].str.split(';')\n res = (res[\"afid\"].apply(Series)\n .merge(res, right_index=True, left_index=True)\n .drop([\"afid\"], axis=1)\n .melt(id_vars=['source_id', 'author_ids'], value_name=\"afid\")\n .drop(\"variable\", axis=1)\n .dropna())\n res['afid'] = res['afid'].astype(float)\n return res","def generateAggregatedCsvData(self, context, obj, entities):\n return sum([long(e.prop1.replace('-', ''), 16) for e in entities])","def _merge(dts):\n df = pd.concat(dts)\n\n ma = df.pivot(index='isomir', columns='sample', values='counts')\n ma_mirna = ma\n ma = ma.fillna(0)\n ma_mirna['mirna'] = [m.split(\":\")[0] for m in ma.index.values]\n ma_mirna = ma_mirna.groupby(['mirna']).sum()\n ma_mirna = ma_mirna.fillna(0)\n return ma, ma_mirna","def synthesize_from_table(df, geo_df, targets):\n # replace NaNs with None\n targets = targets.where(targets.notnull(), None)\n\n new_df = df\n\n for _, row in targets.iterrows():\n new_df = synthesize_one(\n df=new_df,\n target=row['target_value'],\n alloc_id=row['geo_id_col'],\n geo_df=geo_df,\n geo_col=row['capacity_col'],\n constraint_expr=row['capacity_expr'],\n filters=row['filters'],\n count=row['count'],\n stuff=row['stuff'])\n\n return new_df"],"string":"[\n \"def alignAndReformat_countFPKMattrTables(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n #reformat\\n countTable_flat = self.reformat_countTable(\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,);\\n fpkmTable_flat = self.reformat_fpkmTable(\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,);\\n attrTable_dict = self.reformat_attrTable();\\n #align\\n countAndFpkmTable_aligned = [];\\n for row in countTable_flat[:]:\\n row.update(attrTable_dict[row['tracking_id']]);\\n countAndFpkmTable_aligned.append(row);\\n for row in fpkmTable_flat[:]:\\n row.update(attrTable_dict[row['tracking_id']]);\\n countAndFpkmTable_aligned.append(row);\\n return countAndFpkmTable_aligned;\",\n \"def reformat_countTable(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n if self.countTable: countTable = self.countTable[:];\\n else: countTable = [];\\n\\n countTable_flat = self.reformat_countOrFPKMTable(\\n countOrFPKMTable_I=countTable,\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,\\n count_or_FPKM = 'count');\\n return countTable_flat;\",\n \"def reformat_fpkmTable(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n if self.fpkmTable: fpkmTable = self.fpkmTable[:];\\n else: fpkmTable = [];\\n\\n fpkmTable_flat = self.reformat_countOrFPKMTable(\\n countOrFPKMTable_I=fpkmTable,\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,\\n count_or_FPKM = 'fpkm');\\n return fpkmTable_flat;\",\n \"def reformat_attrTable(\\n self):\\n #format into a dictionary of rows for quick aligning with the tracking_id\\n if self.attrTable: attrTable = self.attrTable[:];\\n else: attrTable = [];\\n\\n attrTable_dict = {};\\n for row in attrTable:\\n attrTable_dict[row['tracking_id']] = row;\\n return attrTable_dict;\",\n \"def freq_table(a):\\n Detail_freq = a.loc[:, (a.dtypes == object) | (a.dtypes == long) ].columns.get_values().tolist()\\n print(Detail_freq)\\n for freq in Detail_freq:\\n df1 = pd.DataFrame(a[freq].value_counts(dropna=False).astype(float).map('{:20,.0f}'.format).sort_index()).rename(columns={freq:'Count'})\\n df2 = pd.DataFrame(a[freq].value_counts(normalize = True, dropna=False).map('{:,.2%}'.format).sort_index()).rename(columns={freq:'Percentage'})\\n df = pd.concat([df1, df2], axis = 1)\\n print(df)\",\n \"def _finalize(self, sampled_data):\\n final_data = dict()\\n for table_name, table_rows in sampled_data.items():\\n parents = self.metadata.get_parents(table_name)\\n if parents:\\n for parent_name in parents:\\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\\n if foreign_key not in table_rows:\\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\\n table_rows[foreign_key] = parent_ids\\n\\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\\n\\n fields = self.metadata.get_fields(table_name)\\n\\n final_data[table_name] = reversed_data[list(fields.keys())]\\n\\n return final_data\",\n \"def format_unifrac_sample_mapping(sample_ids, otu_ids, otu_table_array):\\r\\n out = []\\r\\n for i, row in enumerate(otu_table_array):\\r\\n for j, val in enumerate(row):\\r\\n if val > 0:\\r\\n line = [otu_ids[i], sample_ids[j], str(val)]\\r\\n out.append('\\\\t'.join(line))\\r\\n return out\",\n \"def per_sample_taxa_summaries(open_table, output_format):\\n t = parse_biom_table(open_table)\\n header = \\\"#taxon\\\\trelative_abundance\\\\n\\\"\\n\\n for v, id_, md in t.iter():\\n with open(output_format % id_, 'w') as f:\\n f.write(header)\\n\\n for sorted_v, taxa in \\\\\\n sorted(zip(v, t.ids(axis='observation')))[::-1]:\\n if sorted_v:\\n f.write(\\\"%s\\\\t%f\\\\n\\\" % (taxa, sorted_v))\",\n \"def reformat_data(self, df, ids):\\n data = np.zeros((len(ids), self.n_sample_rows + 1, self.n_features))\\n idx = 0\\n for i in ids:\\n sample = df.loc[i]\\n data[idx, 0:89, :] = sample.values\\n data[idx, 89, :] = np.mean(sample.values)\\n idx += 1\\n return data\",\n \"def produce_mirna_single_counts_table(count_files, table_file):\\n merge_count_tables(count_files, table_file, \\\".dedup.single.bam.mirbase_counts.txt\\\")\",\n \"def make_summary_tables( res ):\\n\\n # transform second table to csv and read this as a dataFrame\\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\\\",\\\",index_col=0)\\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\\n\\n # first table is trickier because the data is spread on to columns, and there is title line\\n L = res.tables[0].as_html().split('\\\\n')\\n L.pop(1) # get rid of the title\\n tmp = pd.read_html('\\\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \\n\\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\\n # NB : I exclude the last 2 elements which are empty \\n \\n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\\n \\n return result_general_df , result_fit_df\",\n \"def normalize_counts_to_tpm(counts_dir, gff_dir, out_dir, feat='CDS', id_sym='gene_id='):\\n count_files = [os.path.join(counts_dir, f) for f in os.listdir(counts_dir)]\\n all_tpms = {}\\n for cf in count_files:\\n if \\\"_counts\\\" not in cf:\\n continue\\n tpm = normalize_counts_to_tpm_one_file(cf, gff_dir, feat, id_sym)\\n #out_file = \\\"{}_tpm.csv\\\".format(os.path.basename(cf))\\n #out_path = os.path.join(out_dir, out_file)\\n #with open(out_path, \\\"w\\\") as fo:\\n # for gene, t in tpm.items():\\n # fo.write(\\\"{},{}\\\\n\\\".format(gene, t))\\n prefix = os.path.basename(cf).split(\\\"_trimmed\\\")[0] # this would be specific to my naming convention\\n all_tpms[prefix] = tpm\\n return all_tpms\",\n \"def _create_counts(out_dts, out_dir):\\n ma, ma_mirna = _merge(out_dts)\\n out_ma = op.join(out_dir, \\\"counts.tsv\\\")\\n out_ma_mirna = op.join(out_dir, \\\"counts_mirna.tsv\\\")\\n ma.to_csv(out_ma, sep=\\\"\\\\t\\\")\\n ma_mirna.to_csv(out_ma_mirna, sep=\\\"\\\\t\\\")\\n return out_ma_mirna, out_ma\",\n \"def print_tables(hash_table, f_output, l_samples):\\n\\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\\n 'ExAC_SAS',\\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\\n 'Polyphen2_HVAR_score',\\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\\n l_fields = l_fields + l_samples\\n \\n l_chr = set([item[0] for item in hash_table.keys()])\\n\\n fo = open(f_output, 'w')\\n fo.write(','.join(l_fields) + '\\\\n')\\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\\\n')\\n fo.close()\",\n \"def get_split_summary_table(all_df, train_df, test_df):\\n table = PrettyTable()\\n table.field_names = ['set', 'N total', 'N non-ICH', 'N ICH', 'frac non-ICH', 'frac ICH']\\n for df, name in zip([all_df, train_df, test_df],['All', 'Train', 'Test']):\\n table.add_row([name, len(df), len(df[df.Hemorrhage == 0]), len(df[df.Hemorrhage == 1]),\\n f'{len(df[df.Hemorrhage == 0])/len(df):.3%}', f'{len(df[df.Hemorrhage == 1])/len(df):.3%}'])\\n return table\",\n \"def prepare_batch_sample_set_for_metadata_export(path, tsca_id):\\n raw = pd.read_table(path)\\n print( \\\"%d Samples in this batch\\\" % raw.shape[0] )\\n\\n # Create dfs to upload\\n all_samples = pd.concat([pd.DataFrame(index=raw.index, columns=['membership:sample_set_id'], data=tsca_id), \\\\\\n raw[ ['sample_id', 'sample_type'] ]], axis=1)\\n\\n\\n tumors = all_samples.loc[ all_samples['sample_type'] == \\\"Tumor\\\", ['membership:sample_set_id', 'sample_id'] ]\\n tumors.loc[: , 'membership:sample_set_id'] = \\\"%s_T\\\"%tsca_id\\n \\n normals = all_samples.loc[ all_samples['sample_type'] == \\\"Normal\\\", ['membership:sample_set_id', 'sample_id'] ]\\n normals.loc[: , 'membership:sample_set_id'] = \\\"%s_N\\\"%tsca_id\\n\\n all_samples = all_samples.drop('sample_type', axis=1)\\n return (all_samples, tumors, normals)\",\n \"def produce_mirna_allbest_counts_table(count_files, table_file):\\n merge_count_tables(count_files, table_file, \\\".dedup.bam.mirbase_counts.txt\\\")\",\n \"def write_count_table(filtered_counts, gene_names, ids_liver_header):\\n with open(\\\"filtered_tcga_counts.tsv\\\", \\\"w\\\") as file:\\n file.write(\\\"gene_id\\\\tgene_name\\\\t\\\" + \\\"\\\\t\\\".join(ids_liver_header) + \\\"\\\\n\\\")\\n for gene_name, counts_line in zip(gene_names, filtered_counts):\\n file.write(gene_name + \\\"\\\\t\\\" + \\\"\\\\t\\\" + \\\"\\\\t\\\".join(counts_line) + \\\"\\\\n\\\")\",\n \"def make_lof_table(data_table, my_genes, my_samples, summary_func):\\n table_header = [\\\"Gene\\\"] + my_samples + [\\n \\\"Missense:Benign\\\", \\\"Missense:Possibly\\\", \\\"Missense:Probably\\\",\\n \\\"MissenseNA\\\", \\\"Indel\\\", \\\"Nonsense\\\", \\\"Frameshift\\\", \\\"Splice-site\\\",\\n \\\"Synonymous\\\"]\\n table_records = []\\n\\n gs_lookup = group_data_by_gs(data_table)\\n for gene in my_genes:\\n synonymous = missense_benign = missense_possibly = missense_probably = \\\\\\n missense_na = frameshift = nonsense = splice = indel = 0\\n\\n out_row = [gene]\\n for sample in my_samples:\\n normalized = [0]\\n # Count mutations of each type for this gene and sample\\n for entry in gs_lookup[gene][sample]:\\n if entry['muttype'] == 'Silent':\\n synonymous += 1\\n continue\\n if entry['muttype'] == 'Intron':\\n # Shouldn't be here; ignore\\n continue\\n\\n if entry['muttype'] == 'Missense_Mutation':\\n if entry['consequence'] == 'benign':\\n missense_benign += 1\\n elif entry['consequence'] == 'possibly':\\n missense_possibly += 1\\n elif entry['consequence'] == 'probably':\\n missense_probably += 1\\n elif entry['consequence'] == 'NA':\\n missense_na += 1\\n else:\\n print(\\\"Unhandled missense consequence level:\\\",\\n entry['consequence'], file=sys.stderr)\\n elif entry['muttype'] == 'Nonsense_Mutation':\\n nonsense += 1\\n elif entry['muttype'] == 'Splice_Site':\\n splice += 1\\n elif entry['muttype'] in ('Frame_Shift_Ins', 'Frame_Shift_Del'):\\n frameshift += 1\\n elif entry['muttype'] in ('In_Frame_Ins', 'In_Frame_Del'):\\n indel += 1\\n else:\\n print(\\\"Unhandled mutation type:\\\", entry['muttype'],\\n file=sys.stderr)\\n continue\\n\\n normalized.append(entry['normalized'])\\n # Summarize the normalized mutation counts for this gene and sample\\n out_row.append(summary_func(normalized))\\n out_row.extend((missense_benign, missense_possibly, missense_probably,\\n missense_na, indel, nonsense, frameshift, splice,\\n synonymous))\\n table_records.append(out_row)\\n\\n return pandas.DataFrame.from_records(table_records, columns=table_header)\",\n \"def join_gene_tables(gene_tables,output,verbose=None):\\n \\n gene_table_data={}\\n start_column_id=\\\"\\\"\\n samples=[]\\n file_basenames=[]\\n index=0\\n for gene_table in gene_tables:\\n \\n if verbose:\\n print(\\\"Reading file: \\\" + gene_table)\\n \\n lines=util.process_gene_table_with_header(gene_table, allow_for_missing_header=True)\\n header=next(lines)\\n \\n # get the basename of the file\\n file_basename='.'.join(os.path.basename(gene_table).split('.')[:-1])\\n file_basenames.append(file_basename)\\n \\n if header:\\n header_info=header.split(GENE_TABLE_DELIMITER)\\n if not start_column_id:\\n start_column_id=header_info[0]\\n # allow for multiple samples\\n sample_names=header_info[1:]\\n else:\\n # if there is no header in the file then use the file name as the sample name\\n sample_names=[file_basename]\\n \\n for line in lines:\\n data=line.split(GENE_TABLE_DELIMITER)\\n try:\\n gene=data[0]\\n # if the header names multiple samples, merge all samples\\n # this prevents extra columns from being included in some rows\\n # this requires files containing multiple samples to include a header\\n data_points=data[1:len(sample_names)+1]\\n except IndexError:\\n gene=\\\"\\\"\\n\\n if gene:\\n current_data=gene_table_data.get(gene,\\\"\\\")\\n fill = index - current_data.count(GENE_TABLE_DELIMITER)\\n if fill > 0:\\n # fill in zeros for samples without data then add data point\\n gene_table_data[gene]=current_data + GENE_TABLE_DELIMITER.join([\\\"0\\\"]*fill) + GENE_TABLE_DELIMITER + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\\n elif fill < 0:\\n # add data point to other data point from the same sample\\n current_data_points=current_data.split(GENE_TABLE_DELIMITER)\\n for i,point in enumerate(data_points):\\n store_index=len(data_points)*-1-1+i\\n current_data_points[store_index]=str(float(current_data_points[store_index])+float(point))\\n gene_table_data[gene] = GENE_TABLE_DELIMITER.join(current_data_points)\\n else:\\n # add data point to end of list\\n gene_table_data[gene] = current_data + GENE_TABLE_DELIMITER.join(data_points) + GENE_TABLE_DELIMITER\\n \\n samples+=sample_names\\n index+=len(sample_names)\\n # if all of the header names for the files are the same\\n # then use the file names as headers\\n if samples.count(samples[0]) == len(samples):\\n samples=file_basenames\\n \\n # write the joined gene table\\n if not start_column_id:\\n start_column_id=\\\"# header \\\"\\n sample_header=[start_column_id]+samples\\n total_gene_tables=len(samples)\\n sorted_gene_list=util.fsort(list(gene_table_data))\\n try:\\n file_handle=open(output,\\\"w\\\")\\n file_handle.write(GENE_TABLE_DELIMITER.join(sample_header)+\\\"\\\\n\\\")\\n except EnvironmentError:\\n sys.exit(\\\"Unable to write file: \\\" + output) \\n \\n for gene in sorted_gene_list:\\n # extend gene data for any gene that is not included in all samples\\n current_data=gene_table_data[gene]\\n fill = total_gene_tables - current_data.count(GENE_TABLE_DELIMITER)\\n if fill:\\n current_data=current_data + GENE_TABLE_DELIMITER.join([\\\"0\\\"]*fill) + GENE_TABLE_DELIMITER\\n file_handle.write(gene+GENE_TABLE_DELIMITER+current_data.rstrip(GENE_TABLE_DELIMITER)+\\\"\\\\n\\\")\\n \\n file_handle.close()\",\n \"def samples(app, args):\\n engine = create_engine(args.datafile)\\n meta = MetaData()\\n meta.reflect(engine)\\n print(\\\"\\\\t\\\".join([str(x).replace('counts.', '')\\n for x in meta.tables['counts'].columns\\n if not x == 'counts.index']))\",\n \"def format_df(df):\\n df = df.transpose()\\n df.rename(columns=df.iloc[0], inplace=True)\\n df.drop('gene', inplace=True)\\n df.index.rename('sample_id', inplace=True)\\n return df\",\n \"def format_df(df):\\n df = df.transpose()\\n df.rename(columns=df.iloc[0], inplace=True)\\n df.drop('gene', inplace=True)\\n df.index.rename('sample_id', inplace=True)\\n return df\",\n \"def tranform_data(args):\\n data_dir = args.data_dir\\n save_dir = args.save_dir\\n os.makedirs(save_dir)\\n maxsize = args.maxsize\\n maxlen = args.maxlen \\n ext = args.ext\\n datetime = args.datetime\\n classes = set()\\n nb_files = 0\\n time_col = 'time'\\n event_col = 'event'\\n gt_ids = None\\n if args.ext == \\\"pkl\\\":\\n with open(Path(args.data_dir, \\\"fx_labels\\\"), \\\"rb\\\") as fp:\\n gt_ids = pickle.load(fp)[:maxsize]\\n labels = np.unique(gt_ids)\\n gt_data = []\\n for i in range (len(gt_ids)):\\n gt_data.append(int(np.nonzero(gt_ids[i] == labels)[0]))\\n gt = {'cluster_id': gt_data}\\n print(gt_data)\\n gt_table = pd.DataFrame(data=gt)\\n gt_table.to_csv(Path(save_dir, 'clusters.csv'))\\n if Path(args.data_dir, 'clusters.csv').exists():\\n gt_ids = pd.read_csv(Path(args.data_dir, 'clusters.csv'))[:(maxsize)]\\n gt_ids.to_csv(Path(save_dir, 'clusters.csv'))\",\n \"def preprocess(df):\\n \\n # drop the following columns - irrelevant now\\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\\n , 'tmdbId', 'popularity', 'year']\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n # drop all of the language columns\\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\\\"lan\\\"]\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n\\n # loop through the columns we want to aggregate\\n for col_type in [\\n \\\"original_language_\\\"\\n , \\\"prod_comp_cntry_\\\"\\n , \\\"prod_comp_names_\\\"\\n , \\\"writers_\\\"\\n , \\\"actors_\\\"\\n , \\\"genres_\\\"\\n , \\\"director_\\\"\\n ]:\\n # create a dictionary of each unique value and its frequency\\n val_freq = {}\\n for col in df.columns:\\n if col.startswith(col_type):\\n val_freq[col] = df[col].sum()\\n\\n # create a dataframe from this dictionary; sort by count\\n counts = pd.DataFrame.from_dict(\\n val_freq\\n , orient='index'\\n , columns=['count']\\n ).sort_values('count', ascending=False)\\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\\n\\n # handle special case of production company country\\n if col_type == \\\"prod_comp_cntry_\\\":\\n DROP_COLUMNS = [col for col in counts.index][3:]\\n\\n # handle special case of directors\\n elif col_type == \\\"director_\\\":\\n DIRECTOR_COLS = [col for col in df.columns\\n if col.startswith(\\\"director_\\\")\\n and col!=\\\"director_pop\\\"]\\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\\n DROP_COLUMNS = DIRECTOR_COLS\\n\\n # handle special case of actors\\n elif col_type == \\\"actors_\\\":\\n ACTORS_COLS = [col for col in df.columns if \\\"actors\\\" in col]\\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\\n DROP_COLUMNS = ACTORS_COLS\\n\\n # handle all the other cases\\n else:\\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\\n\\n\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n ##########################################################################\\n # adjust the data for inflation\\n CPI_tf = df['CPIAUCSL'].max()\\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n # no longer need CPI data\\n df.drop('CPIAUCSL', axis=1, inplace=True)\\n \\n ########################################################################## \\n # add in useful features about the cast and crew \\n df['cast_crew_sum_pop'] = (\\n df['director_pop']\\n + df['avg_actor_pop']\\n + df['avg_writer_pop']\\n )\\n df['cast_crew_product_pop'] = (\\n df['director_pop']\\n * df['avg_actor_pop']\\n * df['avg_writer_pop']\\n )\\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\\n df = df.query('10000 <= revenue').copy()\\n df = df.query('100000 <= budget').copy()\\n df.drop('sum_actor_pop', axis=1, inplace=True)\\n df.drop('min_writer_pop', axis=1, inplace=True)\\n\\n # code to transform columns\\n for col in [\\n \\\"budget\\\", \\\"director_pop\\\", \\\"avg_writer_pop\\\"\\n , \\\"max_writer_pop\\\", \\\"avg_actor_pop\\\", \\\"max_actor_pop\\\"\\n , \\\"min_actor_pop\\\", 'cast_crew_sum_pop'\\n , 'cast_crew_product_pop'\\n ]:\\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\\n df.drop(col, axis=1, inplace=True)\\n \\n return df\",\n \"def gen_tab(cat):\\n\\n col = ['FLUX_APER2','FLUX_APER4','FLUX_APER5','FLUX_APER8','FLUX_APER10','FLUX_APER14',\\n 'MAG_APER2','MAG_APER4','MAG_APER5','MAG_APER8','MAG_APER10','MAG_APER14',\\n 'MAG_AUTO','MAG_PETRO','KRON_RADIUS',\\n 'PETRO_RADIUS','FLUX_MAX','ISOAREAF_IMAGE','x',\\n 'y','ra','dec','X2_IMAGE','Y2_IMAGE','XY_IMAGE',\\n 'THETA_IMAGE','X2WIN_IMAGE','Y2WIN_IMAGE','XYWIN_IMAGE','AWIN_IMAGE','BWIN_IMAGE',\\n 'THETAWIN_IMAGE','AWIN_WORLD','BWIN_WORLD','THETAWIN_WORLD',\\n 'MU_MAX','FLAGS','FWHM_IMAGE','ELONGATION','SEX_CLASS','FLUX_RADIUS25',\\n 'FLUX_RADIUS50','FLUX_RADIUS85','FLUX_RADIUS95','FLUX_RADIUS99']\\n print('generating features table: {}'.format(cat))\\n tab = pd.read_table(cat,skiprows=41,sep=r'\\\\s+',header=None, names=col)\\n\\n # crop the image for just using the central part of the image\\n tab = crop(tab)\\n\\n # add concentration column by subtracting mag10 by mag5, rejecting the detections with negative concentration\\n tab['CONCENT'] = tab.MAG_APER5 - tab.MAG_APER10\\n tab = tab[tab.CONCENT > 0]\\n\\n # normalizing the columns\\n print('normalizing features...')\\n seesq_norm = ['X2_IMAGE','Y2_IMAGE','X2WIN_IMAGE',\\n 'Y2WIN_IMAGE','XY_IMAGE','XYWIN_IMAGE',\\n 'ISOAREAF_IMAGE']\\n see_norm = ['AWIN_WORLD','AWIN_WORLD','FWHM_IMAGE',\\n 'KRON_RADIUS','PETRO_RADIUS','FLUX_RADIUS25',\\n 'FLUX_RADIUS50','FLUX_RADIUS85',\\n 'FLUX_RADIUS95','FLUX_RADIUS99']\\n mag_norm = ['MAG_APER4','MAG_APER5','MAG_APER8',\\n 'MAG_APER10','MAG_APER14','MAG_AUTO',\\n 'MAG_PETRO','MU_MAX','CONCENT']\\n flux_norm = ['FLUX_APER2','FLUX_APER4','FLUX_APER5',\\n 'FLUX_APER8','FLUX_APER10','FLUX_APER14']\\n fwhm_mean = tab.FWHM_IMAGE.mean()\\n for seesq_col in seesq_norm:\\n tab[seesq_col] = tab[seesq_col] / (fwhm_mean**2)\\n for see_col in see_norm:\\n tab[see_col] = tab[see_col] / fwhm_mean\\n for mag_col in mag_norm:\\n tab[mag_col] = tab[mag_col] * tab['MAG_APER2']\\n for flux_col in flux_norm:\\n tab[flux_col] = tab[flux_col] * tab['FLUX_MAX']\\n tab['CONCENT'] = -1 * tab['CONCENT']\\n\\n # add column for galactic latitude\\n print('calculating galactic latitude...')\\n ra = np.array(tab['ra'].values)\\n dec = np.array(tab['dec'].values)\\n pos = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, frame='icrs')\\n tab['b'] = list(pos.galactic.b.deg)\\n\\n tab.drop(['MAG_APER2','FLUX_MAX','x','y'], axis=1, inplace=True)\\n tab.to_csv(cat[:-4]+'.csv', index=False, header=True)\",\n \"def build_table():\\n with contextlib.ExitStack() as stack:\\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\\n iters = [(line.split() for line in f) for f in files]\\n for it in iters:\\n next(it)\\n key = operator.itemgetter(0)\\n table = []\\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\\n props = list(g)\\n if len(props) == len(iters):\\n table.append([k] + [x[1] for x in props])\\n for snp in table:\\n print(*snp)\",\n \"def print_table(source, count=False):\\n table_value = []\\n table_header = []\\n for source_key, source_value in source.items():\\n for item in source_value:\\n table_value.append([v for v in item.values()])\\n table_header.append([k for k in item.keys()])\\n if not count:\\n print(tabulate(table_value,\\n headers=table_header[0],\\n tablefmt='orgtbl'))\\n else:\\n print(tabulate([[len(source_value)]],\\n headers=[source_key],\\n tablefmt='orgtbl'))\",\n \"def make_table(ranked_means):\\n fp = open(\\\"table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c|}\\n \\\\\\\\hline\\n \\\\\\\\multicolumn{2}{|c||}{Slowest} & \\\\\\\\multicolumn{2}{|c|}{Fastest} \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n Feature & Rate & Feature & Rate \\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\\"\\\"\\\")\\n top_10 = ranked_means[0:10]\\n bottom_10 = ranked_means[-10:]\\n for ((f_rate, f_name),(s_rate,s_name)) in zip(top_10, bottom_10):\\n f_name = f_name.split(\\\":\\\")[-1]\\n f_name = f_name.rsplit(\\\" \\\", 1)[0] if f_name.endswith(\\\"(V)\\\") else f_name\\n s_name = s_name.split(\\\":\\\")[-1]\\n s_name = s_name.rsplit(\\\" \\\", 1)[0] if s_name.endswith(\\\"(V)\\\") else s_name\\n fp.write(\\\" %s & %.2f & %s & %.2f \\\\\\\\\\\\\\\\ \\\\n\\\" % \\\\\\n (f_name, f_rate, s_name, s_rate))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\\n\\n fp = open(\\\"supp_meaning_table.tex\\\", \\\"w\\\")\\n fp.write(\\\"\\\"\\\"\\\\\\\\begin{tabular}{|l|c||l|c||l|c||l|c|}\\n \\\\\\\\hline\\n Meaning & Category & Meaning & Category & Meaning & Category & Meaning & Category\\\\\\\\\\\\\\\\ \\\\\\\\hline\\n\\n\\\"\\\"\\\")\\n feature_names = [f.split(\\\":\\\")[-1] for (r,f) in ranked_means]\\n feature_names.sort(key=lambda s: s.lower())\\n col1 = feature_names[0:25]\\n col2 = feature_names[25:50]\\n col3 = feature_names[50:75]\\n col4 = feature_names[75:]\\n for a,b,c,d in zip(col1,col2,col3,col4):\\n x,y,z,w = [get_meaning_category(i) or \\\"Excluded\\\" for i in (a,b,c,d)]\\n # Lop off (V)s (we needed them above for get_meaning_category to work)\\n a,b,c,d = [f.rsplit(\\\" \\\", 1)[0] if f.endswith(\\\"(V)\\\") else f for f in (a,b,c,d)]\\n fp.write(\\\"%s & %s & %s & %s & %s & %s & %s & %s\\\\\\\\\\\\\\\\ \\\\n\\\" % (a, x, b, y, c, z, d, w))\\n fp.write(\\\"\\\\\\\\hline\\\\n\\\")\\n fp.write(\\\"\\\\\\\\end{tabular}\\\\n\\\")\\n fp.close()\",\n \"def _transform_idoc(df):\\n global _SIMPLECOUNT_COLUMNS\\n\\n try:\\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\\n\\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\\n \\n c_nc = df['admtypo3'] == 1\\n c_tv = df['admtypo3'] == 2\\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\\n c_viol = df['offtype'] == 1\\n c_male = df['sex'] == 'M'\\n c_female = ~c_male\\n\\n c_first2 = [c_nc, c_tv]\\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\\n \\n def helper(c, indicator_id, first2):\\n df['fk_simplecount_indicator'] = indicator_id\\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\\n if first2:\\n return df[c].groupby(g).size().reset_index(name='value')\\n else:\\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\\n\\n out = pd.DataFrame()\\n for i in range(2):\\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\\n \\n for i in range(len(c_others)):\\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\\n\\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\\n return out[_SIMPLECOUNT_COLUMNS]\\n except:\\n raise\",\n \"def tabular_report(sample_dictionary, blast_dictionary):\\n print(\\\"Writing the report...\\\")\\n sample_dict = sample_dictionary.copy()\\n blast_dict = blast_dictionary.copy()\\n\\n #creating quick dictionary to pull out trimmed sequence\\n trimmed_data_dict={}\\n for key in sample_dict.keys():\\n try:\\n trimmed_sequence=(sample_dict[key]['trimmed_sequence'])\\n key = key.strip('@')\\n trimmed_data_dict.update({key:trimmed_sequence})\\n except KeyError:\\n continue\\n\\n samples = []\\n for sequenceID in sample_dict:\\n samples.append(sequenceID[1:])\\n \\n records = blast_dict.keys()\\n \\n columns = [\\\"SeqID\\\", \\\"Trimmed Sequence\\\", \\\"Trimmed Sequence Length\\\",\\\"BLAST Sequence\\\", \\n \\\"BLAST SeqLength\\\", \\\"Description\\\", \\\"Accession\\\", \\\"Db\\\", \\n \\\"Score\\\", \\\"E_value\\\", \\\"Percent_Identity\\\", \\\"Organism\\\", \\n \\\"Source\\\", \\\"Domain\\\", \\\"Taxonomy\\\"]\\n \\n #Writing\\n OUT = open(\\\"blast_hits_report.txt\\\", \\\"w\\\")\\n OUT.write('\\\\t'.join(columns) + '\\\\n')\\n \\n NO_HITS_OUT = open(\\\"blast_no_hits_report.txt\\\", \\\"w\\\")\\n NO_HITS_OUT.write(\\\"SeqID\\\\tOriginal Seq\\\\tOriginal Seq Length\\\"\\n \\\"\\\\tTrimmed Seq\\\\tTrimmed Seq Length\\\\tResult\\\\n\\\")\\n\\n for record in blast_dict.keys():\\n\\n trimmed_sequence = trimmed_data_dict[record]\\n length_trimmed_sequence=len(trimmed_data_dict[record])\\n \\n #Used Brute force coding to be able to manipulate and add new columns to output\\n try: \\n OUT.write(str(record)\\n +'\\\\t'+str(trimmed_sequence)\\n +'\\\\t'+str(length_trimmed_sequence)\\n +'\\\\t'+str(blast_dict[record]['Sequence'])\\n +'\\\\t'+str(blast_dict[record]['SeqLength'])\\n +'\\\\t'+str(blast_dict[record]['Description'])\\n +'\\\\t'+str(blast_dict[record]['Accession'])\\n +'\\\\t'+str(blast_dict[record]['Db'])\\n +'\\\\t'+str(blast_dict[record]['Score'])\\n +'\\\\t'+str(blast_dict[record]['E_value'])\\n +'\\\\t'+str(blast_dict[record]['Percent_Identity'])\\n +'\\\\t'+str(blast_dict[record]['Organism'])\\n +'\\\\t'+str(blast_dict[record]['Source'])\\n +'\\\\t'+str(blast_dict[record]['Domain'])\\n +'\\\\t'+str(blast_dict[record]['Taxonomy'])+'\\\\n')\\n except KeyError:\\n continue\\n\\n for sample in samples:\\n\\n if sample not in records:\\n sample_stripped = sample.split(\\\"\\\\t\\\")[0]\\n\\n #Get original trimmed sequence for reference\\n try:\\n trimmed_sequence = trimmed_data_dict[sample_stripped]\\n length_trimmed_sequence=len(trimmed_sequence)\\n except KeyError:\\n trimmed_sequence = '-'\\n length_trimmed_sequence=0\\n\\n NO_HITS_OUT.write(sample_stripped\\n #Commend out original data being reported\\n + '\\\\t' + sample_dict['@'+sample]['sequence'] \\n + '\\\\t' + str(len(sample_dict['@'+sample]['sequence'])) \\n + '\\\\t' + str(trimmed_sequence)\\n + '\\\\t' + str(length_trimmed_sequence)\\n\\n + '\\\\t' + 'NO HIT OR SEQUENCE QUALITY BELOW THRESHOLD\\\\n')\\n OUT.close()\\n NO_HITS_OUT.close()\",\n \"def reformat_file(inFile, outFile):\\n \\n with open(outFile, \\\"w\\\") as outHandle:\\n \\n\\t\\t# write header line\\n\\t\\toutLine = [\\\"g1\\\", \\\"g2\\\", \\\"raw_count\\\", \\\"log(obs/exp)\\\"]\\n\\t\\t\\n\\t\\toutHandle.write(\\\"\\\\t\\\".join(outLine) + \\\"\\\\n\\\")\\n\\n\\n\\t\\tfor i, line in enumerate(open(inFile)):\\n\\t\\t\\t\\n\\t\\t\\tif not i == 0:\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tsp = line.strip().split(\\\"\\\\t\\\")\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# get row interaction counts and normalized obs/exp values\\n\\t\\t\\t\\trawCount = sp[12]\\n\\t\\t\\t\\tobsExp = sp[13]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tgenes1 = sp[4].split(\\\"|\\\")\\n\\t\\t\\t\\tgenes2 = sp[10].split(\\\"|\\\")\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t#~ print(g1, g2, rawCount)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# iterate over all pairs\\n\\t\\t\\t\\tfor g1 in genes1:\\n\\n\\t\\t\\t\\t\\tfor g2 in genes2:\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\toutLine = [g1, g2, rawCount, obsExp]\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\toutHandle.write(\\\"\\\\t\\\".join(outLine) + \\\"\\\\n\\\")\",\n \"def to_table(self):\\n table = Table()\\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\\n table['counts'] = self.counts.to_table()['data']\\n if self.counts.data_err is not None:\\n table['counts_err'] = self.counts.to_table()['data_err']\\n table['livetime'] = self.livetime.to_table()['data']\\n table['bkg'] = self.bg_rate.to_table()['data']\\n if self.bg_rate.data_err is not None:\\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\\n table.meta['HDUNAME'] = \\\"bkg_2d\\\"\\n return table\",\n \"def get_normalized_data_table(table_metadata, debug=False):\\n suffix = table_metadata.get('suffix', '')\\n data_table = table_metadata['table_class'](\\n file_path=table_metadata['csv_filename'], suffix=suffix)\\n drop_headers(table_metadata['document_label'], data_table.data)\\n rename_headers(table_metadata['document_label'], data_table.data)\\n print_data_table_length(table_metadata['document_label'],\\n data_table.data,\\n debug=debug)\\n return data_table\",\n \"def _collect_counts(self, instance_list):\\n \\\"\\\"\\\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\\n All \\\"rare words\\\" (those words that appear less than 3 times) are replaced by <UNK>.\\n I also add label|START counts.\\n \\\"\\\"\\\"\\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\\n for instance in instance_list: # Set of <(w, pos), l>\\n index = 0\\n for t in instance.data: # Tuple of (w, pos)\\n index = instance.data.index(t)\\n # print t[0] # word\\n # print instance.label[index] # label\\n if t in self.V:\\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\\n else:\\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\\n if index > 0:\\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\\n else:\\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1\",\n \"def fill_table(table, keep_unknowns=False):\\n if not keep_unknowns:\\n table.drop(table.index[table.phylum == \\\"unknown\\\"], inplace=True)\\n table.drop(table.index[table.genus == \\\"unknown\\\"], inplace=True)\\n table.drop(table.index[table.eggNOG == \\\"unknown\\\"], inplace=True)\\n for cohort in table[\\\"cohort_origin\\\"].unique():\\n cohort_table = table.loc[table[\\\"cohort_origin\\\"] == cohort]\\n\\n if cohort not in table_summary:\\n table_summary[cohort] = {}\\n\\n if \\\"N\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"N\\\"] = 1\\n else:\\n table_summary[cohort][\\\"N\\\"] += 1\\n\\n if \\\"phylum\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"phylum\\\"] = pd.DataFrame(columns=[\\\"phylum\\\"])\\n\\n #count the number of unique values in the phylum column\\n table_phylum = cohort_table.loc[table['genus'] != \\\"unknown\\\",['phylum']].apply(pd.Series.value_counts,axis=0)\\n table_summary[cohort][\\\"phylum\\\"] = table_summary[cohort][\\\"phylum\\\"].add(table_phylum,fill_value=0)\\n \\n if \\\"genus\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"genus\\\"] = pd.DataFrame(columns=[\\\"genus\\\"])\\n\\n #count the number of unique values in the genus column\\n table_genus = cohort_table.loc[table['genus'] != \\\"unknown\\\",['genus']].apply(pd.Series.value_counts,axis=0)\\n table_summary[cohort][\\\"genus\\\"] = table_summary[cohort][\\\"genus\\\"].add(table_genus,fill_value=0)\\n \\n if \\\"eggNOG\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"eggNOG\\\"] = pd.DataFrame(columns=[\\\"eggNOG\\\"])\\n\\n index = 0\\n cogs_table = {}\\n\\n #forced to do this by each row because one gene belongs to multiple cogs\\n for row in cohort_table[\\\"eggNOG\\\"]:\\n if \\\"gene_cog\\\" not in table_summary[cohort]:\\n table_summary[cohort][\\\"gene_cog\\\"] = 0\\n table_summary[cohort][\\\"gene_cog\\\"] += 1\\n if \\\";\\\" in row:\\n cogs = row.split(\\\";\\\")\\n for cog in cogs:\\n if cog not in cogs_table:\\n cogs_table[cog] = 0\\n cogs_table[cog] += 1\\n else:\\n if row not in cogs_table:\\n cogs_table[row] = 0\\n cogs_table[row] += 1\\n\\n #create data frame and create the table\\n cog_pd = pd.DataFrame.from_dict(cogs_table, orient=\\\"index\\\")\\n cog_pd.columns = [\\\"eggNOG\\\"]\\n table_summary[cohort][\\\"eggNOG\\\"] = table_summary[cohort][\\\"eggNOG\\\"].add(cog_pd,fill_value=0)\",\n \"def revise_report_df(report_df):\\n # quick check to see whether report_df column structure is as expected\\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\\n print(\\\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\\\")\\n\\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\\n\\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\\n keys = ['precision', 'recall', 'f1-score', 'support']\\n\\n for col in dict_columns:\\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\\n\\n # iterate row wise through dataframe to normalize dictionary values into flat tables\\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\\n\\n # concat all classifier flat tables into one dataframe\\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\\n\\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\\n\\n # select only created columns\\n report_df = report_df.iloc[:,5:]\\n # sort columns and filter out 'support' related columns\\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\\n\\n return report_df\",\n \"def _get_table_from_samples(self, index):\\n df = pd.DataFrame()\\n for sample in self.samples:\\n sd = sample.to_dict()\\n ser = pd.Series(\\n {k: v for (k, v) in list(sd.items()) if not k.startswith(\\\"_\\\")}\\n )\\n df = df.append(ser, ignore_index=True)\\n index = [index] if isinstance(index, str) else index\\n if not all([i in df.columns for i in index]):\\n _LOGGER.debug(\\n \\\"Could not set {} index. At least one of the \\\"\\n \\\"requested columns does not exist: {}\\\".\\n format(CFG_SAMPLE_TABLE_KEY, index))\\n return df\\n _LOGGER.debug(\\\"Setting sample_table index to: {}\\\".format(index))\\n df.set_index(keys=index, drop=False, inplace=True)\\n return df\",\n \"def produce_mirna_unique_counts_table(count_files, table_file):\\n merge_count_tables(count_files, table_file, \\\".dedup.uniq.bam.mirbase_counts.txt\\\")\",\n \"def prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id):\\n tn_pairs = pairs[(pairs['match_type'] == \\\"tumor_normal\\\") & (pairs['case_sample_tsca_id']==tsca_id)]\\n tp_pairs = pairs[(pairs['match_type'] == \\\"tumor_primary\\\") & (pairs['case_sample_tsca_id']==tsca_id)]\\n\\n tn_pairsets = pd.merge(tn_pairs, all_samples[['entity:sample_id', 'tsca_id']], \\\\\\n left_on='case_sample_id', right_on='entity:sample_id', \\\\\\n how='inner')[['tsca_id', 'entity:pair_id']] \\\\\\n .rename(columns={'tsca_id': 'membership:pair_set_id', 'entity:pair_id': 'pair_id'})\\n\\n tp_pairsets = pd.merge(tp_pairs, all_samples[['entity:sample_id', 'tsca_id']], \\\\\\n left_on='case_sample_id', right_on='entity:sample_id', \\\\\\n how='inner')[['tsca_id', 'entity:pair_id']] \\\\\\n .rename(columns={'tsca_id': 'membership:pair_set_id', 'entity:pair_id': 'pair_id'})\\n\\n # Append _TN/_TP to the end of the tumor-normal/tumor-primary pair set ids\\n tn_pairsets['membership:pair_set_id'] = tn_pairsets['membership:pair_set_id'].apply(lambda x: \\\"%s_TN\\\"%x)\\n tp_pairsets['membership:pair_set_id'] = tp_pairsets['membership:pair_set_id'].apply(lambda x: \\\"%s_TP\\\"%x)\\n \\n return (tn_pairsets, tp_pairsets)\",\n \"def getAllSampleFields(self, sample_id, study_id):\\n sample_tables = []\\n sample_tables.append('sample')\\n sample_tables.append('common_fields')\\n sample_tables.append('extra_sample_')\\n sample_tables.append('air')\\n sample_tables.append('other_environment')\\n sample_tables.append('sediment')\\n sample_tables.append('soil')\\n sample_tables.append('wastewater_sludge')\\n sample_tables.append('water')\\n sample_tables.append('host_assoc_vertibrate')\\n sample_tables.append('host_associated_plant')\\n sample_tables.append('human_associated')\\n sample_tables.append('host_sample')\\n sample_tables.append('host')\\n \\n filled_fields = {}\\n \\n con = self.getMetadataDatabaseConnection()\\n cursor = con.cursor()\\n \\n for table in sample_tables:\\n if 'extra_sample_' in table:\\n statement = 'select * from %s%s where sample_id = %s' % (table, study_id, sample_id)\\n elif table == 'host':\\n statement = 'select * from host h inner join host_sample hs on h.host_id = hs.host_id where sample_id = %s' % sample_id\\n else:\\n statement = 'select * from %s where sample_id = %s' % (table, sample_id)\\n \\n try:\\n cursor.execute(statement)\\n except Exception, e:\\n print str(e)\\n print 'Error running query:\\\\n'\\n print statement\\n print 'Running next query...\\\\n'\\n \\n continue\\n \\n data = cursor.fetchall()\\n\\n # Get the column names\\n col_names = []\\n for i in range(0, len(cursor.description)):\\n col_names.append(cursor.description[i][0])\\n \\n # Find the rows with data\\n for row in data:\\n i = 0\\n for field in row:\\n if field != None and field != '':\\n filled_fields[col_names[i]] = field\\n i += 1\\n \\n return filled_fields\",\n \"def summarize_sample_props(psd_list, sample_list):\\n prop_list = [psd.sample_props for psd in psd_list]\\n cols = ['amplicon median', 'mean size', 'lower size', 'upper size']\\n\\n return pd.DataFrame(prop_list, columns=cols, index=sample_list)\",\n \"def column_creator(path):\\n if not os.path.exists(path+'tables'):\\n os.makedirs(path+'tables')\\n\\n\\n # Sequences\\n if os.path.exists(path+'SEQ.txt'):\\n with open(os.path.join(path+'SEQ.txt')) as f1, open(os.path.join(path+'tables/sequences_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n # Modifications\\n if os.path.exists(path + 'modifications.txt'):\\n\\n with open(os.path.join(path+'modifications.txt')) as f1, open(os.path.join(path+'tables/modifications_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n # Spectrum identify:\\n if os.path.exists(path + 'spectrum_identify.txt'):\\n\\n with open(os.path.join(path+'spectrum_identify.txt')) as f1, open(path+'tables/spectrum_ide_table.txt', 'a') as f3:\\n lines1 = f1.read().count('\\\\n')\\n f3.write(\\\"%s\\\\n%s\\\\n\\\" % (\\\"Spectrum Number\\\",lines1))\\n f1.close()\\n f3.close()\\n\\n if os.path.exists(path + 'spectrum_unidentify.txt'):\\n with open(os.path.join(path+'spectrum_unidentify.txt')) as f2, open(path+'tables/spectrum_unide_table.txt', 'a') as f3:\\n lines2 = f2.read().count('\\\\n')\\n f3.write(\\\"%s\\\\n%s\\\\n\\\" % (\\\"Spectrum Number\\\",lines2))\\n f2.close()\\n f3.close()\\n\\n if os.path.exists(path+'taxonomy_identify.txt'):\\n # Taxonomy ide:\\n with open(os.path.join(path+'taxonomy_identify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_ide_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\\n\\n\\n if os.path.exists(path + 'taxonomy_unidentify.txt'):\\n # Taxonomy unide:\\n with open(os.path.join(path+'taxonomy_unidentify.txt')) as f1, open(os.path.join(path+'tables/taxonomy_unide_table.txt'), 'a') as f2:\\n c = Counter(x.strip() for x in f1)\\n for x in c:\\n f2.write(\\\"%s\\\\t%s\\\\n\\\" % (x, str(c[x])))\\n f1.close()\\n f2.close()\",\n \"def finalize(param, input_files='count_files'):\\n\\n import csv\\n HELPER.writeLog('Collecting featureCount raw counts ... \\\\n', param)\\n\\n #check which of these files are actually available\\n working_files = [iFile for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get feature ID using the first column in the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #For featureCount output, we want to skip the first two lines as they\\n #include the featureCount call and the headers which we don't want\\n next(csv_reader, None)\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n counts = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the expression values\\n header = 'ID'\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n csv_file = open(param[input_files][idx])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n\\n #Here too we want to skip the first two lines, before getting the counts\\n next(csv_reader, None)\\n next(csv_reader, None)\\n #Now start getting the counts (row[6]) and add in the ID (counts[i]) before it\\n idx = 0\\n for row in csv_reader:\\n counts[idx] = counts[idx]+'\\\\t'+row[6]\\n idx += 1\\n csv_file.close()\\n\\n #output the file\\n out_file = param['working_dir']+'deliverables/featureCount_raw_counts.txt'\\n out_handle = open(out_file, 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(counts)):\\n out_handle.write(counts[i]+'\\\\n')\\n out_handle.close()\\n\\n #output_phenotype_file\\n HELPER.writeLog('Writing phenotype data ... \\\\n', param)\\n MODULE_HELPER.output_sample_info(param)\\n\\n #write summary stats\\n #featureCount does this on its own so we can just fetch each summary file\\n #check which of these files are actually available\\n working_files = [iFile+'.summary' for iFile in param[input_files] if iFile != '']\\n\\n if len(working_files) > 0:\\n #get Status column from summary file using the first column in\\n #the first file in the list of working files\\n csv_file = open(working_files[0])\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Here, we want to skip the first line, as it simply points to the\\n #alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start by taking the list of identifier,\\n #which is the first column in the file\\n entry = [row[0] for row in csv_reader]\\n csv_file.close()\\n\\n #get all the summary stats for each sample\\n header = 'Status'\\n\\n for idx in range(param['num_samples']):\\n if param[input_files] != '':\\n header = header+'\\\\t'+param['stub'][idx]\\n #Fetch the corresponding sample's summary file\\n csv_file = open(param[input_files][idx]+'.summary')\\n csv_reader = csv.reader(csv_file, delimiter='\\\\t')\\n #Again, we want to skip the first line, as it simply points\\n #to the alignment file used when running featureCount\\n next(csv_reader, None)\\n\\n #Now start getting the stats (row[1]) and add in the Status\\n # (counts[i]) before it\\n i = 0\\n for row in csv_reader:\\n entry[i] = entry[i]+'\\\\t'+row[1]\\n i += 1\\n csv_file.close()\\n #output the file\\n out_handle = open(param['working_dir']+\\n 'results/featureCount/featureCount_stats.txt',\\n 'w')\\n out_handle.write(header+'\\\\n')\\n\\n for i in range(len(entry)):\\n out_handle.write(entry[i]+'\\\\n')\\n out_handle.close()\\n else:\\n print 'featureCount was not run successfully on any of the files..\\\\n'\",\n \"def test_toTable(self):\\r\\n # Empty results.\\r\\n out_f = StringIO()\\r\\n self.res1.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(),\\r\\n \\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\\\n\\\")\\r\\n out_f.close()\\r\\n\\r\\n # Results with multiple samples.\\r\\n exp = \\\"\\\"\\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\r\\nS1\\\\t5\\\\t21\\\\t1.5\\\\t2.5\\\\t3.5\\r\\nS1\\\\t10\\\\t20\\\\t2.5\\\\t2.5\\\\t3.5\\r\\nS1\\\\t20\\\\t30\\\\t3.5\\\\t2.5\\\\t3.5\\r\\nS2\\\\t1\\\\t3\\\\t0.4\\\\t2.5\\\\t3.5\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n self.res2.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\\r\\n\\r\\n # Custom header.\\r\\n exp = \\\"\\\"\\\"foo\\\\tbar\\\\tbaz\\\\tbazaar\\\\tbazaaar\\\\tbazaaaar\\r\\nS1\\\\t5\\\\t21\\\\t1.5\\\\t2.5\\\\t3.5\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n self.res1.addSample('S1', 42)\\r\\n self.res1.addSampleEstimate('S1', 5, 21, 1.5, 2.5, 3.5)\\r\\n self.res1.toTable(out_f,\\r\\n header=['foo', 'bar', 'baz', 'bazaar', 'bazaaar', 'bazaaaar'])\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\\r\\n\\r\\n # Invalid header.\\r\\n with self.assertRaises(ValueError):\\r\\n out_f = StringIO()\\r\\n self.res1.toTable(out_f, header=['foo'])\\r\\n\\r\\n # Cells with None as their value.\\r\\n exp = \\\"\\\"\\\"SampleID\\\\tSize\\\\tEstimate\\\\tStd Err\\\\tCI (lower)\\\\tCI (upper)\\r\\nS1\\\\t43\\\\tN/A\\\\tN/A\\\\tN/A\\\\tN/A\\r\\n\\\"\\\"\\\"\\r\\n out_f = StringIO()\\r\\n res = RichnessEstimatesResults()\\r\\n res.addSample('S1', 42)\\r\\n res.addSampleEstimate('S1', 43, None, None, None, None)\\r\\n res.toTable(out_f)\\r\\n self.assertEqual(out_f.getvalue(), exp)\\r\\n out_f.close()\",\n \"def split_otu_table_on_sample_metadata(otu_table_f, mapping_f, mapping_field):\\r\\n mapping_f = list(mapping_f)\\r\\n mapping_values = get_mapping_values(mapping_f, mapping_field)\\r\\n otu_table = parse_biom_table(otu_table_f)\\r\\n\\r\\n for v in mapping_values:\\r\\n v_fp_str = v.replace(' ', '_')\\r\\n sample_ids_to_keep = sample_ids_from_metadata_description(\\r\\n mapping_f, valid_states_str=\\\"%s:%s\\\" % (mapping_field, v))\\r\\n\\r\\n try:\\r\\n filtered_otu_table = otu_table.filterSamples(\\r\\n lambda values, id_, metadata: id_ in sample_ids_to_keep)\\r\\n except TableException:\\r\\n # all samples are filtered out, so no otu table to write\\r\\n continue\\r\\n yield v_fp_str, format_biom_table(filtered_otu_table)\",\n \"def _prepare_stats_table(self, pinfos):\\n\\n stats_tbl = OrderedDict()\\n stats_tbl[\\\"Title\\\"] = OrderedDict()\\n for res in self.rsts:\\n stats_tbl[res.reportid] = OrderedDict()\\n\\n for pinfo in pinfos:\\n for colname in (pinfo[\\\"colname\\\"], pinfo[\\\"xcolname\\\"]):\\n if colname in stats_tbl[\\\"Title\\\"]:\\n continue\\n\\n # Each column name is represented by a row in the statistics table. Fill the \\\"Title\\\"\\n # column.\\n title_dict = stats_tbl[\\\"Title\\\"][colname] = OrderedDict()\\n defs = self._refdefs.info[colname]\\n\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\":\\n # Convert nanoseconds to microseconds.\\n unit = \\\"us\\\"\\n else:\\n unit = defs.get(\\\"short_unit\\\", \\\"\\\")\\n\\n title_dict[\\\"colname\\\"] = colname\\n if unit:\\n title_dict[\\\"colname\\\"] += f\\\", {unit}\\\"\\n title_dict[\\\"coldescr\\\"] = defs[\\\"descr\\\"]\\n\\n title_dict[\\\"funcs\\\"] = OrderedDict()\\n for funcname in self._stats_funcs:\\n if funcname in self.rsts[0].cstats[colname]:\\n title_dict[\\\"funcs\\\"][funcname] = RORawResult.get_stat_func_descr(funcname)\\n\\n # Now fill the values for each result.\\n for res in self.rsts:\\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\\n res_dict[\\\"funcs\\\"] = OrderedDict()\\n\\n for funcname in title_dict[\\\"funcs\\\"]:\\n val = res.cstats[colname][funcname]\\n fmt = \\\"{}\\\"\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\" and \\\"index\\\" not in funcname:\\n val /= 1000\\n fmt = \\\"{:.2f}\\\"\\n if defs[\\\"type\\\"] == \\\"float\\\":\\n fmt = \\\"{:.2f}\\\"\\n\\n fdict = res_dict[\\\"funcs\\\"][funcname] = OrderedDict()\\n fdict[\\\"val\\\"] = fmt.format(val)\\n fdict[\\\"raw_val\\\"] = val\\n\\n if self._refres.reportid == res.reportid:\\n fdict[\\\"hovertext\\\"] = \\\"This is the reference result, other results \\\" \\\\\\n \\\"are compared to this one.\\\"\\n continue\\n\\n ref_fdict = stats_tbl[self._refres.reportid][colname][\\\"funcs\\\"][funcname]\\n change = val - ref_fdict[\\\"raw_val\\\"]\\n if ref_fdict[\\\"raw_val\\\"]:\\n percent = (change / ref_fdict[\\\"raw_val\\\"]) * 100\\n else:\\n percent = change\\n change = fmt.format(change) + unit\\n percent = \\\"{:.1f}%\\\".format(percent)\\n fdict[\\\"hovertext\\\"] = f\\\"Change: {change} ({percent})\\\"\\n\\n return stats_tbl\",\n \"def summarize(crosswalk, incidence_table, control_spec):\\n\\n include_integer_colums = not setting('NO_INTEGERIZATION_EVER', False)\\n\\n crosswalk_df = crosswalk.to_frame()\\n incidence_df = incidence_table.to_frame()\\n\\n geographies = setting('geographies')\\n seed_geography = setting('seed_geography')\\n meta_geography = geographies[0]\\n sub_geographies = geographies[geographies.index(seed_geography) + 1:]\\n hh_id_col = setting('household_id_col')\\n\\n meta_ids = crosswalk_df[meta_geography].unique()\\n for meta_id in meta_ids:\\n meta_summary_df = \\\\\\n meta_summary(incidence_df, control_spec, meta_geography,\\n meta_id, sub_geographies, hh_id_col)\\n out_table('%s_%s' % (meta_geography, meta_id), meta_summary_df)\\n\\n hh_weights_summary = pd.DataFrame(index=incidence_df.index)\\n\\n # add seed level summaries\\n seed_weights_df = get_weight_table(seed_geography)\\n hh_weights_summary['%s_balanced_weight' % seed_geography] = seed_weights_df['balanced_weight']\\n if include_integer_colums:\\n hh_weights_summary['%s_integer_weight' % seed_geography] = seed_weights_df['integer_weight']\\n\\n for geography in sub_geographies:\\n\\n weights_df = get_weight_table(geography)\\n\\n if weights_df is None:\\n continue\\n\\n if include_integer_colums:\\n hh_weight_cols = [hh_id_col, 'balanced_weight', 'integer_weight']\\n else:\\n hh_weight_cols = [hh_id_col, 'balanced_weight']\\n\\n hh_weights = weights_df[hh_weight_cols].groupby([hh_id_col]).sum()\\n hh_weights_summary['%s_balanced_weight' % geography] = hh_weights['balanced_weight']\\n if include_integer_colums:\\n hh_weights_summary['%s_integer_weight' % geography] = hh_weights['integer_weight']\\n\\n # aggregate to seed level\\n aggegrate_weights = weights_df.groupby([seed_geography, hh_id_col], as_index=False).sum()\\n aggegrate_weights.set_index(hh_id_col, inplace=True)\\n\\n if include_integer_colums:\\n aggegrate_weight_cols = [seed_geography, 'balanced_weight', 'integer_weight']\\n else:\\n aggegrate_weight_cols = [seed_geography, 'balanced_weight']\\n\\n aggegrate_weights = aggegrate_weights[aggegrate_weight_cols]\\n aggegrate_weights['sample_weight'] = incidence_df['sample_weight']\\n aggegrate_weights['%s_preliminary_balanced_weight' % seed_geography] = \\\\\\n seed_weights_df['preliminary_balanced_weight']\\n aggegrate_weights['%s_balanced_weight' % seed_geography] = \\\\\\n seed_weights_df['balanced_weight']\\n if include_integer_colums:\\n aggegrate_weights['%s_integer_weight' % seed_geography] = \\\\\\n seed_weights_df['integer_weight']\\n\\n out_table('%s_aggregate' % (geography,), aggegrate_weights)\\n\\n summary_col = 'integer_weight' if include_integer_colums else 'balanced_weight'\\n df = summarize_geography(seed_geography, summary_col, hh_id_col,\\n crosswalk_df, weights_df, incidence_df)\\n out_table('%s_%s' % (geography, seed_geography,), df)\\n\\n df = summarize_geography(geography, summary_col, hh_id_col,\\n crosswalk_df, weights_df, incidence_df)\\n out_table('%s' % (geography,), df)\\n\\n out_table('hh_weights', hh_weights_summary)\",\n \"def accumulate_entries_as_tables(entries):\\n name_table = {}\\n num_table = {}\\n for number, name in entries:\\n name_table[name] = number\\n num_table[number] = name\\n\\n return name_table, num_table\",\n \"def table_summary():\\n \\n t = dict()\\n t['name'] = get_names()\\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\\n N = len(t['name'])\\n \\n # host\\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\\\'itoi']\\n \\n # progenitor\\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\\n \\n # progenitor type\\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\\n \\n # metallicity\\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\\n \\n # associations\\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\\n \\n tout = Table(t)\\n tout.pprint()\\n tout.write('../data/stream_origin.fits', overwrite=True)\",\n \"def squash_records(key, sample_lookup, sample_ids, sample_indexes, normal_id):\\n chrom, pos, ref, alt = key\\n ids = []\\n quals = []\\n filters = []\\n infos = []\\n formats = []\\n gtypes = {sid: [] for sid in sample_ids}\\n caller_flags = {sid: collections.OrderedDict([(key, None)\\n for key in CALLER_CODES])\\n for sid in sample_ids}\\n for sample_id, caller_lookup in sample_lookup.iteritems():\\n caller_ids = []\\n caller_quals = []\\n caller_filters = []\\n caller_infos = []\\n for caller, records in caller_lookup.iteritems():\\n # Squash repeat calls on a sample (e.g. FreeBayes single/somatic)\\n caller_ids.append(consense_values([rec.ID for rec in records]))\\n caller_quals.append(consense_values([rec.QUAL for rec in records]))\\n caller_filters.extend(chain(*[rec.FILTER\\n for rec in records if rec.FILTER]))\\n caller_infos.append(consense_dicts([rec.INFO for rec in records]))\\n formats.extend(chain(*[rec.FORMAT.split(':') for rec in records]))\\n calls = [rec.genotype(sample_id) for rec in records]\\n gtypes[sample_id].extend(calls) # ENH: consense\\n caller_code = CALLER_NAMES[caller]\\n caller_val = max(call.is_variant for call in calls)\\n if caller_val is not None:\\n caller_val = int(caller_val)\\n caller_flags[sample_id][caller_code] = caller_val\\n ids.extend(caller_ids)\\n quals.extend(caller_quals)\\n filters.extend(caller_filters)\\n infos.extend(caller_infos)\\n formats = list(unique(formats))\\n out_record = Record(chrom, pos, consense_values(ids),\\n ref, [alt], consense_values(quals),\\n list(unique(filters)),\\n consense_dicts(infos),\\n \\\":\\\".join(formats + CALLER_CODES),\\n sample_indexes)\\n out_record.samples = [Call(out_record, sid,\\n make_call_data(formats, gtypes[sid],\\n caller_flags[sid]))\\n for sid in sample_ids]\\n\\n # Post-processing\\n # Fix AN and AC, which are defined specially in the VCF spec\\n all_alleles = list(chain(*[gt.gt_alleles for gt in out_record.samples\\n if gt.gt_nums]))\\n # AN (1): \\\"Total number of alleles in called genotypes\\\"\\n out_record.INFO[\\\"AN\\\"] = sum(a != '.' for a in all_alleles)\\n # AC (A): \\\"Total number of alternate alleles in called genotypes\\\"\\n # ENH: don't crash on multiallelics?\\n out_record.INFO[\\\"AC\\\"] = sum(sum(gta not in ('.', '0') for gta in gt.gt_alleles)\\n for gt in out_record.samples\\n if gt.gt_nums)\\n\\n # NB: Last 2 validation errors:\\n # CGP-1240 FORMAT tag [GL] expected different number of values (expected 2, found 3)\\n # chr2\\t209113113\\t.\\tG\\tA\\t2714.98\\tPASS\\tSOMATIC;NS=1;DP=653;DPB=716.75;AC=1;AN=3;AF=0.375;RO=590;AO=123;PRO=0.0;PAO=0.0;QR=21870;QA=4741;PQR=0.0;PQA=0.0;SRF=386;SRR=203;SAF=90;SAR=33;SRP=127.1;SAP=60.3689;AB=0.233397;ABP=328.364;RUN=1;RPP=24.6368;RPPR=13.5915625;RPL=44.0;RPR=79.0;EPP=4.44029;EPPR=8.549435;DPRA=1.56225;ODDS=272.57225;GTI=0;TYPE=snp;CIGAR=1X;NUMALT=1;MEANALT=2.0;LEN=1;MQM=60.0;MQMR=60.0177;PAIRED=0.95122;PAIREDR=0.958916;technology.ILLUMINA=1.0;MQ0=0;VT=SNP;BaseQRankSum=3.369;Dels=0.0;FS=2.338;HaplotypeScore=31.1133;MLEAC=1;MLEAF=0.5;MQ=60.02;MQRankSum=0.461;QD=7.05;ReadPosRankSum=-0.95;SOR=0.953;SF=0\\tGT:AD:BQ:DP:FA:SS:GMIMUT:GMIMAF:GMICOV:GQ:RO:QR:AO:QA:GL:PL:CallHC:CallUG:CallFB:CallPI:CallSID:CallMU:LR\\t0/1:387,117:38.0:506:0.239:2:117:23.0:504:120.456:401:14917:123:4741:-285.03,0.0,-1199.66:3594,0,14408:.:1:1:.:.:1:0.0723\\t0:200,0:.:227:0.0:0:0:0.0:226:141.912:252:9271:0:0:0.0,-66.9517,-832.671:.:.:.:0:.:.:0:0.0607\\n # chr5\\t176715884\\t.\\tT\\tG\\t2652.62\\tPASS\\tSOMATIC;NS=1;DP=718;DPB=806.5;AC=1;AN=3;AF=0.375;RO=684;AO=122;PRO=0.0;PAO=0.0;QR=26587;QA=4642;PQR=0.0;PQA=0.0;SRF=334;SRR=350;SAF=62;SAR=60;SRP=3.803265;SAP=3.0815;AB=0.225508;ABP=357.065;RUN=1;RPP=5.57335;RPPR=3.3676575;RPL=67.0;RPR=55.0;EPP=4.14943;EPPR=9.662735;DPRA=1.1461875;ODDS=318.98275;GTI=0;TYPE=snp;CIGAR=1X;NUMALT=1;MEANALT=1.0;LEN=1;MQM=60.0;MQMR=60.0;PAIRED=0.991803;PAIREDR=0.97694275;technology.ILLUMINA=1.0;MQ0=0;VT=SNP;BaseQRankSum=-2.525;Dels=0.0;FS=0.0;HaplotypeScore=15.7349;MLEAC=1;MLEAF=0.5;MQ=60.0;MQRankSum=2.816;QD=6.74;ReadPosRankSum=1.308;SOR=0.691;SF=0\\tGT:AD:BQ:DP:FA:SS:GMIMUT:GMIMAF:GMICOV:GQ:RO:QR:AO:QA:GL:PL:CallHC:CallUG:CallFB:CallPI:CallSID:CallMU:LR\\t0/1:408,121:37.0:529:0.22:2:121:23.0:529:117.5855:419:16330:122:4642:-270.938,0.0,-1322.0:3592,0,16251:.:1:1:.:.:1:0.022\\t0:284,0:.:319:0.0:0:0:0.0:319:136.171:354:13676:0:0:0.0,-95.905,-1230.06:.:.:.:0:.:.:0:0.0006\\n # * FreeBayes somatic produces the 3 GL values like this, w/e\\n # chr2\\t209113113\\t.\\tG\\tA\\t2714.98\\t.\\tAB=0.233397;ABP=328.364;AC=1;AF=0.25;AN=4;AO=123;CIGAR=1X;DP=780;DPB=780;DPRA=2.083;EPP=4.44029;EPPR=9.74419;GTI=0;LEN=1;MEANALT=2;MQM=60;MQMR=60.0153;NS=2;NUMALT=1;ODDS=155.143;PAIRED=0.95122;PAIREDR=0.960184;PAO=0;PQA=0;PQR=0;PRO=0;QA=4741;QR=24188;RO=653;RPL=44;RPP=24.6368;RPPR=15.384;RPR=79;RUN=1;SAF=90;SAP=60.3689;SAR=33;SRF=424;SRP=129.458;SRR=229;TYPE=snp;technology.ILLUMINA=1;SOMATIC\\tGT:GQ:DP:RO:QR:AO:QA:GL:GMIMUT:GMIMAF:GMICOV\\t0/1:141.912:527:401:14917:123:4741:-285.03,0,-1199.66:123:23:524\\t0/0:141.912:253:252:9271:0:0:0,-66.9517,-832.671:0:0:252\\n # chr5\\t176715884\\t.\\tT\\tG\\t2652.62\\t.\\tAB=0.225508;ABP=357.065;AC=1;AF=0.25;AN=4;AO=122;CIGAR=1X;DP=895;DPB=895;DPRA=1.52825;EPP=4.14943;EPPR=7.73248;GTI=0;LEN=1;MEANALT=1;MQM=60;MQMR=60;NS=2;NUMALT=1;ODDS=221.81;PAIRED=0.991803;PAIREDR=0.978008;PAO=0;PQA=0;PQR=0;PRO=0;QA=4642;QR=30006;RO=773;RPL=67;RPP=5.57335;RPPR=3.48505;RPR=55;RUN=1;SAF=62;SAP=3.0815;SAR=60;SRF=377;SRP=4.0244;SRR=396;TYPE=snp;technology.ILLUMINA=1;SOMATIC\\tGT:GQ:DP:RO:QR:AO:QA:GL:GMIMUT:GMIMAF:GMICOV\\t0/1:136.171:541:419:16330:122:4642:-270.938,0,-1322:122:23:541\\t0/0:136.171:354:354:13676:0:0:0,-95.905,-1230.06:0:0:354\\n\\n # Set SOMATIC flag (only add, don't remove existing flags)\\n global CNT_ATYPICAL\\n global CNT_TYPICAL\\n if normal_id:\\n normal_gt = out_record.genotype(normal_id)\\n if ((not normal_gt.is_variant) and\\n (not out_record.INFO.get(\\\"SOMATIC\\\")) and\\n any(gt.is_variant for gt in out_record.samples)):\\n out_record.INFO[\\\"SOMATIC\\\"] = True\\n if normal_gt.gt_nums is None:\\n CNT_TYPICAL += 1\\n else:\\n CNT_ATYPICAL += 1\\n\\n return out_record\",\n \"def summarize(data, verbal=False, using_files=True):\\n\\n if using_files:\\n for file_name in tqdm(data):\\n fill_table(pd.read_csv(file_name))\\n else:\\n for table in tqdm(data):\\n fill_table(table)\\n\\n for cluster in table_summary:\\n #total_genes = sum(table_summary[cluster][\\\"phylum\\\"].values) # number of genes\\n #total_genes = table_summary[cluster][\\\"N\\\"] # number of samples\\n total_genes = table_summary[cluster][\\\"eggNOG\\\"].eggNOG.sum() # number of genes in COGs with duplicates\\n \\n phylum_percent = table_summary[cluster][\\\"phylum\\\"].apply(lambda x: x/total_genes * 100)\\n phylum_percent.columns = [\\\"percent\\\"]\\n table_summary[cluster][\\\"phylum\\\"] = pd.concat([table_summary[cluster][\\\"phylum\\\"],phylum_percent],axis=1)\\n\\n #Read above for fix\\n genus_percent = table_summary[cluster][\\\"genus\\\"].apply(lambda x: x/total_genes * 100)\\n genus_percent.columns = [\\\"percent\\\"]\\n table_summary[cluster][\\\"genus\\\"] = pd.concat([table_summary[cluster][\\\"genus\\\"],genus_percent],axis=1)\\n\\n #read above for fix\\n cog_percent = table_summary[cluster][\\\"eggNOG\\\"].apply(lambda x: x/table_summary[cluster][\\\"gene_cog\\\"] * 100)\\n cog_percent.columns = [\\\"percent\\\"]\\n table_summary[cluster][\\\"eggNOG\\\"] = pd.concat([table_summary[cluster][\\\"eggNOG\\\"],cog_percent],axis=1)\\n\\n #Print the data\\n if verbal:\\n print \\\"Cluster %s:\\\\n\\\" % cluster\\n print \\\"Number of Samples: %d\\\\n\\\" % table_summary[cluster][\\\"N\\\"]\\n print \\\"Taxonomy:\\\"\\n print table_summary[cluster][\\\"phylum\\\"].sort(\\\"percent\\\", ascending=False)\\n print \\\"----------------------------------\\\"\\n print table_summary[cluster][\\\"genus\\\"].sort(\\\"percent\\\", ascending=False)\\n print \\\"-----------------------------------\\\"\\n print \\\"COGS:\\\"\\n print table_summary[cluster][\\\"eggNOG\\\"].sort(\\\"percent\\\", ascending=False)\\n print \\\"------------------------------------\\\"\\n print \\\"End Summary\\\"\",\n \"def table_stats(self, db, dest, kvargs, lines):\\n if 'table' in kvargs:\\n tables = [db.get_table(kvargs['table'])]\\n else:\\n tables = db.tables()\\n options = kvargs.get('options','')\\n done = False\\n for table in db.tables():\\n print(\\\"======================= {} =======================\\\".format(table.name))\\n if 'dump' in options:\\n print(\\\"schema dump:\\\")\\n table.dump()\\n print(\\\"\\\")\\n if 'head' in options:\\n print(\\\"First 5 records:\\\")\\n for source_record in db.read_records_as_dicts(tablename=table.name, limit=5):\\n print(source_record)\\n print(\\\"\\\")\\n # Compute single-variable stats on each of the variables\\n sw = stopwatch().start()\\n print(\\\"Computing statistics...\\\")\\n stats = {}\\n census_checksum = 0\\n \\n if self.spark_context:\\n print(\\\"Using spark to read {} ... assuming first line has headings\\\".format(table.filename))\\n sc = self.spark_context\\n data = sc.textFile(table.filename)\\n header = data.first() # extract the header\\n stats = data.filter(lambda row:row!=header).map(table.parse_line_to_dict).reduce(stats_reducer)\\n else:\\n try:\\n for source_record in db.read_records_as_dicts(tablename=table.name,limit=self.limit):\\n if source_record['RECTYPE']=='P':\\n census_checksum += census_person_polynominal(source_record)\\n stats = stats_reducer(source_record, stats)\\n except KeyboardInterrupt as e:\\n print(\\\"*** KeyboardInterrupt at count: {}\\\".format(stats[':count']))\\n done = True\\n if stats:\\n print(\\\"total records: {} speed: {:8.0f} records/sec\\\".format( stats[':count'], stats[':count']/sw.elapsed()))\\n tt = tytable.ttable()\\n tt.add_head(['variable','min','avg','max'])\\n tt.set_col_alignment(1,tytable.ttable.RIGHT)\\n tt.set_col_alignment(2,tytable.ttable.RIGHT)\\n tt.set_col_alignment(3,tytable.ttable.RIGHT)\\n for key in stats_variable_names(stats):\\n try:\\n tt.add_data([key, stats[key+\\\":min\\\"], stats[key+\\\":sum\\\"] / stats[':count'], stats[key+\\\":max\\\"]])\\n except TypeError:\\n tt.add_data([key, stats[key+\\\":min\\\"], \\\"\\\", stats[key+\\\":max\\\"]])\\n print(tt.typeset(mode=tytable.TEXT))\\n if census_checksum:\\n print(\\\"Census checksum: {}\\\".format(census_checksum))\\n print(\\\"\\\")\\n if done:\\n return True # had the keyboard abort\\n return True\",\n \"def print_all_counts_as_shasta_matrix(all_counts, max_count=50, pseudocount=1):\\n a_t_counts = all_counts[\\\"A\\\"] + all_counts[\\\"T\\\"]\\n g_c_counts = all_counts[\\\"G\\\"] + all_counts[\\\"C\\\"]\\n\\n total = 0\\n for i in range(max_count + 1):\\n total += max(pseudocount, a_t_counts[i])\\n\\n line = list()\\n for i in range(max_count + 1):\\n count = max(pseudocount, a_t_counts[i])\\n line.append(\\\"%.9f\\\" % math.log((count/total),10))\\n\\n print(\\\">AT prior\\\")\\n print(\\\",\\\".join(line))\\n print()\\n\\n total = 0\\n for i in range(max_count + 1):\\n total += max(pseudocount, g_c_counts[i])\\n\\n line = list()\\n for i in range(max_count + 1):\\n count = max(pseudocount, g_c_counts[i])\\n line.append(\\\"%.9f\\\" % math.log((count/total),10))\\n\\n print(\\\">GC prior\\\")\\n print(\\\",\\\".join(line))\\n print()\",\n \"def _create_metrics_table(font, format, base, create_glyph_metrics):\\n # we don't set PCF_COMPRESSED_METRICS\\n metrics = tuple(create_glyph_metrics(_g, base) for _g in font.glyphs)\\n table_bytes = (\\n bytes(le.uint32(format))\\n + bytes(base.int32(len(metrics)))\\n + b''.join(bytes(_t) for _t in metrics)\\n )\\n return table_bytes, format\",\n \"def annotate_tsv_freq(in_tsv_gz,annotation_tsv):\\n sys.stderr.write(\\\"Reading TSV file ...\\\\n\\\")\\n nicollo = pd.read_csv(BOLLI, sep=\\\"\\\\t\\\")\\n nicollo = nicollo.iloc[:,[1,2,4,5,23]]\\n nicollo_counts = nicollo.groupby(['CHR','START'])['MT'].count()\\n nol_var = nicollo.drop(['WT','MT'], axis = 1) \\n nol_var = nol_var.set_index(['CHR', 'START'])\\n\\n #nicollo_counts = nicollo.groupby([\\\"CHR\\\",\\\"START\\\",\\\"WT\\\",\\\"MT\\\"]).size().reset_index(name=\\\"count\\\")\\n #nicollo_counts = nicollo_counts[[\\\"CHR\\\", \\\"START\\\",\\\"count\\\"]].set_index(['CHR','START'])\\n\\n mmrf = pd.read_csv('/ifs/res/leukgen/home/yellapav/MMRF/MMRF_CoMMpass_IA9_All_Canonical_Variants.txt', sep=\\\"\\\\t\\\")\\n mmrf=mmrf.iloc[:,[0,1,2,4,5,19,23]]\\n mmrf=mmrf.drop_duplicates()\\n\\n mmrfM=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].median()\\n mmrfC=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].count()\\n mmrfQ25=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].quantile(q=0.25)\\n mmrfQ75=mmrf.groupby(['CHROM','POS'])['GEN[1].AR'].quantile(q=0.75)\\n \\n\\n #anno_tsv = pd.read_csv(annotation_tsv, comment='#',sep=\\\"\\\\t\\\")\\n anno_tsv = pd.read_csv(annotation_tsv, comment='#',sep=\\\"\\\\t\\\", low_memory=False)\\n #anno_tsv[anno_tsv['FILTER'] == \\\"PASS\\\"]\\n counts_tsv=anno_tsv.groupby([\\\"CHR\\\",\\\"START\\\",\\\"REF\\\",\\\"ALT\\\"]).size().reset_index(name=\\\"count\\\")\\n counts_tsv=counts_tsv[[\\\"CHR\\\", \\\"START\\\",\\\"count\\\"]].set_index(['CHR','START'])\\n counts_median=anno_tsv.groupby(['CHR','START'])['TARGET_VAF'].median()\\n\\n\\n\\n inFile = gzip.open(in_tsv_gz,'r')\\n \\n sys.stderr.write(\\\"Annotating ...\\\\n\\\")\\n for record in inFile:\\n record=record.decode(\\\"utf-8\\\")\\n record=record.rstrip()\\n recArr=record.split(\\\"\\\\t\\\")\\n \\n cl = [] \\n freq = [] \\n medVAF = [] \\n Q25 = [] \\n Q75 = [] \\n positions = [] \\n normal = \\\"0\\\" \\n normalVAF = \\\"0\\\" \\n bolli_cl = [] \\n bolli_freq = [] \\n bolli_positions = [] \\n bolli_anno = [] \\n flag = 0\\n bolli_flag = 0\\n if record.startswith(\\\"#\\\"):\\n continue\\n\\n if recArr[0] == \\\"ID_VARIANT\\\":\\n cl = \\\"MMRF_Class\\\"\\n freq = \\\"MMRF_Frequency\\\"\\n medVAF = \\\"MMRF_VAF\\\"\\n Q25 = \\\"MMRF_Q25\\\"\\n Q75 = \\\"MMRF_Q75\\\"\\n positions = \\\"MMRF_Positions\\\"\\n normal = \\\"Normals_Frequency\\\"\\n normalVAF = \\\"Normals_median_VAF\\\"\\n bolli_cl = \\\"Bolli_Class\\\"\\n bolli_freq = \\\"Bolli_Frequency\\\"\\n bolli_positions = \\\"Bolli_Positions\\\"\\n bolli_anno = \\\"Bolli_Annotation\\\"\\n record = [ record, cl, freq, medVAF, Q25, Q75, positions, bolli_cl, bolli_freq, bolli_anno, bolli_positions, normal, normalVAF ]\\n record = (\\\"\\\\t\\\".join(record))\\n print(record)\\n continue\\n\\n try:\\n chrom = str(recArr[3])\\n pos = int(recArr[4])\\n start = int(recArr[4]) - 9\\n end = int(recArr[4]) + 9\\n if (chrom, pos) in mmrfC.index:\\n cl = \\\"genomic_exact\\\"\\n freq = str(mmrfC.loc[(chrom,pos)])\\n medVAF = str(mmrfM.loc[(chrom,pos)])\\n Q25 = str(mmrfQ25.loc[(chrom,pos)])\\n Q75 = str(mmrfQ75.loc[(chrom,pos)])\\n positions = str(pos)\\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n flag = 1\\n if flag == 0:\\n mmrfCsub=mmrfC.loc[chrom]\\n if not mmrfCsub[(mmrfCsub.index >= start) & (mmrfCsub.index <= end)].empty:\\n for i in mmrfCsub[(mmrfCsub.index >= start) & (mmrfCsub.index <= end)].index.values:\\n cl = \\\"genomic_close\\\"\\n freq.append(str(mmrfC.loc[(chrom,i)]))\\n medVAF.append(str(mmrfM.loc[(chrom,i)]))\\n Q25.append(str(mmrfQ25.loc[(chrom,i)]))\\n Q75.append(str(mmrfQ75.loc[(chrom,i)]))\\n positions.append(str(i))\\n freq = (\\\":\\\".join(freq))\\n medVAF = (\\\":\\\".join(medVAF))\\n Q25 = (\\\":\\\".join(Q25))\\n Q75 = (\\\":\\\".join(Q75))\\n positions = (\\\":\\\".join(positions))\\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n else:\\n cl = \\\"NA\\\"\\n freq = \\\"NA\\\"\\n medVAF = \\\"NA\\\"\\n Q25 = \\\"NA\\\"\\n Q75 = \\\"NA\\\"\\n positions = \\\"NA\\\"\\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n\\n\\n except:\\n cl = \\\"NA\\\"\\n freq = \\\"NA\\\"\\n medVAF = \\\"NA\\\"\\n Q25 = \\\"NA\\\"\\n Q75 = \\\"NA\\\"\\n positions = \\\"NA\\\"\\n record = [ record, cl, freq, medVAF, Q25, Q75, positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n\\n\\n\\n try:\\n chrom = str(recArr[3])\\n pos = int(recArr[4])\\n start = int(recArr[4]) - 9\\n end = int(recArr[4]) + 9\\n if (chrom, pos) in nicollo_counts.index:\\n bolli_cl = \\\"genomic_exact\\\"\\n bolli_freq = str(nicollo_counts.loc[(chrom,pos)]) \\n bolli_positions = str(pos)\\n bolli_anno = str(nol_var.loc[chrom, pos]['Variant_class'].values[0])\\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n bolli_flag = 1\\n\\n\\n if bolli_flag == 0: \\n nicollo_counts_sub=nicollo_counts.loc[chrom]\\n if not nicollo_counts_sub[(nicollo_counts_sub.index >= start) & (nicollo_counts_sub.index <= end)].empty:\\n for i in nicollo_counts_sub[(nicollo_counts_sub.index >= start) & (nicollo_counts_sub.index <= end)].index.values:\\n #if not nicollo_counts_sub.ix[start:end].empty:\\n # for i in nicollo_counts_sub.ix[start:end].index.values:\\n #print(\\\"XXXXXXX\\\",i, nicollo_counts_sub.loc[(chrom,i)], start, end)\\n bolli_cl = \\\"genomic_close\\\"\\n bolli_freq.append(str(nicollo_counts.loc[(chrom,i)]))\\n bolli_anno.append(str(nol_var.loc[(chrom,i)]['Variant_class'].values[0]))\\n bolli_positions.append(str(i))\\n bolli_freq = (\\\":\\\".join(bolli_freq))\\n bolli_positions = (\\\":\\\".join(bolli_positions))\\n bolli_anno = (\\\":\\\".join(bolli_anno))\\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n else:\\n bolli_cl = \\\"NA\\\"\\n bolli_freq = \\\"NA\\\"\\n bolli_positions = \\\"NA\\\"\\n bolli_anno = \\\"NA\\\"\\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n\\n\\n except:\\n bolli_cl = \\\"NA\\\"\\n bolli_freq = \\\"NA\\\"\\n bolli_anno = \\\"NA\\\"\\n bolli_positions = \\\"NA\\\"\\n record = [ record, bolli_cl, bolli_freq, bolli_anno, bolli_positions ]\\n record = (\\\"\\\\t\\\".join(record))\\n\\n\\n normal = \\\"0\\\"\\n normalVAF = \\\"0\\\"\\n try:\\n chrom=str(recArr[3])\\n pos=int(recArr[4])\\n normal = counts_tsv.loc[(chrom,pos),\\\"count\\\"]\\n normal = normal.ix[0]\\n normal = str(normal)\\n\\n normalVAF = str(counts_median.loc[(chrom,pos)])\\n\\n record = [ record, normal, normalVAF ]\\n record = (\\\"\\\\t\\\".join(record))\\n print(record)\\n\\n except:\\n normal = \\\"0\\\"\\n normalVAF = \\\"0\\\"\\n record = [ record, str(normal), str(normalVAF) ]\\n record = (\\\"\\\\t\\\".join(record))\\n print(record)\",\n \"def format_used_features(model_dir):\\n feature_keys = {\\n \\\"indel_complexity\\\": \\\"ICP\\\",\\n \\\"dissimilarity\\\": \\\"DSM\\\",\\n \\\"indel_size\\\": \\\"ISZ\\\",\\n \\\"repeat\\\": \\\"REP\\\",\\n \\\"is_uniq_mapped\\\": \\\"UQM\\\",\\n \\\"is_near_boundary\\\": \\\"NEB\\\",\\n \\\"equivalence_exists\\\": \\\"EQX\\\",\\n \\\"is_bidirectional\\\": \\\"BID\\\",\\n \\\"is_multiallelic\\\": \\\"MTA\\\",\\n \\\"is_inframe\\\": \\\"FRM\\\",\\n \\\"is_splice\\\": \\\"SPL\\\",\\n \\\"is_truncating\\\": \\\"TRN\\\",\\n \\\"is_in_cdd\\\": \\\"CDD\\\",\\n \\\"indel_location\\\": \\\"LOC\\\",\\n \\\"is_nmd_insensitive\\\": \\\"NMD\\\",\\n \\\"ipg\\\": \\\"IPG\\\",\\n \\\"cds_length\\\": \\\"LEN\\\",\\n \\\"lc\\\": \\\"LC\\\",\\n \\\"local_lc\\\": \\\"LLC\\\",\\n \\\"gc\\\": \\\"GC\\\",\\n \\\"local_gc\\\": \\\"LGC\\\",\\n \\\"strength\\\": \\\"SG\\\",\\n \\\"local_strength\\\": \\\"LSG\\\",\\n \\\"is_ins\\\": \\\"INS\\\",\\n \\\"is_at_ins\\\": \\\"ATI\\\",\\n \\\"is_at_del\\\": \\\"ATD\\\",\\n \\\"is_gc_ins\\\": \\\"GCI\\\",\\n \\\"is_gc_del\\\": \\\"GCD\\\",\\n \\\"ref_count\\\": \\\"REFC\\\",\\n \\\"alt_count\\\": \\\"ALTC\\\",\\n \\\"is_on_db\\\": \\\"SNP\\\",\\n }\\n\\n feature_dict = make_feature_dict(model_dir)\\n\\n features_used_for_sni = [\\n feature_keys[f] for f in feature_dict[\\\"single_nucleotide_indels\\\"]\\n ]\\n features_used_for_mni = [\\n feature_keys[f] for f in feature_dict[\\\"multi_nucleotide_indels\\\"]\\n ]\\n features_used_for_sni.sort()\\n features_used_for_mni.sort()\\n\\n d = {}\\n d[\\\"##features_used_for_single_nucleotide_indels\\\"] = \\\";\\\".join(features_used_for_sni)\\n d[\\\"##features_used_for_multi_nucleotide_indels\\\"] = \\\";\\\".join(features_used_for_mni)\\n\\n return d\",\n \"def transpose_labels(df, sort=False):\\n label = 'gtruth'\\n temp_gtruth = df[df['label'] == 'gtruth']\\n for sample_method_to_test in ['lab', 'pier']:\\n temp_gtruth = temp_gtruth \\\\\\n .rename({\\n f'{sample_method_to_test} total abundance': f'{sample_method_to_test} {label} total abundance',\\n f'{sample_method_to_test} raw count': f'{sample_method_to_test} {label} raw count',\\n f'{sample_method_to_test} nrmlzd raw count': f'{sample_method_to_test} {label} nrmlzd raw count',\\n f'{sample_method_to_test} relative abundance': f'{sample_method_to_test} {label} relative abundance',\\n f'{sample_method_to_test} cells/mL': f'{sample_method_to_test} {label} cells/mL'},\\n axis=1)\\n\\n temp_gtruth = temp_gtruth.drop('label', axis=1)\\n\\n label = 'predicted'\\n temp_pred = df[df['label'] == 'prediction']\\n for sample_method_to_test in ['lab', 'pier']:\\n temp_pred = temp_pred \\\\\\n .rename({\\n f'{sample_method_to_test} total abundance': f'{sample_method_to_test} {label} total abundance',\\n f'{sample_method_to_test} raw count': f'{sample_method_to_test} {label} raw count',\\n f'{sample_method_to_test} nrmlzd raw count': f'{sample_method_to_test} {label} nrmlzd raw count',\\n f'{sample_method_to_test} relative abundance': f'{sample_method_to_test} {label} relative abundance',\\n f'{sample_method_to_test} cells/mL': f'{sample_method_to_test} {label} cells/mL'},\\n axis=1)\\n temp_pred = temp_pred.drop('label', axis=1)\\n\\n merge_col = ['class', 'datetime', 'sampling time']\\n micro_col = [col for col in df.columns if col.startswith('micro')]\\n if all(mc in df.columns for mc in micro_col):\\n merge_col += micro_col\\n\\n concat = temp_pred.merge(temp_gtruth, on=merge_col)\\n\\n # sort dataframe\\n if sort:\\n col = sorted(concat.columns)\\n concat = concat[col[:2] + [col[-1]] + col[2:-1]]\\n\\n return concat\",\n \"def convert_to_table_format(package):\\n tables = list()\\n for primary_table_id in package.primary_table_ids:\\n tables.append(StachExtensions.__generate_table(package, primary_table_id))\\n return tables\",\n \"def collate_samples(tool_name, fields, samples):\\n sample_dict = {}\\n for sample in samples:\\n sample_name = sample['name']\\n sample_dict[sample_name] = {}\\n tool_result = sample[tool_name]\\n for field in fields:\\n sample_dict[sample_name][field] = tool_result[field]\\n\\n return sample_dict\",\n \"def data_prep(df, params, if_resample=False):\\n\\n if if_resample and (params['balanced'] in ['Bootstrap', 'Handsample']):\\n if params['balanced'] == 'Bootstrap':\\n df = resample(df=df, balance=params['balanced'], nclass=params['classnum'])\\n elif params['balanced'] == 'Handsample':\\n df = resample(df=df, balance=params['balanced'], nclass=params['classnum'])\\n\\n if params['classnum'] == 6:\\n df.drop(df[df['label']=='PTSD'].index, axis=0, inplace=True)\\n\\n data = list(df.dialog)\\n label_encode = LabelEncoder()\\n output = dict()\\n output['data'] = data\\n output['encoded_label'] = label_encode.fit_transform(df.label)\\n output['binary_label'] = label_binarize(y=output['encoded_label'], classes=np.arange(params['classnum']))\\n return output, label_encode\",\n \"def get_results(self):\\n d = {}\\n# r = {}\\n for analyser in self.xml_tree.getroot():\\n for child in analyser:\\n if child.tag == 'all-records':\\n for record in child:\\n attributes = record.attrib\\n sample = attributes['sampleId']\\n assay_id = attributes['assayId']\\n genotype = attributes['genotypeId']\\n quality = attributes['description'].split('.')[0]\\n if re.match(r'rs\\\\d+', assay_id):\\n if sample in d:\\n if assay_id in d[sample]:\\n for allele in list(genotype):\\n if allele not in d[sample][assay_id]['genotype']:\\n d[sample][assay_id]['genotype'] += allele\\n if quality not in d[sample][assay_id]['quality']:\\n d[sample][assay_id]['quality'].append(quality)\\n else:\\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\\n else:\\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\\n# if sample in r:\\n# if assay_id in r[sample]:\\n# for allele in list(genotype):\\n# if allele not in r[sample][assay_id]:\\n# r[sample][assay_id] += allele\\n# else:\\n# r[sample][assay_id] = genotype\\n# else:\\n# r[sample] = {assay_id: genotype}\\n# for k, v in r.items():\\n# for k1, v1, in v.items():\\n# if len(v1) == 1:\\n# v[k1] += v1\\n# pprint.pprint(r)\\n# df = pd.DataFrame.from_dict(r).transpose()\\n# print(df)\\n# df.to_excel('snpcheck.xlsx')\\n return d\",\n \"def _convert_tracks_to_tabular_format(tracks: List[Track]) -> pd.DataFrame:\\n track_dfs: List[pd.DataFrame] = []\\n\\n for track in tracks:\\n track_df = pd.DataFrame()\\n\\n observed_states: List[bool] = []\\n timesteps: List[int] = []\\n positions_x: List[float] = []\\n positions_y: List[float] = []\\n headings: List[float] = []\\n velocities_x: List[float] = []\\n velocities_y: List[float] = []\\n\\n for object_state in track.object_states:\\n observed_states.append(object_state.observed)\\n timesteps.append(object_state.timestep)\\n positions_x.append(object_state.position[0])\\n positions_y.append(object_state.position[1])\\n headings.append(object_state.heading)\\n velocities_x.append(object_state.velocity[0])\\n velocities_y.append(object_state.velocity[1])\\n\\n track_df[\\\"observed\\\"] = observed_states\\n track_df[\\\"track_id\\\"] = track.track_id\\n track_df[\\\"object_type\\\"] = track.object_type.value\\n track_df[\\\"object_category\\\"] = track.category.value\\n track_df[\\\"timestep\\\"] = timesteps\\n track_df[\\\"position_x\\\"] = positions_x\\n track_df[\\\"position_y\\\"] = positions_y\\n track_df[\\\"heading\\\"] = headings\\n track_df[\\\"velocity_x\\\"] = velocities_x\\n track_df[\\\"velocity_y\\\"] = velocities_y\\n\\n track_dfs.append(track_df)\\n\\n return pd.concat(track_dfs, ignore_index=True)\",\n \"def encoding_labelcount(df, target=None):\\n if not target:\\n target = ['user_id', 'title']\\n\\n norm = round(\\n df.shape[0] / 10000) # normalize the count by /per 100000 entries\\n for col in target:\\n df[col + '_labelcount'] = df[col].map(df[col].value_counts()) / norm\\n df.drop([col], axis=1, inplace=True)\\n return None\",\n \"def _prepare_summary_table(self, raw_stats_paths, descr_paths):\\n\\n sum_tbl = OrderedDict()\\n sum_tbl[\\\"Title\\\"] = OrderedDict()\\n for res in self.rsts:\\n sum_tbl[res.reportid] = OrderedDict()\\n\\n # Add tool information.\\n key = \\\"tool_info\\\"\\n sum_tbl[\\\"Title\\\"][key] = \\\"Data collection tool\\\"\\n for res in self.rsts:\\n sum_tbl[res.reportid][key] = f\\\"{res.info['toolname'].capitalize()} version \\\" \\\\\\n f\\\"{res.info['toolver']}\\\"\\n\\n # Add datapoint counts.\\n key = \\\"datapoints_cnt\\\"\\n sum_tbl[\\\"Title\\\"][key] = \\\"Datapoints Count\\\"\\n for res in self.rsts:\\n sum_tbl[res.reportid][key] = len(res.df.index)\\n\\n # Add measurement resolution.\\n for res in self.rsts:\\n key = \\\"device_resolution\\\"\\n resolution = res.info.get(\\\"resolution\\\")\\n if resolution:\\n sum_tbl[\\\"Title\\\"][key] = \\\"Device Resolution\\\"\\n sum_tbl[res.reportid][key] = f\\\"{resolution}ns\\\"\\n\\n # Add links to the raw statistics directories.\\n if raw_stats_paths:\\n key = \\\"raw_stats\\\"\\n sum_tbl[\\\"Title\\\"][key] = \\\"Raw statistics\\\"\\n for res in self.rsts:\\n path = raw_stats_paths.get(res.reportid, \\\"Not available\\\")\\n sum_tbl[res.reportid][key] = path\\n\\n # Add links to the descriptions.\\n if descr_paths:\\n key = \\\"descr\\\"\\n sum_tbl[\\\"Title\\\"][key] = \\\"Test description\\\"\\n for res in self.rsts:\\n path = descr_paths.get(res.reportid, \\\"Not available\\\")\\n sum_tbl[res.reportid][key] = path\\n\\n return sum_tbl\",\n \"def tabulate(store: ObservationStore) -> \\\\\\n Generator[LabelledObservation, None, None]:\\n for k in store:\\n for ob in store[k]:\\n yield when(ob), measured(ob), k\",\n \"def flatten(counts):\\n single_names = {}\\n long_names = {}\\n for i in range(len(counts.items())):\\n if(len(counts.items()[i][0].split(\\\" \\\")) <= 1):\\n single_names[str(counts.items()[i][0])] = counts.items()[i][1]\\n else:\\n long_names[str(counts.items()[i][0])] = counts.items()[i][1]\\n \\n starter_list = [[[x[0]],x[1]] for x in long_names.items()]\\n for i in range(len(single_names.items())):\\n matched = False\\n for j in range(len(starter_list)):\\n if(single_names.items()[i][0] in starter_list[j][0][0].split(\\\" \\\")):\\n starter_list[j][0].append(single_names.items()[i][0])\\n starter_list[j][1] += single_names.items()[i][1]\\n matched = True\\n break\\n \\n if(matched == False):\\n starter_list.append([[single_names.items()[i][0]], single_names.items()[i][1]]) \\n \\n \\n return starter_list\",\n \"def test_format_unifrac_sample_mapping(self):\\r\\n a = [[1, 0, 0], [0, 2, 4], [7, 0, 9.0]]\\r\\n otu_ids = ['OTUa', 'OTUb', 'OTUc']\\r\\n sample_ids = ['Sa', 'Sb', 'Sc']\\r\\n result = format_unifrac_sample_mapping(sample_ids, otu_ids, a)\\r\\n self.assertEqual(\\r\\n result,\\r\\n ['OTUa\\\\tSa\\\\t1',\\r\\n 'OTUb\\\\tSb\\\\t2',\\r\\n 'OTUb\\\\tSc\\\\t4',\\r\\n 'OTUc\\\\tSa\\\\t7',\\r\\n 'OTUc\\\\tSc\\\\t9.0'])\",\n \"def prepare_participants_for_metadata_export(path_to_samples_info, tsca_id): \\n raw = pd.read_table(path_to_samples_info)\\n print( \\\"%d Participants in this batch\\\" % raw['individual_id'].unique().shape[0] )\\n # Data to upload\\n data = pd.DataFrame(raw.individual_id.drop_duplicates()).rename(columns={'individual_id':'entity:participant_id'})\\n return data\",\n \"def collate_fn_pad_text_only(batch):\\n output = {\\n 'id': [],\\n 'label': {\\n 'intent': [],\\n 'semiotic': [],\\n 'contextual': [],\\n },\\n 'caption': [],\\n }\\n\\n for sample in batch:\\n output['id'].append(sample['id'])\\n output['label']['intent'].append(sample['label']['intent'])\\n output['label']['semiotic'].append(sample['label']['semiotic'])\\n output['label']['contextual'].append(sample['label']['contextual'])\\n output['caption'].append(sample['caption'])\\n\\n output['label']['intent'] = torch.LongTensor(output['label']['intent'])\\n output['label']['semiotic'] = torch.LongTensor(output['label']['semiotic'])\\n output['label']['contextual'] = torch.LongTensor(output['label']['contextual'])\\n output['caption'] = torch.nn.utils.rnn.pad_sequence(output['caption']).t() # (batch_size, sequence_length)\\n return output\",\n \"def format_bgc_metadata(df,float_id):\\n mdf = df[bgc_metadata_columns]\\n bgc_metadata_dict = {}\\n for col in list(mdf):\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\\\"'\\\",'\\\"'))\\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \\n bgc_metadata_df = pd.DataFrame({\\\"float_id\\\": [float_id], \\\"Metadata_Dict\\\": [bgc_metadata_dict]})\\n return bgc_metadata_df\",\n \"def count_variants(vcf_list, sample_list):\\n\\n df_lst = []\\n\\n sample_vcf_dct = dict(zip(sample_list,vcf_list))\\n\\n for s in sample_vcf_dct.keys():\\n\\n vcf_in = sample_vcf_dct[s]\\n vcf = VariantFile(vcf_in)\\n\\n snv = 0\\n indel = 0\\n\\n for rec in vcf:\\n\\n ref_len = len(rec.ref)\\n\\n for a in rec.alts:\\n if len(a) > 1 or ref_len > 1:\\n indel +=1\\n else:\\n snv +=1\\n\\n df_lst.append([s,snv,indel])\\n\\n out_df = pd.DataFrame(df_lst, columns=['sample','snvs','indels'])\\n\\n return out_df\",\n \"def _compute_columns(log: EventLog, prefix_length: int, padding: bool) -> list:\\n return [\\\"trace_id\\\"] + \\\\\\n sorted(list({\\n event['concept:name']\\n for trace in log\\n for event in trace[:prefix_length]\\n })) + \\\\\\n ['0'] if padding else [] + \\\\\\n ['label']\",\n \"def feature_table(chr_id, source, orient, genes, transcripts, cds, exons, unk):\\n for gname, ginfo in genes.items():\\n line = [str(chr_id), \\n 'gbk_to_gff',\\n ginfo[3],\\n str(ginfo[0]),\\n str(ginfo[1]),\\n '.',\\n ginfo[2],\\n '.',\\n 'ID='+str(gname)+';Name='+str(gname)+';Note='+ginfo[-1]]\\n print '\\\\t'.join(line) \\n ## construct the transcript line is not defined in the original file \\n t_line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', ginfo[2], '.'] \\n\\n if not transcripts:\\n t_line.append('ID=Transcript:'+str(gname)+';Parent='+str(gname))\\n\\n if exons: ## get the entire transcript region from the defined feature\\n t_line[3] = str(exons[gname][0][0])\\n t_line[4] = str(exons[gname][0][-1])\\n elif cds:\\n t_line[3] = str(cds[gname][0][0])\\n t_line[4] = str(cds[gname][0][-1])\\n print '\\\\t'.join(t_line) \\n\\n if exons:\\n exon_line_print(t_line, exons[gname], 'Transcript:'+str(gname), 'exon')\\n\\n if cds:\\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'CDS')\\n if not exons:\\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'exon')\\n\\n else: ## transcript is defined \\n for idx in transcripts[gname]: \\n t_line[2] = idx[3]\\n t_line[3] = str(idx[0])\\n t_line[4] = str(idx[1])\\n t_line.append('ID='+str(idx[2])+';Parent='+str(gname))\\n print '\\\\t'.join(t_line) \\n \\n ## feature line print call \\n if exons:\\n exon_line_print(t_line, exons[gname], str(idx[2]), 'exon')\\n if cds:\\n exon_line_print(t_line, cds[gname], str(idx[2]), 'CDS')\\n if not exons:\\n exon_line_print(t_line, cds[gname], str(idx[2]), 'exon')\\n\\n if len(genes) == 0: ## feature entry with fragment information \\n \\n line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', orient, '.'] \\n fStart = fStop = None \\n\\n for eid, ex in cds.items(): \\n fStart = ex[0][0] \\n fStop = ex[0][-1]\\n\\n for eid, ex in exons.items(): \\n fStart = ex[0][0] \\n fStop = ex[0][-1]\\n\\n if fStart or fStart:\\n\\n line[2] = 'gene'\\n line[3] = str(fStart)\\n line[4] = str(fStop)\\n line.append('ID=Unknown_Gene_' + str(unk) + ';Name=Unknown_Gene_' + str(unk))\\n print \\\"\\\\t\\\".join(line)\\n\\n if not cds:\\n line[2] = 'transcript'\\n else:\\n line[2] = 'mRNA'\\n line[8] = 'ID=Unknown_Transcript_' + str(unk) + ';Parent=Unknown_Gene_' + str(unk)\\n print \\\"\\\\t\\\".join(line)\\n \\n if exons:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\\n \\n if cds:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'CDS')\\n if not exons:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\\n \\n unk +=1 \\n\\n return unk\",\n \"def usage_table_format(result):\\n table = []\\n for item in result:\\n row = OrderedDict()\\n row['Value'] = item['name']['localizedValue']\\n row['Usage'] = item['currentValue'] or \\\"0\\\"\\n row['Limit'] = item['limit'] or \\\"0\\\"\\n table.append(row)\\n return table\",\n \"def distinct_cols_multi_table(table_list=[], remove_columns=['PNR_LOC','PNR_CRT_DTE'], output_format='sql'):\\n alfa = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n \\n all_cols=set()\\n all_cols_str=''\\n \\n for i, table in enumerate(table_list):\\n cols=table.columns\\n\\n #find new cols (cols in B but not in A)\\n different_cols = set(table.columns)-all_cols\\n\\n #remove additional removal cols\\n different_cols = different_cols-set(remove_columns)\\n\\n #append table references\\n if output_format == 'sql':\\n ref_cols = ',{0}.'.format(alfa[i])+', {0}.'.join(different_cols).format(alfa[i])\\n #append to full column list & str\\n all_cols = all_cols.union(different_cols)\\n all_cols_str = all_cols_str +\\\"\\\"+ ref_cols\\n \\n elif output_format=='pyspark':\\n ref_cols = ','.join('{1}.{0}'.format(w,alfa[i]) for w in different_cols)\\n #append to full column list & str\\n all_cols = all_cols.union(different_cols)\\n all_cols_str = all_cols_str +\\\",\\\"+ ref_cols\\n else:\\n print(\\\"please select output_format = ['pyspark','sql']\\\")\\n \\n\\n # remove first comma\\n if output_format == 'sql':\\n return all_cols_str[1:]\\n elif output_format=='pyspark':\\n return all_cols_str[1:].split(',')\",\n \"def legacy_reporter(self):\\n logging.info('Creating database-friendly summary report')\\n header = '{}\\\\n'.format(','.join(self.legacy_headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SequencingDate\\n data += GenObject.returnattr(sample.run, 'Date')\\n # Analyst\\n data += GenObject.returnattr(sample.run, 'InvestigatorName')\\n # Legacy ConFindr clean/contaminated call\\n data += 'ND,'\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # NumClustersPF\\n data += GenObject.returnattr(sample.run, 'NumberofClustersPF')\\n # Percentage of reads mapping to PhiX control\\n data += GenObject.returnattr(sample.run, 'phix_aligned')\\n # Error rate calculated from PhiX control\\n data += GenObject.returnattr(sample.run, 'error_rate')\\n # LengthForwardRead\\n data += GenObject.returnattr(sample.run, 'forwardlength',\\n number=True)\\n # LengthReverseRead\\n data += GenObject.returnattr(sample.run, 'reverselength',\\n number=True)\\n # Real time strain\\n data += GenObject.returnattr(sample.run, 'Description')\\n # Flowcell\\n data += GenObject.returnattr(sample.run, 'flowcell')\\n # MachineName\\n data += GenObject.returnattr(sample.run, 'instrument')\\n # PipelineVersion\\n data += self.commit + ','\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # cgMLST\\n try:\\n if type(sample.cgmlst.sequencetype) is list:\\n if sample.cgmlst.sequencetype:\\n cgmlst_seq_type = ';'.join(sorted(sample.cgmlst.sequencetype)).rstrip(';') + ','\\n else:\\n cgmlst_seq_type = 'ND,'\\n else:\\n cgmlst_seq_type = GenObject.returnattr(sample.cgmlst, 'sequencetype')\\n # cgmlst_seq_type = cgmlst_seq_type if cgmlst_seq_type != 'ND,' else 'new,'\\n data += cgmlst_seq_type\\n except AttributeError:\\n data += 'ND,'\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'legacy_combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def generate_aggregates(self) -> None:\\n self.create_count_map()\\n self.create_total_count()\\n self.create_n_1_gram_map()\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def summary_scores(fold_scores_list): \\n d = {}\\n nulls = {}\\n for idx, score in enumerate(fold_scores_list):\\n d.update({f\\\"{idx+1}0 samples:\\\": score[0].T})\\n nulls.update({f\\\"{idx+1}0 samples:\\\": score[1].T})\\n \\n summary_scores = pd.concat(d.values(), axis=1, keys=d.keys()).T\\n summary_nulls = pd.concat(nulls.values(), axis=1, keys=nulls.keys()).T\\n return summary_scores, summary_nulls\",\n \"def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\\n datafile = open(output_table, \\\"w\\\")\\n datafile.write(\\n \\\"Feature_1\\\" + '\\\\t' + \\\"Feature_2\\\" + \\\"\\\\t\\\" + \\\"plus_plus\\\" + '\\\\t' + \\\"minus_minus\\\" + '\\\\t' + \\\"plus_minus\\\" + '\\\\t' + \\\"minus_plus\\\" + '\\\\t' + \\\"p_value_same_opposite\\\" + '\\\\t' + \\\"p-value_same_opposite_Bonferoni_corrected\\\" + '\\\\t' + \\\"Ratio_same_opposite\\\" + '\\\\t' + \\\"p_value_divergent_convergent\\\" + '\\\\t' + \\\"p_value_divergent_convergent Bonferoni corrected\\\" + '\\\\t' + \\\"Ratio divergent convergent\\\" + '\\\\n')\\n for i in range(len(NamesL_pairs)):\\n datafile.write(\\n NamesL_pairs[i][0] + '\\\\t' + NamesL_pairs[i][1] + '\\\\t' + str(p_pL[i]) + '\\\\t' + str(m_mL[i]) + '\\\\t' + str(\\n p_mL[i]) + '\\\\t' + str(m_pL[i]) + '\\\\t' + str(p_valsL[i]) + '\\\\t' + str(p_vals_BonferoniL[i]) + '\\\\t' + str(\\n RatiosL[i]) + '\\\\t' + str(p_valsL_divergent_convergent[i]) + '\\\\t' + str(\\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\\\t' + str(RatiosL_divergent_convergent[i]) + '\\\\n')\\n datafile.close()\\n return\",\n \"def collate_fn(data):\\n\\toutput = dict()\\n\\n\\tfor name in ['answer_ID','query_ID']:\\n\\t\\toutput[name] = [ _[name] for _ in data]\\n\\n\\n\\tfor name in ['query_len','answer_len']:\\n\\t\\ttemp = [ _[name] for _ in data]\\t \\n\\t\\toutput[name] = torch.stack(temp, dim=0) \\n\\t\\n\\t#deal with source and target\\n\\tfor name in ['answer','query']:\\n\\t\\tlength = output['{0}_len'.format(name)]\\n\\t\\tl = length.max().item()\\n\\n\\t\\tfor i in range(len(data)):\\n\\t\\t\\tif(l-length[i].item()>0):\\n\\t\\t\\t\\tdata[i][name] = torch.cat([data[i][name],torch.zeros(l-length[i].item(),dtype=torch.long)],dim=-1)\\n\\n\\t\\ttemp = [ _[name] for _ in data]\\n\\t\\t\\n\\t\\toutput[name] = torch.stack(temp, dim=0).long()\\n\\t\\t\\n\\n\\treturn output\",\n \"def reduce_data_to_necessary_columns(filtered_df):\\n hist_df = filtered_df[\\n [\\n \\\"UniqueName\\\",\\n \\\"Joins\\\",\\n \\\"Projection_Attributes\\\",\\n \\\"Selection_Attributes\\\",\\n \\\"GroupBy\\\",\\n \\\"OrderBy\\\",\\n \\\"Strings\\\",\\n \\\"Tables\\\",\\n ]\\n ].set_index(\\\"UniqueName\\\")\\n return hist_df\",\n \"def buildAdsTable_v1(output_file = None):\\r\\n ads_table = []\\r\\n text_props = ['readability_text', '_all']\\r\\n onto_props_with_mapping = {'phone':['telephone.name', 'telephone.name.raw'], 'email': ['email.name', 'email.name.raw'],\\r\\n 'posting_date':['inferlink_date', 'readability_date', 'high_recall_readability_date'],\\r\\n 'price':['price'], 'location':['addressLocality'],\\r\\n 'name':['name'],\\r\\n 'ethnicity':['ethnicity'],\\r\\n 'eye_color':['eyeColor'], 'title':['title'],\\r\\n 'hair_color':['hairColor'], 'nationality':['nationality'],\\r\\n 'business_type':['business_type'],\\r\\n 'business_name':['streetAddress'], 'services':['serviceType'],\\r\\n 'business': ['streetAddress'],\\r\\n 'physical_address': ['streetAddress'],\\r\\n 'gender':['gender'], 'top_level_domain':['top_level_domain'],\\r\\n 'obfuscation':['telephone.isObfuscated', 'email.isObfuscated'],\\r\\n 'age':['age'], 'hyperlink:':['relatedLink'], 'drug_use':['drug_use'],\\r\\n 'review_site':['review_site'], 'review_id':['review_id'],\\r\\n 'number_of_individuals':['name_count'],\\r\\n 'ad': ['identifier'],\\r\\n 'multiple_phone': ['telephone_count'],\\r\\n 'cluster': ['seller.uri'],\\r\\n 'seed': ['seller.telephone.name', 'seller.email.name']\\r\\n }\\r\\n non_readability_props = ['number_of_individuals', 'ad', 'multiple_phone', 'cluster', 'phone', 'posting_date', 'email']\\r\\n onto_props_without_mapping = ['image_with_email', 'image_with_phone']\\r\\n for property, value_list in onto_props_with_mapping.iteritems():\\r\\n dict = {}\\r\\n dict['onto_prop'] = property\\r\\n mappings = []\\r\\n tmp = {}\\r\\n for v in value_list:\\r\\n if property == 'phone' or v == 'seller.telephone.name':\\r\\n tmp[v] = 'build_phone_match_clause'\\r\\n tmp['_all'] = 'build_phone_match_clause'\\r\\n tmp['url'] = 'build_phone_regexp_clause'\\r\\n elif v == 'email.name':\\r\\n tmp[v] = 'build_email_match_clause'\\r\\n tmp['_all'] = 'build_match_phrase_clause'\\r\\n elif property == 'ad':\\r\\n tmp[v] = 'build_term_clause'\\r\\n elif '_count' in v:\\r\\n tmp[v] = 'build_count_match_clause'\\r\\n elif property == 'gender':\\r\\n tmp[v] = 'build_gender_match_clause'\\r\\n elif property == 'posting_date':\\r\\n tmp[v] = 'build_match_phrase_clause'\\r\\n else:\\r\\n tmp[v] = 'build_match_clause'\\r\\n if property not in non_readability_props:\\r\\n for v in text_props: # will overwrite for seller.telephone.name\\r\\n tmp[v] = 'build_match_clause_inner'\\r\\n mappings.append(tmp)\\r\\n dict['mappings'] = mappings\\r\\n ads_table.append(dict)\\r\\n\\r\\n for property in onto_props_without_mapping:\\r\\n dict = {}\\r\\n dict['onto_prop'] = property\\r\\n mappings = []\\r\\n tmp = {}\\r\\n for v in text_props:\\r\\n tmp[v] = 'build_match_clause_inner'\\r\\n mappings.append(tmp)\\r\\n dict['mappings'] = mappings\\r\\n ads_table.append(dict)\\r\\n\\r\\n if output_file:\\r\\n file = codecs.open(output_file, 'w', 'utf-8')\\r\\n for entry in ads_table:\\r\\n json.dump(entry, file)\\r\\n file.write('\\\\n')\\r\\n file.close()\",\n \"def get_summary_of_records(self):\\n ids = self.get_saleman_ids()\\n table = [\\n [\\\"Seller name\\\",\\\"Number of sales\\\",\\\"Total Value ($)\\\"]\\n ]\\n for id in ids:\\n table_id = [self.get_seller_name(id),self.get_number_of_sales(id),\\n self.get_total_of_saleman(id)]\\n table.append(table_id)\\n data_table = AsciiTable(table)\\n print(data_table.table)\",\n \"def convert_data(test_data,params,list_dict,rational_present=True,topk=2):\\n \\\"\\\"\\\"input: params -- input dict, list_dict -- previous predictions containing rationals\\n rational_present -- whether to keep rational only or remove them only\\n topk -- how many words to select\\\"\\\"\\\"\\n \\n temp_dict={}\\n for ele in list_dict:\\n temp_dict[ele['annotation_id']]=ele['rationales'][0]['soft_rationale_predictions']\\n \\n test_data_modified=[]\\n \\n for index,row in tqdm(test_data.iterrows(),total=len(test_data)):\\n try:\\n attention=temp_dict[row['Post_id']]\\n except KeyError:\\n continue\\n topk_indices = sorted(range(len(attention)), key=lambda i: attention[i])[-topk:]\\n new_text =[]\\n new_attention =[]\\n if(rational_present):\\n if(params['bert_tokens']):\\n new_attention =[0]\\n new_text = [101]\\n for i in range(len(row['Text'])):\\n if(i in topk_indices):\\n new_text.append(row['Text'][i])\\n new_attention.append(row['Attention'][i])\\n if(params['bert_tokens']):\\n new_attention.append(0)\\n new_text.append(102)\\n else:\\n for i in range(len(row['Text'])):\\n if(i not in topk_indices):\\n new_text.append(row['Text'][i])\\n new_attention.append(row['Attention'][i])\\n test_data_modified.append([row['Post_id'],new_text,new_attention,row['Label']])\\n\\n df=pd.DataFrame(test_data_modified,columns=test_data.columns)\\n return df\",\n \"def parse_trflp(lines):\\r\\n\\r\\n sample_ids = []\\r\\n otu_ids = []\\r\\n data = []\\r\\n non_alphanum_mask = re.compile('[^\\\\w|^\\\\t]')\\r\\n # not sure why the above regex doesn't cover the following regex...\\r\\n dash_space_mask = re.compile('[_ -]')\\r\\n\\r\\n for i, line in enumerate(lines):\\r\\n elements = line.strip('\\\\n').split('\\\\t')\\r\\n\\r\\n # special handling for the first line only\\r\\n if i == 0:\\r\\n # validating if the file has a header\\r\\n if elements[0] == '':\\r\\n for otu_id in elements[1:]:\\r\\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\\r\\n continue\\r\\n else:\\r\\n for j, otu_id in enumerate(elements[1:]):\\r\\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n\\r\\n # handling of all other lines\\r\\n current_row = []\\r\\n\\r\\n # converting each value in the row to int\\r\\n for count in elements[1:]:\\r\\n try:\\r\\n current_row.append(int(round(float(count), 0)))\\r\\n except ValueError:\\r\\n current_row.append(0)\\r\\n\\r\\n # if the sum of all the values is equial to 0 ignore line\\r\\n if sum(current_row) == 0:\\r\\n continue\\r\\n\\r\\n # adding sample header to list\\r\\n sample_ids.append(non_alphanum_mask.sub('.',\\r\\n dash_space_mask.sub('.', elements[0])))\\r\\n\\r\\n # validating the size of the headers to add missing columns\\r\\n # this is only valid when there is no header\\r\\n if len(current_row) > len(otu_ids):\\r\\n # modify header data\\r\\n extra_cols = []\\r\\n for j in range(len(otu_ids), len(current_row)):\\r\\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n # modify data\\r\\n for j in range(len(data)):\\r\\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\\r\\n\\r\\n otu_ids.extend(extra_cols)\\r\\n elif len(current_row) < len(otu_ids):\\r\\n # modify data\\r\\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\\r\\n\\r\\n data.append(current_row)\\r\\n\\r\\n return sample_ids, otu_ids, asarray(data).transpose()\",\n \"def reformat_analogs(analogs, prefix = 'modified-analogfile.tsv'):\\n # import numpy as np\\n import pandas as pd\\n\\n try:\\n num_cols = 3 # dateAnlg, Dis, Corr\\n\\n # Create dataframe and read in output csv file of analogs process\\n dfS = pd.DataFrame()\\n dfS = pd.read_csv(analogs, delimiter=r\\\"\\\\s+\\\", index_col=0)\\n\\n # Find number of analogues\\n num_analogues = (dfS.shape[1]) / 3\\n # LOGGER.debug('num_analogues: %s', num_analogues)\\n\\n # Define temporary df\\n df_anlg = dfS.iloc[:, 0:num_analogues] # store only anlg dates\\n df_dis = dfS.iloc[:, num_analogues:2 * num_analogues] # store only dis\\n df_corr = dfS.iloc[:, 2 * num_analogues:3 *\\n num_analogues] # store only corr\\n\\n # remove index name before stacking\\n df_anlg.index.name = \\\"\\\"\\n df_dis.index.name = \\\"\\\"\\n df_corr.index.name = \\\"\\\"\\n\\n dateStack = df_anlg.stack()\\n disStack = df_dis.stack().abs() # raw values < 0 so take abs\\n corrStack = df_corr.stack()\\n\\n # Create df of correct dimensions (n x num_cols) using dfS\\n df_all = dfS.iloc[:, 0:num_cols] # NB data are placeholders\\n # Rename cols\\n df_all.columns = ['dateAnlg', 'Dis', 'Corr']\\n # Replicate each row 20 times (for dcjs format)\\n df_all = df_all.loc[np.repeat(df_all.index.values, num_analogues)]\\n # Replace data placeholders with correct values\\n df_all['dateAnlg'] = list(dateStack)\\n df_all['Dis'] = list(disStack)\\n df_all['Corr'] = list(corrStack)\\n # Name index col\\n df_all.index.name = 'dateRef'\\n\\n # save to tsv file\\n analogs_mod = prefix\\n df_all.to_csv(analogs_mod, sep='\\\\t')\\n LOGGER.info('successfully reformatted analog file')\\n except Exception:\\n msg = 'failed to reformat analog file'\\n LOGGER.exception(msg)\\n raise Exception(msg)\\n return analogs_mod\",\n \"def data_in(input_filename, sample_id):\\n \\n print('Processing sample {}'.format(sample_id))\\n # Encoding set to latin1 due to presence of degree symbol\\n # newlines in CC2 logger details fields will cause issues\\n header_rows = 30\\n df_param = pd.read_table(input_filename, \\n nrows=header_rows, \\n encoding=\\\"latin1\\\", \\n header=None)\\n df_val = pd.read_table(input_filename, skiprows=header_rows)\\n df_param_indexed = df_param.set_index(0)\\n# redundant due to parsing sample id from filename\\n# sample_id = df_param_indexed.loc['Sample ID', 1]\\n \\n d1 = {1: pd.Series(['', ''], index=['Sample ID', 'Label'])}\\n df_val_params = pd.DataFrame(d1)\\n df_val_params.loc['Sample ID', 1] = sample_id\\n df_val_params.loc['Label', 1] = sample_id\\n\\n return df_param_indexed, df_val, df_val_params\",\n \"def prepare_data(base_df, n_seconds_min=3):\\n # Remove too short samples\\n source_df = base_df.loc[base_df['seconds'] > n_seconds_min]\\n # Group speakers duplicated by id\\n df = source_df.loc[:, ['speaker_id', 'dataset_name']]\\n df = df.set_index('speaker_id')\\n df = df.loc[~df.index.duplicated(keep='first')]\\n dfGrouped = source_df.groupby(['speaker_id']).sum()\\n # Count the number of samples for each speaker\\n dfCountAudio = source_df.groupby(['speaker_id']).count().filepath\\n speakers_duration = dfGrouped.join(df)\\n speakers_duration = speakers_duration.join(dfCountAudio)\\n speakers_duration = speakers_duration.rename(columns={'filepath': 'n_samples'})\\n return source_df, speakers_duration\",\n \"def aggregate_counts(counts_files,\\n output_file = '/dev/stdout', \\n sample_names=None, \\n sep=\\\"\\\\t\\\", \\n header=0, \\n comment=\\\"#\\\"):\\n sample_pos = -1\\n \\n if sample_names is not None:\\n if len(sample_names)!=len(counts_files):\\n logging.error(\\\"Number of sample names is not the same length as \\\",\\n \\\"the number of counts files.\\\")\\n raise RuntimeError(\\\"\\\")\\n\\n # read in all counts files\\n counts_df = [pd.read_csv(file, sep=sep, header=header, comment=comment) \\n for file in counts_files]\\n\\n # overwrite the sample names if provided\\n if sample_names:\\n for i, df in enumerate(counts_df):\\n #counts_df[i].columns[sample_pos] = sample_names[i]\\n new_columns = df.columns.tolist()\\n new_columns[sample_pos] = sample_names[i]\\n df.columns = new_columns\\n else:\\n # check sample names are all different\\n sample_names_from_files = [df.columns[sample_pos] for df in counts_df]\\n\\n if (len(set(sample_names_from_files))<len(counts_files)):\\n logging.error(\\\"Sample names in counts files are not unique. Fix \\\",\\n \\\"or provide a list of sample names to use.\\\")\\n raise RunTimeError()\\n\\n\\n # merge the dataframes together\\n merged_df = reduce(lambda x, y: pd.merge(x,y), counts_df)\\n\\n\\n # output\\n if header is not None:\\n out_header = True\\n\\n with open(output_file, 'w') as handle:\\n merged_df.to_csv(handle, sep=sep, header=out_header, index=False)\\n\\n return 0\",\n \"def make_case_metadata_table_query():\\n case_aliquot_table_id, study_table_id, case_external_mapping_table_id = get_mapping_table_ids()\\n\\n file_count_table_name = construct_table_name(API_PARAMS, prefix=BQ_PARAMS['FILE_COUNT_TABLE'])\\n file_count_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{file_count_table_name}\\\"\\n\\n return f\\\"\\\"\\\"\\n WITH case_project_file_count AS (\\n SELECT DISTINCT c.case_id, c.case_submitter_id, c.project_submitter_id, \\n s.project_name, s.program_name, s.project_id,\\n fc.file_id_count as file_count\\n FROM `{case_external_mapping_table_id}` c\\n JOIN `{study_table_id}` s\\n ON c.project_id = s.project_id\\n JOIN `{file_count_table_id}` fc\\n ON c.case_id = fc.case_id\\n )\\n\\n SELECT ca.case_id, ca.case_submitter_id, ca.primary_site, ca.disease_type,\\n cp.project_name, cp.program_name, cp.project_id, cp.file_count\\n FROM `{case_aliquot_table_id}` AS ca\\n JOIN case_project_file_count AS cp\\n ON cp.case_id = ca.case_id\\n \\\"\\\"\\\"\",\n \"def generate_table(self, outtablename,\\n cols=['A', 'B', 'AB'],\\n generateTable=True):\\n if generateTable:\\n new_indices = ['time (s)', 'mean counts']\\n for idx in self.data[cols].describe().index[2:]:\\n new_indices.append(idx)\\n outTable = self.data[cols].describe()\\\\\\n .set_index(pd.Index(new_indices))\\n outTable.to_latex(\\n self.tables_dir + outtablename + \\\".tex\\\", float_format=\\\"%d\\\")\\n print(\\\"Outtable: \\\", outTable)\",\n \"def prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id):\\n # export raw data\\n data = pd.read_table(path_to_samples_info)\\n # Rename columns to match firecloud requirements\\n data = data.rename(columns={'sample_id':'entity:sample_id', 'individual_id':'participant_id'})\\n # Locations of BAM files in google bucket\\n path_in_bucket_full = \\\"gs://%s/seq_data/%s\\\" % (google_bucket_id, tsca_id)\\n # Extract bam filename\\n data['bam_filename'] = data.apply(lambda row: row['clean_bam_file_capture'].split('/')[-1], axis=1)\\n # Create bai filename (change extension on .bam file)\\n data['bai_filename'] = data.apply(lambda row: \\\"%s%s\\\" %(row['bam_filename'][:-3], 'bai'), axis=1)\\n # Change BAM path from xchip to Google cloud\\n data['clean_bam_file_capture'] = \\\\\\n data.apply( lambda row: \\\"%s/%s/%s\\\" \\\\\\n %(path_in_bucket_full, row['external_id_validation'], row['bam_filename']), axis=1)\\n # Add location of .bai file \\n data['clean_bai_file_capture'] = \\\\\\n data.apply( lambda row: \\\"%s/%s/%s\\\" \\\\\\n %(path_in_bucket_full, row['external_id_validation'], row['bai_filename']), axis=1)\\n # Add TSCA ID\\n data['tsca_id'] = tsca_id\\n\\n # Reorganize columns (entity:sample_id at the beginning)\\n columns = ['entity:sample_id'] + [col for col in data if col != 'entity:sample_id']\\n data = data[columns]\\n return data\",\n \"def transform(filtered_list):\\n out_put = {}\\n out_list = []\\n # loop to get the required columns, random ordered\\n for item in filtered_list:\\n for val in item._fields:\\n if val in type_dict:\\n out_put[val] = type_dict.get(val)(getattr(item, val))\\n out_list.append(out_put)\\n out_put = {}\\n\\n # loop to the ordered columns data as per output\\n all_rows = []\\n for item in out_list:\\n tmp_row = []\\n for key in type_dict.keys():\\n out_put[key] = item[key]\\n tmp_row.append(item[key])\\n all_rows.append(tmp_row)\\n\\n col_row = [col.replace('_', '-') for col in type_dict.keys()]\\n all_rows.insert(0, col_row)\\n return all_rows\",\n \"def expand_affiliation(df):\\n from pandas import Series\\n res = df[[\\\"source_id\\\", \\\"author_ids\\\", \\\"afid\\\"]].copy()\\n res['afid'] = res[\\\"afid\\\"].str.split(';')\\n res = (res[\\\"afid\\\"].apply(Series)\\n .merge(res, right_index=True, left_index=True)\\n .drop([\\\"afid\\\"], axis=1)\\n .melt(id_vars=['source_id', 'author_ids'], value_name=\\\"afid\\\")\\n .drop(\\\"variable\\\", axis=1)\\n .dropna())\\n res['afid'] = res['afid'].astype(float)\\n return res\",\n \"def generateAggregatedCsvData(self, context, obj, entities):\\n return sum([long(e.prop1.replace('-', ''), 16) for e in entities])\",\n \"def _merge(dts):\\n df = pd.concat(dts)\\n\\n ma = df.pivot(index='isomir', columns='sample', values='counts')\\n ma_mirna = ma\\n ma = ma.fillna(0)\\n ma_mirna['mirna'] = [m.split(\\\":\\\")[0] for m in ma.index.values]\\n ma_mirna = ma_mirna.groupby(['mirna']).sum()\\n ma_mirna = ma_mirna.fillna(0)\\n return ma, ma_mirna\",\n \"def synthesize_from_table(df, geo_df, targets):\\n # replace NaNs with None\\n targets = targets.where(targets.notnull(), None)\\n\\n new_df = df\\n\\n for _, row in targets.iterrows():\\n new_df = synthesize_one(\\n df=new_df,\\n target=row['target_value'],\\n alloc_id=row['geo_id_col'],\\n geo_df=geo_df,\\n geo_col=row['capacity_col'],\\n constraint_expr=row['capacity_expr'],\\n filters=row['filters'],\\n count=row['count'],\\n stuff=row['stuff'])\\n\\n return new_df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5996871","0.5834174","0.56089836","0.5596423","0.5548671","0.554599","0.5534935","0.548897","0.5448419","0.5394029","0.537485","0.5362557","0.5339601","0.53235745","0.52827585","0.5272961","0.5233933","0.522556","0.52244353","0.5220203","0.5207982","0.5201744","0.5201744","0.5178545","0.51763254","0.5175003","0.5160906","0.51356107","0.5124829","0.51237833","0.51128864","0.5099795","0.50921696","0.50755244","0.506032","0.5059872","0.5051263","0.5048963","0.50474966","0.5036502","0.50320566","0.5028542","0.50064075","0.5003736","0.49984905","0.498694","0.49821743","0.4978145","0.49739316","0.49608037","0.49577188","0.4951192","0.4948323","0.49438456","0.49356404","0.49352336","0.49234664","0.49216846","0.4920609","0.49190423","0.4911877","0.4910904","0.49105003","0.49079865","0.48951948","0.48951668","0.48911723","0.48845515","0.48809332","0.4872626","0.48617828","0.48614907","0.4860857","0.48594263","0.48559833","0.48516807","0.48515028","0.4847017","0.48436916","0.48436916","0.4839328","0.48309308","0.48244923","0.48214206","0.48182416","0.48149982","0.48138413","0.48123798","0.48114213","0.48028022","0.47925562","0.4789258","0.47866267","0.47784677","0.47730643","0.47654584","0.4759721","0.47539186","0.47537482","0.47493646"],"string":"[\n \"0.5996871\",\n \"0.5834174\",\n \"0.56089836\",\n \"0.5596423\",\n \"0.5548671\",\n \"0.554599\",\n \"0.5534935\",\n \"0.548897\",\n \"0.5448419\",\n \"0.5394029\",\n \"0.537485\",\n \"0.5362557\",\n \"0.5339601\",\n \"0.53235745\",\n \"0.52827585\",\n \"0.5272961\",\n \"0.5233933\",\n \"0.522556\",\n \"0.52244353\",\n \"0.5220203\",\n \"0.5207982\",\n \"0.5201744\",\n \"0.5201744\",\n \"0.5178545\",\n \"0.51763254\",\n \"0.5175003\",\n \"0.5160906\",\n \"0.51356107\",\n \"0.5124829\",\n \"0.51237833\",\n \"0.51128864\",\n \"0.5099795\",\n \"0.50921696\",\n \"0.50755244\",\n \"0.506032\",\n \"0.5059872\",\n \"0.5051263\",\n \"0.5048963\",\n \"0.50474966\",\n \"0.5036502\",\n \"0.50320566\",\n \"0.5028542\",\n \"0.50064075\",\n \"0.5003736\",\n \"0.49984905\",\n \"0.498694\",\n \"0.49821743\",\n \"0.4978145\",\n \"0.49739316\",\n \"0.49608037\",\n \"0.49577188\",\n \"0.4951192\",\n \"0.4948323\",\n \"0.49438456\",\n \"0.49356404\",\n \"0.49352336\",\n \"0.49234664\",\n \"0.49216846\",\n \"0.4920609\",\n \"0.49190423\",\n \"0.4911877\",\n \"0.4910904\",\n \"0.49105003\",\n \"0.49079865\",\n \"0.48951948\",\n \"0.48951668\",\n \"0.48911723\",\n \"0.48845515\",\n \"0.48809332\",\n \"0.4872626\",\n \"0.48617828\",\n \"0.48614907\",\n \"0.4860857\",\n \"0.48594263\",\n \"0.48559833\",\n \"0.48516807\",\n \"0.48515028\",\n \"0.4847017\",\n \"0.48436916\",\n \"0.48436916\",\n \"0.4839328\",\n \"0.48309308\",\n \"0.48244923\",\n \"0.48214206\",\n \"0.48182416\",\n \"0.48149982\",\n \"0.48138413\",\n \"0.48123798\",\n \"0.48114213\",\n \"0.48028022\",\n \"0.47925562\",\n \"0.4789258\",\n \"0.47866267\",\n \"0.47784677\",\n \"0.47730643\",\n \"0.47654584\",\n \"0.4759721\",\n \"0.47539186\",\n \"0.47537482\",\n \"0.47493646\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63312393"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7493,"cells":{"query":{"kind":"string","value":"reformat count or FPKM tables into a dictionary for rapid alignment of attr table with tracking_id"},"document":{"kind":"string","value":"def alignAndReformat_countFPKMattrTables(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n #reformat\n countTable_flat = self.reformat_countTable(\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,);\n fpkmTable_flat = self.reformat_fpkmTable(\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,);\n attrTable_dict = self.reformat_attrTable();\n #align\n countAndFpkmTable_aligned = [];\n for row in countTable_flat[:]:\n row.update(attrTable_dict[row['tracking_id']]);\n countAndFpkmTable_aligned.append(row);\n for row in fpkmTable_flat[:]:\n row.update(attrTable_dict[row['tracking_id']]);\n countAndFpkmTable_aligned.append(row);\n return countAndFpkmTable_aligned;"},"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 reformat_attrTable(\n self):\n #format into a dictionary of rows for quick aligning with the tracking_id\n if self.attrTable: attrTable = self.attrTable[:];\n else: attrTable = [];\n\n attrTable_dict = {};\n for row in attrTable:\n attrTable_dict[row['tracking_id']] = row;\n return attrTable_dict;","def reformat_countOrFPKMTable(\n self,\n countOrFPKMTable_I=None,\n analysis_id_I=None,\n sna2experimentID_I=None,\n sna2sns_I=None,\n count_or_FPKM = 'count'):\n #format into a dictionary of rows for quick aligning with the tracking_id\n countOrFPKMTable_flat = [];\n for row in countOrFPKMTable_I:\n for k,v in row.items():\n if k=='tracking_id':continue;\n tmp = {};\n tmp['analysis_id'] = analysis_id_I;\n tmp['tracking_id'] = row['tracking_id'];\n\n sample_name_lst = k.split('_');\n sample_name_base = '_'.join(sample_name_lst[:-1]);\n sample_name_rep = eval(sample_name_lst[-1]);\n if sna2experimentID_I: \n experiment_id = sna2experimentID_I[sample_name_base];\n else:\n experiment_id=None;\n tmp['experiment_id'] = experiment_id;\n if sna2sns_I: \n sample_name = sna2sns_I[sample_name_base][sample_name_rep];\n else:\n sample_name=k;\n tmp['sample_name'] = sample_name;\n\n tmp['value'] = v;\n tmp['value_units'] = count_or_FPKM;\n tmp['used_'] = True;\n tmp['comment_'] = None;\n countOrFPKMTable_flat.append(tmp);\n return countOrFPKMTable_flat;","def format_countOrFPKMTable(self,fpkmTracking_I):\n for fpkmTracking in fpkmTracking_I:\n for k,v in fpkmTracking.items():\n if k=='tracking_id' and type(fpkmTracking['tracking_id'])==type('string'):\n pass;\n elif k!='tracking_id' and type(fpkmTracking[k])==type('string'):\n fpkmTracking[k] = eval(v);\n return fpkmTracking_I;","def tables() -> dict[str, str]:\n return {\n \"land_use\": \"zone_id\",\n \"tours\": \"tour_id\",\n \"trips\": \"trip_id\",\n \"persons\": \"person_id\",\n \"households\": \"household_id\",\n }","def reformat_countTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.countTable: countTable = self.countTable[:];\n else: countTable = [];\n\n countTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=countTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'count');\n return countTable_flat;","def normalize_counts_to_tpm(counts_dir, gff_dir, out_dir, feat='CDS', id_sym='gene_id='):\n count_files = [os.path.join(counts_dir, f) for f in os.listdir(counts_dir)]\n all_tpms = {}\n for cf in count_files:\n if \"_counts\" not in cf:\n continue\n tpm = normalize_counts_to_tpm_one_file(cf, gff_dir, feat, id_sym)\n #out_file = \"{}_tpm.csv\".format(os.path.basename(cf))\n #out_path = os.path.join(out_dir, out_file)\n #with open(out_path, \"w\") as fo:\n # for gene, t in tpm.items():\n # fo.write(\"{},{}\\n\".format(gene, t))\n prefix = os.path.basename(cf).split(\"_trimmed\")[0] # this would be specific to my naming convention\n all_tpms[prefix] = tpm\n return all_tpms","def avail_table_to_dict(avail_data):\n avail_target = avail_data[\"TARGETID\"]\n avail_loc = avail_data[\"LOCATION\"]\n avail = dict()\n for lid, tgid in zip(avail_loc, avail_target):\n if lid in avail:\n avail[lid].append(tgid)\n else:\n avail[lid] = list([tgid])\n avail = {f: np.array(av) for f, av in avail.items()}\n return avail","def accumulate_entries_as_tables(entries):\n name_table = {}\n num_table = {}\n for number, name in entries:\n name_table[name] = number\n num_table[number] = name\n\n return name_table, num_table","def make_category_tables(category_table):\n category2label = {}\n label2category = {}\n for item in category_table.itertuples():\n category_id = item[0]\n label_id = item[4]\n category2label[category_id] = label_id\n label2category[label_id] = category_id\n return category2label, label2category","def create_count_map(self) -> Dict[int, int]:\n res: Dict[int, int] = {}\n for sequence_data in self.model.values():\n sequence_data: NGramsSequence = cast(NGramsSequence, sequence_data)\n for count in sequence_data.next_count.values():\n count: int = cast(int, count)\n if count not in res:\n res[count] = 0\n res[count] += 1\n self.count_map = res\n logger.success('created count map')\n return res","def tallying_genes():\n #Creating a tallying Mechanism of genes with multiple sequences in file and\n # an output file for future alignment of sequences \n blast_hit_results = open('blast_hits_report.txt', 'r')\n gene_dict={}\n\n for line in blast_hit_results:\n data = line.split(\"\\t\")\n \n if line.startswith('SeqID'):\n continue\n else:\n #Test to see if organism in dictionary\n verdict = gene_dict.get(data[6])\n \n if str(verdict) == \"None\":\n #creating new entry\n key = data[6]\n seq_info=str(data[0])+\"|\"+str(data[1])\n counter = 1\n #Value[Counts, Trimmed_Length, Blast Length, Blast_Score, Blast_Percent_Identity]\n value=[data[5], counter, [seq_info]]\n gene_dict.update({key:value})\n else:\n #Fills dictionary based on organism name\n seq_info=str(data[0])+\"|\"+str(data[1])\n gene_dict[data[6]][1]+=1\n gene_dict[data[6]][2].append(seq_info)\n blast_hit_results.close()\n return(gene_dict)","def basic_table_details():\n tbl: pa.table = pa.Table.from_pylist([\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\n ])\n\n results = {\n 'column_names': tbl.column_names,\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\n 'nbytes': tbl.nbytes,\n 'num_columns': tbl.num_columns,\n 'num_rows': tbl.num_rows,\n 'schema': tbl.schema,\n 'shape': tbl.shape,\n }\n\n print(results)","def create_dicts_w_info(df,\n table_visit_diff_string,\n bad_records_string='num_bad_records'):\n\n hpos = df['src_hpo_id'].unique().tolist()\n\n site_dictionaries = {}\n\n for hpo in hpos:\n sample_df = df.loc[df['src_hpo_id'] == hpo]\n\n sample_df.loc[\"Total\"] = sample_df.sum(numeric_only=True)\n\n hpo_dict = sample_df.loc[\"Total\"].to_dict()\n\n site_dictionaries[hpo] = hpo_dict\n\n tot = 0\n\n num_bad_records = {}\n\n for hpo, info in site_dictionaries.items():\n num_bad_records[hpo] = info[bad_records_string]\n\n table_visit_diff_dict = {}\n tot_rec, tot_diff = 0, 0\n\n for hpo, info in site_dictionaries.items():\n bad_records = info[bad_records_string]\n difference = info[table_visit_diff_string]\n\n tot_rec += bad_records\n tot_diff += difference\n\n avg = round(difference / bad_records, 2)\n\n table_visit_diff_dict[hpo] = avg\n\n table_visit_diff_dict['Total'] = round(tot_diff / tot_rec, 2)\n\n return num_bad_records, table_visit_diff_dict","def reformat_fpkmTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.fpkmTable: fpkmTable = self.fpkmTable[:];\n else: fpkmTable = [];\n\n fpkmTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=fpkmTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'fpkm');\n return fpkmTable_flat;","def table_fields() -> Dict[str, TableFieldDetails]:\n return {\n \"REPEATS\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=0,\n bit_high=15,\n description=\"Number of times the line will repeat\",\n labels=None,\n ),\n \"TRIGGER\": TableFieldDetails(\n subtype=\"enum\",\n bit_low=16,\n bit_high=19,\n description=\"The trigger condition to start the phases\",\n labels=[\n \"Immediate\",\n \"BITA=0\",\n \"BITA=1\",\n \"BITB=0\",\n \"BITB=1\",\n \"BITC=0\",\n \"BITC=1\",\n \"POSA>=POSITION\",\n \"POSA<=POSITION\",\n \"POSB>=POSITION\",\n \"POSB<=POSITION\",\n \"POSC>=POSITION\",\n \"POSC<=POSITION\",\n ],\n ),\n \"POSITION\": TableFieldDetails(\n subtype=\"int\",\n bit_low=32,\n bit_high=63,\n description=\"The position that can be used in trigger condition\",\n labels=None,\n ),\n \"TIME1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=64,\n bit_high=95,\n description=\"The time the optional phase 1 should take\",\n labels=None,\n ),\n \"OUTA1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=20,\n bit_high=20,\n description=\"Output A value during phase 1\",\n labels=None,\n ),\n \"OUTB1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=21,\n bit_high=21,\n description=\"Output B value during phase 1\",\n labels=None,\n ),\n \"OUTC1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=22,\n bit_high=22,\n description=\"Output C value during phase 1\",\n labels=None,\n ),\n \"OUTD1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=23,\n bit_high=23,\n description=\"Output D value during phase 1\",\n labels=None,\n ),\n \"OUTE1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=24,\n bit_high=24,\n description=\"Output E value during phase 1\",\n labels=None,\n ),\n \"OUTF1\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=25,\n bit_high=25,\n description=\"Output F value during phase 1\",\n labels=None,\n ),\n \"TIME2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=96,\n bit_high=127,\n description=\"The time the mandatory phase 2 should take\",\n labels=None,\n ),\n \"OUTA2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=26,\n bit_high=26,\n description=\"Output A value during phase 2\",\n labels=None,\n ),\n \"OUTB2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=27,\n bit_high=27,\n description=\"Output B value during phase 2\",\n labels=None,\n ),\n \"OUTC2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=28,\n bit_high=28,\n description=\"Output C value during phase 2\",\n labels=None,\n ),\n \"OUTD2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=29,\n bit_high=29,\n description=\"Output D value during phase 2\",\n labels=None,\n ),\n \"OUTE2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=30,\n bit_high=30,\n description=\"Output E value during phase 2\",\n labels=None,\n ),\n \"OUTF2\": TableFieldDetails(\n subtype=\"uint\",\n bit_low=31,\n bit_high=31,\n description=\"Output F value during phase 2\",\n labels=None,\n ),\n }","def infodump(self):\n idtable = dict.fromkeys(KEYS_IDSTABLE)\n rttable = dict.fromkeys(KEYS_RTTABLE)\n #Construct Common table, IMDb table, Review table\n commontable,imdbtable,reviewtable = self.dump_imdb()\n #Contruct IDTable\n idtable[\"imdb_id\"] = imdbtable[\"imdb_id\"]\n if commontable[\"kind\"] == \"movie\":\n #Contruct RT table\n rttable = self.dump_rt()\n idtable[\"rt_id\"] = rttable[\"rt_id\"]\n return idtable,commontable,imdbtable,rttable,reviewtable","def get_tally_map (self):\n if self._tally_map is None:\n self._tally_map = {}\n # for field in self.header[-3:]:\n for column in self.get_award_id_columns():\n self._tally_map[column] = self.tally_awards_in_column(column)\n return self._tally_map","def get_indices_convert_dict(fn):\n pdb_inp = pdb.input(file_name=fn)\n pdb_hierarchy = pdb_inp.construct_hierarchy()\n \n newids = OrderedDict((atom.id_str(), idx) for (idx, atom) in enumerate(pdb_hierarchy.atoms()))\n oldids= OrderedDict((atom.id_str(), idx) for (idx, atom) in enumerate(pdb_inp.atoms()))\n \n return {'p2a': np.array([newids[atom.id_str()] for atom in pdb_inp.atoms()]),\n 'a2p': np.array([oldids[atom.id_str()] for atom in pdb_hierarchy.atoms()])}","def pre_build_idf_table(self):\r\n doc_per_word_table = dict() # in how many documents does a word occur\r\n \r\n for doc in self.documents:\r\n # converting list to set will delete any duplicate words\r\n doc = self.preprocess_document(doc)\r\n doc_words = set(self.word_tokenize_preprocessed(doc))\r\n\r\n for word in doc_words:\r\n if word in doc_per_word_table:\r\n doc_per_word_table[word] += 1\r\n else:\r\n doc_per_word_table[word] = 1\r\n\r\n total_documents = len(self.documents)\r\n idf_table = dict()\r\n\r\n for word in doc_per_word_table:\r\n idf_table[word] = math.log2(total_documents / float(doc_per_word_table[word]))\r\n\r\n return idf_table","def gfa_table_to_dict(gfa_data):\n gfa_target = gfa_data[\"TARGETID\"]\n gfa_loc = gfa_data[\"GFA_LOC\"]\n gfa_gmag = gfa_data[\"GAIA_PHOT_G_MEAN_MAG\"]\n gfa = dict()\n for lid, tgid, mag in zip(gfa_loc, gfa_target,gfa_gmag):\n print(zip(gfa_loc, gfa_target,gfa_gmag))\n if lid in gfa:\n gfa[lid].append(mag)\n else:\n gfa[lid] = list([mag])\n gfa = {f: np.array(av) for f, av in gfa.items()}\n return gfa","def _convert_table_to_dict(self, data_table):\n column_names = ['star_name', 'distance', 'brightness', 'luminosity']\n stars = {}\n for line in data_table:\n stars[line[0]] = {column_names[i] : line[i] for i in range(1, len(column_names))}\n return stars","def group(df):\r\n t = len(df.columns.values)\r\n ans = {}\r\n names = df.columns.values\r\n for index,row in df.iterrows(): \r\n for j in range(t-1): \r\n index = str(j) \r\n llaveCol = str(row[j])\r\n llaveClase = str(row[t-1]) \r\n if index not in ans:\r\n ans[index] = {}\r\n if llaveCol not in ans[index]:\r\n ans[index][llaveCol] = {} \r\n if llaveClase not in ans[index][llaveCol]:\r\n ans[index][llaveCol][llaveClase] = 0 \r\n ans[index][llaveCol][llaveClase]+=1 \r\n return ans","def get_columns_dict(table, replace):\n # 0 is name, 1 is id\n if type(table.index) == pd.MultiIndex:\n colcount = 1 + len(table.index[0])\n else:\n colcount = 2\n cols = {}\n for c in table.columns:\n c_repres = \",\".join(c)\n if \"Filtergroups\" not in c:\n cols[colcount] = replace_in_str(str(c_repres), replace)\n colcount = colcount + 1\n return cols","def aggregate_data(mts, feature, target):\r\n set_dict = dict()\r\n set_dict['mt'] = mts\r\n set_dict['feature'] = feature\r\n set_dict['target'] = target\r\n \r\n return set_dict","def build_rule_count_dict(counts_iterator):\n rule_count_dict = {}\n for l in counts_iterator:\n if l[1] != 'NONTERMINAL':\n x = l[2]\n y = l[1] == 'UNARYRULE' and l[3] or l[3] + ' ' + l[4]\n # if l[1] == 'UNARYRULE':\n # y = l[3]\n # else: # l[1] == 'BINARYRULE'\n # y = l[3] + ' ' + l[4]\n if x not in rule_count_dict:\n rule_count_dict[x] = {}\n rule_count_dict[x][y] = int(l[0])\n return rule_count_dict","def dictagnum(kind, fname):\n\n with open(fname, 'r') as g:\n g.next()\n g.next()\n m = g.next()\n startdict = agline(m)\n genold = startdict['gen']\n\n f = open(fname)\n f.next()\n f.next()\n d = {}\n y = '1'\n nb = []\n for l in f:\n adict = agline(l)\n ks = kind + 's'\n gen = adict['gen']\n well = adict['well']\n\n if adict['gen'] not in d:\n d[gen] = []\n \n if gen != genold:\n d[genold].append(sum(nb))\n nb = []\n else: \n if adict['well'] != y:\n d[gen].append(sum(nb))\n nb = []\n \n if kind == 'charge':\n if adict[ks] == 'x':\n nb.append(0)\n elif int(adict[ks]) >= 0 and (adict['charget'] == 'c' or \n adict['charget'] == 'o'):\n nb.append(1)\n elif adict[ks] == '-':\n pass\n #print('nb', nb)\n\n if kind == 'escd' or kind == 'escm':\n if adict[ks] == '':\n nb.append(0)\n elif int(adict[ks]) >= 0:\n nb.append(1)\n elif adict[ks] == '-':\n pass\n\n y = adict['well']\n genold = adict['gen']\n \n d[gen].append(sum(nb))\n \n return(d)","def decode_fusionTable_schema(tables: list) -> dict:\n def _map_col(col: dict) -> dict:\n col_schema = {k: col.get(k, '') for k in ('name', 'columnId', 'description')}\n if col['name'] != 'Comment':\n col_schema['mode'] = 'REQUIRED'\n # We can actually use int/float now!\n col_type: str = col['type']\n if col_type == 'NUMBER':\n col_type = 'INT64' if col['formatPattern'] == 'NUMBER_INTEGER' else 'FLOAT64'\n col_schema['type'] = col_type\n return col_schema\n\n def _map_table(table: dict) -> dict:\n table_schema = {k: table.get(k, '') for k in ('name', 'tableId', 'description')}\n table_schema['columns'] = list(map(_map_col, table['columns']))\n return table_schema\n\n return dict((s.get('tableId'), s) for s in map(_map_table, tables))","def format_bgc_metadata(df,float_id):\n mdf = df[bgc_metadata_columns]\n bgc_metadata_dict = {}\n for col in list(mdf):\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\"'\",'\"'))\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \n bgc_metadata_df = pd.DataFrame({\"float_id\": [float_id], \"Metadata_Dict\": [bgc_metadata_dict]})\n return bgc_metadata_df","def get_mapping_table_ids():\n dev_meta_dataset = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}\"\n prod_meta_dataset = f\"{BQ_PARAMS['PROD_PROJECT']}.{BQ_PARAMS['PUBLIC_META_DATASET']}\"\n\n file_metadata_table_name = f\"{BQ_PARAMS['FILE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\"\n file_metadata_table_id = f\"{prod_meta_dataset}.{file_metadata_table_name}\"\n\n case_metadata_table_name = f\"{BQ_PARAMS['CASE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\"\n case_metadata_table_id = f\"{prod_meta_dataset}.{case_metadata_table_name}\"\n\n file_assoc_table_name = f\"{BQ_PARAMS['FILE_ASSOC_MAPPING_TABLE']}_{API_PARAMS['RELEASE']}\"\n file_assoc_table_id = f\"{prod_meta_dataset}.{file_assoc_table_name}\"\n\n aliquot_table_name = f\"{BQ_PARAMS['ALIQUOT_TO_CASE_TABLE']}_{API_PARAMS['RELEASE']}\"\n aliquot_table_id = f\"{prod_meta_dataset}.{aliquot_table_name}\"\n\n # todo switch to published table\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, 'allPrograms'))\n study_table_id = f\"{dev_meta_dataset}.{study_table_name}\"\n\n return file_metadata_table_id, case_metadata_table_id, file_assoc_table_id, aliquot_table_id, study_table_id","def _create_tf_table(self, words) -> dict:\r\n\r\n freqTable = dict()\r\n tfTable = dict()\r\n\r\n totalWords = len(words)\r\n for word in words:\r\n if word in freqTable:\r\n freqTable[word] += 1\r\n else:\r\n freqTable[word] = 1\r\n \r\n uniqueWords = set(words)\r\n for word in uniqueWords:\r\n tfTable[word] = freqTable[word] / float(totalWords)\r\n\r\n return tfTable","def get_tabledata_for(doc):\n plomino_table = doc.getForm().id\n data = dict(ADDITIONAL_VALUES)\n map = FIELDS_MAP[plomino_table]\n for key in doc.getItems():\n if key not in map:\n continue\n value = normalize_value(doc.getItem(key, None))\n data[map[key]] = value\n post_method = plomino_table + '_postprocessing'\n if post_method in globals():\n globals()[post_method](doc, data)\n iride_table = map['__tablename']\n return {\n iride_table: (data,)\n }","def review_counts(stat_info_dict):\n review_counts = {}\n for release, stat_dict in stat_info_dict.items():\n review_counts_per_release = {}\n for key, stat in stat_dict.items():\n # review count\n review_counts_per_release[key] = stat['metric']\n review_counts[release] = review_counts_per_release\n return review_counts","def reprocess_dict (dict1):\n out = {};\n for kk,value in dict1.iteritems():\n # parse keys\n (lo0,dur,decdec,freqmhz,nch),weight = kk[0].split(\"_\"),kk[1]\n if weight != \"natural\":\n weight += \":\" + kk[3];\n dec = -int(decdec.split(\"-\")[1]);\n freq = int(freqmhz[:-3])\n # parse layout\n lo = lo0;\n if lo[-2] in \"abcd\":\n lores = \"0.\"+lo[-1];\n lofreq = dict(a=650,b=800,c=1000,d=1400)[lo[-2]];\n lo = lo[:-2];\n else:\n lores = 0;\n lofreq = 0;\n lo = lo[4:];\n l00 = lo0[4:]\n wbins.add(weight);\n # make new entry\n out[lo0,lores,lofreq,freq,dec,weight] = [value,kk];\n return out;","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def update_data(self):\n for sai_id_key in self.if_id_map:\n namespace, sai_id = mibs.split_sai_id_key(sai_id_key)\n if_idx = mibs.get_index_from_str(self.if_id_map[sai_id_key])\n counter_table = self.namespace_db_map[namespace].get_all(mibs.COUNTERS_DB, \\\n mibs.counter_table(sai_id))\n if counter_table is None:\n counter_table = {}\n self.if_counters[if_idx] = counter_table\n\n\n self.lag_name_if_name_map, \\\n self.if_name_lag_name_map, \\\n self.oid_lag_name_map, _, _ = Namespace.get_sync_d_from_all_namespace(mibs.init_sync_d_lag_tables, self.db_conn)\n\n self.if_range = sorted(list(self.oid_name_map.keys()) + list(self.oid_lag_name_map.keys()))\n self.if_range = [(i,) for i in self.if_range]","def to_table(self):\n table = Table()\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\n table['counts'] = self.counts.to_table()['data']\n if self.counts.data_err is not None:\n table['counts_err'] = self.counts.to_table()['data_err']\n table['livetime'] = self.livetime.to_table()['data']\n table['bkg'] = self.bg_rate.to_table()['data']\n if self.bg_rate.data_err is not None:\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\n table.meta['HDUNAME'] = \"bkg_2d\"\n return table","def calculate_TF_IDF():\n conn = util.getDBConnection()\n sql = \"select word from clean_keywords\"\n print sql\n rows = util.executeSQL(conn, sql)\n word_tf = {}\n word_df = {}\n for row in rows:\n word = row[0]\n sql1 = \"select doc_id from keywords where name='\" + word + \"'\"\n print sql1\n res = util.executeSQL(conn, sql1)\n for row1 in res:\n pkg_id = row1[0]\n key = word + ':' + str(pkg_id)\n if key in word_tf:\n tf_count = word_tf[key]\n word_tf[key] = tf_count + 1\n else:\n word_tf[key] = 1\n if word in word_df:\n df_count = word_df[word]\n word_df[word] = df_count + 1\n else:\n word_df[word] = 1\n\n for word, df in word_df.iteritems():\n sql = 'update clean_keywords set df=' + str(df) + \" where word='\" + word + \"'\"\n print sql\n util.executeSQL(conn, sql)\n\n for word_pkgid, tf in word_tf.iteritems():\n word, pkg_id = word_pkgid.split(\":\")\n sql = 'update keywords set tf=' + str(tf) + \" where name='\" + word + \"' and doc_id=\" + str(pkg_id)\n print sql\n util.executeSQL(conn, sql)","def dict2():\n print(record1)\n record2 = {}\n for k, v in record1.items():\n count = 0\n for letter in v:\n if letter == 't' or letter == 'T':\n count += 1\n record2[k] = count\n print(record2)","def prepare_for_table(data, machine_id):\n\n defint = lambda x: 0 if x == '' else int(x)\n\n keep_and_convert = {\n 'MESSAGE': str,\n 'PRIORITY': defint,\n '__REALTIME_TIMESTAMP': defint,\n '_PID': defint,\n '_UID': defint,\n '_SYSTEMD_UNIT': str,\n 'SYSLOG_IDENTIFIER': str,\n '_COMM': str,\n }\n result = dict((key, converter(data.get(key, ''))) for key, converter in keep_and_convert.items())\n result['MACHINE_ID'] = machine_id\n return data['__CURSOR'], result","def append_counting(dict):\n row_c = []\n # for nuc in NUC: #Scans all the elements and adds it to the table.\n # row_c.append(dict[nuc])\n for mot in MOT:\n row_c.append(dict[mot])\n for nuc_nr in NUC_NR :\n row_c.append(dict[nuc_nr + \"_NR\"])\n # #row.extend([dict[\"AA_NR\"], dict[\"TT_NR\"], dict[\"CC_NR\"], dict[\"GG_NR\"]])\n return row_c","def get_patients_dict(table):\n\tf = open(table)\n\tpatients = f.readline().strip().split(\"\\t\")[1:]\n\t\t \n\tpatients_dict = {}\n\tfor i in patients:\n\t\tpatients_dict[i.replace('\"', '')] = {}\n\t\t \n\tfor i in f:\n\t\tl = i.strip().split(\"\\t\")\n\t\tgene = l[0]\n\n\t\tfor j in range(len(l[1:])):\n\t\t\tpatients_dict[patients[j]][gene] = int(l[1:][j])\n\treturn patients_dict","def build_article_map(f='./wikispeedia_paths-and-graph/articles.tsv'):\n out_dict = {}\n count = 0\n with open(f, 'r') as r:\n for _ in xrange(12):\n next(r)\n for line in r:\n out_dict[line.strip('\\n')] = count\n count += 1\n return out_dict","def files2dictionary(filename, countryID, supp_dict):\r\n\r\n fh = open(filename)\r\n header = next(fh)\r\n\r\n data_dict = {}\r\n data_dict[countryID] = {}\r\n\r\n numlist = range(1, 36)\r\n agelist = []\r\n for line in fh:\r\n linelist = line.strip().split(\",\")\r\n age = linelist[4]\r\n agelist.append(age)\r\n for icdrep in numlist:\r\n if str(age) not in data_dict[countryID]:\r\n data_dict[countryID][str(age)] = {}\r\n #if str(icdrep) not in data_dict[countryID][str(age)]:\r\n data_dict[countryID][str(age)][str(icdrep)] = float(linelist[icdrep+8]) # look into changing this to one million\r\n else:\r\n data_dict[countryID][str(age)][str(icdrep)] = float(linelist[icdrep+8]) # look into changing this to one million\r\n fh.close()\r\n supp_dict.update(support_counter(header.split(\",\"), agelist, supp_dict))\r\n return data_dict, supp_dict","def compactify(self):\n logger.debug(\"rebuilding dictionary, shrinking gaps\")\n\n # build mapping from old id -> new id\n idmap = dict(zip(sorted(itervalues(self.token2id)), range(len(self.token2id))))\n\n # reassign mappings to new ids\n self.token2id = {token: idmap[tokenid] for token, tokenid in iteritems(self.token2id)}\n self.id2token = {}\n self.dfs = {idmap[tokenid]: freq for tokenid, freq in iteritems(self.dfs)}","def _prepare_stats_table(self, pinfos):\n\n stats_tbl = OrderedDict()\n stats_tbl[\"Title\"] = OrderedDict()\n for res in self.rsts:\n stats_tbl[res.reportid] = OrderedDict()\n\n for pinfo in pinfos:\n for colname in (pinfo[\"colname\"], pinfo[\"xcolname\"]):\n if colname in stats_tbl[\"Title\"]:\n continue\n\n # Each column name is represented by a row in the statistics table. Fill the \"Title\"\n # column.\n title_dict = stats_tbl[\"Title\"][colname] = OrderedDict()\n defs = self._refdefs.info[colname]\n\n if defs.get(\"unit\") == \"nanosecond\":\n # Convert nanoseconds to microseconds.\n unit = \"us\"\n else:\n unit = defs.get(\"short_unit\", \"\")\n\n title_dict[\"colname\"] = colname\n if unit:\n title_dict[\"colname\"] += f\", {unit}\"\n title_dict[\"coldescr\"] = defs[\"descr\"]\n\n title_dict[\"funcs\"] = OrderedDict()\n for funcname in self._stats_funcs:\n if funcname in self.rsts[0].cstats[colname]:\n title_dict[\"funcs\"][funcname] = RORawResult.get_stat_func_descr(funcname)\n\n # Now fill the values for each result.\n for res in self.rsts:\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\n res_dict[\"funcs\"] = OrderedDict()\n\n for funcname in title_dict[\"funcs\"]:\n val = res.cstats[colname][funcname]\n fmt = \"{}\"\n if defs.get(\"unit\") == \"nanosecond\" and \"index\" not in funcname:\n val /= 1000\n fmt = \"{:.2f}\"\n if defs[\"type\"] == \"float\":\n fmt = \"{:.2f}\"\n\n fdict = res_dict[\"funcs\"][funcname] = OrderedDict()\n fdict[\"val\"] = fmt.format(val)\n fdict[\"raw_val\"] = val\n\n if self._refres.reportid == res.reportid:\n fdict[\"hovertext\"] = \"This is the reference result, other results \" \\\n \"are compared to this one.\"\n continue\n\n ref_fdict = stats_tbl[self._refres.reportid][colname][\"funcs\"][funcname]\n change = val - ref_fdict[\"raw_val\"]\n if ref_fdict[\"raw_val\"]:\n percent = (change / ref_fdict[\"raw_val\"]) * 100\n else:\n percent = change\n change = fmt.format(change) + unit\n percent = \"{:.1f}%\".format(percent)\n fdict[\"hovertext\"] = f\"Change: {change} ({percent})\"\n\n return stats_tbl","def _collect_counts(self, instance_list):\n \"\"\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\n All \"rare words\" (those words that appear less than 3 times) are replaced by <UNK>.\n I also add label|START counts.\n \"\"\"\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\n for instance in instance_list: # Set of <(w, pos), l>\n index = 0\n for t in instance.data: # Tuple of (w, pos)\n index = instance.data.index(t)\n # print t[0] # word\n # print instance.label[index] # label\n if t in self.V:\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\n else:\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\n if index > 0:\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\n else:\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1","def normalize_keys(df: pd.DataFrame) -> None:\n renames = {'tripUpdate_trip_tripId': 'trip_id', 'tripUpdate_trip_startDate': 'start_date',\n 'tripUpdate_trip_directionId': 'direction_id', 'tripUpdate_trip_routeId': 'route_id',\n 'tripUpdate_trip_scheduleRelationship': 'schedule_relationship',\n 'tripUpdate_trip_startTime': 'start_time',\n 'tripUpdate_timestamp': 'timestamp', 'tripUpdate_vehicle_id': 'vehicle_id',\n 'stopSequence': 'stop_sequence', 'stopId': 'stop_id',\n 'scheduleRelationship': 'schedule_relationship2',\n 'vehicle_trip_tripId': 'trip_id', 'vehicle_trip_scheduleRelationship': 'schedule_relationship',\n 'vehicle_timestamp': 'timestamp', 'vehicle_vehicle_id': 'vehicle_id',\n 'vehicle_trip_startTime': 'start_time', 'vehicle_trip_startDate': 'start_date',\n 'vehicle_trip_routeId': 'route_id', 'vehicle_trip_directionId': 'direction_id',\n 'tripUpdate_stopTimeUpdate_stopSequence': 'stop_sequence',\n 'tripUpdate_stopTimeUpdate_stopId': 'stop_id',\n 'tripUpdate_stopTimeUpdate_arrival_delay': 'arrival_delay',\n 'tripUpdate_stopTimeUpdate_arrival_time': 'arrival_time',\n 'tripUpdate_stopTimeUpdate_departure_delay': 'departure_delay',\n 'tripUpdate_stopTimeUpdate_departure_time': 'departure_time',\n 'tripUpdate_stopTimeUpdate_arrival_uncertainty': 'arrival_uncertainty',\n 'tripUpdate_stopTimeUpdate_departure_uncertainty': 'departure_uncertainty',\n 'alert_activePeriod_start': 'period_start', 'alert_activePeriod_end': 'period_end',\n 'alert_informedEntity_routeId': 'route_id', 'alert_informedEntity_stopId': 'stop_id',\n 'alert_informedEntity_trip_tripId': 'trip_id',\n 'alert_informedEntity_trip_scheduleRelationship': 'schedule_relationship',\n 'alert_headerText_translation_text': 'header_text',\n 'alert_descriptionText_translation_text': 'description_text',\n }\n df.rename(columns=renames, inplace=True)","def aggregate_buff_lines(res_lines, buff_lines, dispel_lines):\n\n res_data = {}\n buff_data = {}\n dispel_data = {}\n\n for lines, data in zip(\n (res_lines, buff_lines, dispel_lines), (res_data, buff_data, dispel_data)\n ):\n for spell_id, spell_name, source, target in lines:\n if spell_id not in data:\n data[spell_id] = {}\n\n spell_dict = data[spell_id]\n\n if source not in spell_dict:\n spell_dict[source] = 0\n\n spell_dict[source] += 1\n\n return res_data, buff_data, dispel_data","def _finalize(self, sampled_data):\n final_data = dict()\n for table_name, table_rows in sampled_data.items():\n parents = self.metadata.get_parents(table_name)\n if parents:\n for parent_name in parents:\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\n if foreign_key not in table_rows:\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\n table_rows[foreign_key] = parent_ids\n\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\n\n fields = self.metadata.get_fields(table_name)\n\n final_data[table_name] = reversed_data[list(fields.keys())]\n\n return final_data","def _transform_idoc(df):\n global _SIMPLECOUNT_COLUMNS\n\n try:\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\n\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\n \n c_nc = df['admtypo3'] == 1\n c_tv = df['admtypo3'] == 2\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\n c_viol = df['offtype'] == 1\n c_male = df['sex'] == 'M'\n c_female = ~c_male\n\n c_first2 = [c_nc, c_tv]\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\n \n def helper(c, indicator_id, first2):\n df['fk_simplecount_indicator'] = indicator_id\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\n if first2:\n return df[c].groupby(g).size().reset_index(name='value')\n else:\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\n\n out = pd.DataFrame()\n for i in range(2):\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\n \n for i in range(len(c_others)):\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\n\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\n return out[_SIMPLECOUNT_COLUMNS]\n except:\n raise","def _get_rank_values(self):\n \n info_gains = {}\n \n #caluclate info gain\n for col in self.cat_cols:\n info_gains[col] = self._get_info_gain(col)\n \n return info_gains","def _getSourceMetrics(self, idKeyTuple, idValList, sourceTableList):\n if self._idKeyTuple is None:\n self._idKeyTuple = tuple(idKeyTuple)\n self._idKeyDTypeList = _getDTypeList(keyTuple = self._idKeyTuple,\n valTuple = idValList[0])\n else:\n if self._idKeyTuple != tuple(idKeyTuple):\n raise RuntimeError(\"idKeyTuple = %s != %s = first idKeyTuple; must be the same each time\" % \\\n (idKeyTuple, self._idKeyTuple))\n\n dataDict = {}\n for idTuple, sourceTable in itertools.izip(idValList, sourceTableList):\n if len(sourceTable) == 0:\n continue\n \n idList = sourceTable.get(\"id\")\n dataList = [sourceTable.get(key) for key in self._sourceKeyTuple]\n\n if self._sourceDTypeList is None:\n self._sourceDTypeList = [(key, arr.dtype)\n for key, arr in itertools.izip(self._sourceKeyTuple, dataList)]\n \n transposedDataList = zip(*dataList)\n del dataList\n\n dataDict.update((srcId, idTuple + tuple(data))\n for srcId, data in itertools.izip(idList, transposedDataList))\n return dataDict","def preprocess_data_by_mapping(dicts_count, alias_mapping):\n\n new_mapping_dict = {}\n for (key, value) in dicts_count.items():\n\n # Replace the key with lower string\n new_key = unicode.lower(key)\n\n # Replace the key with object alias\n if new_key in alias_mapping:\n new_key = alias_mapping[new_key]\n\n # Check if the object key is already exists in the dict\n if new_key in new_mapping_dict:\n new_mapping_dict[new_key] += value\n else:\n new_mapping_dict[new_key] = value\n\n return new_mapping_dict","def new_counts_dict():\n\n\tIN_FILES = [\"../_semtag_dataset_webanno_tfidf_inimigo.txt\",\"../_semtag_dataset_webanno_tfidf_publico.txt\" ]\n\n\ttxt = []\n\tfor in_file in IN_FILES:\n\t with codecs.open(in_file,\"r\",\"utf-8\") as fid:\n\t txt += fid.readlines()\n\t#words\n\twords = [w for m in txt for w in m.split()]\n\t#unique words\n\twords = list(set(words))\n\t#word index\n\twrd2idx = {w:-1 for w in words}\n\n\tset_trace()\n\t\n\twith open(COUNTS_DIC,\"w\") as fod:\n\t\tcPickle.dump(wrd2idx, fod, cPickle.HIGHEST_PROTOCOL)","def aggregate_by_primary_attribute(table):\n result = {}\n for row in table:\n for attribute_to_aggregate_by in row[1].split(','):\n attribute_to_aggregate_by.strip()\n attribute_data = row[0]\n if attribute_to_aggregate_by not in result:\n result[attribute_to_aggregate_by] = [attribute_data]\n else:\n result[attribute_to_aggregate_by] += [attribute_data]\n return result","def _create_metrics_table(font, format, base, create_glyph_metrics):\n # we don't set PCF_COMPRESSED_METRICS\n metrics = tuple(create_glyph_metrics(_g, base) for _g in font.glyphs)\n table_bytes = (\n bytes(le.uint32(format))\n + bytes(base.int32(len(metrics)))\n + b''.join(bytes(_t) for _t in metrics)\n )\n return table_bytes, format","def calcCountDict(TFdict):\n\n countDict = {}\n\n for doc in TFdict:\n for term in doc:\n if term in countDict:\n countDict[term] +=1\n else:\n countDict[term] = 1\n\n return countDict","def get_counts_by_manufacturers(table):\n manufacturers_dict = {}\n for item in table:\n try:\n if item[2]:\n try:\n if item[2] in manufacturers_dict.keys():\n manufacturers_dict[item[2]] += 1\n else:\n manufacturers_dict[item[2]] = 1\n except IndexError:\n pass\n else:\n raise ValueError\n except ValueError:\n pass\n\n return manufacturers_dict","def summarize_metadata(self):\n meta_dict = {}\n for comp in self.dataset.data_vars:\n for mkey, mvalue in self.dataset[comp].attrs.items():\n meta_dict[f\"{comp}.{mkey}\"] = mvalue\n\n return meta_dict","def prepare_data(groups):\n all_dicts = []\n for idx, group in groups:\n res_dict = {'organism': group.organism.iloc[0]}\n for g_idx, row in group.iterrows():\n if pd.notna(row.label):\n res_dict[row.cmp_name] = {'label': row.label, 'mic': row.MIC}\n else:\n res_dict[row.cmp_name] = {'label': '', 'mic': row.MIC}\n all_dicts.append(res_dict)\n return all_dicts","def makeTargetFieldsDict(tgt_fields):\n global RES, NRES, HOTEL\n out_dict = {}\n for fld in tgt_fields:\n use, suffix = fld.split(\"_SF_\")\n if use in RES:\n act_field = \"RES\"\n elif use in NRES:\n act_field = \"JOB\"\n elif use in HOTEL:\n act_field = \"HOTEL\"\n else:\n # This is an untracked ause\n continue\n share_field = \"shr_{}\".format(use)\n sqft_field = \"{}_sqft\".format(use)\n out_dict[fld] = (act_field, share_field, sqft_field)\n return out_dict","def get_tracking_info(self):\n items = [('run_id', b_to_str),\n ('asic_id', b_to_str),\n ('version_name', b_to_str),\n ('asic_temp', float),\n ('heatsink_temp', float),\n ('exp_script_purpose', b_to_str),\n ('flow_cell_id', b_to_str),\n ('device_id', b_to_str),\n ]\n attrs = self['/UniqueGlobalKey/tracking_id'].attrs\n return {key: converter(attrs[key]) for key, converter in items}","def updateAnnoByIdDictFromMeta(syn,idDict,metaDf,refCol,fileExts):\n for key in idDict:\n print \"updating annotaion values for key: %s\" % key\n for synId in idDict[key]:\n print \"> %s\" %synId\n temp = syn.get(synId, downloadFile = False)\n exts = ')|('.join(fileExts)\n exts = r'(' + exts + ')'\n tempName = re.sub(exts,\"\",temp.name)\n row = df.loc[df[refCol] == tempName]\n temp[key] = map(str,row[key])[0]\n temp = syn.store(temp,forceVersion = False)\n print \"\"","def make_contingency_tables(\n y: np.ndarray, flagged_A: np.ndarray, flagged_B: np.ndarray\n) -> Dict[int, np.ndarray]:\n\n y = np.array(y).astype(np.int64).flatten()\n flagged_A = np.array(flagged_A).astype(np.bool_).flatten()\n flagged_B = np.array(flagged_B).astype(np.bool_).flatten()\n\n if len(flagged_A) != len(y) or len(flagged_B) != len(y):\n raise ValueError(\n f\"Expected arrays y, flagged_A, and flagged_B of the same length: \\\n got {len(y)}, {len(flagged_A)}, and {len(flagged_B)}.\"\n )\n\n contingency_tables = {}\n for class_id in np.unique(y):\n\n items_flagged_A = flagged_A[y == class_id]\n items_flagged_B = flagged_B[y == class_id]\n\n a = (~items_flagged_A & ~items_flagged_B).sum()\n b = (~items_flagged_A & items_flagged_B).sum()\n c = (items_flagged_A & ~items_flagged_B).sum()\n d = (items_flagged_A & items_flagged_B).sum()\n\n table = np.array([[a, b], [c, d]])\n contingency_tables[class_id] = table\n\n return contingency_tables","def getTF_IDFSpace():\n sids,documents = getSongTextInfo()\n texts = [[word for word in document.lower().split()] for document in documents]\n dictionary = corpora.Dictionary(texts)\n corpus = [dictionary.doc2bow(text) for text in texts]\n tfidf = models.TfidfModel(corpus)\n corpus_tfidf = tfidf[corpus]\n songMap = {}\n index = 0\n for doc in corpus_tfidf:\n sid = sids[index]\n rMap = {}\n for item in doc:\n wid = item[0]\n count = item[1]\n rMap[wid] = count\n songMap[sid] = rMap\n index += 1\n return songMap","def table_summary():\n \n t = dict()\n t['name'] = get_names()\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\n N = len(t['name'])\n \n # host\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\'itoi']\n \n # progenitor\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\n \n # progenitor type\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\n \n # metallicity\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\n \n # associations\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\n \n tout = Table(t)\n tout.pprint()\n tout.write('../data/stream_origin.fits', overwrite=True)","def produce_mirna_single_counts_table(count_files, table_file):\n merge_count_tables(count_files, table_file, \".dedup.single.bam.mirbase_counts.txt\")","def coalesce_tables(tables):\n ## For each table, we can:\n ## Process the names in the first row, as column names\n ## If we have a \"quantity\" column, convert this from euro style to float\n\n ## If the column names are the same, we can append one to the other.\n \n proc_tables = OrderedDict()\n most_recent_key = None\n for tn,t in enumerate(tables):\n for i, r in enumerate(t):\n ##print(f\"Table {tn}, Row number {i}\")\n col_accessors = [str(x) for x in range(len(r))]\n ## Get the processed row names\n if i == 0: \n cnames = {}\n for c in col_accessors:\n cnames[c] = r[c].lower().strip().replace(\" \", \"\")\n continue\n ## Now, cnames was defined from iteration i==0\n rec = {}\n for c in col_accessors:\n rec[cnames[c]] = r[c]\n\n fixweight = lambda x: float(x.replace(\",\", \".\"))\n \n \n if 'netweight' in rec.keys():\n if rec['netweight'] is not None:\n rec['netweight'] = fixweight(rec['netweight'])\n\n if rec['no.'] is not None:\n ## new record\n most_recent_key = rec['no.']\n proc_tables[most_recent_key] = rec\n else:\n ## append the description to previous\n if rec['description'] is not None:\n proc_tables[most_recent_key]['description'] = proc_tables[most_recent_key]['description'] + \" \" + rec['description']\n\n\n return(list(proc_tables.values()))","def build_table():\n with contextlib.ExitStack() as stack:\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\n iters = [(line.split() for line in f) for f in files]\n for it in iters:\n next(it)\n key = operator.itemgetter(0)\n table = []\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\n props = list(g)\n if len(props) == len(iters):\n table.append([k] + [x[1] for x in props])\n for snp in table:\n print(*snp)","def create_op_set_id_version_map(table: VersionTableType) -> VersionMapType:\n result: VersionMapType = {}\n\n def process(release_version: str, ir_version: int, *args: Any) -> None:\n del release_version # Unused\n for pair in zip([\"ai.onnx\", \"ai.onnx.ml\", \"ai.onnx.training\"], args):\n if pair not in result:\n result[pair] = ir_version\n if pair[0] == \"ai.onnx.training\":\n result[\"ai.onnx.preview.training\", pair[1]] = ir_version\n\n for row in table:\n process(*row)\n return result","def parse_distmat_to_dict(table):\r\n\r\n col_headers, row_headers, data = parse_matrix(table)\r\n assert(col_headers == row_headers)\r\n\r\n result = defaultdict(dict)\r\n for (sample_id_x, row) in zip(col_headers, data):\r\n for (sample_id_y, value) in zip(row_headers, row):\r\n result[sample_id_x][sample_id_y] = value\r\n return result","def freq_table(a):\n Detail_freq = a.loc[:, (a.dtypes == object) | (a.dtypes == long) ].columns.get_values().tolist()\n print(Detail_freq)\n for freq in Detail_freq:\n df1 = pd.DataFrame(a[freq].value_counts(dropna=False).astype(float).map('{:20,.0f}'.format).sort_index()).rename(columns={freq:'Count'})\n df2 = pd.DataFrame(a[freq].value_counts(normalize = True, dropna=False).map('{:,.2%}'.format).sort_index()).rename(columns={freq:'Percentage'})\n df = pd.concat([df1, df2], axis = 1)\n print(df)","def tab(data, column) :\r\n\r\n table = {}\r\n \r\n for i in data :\r\n if i[column] not in table :\r\n # add it to tab\r\n table[i[column]] = 1 \r\n else:\r\n # add tabulation\r\n table[i[column]] += 1\r\n\r\n #place the cursor back at 0\r\n \r\n return table","def dictagfreq2(kind, fname):\n \n #x = ['movie', 'moviecode', 'offset', 'well', 'agmin', 'agsec', 'agdur', \n #'agtype', 'agcomm', 'escmin', 'escsec', 'escdur', 'esctype', 'escbeh', \n #'esccomm', 'gen', 'date', 'assay', 'fps', 'flyid', 'side', 'moviepart']\n\n d = {}\n y = '1'\n b = []\n \n with open(fname) as f:\n for l in f:\n adict = agline2(l)\n #print(l)\n \n if adict['well'] != y:\n agfreqcmd(kind, b, d[gen])\n b = []\n \n if adict['agtype'] != '-':\n b.append(adict['agtype'])\n\n if adict['esctype'] != '':\n b.append(adict['esctype'])\n \n gen = adict['gen'] \n if gen not in d:\n d[gen] = [] \n \n #print('b', b)\n #print('well', adict['well'])\n #print('gen', gen, 'd', d)\n \n y = adict['well']\n \n agfreqcmd(kind, b, d[gen])\n \n return(d)","def _get_identifiers_from_kbs(self) -> dict:\n id_mapping_dict = defaultdict(set)\n\n for kb in self.kbs:\n sys.stdout.write('\\n%s \\n' % kb.name)\n for p in tqdm.tqdm(kb.pathways, total=len(kb.pathways)):\n for ent in p.entities:\n id_set = list(set(ent.xrefs))\n if len(id_set) == 1:\n id_mapping_dict[id_set.pop()] = set([])\n for p, q in itertools.combinations(id_set, 2):\n id_mapping_dict[p].add(q)\n id_mapping_dict[q].add(p)\n\n return id_mapping_dict","def calcTFIDFDict(TFDict, IDFDict):\n\n TFIDFDict = {}\n\n #for each term in the document, multiply the tf and idf values\n\n for term in TFDict:\n TFIDFDict[term] = TFDict[term] * IDFDict[term]\n\n return TFIDFDict","def produce_mirna_allbest_counts_table(count_files, table_file):\n merge_count_tables(count_files, table_file, \".dedup.bam.mirbase_counts.txt\")","def print_tables(hash_table, f_output, l_samples):\n\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\n 'ExAC_SAS',\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\n 'Polyphen2_HVAR_score',\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\n l_fields = l_fields + l_samples\n \n l_chr = set([item[0] for item in hash_table.keys()])\n\n fo = open(f_output, 'w')\n fo.write(','.join(l_fields) + '\\n')\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\n')\n fo.close()","def compute_helper_mempool_dictionaries():\n txn_density_dict = {}\n txn_parents_dict = {}\n txn_size_dict = {}\n mempool_data = parse_mempool_csv()\n for elem in mempool_data:\n size = elem.weight/MAXIMUM_BLOCK_WEIGHT # weight mapped to (0,1)\n txn_size_dict[elem.txid] = size \n txn_density_dict[elem.txid] = elem.fee/size\n if elem.parents != '':\n txn_parents_dict[elem.txid] = elem.parents.strip().split(';')\n return txn_density_dict,txn_parents_dict,txn_size_dict","def calcIDFDict(countDict, numfiles):\n\n IDFDict = {}\n for term in countDict:\n IDFDict[term] = math.log(numfiles / countDict[term])\n\n return IDFDict","def define_info_dict():\n\n d = {\n \"PRED\": {\n \"COLUMN\": [\"predicted_class\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Predicted class: somatic, germline, artifact\",\n },\n \"PROB\": {\n \"COLUMN\": [\"prob_s\", \"prob_g\", \"prob_a\"],\n \"Number\": \"3\",\n \"Type\": \"Float\",\n \"Description\": \"Prediction probability of \"\n \"being somatic, germline, artifact in this order\",\n },\n \"SNP\": {\n \"COLUMN\": [\"is_on_db\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\",\n },\n \"ANNO\": {\n \"COLUMN\": [\"annotation\"],\n \"Number\": \".\",\n \"Type\": \"String\",\n \"Description\": \"Indel annotation formatted as \"\n \"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\"\n \"Delimited by comma for multiple isoforms\",\n },\n \"COSMIC_CNT\": {\n \"COLUMN\": [\"cosmic_cnt\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"COSMIC count in v89\",\n },\n \"MAXMAF\": {\n \"COLUMN\": [\"max_maf\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Maximum minor allele frequency (MAF) \"\n \"reported in dbSNP, ClinVar and gnomAD non-cancer population\",\n },\n \"COMMON\": {\n \"COLUMN\": [\"is_common\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Common in dbSNP or MAXMAF > 0.01\",\n },\n \"CLIN\": {\n \"COLUMN\": [\"clin_info\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"ClinVar annotation formatted as ClinicalSignificance|Condition\",\n },\n \"ICP\": {\n \"COLUMN\": [\"indel_complexity\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel complexity: mismatches around the indel measured by edit distance\",\n },\n \"DSM\": {\n \"COLUMN\": [\"dissimilarity\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Dissimilarity: edit distance between indel and flanking sequences\",\n },\n \"ISZ\": {\n \"COLUMN\": [\"indel_size\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel size\",\n },\n \"REP\": {\n \"COLUMN\": [\"repeat\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Repeat: count of the indel-sequence repeats in flanking region\",\n },\n \"UQM\": {\n \"COLUMN\": [\"is_uniq_mapped\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by uniquely mapped reads\",\n },\n \"NEB\": {\n \"COLUMN\": [\"is_near_boundary\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Near exon boundary\",\n },\n \"EQX\": {\n \"COLUMN\": [\"equivalence_exists\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Equivalent alignments exist for the indel\",\n },\n \"BID\": {\n \"COLUMN\": [\"is_bidirectional\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by forward and reverse reads\",\n },\n \"MTA\": {\n \"COLUMN\": [\"is_multiallelic\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Multialleleic\",\n },\n \"FRM\": {\n \"COLUMN\": [\"is_inframe\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"In-frame indel\",\n },\n \"SPL\": {\n \"COLUMN\": [\"is_splice\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in splice region\",\n },\n \"TRN\": {\n \"COLUMN\": [\"is_truncating\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Truncating indel\",\n },\n \"CDD\": {\n \"COLUMN\": [\"is_in_cdd\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in conserved domain\",\n },\n \"LOC\": {\n \"COLUMN\": [\"indel_location\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Relative indel location within the transcript coding region\",\n },\n \"NMD\": {\n \"COLUMN\": [\"is_nmd_insensitive\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insensitive to nonsense mediated decay\",\n },\n \"IPG\": {\n \"COLUMN\": [\"ipg\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Indels per gene\",\n },\n \"LEN\": {\n \"COLUMN\": [\"cds_length\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Coding sequence length. Median value if multiple isoforms exist\",\n },\n \"LC\": {\n \"COLUMN\": [\"lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Linguistic complexity: diversity of k-mers in flanking 50-bp region\",\n },\n \"LLC\": {\n \"COLUMN\": [\"local_lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\",\n },\n \"GC\": {\n \"COLUMN\": [\"gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"GC-content in flanking 50-bp region\",\n },\n \"LGC\": {\n \"COLUMN\": [\"local_gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local GC-content in flanking 6-bp region\",\n },\n \"SG\": {\n \"COLUMN\": [\"strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"DNA bond strength of 2-mers in flanking 50-bp region\",\n },\n \"LSG\": {\n \"COLUMN\": [\"local_strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local DNA bond strength of 2-mers in flanking 6-bp region\",\n },\n \"INS\": {\n \"COLUMN\": [\"is_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insertion\",\n },\n \"ATI\": {\n \"COLUMN\": [\"is_at_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of A or T\",\n },\n \"ATD\": {\n \"COLUMN\": [\"is_at_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of A or T\",\n },\n \"GCI\": {\n \"COLUMN\": [\"is_gc_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of G or C\",\n },\n \"GCD\": {\n \"COLUMN\": [\"is_gc_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of G or C\",\n },\n \"ALTC\": {\n \"COLUMN\": [\"alt_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Alt count: count of unique reads supporting ALT allele\",\n },\n \"REFC\": {\n \"COLUMN\": [\"ref_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Ref count: count of unique reads supporting REF allele\",\n },\n \"RCF\": {\n \"COLUMN\": [\"reclassified\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Reclassification applied\",\n },\n \"RQB\": {\n \"COLUMN\": [\"filtered\", \"rescued\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\",\n },\n }\n\n return d","def get_mapping_table_ids():\n #\n aliquot_prefix = get_prefix(API_PARAMS, API_PARAMS['ALIQUOT_ENDPOINT'])\n case_aliquot_table_name = construct_table_name(API_PARAMS, prefix=aliquot_prefix)\n case_aliquot_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_aliquot_table_name}\"\n\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, API_PARAMS['STUDY_ENDPOINT']))\n study_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{study_table_name}\"\n\n case_external_mapping_prefix = get_prefix(API_PARAMS, API_PARAMS['CASE_EXTERNAL_MAP_ENDPOINT'])\n case_ext_map_table_name = construct_table_name(API_PARAMS, prefix=case_external_mapping_prefix)\n case_external_mapping_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_ext_map_table_name}\"\n\n return case_aliquot_table_id, study_table_id, case_external_mapping_table_id","def _prepare_links_table(self):\n\n links_tbl = OrderedDict()\n for colname in itertools.islice(self._pinfos, 1, None):\n links_tbl[colname] = {}\n links_tbl[colname][\"name\"] = f\"{colname}\"\n fname = colname.replace(\"%\", \"_pcnt\") + \".html\"\n links_tbl[colname][\"fname\"] = fname\n links_tbl[colname][\"hlink\"] = f\"<a href=\\\"{fname}\\\">{colname}</a>\"\n\n return links_tbl","def parse_mixcr_table(filepath):\n tbl = pd.read_csv(filepath, sep='\\t')\n out = {}\n for _, row in tbl.iterrows():\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\n for kind, motif_counts in motifs.items():\n for motif, count in motif_counts.items():\n for mykind in [kind, 'all_types']:\n key = (mykind, motif)\n if key not in out:\n out[key] = {\n 'num_unique_seqs': 0,\n 'num_clones': 0,\n 'num_unique_occurences': 0,\n 'num_clonal_occurences': 0,\n }\n out[key]['num_unique_seqs'] += 1\n out[key]['num_clones'] += row['cloneCount']\n out[key]['num_unique_occurences'] += count\n out[key]['num_clonal_occurences'] += count * row['cloneCount']\n return out","def tr_nc_dict(dfin):\n\n\ttr_nc_index_dict = OrderedDict()\n\t\n\ttrCount = 0\n\tpreviousTrIndex = 0\n\n\tcurrent_id=\"\"\n\tcurrent_tr=\"\"\n\n\tfor i in range(len(dfin)):\n# print dfin.loc[i]\n\n\t\tif dfin.loc[i,'feature'] == 'transcript':\n\t\t\ttrdict = parse_mod_entry(dfin.loc[i,'transcript_id'])\n\n\t\t\tcurGeneID = trdict['gene_id'][0]\n\n\t\t\tif trCount != 0:\n\t\t\t\tnewTrIndex = i\n\t\t\t\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\n\t\t\t\tpreviousTrIndex = i\n\t\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\t\ttrCount += 1\n\n\t\t\telse:\n\t\t\t\tnewTrIndex = 0\n\t\t\t\ttrCount +=1\n\t\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\t\t\n\t\tif i == (len(dfin)-1):\n\t\t\tnewTrIndex = i+1\n\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\n\t\t\t\n\treturn tr_nc_index_dict","def _make_metric_dict(self, metrics):\n exon = None\n hgvs_c = None\n\n slen = len(metrics.split(':'))\n if slen == 1:\n if \"(dist=\" not in metrics:\n tx = metrics\n else:\n tx = None\n elif slen == 2:\n tx = metrics.split('(')[0]\n hgvs_c = metrics.split(':')[1].rstrip(')')\n elif slen == 3:\n tx = metrics.split('(')[0]\n exon = metrics.split(':')[1][4:]\n hgvs_c = metrics.split(':')[2].rstrip(')')\n else:\n raise UserWarning(\"There are an incorrect number of fields listed in: \" + str(self.info))\n\n if hgvs_c:\n full_c = ':'.join([tx, hgvs_c])\n hgvs_parser = Hgvs(full_c)\n hgvs_basep = hgvs_parser.start\n else:\n hgvs_basep = None\n\n return {\n 'AAP': None,\n 'BASEP': hgvs_basep,\n 'EXON': exon,\n 'HGNC': None,\n 'HGVSC': hgvs_c,\n 'HGVSP1': None,\n 'HGVSP3': None,\n 'SOURCE': self.tfx_type,\n 'SPLICE': self.splicing,\n 'TXC': tx,\n 'PVT': self.vtype,\n 'VFX': None\n }","def __generate_dict(self, test_output_dir, types, _table):\n # dump json from file to dict\n\n table_dict = CaseDump(os.path.join(test_output_dir, _table + OUT_SUFFIX), CASE_FILE_TYPE_JSON).dump()\n\n # foreach key from statistic report, split it into table/type/key/sub_value\n type_dict = {}\n for _key in table_dict.keys():\n # wipe off table\n _key = _key.replace(\"%s_\" % _table, \"\")\n for _type in types:\n # wipe off type\n if _type and _type in _key:\n if _type not in type_dict.keys():\n type_dict[_type] = {}\n _key = _key.replace(\"%s_\" % _type, \"\")\n # if has sub_value, set RESULT_DICT[table][type][key] = sub_value\n # else set RESULT_DICT[table][type][key] = None\n if \"#\" in _key:\n sub_value = _key.split(\"#\")\n type_dict[_type][sub_value[0]] = sub_value[1]\n else:\n type_dict[_type][_key] = \"\"\n break\n\n global RESULT_DICT\n RESULT_DICT[_table] = type_dict","def buildAdsTable_v1(output_file = None):\r\n ads_table = []\r\n text_props = ['readability_text', '_all']\r\n onto_props_with_mapping = {'phone':['telephone.name', 'telephone.name.raw'], 'email': ['email.name', 'email.name.raw'],\r\n 'posting_date':['inferlink_date', 'readability_date', 'high_recall_readability_date'],\r\n 'price':['price'], 'location':['addressLocality'],\r\n 'name':['name'],\r\n 'ethnicity':['ethnicity'],\r\n 'eye_color':['eyeColor'], 'title':['title'],\r\n 'hair_color':['hairColor'], 'nationality':['nationality'],\r\n 'business_type':['business_type'],\r\n 'business_name':['streetAddress'], 'services':['serviceType'],\r\n 'business': ['streetAddress'],\r\n 'physical_address': ['streetAddress'],\r\n 'gender':['gender'], 'top_level_domain':['top_level_domain'],\r\n 'obfuscation':['telephone.isObfuscated', 'email.isObfuscated'],\r\n 'age':['age'], 'hyperlink:':['relatedLink'], 'drug_use':['drug_use'],\r\n 'review_site':['review_site'], 'review_id':['review_id'],\r\n 'number_of_individuals':['name_count'],\r\n 'ad': ['identifier'],\r\n 'multiple_phone': ['telephone_count'],\r\n 'cluster': ['seller.uri'],\r\n 'seed': ['seller.telephone.name', 'seller.email.name']\r\n }\r\n non_readability_props = ['number_of_individuals', 'ad', 'multiple_phone', 'cluster', 'phone', 'posting_date', 'email']\r\n onto_props_without_mapping = ['image_with_email', 'image_with_phone']\r\n for property, value_list in onto_props_with_mapping.iteritems():\r\n dict = {}\r\n dict['onto_prop'] = property\r\n mappings = []\r\n tmp = {}\r\n for v in value_list:\r\n if property == 'phone' or v == 'seller.telephone.name':\r\n tmp[v] = 'build_phone_match_clause'\r\n tmp['_all'] = 'build_phone_match_clause'\r\n tmp['url'] = 'build_phone_regexp_clause'\r\n elif v == 'email.name':\r\n tmp[v] = 'build_email_match_clause'\r\n tmp['_all'] = 'build_match_phrase_clause'\r\n elif property == 'ad':\r\n tmp[v] = 'build_term_clause'\r\n elif '_count' in v:\r\n tmp[v] = 'build_count_match_clause'\r\n elif property == 'gender':\r\n tmp[v] = 'build_gender_match_clause'\r\n elif property == 'posting_date':\r\n tmp[v] = 'build_match_phrase_clause'\r\n else:\r\n tmp[v] = 'build_match_clause'\r\n if property not in non_readability_props:\r\n for v in text_props: # will overwrite for seller.telephone.name\r\n tmp[v] = 'build_match_clause_inner'\r\n mappings.append(tmp)\r\n dict['mappings'] = mappings\r\n ads_table.append(dict)\r\n\r\n for property in onto_props_without_mapping:\r\n dict = {}\r\n dict['onto_prop'] = property\r\n mappings = []\r\n tmp = {}\r\n for v in text_props:\r\n tmp[v] = 'build_match_clause_inner'\r\n mappings.append(tmp)\r\n dict['mappings'] = mappings\r\n ads_table.append(dict)\r\n\r\n if output_file:\r\n file = codecs.open(output_file, 'w', 'utf-8')\r\n for entry in ads_table:\r\n json.dump(entry, file)\r\n file.write('\\n')\r\n file.close()","def generateAggregatedCsvData(self, context, obj, entities):\n return sum([long(e.prop1.replace('-', ''), 16) for e in entities])","def _parse_id_to_taxonomy_file(f):\r\n result = {}\r\n for line in f:\r\n line = line.strip()\r\n if line:\r\n identifier, taxonomy = map(strip, line.split('\\t'))\r\n result[identifier] = taxonomy\r\n return result","def write_count_table(filtered_counts, gene_names, ids_liver_header):\n with open(\"filtered_tcga_counts.tsv\", \"w\") as file:\n file.write(\"gene_id\\tgene_name\\t\" + \"\\t\".join(ids_liver_header) + \"\\n\")\n for gene_name, counts_line in zip(gene_names, filtered_counts):\n file.write(gene_name + \"\\t\" + \"\\t\" + \"\\t\".join(counts_line) + \"\\n\")","def get_table(text, size = 1):\r\n result = {}\r\n for i in range(len(text)):\r\n chars = text[i:i+size]\r\n try:\r\n out = text[i + size]\r\n except IndexError:\r\n break\r\n char_dict = result.get(chars, {})\r\n if out not in char_dict:\r\n char_dict[out] = 0\r\n char_dict[out] += 1\r\n result[chars] = char_dict\r\n return result","def process_ubern(fdict,cdmd,cdm,fn,proflist,campaigns,data):\n good=False\n for d in data:\n if 'Min' in d:\n good=True\n if 'ri_name' in d:\n key=d.split()[-1]\n if key not in fdict.keys():\n fdict[key]={'observations_table':{},'header_table':{}}\n \n if 'COMP018' in fn:\n print('018')\n if not good:\n print(fn,'could not be processed')\n return\n if(not fdict[key]['observations_table']):\n \n for k in cdmd['observations_table'].element_name.values:\n fdict[key]['observations_table'][k]=[]\n for k in cdmd['header_table'].element_name.values:\n fdict[key]['header_table'][k]=[]\n refdate=datetime(year=1900,month=1,day=1)\n header=True\n aux={}\n l=0\n comp=fn[:7]\n for d in data:\n if 'Min' in d:\n head=d.split()\n break\n l+=1\n m=0\n for d in data[l+1:]:\n dlist=d.split()\n if dlist:\n if dlist[0]=='NA':\n continue\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\n \n try:\n x=float(dlist[1])*100\n except ValueError:\n try: \n if 'PPPcorr' in head:\n x=float(dlist[2])*100\n else:\n continue\n except ValueError:\n continue\n m+=1\n \n if m==0:\n print(fn,'no valid obs')\n return\n \n try:\n idx=np.where(cdm['iri_rstype_map'].ri_name==key.split('_')[0])[0][0]\n fdict[key]['sensor_id']=numpy.string_(cdm['iri_rstype_map'].vapor_name[idx]) #.split(b',')[0])\n except:\n fdict[key]['sensor_id']=numpy.string_('NA')\n \n l=0\n for d in data:\n if 'ri_name' in d:\n key=d.split()[1]\n print(comp,fn,key)\n if 'measurement_programme' not in fdict[key]['header_table'].keys():\n fdict[key]['header_table']['measurement_programme']=[]\n idx=campaigns.index[campaigns.ID==comp]\n fdict[key]['header_table']['measurement_programme'].append(campaigns.Name[idx].values[0]) \n\n id_ascent=fn.split('/')[-1][:11]\n #key=np.string_('0-20100-0-0000'+fn[-8])\n sidx=np.where(cdm['station_configuration'].measuring_system_id==numpy.string_(key))[0][0]\n pidx=np.where(proflist['id_ascent']==id_ascent)[0][0]\n fdict[key]['header_table']['primary_station_id'].append(cdm['station_configuration'].primary_id.values[sidx])\n fdict[key]['header_table']['report_id'].append(fdict[key]['header_table']['primary_station_id'][-1]+\n b'-'+numpy.string_(fn.split('_')[-2]))\n print(key,fdict[key]['header_table']['report_id'])\n fdict[key]['header_table']['latitude'].append(cdm['station_configuration'].latitude.values[sidx])\n fdict[key]['header_table']['longitude'].append(cdm['station_configuration'].longitude.values[sidx])\n fdict[key]['header_table']['height_of_station_above_sea_level'].append(\n proflist['alt'].values[pidx])\n\n for k in 'latitude','longitude':\n aux[k]=fdict[key]['header_table'][k][-1]\n\n if 'date ' in d:\n dat=d.split()[-1]\n if 'hour_utc' in d:\n hour=d.split()[-1]\n hh=hour.split(':')\n if len(hh)==2:\n hour=hour+':00'\n relset=datetime.strptime(dat+' '+hour,'%Y-%m-%d %H:%M:%S')\n fdict[key]['header_table']['report_timestamp'].append(int((relset-refdate).total_seconds()))\n fdict[key]['header_table']['record_timestamp'].append(int((relset-refdate).total_seconds()))\n if 'Min' in d:\n head=d.split()\n break\n l+=1\n\n m=0\n dlen=len(head)-2\n ddlen=dlen\n dstart=2\n if 'Notes' in head:\n dlen-=1\n ddlen-=1\n if 'PPPcorr' in head:\n dstart+=1\n ddlen-=2\n ovar={'TTT':85,'TTTcorr':85,'UU':38,'H':117,'DEWPT':36,'DIR':106,'WSPEED':107}\n offset={'TTT':273.15,'TTTcorr':273.15,'UU':0,'H':0,'DEWPT':273.15,'DIR':0,'WSPEED':0}\n scale={'TTT':1,'TTTcorr':1,'UU':1,'H':9.80655,'DEWPT':1,'DIR':1,'WSPEED':1}\n units={'TTT':5,'TTTcorr':5,'UU':0,'H':806,'DEWPT':5,'DIR':110,'WSPEED':731}\n for d in data[l+1:]:\n dlist=d.split()\n if dlist:\n if dlist[0]=='NA':\n continue\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\n \n try:\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[1])*100]\n except ValueError:\n try: \n if 'PPPcorr' in head:\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[2])*100]\n else:\n continue\n except ValueError:\n continue\n #fdict[key]['observations_table']['z_coordinate']+=ddlen*[numpy.nan]\n \n fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]+int(float(dlist[0])*60)]\n if ddlen<len(head[dstart:dlen+2]):\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart+1:dlen+2]]\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart+1:dlen+2]]\n else:\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart:dlen+2]]\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart:dlen+2]]\n for i in range(dstart,dlen+2):\n \n if head[i]!='TTTcorr': \n try:\n fdict[key]['observations_table']['observation_value'].append(offset[head[i]]+scale[head[i]]*float(dlist[i]))\n except ValueError:\n fdict[key]['observations_table']['observation_value'].append(numpy.nan)\n else:\n try:\n fdict[key]['observations_table']['observation_value'][-1]=offset[head[i]]+scale[head[i]]*float(dlist[i])\n except ValueError:\n pass\n \n fdict[key]['observations_table']['observation_id']+=[numpy.string_('{:0>8}'.format(m+i+1)) for i in range(ddlen)]\n m+=ddlen\n else:\n break\n\n aux['sensor_id']='NA'\n try:\n idx=np.where(cdm['iri_rstype_map'].ri_name==key)[0][0]\n aux['sensor_id']=cdm['iri_rstype_map'].vapor_name[idx]\n except:\n print('could not find rs type key for '+key+'!')\n pass\n aux['report_id']=fdict[key]['header_table']['report_id'][-1]\n\n for k,v in aux.items():\n fdict[key]['observations_table'][k]+=[v]*m\n \n return","def cars_dict_to_table(car_data):\n table_data = [[\"ID\", \"Car\", \"Price\", \"Total Sales\"]]\n for item in car_data:\n table_data.append([item[\"id\"], format_car(item[\"car\"]), item[\"price\"], item[\"total_sales\"]])\n return table_data","def convert_trsp_index(geneDictNonCoding, df, TR_index_dict):\n\n\n\tgeneDictCanon = OrderedDict()\n\t\n\tfor gene in geneDictNonCoding:\n\t\ttrDF = df.iloc[geneDictNonCoding[gene][0]:geneDictNonCoding[gene][1]]\n\t\ttrDFz = trDF.reset_index(drop=True)\n\t\t\n\t\ttrCount = 0\n\t\ttrDictLoc = OrderedDict()\n\t\t\n\t\tfor i in range(len(trDFz)):\n\t\t\tif trDFz.loc[i, 'feature'] == 'transcript':\n\t\t\t\ttr = trDFz.loc[i, 'transcript_id']\n\t\t\t\ttrdict = parse_entry(tr)\n\t\t\t\ttrName = trdict['transcript_id'][0]\n\t\t\t\ttrDictLoc[trName] = [trDFz.loc[i, 'chromStart'], trDFz.loc[i, 'chromEnd']]\n\t\t\t\ttrCount += 1\n\t\t\n\t\tif trCount > 1:\n# print gene, \"more than 1 trsp !!! \\n\"\n\t\t\t\n\t\t\trangeDict = OrderedDict() ## store the ranges, and take the longest\n\t\t\tfor key in trDictLoc:\n\t\t\t\ttrRange = len(range(int(trDictLoc[key][0]),int(trDictLoc[key][1])))\n\t\t\t\trangeDict[key] = trRange\n\t\t\t\t\n\t\t\tv=list(rangeDict.values())\n\t\t\tk=list(rangeDict.keys())\n\t\t\ttrOut = k[v.index(max(v))]\n# print trOut\n\t\t\tgeneDictCanon[trOut] = [gene, TR_index_dict[trOut]]\n\t\t\t\n\t\t\t\n\n\t\telse: ## for genes with single transcripts\n\t\t\ttrOut = trDictLoc.keys()[0]\n\t\t\tgeneDictCanon[trOut] = [gene, TR_index_dict[trOut]]\n\treturn geneDictCanon","def get_results(self):\n d = {}\n# r = {}\n for analyser in self.xml_tree.getroot():\n for child in analyser:\n if child.tag == 'all-records':\n for record in child:\n attributes = record.attrib\n sample = attributes['sampleId']\n assay_id = attributes['assayId']\n genotype = attributes['genotypeId']\n quality = attributes['description'].split('.')[0]\n if re.match(r'rs\\d+', assay_id):\n if sample in d:\n if assay_id in d[sample]:\n for allele in list(genotype):\n if allele not in d[sample][assay_id]['genotype']:\n d[sample][assay_id]['genotype'] += allele\n if quality not in d[sample][assay_id]['quality']:\n d[sample][assay_id]['quality'].append(quality)\n else:\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\n else:\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\n# if sample in r:\n# if assay_id in r[sample]:\n# for allele in list(genotype):\n# if allele not in r[sample][assay_id]:\n# r[sample][assay_id] += allele\n# else:\n# r[sample][assay_id] = genotype\n# else:\n# r[sample] = {assay_id: genotype}\n# for k, v in r.items():\n# for k1, v1, in v.items():\n# if len(v1) == 1:\n# v[k1] += v1\n# pprint.pprint(r)\n# df = pd.DataFrame.from_dict(r).transpose()\n# print(df)\n# df.to_excel('snpcheck.xlsx')\n return d","def idf_dict1(l):\n idf = {}\n # first look for idf in abstract field\n solrdict = list_to_dict(l)\n for doc in solrdict.itervalues():\n terms = doc.get('abstract',{})\n for w,t in terms.iteritems():\n if not idf.has_key(w):\n idf[w] = t['tf-idf'][0] / t['tf'][0]\n # next add idf values from title field\n for doc in solrdict.itervalues():\n terms = doc.get('title',{})\n for w,t in terms.iteritems():\n if not idf.has_key(w):\n idf[w] = t['tf-idf'][0] / t['tf'][0]\n return idf","def compile_as_dict(self, data):\r\n fut = {}\r\n index = self.primary_key.index\r\n for i in data:\r\n popped = i.pop(index)\r\n head = self.table_headers.copy()\r\n head.pop(index)\r\n s = {}\r\n for j in range(len(head)):\r\n s[j] = i[j]\r\n fut[popped] = s\r\n return fut","def table_to_dict(self, tab):\n dict = {}\n for colname in tab.colnames:\n dict[colname] = tab[colname].data\n return dict","def analysis():\r\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\r\n data_frame.registerTempTable('business')\r\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\r\n\r\n def merge_count(a, b):\r\n r = {}\r\n for p, c in a.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n for p, c in b.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n return r\r\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\r\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\r\n\r\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\r\n print(result.take(10))\r\n print('----------------pron count-----------------')\r\n print(pron_count.collect())\r\n\r\n print(gd)"],"string":"[\n \"def reformat_attrTable(\\n self):\\n #format into a dictionary of rows for quick aligning with the tracking_id\\n if self.attrTable: attrTable = self.attrTable[:];\\n else: attrTable = [];\\n\\n attrTable_dict = {};\\n for row in attrTable:\\n attrTable_dict[row['tracking_id']] = row;\\n return attrTable_dict;\",\n \"def reformat_countOrFPKMTable(\\n self,\\n countOrFPKMTable_I=None,\\n analysis_id_I=None,\\n sna2experimentID_I=None,\\n sna2sns_I=None,\\n count_or_FPKM = 'count'):\\n #format into a dictionary of rows for quick aligning with the tracking_id\\n countOrFPKMTable_flat = [];\\n for row in countOrFPKMTable_I:\\n for k,v in row.items():\\n if k=='tracking_id':continue;\\n tmp = {};\\n tmp['analysis_id'] = analysis_id_I;\\n tmp['tracking_id'] = row['tracking_id'];\\n\\n sample_name_lst = k.split('_');\\n sample_name_base = '_'.join(sample_name_lst[:-1]);\\n sample_name_rep = eval(sample_name_lst[-1]);\\n if sna2experimentID_I: \\n experiment_id = sna2experimentID_I[sample_name_base];\\n else:\\n experiment_id=None;\\n tmp['experiment_id'] = experiment_id;\\n if sna2sns_I: \\n sample_name = sna2sns_I[sample_name_base][sample_name_rep];\\n else:\\n sample_name=k;\\n tmp['sample_name'] = sample_name;\\n\\n tmp['value'] = v;\\n tmp['value_units'] = count_or_FPKM;\\n tmp['used_'] = True;\\n tmp['comment_'] = None;\\n countOrFPKMTable_flat.append(tmp);\\n return countOrFPKMTable_flat;\",\n \"def format_countOrFPKMTable(self,fpkmTracking_I):\\n for fpkmTracking in fpkmTracking_I:\\n for k,v in fpkmTracking.items():\\n if k=='tracking_id' and type(fpkmTracking['tracking_id'])==type('string'):\\n pass;\\n elif k!='tracking_id' and type(fpkmTracking[k])==type('string'):\\n fpkmTracking[k] = eval(v);\\n return fpkmTracking_I;\",\n \"def tables() -> dict[str, str]:\\n return {\\n \\\"land_use\\\": \\\"zone_id\\\",\\n \\\"tours\\\": \\\"tour_id\\\",\\n \\\"trips\\\": \\\"trip_id\\\",\\n \\\"persons\\\": \\\"person_id\\\",\\n \\\"households\\\": \\\"household_id\\\",\\n }\",\n \"def reformat_countTable(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n if self.countTable: countTable = self.countTable[:];\\n else: countTable = [];\\n\\n countTable_flat = self.reformat_countOrFPKMTable(\\n countOrFPKMTable_I=countTable,\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,\\n count_or_FPKM = 'count');\\n return countTable_flat;\",\n \"def normalize_counts_to_tpm(counts_dir, gff_dir, out_dir, feat='CDS', id_sym='gene_id='):\\n count_files = [os.path.join(counts_dir, f) for f in os.listdir(counts_dir)]\\n all_tpms = {}\\n for cf in count_files:\\n if \\\"_counts\\\" not in cf:\\n continue\\n tpm = normalize_counts_to_tpm_one_file(cf, gff_dir, feat, id_sym)\\n #out_file = \\\"{}_tpm.csv\\\".format(os.path.basename(cf))\\n #out_path = os.path.join(out_dir, out_file)\\n #with open(out_path, \\\"w\\\") as fo:\\n # for gene, t in tpm.items():\\n # fo.write(\\\"{},{}\\\\n\\\".format(gene, t))\\n prefix = os.path.basename(cf).split(\\\"_trimmed\\\")[0] # this would be specific to my naming convention\\n all_tpms[prefix] = tpm\\n return all_tpms\",\n \"def avail_table_to_dict(avail_data):\\n avail_target = avail_data[\\\"TARGETID\\\"]\\n avail_loc = avail_data[\\\"LOCATION\\\"]\\n avail = dict()\\n for lid, tgid in zip(avail_loc, avail_target):\\n if lid in avail:\\n avail[lid].append(tgid)\\n else:\\n avail[lid] = list([tgid])\\n avail = {f: np.array(av) for f, av in avail.items()}\\n return avail\",\n \"def accumulate_entries_as_tables(entries):\\n name_table = {}\\n num_table = {}\\n for number, name in entries:\\n name_table[name] = number\\n num_table[number] = name\\n\\n return name_table, num_table\",\n \"def make_category_tables(category_table):\\n category2label = {}\\n label2category = {}\\n for item in category_table.itertuples():\\n category_id = item[0]\\n label_id = item[4]\\n category2label[category_id] = label_id\\n label2category[label_id] = category_id\\n return category2label, label2category\",\n \"def create_count_map(self) -> Dict[int, int]:\\n res: Dict[int, int] = {}\\n for sequence_data in self.model.values():\\n sequence_data: NGramsSequence = cast(NGramsSequence, sequence_data)\\n for count in sequence_data.next_count.values():\\n count: int = cast(int, count)\\n if count not in res:\\n res[count] = 0\\n res[count] += 1\\n self.count_map = res\\n logger.success('created count map')\\n return res\",\n \"def tallying_genes():\\n #Creating a tallying Mechanism of genes with multiple sequences in file and\\n # an output file for future alignment of sequences \\n blast_hit_results = open('blast_hits_report.txt', 'r')\\n gene_dict={}\\n\\n for line in blast_hit_results:\\n data = line.split(\\\"\\\\t\\\")\\n \\n if line.startswith('SeqID'):\\n continue\\n else:\\n #Test to see if organism in dictionary\\n verdict = gene_dict.get(data[6])\\n \\n if str(verdict) == \\\"None\\\":\\n #creating new entry\\n key = data[6]\\n seq_info=str(data[0])+\\\"|\\\"+str(data[1])\\n counter = 1\\n #Value[Counts, Trimmed_Length, Blast Length, Blast_Score, Blast_Percent_Identity]\\n value=[data[5], counter, [seq_info]]\\n gene_dict.update({key:value})\\n else:\\n #Fills dictionary based on organism name\\n seq_info=str(data[0])+\\\"|\\\"+str(data[1])\\n gene_dict[data[6]][1]+=1\\n gene_dict[data[6]][2].append(seq_info)\\n blast_hit_results.close()\\n return(gene_dict)\",\n \"def basic_table_details():\\n tbl: pa.table = pa.Table.from_pylist([\\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\\n ])\\n\\n results = {\\n 'column_names': tbl.column_names,\\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\\n 'nbytes': tbl.nbytes,\\n 'num_columns': tbl.num_columns,\\n 'num_rows': tbl.num_rows,\\n 'schema': tbl.schema,\\n 'shape': tbl.shape,\\n }\\n\\n print(results)\",\n \"def create_dicts_w_info(df,\\n table_visit_diff_string,\\n bad_records_string='num_bad_records'):\\n\\n hpos = df['src_hpo_id'].unique().tolist()\\n\\n site_dictionaries = {}\\n\\n for hpo in hpos:\\n sample_df = df.loc[df['src_hpo_id'] == hpo]\\n\\n sample_df.loc[\\\"Total\\\"] = sample_df.sum(numeric_only=True)\\n\\n hpo_dict = sample_df.loc[\\\"Total\\\"].to_dict()\\n\\n site_dictionaries[hpo] = hpo_dict\\n\\n tot = 0\\n\\n num_bad_records = {}\\n\\n for hpo, info in site_dictionaries.items():\\n num_bad_records[hpo] = info[bad_records_string]\\n\\n table_visit_diff_dict = {}\\n tot_rec, tot_diff = 0, 0\\n\\n for hpo, info in site_dictionaries.items():\\n bad_records = info[bad_records_string]\\n difference = info[table_visit_diff_string]\\n\\n tot_rec += bad_records\\n tot_diff += difference\\n\\n avg = round(difference / bad_records, 2)\\n\\n table_visit_diff_dict[hpo] = avg\\n\\n table_visit_diff_dict['Total'] = round(tot_diff / tot_rec, 2)\\n\\n return num_bad_records, table_visit_diff_dict\",\n \"def reformat_fpkmTable(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n if self.fpkmTable: fpkmTable = self.fpkmTable[:];\\n else: fpkmTable = [];\\n\\n fpkmTable_flat = self.reformat_countOrFPKMTable(\\n countOrFPKMTable_I=fpkmTable,\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,\\n count_or_FPKM = 'fpkm');\\n return fpkmTable_flat;\",\n \"def table_fields() -> Dict[str, TableFieldDetails]:\\n return {\\n \\\"REPEATS\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=0,\\n bit_high=15,\\n description=\\\"Number of times the line will repeat\\\",\\n labels=None,\\n ),\\n \\\"TRIGGER\\\": TableFieldDetails(\\n subtype=\\\"enum\\\",\\n bit_low=16,\\n bit_high=19,\\n description=\\\"The trigger condition to start the phases\\\",\\n labels=[\\n \\\"Immediate\\\",\\n \\\"BITA=0\\\",\\n \\\"BITA=1\\\",\\n \\\"BITB=0\\\",\\n \\\"BITB=1\\\",\\n \\\"BITC=0\\\",\\n \\\"BITC=1\\\",\\n \\\"POSA>=POSITION\\\",\\n \\\"POSA<=POSITION\\\",\\n \\\"POSB>=POSITION\\\",\\n \\\"POSB<=POSITION\\\",\\n \\\"POSC>=POSITION\\\",\\n \\\"POSC<=POSITION\\\",\\n ],\\n ),\\n \\\"POSITION\\\": TableFieldDetails(\\n subtype=\\\"int\\\",\\n bit_low=32,\\n bit_high=63,\\n description=\\\"The position that can be used in trigger condition\\\",\\n labels=None,\\n ),\\n \\\"TIME1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=64,\\n bit_high=95,\\n description=\\\"The time the optional phase 1 should take\\\",\\n labels=None,\\n ),\\n \\\"OUTA1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=20,\\n bit_high=20,\\n description=\\\"Output A value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"OUTB1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=21,\\n bit_high=21,\\n description=\\\"Output B value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"OUTC1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=22,\\n bit_high=22,\\n description=\\\"Output C value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"OUTD1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=23,\\n bit_high=23,\\n description=\\\"Output D value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"OUTE1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=24,\\n bit_high=24,\\n description=\\\"Output E value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"OUTF1\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=25,\\n bit_high=25,\\n description=\\\"Output F value during phase 1\\\",\\n labels=None,\\n ),\\n \\\"TIME2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=96,\\n bit_high=127,\\n description=\\\"The time the mandatory phase 2 should take\\\",\\n labels=None,\\n ),\\n \\\"OUTA2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=26,\\n bit_high=26,\\n description=\\\"Output A value during phase 2\\\",\\n labels=None,\\n ),\\n \\\"OUTB2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=27,\\n bit_high=27,\\n description=\\\"Output B value during phase 2\\\",\\n labels=None,\\n ),\\n \\\"OUTC2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=28,\\n bit_high=28,\\n description=\\\"Output C value during phase 2\\\",\\n labels=None,\\n ),\\n \\\"OUTD2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=29,\\n bit_high=29,\\n description=\\\"Output D value during phase 2\\\",\\n labels=None,\\n ),\\n \\\"OUTE2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=30,\\n bit_high=30,\\n description=\\\"Output E value during phase 2\\\",\\n labels=None,\\n ),\\n \\\"OUTF2\\\": TableFieldDetails(\\n subtype=\\\"uint\\\",\\n bit_low=31,\\n bit_high=31,\\n description=\\\"Output F value during phase 2\\\",\\n labels=None,\\n ),\\n }\",\n \"def infodump(self):\\n idtable = dict.fromkeys(KEYS_IDSTABLE)\\n rttable = dict.fromkeys(KEYS_RTTABLE)\\n #Construct Common table, IMDb table, Review table\\n commontable,imdbtable,reviewtable = self.dump_imdb()\\n #Contruct IDTable\\n idtable[\\\"imdb_id\\\"] = imdbtable[\\\"imdb_id\\\"]\\n if commontable[\\\"kind\\\"] == \\\"movie\\\":\\n #Contruct RT table\\n rttable = self.dump_rt()\\n idtable[\\\"rt_id\\\"] = rttable[\\\"rt_id\\\"]\\n return idtable,commontable,imdbtable,rttable,reviewtable\",\n \"def get_tally_map (self):\\n if self._tally_map is None:\\n self._tally_map = {}\\n # for field in self.header[-3:]:\\n for column in self.get_award_id_columns():\\n self._tally_map[column] = self.tally_awards_in_column(column)\\n return self._tally_map\",\n \"def get_indices_convert_dict(fn):\\n pdb_inp = pdb.input(file_name=fn)\\n pdb_hierarchy = pdb_inp.construct_hierarchy()\\n \\n newids = OrderedDict((atom.id_str(), idx) for (idx, atom) in enumerate(pdb_hierarchy.atoms()))\\n oldids= OrderedDict((atom.id_str(), idx) for (idx, atom) in enumerate(pdb_inp.atoms()))\\n \\n return {'p2a': np.array([newids[atom.id_str()] for atom in pdb_inp.atoms()]),\\n 'a2p': np.array([oldids[atom.id_str()] for atom in pdb_hierarchy.atoms()])}\",\n \"def pre_build_idf_table(self):\\r\\n doc_per_word_table = dict() # in how many documents does a word occur\\r\\n \\r\\n for doc in self.documents:\\r\\n # converting list to set will delete any duplicate words\\r\\n doc = self.preprocess_document(doc)\\r\\n doc_words = set(self.word_tokenize_preprocessed(doc))\\r\\n\\r\\n for word in doc_words:\\r\\n if word in doc_per_word_table:\\r\\n doc_per_word_table[word] += 1\\r\\n else:\\r\\n doc_per_word_table[word] = 1\\r\\n\\r\\n total_documents = len(self.documents)\\r\\n idf_table = dict()\\r\\n\\r\\n for word in doc_per_word_table:\\r\\n idf_table[word] = math.log2(total_documents / float(doc_per_word_table[word]))\\r\\n\\r\\n return idf_table\",\n \"def gfa_table_to_dict(gfa_data):\\n gfa_target = gfa_data[\\\"TARGETID\\\"]\\n gfa_loc = gfa_data[\\\"GFA_LOC\\\"]\\n gfa_gmag = gfa_data[\\\"GAIA_PHOT_G_MEAN_MAG\\\"]\\n gfa = dict()\\n for lid, tgid, mag in zip(gfa_loc, gfa_target,gfa_gmag):\\n print(zip(gfa_loc, gfa_target,gfa_gmag))\\n if lid in gfa:\\n gfa[lid].append(mag)\\n else:\\n gfa[lid] = list([mag])\\n gfa = {f: np.array(av) for f, av in gfa.items()}\\n return gfa\",\n \"def _convert_table_to_dict(self, data_table):\\n column_names = ['star_name', 'distance', 'brightness', 'luminosity']\\n stars = {}\\n for line in data_table:\\n stars[line[0]] = {column_names[i] : line[i] for i in range(1, len(column_names))}\\n return stars\",\n \"def group(df):\\r\\n t = len(df.columns.values)\\r\\n ans = {}\\r\\n names = df.columns.values\\r\\n for index,row in df.iterrows(): \\r\\n for j in range(t-1): \\r\\n index = str(j) \\r\\n llaveCol = str(row[j])\\r\\n llaveClase = str(row[t-1]) \\r\\n if index not in ans:\\r\\n ans[index] = {}\\r\\n if llaveCol not in ans[index]:\\r\\n ans[index][llaveCol] = {} \\r\\n if llaveClase not in ans[index][llaveCol]:\\r\\n ans[index][llaveCol][llaveClase] = 0 \\r\\n ans[index][llaveCol][llaveClase]+=1 \\r\\n return ans\",\n \"def get_columns_dict(table, replace):\\n # 0 is name, 1 is id\\n if type(table.index) == pd.MultiIndex:\\n colcount = 1 + len(table.index[0])\\n else:\\n colcount = 2\\n cols = {}\\n for c in table.columns:\\n c_repres = \\\",\\\".join(c)\\n if \\\"Filtergroups\\\" not in c:\\n cols[colcount] = replace_in_str(str(c_repres), replace)\\n colcount = colcount + 1\\n return cols\",\n \"def aggregate_data(mts, feature, target):\\r\\n set_dict = dict()\\r\\n set_dict['mt'] = mts\\r\\n set_dict['feature'] = feature\\r\\n set_dict['target'] = target\\r\\n \\r\\n return set_dict\",\n \"def build_rule_count_dict(counts_iterator):\\n rule_count_dict = {}\\n for l in counts_iterator:\\n if l[1] != 'NONTERMINAL':\\n x = l[2]\\n y = l[1] == 'UNARYRULE' and l[3] or l[3] + ' ' + l[4]\\n # if l[1] == 'UNARYRULE':\\n # y = l[3]\\n # else: # l[1] == 'BINARYRULE'\\n # y = l[3] + ' ' + l[4]\\n if x not in rule_count_dict:\\n rule_count_dict[x] = {}\\n rule_count_dict[x][y] = int(l[0])\\n return rule_count_dict\",\n \"def dictagnum(kind, fname):\\n\\n with open(fname, 'r') as g:\\n g.next()\\n g.next()\\n m = g.next()\\n startdict = agline(m)\\n genold = startdict['gen']\\n\\n f = open(fname)\\n f.next()\\n f.next()\\n d = {}\\n y = '1'\\n nb = []\\n for l in f:\\n adict = agline(l)\\n ks = kind + 's'\\n gen = adict['gen']\\n well = adict['well']\\n\\n if adict['gen'] not in d:\\n d[gen] = []\\n \\n if gen != genold:\\n d[genold].append(sum(nb))\\n nb = []\\n else: \\n if adict['well'] != y:\\n d[gen].append(sum(nb))\\n nb = []\\n \\n if kind == 'charge':\\n if adict[ks] == 'x':\\n nb.append(0)\\n elif int(adict[ks]) >= 0 and (adict['charget'] == 'c' or \\n adict['charget'] == 'o'):\\n nb.append(1)\\n elif adict[ks] == '-':\\n pass\\n #print('nb', nb)\\n\\n if kind == 'escd' or kind == 'escm':\\n if adict[ks] == '':\\n nb.append(0)\\n elif int(adict[ks]) >= 0:\\n nb.append(1)\\n elif adict[ks] == '-':\\n pass\\n\\n y = adict['well']\\n genold = adict['gen']\\n \\n d[gen].append(sum(nb))\\n \\n return(d)\",\n \"def decode_fusionTable_schema(tables: list) -> dict:\\n def _map_col(col: dict) -> dict:\\n col_schema = {k: col.get(k, '') for k in ('name', 'columnId', 'description')}\\n if col['name'] != 'Comment':\\n col_schema['mode'] = 'REQUIRED'\\n # We can actually use int/float now!\\n col_type: str = col['type']\\n if col_type == 'NUMBER':\\n col_type = 'INT64' if col['formatPattern'] == 'NUMBER_INTEGER' else 'FLOAT64'\\n col_schema['type'] = col_type\\n return col_schema\\n\\n def _map_table(table: dict) -> dict:\\n table_schema = {k: table.get(k, '') for k in ('name', 'tableId', 'description')}\\n table_schema['columns'] = list(map(_map_col, table['columns']))\\n return table_schema\\n\\n return dict((s.get('tableId'), s) for s in map(_map_table, tables))\",\n \"def format_bgc_metadata(df,float_id):\\n mdf = df[bgc_metadata_columns]\\n bgc_metadata_dict = {}\\n for col in list(mdf):\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\\\"'\\\",'\\\"'))\\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \\n bgc_metadata_df = pd.DataFrame({\\\"float_id\\\": [float_id], \\\"Metadata_Dict\\\": [bgc_metadata_dict]})\\n return bgc_metadata_df\",\n \"def get_mapping_table_ids():\\n dev_meta_dataset = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}\\\"\\n prod_meta_dataset = f\\\"{BQ_PARAMS['PROD_PROJECT']}.{BQ_PARAMS['PUBLIC_META_DATASET']}\\\"\\n\\n file_metadata_table_name = f\\\"{BQ_PARAMS['FILE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n file_metadata_table_id = f\\\"{prod_meta_dataset}.{file_metadata_table_name}\\\"\\n\\n case_metadata_table_name = f\\\"{BQ_PARAMS['CASE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n case_metadata_table_id = f\\\"{prod_meta_dataset}.{case_metadata_table_name}\\\"\\n\\n file_assoc_table_name = f\\\"{BQ_PARAMS['FILE_ASSOC_MAPPING_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n file_assoc_table_id = f\\\"{prod_meta_dataset}.{file_assoc_table_name}\\\"\\n\\n aliquot_table_name = f\\\"{BQ_PARAMS['ALIQUOT_TO_CASE_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n aliquot_table_id = f\\\"{prod_meta_dataset}.{aliquot_table_name}\\\"\\n\\n # todo switch to published table\\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, 'allPrograms'))\\n study_table_id = f\\\"{dev_meta_dataset}.{study_table_name}\\\"\\n\\n return file_metadata_table_id, case_metadata_table_id, file_assoc_table_id, aliquot_table_id, study_table_id\",\n \"def _create_tf_table(self, words) -> dict:\\r\\n\\r\\n freqTable = dict()\\r\\n tfTable = dict()\\r\\n\\r\\n totalWords = len(words)\\r\\n for word in words:\\r\\n if word in freqTable:\\r\\n freqTable[word] += 1\\r\\n else:\\r\\n freqTable[word] = 1\\r\\n \\r\\n uniqueWords = set(words)\\r\\n for word in uniqueWords:\\r\\n tfTable[word] = freqTable[word] / float(totalWords)\\r\\n\\r\\n return tfTable\",\n \"def get_tabledata_for(doc):\\n plomino_table = doc.getForm().id\\n data = dict(ADDITIONAL_VALUES)\\n map = FIELDS_MAP[plomino_table]\\n for key in doc.getItems():\\n if key not in map:\\n continue\\n value = normalize_value(doc.getItem(key, None))\\n data[map[key]] = value\\n post_method = plomino_table + '_postprocessing'\\n if post_method in globals():\\n globals()[post_method](doc, data)\\n iride_table = map['__tablename']\\n return {\\n iride_table: (data,)\\n }\",\n \"def review_counts(stat_info_dict):\\n review_counts = {}\\n for release, stat_dict in stat_info_dict.items():\\n review_counts_per_release = {}\\n for key, stat in stat_dict.items():\\n # review count\\n review_counts_per_release[key] = stat['metric']\\n review_counts[release] = review_counts_per_release\\n return review_counts\",\n \"def reprocess_dict (dict1):\\n out = {};\\n for kk,value in dict1.iteritems():\\n # parse keys\\n (lo0,dur,decdec,freqmhz,nch),weight = kk[0].split(\\\"_\\\"),kk[1]\\n if weight != \\\"natural\\\":\\n weight += \\\":\\\" + kk[3];\\n dec = -int(decdec.split(\\\"-\\\")[1]);\\n freq = int(freqmhz[:-3])\\n # parse layout\\n lo = lo0;\\n if lo[-2] in \\\"abcd\\\":\\n lores = \\\"0.\\\"+lo[-1];\\n lofreq = dict(a=650,b=800,c=1000,d=1400)[lo[-2]];\\n lo = lo[:-2];\\n else:\\n lores = 0;\\n lofreq = 0;\\n lo = lo[4:];\\n l00 = lo0[4:]\\n wbins.add(weight);\\n # make new entry\\n out[lo0,lores,lofreq,freq,dec,weight] = [value,kk];\\n return out;\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def update_data(self):\\n for sai_id_key in self.if_id_map:\\n namespace, sai_id = mibs.split_sai_id_key(sai_id_key)\\n if_idx = mibs.get_index_from_str(self.if_id_map[sai_id_key])\\n counter_table = self.namespace_db_map[namespace].get_all(mibs.COUNTERS_DB, \\\\\\n mibs.counter_table(sai_id))\\n if counter_table is None:\\n counter_table = {}\\n self.if_counters[if_idx] = counter_table\\n\\n\\n self.lag_name_if_name_map, \\\\\\n self.if_name_lag_name_map, \\\\\\n self.oid_lag_name_map, _, _ = Namespace.get_sync_d_from_all_namespace(mibs.init_sync_d_lag_tables, self.db_conn)\\n\\n self.if_range = sorted(list(self.oid_name_map.keys()) + list(self.oid_lag_name_map.keys()))\\n self.if_range = [(i,) for i in self.if_range]\",\n \"def to_table(self):\\n table = Table()\\n table['THETA_LO'] = Quantity([self.counts.offset[:-1]], unit=self.counts.offset.unit)\\n table['THETA_HI'] = Quantity([self.counts.offset[1:]], unit=self.counts.offset.unit)\\n table['ENERG_LO'] = Quantity([self.counts.energy[:-1]], unit=self.counts.energy.unit)\\n table['ENERG_HI'] = Quantity([self.counts.energy[1:]], unit=self.counts.energy.unit)\\n table['counts'] = self.counts.to_table()['data']\\n if self.counts.data_err is not None:\\n table['counts_err'] = self.counts.to_table()['data_err']\\n table['livetime'] = self.livetime.to_table()['data']\\n table['bkg'] = self.bg_rate.to_table()['data']\\n if self.bg_rate.data_err is not None:\\n table['bkg_err'] = self.bg_rate.to_table()['data_err']\\n table.meta['HDUNAME'] = \\\"bkg_2d\\\"\\n return table\",\n \"def calculate_TF_IDF():\\n conn = util.getDBConnection()\\n sql = \\\"select word from clean_keywords\\\"\\n print sql\\n rows = util.executeSQL(conn, sql)\\n word_tf = {}\\n word_df = {}\\n for row in rows:\\n word = row[0]\\n sql1 = \\\"select doc_id from keywords where name='\\\" + word + \\\"'\\\"\\n print sql1\\n res = util.executeSQL(conn, sql1)\\n for row1 in res:\\n pkg_id = row1[0]\\n key = word + ':' + str(pkg_id)\\n if key in word_tf:\\n tf_count = word_tf[key]\\n word_tf[key] = tf_count + 1\\n else:\\n word_tf[key] = 1\\n if word in word_df:\\n df_count = word_df[word]\\n word_df[word] = df_count + 1\\n else:\\n word_df[word] = 1\\n\\n for word, df in word_df.iteritems():\\n sql = 'update clean_keywords set df=' + str(df) + \\\" where word='\\\" + word + \\\"'\\\"\\n print sql\\n util.executeSQL(conn, sql)\\n\\n for word_pkgid, tf in word_tf.iteritems():\\n word, pkg_id = word_pkgid.split(\\\":\\\")\\n sql = 'update keywords set tf=' + str(tf) + \\\" where name='\\\" + word + \\\"' and doc_id=\\\" + str(pkg_id)\\n print sql\\n util.executeSQL(conn, sql)\",\n \"def dict2():\\n print(record1)\\n record2 = {}\\n for k, v in record1.items():\\n count = 0\\n for letter in v:\\n if letter == 't' or letter == 'T':\\n count += 1\\n record2[k] = count\\n print(record2)\",\n \"def prepare_for_table(data, machine_id):\\n\\n defint = lambda x: 0 if x == '' else int(x)\\n\\n keep_and_convert = {\\n 'MESSAGE': str,\\n 'PRIORITY': defint,\\n '__REALTIME_TIMESTAMP': defint,\\n '_PID': defint,\\n '_UID': defint,\\n '_SYSTEMD_UNIT': str,\\n 'SYSLOG_IDENTIFIER': str,\\n '_COMM': str,\\n }\\n result = dict((key, converter(data.get(key, ''))) for key, converter in keep_and_convert.items())\\n result['MACHINE_ID'] = machine_id\\n return data['__CURSOR'], result\",\n \"def append_counting(dict):\\n row_c = []\\n # for nuc in NUC: #Scans all the elements and adds it to the table.\\n # row_c.append(dict[nuc])\\n for mot in MOT:\\n row_c.append(dict[mot])\\n for nuc_nr in NUC_NR :\\n row_c.append(dict[nuc_nr + \\\"_NR\\\"])\\n # #row.extend([dict[\\\"AA_NR\\\"], dict[\\\"TT_NR\\\"], dict[\\\"CC_NR\\\"], dict[\\\"GG_NR\\\"]])\\n return row_c\",\n \"def get_patients_dict(table):\\n\\tf = open(table)\\n\\tpatients = f.readline().strip().split(\\\"\\\\t\\\")[1:]\\n\\t\\t \\n\\tpatients_dict = {}\\n\\tfor i in patients:\\n\\t\\tpatients_dict[i.replace('\\\"', '')] = {}\\n\\t\\t \\n\\tfor i in f:\\n\\t\\tl = i.strip().split(\\\"\\\\t\\\")\\n\\t\\tgene = l[0]\\n\\n\\t\\tfor j in range(len(l[1:])):\\n\\t\\t\\tpatients_dict[patients[j]][gene] = int(l[1:][j])\\n\\treturn patients_dict\",\n \"def build_article_map(f='./wikispeedia_paths-and-graph/articles.tsv'):\\n out_dict = {}\\n count = 0\\n with open(f, 'r') as r:\\n for _ in xrange(12):\\n next(r)\\n for line in r:\\n out_dict[line.strip('\\\\n')] = count\\n count += 1\\n return out_dict\",\n \"def files2dictionary(filename, countryID, supp_dict):\\r\\n\\r\\n fh = open(filename)\\r\\n header = next(fh)\\r\\n\\r\\n data_dict = {}\\r\\n data_dict[countryID] = {}\\r\\n\\r\\n numlist = range(1, 36)\\r\\n agelist = []\\r\\n for line in fh:\\r\\n linelist = line.strip().split(\\\",\\\")\\r\\n age = linelist[4]\\r\\n agelist.append(age)\\r\\n for icdrep in numlist:\\r\\n if str(age) not in data_dict[countryID]:\\r\\n data_dict[countryID][str(age)] = {}\\r\\n #if str(icdrep) not in data_dict[countryID][str(age)]:\\r\\n data_dict[countryID][str(age)][str(icdrep)] = float(linelist[icdrep+8]) # look into changing this to one million\\r\\n else:\\r\\n data_dict[countryID][str(age)][str(icdrep)] = float(linelist[icdrep+8]) # look into changing this to one million\\r\\n fh.close()\\r\\n supp_dict.update(support_counter(header.split(\\\",\\\"), agelist, supp_dict))\\r\\n return data_dict, supp_dict\",\n \"def compactify(self):\\n logger.debug(\\\"rebuilding dictionary, shrinking gaps\\\")\\n\\n # build mapping from old id -> new id\\n idmap = dict(zip(sorted(itervalues(self.token2id)), range(len(self.token2id))))\\n\\n # reassign mappings to new ids\\n self.token2id = {token: idmap[tokenid] for token, tokenid in iteritems(self.token2id)}\\n self.id2token = {}\\n self.dfs = {idmap[tokenid]: freq for tokenid, freq in iteritems(self.dfs)}\",\n \"def _prepare_stats_table(self, pinfos):\\n\\n stats_tbl = OrderedDict()\\n stats_tbl[\\\"Title\\\"] = OrderedDict()\\n for res in self.rsts:\\n stats_tbl[res.reportid] = OrderedDict()\\n\\n for pinfo in pinfos:\\n for colname in (pinfo[\\\"colname\\\"], pinfo[\\\"xcolname\\\"]):\\n if colname in stats_tbl[\\\"Title\\\"]:\\n continue\\n\\n # Each column name is represented by a row in the statistics table. Fill the \\\"Title\\\"\\n # column.\\n title_dict = stats_tbl[\\\"Title\\\"][colname] = OrderedDict()\\n defs = self._refdefs.info[colname]\\n\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\":\\n # Convert nanoseconds to microseconds.\\n unit = \\\"us\\\"\\n else:\\n unit = defs.get(\\\"short_unit\\\", \\\"\\\")\\n\\n title_dict[\\\"colname\\\"] = colname\\n if unit:\\n title_dict[\\\"colname\\\"] += f\\\", {unit}\\\"\\n title_dict[\\\"coldescr\\\"] = defs[\\\"descr\\\"]\\n\\n title_dict[\\\"funcs\\\"] = OrderedDict()\\n for funcname in self._stats_funcs:\\n if funcname in self.rsts[0].cstats[colname]:\\n title_dict[\\\"funcs\\\"][funcname] = RORawResult.get_stat_func_descr(funcname)\\n\\n # Now fill the values for each result.\\n for res in self.rsts:\\n res_dict = stats_tbl[res.reportid][colname] = OrderedDict()\\n res_dict[\\\"funcs\\\"] = OrderedDict()\\n\\n for funcname in title_dict[\\\"funcs\\\"]:\\n val = res.cstats[colname][funcname]\\n fmt = \\\"{}\\\"\\n if defs.get(\\\"unit\\\") == \\\"nanosecond\\\" and \\\"index\\\" not in funcname:\\n val /= 1000\\n fmt = \\\"{:.2f}\\\"\\n if defs[\\\"type\\\"] == \\\"float\\\":\\n fmt = \\\"{:.2f}\\\"\\n\\n fdict = res_dict[\\\"funcs\\\"][funcname] = OrderedDict()\\n fdict[\\\"val\\\"] = fmt.format(val)\\n fdict[\\\"raw_val\\\"] = val\\n\\n if self._refres.reportid == res.reportid:\\n fdict[\\\"hovertext\\\"] = \\\"This is the reference result, other results \\\" \\\\\\n \\\"are compared to this one.\\\"\\n continue\\n\\n ref_fdict = stats_tbl[self._refres.reportid][colname][\\\"funcs\\\"][funcname]\\n change = val - ref_fdict[\\\"raw_val\\\"]\\n if ref_fdict[\\\"raw_val\\\"]:\\n percent = (change / ref_fdict[\\\"raw_val\\\"]) * 100\\n else:\\n percent = change\\n change = fmt.format(change) + unit\\n percent = \\\"{:.1f}%\\\".format(percent)\\n fdict[\\\"hovertext\\\"] = f\\\"Change: {change} ({percent})\\\"\\n\\n return stats_tbl\",\n \"def _collect_counts(self, instance_list):\\n \\\"\\\"\\\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\\n All \\\"rare words\\\" (those words that appear less than 3 times) are replaced by <UNK>.\\n I also add label|START counts.\\n \\\"\\\"\\\"\\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\\n for instance in instance_list: # Set of <(w, pos), l>\\n index = 0\\n for t in instance.data: # Tuple of (w, pos)\\n index = instance.data.index(t)\\n # print t[0] # word\\n # print instance.label[index] # label\\n if t in self.V:\\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\\n else:\\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\\n if index > 0:\\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\\n else:\\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1\",\n \"def normalize_keys(df: pd.DataFrame) -> None:\\n renames = {'tripUpdate_trip_tripId': 'trip_id', 'tripUpdate_trip_startDate': 'start_date',\\n 'tripUpdate_trip_directionId': 'direction_id', 'tripUpdate_trip_routeId': 'route_id',\\n 'tripUpdate_trip_scheduleRelationship': 'schedule_relationship',\\n 'tripUpdate_trip_startTime': 'start_time',\\n 'tripUpdate_timestamp': 'timestamp', 'tripUpdate_vehicle_id': 'vehicle_id',\\n 'stopSequence': 'stop_sequence', 'stopId': 'stop_id',\\n 'scheduleRelationship': 'schedule_relationship2',\\n 'vehicle_trip_tripId': 'trip_id', 'vehicle_trip_scheduleRelationship': 'schedule_relationship',\\n 'vehicle_timestamp': 'timestamp', 'vehicle_vehicle_id': 'vehicle_id',\\n 'vehicle_trip_startTime': 'start_time', 'vehicle_trip_startDate': 'start_date',\\n 'vehicle_trip_routeId': 'route_id', 'vehicle_trip_directionId': 'direction_id',\\n 'tripUpdate_stopTimeUpdate_stopSequence': 'stop_sequence',\\n 'tripUpdate_stopTimeUpdate_stopId': 'stop_id',\\n 'tripUpdate_stopTimeUpdate_arrival_delay': 'arrival_delay',\\n 'tripUpdate_stopTimeUpdate_arrival_time': 'arrival_time',\\n 'tripUpdate_stopTimeUpdate_departure_delay': 'departure_delay',\\n 'tripUpdate_stopTimeUpdate_departure_time': 'departure_time',\\n 'tripUpdate_stopTimeUpdate_arrival_uncertainty': 'arrival_uncertainty',\\n 'tripUpdate_stopTimeUpdate_departure_uncertainty': 'departure_uncertainty',\\n 'alert_activePeriod_start': 'period_start', 'alert_activePeriod_end': 'period_end',\\n 'alert_informedEntity_routeId': 'route_id', 'alert_informedEntity_stopId': 'stop_id',\\n 'alert_informedEntity_trip_tripId': 'trip_id',\\n 'alert_informedEntity_trip_scheduleRelationship': 'schedule_relationship',\\n 'alert_headerText_translation_text': 'header_text',\\n 'alert_descriptionText_translation_text': 'description_text',\\n }\\n df.rename(columns=renames, inplace=True)\",\n \"def aggregate_buff_lines(res_lines, buff_lines, dispel_lines):\\n\\n res_data = {}\\n buff_data = {}\\n dispel_data = {}\\n\\n for lines, data in zip(\\n (res_lines, buff_lines, dispel_lines), (res_data, buff_data, dispel_data)\\n ):\\n for spell_id, spell_name, source, target in lines:\\n if spell_id not in data:\\n data[spell_id] = {}\\n\\n spell_dict = data[spell_id]\\n\\n if source not in spell_dict:\\n spell_dict[source] = 0\\n\\n spell_dict[source] += 1\\n\\n return res_data, buff_data, dispel_data\",\n \"def _finalize(self, sampled_data):\\n final_data = dict()\\n for table_name, table_rows in sampled_data.items():\\n parents = self.metadata.get_parents(table_name)\\n if parents:\\n for parent_name in parents:\\n foreign_key = self.metadata.get_foreign_key(parent_name, table_name)\\n if foreign_key not in table_rows:\\n parent_ids = self._find_parent_ids(table_name, parent_name, sampled_data)\\n table_rows[foreign_key] = parent_ids\\n\\n reversed_data = self.metadata.reverse_transform(table_name, table_rows)\\n\\n fields = self.metadata.get_fields(table_name)\\n\\n final_data[table_name] = reversed_data[list(fields.keys())]\\n\\n return final_data\",\n \"def _transform_idoc(df):\\n global _SIMPLECOUNT_COLUMNS\\n\\n try:\\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\\n\\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\\n \\n c_nc = df['admtypo3'] == 1\\n c_tv = df['admtypo3'] == 2\\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\\n c_viol = df['offtype'] == 1\\n c_male = df['sex'] == 'M'\\n c_female = ~c_male\\n\\n c_first2 = [c_nc, c_tv]\\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\\n \\n def helper(c, indicator_id, first2):\\n df['fk_simplecount_indicator'] = indicator_id\\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\\n if first2:\\n return df[c].groupby(g).size().reset_index(name='value')\\n else:\\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\\n\\n out = pd.DataFrame()\\n for i in range(2):\\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\\n \\n for i in range(len(c_others)):\\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\\n\\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\\n return out[_SIMPLECOUNT_COLUMNS]\\n except:\\n raise\",\n \"def _get_rank_values(self):\\n \\n info_gains = {}\\n \\n #caluclate info gain\\n for col in self.cat_cols:\\n info_gains[col] = self._get_info_gain(col)\\n \\n return info_gains\",\n \"def _getSourceMetrics(self, idKeyTuple, idValList, sourceTableList):\\n if self._idKeyTuple is None:\\n self._idKeyTuple = tuple(idKeyTuple)\\n self._idKeyDTypeList = _getDTypeList(keyTuple = self._idKeyTuple,\\n valTuple = idValList[0])\\n else:\\n if self._idKeyTuple != tuple(idKeyTuple):\\n raise RuntimeError(\\\"idKeyTuple = %s != %s = first idKeyTuple; must be the same each time\\\" % \\\\\\n (idKeyTuple, self._idKeyTuple))\\n\\n dataDict = {}\\n for idTuple, sourceTable in itertools.izip(idValList, sourceTableList):\\n if len(sourceTable) == 0:\\n continue\\n \\n idList = sourceTable.get(\\\"id\\\")\\n dataList = [sourceTable.get(key) for key in self._sourceKeyTuple]\\n\\n if self._sourceDTypeList is None:\\n self._sourceDTypeList = [(key, arr.dtype)\\n for key, arr in itertools.izip(self._sourceKeyTuple, dataList)]\\n \\n transposedDataList = zip(*dataList)\\n del dataList\\n\\n dataDict.update((srcId, idTuple + tuple(data))\\n for srcId, data in itertools.izip(idList, transposedDataList))\\n return dataDict\",\n \"def preprocess_data_by_mapping(dicts_count, alias_mapping):\\n\\n new_mapping_dict = {}\\n for (key, value) in dicts_count.items():\\n\\n # Replace the key with lower string\\n new_key = unicode.lower(key)\\n\\n # Replace the key with object alias\\n if new_key in alias_mapping:\\n new_key = alias_mapping[new_key]\\n\\n # Check if the object key is already exists in the dict\\n if new_key in new_mapping_dict:\\n new_mapping_dict[new_key] += value\\n else:\\n new_mapping_dict[new_key] = value\\n\\n return new_mapping_dict\",\n \"def new_counts_dict():\\n\\n\\tIN_FILES = [\\\"../_semtag_dataset_webanno_tfidf_inimigo.txt\\\",\\\"../_semtag_dataset_webanno_tfidf_publico.txt\\\" ]\\n\\n\\ttxt = []\\n\\tfor in_file in IN_FILES:\\n\\t with codecs.open(in_file,\\\"r\\\",\\\"utf-8\\\") as fid:\\n\\t txt += fid.readlines()\\n\\t#words\\n\\twords = [w for m in txt for w in m.split()]\\n\\t#unique words\\n\\twords = list(set(words))\\n\\t#word index\\n\\twrd2idx = {w:-1 for w in words}\\n\\n\\tset_trace()\\n\\t\\n\\twith open(COUNTS_DIC,\\\"w\\\") as fod:\\n\\t\\tcPickle.dump(wrd2idx, fod, cPickle.HIGHEST_PROTOCOL)\",\n \"def aggregate_by_primary_attribute(table):\\n result = {}\\n for row in table:\\n for attribute_to_aggregate_by in row[1].split(','):\\n attribute_to_aggregate_by.strip()\\n attribute_data = row[0]\\n if attribute_to_aggregate_by not in result:\\n result[attribute_to_aggregate_by] = [attribute_data]\\n else:\\n result[attribute_to_aggregate_by] += [attribute_data]\\n return result\",\n \"def _create_metrics_table(font, format, base, create_glyph_metrics):\\n # we don't set PCF_COMPRESSED_METRICS\\n metrics = tuple(create_glyph_metrics(_g, base) for _g in font.glyphs)\\n table_bytes = (\\n bytes(le.uint32(format))\\n + bytes(base.int32(len(metrics)))\\n + b''.join(bytes(_t) for _t in metrics)\\n )\\n return table_bytes, format\",\n \"def calcCountDict(TFdict):\\n\\n countDict = {}\\n\\n for doc in TFdict:\\n for term in doc:\\n if term in countDict:\\n countDict[term] +=1\\n else:\\n countDict[term] = 1\\n\\n return countDict\",\n \"def get_counts_by_manufacturers(table):\\n manufacturers_dict = {}\\n for item in table:\\n try:\\n if item[2]:\\n try:\\n if item[2] in manufacturers_dict.keys():\\n manufacturers_dict[item[2]] += 1\\n else:\\n manufacturers_dict[item[2]] = 1\\n except IndexError:\\n pass\\n else:\\n raise ValueError\\n except ValueError:\\n pass\\n\\n return manufacturers_dict\",\n \"def summarize_metadata(self):\\n meta_dict = {}\\n for comp in self.dataset.data_vars:\\n for mkey, mvalue in self.dataset[comp].attrs.items():\\n meta_dict[f\\\"{comp}.{mkey}\\\"] = mvalue\\n\\n return meta_dict\",\n \"def prepare_data(groups):\\n all_dicts = []\\n for idx, group in groups:\\n res_dict = {'organism': group.organism.iloc[0]}\\n for g_idx, row in group.iterrows():\\n if pd.notna(row.label):\\n res_dict[row.cmp_name] = {'label': row.label, 'mic': row.MIC}\\n else:\\n res_dict[row.cmp_name] = {'label': '', 'mic': row.MIC}\\n all_dicts.append(res_dict)\\n return all_dicts\",\n \"def makeTargetFieldsDict(tgt_fields):\\n global RES, NRES, HOTEL\\n out_dict = {}\\n for fld in tgt_fields:\\n use, suffix = fld.split(\\\"_SF_\\\")\\n if use in RES:\\n act_field = \\\"RES\\\"\\n elif use in NRES:\\n act_field = \\\"JOB\\\"\\n elif use in HOTEL:\\n act_field = \\\"HOTEL\\\"\\n else:\\n # This is an untracked ause\\n continue\\n share_field = \\\"shr_{}\\\".format(use)\\n sqft_field = \\\"{}_sqft\\\".format(use)\\n out_dict[fld] = (act_field, share_field, sqft_field)\\n return out_dict\",\n \"def get_tracking_info(self):\\n items = [('run_id', b_to_str),\\n ('asic_id', b_to_str),\\n ('version_name', b_to_str),\\n ('asic_temp', float),\\n ('heatsink_temp', float),\\n ('exp_script_purpose', b_to_str),\\n ('flow_cell_id', b_to_str),\\n ('device_id', b_to_str),\\n ]\\n attrs = self['/UniqueGlobalKey/tracking_id'].attrs\\n return {key: converter(attrs[key]) for key, converter in items}\",\n \"def updateAnnoByIdDictFromMeta(syn,idDict,metaDf,refCol,fileExts):\\n for key in idDict:\\n print \\\"updating annotaion values for key: %s\\\" % key\\n for synId in idDict[key]:\\n print \\\"> %s\\\" %synId\\n temp = syn.get(synId, downloadFile = False)\\n exts = ')|('.join(fileExts)\\n exts = r'(' + exts + ')'\\n tempName = re.sub(exts,\\\"\\\",temp.name)\\n row = df.loc[df[refCol] == tempName]\\n temp[key] = map(str,row[key])[0]\\n temp = syn.store(temp,forceVersion = False)\\n print \\\"\\\"\",\n \"def make_contingency_tables(\\n y: np.ndarray, flagged_A: np.ndarray, flagged_B: np.ndarray\\n) -> Dict[int, np.ndarray]:\\n\\n y = np.array(y).astype(np.int64).flatten()\\n flagged_A = np.array(flagged_A).astype(np.bool_).flatten()\\n flagged_B = np.array(flagged_B).astype(np.bool_).flatten()\\n\\n if len(flagged_A) != len(y) or len(flagged_B) != len(y):\\n raise ValueError(\\n f\\\"Expected arrays y, flagged_A, and flagged_B of the same length: \\\\\\n got {len(y)}, {len(flagged_A)}, and {len(flagged_B)}.\\\"\\n )\\n\\n contingency_tables = {}\\n for class_id in np.unique(y):\\n\\n items_flagged_A = flagged_A[y == class_id]\\n items_flagged_B = flagged_B[y == class_id]\\n\\n a = (~items_flagged_A & ~items_flagged_B).sum()\\n b = (~items_flagged_A & items_flagged_B).sum()\\n c = (items_flagged_A & ~items_flagged_B).sum()\\n d = (items_flagged_A & items_flagged_B).sum()\\n\\n table = np.array([[a, b], [c, d]])\\n contingency_tables[class_id] = table\\n\\n return contingency_tables\",\n \"def getTF_IDFSpace():\\n sids,documents = getSongTextInfo()\\n texts = [[word for word in document.lower().split()] for document in documents]\\n dictionary = corpora.Dictionary(texts)\\n corpus = [dictionary.doc2bow(text) for text in texts]\\n tfidf = models.TfidfModel(corpus)\\n corpus_tfidf = tfidf[corpus]\\n songMap = {}\\n index = 0\\n for doc in corpus_tfidf:\\n sid = sids[index]\\n rMap = {}\\n for item in doc:\\n wid = item[0]\\n count = item[1]\\n rMap[wid] = count\\n songMap[sid] = rMap\\n index += 1\\n return songMap\",\n \"def table_summary():\\n \\n t = dict()\\n t['name'] = get_names()\\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\\n N = len(t['name'])\\n \\n # host\\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\\\'itoi']\\n \\n # progenitor\\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\\n \\n # progenitor type\\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\\n \\n # metallicity\\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\\n \\n # associations\\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\\n \\n tout = Table(t)\\n tout.pprint()\\n tout.write('../data/stream_origin.fits', overwrite=True)\",\n \"def produce_mirna_single_counts_table(count_files, table_file):\\n merge_count_tables(count_files, table_file, \\\".dedup.single.bam.mirbase_counts.txt\\\")\",\n \"def coalesce_tables(tables):\\n ## For each table, we can:\\n ## Process the names in the first row, as column names\\n ## If we have a \\\"quantity\\\" column, convert this from euro style to float\\n\\n ## If the column names are the same, we can append one to the other.\\n \\n proc_tables = OrderedDict()\\n most_recent_key = None\\n for tn,t in enumerate(tables):\\n for i, r in enumerate(t):\\n ##print(f\\\"Table {tn}, Row number {i}\\\")\\n col_accessors = [str(x) for x in range(len(r))]\\n ## Get the processed row names\\n if i == 0: \\n cnames = {}\\n for c in col_accessors:\\n cnames[c] = r[c].lower().strip().replace(\\\" \\\", \\\"\\\")\\n continue\\n ## Now, cnames was defined from iteration i==0\\n rec = {}\\n for c in col_accessors:\\n rec[cnames[c]] = r[c]\\n\\n fixweight = lambda x: float(x.replace(\\\",\\\", \\\".\\\"))\\n \\n \\n if 'netweight' in rec.keys():\\n if rec['netweight'] is not None:\\n rec['netweight'] = fixweight(rec['netweight'])\\n\\n if rec['no.'] is not None:\\n ## new record\\n most_recent_key = rec['no.']\\n proc_tables[most_recent_key] = rec\\n else:\\n ## append the description to previous\\n if rec['description'] is not None:\\n proc_tables[most_recent_key]['description'] = proc_tables[most_recent_key]['description'] + \\\" \\\" + rec['description']\\n\\n\\n return(list(proc_tables.values()))\",\n \"def build_table():\\n with contextlib.ExitStack() as stack:\\n files = [stack.enter_context(gzip.open(f, 'rt')) for f in sys.argv[1:]]\\n iters = [(line.split() for line in f) for f in files]\\n for it in iters:\\n next(it)\\n key = operator.itemgetter(0)\\n table = []\\n for k, g in itertools.groupby(merge(*iters, key=key), key=key):\\n props = list(g)\\n if len(props) == len(iters):\\n table.append([k] + [x[1] for x in props])\\n for snp in table:\\n print(*snp)\",\n \"def create_op_set_id_version_map(table: VersionTableType) -> VersionMapType:\\n result: VersionMapType = {}\\n\\n def process(release_version: str, ir_version: int, *args: Any) -> None:\\n del release_version # Unused\\n for pair in zip([\\\"ai.onnx\\\", \\\"ai.onnx.ml\\\", \\\"ai.onnx.training\\\"], args):\\n if pair not in result:\\n result[pair] = ir_version\\n if pair[0] == \\\"ai.onnx.training\\\":\\n result[\\\"ai.onnx.preview.training\\\", pair[1]] = ir_version\\n\\n for row in table:\\n process(*row)\\n return result\",\n \"def parse_distmat_to_dict(table):\\r\\n\\r\\n col_headers, row_headers, data = parse_matrix(table)\\r\\n assert(col_headers == row_headers)\\r\\n\\r\\n result = defaultdict(dict)\\r\\n for (sample_id_x, row) in zip(col_headers, data):\\r\\n for (sample_id_y, value) in zip(row_headers, row):\\r\\n result[sample_id_x][sample_id_y] = value\\r\\n return result\",\n \"def freq_table(a):\\n Detail_freq = a.loc[:, (a.dtypes == object) | (a.dtypes == long) ].columns.get_values().tolist()\\n print(Detail_freq)\\n for freq in Detail_freq:\\n df1 = pd.DataFrame(a[freq].value_counts(dropna=False).astype(float).map('{:20,.0f}'.format).sort_index()).rename(columns={freq:'Count'})\\n df2 = pd.DataFrame(a[freq].value_counts(normalize = True, dropna=False).map('{:,.2%}'.format).sort_index()).rename(columns={freq:'Percentage'})\\n df = pd.concat([df1, df2], axis = 1)\\n print(df)\",\n \"def tab(data, column) :\\r\\n\\r\\n table = {}\\r\\n \\r\\n for i in data :\\r\\n if i[column] not in table :\\r\\n # add it to tab\\r\\n table[i[column]] = 1 \\r\\n else:\\r\\n # add tabulation\\r\\n table[i[column]] += 1\\r\\n\\r\\n #place the cursor back at 0\\r\\n \\r\\n return table\",\n \"def dictagfreq2(kind, fname):\\n \\n #x = ['movie', 'moviecode', 'offset', 'well', 'agmin', 'agsec', 'agdur', \\n #'agtype', 'agcomm', 'escmin', 'escsec', 'escdur', 'esctype', 'escbeh', \\n #'esccomm', 'gen', 'date', 'assay', 'fps', 'flyid', 'side', 'moviepart']\\n\\n d = {}\\n y = '1'\\n b = []\\n \\n with open(fname) as f:\\n for l in f:\\n adict = agline2(l)\\n #print(l)\\n \\n if adict['well'] != y:\\n agfreqcmd(kind, b, d[gen])\\n b = []\\n \\n if adict['agtype'] != '-':\\n b.append(adict['agtype'])\\n\\n if adict['esctype'] != '':\\n b.append(adict['esctype'])\\n \\n gen = adict['gen'] \\n if gen not in d:\\n d[gen] = [] \\n \\n #print('b', b)\\n #print('well', adict['well'])\\n #print('gen', gen, 'd', d)\\n \\n y = adict['well']\\n \\n agfreqcmd(kind, b, d[gen])\\n \\n return(d)\",\n \"def _get_identifiers_from_kbs(self) -> dict:\\n id_mapping_dict = defaultdict(set)\\n\\n for kb in self.kbs:\\n sys.stdout.write('\\\\n%s \\\\n' % kb.name)\\n for p in tqdm.tqdm(kb.pathways, total=len(kb.pathways)):\\n for ent in p.entities:\\n id_set = list(set(ent.xrefs))\\n if len(id_set) == 1:\\n id_mapping_dict[id_set.pop()] = set([])\\n for p, q in itertools.combinations(id_set, 2):\\n id_mapping_dict[p].add(q)\\n id_mapping_dict[q].add(p)\\n\\n return id_mapping_dict\",\n \"def calcTFIDFDict(TFDict, IDFDict):\\n\\n TFIDFDict = {}\\n\\n #for each term in the document, multiply the tf and idf values\\n\\n for term in TFDict:\\n TFIDFDict[term] = TFDict[term] * IDFDict[term]\\n\\n return TFIDFDict\",\n \"def produce_mirna_allbest_counts_table(count_files, table_file):\\n merge_count_tables(count_files, table_file, \\\".dedup.bam.mirbase_counts.txt\\\")\",\n \"def print_tables(hash_table, f_output, l_samples):\\n\\n l_fields = ['chr', 'pos', 'ref', 'alt', 'QUAL', 'FILTER',\\n 'Func.refGene', 'Gene.refGene', 'GeneDetail.refGene', 'ExonicFunc.refGene', 'AAChange.refGene',\\n 'cytoBand', 'ExAC_ALL', 'ExAC_AFR', 'ExAC_AMR', 'ExAC_EAS', 'ExAC_FIN', 'ExAC_NFE', 'ExAC_OTH',\\n 'ExAC_SAS',\\n 'avsnp147', 'SIFT_score', 'SIFT_pred', 'Polyphen2_HDIV_score', 'Polyphen2_HDIV_pred',\\n 'Polyphen2_HVAR_score',\\n 'Polyphen2_HVAR_pred', 'LRT_score', 'LRT_pred', 'MutationTaster_score', 'MutationTaster_pred',\\n 'MutationAssessor_score', 'MutationAssessor_pred', 'FATHMM_score', 'FATHMM_pred', 'PROVEAN_score',\\n 'PROVEAN_pred', 'VEST3_score', 'CADD_raw', 'CADD_phred', 'DANN_score', 'fathmm-MKL_coding_score',\\n 'fathmm-MKL_coding_pred', 'MetaSVM_score', 'MetaSVM_pred', 'MetaLR_score', 'MetaLR_pred',\\n 'integrated_fitCons_score', 'integrated_confidence_value', 'GERP++_RS', 'phyloP7way_vertebrate',\\n 'phyloP20way_mammalian', 'phastCons7way_vertebrate', 'phastCons20way_mammalian', 'SiPhy_29way_logOdds']\\n l_fields = l_fields + l_samples\\n \\n l_chr = set([item[0] for item in hash_table.keys()])\\n\\n fo = open(f_output, 'w')\\n fo.write(','.join(l_fields) + '\\\\n')\\n for key in sorted(hash_table.keys(), key=itemgetter(1)):\\n fo.write(','.join(map(lambda field: hash_table[key].get(field, '.'), l_fields)) + '\\\\n')\\n fo.close()\",\n \"def compute_helper_mempool_dictionaries():\\n txn_density_dict = {}\\n txn_parents_dict = {}\\n txn_size_dict = {}\\n mempool_data = parse_mempool_csv()\\n for elem in mempool_data:\\n size = elem.weight/MAXIMUM_BLOCK_WEIGHT # weight mapped to (0,1)\\n txn_size_dict[elem.txid] = size \\n txn_density_dict[elem.txid] = elem.fee/size\\n if elem.parents != '':\\n txn_parents_dict[elem.txid] = elem.parents.strip().split(';')\\n return txn_density_dict,txn_parents_dict,txn_size_dict\",\n \"def calcIDFDict(countDict, numfiles):\\n\\n IDFDict = {}\\n for term in countDict:\\n IDFDict[term] = math.log(numfiles / countDict[term])\\n\\n return IDFDict\",\n \"def define_info_dict():\\n\\n d = {\\n \\\"PRED\\\": {\\n \\\"COLUMN\\\": [\\\"predicted_class\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Predicted class: somatic, germline, artifact\\\",\\n },\\n \\\"PROB\\\": {\\n \\\"COLUMN\\\": [\\\"prob_s\\\", \\\"prob_g\\\", \\\"prob_a\\\"],\\n \\\"Number\\\": \\\"3\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Prediction probability of \\\"\\n \\\"being somatic, germline, artifact in this order\\\",\\n },\\n \\\"SNP\\\": {\\n \\\"COLUMN\\\": [\\\"is_on_db\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\\\",\\n },\\n \\\"ANNO\\\": {\\n \\\"COLUMN\\\": [\\\"annotation\\\"],\\n \\\"Number\\\": \\\".\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel annotation formatted as \\\"\\n \\\"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\\\"\\n \\\"Delimited by comma for multiple isoforms\\\",\\n },\\n \\\"COSMIC_CNT\\\": {\\n \\\"COLUMN\\\": [\\\"cosmic_cnt\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"COSMIC count in v89\\\",\\n },\\n \\\"MAXMAF\\\": {\\n \\\"COLUMN\\\": [\\\"max_maf\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Maximum minor allele frequency (MAF) \\\"\\n \\\"reported in dbSNP, ClinVar and gnomAD non-cancer population\\\",\\n },\\n \\\"COMMON\\\": {\\n \\\"COLUMN\\\": [\\\"is_common\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Common in dbSNP or MAXMAF > 0.01\\\",\\n },\\n \\\"CLIN\\\": {\\n \\\"COLUMN\\\": [\\\"clin_info\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"ClinVar annotation formatted as ClinicalSignificance|Condition\\\",\\n },\\n \\\"ICP\\\": {\\n \\\"COLUMN\\\": [\\\"indel_complexity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel complexity: mismatches around the indel measured by edit distance\\\",\\n },\\n \\\"DSM\\\": {\\n \\\"COLUMN\\\": [\\\"dissimilarity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Dissimilarity: edit distance between indel and flanking sequences\\\",\\n },\\n \\\"ISZ\\\": {\\n \\\"COLUMN\\\": [\\\"indel_size\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel size\\\",\\n },\\n \\\"REP\\\": {\\n \\\"COLUMN\\\": [\\\"repeat\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Repeat: count of the indel-sequence repeats in flanking region\\\",\\n },\\n \\\"UQM\\\": {\\n \\\"COLUMN\\\": [\\\"is_uniq_mapped\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by uniquely mapped reads\\\",\\n },\\n \\\"NEB\\\": {\\n \\\"COLUMN\\\": [\\\"is_near_boundary\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Near exon boundary\\\",\\n },\\n \\\"EQX\\\": {\\n \\\"COLUMN\\\": [\\\"equivalence_exists\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Equivalent alignments exist for the indel\\\",\\n },\\n \\\"BID\\\": {\\n \\\"COLUMN\\\": [\\\"is_bidirectional\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by forward and reverse reads\\\",\\n },\\n \\\"MTA\\\": {\\n \\\"COLUMN\\\": [\\\"is_multiallelic\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Multialleleic\\\",\\n },\\n \\\"FRM\\\": {\\n \\\"COLUMN\\\": [\\\"is_inframe\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"In-frame indel\\\",\\n },\\n \\\"SPL\\\": {\\n \\\"COLUMN\\\": [\\\"is_splice\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in splice region\\\",\\n },\\n \\\"TRN\\\": {\\n \\\"COLUMN\\\": [\\\"is_truncating\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Truncating indel\\\",\\n },\\n \\\"CDD\\\": {\\n \\\"COLUMN\\\": [\\\"is_in_cdd\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in conserved domain\\\",\\n },\\n \\\"LOC\\\": {\\n \\\"COLUMN\\\": [\\\"indel_location\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Relative indel location within the transcript coding region\\\",\\n },\\n \\\"NMD\\\": {\\n \\\"COLUMN\\\": [\\\"is_nmd_insensitive\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insensitive to nonsense mediated decay\\\",\\n },\\n \\\"IPG\\\": {\\n \\\"COLUMN\\\": [\\\"ipg\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Indels per gene\\\",\\n },\\n \\\"LEN\\\": {\\n \\\"COLUMN\\\": [\\\"cds_length\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Coding sequence length. Median value if multiple isoforms exist\\\",\\n },\\n \\\"LC\\\": {\\n \\\"COLUMN\\\": [\\\"lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Linguistic complexity: diversity of k-mers in flanking 50-bp region\\\",\\n },\\n \\\"LLC\\\": {\\n \\\"COLUMN\\\": [\\\"local_lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\\\",\\n },\\n \\\"GC\\\": {\\n \\\"COLUMN\\\": [\\\"gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"GC-content in flanking 50-bp region\\\",\\n },\\n \\\"LGC\\\": {\\n \\\"COLUMN\\\": [\\\"local_gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local GC-content in flanking 6-bp region\\\",\\n },\\n \\\"SG\\\": {\\n \\\"COLUMN\\\": [\\\"strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"DNA bond strength of 2-mers in flanking 50-bp region\\\",\\n },\\n \\\"LSG\\\": {\\n \\\"COLUMN\\\": [\\\"local_strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local DNA bond strength of 2-mers in flanking 6-bp region\\\",\\n },\\n \\\"INS\\\": {\\n \\\"COLUMN\\\": [\\\"is_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insertion\\\",\\n },\\n \\\"ATI\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of A or T\\\",\\n },\\n \\\"ATD\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of A or T\\\",\\n },\\n \\\"GCI\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of G or C\\\",\\n },\\n \\\"GCD\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of G or C\\\",\\n },\\n \\\"ALTC\\\": {\\n \\\"COLUMN\\\": [\\\"alt_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Alt count: count of unique reads supporting ALT allele\\\",\\n },\\n \\\"REFC\\\": {\\n \\\"COLUMN\\\": [\\\"ref_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Ref count: count of unique reads supporting REF allele\\\",\\n },\\n \\\"RCF\\\": {\\n \\\"COLUMN\\\": [\\\"reclassified\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Reclassification applied\\\",\\n },\\n \\\"RQB\\\": {\\n \\\"COLUMN\\\": [\\\"filtered\\\", \\\"rescued\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\\\",\\n },\\n }\\n\\n return d\",\n \"def get_mapping_table_ids():\\n #\\n aliquot_prefix = get_prefix(API_PARAMS, API_PARAMS['ALIQUOT_ENDPOINT'])\\n case_aliquot_table_name = construct_table_name(API_PARAMS, prefix=aliquot_prefix)\\n case_aliquot_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_aliquot_table_name}\\\"\\n\\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, API_PARAMS['STUDY_ENDPOINT']))\\n study_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{study_table_name}\\\"\\n\\n case_external_mapping_prefix = get_prefix(API_PARAMS, API_PARAMS['CASE_EXTERNAL_MAP_ENDPOINT'])\\n case_ext_map_table_name = construct_table_name(API_PARAMS, prefix=case_external_mapping_prefix)\\n case_external_mapping_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_ext_map_table_name}\\\"\\n\\n return case_aliquot_table_id, study_table_id, case_external_mapping_table_id\",\n \"def _prepare_links_table(self):\\n\\n links_tbl = OrderedDict()\\n for colname in itertools.islice(self._pinfos, 1, None):\\n links_tbl[colname] = {}\\n links_tbl[colname][\\\"name\\\"] = f\\\"{colname}\\\"\\n fname = colname.replace(\\\"%\\\", \\\"_pcnt\\\") + \\\".html\\\"\\n links_tbl[colname][\\\"fname\\\"] = fname\\n links_tbl[colname][\\\"hlink\\\"] = f\\\"<a href=\\\\\\\"{fname}\\\\\\\">{colname}</a>\\\"\\n\\n return links_tbl\",\n \"def parse_mixcr_table(filepath):\\n tbl = pd.read_csv(filepath, sep='\\\\t')\\n out = {}\\n for _, row in tbl.iterrows():\\n motifs = get_binding_motifs(row['aaSeqImputedCDR3'])\\n for kind, motif_counts in motifs.items():\\n for motif, count in motif_counts.items():\\n for mykind in [kind, 'all_types']:\\n key = (mykind, motif)\\n if key not in out:\\n out[key] = {\\n 'num_unique_seqs': 0,\\n 'num_clones': 0,\\n 'num_unique_occurences': 0,\\n 'num_clonal_occurences': 0,\\n }\\n out[key]['num_unique_seqs'] += 1\\n out[key]['num_clones'] += row['cloneCount']\\n out[key]['num_unique_occurences'] += count\\n out[key]['num_clonal_occurences'] += count * row['cloneCount']\\n return out\",\n \"def tr_nc_dict(dfin):\\n\\n\\ttr_nc_index_dict = OrderedDict()\\n\\t\\n\\ttrCount = 0\\n\\tpreviousTrIndex = 0\\n\\n\\tcurrent_id=\\\"\\\"\\n\\tcurrent_tr=\\\"\\\"\\n\\n\\tfor i in range(len(dfin)):\\n# print dfin.loc[i]\\n\\n\\t\\tif dfin.loc[i,'feature'] == 'transcript':\\n\\t\\t\\ttrdict = parse_mod_entry(dfin.loc[i,'transcript_id'])\\n\\n\\t\\t\\tcurGeneID = trdict['gene_id'][0]\\n\\n\\t\\t\\tif trCount != 0:\\n\\t\\t\\t\\tnewTrIndex = i\\n\\t\\t\\t\\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\\n\\t\\t\\t\\tpreviousTrIndex = i\\n\\t\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\t\\ttrCount += 1\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnewTrIndex = 0\\n\\t\\t\\t\\ttrCount +=1\\n\\t\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\t\\t\\n\\t\\tif i == (len(dfin)-1):\\n\\t\\t\\tnewTrIndex = i+1\\n\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\\n\\t\\t\\t\\n\\treturn tr_nc_index_dict\",\n \"def _make_metric_dict(self, metrics):\\n exon = None\\n hgvs_c = None\\n\\n slen = len(metrics.split(':'))\\n if slen == 1:\\n if \\\"(dist=\\\" not in metrics:\\n tx = metrics\\n else:\\n tx = None\\n elif slen == 2:\\n tx = metrics.split('(')[0]\\n hgvs_c = metrics.split(':')[1].rstrip(')')\\n elif slen == 3:\\n tx = metrics.split('(')[0]\\n exon = metrics.split(':')[1][4:]\\n hgvs_c = metrics.split(':')[2].rstrip(')')\\n else:\\n raise UserWarning(\\\"There are an incorrect number of fields listed in: \\\" + str(self.info))\\n\\n if hgvs_c:\\n full_c = ':'.join([tx, hgvs_c])\\n hgvs_parser = Hgvs(full_c)\\n hgvs_basep = hgvs_parser.start\\n else:\\n hgvs_basep = None\\n\\n return {\\n 'AAP': None,\\n 'BASEP': hgvs_basep,\\n 'EXON': exon,\\n 'HGNC': None,\\n 'HGVSC': hgvs_c,\\n 'HGVSP1': None,\\n 'HGVSP3': None,\\n 'SOURCE': self.tfx_type,\\n 'SPLICE': self.splicing,\\n 'TXC': tx,\\n 'PVT': self.vtype,\\n 'VFX': None\\n }\",\n \"def __generate_dict(self, test_output_dir, types, _table):\\n # dump json from file to dict\\n\\n table_dict = CaseDump(os.path.join(test_output_dir, _table + OUT_SUFFIX), CASE_FILE_TYPE_JSON).dump()\\n\\n # foreach key from statistic report, split it into table/type/key/sub_value\\n type_dict = {}\\n for _key in table_dict.keys():\\n # wipe off table\\n _key = _key.replace(\\\"%s_\\\" % _table, \\\"\\\")\\n for _type in types:\\n # wipe off type\\n if _type and _type in _key:\\n if _type not in type_dict.keys():\\n type_dict[_type] = {}\\n _key = _key.replace(\\\"%s_\\\" % _type, \\\"\\\")\\n # if has sub_value, set RESULT_DICT[table][type][key] = sub_value\\n # else set RESULT_DICT[table][type][key] = None\\n if \\\"#\\\" in _key:\\n sub_value = _key.split(\\\"#\\\")\\n type_dict[_type][sub_value[0]] = sub_value[1]\\n else:\\n type_dict[_type][_key] = \\\"\\\"\\n break\\n\\n global RESULT_DICT\\n RESULT_DICT[_table] = type_dict\",\n \"def buildAdsTable_v1(output_file = None):\\r\\n ads_table = []\\r\\n text_props = ['readability_text', '_all']\\r\\n onto_props_with_mapping = {'phone':['telephone.name', 'telephone.name.raw'], 'email': ['email.name', 'email.name.raw'],\\r\\n 'posting_date':['inferlink_date', 'readability_date', 'high_recall_readability_date'],\\r\\n 'price':['price'], 'location':['addressLocality'],\\r\\n 'name':['name'],\\r\\n 'ethnicity':['ethnicity'],\\r\\n 'eye_color':['eyeColor'], 'title':['title'],\\r\\n 'hair_color':['hairColor'], 'nationality':['nationality'],\\r\\n 'business_type':['business_type'],\\r\\n 'business_name':['streetAddress'], 'services':['serviceType'],\\r\\n 'business': ['streetAddress'],\\r\\n 'physical_address': ['streetAddress'],\\r\\n 'gender':['gender'], 'top_level_domain':['top_level_domain'],\\r\\n 'obfuscation':['telephone.isObfuscated', 'email.isObfuscated'],\\r\\n 'age':['age'], 'hyperlink:':['relatedLink'], 'drug_use':['drug_use'],\\r\\n 'review_site':['review_site'], 'review_id':['review_id'],\\r\\n 'number_of_individuals':['name_count'],\\r\\n 'ad': ['identifier'],\\r\\n 'multiple_phone': ['telephone_count'],\\r\\n 'cluster': ['seller.uri'],\\r\\n 'seed': ['seller.telephone.name', 'seller.email.name']\\r\\n }\\r\\n non_readability_props = ['number_of_individuals', 'ad', 'multiple_phone', 'cluster', 'phone', 'posting_date', 'email']\\r\\n onto_props_without_mapping = ['image_with_email', 'image_with_phone']\\r\\n for property, value_list in onto_props_with_mapping.iteritems():\\r\\n dict = {}\\r\\n dict['onto_prop'] = property\\r\\n mappings = []\\r\\n tmp = {}\\r\\n for v in value_list:\\r\\n if property == 'phone' or v == 'seller.telephone.name':\\r\\n tmp[v] = 'build_phone_match_clause'\\r\\n tmp['_all'] = 'build_phone_match_clause'\\r\\n tmp['url'] = 'build_phone_regexp_clause'\\r\\n elif v == 'email.name':\\r\\n tmp[v] = 'build_email_match_clause'\\r\\n tmp['_all'] = 'build_match_phrase_clause'\\r\\n elif property == 'ad':\\r\\n tmp[v] = 'build_term_clause'\\r\\n elif '_count' in v:\\r\\n tmp[v] = 'build_count_match_clause'\\r\\n elif property == 'gender':\\r\\n tmp[v] = 'build_gender_match_clause'\\r\\n elif property == 'posting_date':\\r\\n tmp[v] = 'build_match_phrase_clause'\\r\\n else:\\r\\n tmp[v] = 'build_match_clause'\\r\\n if property not in non_readability_props:\\r\\n for v in text_props: # will overwrite for seller.telephone.name\\r\\n tmp[v] = 'build_match_clause_inner'\\r\\n mappings.append(tmp)\\r\\n dict['mappings'] = mappings\\r\\n ads_table.append(dict)\\r\\n\\r\\n for property in onto_props_without_mapping:\\r\\n dict = {}\\r\\n dict['onto_prop'] = property\\r\\n mappings = []\\r\\n tmp = {}\\r\\n for v in text_props:\\r\\n tmp[v] = 'build_match_clause_inner'\\r\\n mappings.append(tmp)\\r\\n dict['mappings'] = mappings\\r\\n ads_table.append(dict)\\r\\n\\r\\n if output_file:\\r\\n file = codecs.open(output_file, 'w', 'utf-8')\\r\\n for entry in ads_table:\\r\\n json.dump(entry, file)\\r\\n file.write('\\\\n')\\r\\n file.close()\",\n \"def generateAggregatedCsvData(self, context, obj, entities):\\n return sum([long(e.prop1.replace('-', ''), 16) for e in entities])\",\n \"def _parse_id_to_taxonomy_file(f):\\r\\n result = {}\\r\\n for line in f:\\r\\n line = line.strip()\\r\\n if line:\\r\\n identifier, taxonomy = map(strip, line.split('\\\\t'))\\r\\n result[identifier] = taxonomy\\r\\n return result\",\n \"def write_count_table(filtered_counts, gene_names, ids_liver_header):\\n with open(\\\"filtered_tcga_counts.tsv\\\", \\\"w\\\") as file:\\n file.write(\\\"gene_id\\\\tgene_name\\\\t\\\" + \\\"\\\\t\\\".join(ids_liver_header) + \\\"\\\\n\\\")\\n for gene_name, counts_line in zip(gene_names, filtered_counts):\\n file.write(gene_name + \\\"\\\\t\\\" + \\\"\\\\t\\\" + \\\"\\\\t\\\".join(counts_line) + \\\"\\\\n\\\")\",\n \"def get_table(text, size = 1):\\r\\n result = {}\\r\\n for i in range(len(text)):\\r\\n chars = text[i:i+size]\\r\\n try:\\r\\n out = text[i + size]\\r\\n except IndexError:\\r\\n break\\r\\n char_dict = result.get(chars, {})\\r\\n if out not in char_dict:\\r\\n char_dict[out] = 0\\r\\n char_dict[out] += 1\\r\\n result[chars] = char_dict\\r\\n return result\",\n \"def process_ubern(fdict,cdmd,cdm,fn,proflist,campaigns,data):\\n good=False\\n for d in data:\\n if 'Min' in d:\\n good=True\\n if 'ri_name' in d:\\n key=d.split()[-1]\\n if key not in fdict.keys():\\n fdict[key]={'observations_table':{},'header_table':{}}\\n \\n if 'COMP018' in fn:\\n print('018')\\n if not good:\\n print(fn,'could not be processed')\\n return\\n if(not fdict[key]['observations_table']):\\n \\n for k in cdmd['observations_table'].element_name.values:\\n fdict[key]['observations_table'][k]=[]\\n for k in cdmd['header_table'].element_name.values:\\n fdict[key]['header_table'][k]=[]\\n refdate=datetime(year=1900,month=1,day=1)\\n header=True\\n aux={}\\n l=0\\n comp=fn[:7]\\n for d in data:\\n if 'Min' in d:\\n head=d.split()\\n break\\n l+=1\\n m=0\\n for d in data[l+1:]:\\n dlist=d.split()\\n if dlist:\\n if dlist[0]=='NA':\\n continue\\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\\n \\n try:\\n x=float(dlist[1])*100\\n except ValueError:\\n try: \\n if 'PPPcorr' in head:\\n x=float(dlist[2])*100\\n else:\\n continue\\n except ValueError:\\n continue\\n m+=1\\n \\n if m==0:\\n print(fn,'no valid obs')\\n return\\n \\n try:\\n idx=np.where(cdm['iri_rstype_map'].ri_name==key.split('_')[0])[0][0]\\n fdict[key]['sensor_id']=numpy.string_(cdm['iri_rstype_map'].vapor_name[idx]) #.split(b',')[0])\\n except:\\n fdict[key]['sensor_id']=numpy.string_('NA')\\n \\n l=0\\n for d in data:\\n if 'ri_name' in d:\\n key=d.split()[1]\\n print(comp,fn,key)\\n if 'measurement_programme' not in fdict[key]['header_table'].keys():\\n fdict[key]['header_table']['measurement_programme']=[]\\n idx=campaigns.index[campaigns.ID==comp]\\n fdict[key]['header_table']['measurement_programme'].append(campaigns.Name[idx].values[0]) \\n\\n id_ascent=fn.split('/')[-1][:11]\\n #key=np.string_('0-20100-0-0000'+fn[-8])\\n sidx=np.where(cdm['station_configuration'].measuring_system_id==numpy.string_(key))[0][0]\\n pidx=np.where(proflist['id_ascent']==id_ascent)[0][0]\\n fdict[key]['header_table']['primary_station_id'].append(cdm['station_configuration'].primary_id.values[sidx])\\n fdict[key]['header_table']['report_id'].append(fdict[key]['header_table']['primary_station_id'][-1]+\\n b'-'+numpy.string_(fn.split('_')[-2]))\\n print(key,fdict[key]['header_table']['report_id'])\\n fdict[key]['header_table']['latitude'].append(cdm['station_configuration'].latitude.values[sidx])\\n fdict[key]['header_table']['longitude'].append(cdm['station_configuration'].longitude.values[sidx])\\n fdict[key]['header_table']['height_of_station_above_sea_level'].append(\\n proflist['alt'].values[pidx])\\n\\n for k in 'latitude','longitude':\\n aux[k]=fdict[key]['header_table'][k][-1]\\n\\n if 'date ' in d:\\n dat=d.split()[-1]\\n if 'hour_utc' in d:\\n hour=d.split()[-1]\\n hh=hour.split(':')\\n if len(hh)==2:\\n hour=hour+':00'\\n relset=datetime.strptime(dat+' '+hour,'%Y-%m-%d %H:%M:%S')\\n fdict[key]['header_table']['report_timestamp'].append(int((relset-refdate).total_seconds()))\\n fdict[key]['header_table']['record_timestamp'].append(int((relset-refdate).total_seconds()))\\n if 'Min' in d:\\n head=d.split()\\n break\\n l+=1\\n\\n m=0\\n dlen=len(head)-2\\n ddlen=dlen\\n dstart=2\\n if 'Notes' in head:\\n dlen-=1\\n ddlen-=1\\n if 'PPPcorr' in head:\\n dstart+=1\\n ddlen-=2\\n ovar={'TTT':85,'TTTcorr':85,'UU':38,'H':117,'DEWPT':36,'DIR':106,'WSPEED':107}\\n offset={'TTT':273.15,'TTTcorr':273.15,'UU':0,'H':0,'DEWPT':273.15,'DIR':0,'WSPEED':0}\\n scale={'TTT':1,'TTTcorr':1,'UU':1,'H':9.80655,'DEWPT':1,'DIR':1,'WSPEED':1}\\n units={'TTT':5,'TTTcorr':5,'UU':0,'H':806,'DEWPT':5,'DIR':110,'WSPEED':731}\\n for d in data[l+1:]:\\n dlist=d.split()\\n if dlist:\\n if dlist[0]=='NA':\\n continue\\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\\n \\n try:\\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[1])*100]\\n except ValueError:\\n try: \\n if 'PPPcorr' in head:\\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[2])*100]\\n else:\\n continue\\n except ValueError:\\n continue\\n #fdict[key]['observations_table']['z_coordinate']+=ddlen*[numpy.nan]\\n \\n fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]+int(float(dlist[0])*60)]\\n if ddlen<len(head[dstart:dlen+2]):\\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart+1:dlen+2]]\\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart+1:dlen+2]]\\n else:\\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart:dlen+2]]\\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart:dlen+2]]\\n for i in range(dstart,dlen+2):\\n \\n if head[i]!='TTTcorr': \\n try:\\n fdict[key]['observations_table']['observation_value'].append(offset[head[i]]+scale[head[i]]*float(dlist[i]))\\n except ValueError:\\n fdict[key]['observations_table']['observation_value'].append(numpy.nan)\\n else:\\n try:\\n fdict[key]['observations_table']['observation_value'][-1]=offset[head[i]]+scale[head[i]]*float(dlist[i])\\n except ValueError:\\n pass\\n \\n fdict[key]['observations_table']['observation_id']+=[numpy.string_('{:0>8}'.format(m+i+1)) for i in range(ddlen)]\\n m+=ddlen\\n else:\\n break\\n\\n aux['sensor_id']='NA'\\n try:\\n idx=np.where(cdm['iri_rstype_map'].ri_name==key)[0][0]\\n aux['sensor_id']=cdm['iri_rstype_map'].vapor_name[idx]\\n except:\\n print('could not find rs type key for '+key+'!')\\n pass\\n aux['report_id']=fdict[key]['header_table']['report_id'][-1]\\n\\n for k,v in aux.items():\\n fdict[key]['observations_table'][k]+=[v]*m\\n \\n return\",\n \"def cars_dict_to_table(car_data):\\n table_data = [[\\\"ID\\\", \\\"Car\\\", \\\"Price\\\", \\\"Total Sales\\\"]]\\n for item in car_data:\\n table_data.append([item[\\\"id\\\"], format_car(item[\\\"car\\\"]), item[\\\"price\\\"], item[\\\"total_sales\\\"]])\\n return table_data\",\n \"def convert_trsp_index(geneDictNonCoding, df, TR_index_dict):\\n\\n\\n\\tgeneDictCanon = OrderedDict()\\n\\t\\n\\tfor gene in geneDictNonCoding:\\n\\t\\ttrDF = df.iloc[geneDictNonCoding[gene][0]:geneDictNonCoding[gene][1]]\\n\\t\\ttrDFz = trDF.reset_index(drop=True)\\n\\t\\t\\n\\t\\ttrCount = 0\\n\\t\\ttrDictLoc = OrderedDict()\\n\\t\\t\\n\\t\\tfor i in range(len(trDFz)):\\n\\t\\t\\tif trDFz.loc[i, 'feature'] == 'transcript':\\n\\t\\t\\t\\ttr = trDFz.loc[i, 'transcript_id']\\n\\t\\t\\t\\ttrdict = parse_entry(tr)\\n\\t\\t\\t\\ttrName = trdict['transcript_id'][0]\\n\\t\\t\\t\\ttrDictLoc[trName] = [trDFz.loc[i, 'chromStart'], trDFz.loc[i, 'chromEnd']]\\n\\t\\t\\t\\ttrCount += 1\\n\\t\\t\\n\\t\\tif trCount > 1:\\n# print gene, \\\"more than 1 trsp !!! \\\\n\\\"\\n\\t\\t\\t\\n\\t\\t\\trangeDict = OrderedDict() ## store the ranges, and take the longest\\n\\t\\t\\tfor key in trDictLoc:\\n\\t\\t\\t\\ttrRange = len(range(int(trDictLoc[key][0]),int(trDictLoc[key][1])))\\n\\t\\t\\t\\trangeDict[key] = trRange\\n\\t\\t\\t\\t\\n\\t\\t\\tv=list(rangeDict.values())\\n\\t\\t\\tk=list(rangeDict.keys())\\n\\t\\t\\ttrOut = k[v.index(max(v))]\\n# print trOut\\n\\t\\t\\tgeneDictCanon[trOut] = [gene, TR_index_dict[trOut]]\\n\\t\\t\\t\\n\\t\\t\\t\\n\\n\\t\\telse: ## for genes with single transcripts\\n\\t\\t\\ttrOut = trDictLoc.keys()[0]\\n\\t\\t\\tgeneDictCanon[trOut] = [gene, TR_index_dict[trOut]]\\n\\treturn geneDictCanon\",\n \"def get_results(self):\\n d = {}\\n# r = {}\\n for analyser in self.xml_tree.getroot():\\n for child in analyser:\\n if child.tag == 'all-records':\\n for record in child:\\n attributes = record.attrib\\n sample = attributes['sampleId']\\n assay_id = attributes['assayId']\\n genotype = attributes['genotypeId']\\n quality = attributes['description'].split('.')[0]\\n if re.match(r'rs\\\\d+', assay_id):\\n if sample in d:\\n if assay_id in d[sample]:\\n for allele in list(genotype):\\n if allele not in d[sample][assay_id]['genotype']:\\n d[sample][assay_id]['genotype'] += allele\\n if quality not in d[sample][assay_id]['quality']:\\n d[sample][assay_id]['quality'].append(quality)\\n else:\\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\\n else:\\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\\n# if sample in r:\\n# if assay_id in r[sample]:\\n# for allele in list(genotype):\\n# if allele not in r[sample][assay_id]:\\n# r[sample][assay_id] += allele\\n# else:\\n# r[sample][assay_id] = genotype\\n# else:\\n# r[sample] = {assay_id: genotype}\\n# for k, v in r.items():\\n# for k1, v1, in v.items():\\n# if len(v1) == 1:\\n# v[k1] += v1\\n# pprint.pprint(r)\\n# df = pd.DataFrame.from_dict(r).transpose()\\n# print(df)\\n# df.to_excel('snpcheck.xlsx')\\n return d\",\n \"def idf_dict1(l):\\n idf = {}\\n # first look for idf in abstract field\\n solrdict = list_to_dict(l)\\n for doc in solrdict.itervalues():\\n terms = doc.get('abstract',{})\\n for w,t in terms.iteritems():\\n if not idf.has_key(w):\\n idf[w] = t['tf-idf'][0] / t['tf'][0]\\n # next add idf values from title field\\n for doc in solrdict.itervalues():\\n terms = doc.get('title',{})\\n for w,t in terms.iteritems():\\n if not idf.has_key(w):\\n idf[w] = t['tf-idf'][0] / t['tf'][0]\\n return idf\",\n \"def compile_as_dict(self, data):\\r\\n fut = {}\\r\\n index = self.primary_key.index\\r\\n for i in data:\\r\\n popped = i.pop(index)\\r\\n head = self.table_headers.copy()\\r\\n head.pop(index)\\r\\n s = {}\\r\\n for j in range(len(head)):\\r\\n s[j] = i[j]\\r\\n fut[popped] = s\\r\\n return fut\",\n \"def table_to_dict(self, tab):\\n dict = {}\\n for colname in tab.colnames:\\n dict[colname] = tab[colname].data\\n return dict\",\n \"def analysis():\\r\\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\\r\\n data_frame.registerTempTable('business')\\r\\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\\r\\n\\r\\n def merge_count(a, b):\\r\\n r = {}\\r\\n for p, c in a.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n for p, c in b.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n return r\\r\\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\\r\\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\\r\\n\\r\\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\\r\\n print(result.take(10))\\r\\n print('----------------pron count-----------------')\\r\\n print(pron_count.collect())\\r\\n\\r\\n print(gd)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7227093","0.64672595","0.63795495","0.5744344","0.5535398","0.5517264","0.54640967","0.542029","0.53676605","0.5360893","0.5334983","0.5295043","0.5288153","0.5281474","0.52813345","0.5253252","0.52377903","0.5232528","0.51988035","0.51941615","0.5179884","0.5179878","0.5159318","0.5158393","0.51572585","0.5156911","0.5146848","0.5139303","0.51163846","0.5108249","0.5093531","0.50800335","0.5056124","0.50550383","0.5039629","0.503599","0.50350326","0.5017609","0.5017149","0.500882","0.50071186","0.50038755","0.50005776","0.49958292","0.49896255","0.4985664","0.4982965","0.49818128","0.49799496","0.49785832","0.49779838","0.49779204","0.4973262","0.4967225","0.49669284","0.4966263","0.4966084","0.49584803","0.49557778","0.49387944","0.4938789","0.49368963","0.49345854","0.49258658","0.49239764","0.49137437","0.4913335","0.4912436","0.49099764","0.49072957","0.48968863","0.48954186","0.48935357","0.48927847","0.4890293","0.48874718","0.48845693","0.4881233","0.4877495","0.48760763","0.4873524","0.48719233","0.48699364","0.4867528","0.48657075","0.4861294","0.48609498","0.48596883","0.48563698","0.4856012","0.48548603","0.48546404","0.48500413","0.48497498","0.48342848","0.48327482","0.4832731","0.4829764","0.48294696","0.48133487"],"string":"[\n \"0.7227093\",\n \"0.64672595\",\n \"0.63795495\",\n \"0.5744344\",\n \"0.5535398\",\n \"0.5517264\",\n \"0.54640967\",\n \"0.542029\",\n \"0.53676605\",\n \"0.5360893\",\n \"0.5334983\",\n \"0.5295043\",\n \"0.5288153\",\n \"0.5281474\",\n \"0.52813345\",\n \"0.5253252\",\n \"0.52377903\",\n \"0.5232528\",\n \"0.51988035\",\n \"0.51941615\",\n \"0.5179884\",\n \"0.5179878\",\n \"0.5159318\",\n \"0.5158393\",\n \"0.51572585\",\n \"0.5156911\",\n \"0.5146848\",\n \"0.5139303\",\n \"0.51163846\",\n \"0.5108249\",\n \"0.5093531\",\n \"0.50800335\",\n \"0.5056124\",\n \"0.50550383\",\n \"0.5039629\",\n \"0.503599\",\n \"0.50350326\",\n \"0.5017609\",\n \"0.5017149\",\n \"0.500882\",\n \"0.50071186\",\n \"0.50038755\",\n \"0.50005776\",\n \"0.49958292\",\n \"0.49896255\",\n \"0.4985664\",\n \"0.4982965\",\n \"0.49818128\",\n \"0.49799496\",\n \"0.49785832\",\n \"0.49779838\",\n \"0.49779204\",\n \"0.4973262\",\n \"0.4967225\",\n \"0.49669284\",\n \"0.4966263\",\n \"0.4966084\",\n \"0.49584803\",\n \"0.49557778\",\n \"0.49387944\",\n \"0.4938789\",\n \"0.49368963\",\n \"0.49345854\",\n \"0.49258658\",\n \"0.49239764\",\n \"0.49137437\",\n \"0.4913335\",\n \"0.4912436\",\n \"0.49099764\",\n \"0.49072957\",\n \"0.48968863\",\n \"0.48954186\",\n \"0.48935357\",\n \"0.48927847\",\n \"0.4890293\",\n \"0.48874718\",\n \"0.48845693\",\n \"0.4881233\",\n \"0.4877495\",\n \"0.48760763\",\n \"0.4873524\",\n \"0.48719233\",\n \"0.48699364\",\n \"0.4867528\",\n \"0.48657075\",\n \"0.4861294\",\n \"0.48609498\",\n \"0.48596883\",\n \"0.48563698\",\n \"0.4856012\",\n \"0.48548603\",\n \"0.48546404\",\n \"0.48500413\",\n \"0.48497498\",\n \"0.48342848\",\n \"0.48327482\",\n \"0.4832731\",\n \"0.4829764\",\n \"0.48294696\",\n \"0.48133487\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63274735"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":7494,"cells":{"query":{"kind":"string","value":"formats raw string input into their appropriate values"},"document":{"kind":"string","value":"def format_countOrFPKMTable(self,fpkmTracking_I):\n for fpkmTracking in fpkmTracking_I:\n for k,v in fpkmTracking.items():\n if k=='tracking_id' and type(fpkmTracking['tracking_id'])==type('string'):\n pass;\n elif k!='tracking_id' and type(fpkmTracking[k])==type('string'):\n fpkmTracking[k] = eval(v);\n return fpkmTracking_I;"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def format_raw_input(user_input):\n # Replace silly “ or ” characters with \"\n # TODO: Swap out with regex\n raw_input = user_input.strip().replace(\n '“', '\"').replace(\"”\", '\"').replace(\",\", \"\").replace(\"\\n\", \" \")\n # Break apart the string into each coordinate\n raw_inputs = [r.replace('\"', '') for r in raw_input.split('\" \"')]\n # Return coordinates as lists of ints.\n return [[int(i) for i in r.split(\" \")] for r in raw_inputs]","def input_parser(input_string: str) -> str: \n if is_int(input_string):\n return input_string\n #he is int, give back plz.\n else:\n try:\n modified_input: str = input_string.strip()\n\n evaluatable_pairs: str = regex_splitter(modified_input)\n\n while not (is_int(evaluatable_pairs)):\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\n\n return (evaluatable_pairs)\n\n except:\n raise Exception(\"Invalid Input\")","def __call__(self, value): # noqa: D102\n if not isinstance(value, str):\n raise ValueError(f\"Input value must be a string. '{value}' is not.\")\n\n raw_value = value\n for c in self.remove_characters:\n value = value.replace(c, \"\")\n if not bool(re.match(f\"^[{self.allowed_characters},]+$\", value)):\n raise ValueError(\n f\"Input must only contain values '{self.allowed_characters},'. '{raw_value}' does not.\"\n )\n if not bool(\n re.match(\n f\"^([{self.allowed_characters}],)+[{self.allowed_characters}]$\", value\n )\n ):\n raise ValueError(\n f\"Input must have format '(?,?,?,?)'. '{raw_value}' does not.\"\n )\n if not all([c in value for c in self.required_characters]):\n raise ValueError(\n f\"Input must contain {self.required_characters}. '{raw_value}' does not.\"\n )\n return raw_value","def parse(s):\n return s","def get_processed_string(self, input_string):\n if input_string[:6] == '[sic]\"':\n return input_string[6: -1]\n else:\n return input_string.format(**self)","def parse_string(self, data):\n pass","def parse(self,value):\r\n\t\treturn str(value)","def _parse_user_input(self):\n user_input = self.user_input.strip()\n if user_input:\n if user_input.find(',') > -1:\n # Location is either city/state or latitude/longitude.\n if user_input[0].isalpha():\n # City, state (lat/long handled elsewhere)\n city, state = [x.strip() for x in user_input.split(',')]\n self.city = city\n self.state = state\n elif (len(user_input) <= 10 and\n user_input[1].isdigit()): # 2nd char in US/Can. postal codes\n # Postal code\n self.postal_code = user_input.strip()","def _sanitize_string(self, string):\n # get the type of a unicode string\n unicode_type = type(Pyasciigraph._u('t'))\n input_type = type(string)\n if input_type is str:\n if sys.version < '3':\n info = unicode(string)\n else:\n info = string\n elif input_type is unicode_type:\n info = string\n elif input_type is int or input_type is float:\n if sys.version < '3':\n info = unicode(string)\n else:\n info = str(string)\n else:\n info = str(string)\n return info","def __normalize(self, ctx: commands.Context, format: str) -> str:\n\t\t# convert to lowercase\n\t\tlower_format = format.lower()\n\t\t# check if inputted format is recognized\n\t\tif lower_format in self.formats:\n\t\t\treturn lower_format\n\t\t# check for aliases\n\t\telif lower_format in self.aliases:\n\t\t\treturn self.aliases[lower_format]\n\t\t# format is not recognized\n\t\telse:\n\t\t\traise FriendlyError(\n\t\t\t\tf\"'{format}' is not a recognized format.\", ctx.channel, ctx.author\n\t\t\t)","def __format_input_translator(str_to_process):\n return re.sub(r'\\([^)]*\\)', '', str_to_process).replace(' ', '').split('/')","def parseString(self, s):\n pass","def preprocess(inpString):\n\n figure = re.compile(r'\\([^\\W\\d_]+ [0-9]+\\.[0-9]+\\)') # i.e (Tablo 3.2)\n out_string = figure.sub('', inpString)\n\n digit_dot = re.compile(r'([0-9]+)\\.([0-9]{3})') # i.e 19.000 --> 19000\n out_string = digit_dot.sub(r'\\1\\2', out_string)\n out_string = digit_dot.sub(r'\\1\\2', out_string)\n\n centigrade = re.compile(r'(°C)|(°c)|(0C)') # °C --> santigrat\n out_string = centigrade.sub(r' santigrat', out_string)\n\n out_string = re.sub(r'°', ' derece', out_string) # ° --> derece\n\n digit_space = re.compile(r'([0-9]+) ([0-9]+)') # 19 000 --> 19000\n out_string = digit_space.sub(r'\\1\\2', out_string)\n\n out_string = re.sub(r'â', 'a', out_string) # Elâzig --> Elazig\n\n spec_hyphen = re.compile(r'([A-Za-z])-([0-9]+)') # G-20 --> G20\n out_string = spec_hyphen.sub(r'\\1\\2', out_string)\n\n out_string = re.sub(r'-', ' ', out_string) # replace hyphen with space\n\n out_string = re.sub(r'%|‰', 'yüzde ', out_string) # % --> yuzde\n\n year = re.compile(\"([0-9]{4})(’|')([a-z]+)\") # 1815'te --> 1815 yilinda\n out_string = year.sub(r'\\1 yılında', out_string)\n\n out_string = re.sub(r' km2', ' kilometrekare', out_string) # converting km2, m, km\n out_string = re.sub(r' m ', ' metre ', out_string)\n out_string = re.sub(r' km ', ' kilometre ', out_string)\n\n out_string = re.sub(r\"(’|')([a-züşöıç]+)\", '', out_string) # turkiye'de --> turkiye\n\n out_string = re.sub(r'([0-9]+),([0-9]+)', r'\\1CBN\\2', out_string) # replacing comma between\n # digits with a placeholder\n\n puncs = string.punctuation + '”' + '“' + '’' + '‘'\n translator = str.maketrans('', '', puncs)\n out_string = out_string.translate(translator) # removing pucntuations\n\n out_string = re.sub(r'CBN', ',', out_string) # bringing back the comma between numbers\n # out_string= out_string.split(' ') #[s.split(' ') for s in out_string.split('#')] #splitting from end of sentences\n # end sentence into words\n\n return out_string","def decode(self, s):","def decode(self, s):","def format_input(self, args):\n\n new_list = []\n if args[1].find('{') != -1:\n new_list = self.format_dicti(args)\n return new_list\n else:\n new_list = []\n new_list.append(args[0])\n new_str = args[1][ args[1].find('(') + 2 : args[1].find(',') - 1]\n new_str += args[1][ args[1].find(',') : args[1].find(')') - 0]\n new_list.append(\" \".join(new_str.split(\", \") ) )\n\n return \" \".join(i for i in new_list)","def value_from_str(self, s):\n if is_quoted_str(s):\n return s[1:-1]\n return super().value_from_str(s)","def value_from_str(self, s):\n if is_quoted_str(s):\n return s[1:-1]\n return super().value_from_str(s)","async def parse(self, raw: str) -> dict:","def convert(string, sanitize=False):\n return r.convert(string, (preprocess if sanitize else False))","def _handle_string(\n *, artifacts: types.ColumnArtifacts\n) -> typing.Union[String, Binary, Date, DateTime]:\n if artifacts.open_api.format in {None, \"byte\", \"password\"}:\n if artifacts.open_api.max_length is None:\n return String\n return String(length=artifacts.open_api.max_length)\n if artifacts.open_api.format == \"binary\":\n if artifacts.open_api.max_length is None:\n return Binary\n return Binary(length=artifacts.open_api.max_length)\n if artifacts.open_api.format == \"date\":\n return Date\n if artifacts.open_api.format == \"date-time\":\n return DateTime\n raise exceptions.FeatureNotImplementedError(\n f\"{artifacts.open_api.format} format for string is not supported.\"\n )","def get_params(raw):\n parts = raw.split(\" \", 1)\n return None if len(parts) == 1 else parts[1]","def parse(self,value):\r\n\t\treturn unicode(value)","def test_parseUnformattedText(self):\n self.assertEqual(irc.parseFormattedText(\"hello\"), A.normal[\"hello\"])","def tidy_string(s: str\n ) -> str:\n s = s.encode('ascii', errors='ignore').decode(FORMAT)\n s = s.replace(\"\\r\", \"\").replace(\"\\t\", \"\").replace('\\n', '') \n return s","def autoconvert(s):\n try:\n return eval(s)\n except:\n return s","def preprocess_data(raw_text):\n try:\n raw_text = raw_text.replace(\"?\", \"Unknown\")\n raw_text = raw_text.replace(\" or \", \", \")\n raw_text = raw_text.replace(\" and \", \", \")\n raw_text = re.sub(\"\\[.*?\\]\", \"\", raw_text)\n raw_text = re.sub(\"\\(.*?\\)\", \"\", raw_text)\n return raw_text.strip().lower()\n except Exception as e:\n print(e.__str__())\n raise e","def vsepr_parse_user_answer(user_input):\r\n return json.loads(user_input)","def BlindConvertStringToProperValue(value: str, variables: dict, report, run_once=False, allowPrefixArithmetic=False) -> (object, FClass.FiMVariableTypes):\n if value == \"nothing\":\n return 0, FClass.FiMVariableTypes.NUMBER\n\n # Char\n if value.startswith(\"'\") and value.endswith(\"'\") and len(value[1:-1]) == 1:\n return value, FClass.FiMVariableTypes.CHAR\n #return value[1], FClass.FiMVariableTypes.CHAR\n # String\n if value.startswith('\"') and value.endswith('\"'):\n return value, FClass.FiMVariableTypes.STRING\n # Number\n try:\n if _isFloat(value):\n return float(value), FClass.FiMVariableTypes.NUMBER\n return int(value), FClass.FiMVariableTypes.NUMBER\n except:\n pass\n # Boolean\n try:\n return ConvertBooleanString( value ), FClass.FiMVariableTypes.BOOL\n except:\n pass\n # Variable Value\n if value in variables:\n #if variables[value]._isArray(True):\n # raise Exception(\"Cannot convert value from array\")\n return variables[value].getv(), variables[value].Type\n # Array length\n if value.startswith(\"length of \"):\n var_name = value[len(\"length of \"):]\n if not var_name in variables:\n raise Exception(f\"Cannot find variable {var_name}\")\n if not variables[var_name]._isArray():\n raise Exception(\"Cannot get length of non-array variable\")\n return len(variables[var_name].getsv()), FClass.FiMVariableTypes.NUMBER\n # Ascii number to character\n if value.startswith(\"raw char of \"):\n var_name = value[len(\"raw char of \"):]\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\n raise Exception(\"Cannot get ASCII value of a non-number value\")\n return chr(var_value), FClass.FiMVariableTypes.CHAR\n # Character to ascii number\n if value.startswith(\"raw num of \"):\n var_name = value[len(\"raw num of \"):]\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\n if guessed_type != FClass.FiMVariableTypes.CHAR:\n raise Exception(\"Cannot get ASCII number of a non-char value\")\n if var_value.startswith(\"'\") and var_value.endswith(\"'\"):\n var_value = var_value[1]\n return ord(var_value), FClass.FiMVariableTypes.NUMBER\n # Number to string\n if value.startswith(\"string of \"):\n var_name = value[len(\"string of \"):]\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\n raise Exception(\"Cannot convert a non-number value to string\")\n return f'\"{var_value}\"', FClass.FiMVariableTypes.STRING\n # String/Bool/Char to number\n if value.startswith(\"number of \"):\n var_name = value[len(\"number of \"):]\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\n if guessed_type == FClass.FiMVariableTypes.STRING or guessed_type == FClass.FiMVariableTypes.CHAR:\n var_value = var_value[1:-1]\n if _isFloat(var_value):\n return float(var_value), FClass.FiMVariableTypes.NUMBER \n return int(var_value), FClass.FiMVariableTypes.NUMBER\n elif guessed_type == FClass.FiMVariableTypes.BOOL:\n return 1 if var_value else 0, FClass.FiMVariableTypes.NUMBER\n else:\n raise Exception(\"Cannot convert non-string or non-bool value to number\")\n # Square root\n if value.startswith( FGlobal.Methods[\"Variable Square Root\"] ):\n sqrt_prefix = _findFirst(FGlobal.Methods[\"Variable Square Root\"], lambda x: value.startswith(x))\n var_name = value[len(sqrt_prefix):]\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\n raise Exception(\"Cannot square root a non-number value\")\n return math.sqrt(var_value), FClass.FiMVariableTypes.NUMBER\n # Paragraph return 1\n if value in report.Paragraphs:\n if report.Paragraphs[ value ].Return_Type is None:\n raise Exception(\"Paragraph doesn't return anything\")\n return report.RunParagraph(value).getv(), report.Paragraphs[ value ].Return_Type\n # Paragraph return 2\n if value.split(' using ')[0] in report.Paragraphs:\n paragraph_name = value.split(' using ')[0]\n if report.Paragraphs[ paragraph_name ].Return_Type is None:\n raise Exception(\"Paragraph doesn't return anything\")\n paragraph_param = ' using '.join( value.split(' using ')[1:] )\n params = list()\n for param_index, param in enumerate( paragraph_param.split(' and ') ):\n if param_index >= len(report.Paragraphs[ paragraph_name ].Parameters): break \n param_type,param_len = GrabVariableInitializerType(param)\n if param_type!=FClass.FiMVariableTypes.UNKNOWN:\n param = param[param_len:].strip()\n param_value, guessed_type = BlindConvertStringToProperValue(param, variables, report, run_once=True)\n if guessed_type != report.Paragraphs[ paragraph_name ].Parameters[ param_index ].Type:\n raise Exception(\"Invalid variable type\")\n params.append(param_value)\n return report.RunParagraph(paragraph_name, params).getv(), report.Paragraphs[ paragraph_name ].Return_Type\n # Variable array value 1 and 2\n if (re.sub(r\" (\\d+)$\", \"\", value) in variables) or (\" of \" in value):\n var_name, var_index = GetVariableArrayDetails(value, variables, report)\n if var_name is None and var_index is None:\n raise Exception(\"Invalid variable array initializer\")\n return variables[var_name].getav(var_index), FClass.FiMVariableTypes.GetArrayType( variables[var_name].Type )\n # Arithmetic\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( value )\n if arith_check:\n if (allowPrefixArithmetic) or (not allowPrefixArithmetic and not arith_value.is_prefix):\n arith = FCalc.FiMArithmetic(value, arith_value)\n arith.GetValues(variables, report)\n return arith.Calculate(), FClass.FiMVariableTypes.NUMBER\n\n # Return\n if run_once:\n raise Exception(f\"Cannot blind convert value {value}\")\n else:\n return SanitizeString(value, variables, report), FClass.FiMVariableTypes.STRING","def raw(string):\n string = string or \"\"\n return string.replace(\"{\", \"{{\").replace(\"|\", \"||\")","def value_from_str(self, s):\n raise ValueError()","def _process_value(self, value):\n if isinstance(value, str):\n try:\n value.decode('ascii')\n except UnicodeDecodeError:\n return unicode(''.join([unichr(ord(c)) for c in value]))\n return value","def get_data_from_nonformat_text():\n pass","def beautify(self, string):\n\n\t\tif not string:\n\t\t\treturn string\n\n\t\t# string may differ because of escaped characters\n\t\tstring, phrases = self.parse(string)\n\n\t\tif not phrases:\n\t\t\treturn string\n\n\t\tif not self.positional and not self.always:\n\t\t\traise errors.ArgumentError(\"Found phrases, but no styles \"\n\t\t\t\t\t\t\t\t\t \"were supplied!\")\n\n\t\treturn self.stringify(string, phrases)","def _normalize(fmt):\n if re.search(r\"\\d\\s+\", fmt):\n raise StructError(\"bad char in struct format\")\n return fmt.replace(\" \", \"\")","def haiku_string_parser():\n pass","def parse(self, input):\n pass","def format_value(text):\n return text.encode('utf8').replace('\\n', ' ').replace('\\r', ' ')","def get_correct_data_types(user_input, answer_type, alpha_string):\n if answer_type == int:\n try:\n user_input = int(user_input)\n except ValueError:\n user_input = None\n ui.print_error_message(\"Wrong value provided.\\n\")\n\n elif answer_type == str:\n if alpha_string:\n user_input_copy = user_input.replace(' ', '')\n\n if not user_input_copy.isalpha():\n user_input = None\n ui.print_error_message('It not alpha string.')\n\n return user_input","def normalizeRawFromHeader(value):\n return value.replace('\\n', '').replace('\\r', '').strip()","def from_str(cls, string):","def scanf(format, string):\n re_fmt = compile(format)\n result = re_fmt.scanf(string)\n _log.debug('%r <- %r = %r', format, string, result)\n return result","def prepare_input(self, extracted_str):\n\n # Remove withspace\n if self.options['remove_whitespace']:\n optimized_str = re.sub(' +', '', extracted_str)\n else:\n optimized_str = extracted_str\n \n # Remove accents\n if self.options['remove_accents']:\n optimized_str = unidecode(optimized_str)\n\n # specific replace\n for replace in self.options['replace']:\n assert len(replace) == 2, 'A replace should be a list of 2 items'\n optimized_str = optimized_str.replace(replace[0], replace[1])\n\n return optimized_str","def format_input(user_input):\n values = [int(i) for i in user_input.strip().split(\" \")]\n start = Coord(values[0], values[1])\n end = Coord(values[2], values[3])\n return Zone(start, end)","def test_prepare_value_string(self):\n field = FractionField()\n result = field.prepare_value(\"1/4\")\n self.assertEqual(\"1/4\", result)\n\n result = field.prepare_value(\"1 1/4\")\n self.assertEqual(\"1 1/4\", result)","def normalize_string(string):\n return string.replace(\"\\xa0\", \" \")\\\n .replace(\"\\\\\", \"\")\\\n .replace(\"-LRB-\", \"(\")\\\n .replace(\"-RRB-\", \")\")\\\n .replace(\"-LCB-\", \"{\")\\\n .replace(\"-RCB-\", \"}\")\\\n .replace(\"-LSB-\", \"[\")\\\n .replace(\"-RSB-\", \"]\")","def format_input(input):\n return f\"{input}> \"","def _massage_raw_pg_output_vals(self):\n pass","def __parse__(self, filter):\n \n if filter == 'zipcode':\n # Return 5 digit zip or, if applicable, Concatenate 5 digit and \n # 4 digit zipcode\n if self.data['Mailing Zip 4']:\n return \"%s-%s\" %(str(self.data['Mailing Zip Code'])[:-2],\n str(self.data['Mailing Zip 4'])[:-2]\n )\n else:\n return str(self.data['Mailing Zip Code'])[:-2]\n elif filter == 'employee_count':\n # Convert employee count string to digit\n pattern = '.+to\\s([0-9]+)'\n try:\n return re.findall(\n pattern, self.data['Location Employee Size Range'])[0]\n except IndexError:\n pass\n elif filter == 'phone':\n # Regex phone number digits and concatenate\n number = ''.join(re.findall('[0-9]+', \n self.data['Phone Number Combined']))\n return number if len(number) == 10 else 0","def format(self, data):","def _parse_input(self):\n #temperature\n regex = re.compile(\"TEMP=(\\d+\\.\\d*|\\d+)\")\n r = regex.search(self.file_dic['input'])\n if r:\n self.temperature = r.groups()[0]\n else:\n self.temperature = 298.15\n #theory\n regex = re.compile('(\\$contrl.+\\$end|\\$basis.+ \\$end)')\n temp_theory = regex.findall(self.file_dic['input'])\n contrl = temp_theory[0][:-4][7:].strip()\n basis = temp_theory[1][:-4][6:].strip()\n self.theory = contrl + ' ' + basis","def parseInput(self, inputString):\n stringArray = self.inputChecker(inputString)\n if stringArray == \"Error\":\n return stringArray\n # Correctly assigns negative numbers\n stringArray = self.negativeCheck(stringArray)\n # Checks whether any of the numbers have been written in scientific notation\n stringArray = self.scientificNotationCheck(stringArray)\n# print(stringArray)\n return stringArray","def format_data(self, data):","def simple_format_string(self, value, force_raw=False):\n\n m = re.search(r'((\\n)|\\r|\\n|\\t|\\\\)', value)\n newlines = False\n raw = ''\n if m:\n if m.group(2):\n newlines = True\n raw = 'r'\n if force_raw and not raw:\n raw = 'r'\n\n single = self.needs_escape(value, '\\'')\n double = self.needs_escape(value, '\"')\n tsingle = self.needs_escape(value, '\\'', quote_count=3)\n tdouble = self.needs_escape(value, '\"', quote_count=3)\n if not double and not newlines:\n string = '%s\"%s\"' % (raw, value)\n elif not single and not newlines:\n string = '%s\\'%s\\'' % (raw, value)\n elif not tdouble:\n string = '%s\"\"\"%s\"\"\"' % (raw, value)\n elif not tsingle:\n string = '%s\\'\\'\\'%s\\'\\'\\'' % (raw, value)\n elif not newlines:\n string = '%s\"%s\"' % (raw, self.fix_escape(value, '\"'))\n else:\n string = '%s\"\"\"%s\"\"\"' % (raw, self.fix_escape(value, '\"', quote_count=3))\n return string","def preprocess_raw(self):\n pass","def __str__(self):\n\n\t\tif self.rawValue == None: return str()\n\n\t\tx = self.rawValue\n\n\t\tif not x.isdigit() or len(x) != 44 or len(set(x)) == 1:\n\t\t\treturn self.rawValue\n\n\t\treturn '{} {} {} {} {} {} {} {} {} {} {}'.format(x[:4], x[4:8], x[8:12], x[12:16], x[16:20], x[20:24], x[24:28], x[28:32], x[32:36], x[36:40], x[40:44])","def ConvertUnknownStringToProperValue(value: str, vtype: FClass.FiMVariableTypes):\n if value == \"nothing\":\n no_value = {\n FClass.FiMVariableTypes.BOOL: False,\n FClass.FiMVariableTypes.NUMBER: 0,\n FClass.FiMVariableTypes.STRING: \"\\\"\\\"\",\n FClass.FiMVariableTypes.CHAR: \"'?'\"\n }\n if vtype in no_value: return no_value[ vtype ]\n \n if vtype == FClass.FiMVariableTypes.CHAR:\n if not value.startswith(\"'\") and not value.endswith(\"'\") and len(value[1:-1]) != 1:\n raise Exception(f\"Invalid char {value}\")\n return value\n if vtype == FClass.FiMVariableTypes.STRING:\n if not value.startswith('\"') and not value.endswith('\"'):\n raise Exception(f\"Invalid string {value}\")\n return value\n if vtype == FClass.FiMVariableTypes.NUMBER:\n try:\n return float(value)\n except:\n raise Exception(f\"Invalid number {value}\")\n if vtype == FClass.FiMVariableTypes.BOOL:\n try:\n return ConvertBooleanString( value )\n except:\n raise Exception(f\"Invalid boolean {value}\")","def process(line, form):\n return unicodedata.normalize(form, line)","def string_from_interwebs(input_value):\n \n return escape(unquote(input_value))","def _convert_metastr(metastr, dtype=None):\n if dtype is None:\n dtype = str\n\n if issubclass(dtype, tuple):\n return tuple(float(f.strip()) for f in metastr.split(\",\"))\n else:\n return dtype(metastr)","def test_format_phone_raw(self):\n number = '8095551234'\n self.assertEqual(format_phone(number), '(809) 555-1234')","def _decode_string(box_string):\r\n if box_string == \"no_box\":\r\n return np.zeros((0,4))\r\n else:\r\n try:\r\n boxes = np.array([np.array([int(eval(i)) for i in box.split(\" \")])\r\n for box in box_string.split(\";\")])\r\n return boxes\r\n except:\r\n print(box_string)\r\n print(\"Submission is not well formatted. empty boxes will be returned\")\r\n return np.zeros((0,4))","def reformat(ctx):\n pass","def parse_input(userstring):\n xsplit = userstring.split()\n stringtovalues = [float(x) for x in xsplit]\n\n return stringtovalues","def form(s):\r\n \r\n # removes leading and trailing apostrophe's from string\r\n s = s.strip(\"'\")\r\n \r\n # converts HTML hex back to characters\r\n s = s.replace(\"'\", \"'\")\r\n s = s.replace(\"’\", \"’\")\r\n s = s.replace(\"‘\", '\"')\r\n s = s.replace(\"”\", \"'\")\r\n s = s.replace(\"“\", \"'\")\r\n \r\n # success\r\n return s","def decode_string(self, value):\r\n return value","def preparse(self, raw):\n return raw","def normalize(self, text: str) -> str:","def _convert(string, type, message):\n try:\n return type(string)\n except ValueError as e:\n print(e)\n raise CharmmPSFError('Could not convert %s' % message)","def normalize_string(string):\n return string.replace(u\"\\xa0\", \" \")\\\n .replace(\"\\\\\", \"\")\\\n .replace(\"-LRB-\", \"(\")\\\n .replace(\"-RRB-\", \")\")\\\n .replace(\"-LCB-\", \"{\")\\\n .replace(\"-RCB-\", \"}\")\\\n .replace(\"-LSB-\", \"[\")\\\n .replace(\"-RSB-\", \"]\")","def parse_input(input_data):\n cleaned_input = input_data.replace('@ ', '').replace(':', '')\n split_data = cleaned_input.split(' ')\n xy = split_data[1].split(',')\n wh = split_data[2].split('x')\n return FabricRectangle(\n claim_id=split_data[0],\n x=int(xy[0]),\n y=int(xy[1]),\n width=int(wh[0]),\n height=int(wh[1])\n )","def string_to_param(self,string):\n\n if (string.startswith(\"log_\")): return math.log10(self.string_to_param(string[4:]))\n if (string.startswith(\"ln_\")): return math.log(self.string_to_param(string[3:]))\n if (string.startswith(\"exp_\")): return math.exp(self.string_to_param(string[4:]))\n if (string == \"Mass\"): return self.glb[imass]/constants.solar_mass\n if (string == \"Radius\"): return self.glb[iradius]/constants.solar_radius\n if (string == \"Luminosity\"): return self.glb[iluminosity]/constants.solar_luminosity\n if (string == \"Z\"): return self.glb[iz0]\n if (string == \"Y\"): return 1.0-self.glb[iz0]-self.glb[ix0]\n if (string == \"X\"): return self.glb[ix0]\n if (string == \"Ys\"): return 1.0-self.glb[user_params_index[\"Zs\"]]-self.glb[user_params_index[\"Xs\"]]\n if (string == \"zsx_s\"): return self.zsx_s\n if (string == \"zsx_0\"): return self.zsx_0\n if (string == \"Fe_H\"): return self.FeH\n if (string == \"M_H\"): return self.MH\n if (string == \"Age\"): return self.glb[iage]\n if (string == \"Teff\"): return self.glb[itemperature]\n if (string == \"Dnu\"): return self.find_large_separation()*self.glb[ifreq_ref]\n if (string == \"numax\"): return self.numax\n if (string == \"Rho\"): return 3.0*self.glb[imass]/(4.0*math.pi*self.glb[iradius]**3)\n if (string == \"g\"): return constants.G*self.glb[imass]/self.glb[iradius]**2\n if (string == \"beta_Sonoi2015\"): return self.beta_Sonoi2015\n if (string == \"b_Kjeldsen2008\"): return self.b_Kjeldsen2008\n\n try:\n return self.glb[user_params_index[string]]\n except KeyError:\n sys.exit(\"ERROR: unrecognised model quantity: \"+string)","def normalize_format(fmt):\n # Remove shape '()' at the forefront which is equivalent to an scalar\n if fmt[:2] == '()':\n fmt = fmt[2:]\n # Accept 'S' as a synonym of 'a'\n if fmt.find('S') >= 0:\n fmt = fmt.replace('S', 'a')\n return fmt","def get_formatted_string(self, input_string):\n if isinstance(input_string, str):\n try:\n return self.get_processed_string(input_string)\n except KeyError as err:\n # Wrapping the KeyError into a less cryptic error for end-user\n # friendliness\n missing_key = err.args[0]\n raise KeyNotInContextError(\n f'Unable to format \\'{input_string}\\' with '\n f'{{{missing_key}}}, because '\n f'context[\\'{missing_key}\\'] doesn\\'t exist') from err\n else:\n raise TypeError(f\"can only format on strings. {input_string} is a \"\n f\"{type(input_string)} instead.\")","def native_string(input_var):\n if isinstance(input_var, str):\n return input_var\n\n return input_var.decode('utf-8', 'replace')","def handle_input(data: dict):","def preprocess(string):\r\n # string = [strQ2B(ch) for ch in string.strip()]\r\n # return ''.join(string)\r\n return string","def cleanData(rawData):\n\trawData = re.sub(r'R-LRB- \\(', r'R-LRB- -LRB-', rawData)\n\trawData = re.sub(r'R-RRB- \\)', r'R-RRB- -RRB-', rawData)\n\trawData = re.sub(r'R-RRB- \\(', r'R-RRB- -LRB-', rawData)\n\trawData = re.sub(r'-LRB- \\(', r'-LRB- -LRB-', rawData)\n\trawData = re.sub(r'-RRB- \\)', r'-RRB- -RRB-', rawData)\n\trawData = re.sub(r'PU \\(', r'PU -LRB-', rawData)\n\trawData = re.sub(r'PU \\)', r'PU -RRB-', rawData)\n\trawData = re.sub(r':-\\)', r'smileyface', rawData)\n\n\treturn rawData","def convert_str(input_obj):\n if isinstance(input_obj, str):\n return(input_obj)\n elif input_obj is True:\n return('1')\n elif (input_obj is None):\n return('none')\n elif input_obj is False:\n return('0')\n else:\n return(str(input_obj))","def getString(self):\n print \"Enter String value:\",\n self.string = raw_input()","def _parseSingle(string):\n string = string.strip()\n \n if len(string) == 0:\n return ''\n \n pattern = re.compile(r'[^0-9]')\n if not pattern.search(string):\n return int(string)\n pattern = re.compile(r'[^0-9\\.eE]')\n if not pattern.search(string):\n if (string.count('.') <= 1 and \n (string.count('e') + string.count('E') <= 1)):\n return float(string)\n \n boolValue = _bool(string)\n if boolValue is not None:\n return boolValue\n \n if string[0] == string[-1]:\n if string[0] == '\"' or string[0] == \"'\":\n return string[1:-1]\n elif string[1] == string[-1]:\n if ((string[0] == 'u' or string[0] == 'r') and \n (string[1] == '\"' or string[1] == \"'\")):\n return string[2:-1]\n \n if string == 'None':\n return None\n \n return string","def _sanitize_string_for_python(self, s):\n s = repr(s)\n\n if s.startswith('u'):\n s = s[1:]\n\n return s","def main():\n haiku_string = \"clouds ,mur,mur ,dark,ly/it ,is ,a ,blin,ding ,ha,bit/ga,zing ,at ,the ,moon\"\n formatted_haiku = haiku_string_parser(haiku_string)\n print(formatted_haiku)","def make_input(value):\n what = value[0]\n if what.startswith('not editable'):\n what = what.replace('not editable:', '').replace(\"'\", \"\\'\")\n if what.startswith('attribute:value:'):\n what = what.replace('attribute:value:', '').replace(\"'\", \"\\'\")\n return what","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 parts.append(raw[s_index:])\n return GenericHelperFn({u\"Fn::Join\": [u\"\", parts]})","def parse_value(string: str) -> Union[str, dict, bool, int, float]:\n unesc_str = unescape(string)\n stripped = string.strip()\n if REGEX_RE.match(stripped):\n return {\"regex\": unesc_str.strip()[7:-2]}\n elif BOOL_RE.match(stripped):\n return stripped.lower() == \"true\"\n elif INT_RE.match(stripped):\n return int(stripped)\n elif FLOAT_RE.match(stripped):\n return float(stripped)\n else:\n return unesc_str[1:-1]","def process_input(self, message: str) -> str:\n processed_input = message\n # Convert user ids to just nick names\n processed_input.replace(\"@\"+self.user.name+\"#\"+self.user.discriminator, \"\")\n processed_input.replace(\"@\"+self.user.name, \"\")\n return processed_input","def parse_input_string(self, string_name):\n list_of_parts = string_name.split(\".\")\n if list_of_parts[0] == \"inputs\":\n return string_name\n else:\n # return only the integer part\n return int(list_of_parts[1])","def psych(output_str, input_str_1, input_1, input_str_2, input_2, pressure=29.92):\n\n dry_bulb = 0\n wet_bulb = 0\n dewpoint = 0\n rel_hum = 0\n hum_rat = 0\n spec_vol = 0\n enthalpy = 0\n pressure *= 0.491154\n\n if input_str_1 in ('db', 'DB', 'wb', 'WB', 'dp', 'DP', 'rh', 'RH', 'hr', 'HR', 'sv', 'SV', 'en', 'EN') and \\\n input_str_2 in ('db', 'DB', 'wb', 'WB', 'dp', 'DP', 'rh', 'RH', 'hr', 'HR', 'sv', 'SV', 'en', 'EN'):\n\n if input_str_1 in ('db', 'DB'):\n dry_bulb = input_1\n elif input_str_2 in ('db', 'DB'):\n dry_bulb = input_2\n\n if input_str_1 in ('wb', 'WB'):\n wet_bulb = input_1\n elif input_str_2 in ('wb', 'WB'):\n wet_bulb = input_2\n\n if input_str_1 in ('dp', 'DP'):\n dewpoint = input_1\n elif input_str_2 in ('dp', 'DP'):\n dewpoint = input_2\n\n if input_str_1 in ('rh', 'RH'):\n rel_hum = input_1 / 100\n elif input_str_2 in ('rh', 'RH'):\n rel_hum = input_2 / 100\n\n if input_str_1 in ('hr', 'HR'):\n hum_rat = input_1\n elif input_str_2 in ('hr', 'HR'):\n hum_rat = input_2\n\n if input_str_1 in ('sv', 'SV'):\n spec_vol = input_1\n elif input_str_2 in ('sv', 'SV'):\n spec_vol = input_2\n\n if input_str_1 in ('en', 'EN'):\n enthalpy = input_1\n elif input_str_2 in ('en', 'EN'):\n enthalpy = input_2\n else:\n return ValueError('Invalid input types')\n\n if hum_rat < 0:\n return ValueError('Humidity ratio less than 0')\n if rel_hum < 0 or rel_hum > 1:\n return ValueError('Relative humidity less than 0 or greater than 100')\n\n ############################################################################################\n\n if input_str_1 in ('db', 'DB') or input_str_2 in ('db', 'DB'):\n if output_str in ('db', 'DB'):\n return dry_bulb\n\n db_r = dry_bulb + 459.67\n\n if input_str_1 in ('wb', 'WB') or input_str_2 in ('wb', 'WB'):\n if output_str in ('wb', 'WB'):\n return wet_bulb\n\n db_r = dry_bulb + 459.67\n wb_r = wet_bulb + 459.67\n\n pres_wb_sat = sat_pres(wb_r)\n\n hr_wb_sat = 0.62198 * pres_wb_sat / (pressure - pres_wb_sat)\n\n hum_rat = (hr_wb_sat * (1093 - .556 * wet_bulb) - 0.24 * (dry_bulb - wet_bulb)) / \\\n (1093 + .444 * dry_bulb - wet_bulb)\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_db_sat = sat_pres(db_r)\n\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n\n mu = hum_rat / hr_db_sat\n\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\n if rel_hum < 0 or rel_hum > 1:\n return -1 # ValueError('Calculated relative humidity less than 0')\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n\n if output_str in ('dp', 'DP'):\n pres_vapor = (pressure * hum_rat) / (0.62198 + hum_rat)\n\n dewpoint = calc_dewpoint(pres_vapor)\n return dewpoint\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\n if output_str in ('dp', 'DP'):\n return dewpoint\n\n dp_r = dewpoint + 459.67\n\n pres_vapor = sat_pres(dp_r)\n\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\n if hum_rat < 0:\n return -1 # ValueError('Calculated humidity ratio below 0')\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_db_sat = sat_pres(db_r)\n\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n\n mu = hum_rat / hr_db_sat\n\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\n if rel_hum < 0 or rel_hum > 1:\n return -1\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n\n if output_str in ('wb', 'WB'):\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\n return wet_bulb\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n pres_db_sat = sat_pres(db_r)\n\n pres_vapor = pres_db_sat * rel_hum\n\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\n if hum_rat < 0:\n return -1\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n\n # mu = hum_rat / hr_db_sat\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n\n if output_str in ('dp', 'DP'):\n dewpoint = calc_dewpoint(pres_vapor)\n return dewpoint\n\n if output_str in ('wb', 'WB'):\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\n return wet_bulb\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_db_sat = sat_pres(db_r)\n\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\n\n rel_hum = pres_vapor / pres_db_sat\n if rel_hum < 0 or rel_hum > 1:\n return -1\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n\n # mu = hum_rat / hr_db_sat\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n\n if output_str in ('dp', 'DP'):\n dewpoint = calc_dewpoint(pres_vapor)\n return dewpoint\n\n if output_str in ('wb', 'WB'):\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\n return wet_bulb\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n\n # pres_db_sat = sat_pres(db_r)\n\n hum_rat = (spec_vol * 28.9645 * (pressure * 144) / (1545.32 * db_r) - 1) / 1.6078\n if hum_rat < 0:\n return -1\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\n\n rel_hum = pres_vapor / pressure\n if rel_hum < 0 or rel_hum > 1:\n return -1\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n\n if output_str in ('dp', 'DP'):\n dewpoint = calc_dewpoint(pres_vapor)\n return dewpoint\n\n if output_str in ('wb', 'WB'):\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\n return wet_bulb\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy\n\n pres_db_sat = sat_pres(dry_bulb)\n\n hum_rat = (enthalpy - 0.24 * dry_bulb) / (1061 + 0.444 * dry_bulb)\n if hum_rat < 0:\n return -1\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\n\n rel_hum = pres_vapor / pres_db_sat\n if rel_hum < 0 or rel_hum > 1:\n return -1\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n\n # mu = hum_rat / hr_db_sat\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('dp', 'DP'):\n dewpoint = calc_dewpoint(pres_vapor)\n return dewpoint\n\n if output_str in ('wb', 'WB'):\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\n return wet_bulb\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('wb', 'WB') or input_str_2 in ('wb', 'WB'):\n if output_str in ('wb', 'WB'):\n return wet_bulb\n\n wb_r = wet_bulb + 459.67\n\n if input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\n if output_str in ('dp', 'DP'):\n return dewpoint\n\n dp_r = dewpoint + 459.67\n\n pres_vapor = sat_pres(dp_r)\n\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\n if hum_rat < 0:\n return -1\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n pres_wb_sat = sat_pres(wb_r)\n\n hr_wb_sat = 0.62198 * pres_wb_sat / (pressure - pres_wb_sat)\n\n dry_bulb = ((1093 - 0.556 * wet_bulb) * hr_wb_sat + 0.24 * wet_bulb - (1093 - wet_bulb) * hum_rat) / \\\n (0.444 * hum_rat + 0.24)\n if output_str in ('db', 'DB'):\n return dry_bulb\n\n db_r = dry_bulb + 459.67\n\n pres_db_sat = sat_pres(dry_bulb)\n\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\n mu = hum_rat / hr_db_sat\n\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\n if rel_hum < 0 or rel_hum > 1:\n return -1\n if output_str in ('rh', 'RH'):\n return rel_hum\n\n if output_str in ('sv', 'SV'):\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\n return spec_vol\n\n if output_str in ('en', 'EN'):\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\n return enthalpy\n else:\n return ValueError('Unknown output request')\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\n if output_str in ('hr', 'HR'):\n return hum_rat\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy\n return -1 # no enthalpy, wet bulb and enthalpy are too closely related to avoid problems\n elif input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\n if output_str in ('dp', 'DP'):\n return dewpoint\n\n dp_r = dewpoint + 459.67\n\n if input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\n if output_str in ('hr', 'HR'):\n return hum_rat\n return -1 # no humidity ratio - it is the dew point more or less\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\n if output_str in ('hr', 'HR'):\n return hum_rat\n\n if input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\n if output_str in ('rh', 'RH'):\n return rel_hum * 100\n\n if input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\n if output_str in ('sv', 'SV'):\n return spec_vol\n\n if input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\n if output_str in ('en', 'EN'):\n return enthalpy","def preprocess_input(self, text):\n text = re.sub(r\"([^a-zA-Z0-9 -]+ +[^a-zA-Z0-9 -]*|[^a-zA-Z0-9 -]*\" +\n \" +[^a-zA-Z0-9 -]+)\", ' ', text, flags=re.UNICODE)\n text = re.sub(r\"([^a-zA-Z0-9 -]+$|^[^a-zA-Z0-9 -]+)\", '', text)\n text = re.sub(r\"([a-zA-Z0-9 -]+?)([^a-zA-Z0-9 -])([a-zA-Z0-9 -]+?)\",\n r\"\\1'\\3\", text, flags=re.UNICODE)\n text = re.sub(r\"([\\x00-\\x7F -]+?)([^a-zA-Z0-9 -]+)([\\x00-\\x7F -]+?)\",\n r\"\\1'\\3\", text, flags=re.UNICODE).encode(\"utf-8\")\n return re.sub(r\"([^a-zA-Z0-9 \\-\\'])\", '', text, flags=re.UNICODE)","def prepare_value(self, value):\n if value is None:\n return value\n value = value.replace(\" \", \"\").replace(\".\", \"\")\n if value:\n return \"%s.%s.%s.%s\" % (value[0:3], value[3:7], value[7:11], value[11:])\n return value","def format_value(self, val):\n\n if isinstance(val, (unicode, str)):\n val = self.standardise_quotes(val)\n \n try:\n val = unicode(val)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(val).encode('string_escape')\n val = unicode(ascii_text)\n\n return val","def handle_value(value):\n\n if value[-1] == 'x':\n return float(value[0:-1])\n\n if value[-1] == '%':\n return float(value[0:-1])\n\n if value[0].isdigit():\n return bytify(value)\n\n raise ValueError","def parse_string(self, in_str):\n match = MAIN_REGEX.search(in_str)\n if not match:\n err_str = \"Unable to parse string: %s\" % in_str\n raise ValueError(err_str)\n self.parse_completed(match.group(1))\n self.parse_priority(match.group(2))\n if match.group(3) and match.group(4):\n self.parse_completion_date(match.group(3))\n self.parse_creation_date(match.group(4))\n else:\n self.parse_creation_date(match.group(3))\n self.parse_description(match.group(5))","def clean_string(raw_string):\n if raw_string == None:\n return\n\n clean_string = raw_string.strip('\\n')\n clean_string = ' '.join(clean_string.split())\n return clean_string","def from_str (s):\n try: \n return from_csv(s)\n except Exception: \n pass\n \n try: \n return from_hex(s)\n except Exception: \n pass\n\n try:\n return from_name(s)\n except Exception: \n pass\n\n raise ColourFormatError(\"'%s' is not a recognized colour string\"%s)","def str_to_rule(str_in):\r\n log.debug(\"str_to_rule: \"+str_in.strip())\r\n str_i = str_in.strip().split('#')[0].strip()\r\n if len(str_i)>0:\r\n dic_rule = dict(valid=False,type='para',errors=list(),key=\"\",val=\"\")\r\n if(str_i[0]==\"%\"): # % Parameter str_i\r\n lst_par = str_i[1:].split('=')\r\n lst_par = [par.strip() for par in lst_par]\r\n if lst_par[0] in lst_known_para:\r\n dic_rule['key']=lst_par[0].strip()\r\n dic_rule['val']=lst_par[1].strip()\r\n dic_rule['valid']=True\r\n log.info('Parameter recognised: '+str(dic_rule['key'])+' = '+str(dic_rule['val']))\r\n else:\r\n dic_rule['valid']=False\r\n dic_rule['errors'].append(\"Unrecognised parameter: \"+lst_par[0])\r\n log.warning('#205 > '+str(dic_rule['errors'])+' raw line: '+str_i)\r\n elif(str_i[0]==\":\"): # : Rule str_i\r\n dic_rule = dict(valid=False,type='rule',errors=list(),id=\"\",title=\"\",mode=\"\",data_table=\"\",condition=\"\",action=\"\",act_param=\"\",comment=\"\")\r\n lst_items = str_i[1:].split(\":\")\r\n lst_items = [itm.strip() for itm in lst_items]\r\n if len(lst_items)==8:\r\n dic_rule['id']=lst_items[0]\r\n dic_rule['title']=lst_items[1]\r\n dic_rule['mode']=lst_items[2].upper()\r\n dic_rule['layer']=lst_items[3]\r\n dic_rule['condition']=lst_items[4]\r\n dic_rule['action']=lst_items[5]\r\n dic_rule['act_param']=lst_items[6]\r\n dic_rule['comment']=lst_items[7]\r\n dic_rule = sanity_check(dic_rule)\r\n if not dic_rule['valid']:\r\n log.warning('#203 invalid rule > '+str(dic_rule['errors'])+' raw line: '+str_in)\r\n log.debug('parsed good rule: '+str(dic_rule))\r\n else:\r\n dic_rule['errors'].append(\"Rule string does not contain the correct number of elements - Check that you comment do not contain ':'. Ignoring this rule. \\n\\t\"+str_in.strip()+\"\\n\\t\"+str(len(lst_items))+'\\t'+str(lst_items))\r\n log.warning('#202 '+dic_rule['errors'])\r\n dic_rule['valid']=False\r\n else:\r\n dic_rule['errors'].append(\"Rule string must start with #, % or : But I found: \"+str_in[0]+\" in line (\"+str_in+\")\")\r\n log.warning('#201 '+str(dic_rule['errors'][-1:]))\r\n dic_rule['valid']=False\r\n else: # Empty (or only comments) str_i\r\n return {'type':'null', 'valid':True}\r\n return dic_rule","def formatengsrt(input, output):\n \n p = ( (r\"-=.*?=-\\s+\", \"\", re.U), #类似 ==http://a.b.c/=- 删除\n (r\"<.*?>\", \"\", re.U), #类似 <...> 删除\n (r\"^[\\s\\d\\-:>,]*[\\r\\n]+\", r\"\", re.M|re.U), #'-'是特殊字符\n (r\"(\\S+)\\s+$\", r\"\\1\", re.M|re.U), #删除结尾的空余字符\n (r\"\\.{3}[\\r\\n]+([a-z])\", r\" \\1\", re.U), #结尾是...的，下一行开始是一个小写字母的。\n (r\"([^\\.?!])[\\r\\n]+\", r\"\\1 \", re.U), #结尾没有 .?!的，添加空格，去掉回车换行\n (r\"(\\w+)[,.?!](\\w)\", r\"\\1, \\2\", re.U), #有些单词后逗号后面没有空格，加上\n )\n\n d = chardet.detect(open(input, \"r\").read())\n print \"[%s] 自动检测为 %s\" %(input, d)\n\n with codecs.open(input, encoding=d['encoding'], mode='r') as fi:\n t = fi.read()\n \n for a, b, c in p:\n t = re.sub(a, b, t, 0, c)\n\n with codecs.open(output, encoding=d['encoding'], mode='w') as fo:\n fo.write(t)\n print \"[{}] compeleted.\".format(output)","def street_parser(*street_data):\n\n # parsing tuples\n if len(street_data) == 2:\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\n raise WrongInput(\"Invalid format\")\n # street name as the tuple's first item\n strname, strnumber = street_data\n # street number as the tuple's first item\n if street_data[0][0] in digits:\n strname, strnumber = strnumber, strname\n\n # parsing strings\n else:\n if not isinstance(street_data[0], str):\n raise WrongInput(\"Invalid format\")\n if not street_data[0]:\n raise WrongInput(\"Input cannot be blank\")\n\n # string starting with street number\n if street_data[0][0] in digits:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\d+) # street number is any number of digits\n \\W+ # separator\n (\\w+\\W*\\w*\\W*) # street name is one or more words with optional separators\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n strnumber, strname = street_obj.groups()\n\n # string starting with street name\n else:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\w+\\W*\\w*\\s*) # street name is one or more words with optional separators\n \\W+ # separator\n (\\d+) # street number is any number of digits\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n (strname, strnumber) = street_obj.groups()\n\n # replace specific words in street name with their abbreviates\n strname = strname.lower()\n special = {r'\\baleje\\b': 'Al.', r'\\bavenue\\b': 'Av.', r'\\broad\\b': 'Rd.', r'\\bsquare\\b': 'Sq.',\n r'\\bstreet\\b': 'St.', r'\\bdrive\\b': 'Dr.'}\n for key in special:\n strname = re.sub(key, special[key], strname)\n return strname.title(), strnumber","def makeinputstring(variabel):\r\n if type(variabel) == int:\r\n return str(variabel)\r\n elif type(variabel) == float:\r\n return str(int(float(variabel)))\r\n else:\r\n return str(variabel)","def inputString(self):\n return self.__inputString"],"string":"[\n \"def format_raw_input(user_input):\\n # Replace silly “ or ” characters with \\\"\\n # TODO: Swap out with regex\\n raw_input = user_input.strip().replace(\\n '“', '\\\"').replace(\\\"”\\\", '\\\"').replace(\\\",\\\", \\\"\\\").replace(\\\"\\\\n\\\", \\\" \\\")\\n # Break apart the string into each coordinate\\n raw_inputs = [r.replace('\\\"', '') for r in raw_input.split('\\\" \\\"')]\\n # Return coordinates as lists of ints.\\n return [[int(i) for i in r.split(\\\" \\\")] for r in raw_inputs]\",\n \"def input_parser(input_string: str) -> str: \\n if is_int(input_string):\\n return input_string\\n #he is int, give back plz.\\n else:\\n try:\\n modified_input: str = input_string.strip()\\n\\n evaluatable_pairs: str = regex_splitter(modified_input)\\n\\n while not (is_int(evaluatable_pairs)):\\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\\n\\n return (evaluatable_pairs)\\n\\n except:\\n raise Exception(\\\"Invalid Input\\\")\",\n \"def __call__(self, value): # noqa: D102\\n if not isinstance(value, str):\\n raise ValueError(f\\\"Input value must be a string. '{value}' is not.\\\")\\n\\n raw_value = value\\n for c in self.remove_characters:\\n value = value.replace(c, \\\"\\\")\\n if not bool(re.match(f\\\"^[{self.allowed_characters},]+$\\\", value)):\\n raise ValueError(\\n f\\\"Input must only contain values '{self.allowed_characters},'. '{raw_value}' does not.\\\"\\n )\\n if not bool(\\n re.match(\\n f\\\"^([{self.allowed_characters}],)+[{self.allowed_characters}]$\\\", value\\n )\\n ):\\n raise ValueError(\\n f\\\"Input must have format '(?,?,?,?)'. '{raw_value}' does not.\\\"\\n )\\n if not all([c in value for c in self.required_characters]):\\n raise ValueError(\\n f\\\"Input must contain {self.required_characters}. '{raw_value}' does not.\\\"\\n )\\n return raw_value\",\n \"def parse(s):\\n return s\",\n \"def get_processed_string(self, input_string):\\n if input_string[:6] == '[sic]\\\"':\\n return input_string[6: -1]\\n else:\\n return input_string.format(**self)\",\n \"def parse_string(self, data):\\n pass\",\n \"def parse(self,value):\\r\\n\\t\\treturn str(value)\",\n \"def _parse_user_input(self):\\n user_input = self.user_input.strip()\\n if user_input:\\n if user_input.find(',') > -1:\\n # Location is either city/state or latitude/longitude.\\n if user_input[0].isalpha():\\n # City, state (lat/long handled elsewhere)\\n city, state = [x.strip() for x in user_input.split(',')]\\n self.city = city\\n self.state = state\\n elif (len(user_input) <= 10 and\\n user_input[1].isdigit()): # 2nd char in US/Can. postal codes\\n # Postal code\\n self.postal_code = user_input.strip()\",\n \"def _sanitize_string(self, string):\\n # get the type of a unicode string\\n unicode_type = type(Pyasciigraph._u('t'))\\n input_type = type(string)\\n if input_type is str:\\n if sys.version < '3':\\n info = unicode(string)\\n else:\\n info = string\\n elif input_type is unicode_type:\\n info = string\\n elif input_type is int or input_type is float:\\n if sys.version < '3':\\n info = unicode(string)\\n else:\\n info = str(string)\\n else:\\n info = str(string)\\n return info\",\n \"def __normalize(self, ctx: commands.Context, format: str) -> str:\\n\\t\\t# convert to lowercase\\n\\t\\tlower_format = format.lower()\\n\\t\\t# check if inputted format is recognized\\n\\t\\tif lower_format in self.formats:\\n\\t\\t\\treturn lower_format\\n\\t\\t# check for aliases\\n\\t\\telif lower_format in self.aliases:\\n\\t\\t\\treturn self.aliases[lower_format]\\n\\t\\t# format is not recognized\\n\\t\\telse:\\n\\t\\t\\traise FriendlyError(\\n\\t\\t\\t\\tf\\\"'{format}' is not a recognized format.\\\", ctx.channel, ctx.author\\n\\t\\t\\t)\",\n \"def __format_input_translator(str_to_process):\\n return re.sub(r'\\\\([^)]*\\\\)', '', str_to_process).replace(' ', '').split('/')\",\n \"def parseString(self, s):\\n pass\",\n \"def preprocess(inpString):\\n\\n figure = re.compile(r'\\\\([^\\\\W\\\\d_]+ [0-9]+\\\\.[0-9]+\\\\)') # i.e (Tablo 3.2)\\n out_string = figure.sub('', inpString)\\n\\n digit_dot = re.compile(r'([0-9]+)\\\\.([0-9]{3})') # i.e 19.000 --> 19000\\n out_string = digit_dot.sub(r'\\\\1\\\\2', out_string)\\n out_string = digit_dot.sub(r'\\\\1\\\\2', out_string)\\n\\n centigrade = re.compile(r'(°C)|(°c)|(0C)') # °C --> santigrat\\n out_string = centigrade.sub(r' santigrat', out_string)\\n\\n out_string = re.sub(r'°', ' derece', out_string) # ° --> derece\\n\\n digit_space = re.compile(r'([0-9]+) ([0-9]+)') # 19 000 --> 19000\\n out_string = digit_space.sub(r'\\\\1\\\\2', out_string)\\n\\n out_string = re.sub(r'â', 'a', out_string) # Elâzig --> Elazig\\n\\n spec_hyphen = re.compile(r'([A-Za-z])-([0-9]+)') # G-20 --> G20\\n out_string = spec_hyphen.sub(r'\\\\1\\\\2', out_string)\\n\\n out_string = re.sub(r'-', ' ', out_string) # replace hyphen with space\\n\\n out_string = re.sub(r'%|‰', 'yüzde ', out_string) # % --> yuzde\\n\\n year = re.compile(\\\"([0-9]{4})(’|')([a-z]+)\\\") # 1815'te --> 1815 yilinda\\n out_string = year.sub(r'\\\\1 yılında', out_string)\\n\\n out_string = re.sub(r' km2', ' kilometrekare', out_string) # converting km2, m, km\\n out_string = re.sub(r' m ', ' metre ', out_string)\\n out_string = re.sub(r' km ', ' kilometre ', out_string)\\n\\n out_string = re.sub(r\\\"(’|')([a-züşöıç]+)\\\", '', out_string) # turkiye'de --> turkiye\\n\\n out_string = re.sub(r'([0-9]+),([0-9]+)', r'\\\\1CBN\\\\2', out_string) # replacing comma between\\n # digits with a placeholder\\n\\n puncs = string.punctuation + '”' + '“' + '’' + '‘'\\n translator = str.maketrans('', '', puncs)\\n out_string = out_string.translate(translator) # removing pucntuations\\n\\n out_string = re.sub(r'CBN', ',', out_string) # bringing back the comma between numbers\\n # out_string= out_string.split(' ') #[s.split(' ') for s in out_string.split('#')] #splitting from end of sentences\\n # end sentence into words\\n\\n return out_string\",\n \"def decode(self, s):\",\n \"def decode(self, s):\",\n \"def format_input(self, args):\\n\\n new_list = []\\n if args[1].find('{') != -1:\\n new_list = self.format_dicti(args)\\n return new_list\\n else:\\n new_list = []\\n new_list.append(args[0])\\n new_str = args[1][ args[1].find('(') + 2 : args[1].find(',') - 1]\\n new_str += args[1][ args[1].find(',') : args[1].find(')') - 0]\\n new_list.append(\\\" \\\".join(new_str.split(\\\", \\\") ) )\\n\\n return \\\" \\\".join(i for i in new_list)\",\n \"def value_from_str(self, s):\\n if is_quoted_str(s):\\n return s[1:-1]\\n return super().value_from_str(s)\",\n \"def value_from_str(self, s):\\n if is_quoted_str(s):\\n return s[1:-1]\\n return super().value_from_str(s)\",\n \"async def parse(self, raw: str) -> dict:\",\n \"def convert(string, sanitize=False):\\n return r.convert(string, (preprocess if sanitize else False))\",\n \"def _handle_string(\\n *, artifacts: types.ColumnArtifacts\\n) -> typing.Union[String, Binary, Date, DateTime]:\\n if artifacts.open_api.format in {None, \\\"byte\\\", \\\"password\\\"}:\\n if artifacts.open_api.max_length is None:\\n return String\\n return String(length=artifacts.open_api.max_length)\\n if artifacts.open_api.format == \\\"binary\\\":\\n if artifacts.open_api.max_length is None:\\n return Binary\\n return Binary(length=artifacts.open_api.max_length)\\n if artifacts.open_api.format == \\\"date\\\":\\n return Date\\n if artifacts.open_api.format == \\\"date-time\\\":\\n return DateTime\\n raise exceptions.FeatureNotImplementedError(\\n f\\\"{artifacts.open_api.format} format for string is not supported.\\\"\\n )\",\n \"def get_params(raw):\\n parts = raw.split(\\\" \\\", 1)\\n return None if len(parts) == 1 else parts[1]\",\n \"def parse(self,value):\\r\\n\\t\\treturn unicode(value)\",\n \"def test_parseUnformattedText(self):\\n self.assertEqual(irc.parseFormattedText(\\\"hello\\\"), A.normal[\\\"hello\\\"])\",\n \"def tidy_string(s: str\\n ) -> str:\\n s = s.encode('ascii', errors='ignore').decode(FORMAT)\\n s = s.replace(\\\"\\\\r\\\", \\\"\\\").replace(\\\"\\\\t\\\", \\\"\\\").replace('\\\\n', '') \\n return s\",\n \"def autoconvert(s):\\n try:\\n return eval(s)\\n except:\\n return s\",\n \"def preprocess_data(raw_text):\\n try:\\n raw_text = raw_text.replace(\\\"?\\\", \\\"Unknown\\\")\\n raw_text = raw_text.replace(\\\" or \\\", \\\", \\\")\\n raw_text = raw_text.replace(\\\" and \\\", \\\", \\\")\\n raw_text = re.sub(\\\"\\\\[.*?\\\\]\\\", \\\"\\\", raw_text)\\n raw_text = re.sub(\\\"\\\\(.*?\\\\)\\\", \\\"\\\", raw_text)\\n return raw_text.strip().lower()\\n except Exception as e:\\n print(e.__str__())\\n raise e\",\n \"def vsepr_parse_user_answer(user_input):\\r\\n return json.loads(user_input)\",\n \"def BlindConvertStringToProperValue(value: str, variables: dict, report, run_once=False, allowPrefixArithmetic=False) -> (object, FClass.FiMVariableTypes):\\n if value == \\\"nothing\\\":\\n return 0, FClass.FiMVariableTypes.NUMBER\\n\\n # Char\\n if value.startswith(\\\"'\\\") and value.endswith(\\\"'\\\") and len(value[1:-1]) == 1:\\n return value, FClass.FiMVariableTypes.CHAR\\n #return value[1], FClass.FiMVariableTypes.CHAR\\n # String\\n if value.startswith('\\\"') and value.endswith('\\\"'):\\n return value, FClass.FiMVariableTypes.STRING\\n # Number\\n try:\\n if _isFloat(value):\\n return float(value), FClass.FiMVariableTypes.NUMBER\\n return int(value), FClass.FiMVariableTypes.NUMBER\\n except:\\n pass\\n # Boolean\\n try:\\n return ConvertBooleanString( value ), FClass.FiMVariableTypes.BOOL\\n except:\\n pass\\n # Variable Value\\n if value in variables:\\n #if variables[value]._isArray(True):\\n # raise Exception(\\\"Cannot convert value from array\\\")\\n return variables[value].getv(), variables[value].Type\\n # Array length\\n if value.startswith(\\\"length of \\\"):\\n var_name = value[len(\\\"length of \\\"):]\\n if not var_name in variables:\\n raise Exception(f\\\"Cannot find variable {var_name}\\\")\\n if not variables[var_name]._isArray():\\n raise Exception(\\\"Cannot get length of non-array variable\\\")\\n return len(variables[var_name].getsv()), FClass.FiMVariableTypes.NUMBER\\n # Ascii number to character\\n if value.startswith(\\\"raw char of \\\"):\\n var_name = value[len(\\\"raw char of \\\"):]\\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\\n raise Exception(\\\"Cannot get ASCII value of a non-number value\\\")\\n return chr(var_value), FClass.FiMVariableTypes.CHAR\\n # Character to ascii number\\n if value.startswith(\\\"raw num of \\\"):\\n var_name = value[len(\\\"raw num of \\\"):]\\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\\n if guessed_type != FClass.FiMVariableTypes.CHAR:\\n raise Exception(\\\"Cannot get ASCII number of a non-char value\\\")\\n if var_value.startswith(\\\"'\\\") and var_value.endswith(\\\"'\\\"):\\n var_value = var_value[1]\\n return ord(var_value), FClass.FiMVariableTypes.NUMBER\\n # Number to string\\n if value.startswith(\\\"string of \\\"):\\n var_name = value[len(\\\"string of \\\"):]\\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\\n raise Exception(\\\"Cannot convert a non-number value to string\\\")\\n return f'\\\"{var_value}\\\"', FClass.FiMVariableTypes.STRING\\n # String/Bool/Char to number\\n if value.startswith(\\\"number of \\\"):\\n var_name = value[len(\\\"number of \\\"):]\\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\\n if guessed_type == FClass.FiMVariableTypes.STRING or guessed_type == FClass.FiMVariableTypes.CHAR:\\n var_value = var_value[1:-1]\\n if _isFloat(var_value):\\n return float(var_value), FClass.FiMVariableTypes.NUMBER \\n return int(var_value), FClass.FiMVariableTypes.NUMBER\\n elif guessed_type == FClass.FiMVariableTypes.BOOL:\\n return 1 if var_value else 0, FClass.FiMVariableTypes.NUMBER\\n else:\\n raise Exception(\\\"Cannot convert non-string or non-bool value to number\\\")\\n # Square root\\n if value.startswith( FGlobal.Methods[\\\"Variable Square Root\\\"] ):\\n sqrt_prefix = _findFirst(FGlobal.Methods[\\\"Variable Square Root\\\"], lambda x: value.startswith(x))\\n var_name = value[len(sqrt_prefix):]\\n var_value, guessed_type = BlindConvertStringToProperValue(var_name, variables, report, True)\\n if guessed_type != FClass.FiMVariableTypes.NUMBER:\\n raise Exception(\\\"Cannot square root a non-number value\\\")\\n return math.sqrt(var_value), FClass.FiMVariableTypes.NUMBER\\n # Paragraph return 1\\n if value in report.Paragraphs:\\n if report.Paragraphs[ value ].Return_Type is None:\\n raise Exception(\\\"Paragraph doesn't return anything\\\")\\n return report.RunParagraph(value).getv(), report.Paragraphs[ value ].Return_Type\\n # Paragraph return 2\\n if value.split(' using ')[0] in report.Paragraphs:\\n paragraph_name = value.split(' using ')[0]\\n if report.Paragraphs[ paragraph_name ].Return_Type is None:\\n raise Exception(\\\"Paragraph doesn't return anything\\\")\\n paragraph_param = ' using '.join( value.split(' using ')[1:] )\\n params = list()\\n for param_index, param in enumerate( paragraph_param.split(' and ') ):\\n if param_index >= len(report.Paragraphs[ paragraph_name ].Parameters): break \\n param_type,param_len = GrabVariableInitializerType(param)\\n if param_type!=FClass.FiMVariableTypes.UNKNOWN:\\n param = param[param_len:].strip()\\n param_value, guessed_type = BlindConvertStringToProperValue(param, variables, report, run_once=True)\\n if guessed_type != report.Paragraphs[ paragraph_name ].Parameters[ param_index ].Type:\\n raise Exception(\\\"Invalid variable type\\\")\\n params.append(param_value)\\n return report.RunParagraph(paragraph_name, params).getv(), report.Paragraphs[ paragraph_name ].Return_Type\\n # Variable array value 1 and 2\\n if (re.sub(r\\\" (\\\\d+)$\\\", \\\"\\\", value) in variables) or (\\\" of \\\" in value):\\n var_name, var_index = GetVariableArrayDetails(value, variables, report)\\n if var_name is None and var_index is None:\\n raise Exception(\\\"Invalid variable array initializer\\\")\\n return variables[var_name].getav(var_index), FClass.FiMVariableTypes.GetArrayType( variables[var_name].Type )\\n # Arithmetic\\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( value )\\n if arith_check:\\n if (allowPrefixArithmetic) or (not allowPrefixArithmetic and not arith_value.is_prefix):\\n arith = FCalc.FiMArithmetic(value, arith_value)\\n arith.GetValues(variables, report)\\n return arith.Calculate(), FClass.FiMVariableTypes.NUMBER\\n\\n # Return\\n if run_once:\\n raise Exception(f\\\"Cannot blind convert value {value}\\\")\\n else:\\n return SanitizeString(value, variables, report), FClass.FiMVariableTypes.STRING\",\n \"def raw(string):\\n string = string or \\\"\\\"\\n return string.replace(\\\"{\\\", \\\"{{\\\").replace(\\\"|\\\", \\\"||\\\")\",\n \"def value_from_str(self, s):\\n raise ValueError()\",\n \"def _process_value(self, value):\\n if isinstance(value, str):\\n try:\\n value.decode('ascii')\\n except UnicodeDecodeError:\\n return unicode(''.join([unichr(ord(c)) for c in value]))\\n return value\",\n \"def get_data_from_nonformat_text():\\n pass\",\n \"def beautify(self, string):\\n\\n\\t\\tif not string:\\n\\t\\t\\treturn string\\n\\n\\t\\t# string may differ because of escaped characters\\n\\t\\tstring, phrases = self.parse(string)\\n\\n\\t\\tif not phrases:\\n\\t\\t\\treturn string\\n\\n\\t\\tif not self.positional and not self.always:\\n\\t\\t\\traise errors.ArgumentError(\\\"Found phrases, but no styles \\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"were supplied!\\\")\\n\\n\\t\\treturn self.stringify(string, phrases)\",\n \"def _normalize(fmt):\\n if re.search(r\\\"\\\\d\\\\s+\\\", fmt):\\n raise StructError(\\\"bad char in struct format\\\")\\n return fmt.replace(\\\" \\\", \\\"\\\")\",\n \"def haiku_string_parser():\\n pass\",\n \"def parse(self, input):\\n pass\",\n \"def format_value(text):\\n return text.encode('utf8').replace('\\\\n', ' ').replace('\\\\r', ' ')\",\n \"def get_correct_data_types(user_input, answer_type, alpha_string):\\n if answer_type == int:\\n try:\\n user_input = int(user_input)\\n except ValueError:\\n user_input = None\\n ui.print_error_message(\\\"Wrong value provided.\\\\n\\\")\\n\\n elif answer_type == str:\\n if alpha_string:\\n user_input_copy = user_input.replace(' ', '')\\n\\n if not user_input_copy.isalpha():\\n user_input = None\\n ui.print_error_message('It not alpha string.')\\n\\n return user_input\",\n \"def normalizeRawFromHeader(value):\\n return value.replace('\\\\n', '').replace('\\\\r', '').strip()\",\n \"def from_str(cls, string):\",\n \"def scanf(format, string):\\n re_fmt = compile(format)\\n result = re_fmt.scanf(string)\\n _log.debug('%r <- %r = %r', format, string, result)\\n return result\",\n \"def prepare_input(self, extracted_str):\\n\\n # Remove withspace\\n if self.options['remove_whitespace']:\\n optimized_str = re.sub(' +', '', extracted_str)\\n else:\\n optimized_str = extracted_str\\n \\n # Remove accents\\n if self.options['remove_accents']:\\n optimized_str = unidecode(optimized_str)\\n\\n # specific replace\\n for replace in self.options['replace']:\\n assert len(replace) == 2, 'A replace should be a list of 2 items'\\n optimized_str = optimized_str.replace(replace[0], replace[1])\\n\\n return optimized_str\",\n \"def format_input(user_input):\\n values = [int(i) for i in user_input.strip().split(\\\" \\\")]\\n start = Coord(values[0], values[1])\\n end = Coord(values[2], values[3])\\n return Zone(start, end)\",\n \"def test_prepare_value_string(self):\\n field = FractionField()\\n result = field.prepare_value(\\\"1/4\\\")\\n self.assertEqual(\\\"1/4\\\", result)\\n\\n result = field.prepare_value(\\\"1 1/4\\\")\\n self.assertEqual(\\\"1 1/4\\\", result)\",\n \"def normalize_string(string):\\n return string.replace(\\\"\\\\xa0\\\", \\\" \\\")\\\\\\n .replace(\\\"\\\\\\\\\\\", \\\"\\\")\\\\\\n .replace(\\\"-LRB-\\\", \\\"(\\\")\\\\\\n .replace(\\\"-RRB-\\\", \\\")\\\")\\\\\\n .replace(\\\"-LCB-\\\", \\\"{\\\")\\\\\\n .replace(\\\"-RCB-\\\", \\\"}\\\")\\\\\\n .replace(\\\"-LSB-\\\", \\\"[\\\")\\\\\\n .replace(\\\"-RSB-\\\", \\\"]\\\")\",\n \"def format_input(input):\\n return f\\\"{input}> \\\"\",\n \"def _massage_raw_pg_output_vals(self):\\n pass\",\n \"def __parse__(self, filter):\\n \\n if filter == 'zipcode':\\n # Return 5 digit zip or, if applicable, Concatenate 5 digit and \\n # 4 digit zipcode\\n if self.data['Mailing Zip 4']:\\n return \\\"%s-%s\\\" %(str(self.data['Mailing Zip Code'])[:-2],\\n str(self.data['Mailing Zip 4'])[:-2]\\n )\\n else:\\n return str(self.data['Mailing Zip Code'])[:-2]\\n elif filter == 'employee_count':\\n # Convert employee count string to digit\\n pattern = '.+to\\\\s([0-9]+)'\\n try:\\n return re.findall(\\n pattern, self.data['Location Employee Size Range'])[0]\\n except IndexError:\\n pass\\n elif filter == 'phone':\\n # Regex phone number digits and concatenate\\n number = ''.join(re.findall('[0-9]+', \\n self.data['Phone Number Combined']))\\n return number if len(number) == 10 else 0\",\n \"def format(self, data):\",\n \"def _parse_input(self):\\n #temperature\\n regex = re.compile(\\\"TEMP=(\\\\d+\\\\.\\\\d*|\\\\d+)\\\")\\n r = regex.search(self.file_dic['input'])\\n if r:\\n self.temperature = r.groups()[0]\\n else:\\n self.temperature = 298.15\\n #theory\\n regex = re.compile('(\\\\$contrl.+\\\\$end|\\\\$basis.+ \\\\$end)')\\n temp_theory = regex.findall(self.file_dic['input'])\\n contrl = temp_theory[0][:-4][7:].strip()\\n basis = temp_theory[1][:-4][6:].strip()\\n self.theory = contrl + ' ' + basis\",\n \"def parseInput(self, inputString):\\n stringArray = self.inputChecker(inputString)\\n if stringArray == \\\"Error\\\":\\n return stringArray\\n # Correctly assigns negative numbers\\n stringArray = self.negativeCheck(stringArray)\\n # Checks whether any of the numbers have been written in scientific notation\\n stringArray = self.scientificNotationCheck(stringArray)\\n# print(stringArray)\\n return stringArray\",\n \"def format_data(self, data):\",\n \"def simple_format_string(self, value, force_raw=False):\\n\\n m = re.search(r'((\\\\n)|\\\\r|\\\\n|\\\\t|\\\\\\\\)', value)\\n newlines = False\\n raw = ''\\n if m:\\n if m.group(2):\\n newlines = True\\n raw = 'r'\\n if force_raw and not raw:\\n raw = 'r'\\n\\n single = self.needs_escape(value, '\\\\'')\\n double = self.needs_escape(value, '\\\"')\\n tsingle = self.needs_escape(value, '\\\\'', quote_count=3)\\n tdouble = self.needs_escape(value, '\\\"', quote_count=3)\\n if not double and not newlines:\\n string = '%s\\\"%s\\\"' % (raw, value)\\n elif not single and not newlines:\\n string = '%s\\\\'%s\\\\'' % (raw, value)\\n elif not tdouble:\\n string = '%s\\\"\\\"\\\"%s\\\"\\\"\\\"' % (raw, value)\\n elif not tsingle:\\n string = '%s\\\\'\\\\'\\\\'%s\\\\'\\\\'\\\\'' % (raw, value)\\n elif not newlines:\\n string = '%s\\\"%s\\\"' % (raw, self.fix_escape(value, '\\\"'))\\n else:\\n string = '%s\\\"\\\"\\\"%s\\\"\\\"\\\"' % (raw, self.fix_escape(value, '\\\"', quote_count=3))\\n return string\",\n \"def preprocess_raw(self):\\n pass\",\n \"def __str__(self):\\n\\n\\t\\tif self.rawValue == None: return str()\\n\\n\\t\\tx = self.rawValue\\n\\n\\t\\tif not x.isdigit() or len(x) != 44 or len(set(x)) == 1:\\n\\t\\t\\treturn self.rawValue\\n\\n\\t\\treturn '{} {} {} {} {} {} {} {} {} {} {}'.format(x[:4], x[4:8], x[8:12], x[12:16], x[16:20], x[20:24], x[24:28], x[28:32], x[32:36], x[36:40], x[40:44])\",\n \"def ConvertUnknownStringToProperValue(value: str, vtype: FClass.FiMVariableTypes):\\n if value == \\\"nothing\\\":\\n no_value = {\\n FClass.FiMVariableTypes.BOOL: False,\\n FClass.FiMVariableTypes.NUMBER: 0,\\n FClass.FiMVariableTypes.STRING: \\\"\\\\\\\"\\\\\\\"\\\",\\n FClass.FiMVariableTypes.CHAR: \\\"'?'\\\"\\n }\\n if vtype in no_value: return no_value[ vtype ]\\n \\n if vtype == FClass.FiMVariableTypes.CHAR:\\n if not value.startswith(\\\"'\\\") and not value.endswith(\\\"'\\\") and len(value[1:-1]) != 1:\\n raise Exception(f\\\"Invalid char {value}\\\")\\n return value\\n if vtype == FClass.FiMVariableTypes.STRING:\\n if not value.startswith('\\\"') and not value.endswith('\\\"'):\\n raise Exception(f\\\"Invalid string {value}\\\")\\n return value\\n if vtype == FClass.FiMVariableTypes.NUMBER:\\n try:\\n return float(value)\\n except:\\n raise Exception(f\\\"Invalid number {value}\\\")\\n if vtype == FClass.FiMVariableTypes.BOOL:\\n try:\\n return ConvertBooleanString( value )\\n except:\\n raise Exception(f\\\"Invalid boolean {value}\\\")\",\n \"def process(line, form):\\n return unicodedata.normalize(form, line)\",\n \"def string_from_interwebs(input_value):\\n \\n return escape(unquote(input_value))\",\n \"def _convert_metastr(metastr, dtype=None):\\n if dtype is None:\\n dtype = str\\n\\n if issubclass(dtype, tuple):\\n return tuple(float(f.strip()) for f in metastr.split(\\\",\\\"))\\n else:\\n return dtype(metastr)\",\n \"def test_format_phone_raw(self):\\n number = '8095551234'\\n self.assertEqual(format_phone(number), '(809) 555-1234')\",\n \"def _decode_string(box_string):\\r\\n if box_string == \\\"no_box\\\":\\r\\n return np.zeros((0,4))\\r\\n else:\\r\\n try:\\r\\n boxes = np.array([np.array([int(eval(i)) for i in box.split(\\\" \\\")])\\r\\n for box in box_string.split(\\\";\\\")])\\r\\n return boxes\\r\\n except:\\r\\n print(box_string)\\r\\n print(\\\"Submission is not well formatted. empty boxes will be returned\\\")\\r\\n return np.zeros((0,4))\",\n \"def reformat(ctx):\\n pass\",\n \"def parse_input(userstring):\\n xsplit = userstring.split()\\n stringtovalues = [float(x) for x in xsplit]\\n\\n return stringtovalues\",\n \"def form(s):\\r\\n \\r\\n # removes leading and trailing apostrophe's from string\\r\\n s = s.strip(\\\"'\\\")\\r\\n \\r\\n # converts HTML hex back to characters\\r\\n s = s.replace(\\\"'\\\", \\\"'\\\")\\r\\n s = s.replace(\\\"’\\\", \\\"’\\\")\\r\\n s = s.replace(\\\"‘\\\", '\\\"')\\r\\n s = s.replace(\\\"”\\\", \\\"'\\\")\\r\\n s = s.replace(\\\"“\\\", \\\"'\\\")\\r\\n \\r\\n # success\\r\\n return s\",\n \"def decode_string(self, value):\\r\\n return value\",\n \"def preparse(self, raw):\\n return raw\",\n \"def normalize(self, text: str) -> str:\",\n \"def _convert(string, type, message):\\n try:\\n return type(string)\\n except ValueError as e:\\n print(e)\\n raise CharmmPSFError('Could not convert %s' % message)\",\n \"def normalize_string(string):\\n return string.replace(u\\\"\\\\xa0\\\", \\\" \\\")\\\\\\n .replace(\\\"\\\\\\\\\\\", \\\"\\\")\\\\\\n .replace(\\\"-LRB-\\\", \\\"(\\\")\\\\\\n .replace(\\\"-RRB-\\\", \\\")\\\")\\\\\\n .replace(\\\"-LCB-\\\", \\\"{\\\")\\\\\\n .replace(\\\"-RCB-\\\", \\\"}\\\")\\\\\\n .replace(\\\"-LSB-\\\", \\\"[\\\")\\\\\\n .replace(\\\"-RSB-\\\", \\\"]\\\")\",\n \"def parse_input(input_data):\\n cleaned_input = input_data.replace('@ ', '').replace(':', '')\\n split_data = cleaned_input.split(' ')\\n xy = split_data[1].split(',')\\n wh = split_data[2].split('x')\\n return FabricRectangle(\\n claim_id=split_data[0],\\n x=int(xy[0]),\\n y=int(xy[1]),\\n width=int(wh[0]),\\n height=int(wh[1])\\n )\",\n \"def string_to_param(self,string):\\n\\n if (string.startswith(\\\"log_\\\")): return math.log10(self.string_to_param(string[4:]))\\n if (string.startswith(\\\"ln_\\\")): return math.log(self.string_to_param(string[3:]))\\n if (string.startswith(\\\"exp_\\\")): return math.exp(self.string_to_param(string[4:]))\\n if (string == \\\"Mass\\\"): return self.glb[imass]/constants.solar_mass\\n if (string == \\\"Radius\\\"): return self.glb[iradius]/constants.solar_radius\\n if (string == \\\"Luminosity\\\"): return self.glb[iluminosity]/constants.solar_luminosity\\n if (string == \\\"Z\\\"): return self.glb[iz0]\\n if (string == \\\"Y\\\"): return 1.0-self.glb[iz0]-self.glb[ix0]\\n if (string == \\\"X\\\"): return self.glb[ix0]\\n if (string == \\\"Ys\\\"): return 1.0-self.glb[user_params_index[\\\"Zs\\\"]]-self.glb[user_params_index[\\\"Xs\\\"]]\\n if (string == \\\"zsx_s\\\"): return self.zsx_s\\n if (string == \\\"zsx_0\\\"): return self.zsx_0\\n if (string == \\\"Fe_H\\\"): return self.FeH\\n if (string == \\\"M_H\\\"): return self.MH\\n if (string == \\\"Age\\\"): return self.glb[iage]\\n if (string == \\\"Teff\\\"): return self.glb[itemperature]\\n if (string == \\\"Dnu\\\"): return self.find_large_separation()*self.glb[ifreq_ref]\\n if (string == \\\"numax\\\"): return self.numax\\n if (string == \\\"Rho\\\"): return 3.0*self.glb[imass]/(4.0*math.pi*self.glb[iradius]**3)\\n if (string == \\\"g\\\"): return constants.G*self.glb[imass]/self.glb[iradius]**2\\n if (string == \\\"beta_Sonoi2015\\\"): return self.beta_Sonoi2015\\n if (string == \\\"b_Kjeldsen2008\\\"): return self.b_Kjeldsen2008\\n\\n try:\\n return self.glb[user_params_index[string]]\\n except KeyError:\\n sys.exit(\\\"ERROR: unrecognised model quantity: \\\"+string)\",\n \"def normalize_format(fmt):\\n # Remove shape '()' at the forefront which is equivalent to an scalar\\n if fmt[:2] == '()':\\n fmt = fmt[2:]\\n # Accept 'S' as a synonym of 'a'\\n if fmt.find('S') >= 0:\\n fmt = fmt.replace('S', 'a')\\n return fmt\",\n \"def get_formatted_string(self, input_string):\\n if isinstance(input_string, str):\\n try:\\n return self.get_processed_string(input_string)\\n except KeyError as err:\\n # Wrapping the KeyError into a less cryptic error for end-user\\n # friendliness\\n missing_key = err.args[0]\\n raise KeyNotInContextError(\\n f'Unable to format \\\\'{input_string}\\\\' with '\\n f'{{{missing_key}}}, because '\\n f'context[\\\\'{missing_key}\\\\'] doesn\\\\'t exist') from err\\n else:\\n raise TypeError(f\\\"can only format on strings. {input_string} is a \\\"\\n f\\\"{type(input_string)} instead.\\\")\",\n \"def native_string(input_var):\\n if isinstance(input_var, str):\\n return input_var\\n\\n return input_var.decode('utf-8', 'replace')\",\n \"def handle_input(data: dict):\",\n \"def preprocess(string):\\r\\n # string = [strQ2B(ch) for ch in string.strip()]\\r\\n # return ''.join(string)\\r\\n return string\",\n \"def cleanData(rawData):\\n\\trawData = re.sub(r'R-LRB- \\\\(', r'R-LRB- -LRB-', rawData)\\n\\trawData = re.sub(r'R-RRB- \\\\)', r'R-RRB- -RRB-', rawData)\\n\\trawData = re.sub(r'R-RRB- \\\\(', r'R-RRB- -LRB-', rawData)\\n\\trawData = re.sub(r'-LRB- \\\\(', r'-LRB- -LRB-', rawData)\\n\\trawData = re.sub(r'-RRB- \\\\)', r'-RRB- -RRB-', rawData)\\n\\trawData = re.sub(r'PU \\\\(', r'PU -LRB-', rawData)\\n\\trawData = re.sub(r'PU \\\\)', r'PU -RRB-', rawData)\\n\\trawData = re.sub(r':-\\\\)', r'smileyface', rawData)\\n\\n\\treturn rawData\",\n \"def convert_str(input_obj):\\n if isinstance(input_obj, str):\\n return(input_obj)\\n elif input_obj is True:\\n return('1')\\n elif (input_obj is None):\\n return('none')\\n elif input_obj is False:\\n return('0')\\n else:\\n return(str(input_obj))\",\n \"def getString(self):\\n print \\\"Enter String value:\\\",\\n self.string = raw_input()\",\n \"def _parseSingle(string):\\n string = string.strip()\\n \\n if len(string) == 0:\\n return ''\\n \\n pattern = re.compile(r'[^0-9]')\\n if not pattern.search(string):\\n return int(string)\\n pattern = re.compile(r'[^0-9\\\\.eE]')\\n if not pattern.search(string):\\n if (string.count('.') <= 1 and \\n (string.count('e') + string.count('E') <= 1)):\\n return float(string)\\n \\n boolValue = _bool(string)\\n if boolValue is not None:\\n return boolValue\\n \\n if string[0] == string[-1]:\\n if string[0] == '\\\"' or string[0] == \\\"'\\\":\\n return string[1:-1]\\n elif string[1] == string[-1]:\\n if ((string[0] == 'u' or string[0] == 'r') and \\n (string[1] == '\\\"' or string[1] == \\\"'\\\")):\\n return string[2:-1]\\n \\n if string == 'None':\\n return None\\n \\n return string\",\n \"def _sanitize_string_for_python(self, s):\\n s = repr(s)\\n\\n if s.startswith('u'):\\n s = s[1:]\\n\\n return s\",\n \"def main():\\n haiku_string = \\\"clouds ,mur,mur ,dark,ly/it ,is ,a ,blin,ding ,ha,bit/ga,zing ,at ,the ,moon\\\"\\n formatted_haiku = haiku_string_parser(haiku_string)\\n print(formatted_haiku)\",\n \"def make_input(value):\\n what = value[0]\\n if what.startswith('not editable'):\\n what = what.replace('not editable:', '').replace(\\\"'\\\", \\\"\\\\'\\\")\\n if what.startswith('attribute:value:'):\\n what = what.replace('attribute:value:', '').replace(\\\"'\\\", \\\"\\\\'\\\")\\n return what\",\n \"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 parts.append(raw[s_index:])\\n return GenericHelperFn({u\\\"Fn::Join\\\": [u\\\"\\\", parts]})\",\n \"def parse_value(string: str) -> Union[str, dict, bool, int, float]:\\n unesc_str = unescape(string)\\n stripped = string.strip()\\n if REGEX_RE.match(stripped):\\n return {\\\"regex\\\": unesc_str.strip()[7:-2]}\\n elif BOOL_RE.match(stripped):\\n return stripped.lower() == \\\"true\\\"\\n elif INT_RE.match(stripped):\\n return int(stripped)\\n elif FLOAT_RE.match(stripped):\\n return float(stripped)\\n else:\\n return unesc_str[1:-1]\",\n \"def process_input(self, message: str) -> str:\\n processed_input = message\\n # Convert user ids to just nick names\\n processed_input.replace(\\\"@\\\"+self.user.name+\\\"#\\\"+self.user.discriminator, \\\"\\\")\\n processed_input.replace(\\\"@\\\"+self.user.name, \\\"\\\")\\n return processed_input\",\n \"def parse_input_string(self, string_name):\\n list_of_parts = string_name.split(\\\".\\\")\\n if list_of_parts[0] == \\\"inputs\\\":\\n return string_name\\n else:\\n # return only the integer part\\n return int(list_of_parts[1])\",\n \"def psych(output_str, input_str_1, input_1, input_str_2, input_2, pressure=29.92):\\n\\n dry_bulb = 0\\n wet_bulb = 0\\n dewpoint = 0\\n rel_hum = 0\\n hum_rat = 0\\n spec_vol = 0\\n enthalpy = 0\\n pressure *= 0.491154\\n\\n if input_str_1 in ('db', 'DB', 'wb', 'WB', 'dp', 'DP', 'rh', 'RH', 'hr', 'HR', 'sv', 'SV', 'en', 'EN') and \\\\\\n input_str_2 in ('db', 'DB', 'wb', 'WB', 'dp', 'DP', 'rh', 'RH', 'hr', 'HR', 'sv', 'SV', 'en', 'EN'):\\n\\n if input_str_1 in ('db', 'DB'):\\n dry_bulb = input_1\\n elif input_str_2 in ('db', 'DB'):\\n dry_bulb = input_2\\n\\n if input_str_1 in ('wb', 'WB'):\\n wet_bulb = input_1\\n elif input_str_2 in ('wb', 'WB'):\\n wet_bulb = input_2\\n\\n if input_str_1 in ('dp', 'DP'):\\n dewpoint = input_1\\n elif input_str_2 in ('dp', 'DP'):\\n dewpoint = input_2\\n\\n if input_str_1 in ('rh', 'RH'):\\n rel_hum = input_1 / 100\\n elif input_str_2 in ('rh', 'RH'):\\n rel_hum = input_2 / 100\\n\\n if input_str_1 in ('hr', 'HR'):\\n hum_rat = input_1\\n elif input_str_2 in ('hr', 'HR'):\\n hum_rat = input_2\\n\\n if input_str_1 in ('sv', 'SV'):\\n spec_vol = input_1\\n elif input_str_2 in ('sv', 'SV'):\\n spec_vol = input_2\\n\\n if input_str_1 in ('en', 'EN'):\\n enthalpy = input_1\\n elif input_str_2 in ('en', 'EN'):\\n enthalpy = input_2\\n else:\\n return ValueError('Invalid input types')\\n\\n if hum_rat < 0:\\n return ValueError('Humidity ratio less than 0')\\n if rel_hum < 0 or rel_hum > 1:\\n return ValueError('Relative humidity less than 0 or greater than 100')\\n\\n ############################################################################################\\n\\n if input_str_1 in ('db', 'DB') or input_str_2 in ('db', 'DB'):\\n if output_str in ('db', 'DB'):\\n return dry_bulb\\n\\n db_r = dry_bulb + 459.67\\n\\n if input_str_1 in ('wb', 'WB') or input_str_2 in ('wb', 'WB'):\\n if output_str in ('wb', 'WB'):\\n return wet_bulb\\n\\n db_r = dry_bulb + 459.67\\n wb_r = wet_bulb + 459.67\\n\\n pres_wb_sat = sat_pres(wb_r)\\n\\n hr_wb_sat = 0.62198 * pres_wb_sat / (pressure - pres_wb_sat)\\n\\n hum_rat = (hr_wb_sat * (1093 - .556 * wet_bulb) - 0.24 * (dry_bulb - wet_bulb)) / \\\\\\n (1093 + .444 * dry_bulb - wet_bulb)\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_db_sat = sat_pres(db_r)\\n\\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n\\n mu = hum_rat / hr_db_sat\\n\\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\\n if rel_hum < 0 or rel_hum > 1:\\n return -1 # ValueError('Calculated relative humidity less than 0')\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n\\n if output_str in ('dp', 'DP'):\\n pres_vapor = (pressure * hum_rat) / (0.62198 + hum_rat)\\n\\n dewpoint = calc_dewpoint(pres_vapor)\\n return dewpoint\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\\n if output_str in ('dp', 'DP'):\\n return dewpoint\\n\\n dp_r = dewpoint + 459.67\\n\\n pres_vapor = sat_pres(dp_r)\\n\\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\\n if hum_rat < 0:\\n return -1 # ValueError('Calculated humidity ratio below 0')\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_db_sat = sat_pres(db_r)\\n\\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n\\n mu = hum_rat / hr_db_sat\\n\\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\\n if rel_hum < 0 or rel_hum > 1:\\n return -1\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n\\n if output_str in ('wb', 'WB'):\\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\\n return wet_bulb\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n pres_db_sat = sat_pres(db_r)\\n\\n pres_vapor = pres_db_sat * rel_hum\\n\\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\\n if hum_rat < 0:\\n return -1\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n\\n # mu = hum_rat / hr_db_sat\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n\\n if output_str in ('dp', 'DP'):\\n dewpoint = calc_dewpoint(pres_vapor)\\n return dewpoint\\n\\n if output_str in ('wb', 'WB'):\\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\\n return wet_bulb\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_db_sat = sat_pres(db_r)\\n\\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\\n\\n rel_hum = pres_vapor / pres_db_sat\\n if rel_hum < 0 or rel_hum > 1:\\n return -1\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n\\n # mu = hum_rat / hr_db_sat\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n\\n if output_str in ('dp', 'DP'):\\n dewpoint = calc_dewpoint(pres_vapor)\\n return dewpoint\\n\\n if output_str in ('wb', 'WB'):\\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\\n return wet_bulb\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n\\n # pres_db_sat = sat_pres(db_r)\\n\\n hum_rat = (spec_vol * 28.9645 * (pressure * 144) / (1545.32 * db_r) - 1) / 1.6078\\n if hum_rat < 0:\\n return -1\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\\n\\n rel_hum = pres_vapor / pressure\\n if rel_hum < 0 or rel_hum > 1:\\n return -1\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n\\n if output_str in ('dp', 'DP'):\\n dewpoint = calc_dewpoint(pres_vapor)\\n return dewpoint\\n\\n if output_str in ('wb', 'WB'):\\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\\n return wet_bulb\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\\n\\n pres_db_sat = sat_pres(dry_bulb)\\n\\n hum_rat = (enthalpy - 0.24 * dry_bulb) / (1061 + 0.444 * dry_bulb)\\n if hum_rat < 0:\\n return -1\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_vapor = hum_rat * pressure / (hum_rat + 0.62198)\\n\\n rel_hum = pres_vapor / pres_db_sat\\n if rel_hum < 0 or rel_hum > 1:\\n return -1\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n # hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n\\n # mu = hum_rat / hr_db_sat\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('dp', 'DP'):\\n dewpoint = calc_dewpoint(pres_vapor)\\n return dewpoint\\n\\n if output_str in ('wb', 'WB'):\\n wet_bulb = calc_wetbulb(dry_bulb, hum_rat, pressure)\\n return wet_bulb\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('wb', 'WB') or input_str_2 in ('wb', 'WB'):\\n if output_str in ('wb', 'WB'):\\n return wet_bulb\\n\\n wb_r = wet_bulb + 459.67\\n\\n if input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\\n if output_str in ('dp', 'DP'):\\n return dewpoint\\n\\n dp_r = dewpoint + 459.67\\n\\n pres_vapor = sat_pres(dp_r)\\n\\n hum_rat = 0.62198 * pres_vapor / (pressure - pres_vapor)\\n if hum_rat < 0:\\n return -1\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n pres_wb_sat = sat_pres(wb_r)\\n\\n hr_wb_sat = 0.62198 * pres_wb_sat / (pressure - pres_wb_sat)\\n\\n dry_bulb = ((1093 - 0.556 * wet_bulb) * hr_wb_sat + 0.24 * wet_bulb - (1093 - wet_bulb) * hum_rat) / \\\\\\n (0.444 * hum_rat + 0.24)\\n if output_str in ('db', 'DB'):\\n return dry_bulb\\n\\n db_r = dry_bulb + 459.67\\n\\n pres_db_sat = sat_pres(dry_bulb)\\n\\n hr_db_sat = 0.62198 * pres_db_sat / (pressure - pres_db_sat)\\n mu = hum_rat / hr_db_sat\\n\\n rel_hum = mu / (1 - (1 - mu) * (pres_db_sat / pressure))\\n if rel_hum < 0 or rel_hum > 1:\\n return -1\\n if output_str in ('rh', 'RH'):\\n return rel_hum\\n\\n if output_str in ('sv', 'SV'):\\n spec_vol = 1545.32 * db_r * (1 + 1.6078 * hum_rat) / (28.9645 * pressure * 144)\\n return spec_vol\\n\\n if output_str in ('en', 'EN'):\\n enthalpy = 0.24 * dry_bulb + hum_rat * (1061 + 0.444 * dry_bulb)\\n return enthalpy\\n else:\\n return ValueError('Unknown output request')\\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\\n return -1 # no enthalpy, wet bulb and enthalpy are too closely related to avoid problems\\n elif input_str_1 in ('dp', 'DP') or input_str_2 in ('dp', 'DP'):\\n if output_str in ('dp', 'DP'):\\n return dewpoint\\n\\n dp_r = dewpoint + 459.67\\n\\n if input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n return -1 # no humidity ratio - it is the dew point more or less\\n elif input_str_1 in ('hr', 'HR') or input_str_2 in ('hr', 'HR'):\\n if output_str in ('hr', 'HR'):\\n return hum_rat\\n\\n if input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\\n elif input_str_1 in ('rh', 'RH') or input_str_2 in ('rh', 'RH'):\\n if output_str in ('rh', 'RH'):\\n return rel_hum * 100\\n\\n if input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n elif input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\\n elif input_str_1 in ('sv', 'SV') or input_str_2 in ('sv', 'SV'):\\n if output_str in ('sv', 'SV'):\\n return spec_vol\\n\\n if input_str_1 in ('en', 'EN') or input_str_2 in ('en', 'EN'):\\n if output_str in ('en', 'EN'):\\n return enthalpy\",\n \"def preprocess_input(self, text):\\n text = re.sub(r\\\"([^a-zA-Z0-9 -]+ +[^a-zA-Z0-9 -]*|[^a-zA-Z0-9 -]*\\\" +\\n \\\" +[^a-zA-Z0-9 -]+)\\\", ' ', text, flags=re.UNICODE)\\n text = re.sub(r\\\"([^a-zA-Z0-9 -]+$|^[^a-zA-Z0-9 -]+)\\\", '', text)\\n text = re.sub(r\\\"([a-zA-Z0-9 -]+?)([^a-zA-Z0-9 -])([a-zA-Z0-9 -]+?)\\\",\\n r\\\"\\\\1'\\\\3\\\", text, flags=re.UNICODE)\\n text = re.sub(r\\\"([\\\\x00-\\\\x7F -]+?)([^a-zA-Z0-9 -]+)([\\\\x00-\\\\x7F -]+?)\\\",\\n r\\\"\\\\1'\\\\3\\\", text, flags=re.UNICODE).encode(\\\"utf-8\\\")\\n return re.sub(r\\\"([^a-zA-Z0-9 \\\\-\\\\'])\\\", '', text, flags=re.UNICODE)\",\n \"def prepare_value(self, value):\\n if value is None:\\n return value\\n value = value.replace(\\\" \\\", \\\"\\\").replace(\\\".\\\", \\\"\\\")\\n if value:\\n return \\\"%s.%s.%s.%s\\\" % (value[0:3], value[3:7], value[7:11], value[11:])\\n return value\",\n \"def format_value(self, val):\\n\\n if isinstance(val, (unicode, str)):\\n val = self.standardise_quotes(val)\\n \\n try:\\n val = unicode(val)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(val).encode('string_escape')\\n val = unicode(ascii_text)\\n\\n return val\",\n \"def handle_value(value):\\n\\n if value[-1] == 'x':\\n return float(value[0:-1])\\n\\n if value[-1] == '%':\\n return float(value[0:-1])\\n\\n if value[0].isdigit():\\n return bytify(value)\\n\\n raise ValueError\",\n \"def parse_string(self, in_str):\\n match = MAIN_REGEX.search(in_str)\\n if not match:\\n err_str = \\\"Unable to parse string: %s\\\" % in_str\\n raise ValueError(err_str)\\n self.parse_completed(match.group(1))\\n self.parse_priority(match.group(2))\\n if match.group(3) and match.group(4):\\n self.parse_completion_date(match.group(3))\\n self.parse_creation_date(match.group(4))\\n else:\\n self.parse_creation_date(match.group(3))\\n self.parse_description(match.group(5))\",\n \"def clean_string(raw_string):\\n if raw_string == None:\\n return\\n\\n clean_string = raw_string.strip('\\\\n')\\n clean_string = ' '.join(clean_string.split())\\n return clean_string\",\n \"def from_str (s):\\n try: \\n return from_csv(s)\\n except Exception: \\n pass\\n \\n try: \\n return from_hex(s)\\n except Exception: \\n pass\\n\\n try:\\n return from_name(s)\\n except Exception: \\n pass\\n\\n raise ColourFormatError(\\\"'%s' is not a recognized colour string\\\"%s)\",\n \"def str_to_rule(str_in):\\r\\n log.debug(\\\"str_to_rule: \\\"+str_in.strip())\\r\\n str_i = str_in.strip().split('#')[0].strip()\\r\\n if len(str_i)>0:\\r\\n dic_rule = dict(valid=False,type='para',errors=list(),key=\\\"\\\",val=\\\"\\\")\\r\\n if(str_i[0]==\\\"%\\\"): # % Parameter str_i\\r\\n lst_par = str_i[1:].split('=')\\r\\n lst_par = [par.strip() for par in lst_par]\\r\\n if lst_par[0] in lst_known_para:\\r\\n dic_rule['key']=lst_par[0].strip()\\r\\n dic_rule['val']=lst_par[1].strip()\\r\\n dic_rule['valid']=True\\r\\n log.info('Parameter recognised: '+str(dic_rule['key'])+' = '+str(dic_rule['val']))\\r\\n else:\\r\\n dic_rule['valid']=False\\r\\n dic_rule['errors'].append(\\\"Unrecognised parameter: \\\"+lst_par[0])\\r\\n log.warning('#205 > '+str(dic_rule['errors'])+' raw line: '+str_i)\\r\\n elif(str_i[0]==\\\":\\\"): # : Rule str_i\\r\\n dic_rule = dict(valid=False,type='rule',errors=list(),id=\\\"\\\",title=\\\"\\\",mode=\\\"\\\",data_table=\\\"\\\",condition=\\\"\\\",action=\\\"\\\",act_param=\\\"\\\",comment=\\\"\\\")\\r\\n lst_items = str_i[1:].split(\\\":\\\")\\r\\n lst_items = [itm.strip() for itm in lst_items]\\r\\n if len(lst_items)==8:\\r\\n dic_rule['id']=lst_items[0]\\r\\n dic_rule['title']=lst_items[1]\\r\\n dic_rule['mode']=lst_items[2].upper()\\r\\n dic_rule['layer']=lst_items[3]\\r\\n dic_rule['condition']=lst_items[4]\\r\\n dic_rule['action']=lst_items[5]\\r\\n dic_rule['act_param']=lst_items[6]\\r\\n dic_rule['comment']=lst_items[7]\\r\\n dic_rule = sanity_check(dic_rule)\\r\\n if not dic_rule['valid']:\\r\\n log.warning('#203 invalid rule > '+str(dic_rule['errors'])+' raw line: '+str_in)\\r\\n log.debug('parsed good rule: '+str(dic_rule))\\r\\n else:\\r\\n dic_rule['errors'].append(\\\"Rule string does not contain the correct number of elements - Check that you comment do not contain ':'. Ignoring this rule. \\\\n\\\\t\\\"+str_in.strip()+\\\"\\\\n\\\\t\\\"+str(len(lst_items))+'\\\\t'+str(lst_items))\\r\\n log.warning('#202 '+dic_rule['errors'])\\r\\n dic_rule['valid']=False\\r\\n else:\\r\\n dic_rule['errors'].append(\\\"Rule string must start with #, % or : But I found: \\\"+str_in[0]+\\\" in line (\\\"+str_in+\\\")\\\")\\r\\n log.warning('#201 '+str(dic_rule['errors'][-1:]))\\r\\n dic_rule['valid']=False\\r\\n else: # Empty (or only comments) str_i\\r\\n return {'type':'null', 'valid':True}\\r\\n return dic_rule\",\n \"def formatengsrt(input, output):\\n \\n p = ( (r\\\"-=.*?=-\\\\s+\\\", \\\"\\\", re.U), #类似 ==http://a.b.c/=- 删除\\n (r\\\"<.*?>\\\", \\\"\\\", re.U), #类似 <...> 删除\\n (r\\\"^[\\\\s\\\\d\\\\-:>,]*[\\\\r\\\\n]+\\\", r\\\"\\\", re.M|re.U), #'-'是特殊字符\\n (r\\\"(\\\\S+)\\\\s+$\\\", r\\\"\\\\1\\\", re.M|re.U), #删除结尾的空余字符\\n (r\\\"\\\\.{3}[\\\\r\\\\n]+([a-z])\\\", r\\\" \\\\1\\\", re.U), #结尾是...的，下一行开始是一个小写字母的。\\n (r\\\"([^\\\\.?!])[\\\\r\\\\n]+\\\", r\\\"\\\\1 \\\", re.U), #结尾没有 .?!的，添加空格，去掉回车换行\\n (r\\\"(\\\\w+)[,.?!](\\\\w)\\\", r\\\"\\\\1, \\\\2\\\", re.U), #有些单词后逗号后面没有空格，加上\\n )\\n\\n d = chardet.detect(open(input, \\\"r\\\").read())\\n print \\\"[%s] 自动检测为 %s\\\" %(input, d)\\n\\n with codecs.open(input, encoding=d['encoding'], mode='r') as fi:\\n t = fi.read()\\n \\n for a, b, c in p:\\n t = re.sub(a, b, t, 0, c)\\n\\n with codecs.open(output, encoding=d['encoding'], mode='w') as fo:\\n fo.write(t)\\n print \\\"[{}] compeleted.\\\".format(output)\",\n \"def street_parser(*street_data):\\n\\n # parsing tuples\\n if len(street_data) == 2:\\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n # street name as the tuple's first item\\n strname, strnumber = street_data\\n # street number as the tuple's first item\\n if street_data[0][0] in digits:\\n strname, strnumber = strnumber, strname\\n\\n # parsing strings\\n else:\\n if not isinstance(street_data[0], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n if not street_data[0]:\\n raise WrongInput(\\\"Input cannot be blank\\\")\\n\\n # string starting with street number\\n if street_data[0][0] in digits:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\d+) # street number is any number of digits\\n \\\\W+ # separator\\n (\\\\w+\\\\W*\\\\w*\\\\W*) # street name is one or more words with optional separators\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n strnumber, strname = street_obj.groups()\\n\\n # string starting with street name\\n else:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\w+\\\\W*\\\\w*\\\\s*) # street name is one or more words with optional separators\\n \\\\W+ # separator\\n (\\\\d+) # street number is any number of digits\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n (strname, strnumber) = street_obj.groups()\\n\\n # replace specific words in street name with their abbreviates\\n strname = strname.lower()\\n special = {r'\\\\baleje\\\\b': 'Al.', r'\\\\bavenue\\\\b': 'Av.', r'\\\\broad\\\\b': 'Rd.', r'\\\\bsquare\\\\b': 'Sq.',\\n r'\\\\bstreet\\\\b': 'St.', r'\\\\bdrive\\\\b': 'Dr.'}\\n for key in special:\\n strname = re.sub(key, special[key], strname)\\n return strname.title(), strnumber\",\n \"def makeinputstring(variabel):\\r\\n if type(variabel) == int:\\r\\n return str(variabel)\\r\\n elif type(variabel) == float:\\r\\n return str(int(float(variabel)))\\r\\n else:\\r\\n return str(variabel)\",\n \"def inputString(self):\\n return self.__inputString\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66427","0.6007272","0.5868643","0.58346975","0.57762647","0.5764655","0.57391506","0.57352734","0.5719354","0.5683081","0.5664899","0.5664475","0.5585627","0.5576046","0.5576046","0.55689394","0.5552692","0.5552692","0.5544937","0.5495645","0.54807526","0.5418459","0.53892833","0.5384292","0.5382638","0.5378241","0.5371518","0.53586084","0.5351287","0.53503245","0.53364813","0.5335731","0.530559","0.5294708","0.5285341","0.5279502","0.52728873","0.5267332","0.5266283","0.52463883","0.524558","0.52444476","0.5239453","0.5236715","0.5220451","0.52135247","0.5212925","0.5206414","0.5201156","0.5199003","0.51982063","0.5190856","0.51889104","0.5188516","0.51849896","0.518319","0.5181675","0.5181284","0.51721823","0.5169754","0.5168834","0.51686615","0.5158072","0.51426816","0.51420003","0.51383287","0.5128351","0.51230925","0.51162034","0.5116013","0.5115869","0.5113908","0.5112067","0.5111067","0.51051027","0.5100286","0.50986123","0.50967115","0.50910944","0.5083668","0.5077099","0.5059676","0.5059358","0.50516516","0.50502443","0.5046505","0.50324017","0.5030788","0.502869","0.5026832","0.50093925","0.50085175","0.50051737","0.50009084","0.49959996","0.49952844","0.49935725","0.4990902","0.49905077","0.4986093","0.49807656"],"string":"[\n \"0.66427\",\n \"0.6007272\",\n \"0.5868643\",\n \"0.58346975\",\n \"0.57762647\",\n \"0.5764655\",\n \"0.57391506\",\n \"0.57352734\",\n \"0.5719354\",\n \"0.5683081\",\n \"0.5664899\",\n \"0.5664475\",\n \"0.5585627\",\n \"0.5576046\",\n \"0.5576046\",\n \"0.55689394\",\n \"0.5552692\",\n \"0.5552692\",\n \"0.5544937\",\n \"0.5495645\",\n \"0.54807526\",\n \"0.5418459\",\n \"0.53892833\",\n \"0.5384292\",\n \"0.5382638\",\n \"0.5378241\",\n \"0.5371518\",\n \"0.53586084\",\n \"0.5351287\",\n \"0.53503245\",\n \"0.53364813\",\n \"0.5335731\",\n \"0.530559\",\n \"0.5294708\",\n \"0.5285341\",\n \"0.5279502\",\n \"0.52728873\",\n \"0.5267332\",\n \"0.5266283\",\n \"0.52463883\",\n \"0.524558\",\n \"0.52444476\",\n \"0.5239453\",\n \"0.5236715\",\n \"0.5220451\",\n \"0.52135247\",\n \"0.5212925\",\n \"0.5206414\",\n \"0.5201156\",\n \"0.5199003\",\n \"0.51982063\",\n \"0.5190856\",\n \"0.51889104\",\n \"0.5188516\",\n \"0.51849896\",\n \"0.518319\",\n \"0.5181675\",\n \"0.5181284\",\n \"0.51721823\",\n \"0.5169754\",\n \"0.5168834\",\n \"0.51686615\",\n \"0.5158072\",\n \"0.51426816\",\n \"0.51420003\",\n \"0.51383287\",\n \"0.5128351\",\n \"0.51230925\",\n \"0.51162034\",\n \"0.5116013\",\n \"0.5115869\",\n \"0.5113908\",\n \"0.5112067\",\n \"0.5111067\",\n \"0.51051027\",\n \"0.5100286\",\n \"0.50986123\",\n \"0.50967115\",\n \"0.50910944\",\n \"0.5083668\",\n \"0.5077099\",\n \"0.5059676\",\n \"0.5059358\",\n \"0.50516516\",\n \"0.50502443\",\n \"0.5046505\",\n \"0.50324017\",\n \"0.5030788\",\n \"0.502869\",\n \"0.5026832\",\n \"0.50093925\",\n \"0.50085175\",\n \"0.50051737\",\n \"0.50009084\",\n \"0.49959996\",\n \"0.49952844\",\n \"0.49935725\",\n \"0.4990902\",\n \"0.49905077\",\n \"0.4986093\",\n \"0.49807656\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":7495,"cells":{"query":{"kind":"string","value":"View for rendering hours as json."},"document":{"kind":"string","value":"def json_hours(request):\n current_site = Site.find_for_request(request)\n if request.method == 'GET':\n if request.GET.get('fallback'):\n fallback = request.GET['fallback']\n return JsonResponse(\n {\n 'llid': get_default_unit().location.libcal_library_id,\n }\n )\n else:\n libcalid = request.GET['libcalid']\n all_building_hours = json.dumps(get_building_hours_and_lid(current_site))\n return JsonResponse(\n {\n 'all_building_hours': all_building_hours,\n 'current_hours': get_json_hours_by_id(int(libcalid), all_building_hours),\n 'llid': libcalid,\n 'llid_fallback': get_default_unit().location.libcal_library_id,\n }\n )"},"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 display_hours(employee_id):\n\n if not g.user:\n flash(\"Please Login to continue.\", \"danger\")\n return redirect(\"/\")\n \n employee = Employee.query.get_or_404(employee_id)\n\n labels = json.dumps( [\"Completed\", \"Required\"])\n data = json.dumps([employee.completed, employee.required])\n \n return render_template(\"users/display_hours.html\", employee = employee, labels = labels, data = data)","def _draw_hours(self):\n tmp_str_list = []\n for i in range(0, self._g_width, self._min_grid):\n if i % self._hour_grid == 0:\n tmp_str_list.append('<polyline class=\"FullHour\" points=\"%d,%d, %d,%d\" />' % (\n i + .5 + 20, 20, i + .5 + 20, self._g_height))\n tmp_str_list.append('<text class=\"Label\" x=\"%d\" y=\"%d\">%d</text>' % (\n i + 20, 20, (i / self._hour_grid + self._offset) % 24))\n else:\n tmp_str_list.append('<polyline class=\"SubHour\" points=\"%d,%d,%d,%d\" />' % (\n i + .5 + 20, 20, i + .5 + 20, self._g_height))\n return \"\".join(tmp_str_list)","def get(self, request):\n\t\tworkingHours = GymModels.WorkingHours.objects.all()\n\t\tserializer = PeopleSerializer.WorkingHourSerializer(workingHours, many=True)\n\t\treturn Response(serializer.data)","def hours(self, venue_id):\n response = self._request(V2_ENDPOINTS['HOURS'] + venue_id)\n return response","def hours(input=None):\n return get(input).hours","def hours(self):\n return self.config['hours']","def json_frapp(request):\n from pv.settings import MEDIA_URL\n\n if request.GET.get('date') == None:\n start = datetime.combine(date.today(), time(0, 0))\n else:\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\n\n end = datetime.combine(start, time(23, 59))\n\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\n\n\n '''Generate categories object for output'''\n\n categories = Category.objects.all()\n categories_output = []\n\n for c in categories:\n c_entry = {\n 'id': c.id,\n 'color': c.color.replace('#', '').upper(),\n 'namedisplay': c.category,\n 'description': c.description\n }\n\n categories_output.append(c_entry)\n\n # Get all series for timeslots\n series = set()\n for ts in timeslots:\n series.add(ts.show)\n\n\n '''Generate series object for output'''\n\n series_output = []\n\n for s in series:\n metainfos = []\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\n\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\n url = '' if s.website == None or s.website == '' else s.website\n\n # Get active schedules for the given date\n # But include upcoming single timeslots (with rrule_id=1)\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n broadcastinfos = ''\n\n if not schedules.exists():\n continue\n\n for schedule in schedules:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n if schedules_repetition.exists():\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\n for schedule in schedules_repetition:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n s_entry = {\n 'id': s.id,\n 'categoryid': s.category.values_list('id', flat=True)[0],\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\n 'namedisplay': s.name,\n 'description': s.description,\n 'url': url,\n 'image': image,\n 'broadcastinfos': broadcastinfos,\n 'metainfos': metainfos\n }\n\n series_output.append(s_entry)\n\n\n '''Generate shows object for output'''\n\n shows_output = []\n\n for ts in timeslots:\n\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\n namedisplay = ts.show.name + is_repetition\n description = ts.show.description\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\n urlmp3 = ''\n\n # If there's a note to the timeslot use its title, description and url\n try:\n note = Note.objects.get(timeslot=ts.id)\n namedisplay = note.title + is_repetition\n description = note.content\n url = str(get_current_site(request)) + '/notes/' + note.slug\n urlmp3 = note.audio_url\n except ObjectDoesNotExist:\n pass\n\n ts_entry = {\n 'id': ts.id,\n 'seriesid': ts.show.id,\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\n 'namedisplay': namedisplay,\n 'description': description,\n 'url': url,\n 'urlmp3': urlmp3,\n }\n\n shows_output.append(ts_entry)\n\n output = {}\n output['categories'] = categories_output\n output['series'] = series_output\n output['shows'] = shows_output\n\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\n content_type=\"application/json; charset=utf-8\")","def format_hours(self, data):\n return unicode('%f' % data).rstrip('0').rstrip('.')","def hourly(self):\n return c.Hourly(self)","def output(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 2,\n \"enabled\": 1,\n \"days_of_week\": self.days,\n \"time\": self.time.output()\n }","def open_hours_detail(self):\n return self._open_hours_detail","def get_hourly_weather_details(self, hours: int = None):\n if hours is None:\n hours = 11\n forecast = super().get_weather_forecast(self.BASE_URL)\n headers = [\"date_time\",\n \"temp\",\n \"real_feel_temp\",\n \"wind_speed\",\n \"rain_probability\",\n \"cloud_cover\",\n ]\n for number in range(hours):\n data = []\n date_time = forecast[number]['DateTime']\n date_time = date_time[:16]\n date_time = date_time.replace('T', ' ')\n data.append(date_time)\n temp = round((int(\n forecast[number][\"Temperature\"][\"Value\"]) - 32) / 1.8)\n data.append(temp)\n real_feel_temp = round((int(\n forecast[number][\"RealFeelTemperature\"][\"Value\"]) - 32) / 1.8)\n data.append(real_feel_temp)\n wind_speed = forecast[number][\"Wind\"][\"Speed\"][\"Value\"]\n data.append(wind_speed)\n rain_probability = forecast[number][\"RainProbability\"]\n data.append(rain_probability)\n cloud_cover = forecast[number][\"CloudCover\"]\n data.append(cloud_cover)\n yield dict(zip(headers, data))","def get_24h(self):\n records = self.level_model.get_for_period(1)\n self.set_attributes(records, '24 hours')","def output(self):\n \n if self.days > 0:\n interval = \"%dd\" % self.days\n if self.hours > 0:\n interval = \"%dh\" % self.hours\n if self.minutes > 0:\n interval = \"%dm\" % self.minutes\n else:\n interval = \"%ds\" % self.seconds\n \n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 1,\n \"enabled\": 1,\n \"interval\": interval\n }","def output(self):\n time = \"%04d-%02d-%02d %02d:%02d:%02d\" % (self.year, self.month, \\\n self.date, self.hours, self.minutes, self.seconds)\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 4,\n \"enabled\": 1,\n \"abstime\": time\n }","def timesheet(request):\r\n return render(\r\n request,\r\n 'timesheet/timesheet.html'\r\n )","def get_hourly(self):\n pass","def output(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"type\": 3,\n \"enabled\": 1,\n \"days_of_month\": self.days,\n \"time\": self.time.output()\n }","def hourly_data(self):\n return self._hourly_data","def stats_format_hours(app_id, hours, hours_anon, hours_auth,\r\n max_hours, max_hours_anon, max_hours_auth):\r\n hourNewStats = dict(label=\"Anon + Auth\", disabled=\"True\", values=[], max=0)\r\n hourNewAnonStats = dict(label=\"Anonymous\", values=[], max=0)\r\n hourNewAuthStats = dict(label=\"Authenticated\", values=[], max=0)\r\n\r\n hourNewStats['max'] = max_hours\r\n hourNewAnonStats['max'] = max_hours_anon\r\n hourNewAuthStats['max'] = max_hours_auth\r\n\r\n for h in sorted(hours.keys()):\r\n # New answers per hour\r\n #hourNewStats['values'].append(dict(x=int(h), y=hours[h], size=hours[h]*10))\r\n if (hours[h] != 0):\r\n hourNewStats['values'].append([int(h), hours[h],\r\n (hours[h] * 5) / max_hours])\r\n else:\r\n hourNewStats['values'].append([int(h), hours[h], 0])\r\n\r\n # New Anonymous answers per hour\r\n if h in hours_anon.keys():\r\n #hourNewAnonStats['values'].append(dict(x=int(h), y=hours[h], size=hours_anon[h]*10))\r\n if (hours_anon[h] != 0):\r\n hourNewAnonStats['values'].append([int(h), hours_anon[h],\r\n (hours_anon[h] * 5) / max_hours])\r\n else:\r\n hourNewAnonStats['values'].append([int(h), hours_anon[h], 0])\r\n\r\n # New Authenticated answers per hour\r\n if h in hours_auth.keys():\r\n #hourNewAuthStats['values'].append(dict(x=int(h), y=hours[h], size=hours_auth[h]*10))\r\n if (hours_auth[h] != 0):\r\n hourNewAuthStats['values'].append([int(h), hours_auth[h],\r\n (hours_auth[h] * 5) / max_hours])\r\n else:\r\n hourNewAuthStats['values'].append([int(h), hours_auth[h], 0])\r\n return hourNewStats, hourNewAnonStats, hourNewAuthStats","def json(self):\n return {\n 'uri': self.view_uri,\n 'created': time.strftime('%c', time.gmtime(self.created)),\n 'created_timestamp': self.created,\n 'exception_type': str(self.exc_type),\n 'exception': str(self.exc_value),\n }","def to_json(self):\n new_dict = self.__dict__.copy()\n new_dict.update({'__class__': str(self.__class__.__name__)})\n for key, value in new_dict.items():\n if isinstance(value, datetime):\n new_dict[key] = value.strftime(self.timeformat)\n return new_dict","def getJSON(self):\n text = super().getJSON() + f', \"exchange\": \"{self.__exchange}\"'\n text += f', \"market pair\": \"{self.__market_pairs}\"'\n text += f', \"interval\": \"{self.__interval}\"}}'\n return text","def getHourColumn(self): \n return self.hourcol","def get_working_hour(self):\n working_hrs_id = self.search([('active', '=', True)])\n if working_hrs_id:\n return {\n 'biz_open_time': time(int(working_hrs_id.start_hour), int(working_hrs_id.start_minute), 0),\n 'biz_close_time': time(int(working_hrs_id.end_hour), int(working_hrs_id.end_minute), 0),\n 'holiday_list': {line.occ_date: line.name for line in working_hrs_id.non_working_days_line},\n 'deadline_revise_percentage': working_hrs_id.deadline_revise_percentage,\n }\n raise ValidationError(\"Working Hour configuration is missing!\")","def hour(self):\n return \"%s:00:00\" % self.timestamp[:13]","def get_hours(self, date = \"\"):\n\n if date == \"\":\n DATE = datetime.today()\n else:\n year, month, day = date.split('-')\n DATE = datetime(int(year), int(month), int(day))\n\n s = requests.get(\"https://api.wdpro.disney.go.com/facility-service/schedules/{}?date={}-{}-{}\".format(self.__id, DATE.year, self.__formatDate(str(DATE.month)), self.__formatDate(str(DATE.day))), headers=getHeaders())\n data = json.loads(s.content)\n\n operating_hours_start = None\n operating_hours_end = None\n extra_hours_start = None\n extra_hours_end = None\n\n try:\n for i in range(len(data['schedules'])):\n if data['schedules'][i]['type'] == 'Operating':\n operating_hours_start = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['startTime'][0:2]), int(data['schedules'][i]['startTime'][3:5]))\n if int(data['schedules'][i]['endTime'][0:2]) >= 0 and int(data['schedules'][i]['endTime'][0:2]) <= 7:\n DATETEMP = DATE + timedelta(days=1)\n operating_hours_end = datetime(DATETEMP.year, DATETEMP.month, DATETEMP.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\n else:\n operating_hours_end = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\n\n if data['schedules'][i]['type'] == \"Special Ticketed Event\":\n extra_hours_start = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['startTime'][0:2]), int(data['schedules'][i]['startTime'][3:5]))\n if int(data['schedules'][i]['endTime'][0:2]) >= 0 and int(data['schedules'][i]['endTime'][0:2]) <= 7:\n DATETEMP = DATE + timedelta(days=1)\n extra_hours_end = datetime(DATETEMP.year, DATETEMP.month, DATETEMP.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\n else:\n operating_hours_end = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\n\n except KeyError:\n pass\n return operating_hours_start, operating_hours_end, extra_hours_start, extra_hours_end","def active_hours(self):\n return self._active_hours","def post(self, request):\n\t\tserializer = PeopleSerializer.WorkingHourSerializer(data=request.data)\n\t\tif serializer.is_valid():\n\t\t\tserializer.save()\n\t\t\treturn Response(serializer.data, status=200)\n\t\treturn Response(serializer.errors, status=422)","def hour(self):\n return self._hour","def heartbeat(request):\r\n output = {\r\n 'date': datetime.now(UTC).isoformat(),\r\n 'courses': [course.location.to_deprecated_string() for course in modulestore().get_courses()],\r\n }\r\n return HttpResponse(json.dumps(output, indent=4))","def get_json(self):\n return {\n \"power\": self.get_power(), \n \"timestamp\": self.get_timestamp(), \n \"shortage\": self.get_shortage()\n }","def hour(self) -> int:\n return pulumi.get(self, \"hour\")","def json_events(request):\n if request.method == 'GET':\n ttrss_url = request.GET['feed']\n\n # need xml for this. \n university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'\n\n n = datetime.datetime.now()\n return JsonResponse(\n {\n 'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))\n }\n )","def getHour(self, parent):\r\n self.now = datetime.now()\r\n self.current_time = self.now.strftime(\"%H:%M:%S\")\r\n self.lineEditWidgets[\"HORA\"].setText(self.current_time)","def get_hourly(station_id):\n hourdata = db.session.query(func.avg(DublinBike.available_bike)) \\\n .filter(DublinBike.number == station_id) \\\n .group_by(extract('hour', DublinBike.localtime)) \\\n .order_by(extract('hour', DublinBike.localtime)) \\\n .all()\n return jsonify([\n {'hour': i,\n 'available_bike': float(hourdata[i][0])\n } for i in range(24)\n ])","def __hour(self):\n return _VirtualColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"hour\",\n operand1=self,\n operand2=None\n )","def view_meetups():\n\n return make_response(jsonify({\n \"status\": 200,\n \"data\": meetups.view_meetups()\n })), 200","def to_json(self):\n json = {\n \"url\": url_for(\n \"api.get_week\",\n year=self.year,\n week=self.week,\n ),\n \"year\": self.year,\n \"week\": self.week,\n \"text\": self.text or \"\",\n \"tags\": [tag.text for tag in self.tags],\n }\n return json","def mean_time():\n return render_template('mean_time_weekday.html')","def show_time(self):\n hour = str(datetime.datetime.now().strftime(\"%H\"))\n minute = str(datetime.datetime.now().strftime(\"%M\"))\n\n hour1 = int(hour[0])\n hour2 = int(hour[1])\n minute1 = int(minute[0])\n minute2 = int(minute[1])\n\n self.light_number(self.numbers[hour1], [0, 5])\n self.light_number(self.numbers[hour2], [0, 0])\n self.light_number(self.numbers[minute1], [5, 5])\n self.light_number(self.numbers[minute2], [5, 0])","def to_representation(self, instance):\n rep = super(TicketSerializer, self).to_representation(instance)\n rep['showtime'] = f\"{instance.showtime.room.name} {instance.showtime.movie.title} \" \\\n f\"{instance.showtime.start_date}\"\n return rep","def get_timetable():\n# NAME,DEGREE_CODE,SEM_CODE,EMPLOYEE,SUBJECT,TIMETABLE,EXAMDATE\n rows = db.engine.execute(f\"SELECT * FROM stutimetable WHERE ALTCODE = {g.user.ALTCODE}\")\n res = []\n for row in rows:\n res.append(dict(row))\n return jsonify(res)","def h_24(self):\n return self._hour","def get_events_for_next_hours(hours):\n\tresults = session.query(\"event_name\",\"date\",\"start_time\").\\\n\tfrom_statement(\"select event_name,date,start_time from event where timestampdiff(HOUR,now(),concat(date,' ',start_time))>0 and \\\n\t timestampdiff(HOUR,now(),concat(date,' ',start_time)) < :numhours\").\\\n\tparams(numhours = int(hours)).all()\n\t#return str(results)\n\tret_dict = {}\n\tevents = []\n\tfor event_tuple in results:\n\t\ttemp = {}\n\t\ttemp['event_name'] = event_tuple[0]\n\t\ttemp['start_date'] = str(event_tuple[1])\n\t\ttemp['start_time'] = str(event_tuple[2])\n\t\tevents.append(temp)\n\tret_dict['events'] = events\n\treturn jsonify(ret_dict)","def __str__(self):\n return \"({0}:{1}:{2})\".format(self.hours, self.minutes, self.seconds)","def __json__(self, request=None):\n # start = self.start.isoformat() if self.start else None\n # end = self.end.isoformat() if self.end else None\n return dict(\n timeref_type=\"daterange\",\n interval=self.interval,\n start=self.start.isoformat(),\n end=self.end.isoformat(),\n )","def json_view(self, recursive=False):\n\n context = self.context.aq_inner\n data = self.export(context, recursive=recursive)\n pretty = json.dumps(data, sort_keys=True, indent=4)\n self.request.response.setHeader(\"Content-type\", \"application/json\")\n return pretty","def schedule(request):\n return render(request, 'vaxcharts/schedule.html')","def continent_json(request, continent_code):\n raise Http404(\"Not implemented\")","def hour(self) -> int:\r\n return self._hour","def hour(self) -> int:\r\n return self._hour","def getHour(self):\n return _libsbml.Date_getHour(self)","def _calc_times():\n app.logger.debug(\"Got a JSON request\")\n km = request.args.get('km', 999, type=float)\n distance = request.args.get('distance', type = int)\n begin_date = request.args.get('begin_date', type = str)\n begin_time = request.args.get('begin_time', type = str)\n dateAndTime = begin_date + \" \" + begin_time\n time = arrow.get(dateAndTime, 'YYYY-MM-DD HH:mm') \n \n open_time = acp_times.open_time(km, distance, time.isoformat())\n close_time = acp_times.close_time(km, distance, time.isoformat())\n result = {\"open\": open_time, \"close\": close_time}\n return flask.jsonify(result=result)","def hours(self):\n return int(self.minutes / 60)","def serialize(self):\n return {\n 'id' : self.id,\n #had to change to 'title' for full calendar, might change\n 'title' : self.name,\n 'host' : self.created_by,\n 'start' : self.start_on.isoformat(),\n 'end' : self.end_on.isoformat(),\n 'description' : self.description,\n 'color' : 'blue',\n }","def render_time(dt):\n return dt.strftime('%H:%M:%S')","def json(self):\n\t\treturn datetime.now()","def list(self, request):\n teams = self.controller.retrieve_all_work_times()\n serializer = data_serializers.PresentWorkTimeDataSerializer(teams, many=True)\n return Response(serializer.data)","def _task_data(self):\n output = {\n 'all': [],\n 'open': [],\n 'open_hours': 0,\n 'done': [],\n 'done_hours': 0,\n 'week_done': [],\n 'week_done_hours': 0,\n 'week_due': [],\n 'week_due_hours': 0,\n 'velocity': [],\n 'velocity_hours': 0,\n 'velocity_count': 0,\n }\n\n last_sunday = SUNDAY - timedelta(weeks=1)\n three_weeks_ago = MONDAY - timedelta(weeks=4)\n\n tasks = Task.originals.owner_id(self.pk).order_by('due_dt')\n for t in tasks:\n output['all'].append(t)\n # process open tasks\n if not t.completed:\n output['open'].append(t)\n output['open_hours'] += t.task_time\n\n # Process done tasks\n else:\n output['done'].append(t)\n output['done_hours'] += t.task_time\n if t.completed_dt >= three_weeks_ago and t.completed_dt <= last_sunday:\n output['velocity'].append(t)\n output['velocity_hours'] += t.task_time\n\n if t.due_dt >= MONDAY and t.due_dt <= SUNDAY:\n output['week_due'].append(t)\n output['week_due_hours'] += t.task_time\n\n if t.completed and t.completed_dt >= MONDAY and t.completed_dt <= SUNDAY:\n output['week_done'].append(t)\n output['week_done_hours'] += t.task_time\n\n output['all_hours'] = output['open_hours'] + output['done_hours']\n\n # Extra calcs for the velocity\n output['velocity_count'] = len(output['velocity'])\n\n if output['velocity_hours'] > 0:\n output['velocity_hours'] = round(output['velocity_hours']/3,2)\n if output['velocity_count'] > 0:\n output['velocity_count'] = round(Decimal(output['velocity_count'])/3,2)\n\n return output","def get_events_for_specific_hours(start,end):\n\tresults = session.query(\"event_name\",\"date\",\"start_time\",\"end_time\").\\\n\tfrom_statement(\"select event_name,date,start_time,end_time from event where date=curdate() and \\\n\t\tstart_time >= :starttime and end_time <= :endtime\").\\\n\tparams(starttime = start, endtime = end).all()\n\tif(len(results) > 0):\n\t\tret_dict = {}\n\t\tevents = []\n\n\t\tfor event_tuple in results:\n\t\t\ttemp = {}\n\t\t\ttemp['event_name'] = event_tuple[0]\n\t\t\ttemp['start_date'] = str(event_tuple[1])\n\t\t\ttemp['start_time'] = str(event_tuple[2])\n\t\t\ttemp['end_time'] = str(event_tuple[3])\n\t\t\tevents.append(temp)\n\n\t\tret_dict['events'] = events\n\t\treturn jsonify(ret_dict)\n\telse:\n\t\treturn \"{'events':'no results returned'}\"","def json(self):\n return {'User_uuid': self.uuid, 'School_id': self.school_id, 'Earned_points': self.us_dollar}","def json(self):\n beat = self.beat + 1.4 # replace with hjd\n w, h = self.getWidth(), self.getHeight()\n \n return {\n \"_time\": beat,\n \"_duration\": self.dur,\n #\"_lineIndex\": 0,\n #\"_type\": 0,\n #\"_width\": 0,\n \"_customData\": {\n # to undo the local rotation z transform we have to take trig parts of it and multiply them by the dimensions of the wall, then add them to the position\n \"_position\": [self.l + math.cos(math.radians(self.lrot[2] - 90)) * h / 2, self.d + math.sin(math.radians(self.lrot[2]-90)) * h / 2 + h / 2],\n \"_scale\": [w, h],\n \"_rotation\": self.rot,\n \"_localRotation\": self.lrot\n }\n }","def hourly_table(self):\n htable = [0 for i in range(24)]\n for i in range(self.dataset.shape[0]):\n stime = time.localtime(np.int32(self.dataset[i][2]))\n evtime = stime[3]\n htable[evtime] += 1\n return htable","def hour(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"hour\")","def hours_to_24h_time(hours: float) -> str:\n return (\n dt.datetime(2001, 1, 2) + dt.timedelta(hours=hours)\n ).time().isoformat()","def index(req):\n secs = Section.objects.all().values()\n return Controller.render_json({'sections':list(secs), \"total\": len(secs)})","def timesheet_all(request):\r\n return render(\r\n request,\r\n 'timesheet/timesheet_all.html'\r\n )","def hour(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"hour\")","def hour(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"hour\")","def hour(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"hour\")","def set_Hour(self, value):\n super(GetTimestampFromDateParametersInputSet, self)._set_input('Hour', value)","def test_02_stats_hours(self):\r\n hour = unicode(datetime.datetime.utcnow().strftime('%H'))\r\n with self.flask_app.test_request_context('/'):\r\n hours, hours_anon, hours_auth, max_hours,\\\r\n max_hours_anon, max_hours_auth = stats.stats_hours(1)\r\n print hours\r\n for i in range(0, 24):\r\n # There should be only 10 answers at current hour\r\n if str(i).zfill(2) == hour:\r\n err_msg = \"At time %s there should be 10 answers\" \\\r\n \"but there are %s\" % (str(i).zfill(2),\r\n hours[str(i).zfill(2)])\r\n assert hours[str(i).zfill(2)] == 10, \"There should be 10 answers\"\r\n else:\r\n err_msg = \"At time %s there should be 0 answers\" \\\r\n \"but there are %s\" % (str(i).zfill(2),\r\n hours[str(i).zfill(2)])\r\n assert hours[str(i).zfill(2)] == 0, err_msg\r\n\r\n if str(i).zfill(2) == hour:\r\n tmp = (hours_anon[hour] + hours_auth[hour])\r\n assert tmp == 10, \"There should be 10 answers\"\r\n else:\r\n tmp = (hours_anon[str(i).zfill(2)] + hours_auth[str(i).zfill(2)])\r\n assert tmp == 0, \"There should be 0 answers\"\r\n err_msg = \"It should be 10, as all answers are submitted in the same hour\"\r\n tr = db.session.query(TaskRun).all()\r\n for t in tr:\r\n print t.finish_time\r\n assert max_hours == 10, err_msg\r\n assert (max_hours_anon + max_hours_auth) == 10, err_msg","def view_events():\n result = get_events_helper(Event)\n return jsonify(result[0]), result[1]","def reservetime_hour(self):\n return self._get_time_info([\"Reserve_Time_H\", \"reserveTimeHour\"])","def __str__(self):\n return (\"\"\"\n{\n %s: {\n start_time: %s,\n stop_time: %s\n }\n}\"\"\" % (self._name, self._start_time, self._stop_time)\n )","def get(self, request, pk, format=None):\n\n user = self.get_user(pk=pk)\n serializer = TimeReminderSerializer(user.time_reminders, many=True)\n \n if 'get_current' not in request.GET: #this means we just want all of the time reminders\n return Response(serializer.data)\n\n #else, we want to see if any time reminders should be going off\n time_to_display = []\n for element in serializer.data:\n reminder_time = element['time']\n curr_time = datetime.datetime.now()\n\n datetime_of_reminder = datetime.datetime.strptime(reminder_time, \"%Y-%m-%dT%H:%M:00Z\")\n test = datetime_of_reminder - curr_time #get time between event and now\n seconds = test.total_seconds()\n if (seconds < 35):\n time_to_display.append(element) #if less than 35 seconds in between, send out notification\n\n return Response(time_to_display)","def info_event_json(event_id):\n event = Event.query.filter_by(id=event_id).first_or_404()\n timeuntil = timesince(event.countdown, until=True)\n return jsonify(event=event.data, timeuntil=timeuntil)","def tradeHours(self, context):\n raise NotImplementedError","def _get_hours_pro_entry(time_entries):\n events = []\n for event in time_entries:\n start_time = datetime.datetime(\n date.today().year,\n date.today().month,\n date.today().day,\n event.start_at.hour,\n event.start_at.minute,\n event.start_at.second,\n )\n end_time = datetime.datetime(\n date.today().year,\n date.today().month,\n date.today().day,\n event.finish_at.hour,\n event.finish_at.minute,\n event.finish_at.second,\n )\n\n timediff = end_time - start_time\n events.append(\n {\n \"worked_hours\": round(timediff.total_seconds() / 3600, DECIMALS_HOUR),\n \"event\": event,\n }\n )\n return events","def get_zakopane_hourly_weather():\n zakopane = TwelveHoursWeatherForecast(location.get(\"zakopane\", \"\"))\n zakopane_weather_detail = zakopane.get_hourly_weather_details()\n zakopane_hourly_weather_detail = []\n for data in zakopane_weather_detail:\n zakopane_hourly_weather_detail.append(data)\n return zakopane_hourly_weather_detail","def date_hour(date):\n return date.hour","def export_to_json(self, hparams=None):\n the_dict = vars(self)\n the_dict.pop('TimeLimit.truncated', None)\n with open(f\"{self.folder}/{self.name}.json\", \"w\") as f:\n json.dump(the_dict, f)","def _disp_times():\n fields = request.args.get('fields', type=str)\n format_type = request.args.get('format', type=str)\n top = request.args.get('top', type=int)\n token = request.args.get('token', type=str)\n results = {}\n\n result, length, code = retrieve(token, format_type, top, request_table[fields])\n return flask.jsonify(result=result, length=length, code=code)\n\n # elif code == 401: # Unauthorized\n # app.logger.debug(\"Token Expired! Let's log the user out.\")\n # return render_template('calc.html')","def as_json(self):","def get_time():\n return {\n 'timestamp': datetime.now()+ timedelta(hours=-1)\n }","def to_hours(self, timesteps, to_label=False):\n out = timesteps*self.dt/(60*60)\n if to_label:\n out = [ '{:.2f} hours'.format(el) for el in out ]\n return out","def to_hours(self, timesteps, to_label=False):\n out = timesteps*self.dt/(60*60)\n if to_label:\n out = [ '{:.2f} hours'.format(el) for el in out ]\n return out","def to_hours(self, timesteps, to_label=False):\n out = timesteps*self.dt/(60*60)\n if to_label:\n out = [ '{:.2f} hours'.format(el) for el in out ]\n return out","def to_hours(self, timesteps, to_label=False):\n out = timesteps*self.dt/(60*60)\n if to_label:\n out = [ '{:.2f} hours'.format(el) for el in out ]\n return out","def default(request):\n return direct_to_template(request,\n 'hours/app.html',\n {})","def report_hour_distribution(self):\n self.histogram_granularities.add(histogram_granularity.HOUR)\n return self","def get_hr_graph():\n signames_hr = queries['signames_hr']\n hrvariability = queries['hrvariability']\n return html.Div(style={'height': '30vh'}, className='hr', children=[dcc.Graph(\n id='hr-' + signame,\n style={'width': '100%'},\n figure={\n 'data': [\n {'x': np.array(hrvariability[signame])[:, 0],\n 'y': np.array(hrvariability[signame])[:, 1],\n 'mode': 'line', 'name': signame, 'line':{'color':'rgb(0,0,255)'}}\n ],\n 'layout': {\n 'font': {'color': '#fff'},\n 'title': signame + ' hr',\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\n 'yaxis': {'title': 'HR (beats/min', 'color': '#fff', 'showgrid': 'False'},\n 'paper_bgcolor': '#000', 'plot_bgcolor': '#000'\n }\n }) for signame in signames_hr])","def proxy_hours_minutes(self):\n\n td = self.convert_last_col_filtered()\n resultat = td.days * 24 + td.seconds // 3600, (td.seconds // 60) % 60\n # print('{} H {} M'.format(*resultat))\n print(resultat)\n return resultat","def for_json(self) -> str:\n return self.isoformat()","def report_gpustat():\n\n def _date_handler(obj):\n if hasattr(obj, 'isoformat'):\n return obj.isoformat()\n else:\n raise TypeError(type(obj))\n\n response.content_type = 'application/json'\n if EXCLUDE_SELF:\n resp = {'error': 'Excluded self!'}\n else:\n resp = core.my_gpustat()\n return json.dumps(resp, default=_date_handler)","def calendar_view(request, calendar_id):\n calendar_obj = Calendar.objects.get(pk=calendar_id)\n try:\n appointments = Appointment.objects.all().filter(calendar=calendar_obj)\n appointments = jsonify(appointments)\n except:\n appointments = []\n calendar_obj = calendar_obj.serialize()\n calendar_obj[\"non_working_days\"] = [day for day in [0, 1, 2, 3, 4, 5, 6] if day not in calendar_obj[\"working_days\"]]\n return render(request, 'calendar_view.html', {'calendar_obj': calendar_obj, 'appointments': appointments})","def _task_data(self):\n output = {\n 'all': [],\n 'all_hours': 0,\n 'open': [],\n 'open_hours': 0,\n 'done': [],\n 'done_hours': 0,\n }\n\n tasks = Task.originals.project_id(self.pk).order_by('due_dt')\n for t in tasks:\n # process open tasks\n if not t.completed:\n output['open'].append(t)\n output['open_hours'] += t.task_time\n\n # Process done tasks\n else:\n output['done'].append(t)\n output['done_hours'] += t.task_time\n\n # Included in the loop to keep the ordering\n output['all'].append(t)\n\n output['all_hours'] = output['open_hours'] + output['done_hours']\n\n return output","def h_12(self):\n return self._pmhour","def get(self, request, *args, **kwargs):\n query = '''\n SELECT\n DATE(bs.\"CreatedAt\"),\n count(1)\n FROM\n blood_sample_bloodsample as bs\n WHERE now() - '36 hour'::interval > bs.\"CreatedAt\" AND \\\n bs.\"State\" in ('0','4')\n GROUP BY DATE(bs.\"CreatedAt\") order by DATE(bs.\"CreatedAt\")\n '''\n\n with connection.cursor() as cursor:\n cursor.execute(query)\n processed_not_ontime = [\n [row[0], row[1]]\n for row in cursor.fetchall() if row[1]\n ]\n\n return JsonResponse(\n {\n 'status': 200,\n 'processed_not_ontime': processed_not_ontime,\n 'processed_hours': settings.PROCESSING_HOURS,\n }\n )"],"string":"[\n \"def display_hours(employee_id):\\n\\n if not g.user:\\n flash(\\\"Please Login to continue.\\\", \\\"danger\\\")\\n return redirect(\\\"/\\\")\\n \\n employee = Employee.query.get_or_404(employee_id)\\n\\n labels = json.dumps( [\\\"Completed\\\", \\\"Required\\\"])\\n data = json.dumps([employee.completed, employee.required])\\n \\n return render_template(\\\"users/display_hours.html\\\", employee = employee, labels = labels, data = data)\",\n \"def _draw_hours(self):\\n tmp_str_list = []\\n for i in range(0, self._g_width, self._min_grid):\\n if i % self._hour_grid == 0:\\n tmp_str_list.append('<polyline class=\\\"FullHour\\\" points=\\\"%d,%d, %d,%d\\\" />' % (\\n i + .5 + 20, 20, i + .5 + 20, self._g_height))\\n tmp_str_list.append('<text class=\\\"Label\\\" x=\\\"%d\\\" y=\\\"%d\\\">%d</text>' % (\\n i + 20, 20, (i / self._hour_grid + self._offset) % 24))\\n else:\\n tmp_str_list.append('<polyline class=\\\"SubHour\\\" points=\\\"%d,%d,%d,%d\\\" />' % (\\n i + .5 + 20, 20, i + .5 + 20, self._g_height))\\n return \\\"\\\".join(tmp_str_list)\",\n \"def get(self, request):\\n\\t\\tworkingHours = GymModels.WorkingHours.objects.all()\\n\\t\\tserializer = PeopleSerializer.WorkingHourSerializer(workingHours, many=True)\\n\\t\\treturn Response(serializer.data)\",\n \"def hours(self, venue_id):\\n response = self._request(V2_ENDPOINTS['HOURS'] + venue_id)\\n return response\",\n \"def hours(input=None):\\n return get(input).hours\",\n \"def hours(self):\\n return self.config['hours']\",\n \"def json_frapp(request):\\n from pv.settings import MEDIA_URL\\n\\n if request.GET.get('date') == None:\\n start = datetime.combine(date.today(), time(0, 0))\\n else:\\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\\n\\n end = datetime.combine(start, time(23, 59))\\n\\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\\n\\n\\n '''Generate categories object for output'''\\n\\n categories = Category.objects.all()\\n categories_output = []\\n\\n for c in categories:\\n c_entry = {\\n 'id': c.id,\\n 'color': c.color.replace('#', '').upper(),\\n 'namedisplay': c.category,\\n 'description': c.description\\n }\\n\\n categories_output.append(c_entry)\\n\\n # Get all series for timeslots\\n series = set()\\n for ts in timeslots:\\n series.add(ts.show)\\n\\n\\n '''Generate series object for output'''\\n\\n series_output = []\\n\\n for s in series:\\n metainfos = []\\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\\n\\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\\n url = '' if s.website == None or s.website == '' else s.website\\n\\n # Get active schedules for the given date\\n # But include upcoming single timeslots (with rrule_id=1)\\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n broadcastinfos = ''\\n\\n if not schedules.exists():\\n continue\\n\\n for schedule in schedules:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n if schedules_repetition.exists():\\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\\n for schedule in schedules_repetition:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n s_entry = {\\n 'id': s.id,\\n 'categoryid': s.category.values_list('id', flat=True)[0],\\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\\n 'namedisplay': s.name,\\n 'description': s.description,\\n 'url': url,\\n 'image': image,\\n 'broadcastinfos': broadcastinfos,\\n 'metainfos': metainfos\\n }\\n\\n series_output.append(s_entry)\\n\\n\\n '''Generate shows object for output'''\\n\\n shows_output = []\\n\\n for ts in timeslots:\\n\\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\\n namedisplay = ts.show.name + is_repetition\\n description = ts.show.description\\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\\n urlmp3 = ''\\n\\n # If there's a note to the timeslot use its title, description and url\\n try:\\n note = Note.objects.get(timeslot=ts.id)\\n namedisplay = note.title + is_repetition\\n description = note.content\\n url = str(get_current_site(request)) + '/notes/' + note.slug\\n urlmp3 = note.audio_url\\n except ObjectDoesNotExist:\\n pass\\n\\n ts_entry = {\\n 'id': ts.id,\\n 'seriesid': ts.show.id,\\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\\n 'namedisplay': namedisplay,\\n 'description': description,\\n 'url': url,\\n 'urlmp3': urlmp3,\\n }\\n\\n shows_output.append(ts_entry)\\n\\n output = {}\\n output['categories'] = categories_output\\n output['series'] = series_output\\n output['shows'] = shows_output\\n\\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\\n content_type=\\\"application/json; charset=utf-8\\\")\",\n \"def format_hours(self, data):\\n return unicode('%f' % data).rstrip('0').rstrip('.')\",\n \"def hourly(self):\\n return c.Hourly(self)\",\n \"def output(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 2,\\n \\\"enabled\\\": 1,\\n \\\"days_of_week\\\": self.days,\\n \\\"time\\\": self.time.output()\\n }\",\n \"def open_hours_detail(self):\\n return self._open_hours_detail\",\n \"def get_hourly_weather_details(self, hours: int = None):\\n if hours is None:\\n hours = 11\\n forecast = super().get_weather_forecast(self.BASE_URL)\\n headers = [\\\"date_time\\\",\\n \\\"temp\\\",\\n \\\"real_feel_temp\\\",\\n \\\"wind_speed\\\",\\n \\\"rain_probability\\\",\\n \\\"cloud_cover\\\",\\n ]\\n for number in range(hours):\\n data = []\\n date_time = forecast[number]['DateTime']\\n date_time = date_time[:16]\\n date_time = date_time.replace('T', ' ')\\n data.append(date_time)\\n temp = round((int(\\n forecast[number][\\\"Temperature\\\"][\\\"Value\\\"]) - 32) / 1.8)\\n data.append(temp)\\n real_feel_temp = round((int(\\n forecast[number][\\\"RealFeelTemperature\\\"][\\\"Value\\\"]) - 32) / 1.8)\\n data.append(real_feel_temp)\\n wind_speed = forecast[number][\\\"Wind\\\"][\\\"Speed\\\"][\\\"Value\\\"]\\n data.append(wind_speed)\\n rain_probability = forecast[number][\\\"RainProbability\\\"]\\n data.append(rain_probability)\\n cloud_cover = forecast[number][\\\"CloudCover\\\"]\\n data.append(cloud_cover)\\n yield dict(zip(headers, data))\",\n \"def get_24h(self):\\n records = self.level_model.get_for_period(1)\\n self.set_attributes(records, '24 hours')\",\n \"def output(self):\\n \\n if self.days > 0:\\n interval = \\\"%dd\\\" % self.days\\n if self.hours > 0:\\n interval = \\\"%dh\\\" % self.hours\\n if self.minutes > 0:\\n interval = \\\"%dm\\\" % self.minutes\\n else:\\n interval = \\\"%ds\\\" % self.seconds\\n \\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 1,\\n \\\"enabled\\\": 1,\\n \\\"interval\\\": interval\\n }\",\n \"def output(self):\\n time = \\\"%04d-%02d-%02d %02d:%02d:%02d\\\" % (self.year, self.month, \\\\\\n self.date, self.hours, self.minutes, self.seconds)\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 4,\\n \\\"enabled\\\": 1,\\n \\\"abstime\\\": time\\n }\",\n \"def timesheet(request):\\r\\n return render(\\r\\n request,\\r\\n 'timesheet/timesheet.html'\\r\\n )\",\n \"def get_hourly(self):\\n pass\",\n \"def output(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"type\\\": 3,\\n \\\"enabled\\\": 1,\\n \\\"days_of_month\\\": self.days,\\n \\\"time\\\": self.time.output()\\n }\",\n \"def hourly_data(self):\\n return self._hourly_data\",\n \"def stats_format_hours(app_id, hours, hours_anon, hours_auth,\\r\\n max_hours, max_hours_anon, max_hours_auth):\\r\\n hourNewStats = dict(label=\\\"Anon + Auth\\\", disabled=\\\"True\\\", values=[], max=0)\\r\\n hourNewAnonStats = dict(label=\\\"Anonymous\\\", values=[], max=0)\\r\\n hourNewAuthStats = dict(label=\\\"Authenticated\\\", values=[], max=0)\\r\\n\\r\\n hourNewStats['max'] = max_hours\\r\\n hourNewAnonStats['max'] = max_hours_anon\\r\\n hourNewAuthStats['max'] = max_hours_auth\\r\\n\\r\\n for h in sorted(hours.keys()):\\r\\n # New answers per hour\\r\\n #hourNewStats['values'].append(dict(x=int(h), y=hours[h], size=hours[h]*10))\\r\\n if (hours[h] != 0):\\r\\n hourNewStats['values'].append([int(h), hours[h],\\r\\n (hours[h] * 5) / max_hours])\\r\\n else:\\r\\n hourNewStats['values'].append([int(h), hours[h], 0])\\r\\n\\r\\n # New Anonymous answers per hour\\r\\n if h in hours_anon.keys():\\r\\n #hourNewAnonStats['values'].append(dict(x=int(h), y=hours[h], size=hours_anon[h]*10))\\r\\n if (hours_anon[h] != 0):\\r\\n hourNewAnonStats['values'].append([int(h), hours_anon[h],\\r\\n (hours_anon[h] * 5) / max_hours])\\r\\n else:\\r\\n hourNewAnonStats['values'].append([int(h), hours_anon[h], 0])\\r\\n\\r\\n # New Authenticated answers per hour\\r\\n if h in hours_auth.keys():\\r\\n #hourNewAuthStats['values'].append(dict(x=int(h), y=hours[h], size=hours_auth[h]*10))\\r\\n if (hours_auth[h] != 0):\\r\\n hourNewAuthStats['values'].append([int(h), hours_auth[h],\\r\\n (hours_auth[h] * 5) / max_hours])\\r\\n else:\\r\\n hourNewAuthStats['values'].append([int(h), hours_auth[h], 0])\\r\\n return hourNewStats, hourNewAnonStats, hourNewAuthStats\",\n \"def json(self):\\n return {\\n 'uri': self.view_uri,\\n 'created': time.strftime('%c', time.gmtime(self.created)),\\n 'created_timestamp': self.created,\\n 'exception_type': str(self.exc_type),\\n 'exception': str(self.exc_value),\\n }\",\n \"def to_json(self):\\n new_dict = self.__dict__.copy()\\n new_dict.update({'__class__': str(self.__class__.__name__)})\\n for key, value in new_dict.items():\\n if isinstance(value, datetime):\\n new_dict[key] = value.strftime(self.timeformat)\\n return new_dict\",\n \"def getJSON(self):\\n text = super().getJSON() + f', \\\"exchange\\\": \\\"{self.__exchange}\\\"'\\n text += f', \\\"market pair\\\": \\\"{self.__market_pairs}\\\"'\\n text += f', \\\"interval\\\": \\\"{self.__interval}\\\"}}'\\n return text\",\n \"def getHourColumn(self): \\n return self.hourcol\",\n \"def get_working_hour(self):\\n working_hrs_id = self.search([('active', '=', True)])\\n if working_hrs_id:\\n return {\\n 'biz_open_time': time(int(working_hrs_id.start_hour), int(working_hrs_id.start_minute), 0),\\n 'biz_close_time': time(int(working_hrs_id.end_hour), int(working_hrs_id.end_minute), 0),\\n 'holiday_list': {line.occ_date: line.name for line in working_hrs_id.non_working_days_line},\\n 'deadline_revise_percentage': working_hrs_id.deadline_revise_percentage,\\n }\\n raise ValidationError(\\\"Working Hour configuration is missing!\\\")\",\n \"def hour(self):\\n return \\\"%s:00:00\\\" % self.timestamp[:13]\",\n \"def get_hours(self, date = \\\"\\\"):\\n\\n if date == \\\"\\\":\\n DATE = datetime.today()\\n else:\\n year, month, day = date.split('-')\\n DATE = datetime(int(year), int(month), int(day))\\n\\n s = requests.get(\\\"https://api.wdpro.disney.go.com/facility-service/schedules/{}?date={}-{}-{}\\\".format(self.__id, DATE.year, self.__formatDate(str(DATE.month)), self.__formatDate(str(DATE.day))), headers=getHeaders())\\n data = json.loads(s.content)\\n\\n operating_hours_start = None\\n operating_hours_end = None\\n extra_hours_start = None\\n extra_hours_end = None\\n\\n try:\\n for i in range(len(data['schedules'])):\\n if data['schedules'][i]['type'] == 'Operating':\\n operating_hours_start = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['startTime'][0:2]), int(data['schedules'][i]['startTime'][3:5]))\\n if int(data['schedules'][i]['endTime'][0:2]) >= 0 and int(data['schedules'][i]['endTime'][0:2]) <= 7:\\n DATETEMP = DATE + timedelta(days=1)\\n operating_hours_end = datetime(DATETEMP.year, DATETEMP.month, DATETEMP.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\\n else:\\n operating_hours_end = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\\n\\n if data['schedules'][i]['type'] == \\\"Special Ticketed Event\\\":\\n extra_hours_start = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['startTime'][0:2]), int(data['schedules'][i]['startTime'][3:5]))\\n if int(data['schedules'][i]['endTime'][0:2]) >= 0 and int(data['schedules'][i]['endTime'][0:2]) <= 7:\\n DATETEMP = DATE + timedelta(days=1)\\n extra_hours_end = datetime(DATETEMP.year, DATETEMP.month, DATETEMP.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\\n else:\\n operating_hours_end = datetime(DATE.year, DATE.month, DATE.day, int(data['schedules'][i]['endTime'][0:2]), int(data['schedules'][i]['endTime'][3:5]))\\n\\n except KeyError:\\n pass\\n return operating_hours_start, operating_hours_end, extra_hours_start, extra_hours_end\",\n \"def active_hours(self):\\n return self._active_hours\",\n \"def post(self, request):\\n\\t\\tserializer = PeopleSerializer.WorkingHourSerializer(data=request.data)\\n\\t\\tif serializer.is_valid():\\n\\t\\t\\tserializer.save()\\n\\t\\t\\treturn Response(serializer.data, status=200)\\n\\t\\treturn Response(serializer.errors, status=422)\",\n \"def hour(self):\\n return self._hour\",\n \"def heartbeat(request):\\r\\n output = {\\r\\n 'date': datetime.now(UTC).isoformat(),\\r\\n 'courses': [course.location.to_deprecated_string() for course in modulestore().get_courses()],\\r\\n }\\r\\n return HttpResponse(json.dumps(output, indent=4))\",\n \"def get_json(self):\\n return {\\n \\\"power\\\": self.get_power(), \\n \\\"timestamp\\\": self.get_timestamp(), \\n \\\"shortage\\\": self.get_shortage()\\n }\",\n \"def hour(self) -> int:\\n return pulumi.get(self, \\\"hour\\\")\",\n \"def json_events(request):\\n if request.method == 'GET':\\n ttrss_url = request.GET['feed']\\n\\n # need xml for this. \\n university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'\\n\\n n = datetime.datetime.now()\\n return JsonResponse(\\n {\\n 'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))\\n }\\n )\",\n \"def getHour(self, parent):\\r\\n self.now = datetime.now()\\r\\n self.current_time = self.now.strftime(\\\"%H:%M:%S\\\")\\r\\n self.lineEditWidgets[\\\"HORA\\\"].setText(self.current_time)\",\n \"def get_hourly(station_id):\\n hourdata = db.session.query(func.avg(DublinBike.available_bike)) \\\\\\n .filter(DublinBike.number == station_id) \\\\\\n .group_by(extract('hour', DublinBike.localtime)) \\\\\\n .order_by(extract('hour', DublinBike.localtime)) \\\\\\n .all()\\n return jsonify([\\n {'hour': i,\\n 'available_bike': float(hourdata[i][0])\\n } for i in range(24)\\n ])\",\n \"def __hour(self):\\n return _VirtualColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"hour\\\",\\n operand1=self,\\n operand2=None\\n )\",\n \"def view_meetups():\\n\\n return make_response(jsonify({\\n \\\"status\\\": 200,\\n \\\"data\\\": meetups.view_meetups()\\n })), 200\",\n \"def to_json(self):\\n json = {\\n \\\"url\\\": url_for(\\n \\\"api.get_week\\\",\\n year=self.year,\\n week=self.week,\\n ),\\n \\\"year\\\": self.year,\\n \\\"week\\\": self.week,\\n \\\"text\\\": self.text or \\\"\\\",\\n \\\"tags\\\": [tag.text for tag in self.tags],\\n }\\n return json\",\n \"def mean_time():\\n return render_template('mean_time_weekday.html')\",\n \"def show_time(self):\\n hour = str(datetime.datetime.now().strftime(\\\"%H\\\"))\\n minute = str(datetime.datetime.now().strftime(\\\"%M\\\"))\\n\\n hour1 = int(hour[0])\\n hour2 = int(hour[1])\\n minute1 = int(minute[0])\\n minute2 = int(minute[1])\\n\\n self.light_number(self.numbers[hour1], [0, 5])\\n self.light_number(self.numbers[hour2], [0, 0])\\n self.light_number(self.numbers[minute1], [5, 5])\\n self.light_number(self.numbers[minute2], [5, 0])\",\n \"def to_representation(self, instance):\\n rep = super(TicketSerializer, self).to_representation(instance)\\n rep['showtime'] = f\\\"{instance.showtime.room.name} {instance.showtime.movie.title} \\\" \\\\\\n f\\\"{instance.showtime.start_date}\\\"\\n return rep\",\n \"def get_timetable():\\n# NAME,DEGREE_CODE,SEM_CODE,EMPLOYEE,SUBJECT,TIMETABLE,EXAMDATE\\n rows = db.engine.execute(f\\\"SELECT * FROM stutimetable WHERE ALTCODE = {g.user.ALTCODE}\\\")\\n res = []\\n for row in rows:\\n res.append(dict(row))\\n return jsonify(res)\",\n \"def h_24(self):\\n return self._hour\",\n \"def get_events_for_next_hours(hours):\\n\\tresults = session.query(\\\"event_name\\\",\\\"date\\\",\\\"start_time\\\").\\\\\\n\\tfrom_statement(\\\"select event_name,date,start_time from event where timestampdiff(HOUR,now(),concat(date,' ',start_time))>0 and \\\\\\n\\t timestampdiff(HOUR,now(),concat(date,' ',start_time)) < :numhours\\\").\\\\\\n\\tparams(numhours = int(hours)).all()\\n\\t#return str(results)\\n\\tret_dict = {}\\n\\tevents = []\\n\\tfor event_tuple in results:\\n\\t\\ttemp = {}\\n\\t\\ttemp['event_name'] = event_tuple[0]\\n\\t\\ttemp['start_date'] = str(event_tuple[1])\\n\\t\\ttemp['start_time'] = str(event_tuple[2])\\n\\t\\tevents.append(temp)\\n\\tret_dict['events'] = events\\n\\treturn jsonify(ret_dict)\",\n \"def __str__(self):\\n return \\\"({0}:{1}:{2})\\\".format(self.hours, self.minutes, self.seconds)\",\n \"def __json__(self, request=None):\\n # start = self.start.isoformat() if self.start else None\\n # end = self.end.isoformat() if self.end else None\\n return dict(\\n timeref_type=\\\"daterange\\\",\\n interval=self.interval,\\n start=self.start.isoformat(),\\n end=self.end.isoformat(),\\n )\",\n \"def json_view(self, recursive=False):\\n\\n context = self.context.aq_inner\\n data = self.export(context, recursive=recursive)\\n pretty = json.dumps(data, sort_keys=True, indent=4)\\n self.request.response.setHeader(\\\"Content-type\\\", \\\"application/json\\\")\\n return pretty\",\n \"def schedule(request):\\n return render(request, 'vaxcharts/schedule.html')\",\n \"def continent_json(request, continent_code):\\n raise Http404(\\\"Not implemented\\\")\",\n \"def hour(self) -> int:\\r\\n return self._hour\",\n \"def hour(self) -> int:\\r\\n return self._hour\",\n \"def getHour(self):\\n return _libsbml.Date_getHour(self)\",\n \"def _calc_times():\\n app.logger.debug(\\\"Got a JSON request\\\")\\n km = request.args.get('km', 999, type=float)\\n distance = request.args.get('distance', type = int)\\n begin_date = request.args.get('begin_date', type = str)\\n begin_time = request.args.get('begin_time', type = str)\\n dateAndTime = begin_date + \\\" \\\" + begin_time\\n time = arrow.get(dateAndTime, 'YYYY-MM-DD HH:mm') \\n \\n open_time = acp_times.open_time(km, distance, time.isoformat())\\n close_time = acp_times.close_time(km, distance, time.isoformat())\\n result = {\\\"open\\\": open_time, \\\"close\\\": close_time}\\n return flask.jsonify(result=result)\",\n \"def hours(self):\\n return int(self.minutes / 60)\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n #had to change to 'title' for full calendar, might change\\n 'title' : self.name,\\n 'host' : self.created_by,\\n 'start' : self.start_on.isoformat(),\\n 'end' : self.end_on.isoformat(),\\n 'description' : self.description,\\n 'color' : 'blue',\\n }\",\n \"def render_time(dt):\\n return dt.strftime('%H:%M:%S')\",\n \"def json(self):\\n\\t\\treturn datetime.now()\",\n \"def list(self, request):\\n teams = self.controller.retrieve_all_work_times()\\n serializer = data_serializers.PresentWorkTimeDataSerializer(teams, many=True)\\n return Response(serializer.data)\",\n \"def _task_data(self):\\n output = {\\n 'all': [],\\n 'open': [],\\n 'open_hours': 0,\\n 'done': [],\\n 'done_hours': 0,\\n 'week_done': [],\\n 'week_done_hours': 0,\\n 'week_due': [],\\n 'week_due_hours': 0,\\n 'velocity': [],\\n 'velocity_hours': 0,\\n 'velocity_count': 0,\\n }\\n\\n last_sunday = SUNDAY - timedelta(weeks=1)\\n three_weeks_ago = MONDAY - timedelta(weeks=4)\\n\\n tasks = Task.originals.owner_id(self.pk).order_by('due_dt')\\n for t in tasks:\\n output['all'].append(t)\\n # process open tasks\\n if not t.completed:\\n output['open'].append(t)\\n output['open_hours'] += t.task_time\\n\\n # Process done tasks\\n else:\\n output['done'].append(t)\\n output['done_hours'] += t.task_time\\n if t.completed_dt >= three_weeks_ago and t.completed_dt <= last_sunday:\\n output['velocity'].append(t)\\n output['velocity_hours'] += t.task_time\\n\\n if t.due_dt >= MONDAY and t.due_dt <= SUNDAY:\\n output['week_due'].append(t)\\n output['week_due_hours'] += t.task_time\\n\\n if t.completed and t.completed_dt >= MONDAY and t.completed_dt <= SUNDAY:\\n output['week_done'].append(t)\\n output['week_done_hours'] += t.task_time\\n\\n output['all_hours'] = output['open_hours'] + output['done_hours']\\n\\n # Extra calcs for the velocity\\n output['velocity_count'] = len(output['velocity'])\\n\\n if output['velocity_hours'] > 0:\\n output['velocity_hours'] = round(output['velocity_hours']/3,2)\\n if output['velocity_count'] > 0:\\n output['velocity_count'] = round(Decimal(output['velocity_count'])/3,2)\\n\\n return output\",\n \"def get_events_for_specific_hours(start,end):\\n\\tresults = session.query(\\\"event_name\\\",\\\"date\\\",\\\"start_time\\\",\\\"end_time\\\").\\\\\\n\\tfrom_statement(\\\"select event_name,date,start_time,end_time from event where date=curdate() and \\\\\\n\\t\\tstart_time >= :starttime and end_time <= :endtime\\\").\\\\\\n\\tparams(starttime = start, endtime = end).all()\\n\\tif(len(results) > 0):\\n\\t\\tret_dict = {}\\n\\t\\tevents = []\\n\\n\\t\\tfor event_tuple in results:\\n\\t\\t\\ttemp = {}\\n\\t\\t\\ttemp['event_name'] = event_tuple[0]\\n\\t\\t\\ttemp['start_date'] = str(event_tuple[1])\\n\\t\\t\\ttemp['start_time'] = str(event_tuple[2])\\n\\t\\t\\ttemp['end_time'] = str(event_tuple[3])\\n\\t\\t\\tevents.append(temp)\\n\\n\\t\\tret_dict['events'] = events\\n\\t\\treturn jsonify(ret_dict)\\n\\telse:\\n\\t\\treturn \\\"{'events':'no results returned'}\\\"\",\n \"def json(self):\\n return {'User_uuid': self.uuid, 'School_id': self.school_id, 'Earned_points': self.us_dollar}\",\n \"def json(self):\\n beat = self.beat + 1.4 # replace with hjd\\n w, h = self.getWidth(), self.getHeight()\\n \\n return {\\n \\\"_time\\\": beat,\\n \\\"_duration\\\": self.dur,\\n #\\\"_lineIndex\\\": 0,\\n #\\\"_type\\\": 0,\\n #\\\"_width\\\": 0,\\n \\\"_customData\\\": {\\n # to undo the local rotation z transform we have to take trig parts of it and multiply them by the dimensions of the wall, then add them to the position\\n \\\"_position\\\": [self.l + math.cos(math.radians(self.lrot[2] - 90)) * h / 2, self.d + math.sin(math.radians(self.lrot[2]-90)) * h / 2 + h / 2],\\n \\\"_scale\\\": [w, h],\\n \\\"_rotation\\\": self.rot,\\n \\\"_localRotation\\\": self.lrot\\n }\\n }\",\n \"def hourly_table(self):\\n htable = [0 for i in range(24)]\\n for i in range(self.dataset.shape[0]):\\n stime = time.localtime(np.int32(self.dataset[i][2]))\\n evtime = stime[3]\\n htable[evtime] += 1\\n return htable\",\n \"def hour(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"hour\\\")\",\n \"def hours_to_24h_time(hours: float) -> str:\\n return (\\n dt.datetime(2001, 1, 2) + dt.timedelta(hours=hours)\\n ).time().isoformat()\",\n \"def index(req):\\n secs = Section.objects.all().values()\\n return Controller.render_json({'sections':list(secs), \\\"total\\\": len(secs)})\",\n \"def timesheet_all(request):\\r\\n return render(\\r\\n request,\\r\\n 'timesheet/timesheet_all.html'\\r\\n )\",\n \"def hour(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"hour\\\")\",\n \"def hour(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"hour\\\")\",\n \"def hour(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"hour\\\")\",\n \"def set_Hour(self, value):\\n super(GetTimestampFromDateParametersInputSet, self)._set_input('Hour', value)\",\n \"def test_02_stats_hours(self):\\r\\n hour = unicode(datetime.datetime.utcnow().strftime('%H'))\\r\\n with self.flask_app.test_request_context('/'):\\r\\n hours, hours_anon, hours_auth, max_hours,\\\\\\r\\n max_hours_anon, max_hours_auth = stats.stats_hours(1)\\r\\n print hours\\r\\n for i in range(0, 24):\\r\\n # There should be only 10 answers at current hour\\r\\n if str(i).zfill(2) == hour:\\r\\n err_msg = \\\"At time %s there should be 10 answers\\\" \\\\\\r\\n \\\"but there are %s\\\" % (str(i).zfill(2),\\r\\n hours[str(i).zfill(2)])\\r\\n assert hours[str(i).zfill(2)] == 10, \\\"There should be 10 answers\\\"\\r\\n else:\\r\\n err_msg = \\\"At time %s there should be 0 answers\\\" \\\\\\r\\n \\\"but there are %s\\\" % (str(i).zfill(2),\\r\\n hours[str(i).zfill(2)])\\r\\n assert hours[str(i).zfill(2)] == 0, err_msg\\r\\n\\r\\n if str(i).zfill(2) == hour:\\r\\n tmp = (hours_anon[hour] + hours_auth[hour])\\r\\n assert tmp == 10, \\\"There should be 10 answers\\\"\\r\\n else:\\r\\n tmp = (hours_anon[str(i).zfill(2)] + hours_auth[str(i).zfill(2)])\\r\\n assert tmp == 0, \\\"There should be 0 answers\\\"\\r\\n err_msg = \\\"It should be 10, as all answers are submitted in the same hour\\\"\\r\\n tr = db.session.query(TaskRun).all()\\r\\n for t in tr:\\r\\n print t.finish_time\\r\\n assert max_hours == 10, err_msg\\r\\n assert (max_hours_anon + max_hours_auth) == 10, err_msg\",\n \"def view_events():\\n result = get_events_helper(Event)\\n return jsonify(result[0]), result[1]\",\n \"def reservetime_hour(self):\\n return self._get_time_info([\\\"Reserve_Time_H\\\", \\\"reserveTimeHour\\\"])\",\n \"def __str__(self):\\n return (\\\"\\\"\\\"\\n{\\n %s: {\\n start_time: %s,\\n stop_time: %s\\n }\\n}\\\"\\\"\\\" % (self._name, self._start_time, self._stop_time)\\n )\",\n \"def get(self, request, pk, format=None):\\n\\n user = self.get_user(pk=pk)\\n serializer = TimeReminderSerializer(user.time_reminders, many=True)\\n \\n if 'get_current' not in request.GET: #this means we just want all of the time reminders\\n return Response(serializer.data)\\n\\n #else, we want to see if any time reminders should be going off\\n time_to_display = []\\n for element in serializer.data:\\n reminder_time = element['time']\\n curr_time = datetime.datetime.now()\\n\\n datetime_of_reminder = datetime.datetime.strptime(reminder_time, \\\"%Y-%m-%dT%H:%M:00Z\\\")\\n test = datetime_of_reminder - curr_time #get time between event and now\\n seconds = test.total_seconds()\\n if (seconds < 35):\\n time_to_display.append(element) #if less than 35 seconds in between, send out notification\\n\\n return Response(time_to_display)\",\n \"def info_event_json(event_id):\\n event = Event.query.filter_by(id=event_id).first_or_404()\\n timeuntil = timesince(event.countdown, until=True)\\n return jsonify(event=event.data, timeuntil=timeuntil)\",\n \"def tradeHours(self, context):\\n raise NotImplementedError\",\n \"def _get_hours_pro_entry(time_entries):\\n events = []\\n for event in time_entries:\\n start_time = datetime.datetime(\\n date.today().year,\\n date.today().month,\\n date.today().day,\\n event.start_at.hour,\\n event.start_at.minute,\\n event.start_at.second,\\n )\\n end_time = datetime.datetime(\\n date.today().year,\\n date.today().month,\\n date.today().day,\\n event.finish_at.hour,\\n event.finish_at.minute,\\n event.finish_at.second,\\n )\\n\\n timediff = end_time - start_time\\n events.append(\\n {\\n \\\"worked_hours\\\": round(timediff.total_seconds() / 3600, DECIMALS_HOUR),\\n \\\"event\\\": event,\\n }\\n )\\n return events\",\n \"def get_zakopane_hourly_weather():\\n zakopane = TwelveHoursWeatherForecast(location.get(\\\"zakopane\\\", \\\"\\\"))\\n zakopane_weather_detail = zakopane.get_hourly_weather_details()\\n zakopane_hourly_weather_detail = []\\n for data in zakopane_weather_detail:\\n zakopane_hourly_weather_detail.append(data)\\n return zakopane_hourly_weather_detail\",\n \"def date_hour(date):\\n return date.hour\",\n \"def export_to_json(self, hparams=None):\\n the_dict = vars(self)\\n the_dict.pop('TimeLimit.truncated', None)\\n with open(f\\\"{self.folder}/{self.name}.json\\\", \\\"w\\\") as f:\\n json.dump(the_dict, f)\",\n \"def _disp_times():\\n fields = request.args.get('fields', type=str)\\n format_type = request.args.get('format', type=str)\\n top = request.args.get('top', type=int)\\n token = request.args.get('token', type=str)\\n results = {}\\n\\n result, length, code = retrieve(token, format_type, top, request_table[fields])\\n return flask.jsonify(result=result, length=length, code=code)\\n\\n # elif code == 401: # Unauthorized\\n # app.logger.debug(\\\"Token Expired! Let's log the user out.\\\")\\n # return render_template('calc.html')\",\n \"def as_json(self):\",\n \"def get_time():\\n return {\\n 'timestamp': datetime.now()+ timedelta(hours=-1)\\n }\",\n \"def to_hours(self, timesteps, to_label=False):\\n out = timesteps*self.dt/(60*60)\\n if to_label:\\n out = [ '{:.2f} hours'.format(el) for el in out ]\\n return out\",\n \"def to_hours(self, timesteps, to_label=False):\\n out = timesteps*self.dt/(60*60)\\n if to_label:\\n out = [ '{:.2f} hours'.format(el) for el in out ]\\n return out\",\n \"def to_hours(self, timesteps, to_label=False):\\n out = timesteps*self.dt/(60*60)\\n if to_label:\\n out = [ '{:.2f} hours'.format(el) for el in out ]\\n return out\",\n \"def to_hours(self, timesteps, to_label=False):\\n out = timesteps*self.dt/(60*60)\\n if to_label:\\n out = [ '{:.2f} hours'.format(el) for el in out ]\\n return out\",\n \"def default(request):\\n return direct_to_template(request,\\n 'hours/app.html',\\n {})\",\n \"def report_hour_distribution(self):\\n self.histogram_granularities.add(histogram_granularity.HOUR)\\n return self\",\n \"def get_hr_graph():\\n signames_hr = queries['signames_hr']\\n hrvariability = queries['hrvariability']\\n return html.Div(style={'height': '30vh'}, className='hr', children=[dcc.Graph(\\n id='hr-' + signame,\\n style={'width': '100%'},\\n figure={\\n 'data': [\\n {'x': np.array(hrvariability[signame])[:, 0],\\n 'y': np.array(hrvariability[signame])[:, 1],\\n 'mode': 'line', 'name': signame, 'line':{'color':'rgb(0,0,255)'}}\\n ],\\n 'layout': {\\n 'font': {'color': '#fff'},\\n 'title': signame + ' hr',\\n 'xaxis': {'title': 'time', 'color': '#fff', 'showgrid': 'False'},\\n 'yaxis': {'title': 'HR (beats/min', 'color': '#fff', 'showgrid': 'False'},\\n 'paper_bgcolor': '#000', 'plot_bgcolor': '#000'\\n }\\n }) for signame in signames_hr])\",\n \"def proxy_hours_minutes(self):\\n\\n td = self.convert_last_col_filtered()\\n resultat = td.days * 24 + td.seconds // 3600, (td.seconds // 60) % 60\\n # print('{} H {} M'.format(*resultat))\\n print(resultat)\\n return resultat\",\n \"def for_json(self) -> str:\\n return self.isoformat()\",\n \"def report_gpustat():\\n\\n def _date_handler(obj):\\n if hasattr(obj, 'isoformat'):\\n return obj.isoformat()\\n else:\\n raise TypeError(type(obj))\\n\\n response.content_type = 'application/json'\\n if EXCLUDE_SELF:\\n resp = {'error': 'Excluded self!'}\\n else:\\n resp = core.my_gpustat()\\n return json.dumps(resp, default=_date_handler)\",\n \"def calendar_view(request, calendar_id):\\n calendar_obj = Calendar.objects.get(pk=calendar_id)\\n try:\\n appointments = Appointment.objects.all().filter(calendar=calendar_obj)\\n appointments = jsonify(appointments)\\n except:\\n appointments = []\\n calendar_obj = calendar_obj.serialize()\\n calendar_obj[\\\"non_working_days\\\"] = [day for day in [0, 1, 2, 3, 4, 5, 6] if day not in calendar_obj[\\\"working_days\\\"]]\\n return render(request, 'calendar_view.html', {'calendar_obj': calendar_obj, 'appointments': appointments})\",\n \"def _task_data(self):\\n output = {\\n 'all': [],\\n 'all_hours': 0,\\n 'open': [],\\n 'open_hours': 0,\\n 'done': [],\\n 'done_hours': 0,\\n }\\n\\n tasks = Task.originals.project_id(self.pk).order_by('due_dt')\\n for t in tasks:\\n # process open tasks\\n if not t.completed:\\n output['open'].append(t)\\n output['open_hours'] += t.task_time\\n\\n # Process done tasks\\n else:\\n output['done'].append(t)\\n output['done_hours'] += t.task_time\\n\\n # Included in the loop to keep the ordering\\n output['all'].append(t)\\n\\n output['all_hours'] = output['open_hours'] + output['done_hours']\\n\\n return output\",\n \"def h_12(self):\\n return self._pmhour\",\n \"def get(self, request, *args, **kwargs):\\n query = '''\\n SELECT\\n DATE(bs.\\\"CreatedAt\\\"),\\n count(1)\\n FROM\\n blood_sample_bloodsample as bs\\n WHERE now() - '36 hour'::interval > bs.\\\"CreatedAt\\\" AND \\\\\\n bs.\\\"State\\\" in ('0','4')\\n GROUP BY DATE(bs.\\\"CreatedAt\\\") order by DATE(bs.\\\"CreatedAt\\\")\\n '''\\n\\n with connection.cursor() as cursor:\\n cursor.execute(query)\\n processed_not_ontime = [\\n [row[0], row[1]]\\n for row in cursor.fetchall() if row[1]\\n ]\\n\\n return JsonResponse(\\n {\\n 'status': 200,\\n 'processed_not_ontime': processed_not_ontime,\\n 'processed_hours': settings.PROCESSING_HOURS,\\n }\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6702672","0.63559496","0.6134469","0.60062844","0.58493066","0.5845087","0.5821502","0.58081955","0.57617134","0.5753844","0.5742273","0.5733102","0.57142144","0.56817853","0.56517273","0.5609031","0.558943","0.5580721","0.5514128","0.549668","0.5485034","0.5459778","0.5456188","0.543028","0.54280025","0.5410326","0.5409438","0.5405698","0.5384301","0.5382816","0.53643906","0.5364096","0.5350562","0.5346681","0.534165","0.53281605","0.5321181","0.5315334","0.53138715","0.53118694","0.529093","0.5278395","0.52764416","0.5244283","0.5243668","0.5238784","0.52286446","0.5214764","0.519391","0.51796293","0.51778084","0.51778084","0.5173355","0.5165089","0.5156235","0.5146726","0.5145232","0.5117731","0.51170164","0.5115999","0.5113561","0.51118493","0.5102339","0.509763","0.5090351","0.50811523","0.5078924","0.5076933","0.5074425","0.5074425","0.5074425","0.5066255","0.5065117","0.50619555","0.5061919","0.5059843","0.505833","0.5053662","0.5047068","0.50425196","0.5036175","0.5029547","0.49995652","0.49919927","0.4981201","0.49804777","0.49691486","0.49691486","0.49691486","0.49691486","0.49672708","0.4960847","0.49563646","0.49507585","0.493862","0.49349773","0.49343115","0.4926809","0.49228948","0.49207303"],"string":"[\n \"0.6702672\",\n \"0.63559496\",\n \"0.6134469\",\n \"0.60062844\",\n \"0.58493066\",\n \"0.5845087\",\n \"0.5821502\",\n \"0.58081955\",\n \"0.57617134\",\n \"0.5753844\",\n \"0.5742273\",\n \"0.5733102\",\n \"0.57142144\",\n \"0.56817853\",\n \"0.56517273\",\n \"0.5609031\",\n \"0.558943\",\n \"0.5580721\",\n \"0.5514128\",\n \"0.549668\",\n \"0.5485034\",\n \"0.5459778\",\n \"0.5456188\",\n \"0.543028\",\n \"0.54280025\",\n \"0.5410326\",\n \"0.5409438\",\n \"0.5405698\",\n \"0.5384301\",\n \"0.5382816\",\n \"0.53643906\",\n \"0.5364096\",\n \"0.5350562\",\n \"0.5346681\",\n \"0.534165\",\n \"0.53281605\",\n \"0.5321181\",\n \"0.5315334\",\n \"0.53138715\",\n \"0.53118694\",\n \"0.529093\",\n \"0.5278395\",\n \"0.52764416\",\n \"0.5244283\",\n \"0.5243668\",\n \"0.5238784\",\n \"0.52286446\",\n \"0.5214764\",\n \"0.519391\",\n \"0.51796293\",\n \"0.51778084\",\n \"0.51778084\",\n \"0.5173355\",\n \"0.5165089\",\n \"0.5156235\",\n \"0.5146726\",\n \"0.5145232\",\n \"0.5117731\",\n \"0.51170164\",\n \"0.5115999\",\n \"0.5113561\",\n \"0.51118493\",\n \"0.5102339\",\n \"0.509763\",\n \"0.5090351\",\n \"0.50811523\",\n \"0.5078924\",\n \"0.5076933\",\n \"0.5074425\",\n \"0.5074425\",\n \"0.5074425\",\n \"0.5066255\",\n \"0.5065117\",\n \"0.50619555\",\n \"0.5061919\",\n \"0.5059843\",\n \"0.505833\",\n \"0.5053662\",\n \"0.5047068\",\n \"0.50425196\",\n \"0.5036175\",\n \"0.5029547\",\n \"0.49995652\",\n \"0.49919927\",\n \"0.4981201\",\n \"0.49804777\",\n \"0.49691486\",\n \"0.49691486\",\n \"0.49691486\",\n \"0.49691486\",\n \"0.49672708\",\n \"0.4960847\",\n \"0.49563646\",\n \"0.49507585\",\n \"0.493862\",\n \"0.49349773\",\n \"0.49343115\",\n \"0.4926809\",\n \"0.49228948\",\n \"0.49207303\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72261083"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7496,"cells":{"query":{"kind":"string","value":"View for rendering events feed data as json."},"document":{"kind":"string","value":"def json_events(request):\n if request.method == 'GET':\n ttrss_url = request.GET['feed']\n\n # need xml for this. \n university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'\n\n n = datetime.datetime.now()\n return JsonResponse(\n {\n 'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))\n }\n )"},"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 view_events():\n result = get_events_helper(Event)\n return jsonify(result[0]), result[1]","def get():\n return jsonify({'events': 'Events API'}), 200","def get_event():\n json_data = request.args or {}\n return make_response(jsonify({ \"data\" : Event.get_events(json_data)}))","def to_json(self):\n self._load_all_events()\n return json.dumps(\n [x.to_dict() for events in self._events.values() for x in events], indent=2\n )","def show_events_list():\r\n\tevents_list = Page.objects.filter(tags='events').order_by('-created')\r\n\treturn {'events_list': events_list}","def list(self):\n return JSONResponse(self.request).data(items=self._get_agenda_items()).dump()","def update_events(request):\n events_data = request.data\n events_manager.deserialize_event(events_data)\n # print(events_manager.serialize_events())\n events_manager.apply()\n return JsonResponse({'nodes': []})","def show_events(request):\n event_list = Event.objects.order_by('-date')\n\n event_form = EventForm()\n\n context = {'events': event_list, 'form': event_form}\n return render(request, 'metro_app/events_view.html', context)","def json_news(request):\n if request.method == 'GET':\n feed = request.GET['feed']\n return JsonResponse(\n {\n 'news': get_news(feed),\n }\n )","def json(self):\n return {\n 'uri': self.view_uri,\n 'created': time.strftime('%c', time.gmtime(self.created)),\n 'created_timestamp': self.created,\n 'exception_type': str(self.exc_type),\n 'exception': str(self.exc_value),\n }","def info_event_json(event_id):\n event = Event.query.filter_by(id=event_id).first_or_404()\n timeuntil = timesince(event.countdown, until=True)\n return jsonify(event=event.data, timeuntil=timeuntil)","def events_to_json(events):\n result = {}\n index = 0\n for e in events:\n event = {}\n event['id'] = e.id\n event['name'] = e.name\n event['datetime'] = e.datetime\n event['fee'] = e.fee\n event['max_capacity'] = e.max_capacity\n event['min_capacity'] = e.min_capacity\n result['event'+str(index)] = event\n index += 1\n return result","def json_view(self, recursive=False):\n\n context = self.context.aq_inner\n data = self.export(context, recursive=recursive)\n pretty = json.dumps(data, sort_keys=True, indent=4)\n self.request.response.setHeader(\"Content-type\", \"application/json\")\n return pretty","def list(self, request):\n user = request.auth.user\n events = Event.objects.order_by('datetime')\n search_text = self.request.query_params.get('q', None)\n if search_text is not None:\n events = events.filter(\n Q(cost__icontains=search_text)\n )\n search_text = self.request.query_params.get('date', None)\n if search_text is not None:\n events = events.filter(\n Q(datetime__icontains=search_text)\n )\n for event in events:\n event.bookmarked = None\n try:\n Bookmark.objects.get(event=event, user=user)\n event.bookmarked = True\n except Bookmark.DoesNotExist:\n event.bookmarked = False\n # game = self.request.query_params.get('gameId', None)\n # if game is not None:\n # events = events.filter(game__id=game)\n serializer = EventSerializer(\n events, many=True, context={'request': request})\n return Response(serializer.data)","def serialize(self):\n return {\n 'id' : self.id,\n 'name' : self.name,\n 'event_url' : self.event_url,\n 'event_thumbnail_url' : self.event_thumbnail_url,\n 'description' : self.description,\n 'ticket_price' : self.ticket_price,\n 'start_date' : str(self.start_date),\n 'featued' : self.featured\n }","def get_events_json(self, query_string, **kwargs):\n\n response = self._search_events(query_string, output_format=\"json\", **kwargs)\n\n return response.text","def list(request, template='events/list.html'):\n return render(request, template, {\n 'events': Event.objects.get_upcoming().order_by('start_date'),\n })","def events(self) -> [redirect, HTMLBody]:\n\t\t# Get all events and split into 2 groups\n\t\teventsl, eventsr = prepare_events(get_events())\n\t\treturn render_template(\"events.jinja2\", eventsl=eventsl, eventsr=eventsr)","def myevents(self, request, pk=None):\n user = request.auth.user\n myevents = user.events\n serializer = EventSerializer(\n myevents, many=True, context={'request': request})\n return Response(serializer.data)","def userevent_list(request):\n if request.method == 'GET':\n with sqlite3.connect(Connection.db_path) as conn:\n conn.row_factory = sqlite3.Row\n db_cursor = conn.cursor()\n\n db_cursor.execute(\"\"\"\n SELECT\n full_name,\n user_id,\n id,\n title,\n description,\n date,\n time,\n name\n FROM\n EVENTS_BY_USER\n \"\"\")\n\n dataset = db_cursor.fetchall()\n\n # Take the flat data from the database, and build the\n # following data structure for each gamer.\n #\n # {\n # 1: {\n # \"id\": 1,\n # \"full_name\": \"Admina Straytor\",\n # \"games\": [\n # {\n # \"id\": 1,\n # \"title\": \"Foo\",\n # \"maker\": \"Bar Games\",\n # \"skill_level\": 3,\n # \"number_of_players\": 4,\n # \"game_type_id\": 2\n # }\n # ]\n # }\n # }\n\n events_by_user = {}\n\n for row in dataset:\n uid = row['user_id']\n if uid in events_by_user:\n events_by_user[uid]['events'].append({\n \"id\": row['id'],\n \"title\": row['title'],\n \"description\": row['description'],\n \"date\": row['date'],\n \"time\": row['time'],\n \"game_name\": row[\"name\"]\n })\n else:\n events_by_user[uid] = {\n \"gamer_id\": uid,\n \"full_name\": row['full_name'],\n \"events\": [{\n \"id\": row['id'],\n \"title\": row['title'],\n \"description\": row['description'],\n \"date\": row['date'],\n \"time\": row['time'],\n \"game_name\": row[\"name\"]\n }]\n }\n\n events = events_by_user.values() \n\n template = 'users/list_with_events.html'\n context = {\n 'userevent_list': events\n }\n\n return render(request, template, context)","def list_all_events(as_format='json'):\n eventlist = []\n for event in Event.query \\\n .filter_by(is_hidden=False, lock_resources=False) \\\n .order_by(Event.starts_at.desc()).all():\n eventlist.append(event.data)\n if as_format == 'json':\n return jsonify(events=eventlist)\n headers = {'Content-Disposition': 'attachment; filename=events.csv'}\n csvlist = gen_csv(eventlist)\n return Response(stream_with_context(csvlist),\n mimetype='text/csv',\n headers=headers)","def json_frapp(request):\n from pv.settings import MEDIA_URL\n\n if request.GET.get('date') == None:\n start = datetime.combine(date.today(), time(0, 0))\n else:\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\n\n end = datetime.combine(start, time(23, 59))\n\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\n\n\n '''Generate categories object for output'''\n\n categories = Category.objects.all()\n categories_output = []\n\n for c in categories:\n c_entry = {\n 'id': c.id,\n 'color': c.color.replace('#', '').upper(),\n 'namedisplay': c.category,\n 'description': c.description\n }\n\n categories_output.append(c_entry)\n\n # Get all series for timeslots\n series = set()\n for ts in timeslots:\n series.add(ts.show)\n\n\n '''Generate series object for output'''\n\n series_output = []\n\n for s in series:\n metainfos = []\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\n\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\n url = '' if s.website == None or s.website == '' else s.website\n\n # Get active schedules for the given date\n # But include upcoming single timeslots (with rrule_id=1)\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n broadcastinfos = ''\n\n if not schedules.exists():\n continue\n\n for schedule in schedules:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n if schedules_repetition.exists():\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\n for schedule in schedules_repetition:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n s_entry = {\n 'id': s.id,\n 'categoryid': s.category.values_list('id', flat=True)[0],\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\n 'namedisplay': s.name,\n 'description': s.description,\n 'url': url,\n 'image': image,\n 'broadcastinfos': broadcastinfos,\n 'metainfos': metainfos\n }\n\n series_output.append(s_entry)\n\n\n '''Generate shows object for output'''\n\n shows_output = []\n\n for ts in timeslots:\n\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\n namedisplay = ts.show.name + is_repetition\n description = ts.show.description\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\n urlmp3 = ''\n\n # If there's a note to the timeslot use its title, description and url\n try:\n note = Note.objects.get(timeslot=ts.id)\n namedisplay = note.title + is_repetition\n description = note.content\n url = str(get_current_site(request)) + '/notes/' + note.slug\n urlmp3 = note.audio_url\n except ObjectDoesNotExist:\n pass\n\n ts_entry = {\n 'id': ts.id,\n 'seriesid': ts.show.id,\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\n 'namedisplay': namedisplay,\n 'description': description,\n 'url': url,\n 'urlmp3': urlmp3,\n }\n\n shows_output.append(ts_entry)\n\n output = {}\n output['categories'] = categories_output\n output['series'] = series_output\n output['shows'] = shows_output\n\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\n content_type=\"application/json; charset=utf-8\")","def serialize(self):\n return {\n 'id' : self.id,\n #had to change to 'title' for full calendar, might change\n 'title' : self.name,\n 'host' : self.created_by,\n 'start' : self.start_on.isoformat(),\n 'end' : self.end_on.isoformat(),\n 'description' : self.description,\n 'color' : 'blue',\n }","def event_activity_json(event_id):\n limit = request.args.get('limit') or 50\n q = request.args.get('q') or None\n if q and len(q) < 3:\n q = None\n return jsonify(activities=get_event_activities(event_id, limit, q))","def data_json(request):\n json_data = []\n for resource in Resource.objects.all():\n record = {} \n record['title'] = resource.name\n record['description'] = resource.description\n record['keyword'] = resource.csw_keywords.split(',')\n record['modified'] = resource.last_updated\n record['publisher'] = resource.organization\n record['contactPoint'] = resource.metadata_contact\n record['mbox'] = resource.contact_email\n record['identifier'] = resource.csw_identifier\n if resource.is_published:\n record['accessLevel'] = 'public'\n else:\n record['accessLevel'] = 'non-public'\n\n json_data.append(record)\n\n return HttpResponse(json.dumps(json_data), 'application/json')","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(self.get_data(context))","def apigw_event():\n with open(\"events/event.json\") as json_file:\n return json.load(json_file)","def info_current_event_json():\n event = Event.query.filter_by(is_current=True).first() or \\\n Event.query.order_by(Event.id.desc()).first_or_404()\n timeuntil = timesince(event.countdown, until=True)\n return jsonify(event=event.data, timeuntil=timeuntil)","def home_feeds(request):\n result = {}\n \n result['feeds'] = []\n\n u = request.user\n\n\n # get other people's feeds, filter by friends if in social group\n feeds = Feed.objects.exclude(actor=u).order_by('-timestamp')\n result['feeds'] = [ f.get_json(me=u, android=True) for f in feeds ]\n\n return JSONHttpResponse(result)","def events_info(request):\n \n global input\n \n if request == 'event-based':\n client_neries = Client_neries()\n \n events = client_neries.getEvents(min_datetime=input['min_date'], \\\n max_datetime=input['max_date'], min_magnitude=input['min_mag'], \\\n max_magnitude=input['max_mag'], min_latitude=input['evlatmin'], \\\n max_latitude=input['evlatmax'], min_longitude=input['evlonmin'], \\\n max_longitude=input['evlonmax'], min_depth = input['min_depth'], \\\n max_depth=input['max_depth'], max_results=input['max_result'])\n \n for i in range(0, len(events)):\n events[i]['t1'] = events[i]['datetime'] - input['preset']\n events[i]['t2'] = events[i]['datetime'] + input['offset']\n \n elif request == 'continuous':\n m_date = UTCDateTime(input['min_date'])\n M_date = UTCDateTime(input['max_date'])\n \n t_cont = M_date - m_date\n \n events = []\n \n if t_cont > input['interval']:\n num_div = int(t_cont/input['interval'])\n t_res = t_cont - num_div*input['interval']\n \n for i in range(0, num_div):\n events.append({'author': 'NAN', 'event_id': 'continuous' + str(i), \\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\n 'datetime': m_date + i*input['interval'], \\\n 't1': m_date + i*input['interval'],\\\n 't2': m_date + (i+1)*input['interval'] + 60.0,\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\n 'flynn_region': 'NAN'})\n \n events.append({'author': 'NAN', 'event_id': 'continuous' + str(i+1), \\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\n 'datetime': m_date + (i+1)*input['interval'], \\\n 't1': m_date + (i+1)*input['interval'],\\\n 't2': M_date,\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\n 'flynn_region': 'NAN'})\n else:\n events.append({'author': 'NAN', 'event_id': 'continuous0', \\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\n 'datetime': m_date, \\\n 't1': m_date,\\\n 't2': M_date,\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\n 'flynn_region': 'NAN'})\n\n return events","def render_json(object):\r\n return HttpResponse(jsonify(object), content_type='application/json')","def list(self, request):\n return HttpResponse(request.user.series_list, content_type=\"application/json\")","def index():\n\n if request.method == 'POST':\n app.logger.info('Event: ' + str(request.get_json()))\n process_event(request.get_json())\n return ''\n else:\n return render_template('index.html')","def _serialize_event_data_as_json(event_data):\n return json.dumps(event_data)","def show_events():\n try:\n return render_template(\n 'events.html',\n events=get_events(),\n auth=is_organizer(get_user()),\n app_config=app.config\n )\n except RuntimeError as error:\n return str(error), 500","def as_json(self):","def events(self):\r\n return resources.Events(self)","def vaccine_data_chart(request):\n\n print(request)\n labels = []\n data = []\n\n queryset = VaccineData.objects.filter(country=\"Australia\").order_by(\"date\")\n for entry in queryset:\n labels.append(entry.date)\n data.append(entry.doses_administered)\n\n return JsonResponse(\n data={\n \"country\": \"Australia Vaccine Doses Administered\",\n \"labels\": labels,\n \"data\": data,\n }\n )","def EventToJSON(_object):\n return json.dumps(_object, default=jsonDefault)","def get_all_feeds(request):\n feed = Feeds.objects.all()\n\n\n\n\n serializer = FeedsSerializer(feed,many=True)\n return JsonResponse({\"FeedList\":serializer.data,\"ResponseCode\": \"200\",\"ResponseMessage\":\"Successfully\"}, safe=False)","def get_events():\n url = app.config['EVENTS_ENDPOINT']\n response = requests.get(url, params={})\n if response.status_code == 200:\n return parse_events(response.json())\n raise RuntimeError('Error in retrieving events.')","def get_feed():\n return jsonify(dict({\n \"result\": mongo.get_hpfeed(),\n \"code\": 200\n }))","def render_get(self, request):\n\n thing_event = get_thing_event(self._server, request)\n last_item = self._last_events.get(self._event_key(thing_event), None)\n payload = json.dumps(last_item).encode(\"utf-8\") if last_item else b\"\"\n response = aiocoap.Message(code=aiocoap.Code.CONTENT, payload=payload)\n response.opt.content_format = JSON_CONTENT_FORMAT\n\n raise tornado.gen.Return(response)","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(self.get_data(**context), **response_kwargs)","def data_json(self, extra_context=None, publish=False):\n if not self.project.CREATE_JSON:\n # nothing to see here, but the right mimetype\n return jsonify()\n\n if not self.data:\n # this sets site.data by spreadsheet or gdoc\n self.get_context(publish)\n\n return jsonify(self.data)","def event_activity_current_json():\n event = Event.query.filter_by(is_current=True).first()\n if not event:\n return jsonify(activities=[])\n return event_activity_json(event.id)","def dumps(self):\n evn_data = {\"id\": self.id,\n \"name\": self.name,\n \"code\": [line.strip() for line in self.source_code]}\n\n return {\"event\": evn_data}","def to_json(self):\n\n columns = list()\n points = list()\n\n if self._collection.type() == Event:\n columns += ['time']\n elif self._collection.type() == TimeRangeEvent:\n columns += ['timerange']\n elif self._collection.type() == IndexedEvent:\n columns += ['index']\n\n columns += self.columns()\n\n for i in self._collection.events():\n points.append(i.to_point(columns[1:]))\n\n cols_and_points = dict(\n columns=columns,\n points=points,\n )\n\n # fold in the rest of the payload\n cols_and_points.update(self._data)\n\n # Turn the index back into a string for the json representation.\n # The Index object can still be accessed via TimeSeries.index()\n if 'index' in cols_and_points and \\\n isinstance(cols_and_points.get('index'), Index):\n cols_and_points['index'] = cols_and_points.get('index').to_string()\n\n return cols_and_points","def to_json(self):\n return json.dumps({\"data\": self._data.tolist(),\n \"header\": self._header.tolist(),\n \"dates\": self._dates.tolist()})","def feed_entries(self):\n date_format = \"%Y-%m-%dT%H:%M:%SZ\"\n entries = self.mapper.list_entries(limit=10)\n if entries:\n updated = max([e.updated for e in entries]).strftime(date_format)\n else:\n updated = datetime.utcnow().strftime(date_format)\n return {\"entries\": entries, \"updated\": updated}","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(\n self.get_data(context),\n **response_kwargs\n )","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(\n self.get_data(context),\n **response_kwargs\n )","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(\n self.get_data(context),\n **response_kwargs\n )","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(\n self.get_data(context),\n **response_kwargs\n )","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(\n self.get_data(context),\n **response_kwargs\n )","def json(data):\n if isinstance(data, dict):\n data = ujson.encode(data)\n uid = str(uuid.uuid4())\n display(HTML('<div id=\"{0}\" style=\"height: 600px; width:100%;\"></div>'.format(uid)))\n display(Javascript(\"\"\"\n require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"], function() {\n document.getElementById('%s').appendChild(renderjson(%s))\n });\n \"\"\" % (uid, data)))","def export_event(self):\n\n cal = Eve()\n cal.add('summary', str(self.categories))\n cal.add('description', self.label)\n cal.add('dtstart', vDatetime(self.start))\n cal.add('dtend', vDatetime(self.end))\n return cal.to_ical()","def __json__(self, request=None):\n # start = self.start.isoformat() if self.start else None\n # end = self.end.isoformat() if self.end else None\n return dict(\n timeref_type=\"daterange\",\n interval=self.interval,\n start=self.start.isoformat(),\n end=self.end.isoformat(),\n )","def render_to_json_response(self, context, **response_kwargs):\n return HttpResponse(\n self.convert_context_to_json(context),\n content_type='application/json',\n **response_kwargs\n )","def render_to_json_response(self, context, **response_kwargs):\n return HttpResponse(\n self.convert_context_to_json(context),\n content_type='application/json',\n **response_kwargs\n )","def to_json(self):\n\t\tevents = [{\"name\": \"Killstreak\", \"value\": v, \"tick\": t} for v, t, _ in self.killstreaks]\n\t\tevents += [{\"name\": \"Bookmark\", \"value\": v, \"tick\": t} for v, t, _ in self.bookmarks]\n\t\treturn {\"events\": sorted(events, key = lambda e: int(e[\"tick\"]))}","def to_json_dict(self):\n json_dict = {}\n json_dict['event_status'] = self.event_status\n json_dict['event_status_list'] = self.event_status_list\n json_dict['spaces'] = [s.get_id() for s in self.spaces]\n json_dict['characters'] = [c.get_id() for c in self.characters]\n json_dict['exits'] = [e.get_id() for e in self.exits]\n json_dict['items'] = [i.get_id() for i in self.items]\n return json_dict","def view_event_list(request, **kwargs):\n #lu = get_common_lookup(request)\n lu = { 'page_title' : 'MCB Event Tweets'\\\n , 'IS_TWEET_EVENT_PAGE' : True\n , 'TWEET_SUCCESS' : kwargs.get('success_msg', False)\n }\n \n if not request.user.is_authenticated():\n return HttpResponse('not logged in')\n \n if not is_user_in_group(request, TWEET_GROUP_NAME):\n return HttpResponse('not in tweet group')\n \n upcoming_events = MCBTweetEvent.get_events_awaiting_approval()\n \n lu.update({ 'upcoming_events' : upcoming_events\\\n #, 'my_checked_codes' : get_previously_checked_expense_codes(request)\\\n })\n #\n return render_to_response('tweet/events/event_list.html', lu, context_instance=RequestContext(request))","def get_event():\n\t#Get HTTP query args.\n\tno = request.args.get('no')\n\tfilename = request.args.get('filename')\n\tcollection = mongo.db[filename]\n\n\tjsonEncoder = hepmcio_json.HepMCJSONEncoder()\n\thepMCDecoder = hepmcio_json.HepMCJSONDecoder()\n\tjsonDecoder = json.JSONDecoder()\n\t#Everything below same as in the Visualiser view.\n\tevent = collection.find_one({\"type\":\"event\", \"no\":int(no)}, {\"_id\":False})\n\tparticleJson = collection.find({\"type\":\"particle\", \"event\":event[\"barcode\"]}, {\"_id\":False})\n\tparticles = []\n\tfor particle in particleJson:\n\t\tparticles.append(jsonEncoder.encode(particle))\n\tvertices = []\n\tvertexJson = collection.find({\"type\":\"vertex\", \"event\":event[\"barcode\"]}, {\"_id\":False})\n\tfor vertex in vertexJson:\n\t\tvertices.append(jsonEncoder.encode(vertex))\n\tevent = jsonEncoder.encode(event)\n\n\teventObject = hepmcio_json.EventJSONObject(event, particles, vertices)\n\t\n\tdecodedEvent = hepMCDecoder.decode(eventObject)\n\n\tPT_CUTOFF = 0.0\n\tintParticles = [particle for particle in decodedEvent.particles.values() if particle.status!=1 and \\\n\t\tparticle.mom[0]**2 + particle.mom[1]**2 > PT_CUTOFF**2]\n\t\n\tintParticleAncestors = reduce(operator.concat, [hepmcio.get_ancestors(particle)[:-1] for particle in intParticles])\n\n\tparticles = []\n\tfor particle in (intParticles + intParticleAncestors):\n\t\tparticles.append(jsonDecoder.decode(jsonEncoder.encode(particle)))\n\t\n\tvertices = list(map(jsonDecoder.decode, vertices))\n\t\n\treturn {\"particles\":jsonEncoder.encode(particles), \"vertices\":jsonEncoder.encode(vertices)}","def get(self, request, *arg, **kwargs):\r\n response = {\r\n 'status' : False,\r\n 'response' : {},\r\n 'error' : {\r\n 'internal' : None,\r\n 'external' : None\r\n }\r\n }\r\n\r\n try:\r\n #list comprehencion querying a low values table for names\r\n events = [event.name for event in EventType.objects.filter(is_active=True)]\r\n\r\n events = self.event_processor(_events=events)\r\n\r\n response['status'] = True\r\n response['response'] = events\r\n\r\n\r\n except Exception as GetException:\r\n response['error']['internal'] = GetException.__str__()\r\n response['error']['external'] = \"Something went wrong\"\r\n \r\n return JsonResponse(response)","def to_json(self):\n return {\n \"item_name\": self.item_name,\n \"summary\": self.summary,\n \"content\": self.content,\n \"date_published\": self.date_published,\n \"item_slug\": self.item_slug,\n \"category_name\": self.category_name,\n }","def render(self, data):\n logging.info(\"render (start)\")\n\n seria = json.dumps(data, ensure_ascii=False, indent=4)\n logging.info(\"rendered %s characters (end)\" % len(seria))\n return seria","def json(self):\n return {'id': self.id, 'name': self.name, 'description': self.description}","def render_json(self, obj):\n self.response.content_type = \"application/json\"\n self.response.out.write(json.encode(obj))","def all_bugs_chart(request):\n labels = []\n data = []\n\n queryset = Bug.objects.values('title', 'id').order_by('-created').exclude(status='Resolved').annotate(\n bug_votes=Count('votes'))[:5]\n for entry in queryset:\n labels.append(entry['title'])\n data.append(entry['bug_votes'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n })","def events(self):\n return get_tsv(self.path, self.values, 'events.tsv')","def json_format(data):\n return {\n 'Title': data[\"title\"],\n 'Publication date': data['pubDate'],\n 'News link': data['link'],\n 'Image link': data['media'],\n }","def all_features_chart(request):\n labels = []\n data = []\n\n queryset = Feature.objects.values('title').order_by('-created').exclude(status='Implemented').annotate(\n feature_purchases=Sum('purchases'))[:5]\n for entry in queryset:\n labels.append(entry['title'])\n data.append(entry['feature_purchases'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n })","def getchart(request):\n data = ast.literal_eval(request.body.decode('utf-8'))\n data['user'] = request.user\n data = newchart(data)\n return JsonResponse(data)","def get_context_data(self, **kwargs):\n context = super(HomeView, self).get_context_data(**kwargs)\n context['events'] = Events.objects.filter(published=True).all()\n return context","def covid_data_chart(request):\n\n print(request)\n labels = []\n data = []\n\n queryset = CovidData.objects.filter(country=\"Australia\").order_by(\"date\")\n for entry in queryset:\n labels.append(entry.date)\n data.append(entry.confirmed)\n\n return JsonResponse(\n data={\n \"country\": \"Australia Covid Cases\",\n \"labels\": labels,\n \"data\": data,\n }\n )","def get_events(self):\n\n url = '/v2.4/'+self.page_id+'/events'\n data = self.graph.request(url)\n\n while 'next' in data['paging'].keys():\n print data['paging']['next']\n data = self.graph.request(url, args={\n 'limit' : 100,\n 'after' : data['paging']['cursors']['after']\n })\n\n return data","def json(self):\n return {\n 'author': self.author,\n 'email': self.email,\n 'display_email': self.display_email,\n 'title': self.title,\n 'trailer_path': self.trailer_path,\n 'date': self.date,\n 'link': self.link,\n '_id': self._id\n }","def format_events(self):\n\n log.debug('formatting events ({}) for \"{}\"'.format(len(self.unformatted_events), self.school_year))\n for event in self.unformatted_events:\n name = event.module.get_text().replace('\\n', '') if event.module else 'Matière non définie'\n category = event.category.get_text() if event.category else 'Catégorie de cours non définie'\n starttime = event.starttime.get_text() if event.starttime else 'Heure de début de cours non définie'\n endtime = event.endtime.get_text() if event.endtime else 'Heure de début de cours non définie'\n room = event.room.item.get_text() if event.room and event.room.item else 'Aucune salle définie'\n group = event.group.item.get_text() if event.group and event.group.item else 'Classe entière'\n nday = int(event.day.get_text()) if event.day else None\n date_first_weekday = self.week_dates_mapping[event.rawweeks.get_text()] if event.rawweeks else None\n start = '{}-{}'.format(date_first_weekday, starttime)\n end = '{}-{}'.format(date_first_weekday, endtime)\n dtstart = datetime.datetime.strptime(start, '%d/%m/%Y-%H:%M') + datetime.timedelta(days=nday, hours=-2)\n dtend = datetime.datetime.strptime(end, '%d/%m/%Y-%H:%M') + datetime.timedelta(days=nday, hours=-2)\n\n start_date = dtstart.isoformat() + 'Z'\n end_date = dtend.isoformat() + 'Z'\n\n calendar_event = GoogleCalendarEvent(\n location=room,\n summary='({}) - {} - {}'.format(category, name, group),\n description=group,\n dtstart=start_date,\n dtend=end_date\n )\n self.formatted_events.append(calendar_event.json)","def getLineData(self):\n # Each view must have exactly one DateRange object\n date_range = DateRange.objects.filter(foreign_key=self.id).first()\n assert(date_range is not None)\n\n begin, end = date_range.getBeginEnd()\n return {\n \"begin\" : begin.strftime(\"%Y%m%d\"),\n \"end\" : end.strftime(\"%Y%m%d\"),\n \"data_sets\" : [\n {\n \"label\" : filter_set.label,\n \"color\" : filter_set.color,\n \"data\" : [\n {\n \"cnt\": row[\"cnt\"],\n \"date\": row[\"date\"].strftime(\"%Y%m%d\")\n } for row in filter_set.getMessageCountPerDay()\n ],\n } for filter_set in self.filterset_set.all()\n ]\n }","def listings(request, category1, category2, category3, page = 1):\n \n # Creating URL for request\n base_url = \"https://www.eventbriteapi.com/v3/events/search/\"\n token_component = \"token=BKKRDKVUVRC5WG4HAVLT\" #I had this token in my mail link\n category_component = \"categories=\" + category1 + ',' + category2 + ',' + category3\n page_component = \"page=\" + str(page)\n url_without_page = base_url + \"?\" + token_component + \"&\" + category_component\n url_complete = url_without_page + \"&\" + page_component\n \n # GET events from Eventbrite\n f = urllib2.urlopen(url_complete) \n json_string = f.read() \n parsed_json = json.loads(json_string) \n\n # Parse through JSON\n events = parsed_json['events']\n eventsList = []\n \n for i in events:\n eventsList.append(event_container())\n \n # Parse further through JSON\n eventsList[-1].name = i['name']['text']\n eventsList[-1].id = i['id']\n eventsList[-1].url = i['url']\n try:\n eventsList[-1].description = i['description']['text']\n except:\n eventsList[-1].description = \"No description available\"\n eventsList[-1].resource_uri = i['resource_uri']\n \n \n listings_url_base = '/topthree/listings/'+ category1 + '/' + category2 + '/' + category3 + '/'\n \n # Pagination\n \n \"\"\"\n Performing manual pagination instead of Django pagination \n because GET request for events pulls in paginated data already\n \"\"\"\n \n next_page = int(page) + 1\n next_page_url = listings_url_base + str(next_page) \n \n if int(page)>1:\n prev_page = int(page) - 1\n prev_page_url = listings_url_base + str(prev_page) \n\n else:\n prev_page = 0\n prev_page_url = \"#\"\n \n \n # Sending values to template\n \n template = loader.get_template('listings.html')\n\n context = RequestContext(request, {\n 'eventsList': eventsList,\n 'prev_page_url':prev_page_url,\n 'next_page_url':next_page_url,\n 'prev_page':prev_page,\n 'page':page,\n 'category1':category1,\n 'category2':category2,\n 'category3':category3,\n })\n \n return HttpResponse(template.render(context))","def format(self, *args):\n\t\tweb.header('Content-Type', 'application/json; charset=utf-8')\n\t\treturn json.dumps(self.content)","def schedule_content(request):\r\n\r\n stories = Story.objects.filter(organization=request.user.organization).exclude(archived=True)\r\n\r\n # data = {}\r\n # data['success'] = 1\r\n # data['result'] = []\r\n data = []\r\n\r\n for story in stories:\r\n # Facet Schedules\r\n for facet in story.facetstory.all():\r\n credit = {}\r\n for user in facet.credit.all():\r\n credit['id'] = []\r\n credit['id'].append(user.credit_name)\r\n credit['id'].append(user.get_absolute_url())\r\n editor = {}\r\n for user in facet.editor.all():\r\n editor['id'] = []\r\n editor['id'].append(user.credit_name)\r\n editor['id'].append(user.get_absolute_url())\r\n print credit\r\n if facet.due_edit:\r\n edit_event_dict = {}\r\n edit_event_dict['id'] = facet.id\r\n edit_event_dict['title'] = facet.name.encode('utf-8')\r\n edit_event_dict['description'] = facet.description.encode('utf-8')\r\n edit_event_dict['due-edit'] = time.mktime(facet.due_edit.timetuple())\r\n edit_event_dict['editor'] = facet.editor.credit_name\r\n edit_event_dict['credit'] = credit\r\n edit_event_dict['url'] = facet.get_absolute_url()\r\n edit_event_dict['start'] = time.mktime(facet.due_edit.timetuple()) * 1000\r\n edit_event_dict['end'] = (time.mktime(facet.due_edit.timetuple()) * 1000) + 60\r\n edit_event_dict['overlap'] = True\r\n edit_event_dict['allDay'] = False\r\n edit_event_dict['backgroundColor'] = '#00aced'\r\n edit_event_dict['textColor'] = '#fff'\r\n data.append(edit_event_dict)\r\n if facet.run_date:\r\n run_event_dict = {}\r\n run_event_dict['id'] = facet.id\r\n run_event_dict['title'] = facet.name.encode('utf-8')\r\n run_event_dict['description'] = facet.description.encode('utf-8')\r\n run_event_dict['due-edit'] = time.mktime(facet.due_edit.timetuple())\r\n run_event_dict['editor'] = facet.editor.credit_name\r\n run_event_dict['credit'] = credit\r\n run_event_dict['url'] = facet.get_absolute_url()\r\n run_event_dict['class'] = 'event_run'\r\n run_event_dict['start'] = time.mktime(facet.run_date.timetuple()) * 1000\r\n run_event_dict['end'] = (time.mktime(facet.run_date.timetuple()) * 1000) + 60\r\n run_event_dict['overlap'] = True\r\n run_event_dict['backgroundColor'] = '#5cb85c'\r\n run_event_dict['textColor'] = '#fff'\r\n data.append(run_event_dict)\r\n\r\n # print \"DATA: \", data\r\n\r\n return HttpResponse(json.dumps(data), content_type='application/json')","def render_to_response(self, context, **response_kwargs):\n return JsonResponse(context)","def showEvents(self, year, month, language, filterIDs=None, negFilterIDs=None):\n try:\n returnEvents = self.newsFeedModel.getEvents(year, month, language, filterIDs, negFilterIDs)\n if language != 'de' and len(returnEvents) == 0:\n returnEvents = self.newsFeedModel.getEvents(year, month, 'de', filterIDs, negFilterIDs)\n returnEventsJSON = self.newsFeedView.toJSONEvents(returnEvents, self.newsFeedModel.getEventCategoriesLastChanged())\n return returnEventsJSON\n except Exception as e:\n print(\"there was a problem while retrieving events\")\n raise e","def get_data(self):\n return self.data.to_json()","def to_json(self, format=None):\n if format == \"widget\":\n return {\n \"source\": self.source,\n \"target\": self.target,\n \"type\": self.type,\n \"time\": self.time,\n \"value\": self.link_width,\n \"title\": self.title,\n \"color\": self.color,\n \"opacity\": self.opacity,\n \"data\": self.data,\n }\n else:\n return {\n \"source\": self.source,\n \"target\": self.target,\n \"type\": self.type,\n \"title\": self.title,\n \"time\": self.time,\n \"link_width\": self.link_width,\n \"data\": self.data,\n \"style\": {\"color\": self.color, \"opacity\": self.opacity,},\n }","def to_json(self):\n json = {\n \"url\": url_for(\n \"api.get_week\",\n year=self.year,\n week=self.week,\n ),\n \"year\": self.year,\n \"week\": self.week,\n \"text\": self.text or \"\",\n \"tags\": [tag.text for tag in self.tags],\n }\n return json","def get(self):\r\n #\"SELECT * FROM DBEvent\"\r\n self.insertContent(\"<hr> Грядущие события:<br>\")\r\n event = self.event #db.GqlQuery(self.query) \r\n eventlist=''\r\n #self.checkSession(self.request.headers.get('Cookie'), False)\r\n found_events = False\r\n \r\n ec = DBEventCat()\r\n cats = ec.get_categories()\r\n \r\n for this_event in event:\r\n try:\r\n if not found_events: found_events = True\r\n if self.Session['access'] >= this_event.access or int(self.Session['userid']) == int(this_event.userid) or this_event.access <= 0:\r\n eventlist += '<a href=\"/event/'+str(this_event.key().id())+'\">'+this_event.name.encode(\"utf8\")+'</a>'\r\n users = db.GqlQuery(\"SELECT * FROM DBEventAnketa WHERE eventid = :eventid\",\r\n eventid = this_event.key().id())\r\n if self.Session['access'] >= 8 or int(self.Session['userid']) == int(this_event.userid): \r\n eventlist += ' [ <i><a href=\"/event/info/' + str(this_event.key().id()) + '\">Участников зарегистрировано: ' + str(users.count()) + '</i></a> ]<br>'\r\n elif self.Session['access'] >= this_event.access:\r\n eventlist += ' [ <i>Участников зарегистрировано: ' + str(users.count()) + '</i> ]<br>'\r\n except: continue\r\n if found_events:\r\n self.insertTemplate('tpl_event_add.html', { 'eventlist': eventlist, 'cats' : cats })\r\n else:\r\n self.insertContent(\" Пока мероприятий не запланировано!\")\r\n self.insertContent(\"<hr> Недавно прошедшие события:<br>\")\r\n \r\n eventlist = ''\r\n events = db.GqlQuery(\"SELECT * FROM DBEvent where date<:today order by date desc limit 10\", today = db.datetime.date.today())\r\n for this_event in events:\r\n if self.Session['access'] >= this_event.access or int(self.Session['userid']) == int(this_event.userid):\r\n eventlist += '<a href=\"/event/'+str(this_event.key().id())+'\">'+this_event.name.encode(\"utf8\")+'</a>'\r\n users = db.GqlQuery(\"SELECT * FROM DBEventAnketa WHERE eventid = :eventid\",\r\n eventid = this_event.key().id())\r\n if self.Session['access'] >= 8 or int(self.Session['userid']) == int(this_event.userid): \r\n eventlist += ' [ <i><a href=\"/event/info/' + str(this_event.key().id()) + '\">Участников зарегестрировано: ' + str(users.count()) + '</i></a> ]<br>'\r\n elif self.Session['access'] >= this_event.access:\r\n eventlist += ' [ <i>Участников зарегистрировано: ' + str(users.count()) + '</i> ]<br>'\r\n self.insertTemplate('tpl_event_add.html', { 'eventlist': eventlist })\r\n\r\n \r\n #self.drawPage()\r","def serialized_data(self):\n return {\n 'id': self.id,\n 'start_time': str(self.start_time),\n 'venue_id': self.venue_id,\n 'venue_name': self.venue.name,\n 'venue_image_link': self.venue.image_link,\n 'artist_id': self.artist_id,\n 'artist_name': self.artist.name,\n 'artist_image_link': self.artist.image_link\n }","def get_user_events_json_list(user_events):\n events = []\n for user_event in user_events:\n events.append(user_event.json())\n return events","def get_chart_one(request):\r\n json_str = []\r\n \r\n usuarios = Usuario.objects.all()\r\n for usuario in usuarios:\r\n peticiones = Peticion.objects.filter(usuario=usuario)\r\n json_str.append({ \r\n 'name': u'%s %s' % (usuario.persona.nombre,\r\n usuario.persona.apellidos),\r\n 'data': len(peticiones)\r\n }) \r\n json_obj = json.dumps(json_str, sort_keys=True, indent=4)\r\n response = HttpResponse(json_obj, mimetype=\"application/json\") \r\n return response","def send_event():\n range = request.args.get('range', '60')\n time = arrow.utcnow().replace(minutes=-int(range))\n data = Event.query.filter(Event.timestamp > time).order_by(Event.timestamp.desc()).all()\n return jsonify(results=[i.serialize for i in data])","def index(self):\r\n shows = Shows.query.order_by(asc(Shows.ShowID)).all()\r\n content = jsonify({\r\n \"shows\": [{\r\n \"date\": get_iso_format(show.ShowDate),\r\n \"countryCode\": show.CountryCode,\r\n \"country\": show.Country,\r\n \"city\": show.City,\r\n \"venue\": show.Venue,\r\n \"setlist\": self.get_setlist(show.ShowID),\r\n \"otherBands\": self.get_other_bands(show.ShowID),\r\n \"people\": self.get_show_people(show.ShowID),\r\n } for show in shows]\r\n })\r\n\r\n return make_response(content, 200)","def json_response(self, request, *args, **kwargs):\n\n return HttpResponse(self.construct_json(),\n content_type='application/json',\n mimetype='application/json', status=self.status)","def events(self):\n self.add_events(Event.objects.filter(event_end__gt=timezone.now()).order_by('event_start'))\n self.filename = 'events'","def events(self):\n self.add_events(Event.objects.filter(event_end__gt=timezone.now()).order_by('event_start'))\n self.filename = 'events'","def calendar_view(request, calendar_id):\n calendar_obj = Calendar.objects.get(pk=calendar_id)\n try:\n appointments = Appointment.objects.all().filter(calendar=calendar_obj)\n appointments = jsonify(appointments)\n except:\n appointments = []\n calendar_obj = calendar_obj.serialize()\n calendar_obj[\"non_working_days\"] = [day for day in [0, 1, 2, 3, 4, 5, 6] if day not in calendar_obj[\"working_days\"]]\n return render(request, 'calendar_view.html', {'calendar_obj': calendar_obj, 'appointments': appointments})","def view_meetups():\n\n return make_response(jsonify({\n \"status\": 200,\n \"data\": meetups.view_meetups()\n })), 200","def tags_JSON(request):\n tags_as_json = serializers.serialize('json', Tag.objects.all())\n return HttpResponse(json.dumps(tags_as_json), content_type='json')"],"string":"[\n \"def view_events():\\n result = get_events_helper(Event)\\n return jsonify(result[0]), result[1]\",\n \"def get():\\n return jsonify({'events': 'Events API'}), 200\",\n \"def get_event():\\n json_data = request.args or {}\\n return make_response(jsonify({ \\\"data\\\" : Event.get_events(json_data)}))\",\n \"def to_json(self):\\n self._load_all_events()\\n return json.dumps(\\n [x.to_dict() for events in self._events.values() for x in events], indent=2\\n )\",\n \"def show_events_list():\\r\\n\\tevents_list = Page.objects.filter(tags='events').order_by('-created')\\r\\n\\treturn {'events_list': events_list}\",\n \"def list(self):\\n return JSONResponse(self.request).data(items=self._get_agenda_items()).dump()\",\n \"def update_events(request):\\n events_data = request.data\\n events_manager.deserialize_event(events_data)\\n # print(events_manager.serialize_events())\\n events_manager.apply()\\n return JsonResponse({'nodes': []})\",\n \"def show_events(request):\\n event_list = Event.objects.order_by('-date')\\n\\n event_form = EventForm()\\n\\n context = {'events': event_list, 'form': event_form}\\n return render(request, 'metro_app/events_view.html', context)\",\n \"def json_news(request):\\n if request.method == 'GET':\\n feed = request.GET['feed']\\n return JsonResponse(\\n {\\n 'news': get_news(feed),\\n }\\n )\",\n \"def json(self):\\n return {\\n 'uri': self.view_uri,\\n 'created': time.strftime('%c', time.gmtime(self.created)),\\n 'created_timestamp': self.created,\\n 'exception_type': str(self.exc_type),\\n 'exception': str(self.exc_value),\\n }\",\n \"def info_event_json(event_id):\\n event = Event.query.filter_by(id=event_id).first_or_404()\\n timeuntil = timesince(event.countdown, until=True)\\n return jsonify(event=event.data, timeuntil=timeuntil)\",\n \"def events_to_json(events):\\n result = {}\\n index = 0\\n for e in events:\\n event = {}\\n event['id'] = e.id\\n event['name'] = e.name\\n event['datetime'] = e.datetime\\n event['fee'] = e.fee\\n event['max_capacity'] = e.max_capacity\\n event['min_capacity'] = e.min_capacity\\n result['event'+str(index)] = event\\n index += 1\\n return result\",\n \"def json_view(self, recursive=False):\\n\\n context = self.context.aq_inner\\n data = self.export(context, recursive=recursive)\\n pretty = json.dumps(data, sort_keys=True, indent=4)\\n self.request.response.setHeader(\\\"Content-type\\\", \\\"application/json\\\")\\n return pretty\",\n \"def list(self, request):\\n user = request.auth.user\\n events = Event.objects.order_by('datetime')\\n search_text = self.request.query_params.get('q', None)\\n if search_text is not None:\\n events = events.filter(\\n Q(cost__icontains=search_text)\\n )\\n search_text = self.request.query_params.get('date', None)\\n if search_text is not None:\\n events = events.filter(\\n Q(datetime__icontains=search_text)\\n )\\n for event in events:\\n event.bookmarked = None\\n try:\\n Bookmark.objects.get(event=event, user=user)\\n event.bookmarked = True\\n except Bookmark.DoesNotExist:\\n event.bookmarked = False\\n # game = self.request.query_params.get('gameId', None)\\n # if game is not None:\\n # events = events.filter(game__id=game)\\n serializer = EventSerializer(\\n events, many=True, context={'request': request})\\n return Response(serializer.data)\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'name' : self.name,\\n 'event_url' : self.event_url,\\n 'event_thumbnail_url' : self.event_thumbnail_url,\\n 'description' : self.description,\\n 'ticket_price' : self.ticket_price,\\n 'start_date' : str(self.start_date),\\n 'featued' : self.featured\\n }\",\n \"def get_events_json(self, query_string, **kwargs):\\n\\n response = self._search_events(query_string, output_format=\\\"json\\\", **kwargs)\\n\\n return response.text\",\n \"def list(request, template='events/list.html'):\\n return render(request, template, {\\n 'events': Event.objects.get_upcoming().order_by('start_date'),\\n })\",\n \"def events(self) -> [redirect, HTMLBody]:\\n\\t\\t# Get all events and split into 2 groups\\n\\t\\teventsl, eventsr = prepare_events(get_events())\\n\\t\\treturn render_template(\\\"events.jinja2\\\", eventsl=eventsl, eventsr=eventsr)\",\n \"def myevents(self, request, pk=None):\\n user = request.auth.user\\n myevents = user.events\\n serializer = EventSerializer(\\n myevents, many=True, context={'request': request})\\n return Response(serializer.data)\",\n \"def userevent_list(request):\\n if request.method == 'GET':\\n with sqlite3.connect(Connection.db_path) as conn:\\n conn.row_factory = sqlite3.Row\\n db_cursor = conn.cursor()\\n\\n db_cursor.execute(\\\"\\\"\\\"\\n SELECT\\n full_name,\\n user_id,\\n id,\\n title,\\n description,\\n date,\\n time,\\n name\\n FROM\\n EVENTS_BY_USER\\n \\\"\\\"\\\")\\n\\n dataset = db_cursor.fetchall()\\n\\n # Take the flat data from the database, and build the\\n # following data structure for each gamer.\\n #\\n # {\\n # 1: {\\n # \\\"id\\\": 1,\\n # \\\"full_name\\\": \\\"Admina Straytor\\\",\\n # \\\"games\\\": [\\n # {\\n # \\\"id\\\": 1,\\n # \\\"title\\\": \\\"Foo\\\",\\n # \\\"maker\\\": \\\"Bar Games\\\",\\n # \\\"skill_level\\\": 3,\\n # \\\"number_of_players\\\": 4,\\n # \\\"game_type_id\\\": 2\\n # }\\n # ]\\n # }\\n # }\\n\\n events_by_user = {}\\n\\n for row in dataset:\\n uid = row['user_id']\\n if uid in events_by_user:\\n events_by_user[uid]['events'].append({\\n \\\"id\\\": row['id'],\\n \\\"title\\\": row['title'],\\n \\\"description\\\": row['description'],\\n \\\"date\\\": row['date'],\\n \\\"time\\\": row['time'],\\n \\\"game_name\\\": row[\\\"name\\\"]\\n })\\n else:\\n events_by_user[uid] = {\\n \\\"gamer_id\\\": uid,\\n \\\"full_name\\\": row['full_name'],\\n \\\"events\\\": [{\\n \\\"id\\\": row['id'],\\n \\\"title\\\": row['title'],\\n \\\"description\\\": row['description'],\\n \\\"date\\\": row['date'],\\n \\\"time\\\": row['time'],\\n \\\"game_name\\\": row[\\\"name\\\"]\\n }]\\n }\\n\\n events = events_by_user.values() \\n\\n template = 'users/list_with_events.html'\\n context = {\\n 'userevent_list': events\\n }\\n\\n return render(request, template, context)\",\n \"def list_all_events(as_format='json'):\\n eventlist = []\\n for event in Event.query \\\\\\n .filter_by(is_hidden=False, lock_resources=False) \\\\\\n .order_by(Event.starts_at.desc()).all():\\n eventlist.append(event.data)\\n if as_format == 'json':\\n return jsonify(events=eventlist)\\n headers = {'Content-Disposition': 'attachment; filename=events.csv'}\\n csvlist = gen_csv(eventlist)\\n return Response(stream_with_context(csvlist),\\n mimetype='text/csv',\\n headers=headers)\",\n \"def json_frapp(request):\\n from pv.settings import MEDIA_URL\\n\\n if request.GET.get('date') == None:\\n start = datetime.combine(date.today(), time(0, 0))\\n else:\\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\\n\\n end = datetime.combine(start, time(23, 59))\\n\\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\\n\\n\\n '''Generate categories object for output'''\\n\\n categories = Category.objects.all()\\n categories_output = []\\n\\n for c in categories:\\n c_entry = {\\n 'id': c.id,\\n 'color': c.color.replace('#', '').upper(),\\n 'namedisplay': c.category,\\n 'description': c.description\\n }\\n\\n categories_output.append(c_entry)\\n\\n # Get all series for timeslots\\n series = set()\\n for ts in timeslots:\\n series.add(ts.show)\\n\\n\\n '''Generate series object for output'''\\n\\n series_output = []\\n\\n for s in series:\\n metainfos = []\\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\\n\\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\\n url = '' if s.website == None or s.website == '' else s.website\\n\\n # Get active schedules for the given date\\n # But include upcoming single timeslots (with rrule_id=1)\\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n broadcastinfos = ''\\n\\n if not schedules.exists():\\n continue\\n\\n for schedule in schedules:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n if schedules_repetition.exists():\\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\\n for schedule in schedules_repetition:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n s_entry = {\\n 'id': s.id,\\n 'categoryid': s.category.values_list('id', flat=True)[0],\\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\\n 'namedisplay': s.name,\\n 'description': s.description,\\n 'url': url,\\n 'image': image,\\n 'broadcastinfos': broadcastinfos,\\n 'metainfos': metainfos\\n }\\n\\n series_output.append(s_entry)\\n\\n\\n '''Generate shows object for output'''\\n\\n shows_output = []\\n\\n for ts in timeslots:\\n\\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\\n namedisplay = ts.show.name + is_repetition\\n description = ts.show.description\\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\\n urlmp3 = ''\\n\\n # If there's a note to the timeslot use its title, description and url\\n try:\\n note = Note.objects.get(timeslot=ts.id)\\n namedisplay = note.title + is_repetition\\n description = note.content\\n url = str(get_current_site(request)) + '/notes/' + note.slug\\n urlmp3 = note.audio_url\\n except ObjectDoesNotExist:\\n pass\\n\\n ts_entry = {\\n 'id': ts.id,\\n 'seriesid': ts.show.id,\\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\\n 'namedisplay': namedisplay,\\n 'description': description,\\n 'url': url,\\n 'urlmp3': urlmp3,\\n }\\n\\n shows_output.append(ts_entry)\\n\\n output = {}\\n output['categories'] = categories_output\\n output['series'] = series_output\\n output['shows'] = shows_output\\n\\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\\n content_type=\\\"application/json; charset=utf-8\\\")\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n #had to change to 'title' for full calendar, might change\\n 'title' : self.name,\\n 'host' : self.created_by,\\n 'start' : self.start_on.isoformat(),\\n 'end' : self.end_on.isoformat(),\\n 'description' : self.description,\\n 'color' : 'blue',\\n }\",\n \"def event_activity_json(event_id):\\n limit = request.args.get('limit') or 50\\n q = request.args.get('q') or None\\n if q and len(q) < 3:\\n q = None\\n return jsonify(activities=get_event_activities(event_id, limit, q))\",\n \"def data_json(request):\\n json_data = []\\n for resource in Resource.objects.all():\\n record = {} \\n record['title'] = resource.name\\n record['description'] = resource.description\\n record['keyword'] = resource.csw_keywords.split(',')\\n record['modified'] = resource.last_updated\\n record['publisher'] = resource.organization\\n record['contactPoint'] = resource.metadata_contact\\n record['mbox'] = resource.contact_email\\n record['identifier'] = resource.csw_identifier\\n if resource.is_published:\\n record['accessLevel'] = 'public'\\n else:\\n record['accessLevel'] = 'non-public'\\n\\n json_data.append(record)\\n\\n return HttpResponse(json.dumps(json_data), 'application/json')\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(self.get_data(context))\",\n \"def apigw_event():\\n with open(\\\"events/event.json\\\") as json_file:\\n return json.load(json_file)\",\n \"def info_current_event_json():\\n event = Event.query.filter_by(is_current=True).first() or \\\\\\n Event.query.order_by(Event.id.desc()).first_or_404()\\n timeuntil = timesince(event.countdown, until=True)\\n return jsonify(event=event.data, timeuntil=timeuntil)\",\n \"def home_feeds(request):\\n result = {}\\n \\n result['feeds'] = []\\n\\n u = request.user\\n\\n\\n # get other people's feeds, filter by friends if in social group\\n feeds = Feed.objects.exclude(actor=u).order_by('-timestamp')\\n result['feeds'] = [ f.get_json(me=u, android=True) for f in feeds ]\\n\\n return JSONHttpResponse(result)\",\n \"def events_info(request):\\n \\n global input\\n \\n if request == 'event-based':\\n client_neries = Client_neries()\\n \\n events = client_neries.getEvents(min_datetime=input['min_date'], \\\\\\n max_datetime=input['max_date'], min_magnitude=input['min_mag'], \\\\\\n max_magnitude=input['max_mag'], min_latitude=input['evlatmin'], \\\\\\n max_latitude=input['evlatmax'], min_longitude=input['evlonmin'], \\\\\\n max_longitude=input['evlonmax'], min_depth = input['min_depth'], \\\\\\n max_depth=input['max_depth'], max_results=input['max_result'])\\n \\n for i in range(0, len(events)):\\n events[i]['t1'] = events[i]['datetime'] - input['preset']\\n events[i]['t2'] = events[i]['datetime'] + input['offset']\\n \\n elif request == 'continuous':\\n m_date = UTCDateTime(input['min_date'])\\n M_date = UTCDateTime(input['max_date'])\\n \\n t_cont = M_date - m_date\\n \\n events = []\\n \\n if t_cont > input['interval']:\\n num_div = int(t_cont/input['interval'])\\n t_res = t_cont - num_div*input['interval']\\n \\n for i in range(0, num_div):\\n events.append({'author': 'NAN', 'event_id': 'continuous' + str(i), \\\\\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\\\\n 'datetime': m_date + i*input['interval'], \\\\\\n 't1': m_date + i*input['interval'],\\\\\\n 't2': m_date + (i+1)*input['interval'] + 60.0,\\\\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\\\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\\\\n 'flynn_region': 'NAN'})\\n \\n events.append({'author': 'NAN', 'event_id': 'continuous' + str(i+1), \\\\\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\\\\n 'datetime': m_date + (i+1)*input['interval'], \\\\\\n 't1': m_date + (i+1)*input['interval'],\\\\\\n 't2': M_date,\\\\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\\\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\\\\n 'flynn_region': 'NAN'})\\n else:\\n events.append({'author': 'NAN', 'event_id': 'continuous0', \\\\\\n 'origin_id': -12345.0, 'longitude': -12345.0, \\\\\\n 'datetime': m_date, \\\\\\n 't1': m_date,\\\\\\n 't2': M_date,\\\\\\n 'depth': -12345.0, 'magnitude': -12345.0, \\\\\\n 'magnitude_type': 'NAN', 'latitude': -12345.0, \\\\\\n 'flynn_region': 'NAN'})\\n\\n return events\",\n \"def render_json(object):\\r\\n return HttpResponse(jsonify(object), content_type='application/json')\",\n \"def list(self, request):\\n return HttpResponse(request.user.series_list, content_type=\\\"application/json\\\")\",\n \"def index():\\n\\n if request.method == 'POST':\\n app.logger.info('Event: ' + str(request.get_json()))\\n process_event(request.get_json())\\n return ''\\n else:\\n return render_template('index.html')\",\n \"def _serialize_event_data_as_json(event_data):\\n return json.dumps(event_data)\",\n \"def show_events():\\n try:\\n return render_template(\\n 'events.html',\\n events=get_events(),\\n auth=is_organizer(get_user()),\\n app_config=app.config\\n )\\n except RuntimeError as error:\\n return str(error), 500\",\n \"def as_json(self):\",\n \"def events(self):\\r\\n return resources.Events(self)\",\n \"def vaccine_data_chart(request):\\n\\n print(request)\\n labels = []\\n data = []\\n\\n queryset = VaccineData.objects.filter(country=\\\"Australia\\\").order_by(\\\"date\\\")\\n for entry in queryset:\\n labels.append(entry.date)\\n data.append(entry.doses_administered)\\n\\n return JsonResponse(\\n data={\\n \\\"country\\\": \\\"Australia Vaccine Doses Administered\\\",\\n \\\"labels\\\": labels,\\n \\\"data\\\": data,\\n }\\n )\",\n \"def EventToJSON(_object):\\n return json.dumps(_object, default=jsonDefault)\",\n \"def get_all_feeds(request):\\n feed = Feeds.objects.all()\\n\\n\\n\\n\\n serializer = FeedsSerializer(feed,many=True)\\n return JsonResponse({\\\"FeedList\\\":serializer.data,\\\"ResponseCode\\\": \\\"200\\\",\\\"ResponseMessage\\\":\\\"Successfully\\\"}, safe=False)\",\n \"def get_events():\\n url = app.config['EVENTS_ENDPOINT']\\n response = requests.get(url, params={})\\n if response.status_code == 200:\\n return parse_events(response.json())\\n raise RuntimeError('Error in retrieving events.')\",\n \"def get_feed():\\n return jsonify(dict({\\n \\\"result\\\": mongo.get_hpfeed(),\\n \\\"code\\\": 200\\n }))\",\n \"def render_get(self, request):\\n\\n thing_event = get_thing_event(self._server, request)\\n last_item = self._last_events.get(self._event_key(thing_event), None)\\n payload = json.dumps(last_item).encode(\\\"utf-8\\\") if last_item else b\\\"\\\"\\n response = aiocoap.Message(code=aiocoap.Code.CONTENT, payload=payload)\\n response.opt.content_format = JSON_CONTENT_FORMAT\\n\\n raise tornado.gen.Return(response)\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(self.get_data(**context), **response_kwargs)\",\n \"def data_json(self, extra_context=None, publish=False):\\n if not self.project.CREATE_JSON:\\n # nothing to see here, but the right mimetype\\n return jsonify()\\n\\n if not self.data:\\n # this sets site.data by spreadsheet or gdoc\\n self.get_context(publish)\\n\\n return jsonify(self.data)\",\n \"def event_activity_current_json():\\n event = Event.query.filter_by(is_current=True).first()\\n if not event:\\n return jsonify(activities=[])\\n return event_activity_json(event.id)\",\n \"def dumps(self):\\n evn_data = {\\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n \\\"code\\\": [line.strip() for line in self.source_code]}\\n\\n return {\\\"event\\\": evn_data}\",\n \"def to_json(self):\\n\\n columns = list()\\n points = list()\\n\\n if self._collection.type() == Event:\\n columns += ['time']\\n elif self._collection.type() == TimeRangeEvent:\\n columns += ['timerange']\\n elif self._collection.type() == IndexedEvent:\\n columns += ['index']\\n\\n columns += self.columns()\\n\\n for i in self._collection.events():\\n points.append(i.to_point(columns[1:]))\\n\\n cols_and_points = dict(\\n columns=columns,\\n points=points,\\n )\\n\\n # fold in the rest of the payload\\n cols_and_points.update(self._data)\\n\\n # Turn the index back into a string for the json representation.\\n # The Index object can still be accessed via TimeSeries.index()\\n if 'index' in cols_and_points and \\\\\\n isinstance(cols_and_points.get('index'), Index):\\n cols_and_points['index'] = cols_and_points.get('index').to_string()\\n\\n return cols_and_points\",\n \"def to_json(self):\\n return json.dumps({\\\"data\\\": self._data.tolist(),\\n \\\"header\\\": self._header.tolist(),\\n \\\"dates\\\": self._dates.tolist()})\",\n \"def feed_entries(self):\\n date_format = \\\"%Y-%m-%dT%H:%M:%SZ\\\"\\n entries = self.mapper.list_entries(limit=10)\\n if entries:\\n updated = max([e.updated for e in entries]).strftime(date_format)\\n else:\\n updated = datetime.utcnow().strftime(date_format)\\n return {\\\"entries\\\": entries, \\\"updated\\\": updated}\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(\\n self.get_data(context),\\n **response_kwargs\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(\\n self.get_data(context),\\n **response_kwargs\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(\\n self.get_data(context),\\n **response_kwargs\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(\\n self.get_data(context),\\n **response_kwargs\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(\\n self.get_data(context),\\n **response_kwargs\\n )\",\n \"def json(data):\\n if isinstance(data, dict):\\n data = ujson.encode(data)\\n uid = str(uuid.uuid4())\\n display(HTML('<div id=\\\"{0}\\\" style=\\\"height: 600px; width:100%;\\\"></div>'.format(uid)))\\n display(Javascript(\\\"\\\"\\\"\\n require([\\\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\\\"], function() {\\n document.getElementById('%s').appendChild(renderjson(%s))\\n });\\n \\\"\\\"\\\" % (uid, data)))\",\n \"def export_event(self):\\n\\n cal = Eve()\\n cal.add('summary', str(self.categories))\\n cal.add('description', self.label)\\n cal.add('dtstart', vDatetime(self.start))\\n cal.add('dtend', vDatetime(self.end))\\n return cal.to_ical()\",\n \"def __json__(self, request=None):\\n # start = self.start.isoformat() if self.start else None\\n # end = self.end.isoformat() if self.end else None\\n return dict(\\n timeref_type=\\\"daterange\\\",\\n interval=self.interval,\\n start=self.start.isoformat(),\\n end=self.end.isoformat(),\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return HttpResponse(\\n self.convert_context_to_json(context),\\n content_type='application/json',\\n **response_kwargs\\n )\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return HttpResponse(\\n self.convert_context_to_json(context),\\n content_type='application/json',\\n **response_kwargs\\n )\",\n \"def to_json(self):\\n\\t\\tevents = [{\\\"name\\\": \\\"Killstreak\\\", \\\"value\\\": v, \\\"tick\\\": t} for v, t, _ in self.killstreaks]\\n\\t\\tevents += [{\\\"name\\\": \\\"Bookmark\\\", \\\"value\\\": v, \\\"tick\\\": t} for v, t, _ in self.bookmarks]\\n\\t\\treturn {\\\"events\\\": sorted(events, key = lambda e: int(e[\\\"tick\\\"]))}\",\n \"def to_json_dict(self):\\n json_dict = {}\\n json_dict['event_status'] = self.event_status\\n json_dict['event_status_list'] = self.event_status_list\\n json_dict['spaces'] = [s.get_id() for s in self.spaces]\\n json_dict['characters'] = [c.get_id() for c in self.characters]\\n json_dict['exits'] = [e.get_id() for e in self.exits]\\n json_dict['items'] = [i.get_id() for i in self.items]\\n return json_dict\",\n \"def view_event_list(request, **kwargs):\\n #lu = get_common_lookup(request)\\n lu = { 'page_title' : 'MCB Event Tweets'\\\\\\n , 'IS_TWEET_EVENT_PAGE' : True\\n , 'TWEET_SUCCESS' : kwargs.get('success_msg', False)\\n }\\n \\n if not request.user.is_authenticated():\\n return HttpResponse('not logged in')\\n \\n if not is_user_in_group(request, TWEET_GROUP_NAME):\\n return HttpResponse('not in tweet group')\\n \\n upcoming_events = MCBTweetEvent.get_events_awaiting_approval()\\n \\n lu.update({ 'upcoming_events' : upcoming_events\\\\\\n #, 'my_checked_codes' : get_previously_checked_expense_codes(request)\\\\\\n })\\n #\\n return render_to_response('tweet/events/event_list.html', lu, context_instance=RequestContext(request))\",\n \"def get_event():\\n\\t#Get HTTP query args.\\n\\tno = request.args.get('no')\\n\\tfilename = request.args.get('filename')\\n\\tcollection = mongo.db[filename]\\n\\n\\tjsonEncoder = hepmcio_json.HepMCJSONEncoder()\\n\\thepMCDecoder = hepmcio_json.HepMCJSONDecoder()\\n\\tjsonDecoder = json.JSONDecoder()\\n\\t#Everything below same as in the Visualiser view.\\n\\tevent = collection.find_one({\\\"type\\\":\\\"event\\\", \\\"no\\\":int(no)}, {\\\"_id\\\":False})\\n\\tparticleJson = collection.find({\\\"type\\\":\\\"particle\\\", \\\"event\\\":event[\\\"barcode\\\"]}, {\\\"_id\\\":False})\\n\\tparticles = []\\n\\tfor particle in particleJson:\\n\\t\\tparticles.append(jsonEncoder.encode(particle))\\n\\tvertices = []\\n\\tvertexJson = collection.find({\\\"type\\\":\\\"vertex\\\", \\\"event\\\":event[\\\"barcode\\\"]}, {\\\"_id\\\":False})\\n\\tfor vertex in vertexJson:\\n\\t\\tvertices.append(jsonEncoder.encode(vertex))\\n\\tevent = jsonEncoder.encode(event)\\n\\n\\teventObject = hepmcio_json.EventJSONObject(event, particles, vertices)\\n\\t\\n\\tdecodedEvent = hepMCDecoder.decode(eventObject)\\n\\n\\tPT_CUTOFF = 0.0\\n\\tintParticles = [particle for particle in decodedEvent.particles.values() if particle.status!=1 and \\\\\\n\\t\\tparticle.mom[0]**2 + particle.mom[1]**2 > PT_CUTOFF**2]\\n\\t\\n\\tintParticleAncestors = reduce(operator.concat, [hepmcio.get_ancestors(particle)[:-1] for particle in intParticles])\\n\\n\\tparticles = []\\n\\tfor particle in (intParticles + intParticleAncestors):\\n\\t\\tparticles.append(jsonDecoder.decode(jsonEncoder.encode(particle)))\\n\\t\\n\\tvertices = list(map(jsonDecoder.decode, vertices))\\n\\t\\n\\treturn {\\\"particles\\\":jsonEncoder.encode(particles), \\\"vertices\\\":jsonEncoder.encode(vertices)}\",\n \"def get(self, request, *arg, **kwargs):\\r\\n response = {\\r\\n 'status' : False,\\r\\n 'response' : {},\\r\\n 'error' : {\\r\\n 'internal' : None,\\r\\n 'external' : None\\r\\n }\\r\\n }\\r\\n\\r\\n try:\\r\\n #list comprehencion querying a low values table for names\\r\\n events = [event.name for event in EventType.objects.filter(is_active=True)]\\r\\n\\r\\n events = self.event_processor(_events=events)\\r\\n\\r\\n response['status'] = True\\r\\n response['response'] = events\\r\\n\\r\\n\\r\\n except Exception as GetException:\\r\\n response['error']['internal'] = GetException.__str__()\\r\\n response['error']['external'] = \\\"Something went wrong\\\"\\r\\n \\r\\n return JsonResponse(response)\",\n \"def to_json(self):\\n return {\\n \\\"item_name\\\": self.item_name,\\n \\\"summary\\\": self.summary,\\n \\\"content\\\": self.content,\\n \\\"date_published\\\": self.date_published,\\n \\\"item_slug\\\": self.item_slug,\\n \\\"category_name\\\": self.category_name,\\n }\",\n \"def render(self, data):\\n logging.info(\\\"render (start)\\\")\\n\\n seria = json.dumps(data, ensure_ascii=False, indent=4)\\n logging.info(\\\"rendered %s characters (end)\\\" % len(seria))\\n return seria\",\n \"def json(self):\\n return {'id': self.id, 'name': self.name, 'description': self.description}\",\n \"def render_json(self, obj):\\n self.response.content_type = \\\"application/json\\\"\\n self.response.out.write(json.encode(obj))\",\n \"def all_bugs_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Bug.objects.values('title', 'id').order_by('-created').exclude(status='Resolved').annotate(\\n bug_votes=Count('votes'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['bug_votes'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def events(self):\\n return get_tsv(self.path, self.values, 'events.tsv')\",\n \"def json_format(data):\\n return {\\n 'Title': data[\\\"title\\\"],\\n 'Publication date': data['pubDate'],\\n 'News link': data['link'],\\n 'Image link': data['media'],\\n }\",\n \"def all_features_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Feature.objects.values('title').order_by('-created').exclude(status='Implemented').annotate(\\n feature_purchases=Sum('purchases'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['feature_purchases'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def getchart(request):\\n data = ast.literal_eval(request.body.decode('utf-8'))\\n data['user'] = request.user\\n data = newchart(data)\\n return JsonResponse(data)\",\n \"def get_context_data(self, **kwargs):\\n context = super(HomeView, self).get_context_data(**kwargs)\\n context['events'] = Events.objects.filter(published=True).all()\\n return context\",\n \"def covid_data_chart(request):\\n\\n print(request)\\n labels = []\\n data = []\\n\\n queryset = CovidData.objects.filter(country=\\\"Australia\\\").order_by(\\\"date\\\")\\n for entry in queryset:\\n labels.append(entry.date)\\n data.append(entry.confirmed)\\n\\n return JsonResponse(\\n data={\\n \\\"country\\\": \\\"Australia Covid Cases\\\",\\n \\\"labels\\\": labels,\\n \\\"data\\\": data,\\n }\\n )\",\n \"def get_events(self):\\n\\n url = '/v2.4/'+self.page_id+'/events'\\n data = self.graph.request(url)\\n\\n while 'next' in data['paging'].keys():\\n print data['paging']['next']\\n data = self.graph.request(url, args={\\n 'limit' : 100,\\n 'after' : data['paging']['cursors']['after']\\n })\\n\\n return data\",\n \"def json(self):\\n return {\\n 'author': self.author,\\n 'email': self.email,\\n 'display_email': self.display_email,\\n 'title': self.title,\\n 'trailer_path': self.trailer_path,\\n 'date': self.date,\\n 'link': self.link,\\n '_id': self._id\\n }\",\n \"def format_events(self):\\n\\n log.debug('formatting events ({}) for \\\"{}\\\"'.format(len(self.unformatted_events), self.school_year))\\n for event in self.unformatted_events:\\n name = event.module.get_text().replace('\\\\n', '') if event.module else 'Matière non définie'\\n category = event.category.get_text() if event.category else 'Catégorie de cours non définie'\\n starttime = event.starttime.get_text() if event.starttime else 'Heure de début de cours non définie'\\n endtime = event.endtime.get_text() if event.endtime else 'Heure de début de cours non définie'\\n room = event.room.item.get_text() if event.room and event.room.item else 'Aucune salle définie'\\n group = event.group.item.get_text() if event.group and event.group.item else 'Classe entière'\\n nday = int(event.day.get_text()) if event.day else None\\n date_first_weekday = self.week_dates_mapping[event.rawweeks.get_text()] if event.rawweeks else None\\n start = '{}-{}'.format(date_first_weekday, starttime)\\n end = '{}-{}'.format(date_first_weekday, endtime)\\n dtstart = datetime.datetime.strptime(start, '%d/%m/%Y-%H:%M') + datetime.timedelta(days=nday, hours=-2)\\n dtend = datetime.datetime.strptime(end, '%d/%m/%Y-%H:%M') + datetime.timedelta(days=nday, hours=-2)\\n\\n start_date = dtstart.isoformat() + 'Z'\\n end_date = dtend.isoformat() + 'Z'\\n\\n calendar_event = GoogleCalendarEvent(\\n location=room,\\n summary='({}) - {} - {}'.format(category, name, group),\\n description=group,\\n dtstart=start_date,\\n dtend=end_date\\n )\\n self.formatted_events.append(calendar_event.json)\",\n \"def getLineData(self):\\n # Each view must have exactly one DateRange object\\n date_range = DateRange.objects.filter(foreign_key=self.id).first()\\n assert(date_range is not None)\\n\\n begin, end = date_range.getBeginEnd()\\n return {\\n \\\"begin\\\" : begin.strftime(\\\"%Y%m%d\\\"),\\n \\\"end\\\" : end.strftime(\\\"%Y%m%d\\\"),\\n \\\"data_sets\\\" : [\\n {\\n \\\"label\\\" : filter_set.label,\\n \\\"color\\\" : filter_set.color,\\n \\\"data\\\" : [\\n {\\n \\\"cnt\\\": row[\\\"cnt\\\"],\\n \\\"date\\\": row[\\\"date\\\"].strftime(\\\"%Y%m%d\\\")\\n } for row in filter_set.getMessageCountPerDay()\\n ],\\n } for filter_set in self.filterset_set.all()\\n ]\\n }\",\n \"def listings(request, category1, category2, category3, page = 1):\\n \\n # Creating URL for request\\n base_url = \\\"https://www.eventbriteapi.com/v3/events/search/\\\"\\n token_component = \\\"token=BKKRDKVUVRC5WG4HAVLT\\\" #I had this token in my mail link\\n category_component = \\\"categories=\\\" + category1 + ',' + category2 + ',' + category3\\n page_component = \\\"page=\\\" + str(page)\\n url_without_page = base_url + \\\"?\\\" + token_component + \\\"&\\\" + category_component\\n url_complete = url_without_page + \\\"&\\\" + page_component\\n \\n # GET events from Eventbrite\\n f = urllib2.urlopen(url_complete) \\n json_string = f.read() \\n parsed_json = json.loads(json_string) \\n\\n # Parse through JSON\\n events = parsed_json['events']\\n eventsList = []\\n \\n for i in events:\\n eventsList.append(event_container())\\n \\n # Parse further through JSON\\n eventsList[-1].name = i['name']['text']\\n eventsList[-1].id = i['id']\\n eventsList[-1].url = i['url']\\n try:\\n eventsList[-1].description = i['description']['text']\\n except:\\n eventsList[-1].description = \\\"No description available\\\"\\n eventsList[-1].resource_uri = i['resource_uri']\\n \\n \\n listings_url_base = '/topthree/listings/'+ category1 + '/' + category2 + '/' + category3 + '/'\\n \\n # Pagination\\n \\n \\\"\\\"\\\"\\n Performing manual pagination instead of Django pagination \\n because GET request for events pulls in paginated data already\\n \\\"\\\"\\\"\\n \\n next_page = int(page) + 1\\n next_page_url = listings_url_base + str(next_page) \\n \\n if int(page)>1:\\n prev_page = int(page) - 1\\n prev_page_url = listings_url_base + str(prev_page) \\n\\n else:\\n prev_page = 0\\n prev_page_url = \\\"#\\\"\\n \\n \\n # Sending values to template\\n \\n template = loader.get_template('listings.html')\\n\\n context = RequestContext(request, {\\n 'eventsList': eventsList,\\n 'prev_page_url':prev_page_url,\\n 'next_page_url':next_page_url,\\n 'prev_page':prev_page,\\n 'page':page,\\n 'category1':category1,\\n 'category2':category2,\\n 'category3':category3,\\n })\\n \\n return HttpResponse(template.render(context))\",\n \"def format(self, *args):\\n\\t\\tweb.header('Content-Type', 'application/json; charset=utf-8')\\n\\t\\treturn json.dumps(self.content)\",\n \"def schedule_content(request):\\r\\n\\r\\n stories = Story.objects.filter(organization=request.user.organization).exclude(archived=True)\\r\\n\\r\\n # data = {}\\r\\n # data['success'] = 1\\r\\n # data['result'] = []\\r\\n data = []\\r\\n\\r\\n for story in stories:\\r\\n # Facet Schedules\\r\\n for facet in story.facetstory.all():\\r\\n credit = {}\\r\\n for user in facet.credit.all():\\r\\n credit['id'] = []\\r\\n credit['id'].append(user.credit_name)\\r\\n credit['id'].append(user.get_absolute_url())\\r\\n editor = {}\\r\\n for user in facet.editor.all():\\r\\n editor['id'] = []\\r\\n editor['id'].append(user.credit_name)\\r\\n editor['id'].append(user.get_absolute_url())\\r\\n print credit\\r\\n if facet.due_edit:\\r\\n edit_event_dict = {}\\r\\n edit_event_dict['id'] = facet.id\\r\\n edit_event_dict['title'] = facet.name.encode('utf-8')\\r\\n edit_event_dict['description'] = facet.description.encode('utf-8')\\r\\n edit_event_dict['due-edit'] = time.mktime(facet.due_edit.timetuple())\\r\\n edit_event_dict['editor'] = facet.editor.credit_name\\r\\n edit_event_dict['credit'] = credit\\r\\n edit_event_dict['url'] = facet.get_absolute_url()\\r\\n edit_event_dict['start'] = time.mktime(facet.due_edit.timetuple()) * 1000\\r\\n edit_event_dict['end'] = (time.mktime(facet.due_edit.timetuple()) * 1000) + 60\\r\\n edit_event_dict['overlap'] = True\\r\\n edit_event_dict['allDay'] = False\\r\\n edit_event_dict['backgroundColor'] = '#00aced'\\r\\n edit_event_dict['textColor'] = '#fff'\\r\\n data.append(edit_event_dict)\\r\\n if facet.run_date:\\r\\n run_event_dict = {}\\r\\n run_event_dict['id'] = facet.id\\r\\n run_event_dict['title'] = facet.name.encode('utf-8')\\r\\n run_event_dict['description'] = facet.description.encode('utf-8')\\r\\n run_event_dict['due-edit'] = time.mktime(facet.due_edit.timetuple())\\r\\n run_event_dict['editor'] = facet.editor.credit_name\\r\\n run_event_dict['credit'] = credit\\r\\n run_event_dict['url'] = facet.get_absolute_url()\\r\\n run_event_dict['class'] = 'event_run'\\r\\n run_event_dict['start'] = time.mktime(facet.run_date.timetuple()) * 1000\\r\\n run_event_dict['end'] = (time.mktime(facet.run_date.timetuple()) * 1000) + 60\\r\\n run_event_dict['overlap'] = True\\r\\n run_event_dict['backgroundColor'] = '#5cb85c'\\r\\n run_event_dict['textColor'] = '#fff'\\r\\n data.append(run_event_dict)\\r\\n\\r\\n # print \\\"DATA: \\\", data\\r\\n\\r\\n return HttpResponse(json.dumps(data), content_type='application/json')\",\n \"def render_to_response(self, context, **response_kwargs):\\n return JsonResponse(context)\",\n \"def showEvents(self, year, month, language, filterIDs=None, negFilterIDs=None):\\n try:\\n returnEvents = self.newsFeedModel.getEvents(year, month, language, filterIDs, negFilterIDs)\\n if language != 'de' and len(returnEvents) == 0:\\n returnEvents = self.newsFeedModel.getEvents(year, month, 'de', filterIDs, negFilterIDs)\\n returnEventsJSON = self.newsFeedView.toJSONEvents(returnEvents, self.newsFeedModel.getEventCategoriesLastChanged())\\n return returnEventsJSON\\n except Exception as e:\\n print(\\\"there was a problem while retrieving events\\\")\\n raise e\",\n \"def get_data(self):\\n return self.data.to_json()\",\n \"def to_json(self, format=None):\\n if format == \\\"widget\\\":\\n return {\\n \\\"source\\\": self.source,\\n \\\"target\\\": self.target,\\n \\\"type\\\": self.type,\\n \\\"time\\\": self.time,\\n \\\"value\\\": self.link_width,\\n \\\"title\\\": self.title,\\n \\\"color\\\": self.color,\\n \\\"opacity\\\": self.opacity,\\n \\\"data\\\": self.data,\\n }\\n else:\\n return {\\n \\\"source\\\": self.source,\\n \\\"target\\\": self.target,\\n \\\"type\\\": self.type,\\n \\\"title\\\": self.title,\\n \\\"time\\\": self.time,\\n \\\"link_width\\\": self.link_width,\\n \\\"data\\\": self.data,\\n \\\"style\\\": {\\\"color\\\": self.color, \\\"opacity\\\": self.opacity,},\\n }\",\n \"def to_json(self):\\n json = {\\n \\\"url\\\": url_for(\\n \\\"api.get_week\\\",\\n year=self.year,\\n week=self.week,\\n ),\\n \\\"year\\\": self.year,\\n \\\"week\\\": self.week,\\n \\\"text\\\": self.text or \\\"\\\",\\n \\\"tags\\\": [tag.text for tag in self.tags],\\n }\\n return json\",\n \"def get(self):\\r\\n #\\\"SELECT * FROM DBEvent\\\"\\r\\n self.insertContent(\\\"<hr> Грядущие события:<br>\\\")\\r\\n event = self.event #db.GqlQuery(self.query) \\r\\n eventlist=''\\r\\n #self.checkSession(self.request.headers.get('Cookie'), False)\\r\\n found_events = False\\r\\n \\r\\n ec = DBEventCat()\\r\\n cats = ec.get_categories()\\r\\n \\r\\n for this_event in event:\\r\\n try:\\r\\n if not found_events: found_events = True\\r\\n if self.Session['access'] >= this_event.access or int(self.Session['userid']) == int(this_event.userid) or this_event.access <= 0:\\r\\n eventlist += '<a href=\\\"/event/'+str(this_event.key().id())+'\\\">'+this_event.name.encode(\\\"utf8\\\")+'</a>'\\r\\n users = db.GqlQuery(\\\"SELECT * FROM DBEventAnketa WHERE eventid = :eventid\\\",\\r\\n eventid = this_event.key().id())\\r\\n if self.Session['access'] >= 8 or int(self.Session['userid']) == int(this_event.userid): \\r\\n eventlist += ' [ <i><a href=\\\"/event/info/' + str(this_event.key().id()) + '\\\">Участников зарегистрировано: ' + str(users.count()) + '</i></a> ]<br>'\\r\\n elif self.Session['access'] >= this_event.access:\\r\\n eventlist += ' [ <i>Участников зарегистрировано: ' + str(users.count()) + '</i> ]<br>'\\r\\n except: continue\\r\\n if found_events:\\r\\n self.insertTemplate('tpl_event_add.html', { 'eventlist': eventlist, 'cats' : cats })\\r\\n else:\\r\\n self.insertContent(\\\" Пока мероприятий не запланировано!\\\")\\r\\n self.insertContent(\\\"<hr> Недавно прошедшие события:<br>\\\")\\r\\n \\r\\n eventlist = ''\\r\\n events = db.GqlQuery(\\\"SELECT * FROM DBEvent where date<:today order by date desc limit 10\\\", today = db.datetime.date.today())\\r\\n for this_event in events:\\r\\n if self.Session['access'] >= this_event.access or int(self.Session['userid']) == int(this_event.userid):\\r\\n eventlist += '<a href=\\\"/event/'+str(this_event.key().id())+'\\\">'+this_event.name.encode(\\\"utf8\\\")+'</a>'\\r\\n users = db.GqlQuery(\\\"SELECT * FROM DBEventAnketa WHERE eventid = :eventid\\\",\\r\\n eventid = this_event.key().id())\\r\\n if self.Session['access'] >= 8 or int(self.Session['userid']) == int(this_event.userid): \\r\\n eventlist += ' [ <i><a href=\\\"/event/info/' + str(this_event.key().id()) + '\\\">Участников зарегестрировано: ' + str(users.count()) + '</i></a> ]<br>'\\r\\n elif self.Session['access'] >= this_event.access:\\r\\n eventlist += ' [ <i>Участников зарегистрировано: ' + str(users.count()) + '</i> ]<br>'\\r\\n self.insertTemplate('tpl_event_add.html', { 'eventlist': eventlist })\\r\\n\\r\\n \\r\\n #self.drawPage()\\r\",\n \"def serialized_data(self):\\n return {\\n 'id': self.id,\\n 'start_time': str(self.start_time),\\n 'venue_id': self.venue_id,\\n 'venue_name': self.venue.name,\\n 'venue_image_link': self.venue.image_link,\\n 'artist_id': self.artist_id,\\n 'artist_name': self.artist.name,\\n 'artist_image_link': self.artist.image_link\\n }\",\n \"def get_user_events_json_list(user_events):\\n events = []\\n for user_event in user_events:\\n events.append(user_event.json())\\n return events\",\n \"def get_chart_one(request):\\r\\n json_str = []\\r\\n \\r\\n usuarios = Usuario.objects.all()\\r\\n for usuario in usuarios:\\r\\n peticiones = Peticion.objects.filter(usuario=usuario)\\r\\n json_str.append({ \\r\\n 'name': u'%s %s' % (usuario.persona.nombre,\\r\\n usuario.persona.apellidos),\\r\\n 'data': len(peticiones)\\r\\n }) \\r\\n json_obj = json.dumps(json_str, sort_keys=True, indent=4)\\r\\n response = HttpResponse(json_obj, mimetype=\\\"application/json\\\") \\r\\n return response\",\n \"def send_event():\\n range = request.args.get('range', '60')\\n time = arrow.utcnow().replace(minutes=-int(range))\\n data = Event.query.filter(Event.timestamp > time).order_by(Event.timestamp.desc()).all()\\n return jsonify(results=[i.serialize for i in data])\",\n \"def index(self):\\r\\n shows = Shows.query.order_by(asc(Shows.ShowID)).all()\\r\\n content = jsonify({\\r\\n \\\"shows\\\": [{\\r\\n \\\"date\\\": get_iso_format(show.ShowDate),\\r\\n \\\"countryCode\\\": show.CountryCode,\\r\\n \\\"country\\\": show.Country,\\r\\n \\\"city\\\": show.City,\\r\\n \\\"venue\\\": show.Venue,\\r\\n \\\"setlist\\\": self.get_setlist(show.ShowID),\\r\\n \\\"otherBands\\\": self.get_other_bands(show.ShowID),\\r\\n \\\"people\\\": self.get_show_people(show.ShowID),\\r\\n } for show in shows]\\r\\n })\\r\\n\\r\\n return make_response(content, 200)\",\n \"def json_response(self, request, *args, **kwargs):\\n\\n return HttpResponse(self.construct_json(),\\n content_type='application/json',\\n mimetype='application/json', status=self.status)\",\n \"def events(self):\\n self.add_events(Event.objects.filter(event_end__gt=timezone.now()).order_by('event_start'))\\n self.filename = 'events'\",\n \"def events(self):\\n self.add_events(Event.objects.filter(event_end__gt=timezone.now()).order_by('event_start'))\\n self.filename = 'events'\",\n \"def calendar_view(request, calendar_id):\\n calendar_obj = Calendar.objects.get(pk=calendar_id)\\n try:\\n appointments = Appointment.objects.all().filter(calendar=calendar_obj)\\n appointments = jsonify(appointments)\\n except:\\n appointments = []\\n calendar_obj = calendar_obj.serialize()\\n calendar_obj[\\\"non_working_days\\\"] = [day for day in [0, 1, 2, 3, 4, 5, 6] if day not in calendar_obj[\\\"working_days\\\"]]\\n return render(request, 'calendar_view.html', {'calendar_obj': calendar_obj, 'appointments': appointments})\",\n \"def view_meetups():\\n\\n return make_response(jsonify({\\n \\\"status\\\": 200,\\n \\\"data\\\": meetups.view_meetups()\\n })), 200\",\n \"def tags_JSON(request):\\n tags_as_json = serializers.serialize('json', Tag.objects.all())\\n return HttpResponse(json.dumps(tags_as_json), content_type='json')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76628214","0.67185247","0.6591272","0.6508852","0.6443426","0.62528884","0.6215547","0.6096349","0.60427344","0.604139","0.5985696","0.5972402","0.5937236","0.5932184","0.59189314","0.58994985","0.58941627","0.5889959","0.58717847","0.58609265","0.5860657","0.58587974","0.5854207","0.58268523","0.5809858","0.5797601","0.5782626","0.57593095","0.57396215","0.5730987","0.5728105","0.5720628","0.5711635","0.57098824","0.56965226","0.56944025","0.56931734","0.56814444","0.56772304","0.5675143","0.56580865","0.56381464","0.56306446","0.5622252","0.561796","0.56102","0.56038415","0.5595678","0.55928624","0.5588356","0.55834657","0.55834657","0.55834657","0.55834657","0.55834657","0.55779874","0.55724764","0.55564684","0.555611","0.555611","0.5556058","0.55436075","0.55392617","0.55330795","0.5528677","0.552349","0.55162275","0.550388","0.55001837","0.5494819","0.54897916","0.5478243","0.54735506","0.54713285","0.54690045","0.5462745","0.54611033","0.54578495","0.5455085","0.5428083","0.54187113","0.54183817","0.5405203","0.5403234","0.5401993","0.53958297","0.5394985","0.53947437","0.53914154","0.5384819","0.53777975","0.53663635","0.53563696","0.5355197","0.5354023","0.5346089","0.5346089","0.5332492","0.5329287","0.5328116"],"string":"[\n \"0.76628214\",\n \"0.67185247\",\n \"0.6591272\",\n \"0.6508852\",\n \"0.6443426\",\n \"0.62528884\",\n \"0.6215547\",\n \"0.6096349\",\n \"0.60427344\",\n \"0.604139\",\n \"0.5985696\",\n \"0.5972402\",\n \"0.5937236\",\n \"0.5932184\",\n \"0.59189314\",\n \"0.58994985\",\n \"0.58941627\",\n \"0.5889959\",\n \"0.58717847\",\n \"0.58609265\",\n \"0.5860657\",\n \"0.58587974\",\n \"0.5854207\",\n \"0.58268523\",\n \"0.5809858\",\n \"0.5797601\",\n \"0.5782626\",\n \"0.57593095\",\n \"0.57396215\",\n \"0.5730987\",\n \"0.5728105\",\n \"0.5720628\",\n \"0.5711635\",\n \"0.57098824\",\n \"0.56965226\",\n \"0.56944025\",\n \"0.56931734\",\n \"0.56814444\",\n \"0.56772304\",\n \"0.5675143\",\n \"0.56580865\",\n \"0.56381464\",\n \"0.56306446\",\n \"0.5622252\",\n \"0.561796\",\n \"0.56102\",\n \"0.56038415\",\n \"0.5595678\",\n \"0.55928624\",\n \"0.5588356\",\n \"0.55834657\",\n \"0.55834657\",\n \"0.55834657\",\n \"0.55834657\",\n \"0.55834657\",\n \"0.55779874\",\n \"0.55724764\",\n \"0.55564684\",\n \"0.555611\",\n \"0.555611\",\n \"0.5556058\",\n \"0.55436075\",\n \"0.55392617\",\n \"0.55330795\",\n \"0.5528677\",\n \"0.552349\",\n \"0.55162275\",\n \"0.550388\",\n \"0.55001837\",\n \"0.5494819\",\n \"0.54897916\",\n \"0.5478243\",\n \"0.54735506\",\n \"0.54713285\",\n \"0.54690045\",\n \"0.5462745\",\n \"0.54611033\",\n \"0.54578495\",\n \"0.5455085\",\n \"0.5428083\",\n \"0.54187113\",\n \"0.54183817\",\n \"0.5405203\",\n \"0.5403234\",\n \"0.5401993\",\n \"0.53958297\",\n \"0.5394985\",\n \"0.53947437\",\n \"0.53914154\",\n \"0.5384819\",\n \"0.53777975\",\n \"0.53663635\",\n \"0.53563696\",\n \"0.5355197\",\n \"0.5354023\",\n \"0.5346089\",\n \"0.5346089\",\n \"0.5332492\",\n \"0.5329287\",\n \"0.5328116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.778918"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7497,"cells":{"query":{"kind":"string","value":"View for rendering news feed data as json."},"document":{"kind":"string","value":"def json_news(request):\n if request.method == 'GET':\n feed = request.GET['feed']\n return JsonResponse(\n {\n 'news': get_news(feed),\n }\n )"},"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_news():\n data = get_top_news()\n return jsonify(data)","def news():\n\n # ensure parameters are present\n # geo = request.args.get(\"geo\")\n geo = '95060'\n if not geo:\n raise RuntimeError(\"missing geo\")\n\n # lookup articles and store them as JSON array\n article_list = lookup(geo)\n\n # TODO\n print(article_list)\n news = jsonify(article_list) \n print(news)\n # return render_template(\"index.html\")\n return article_list","def json_events(request):\n if request.method == 'GET':\n ttrss_url = request.GET['feed']\n\n # need xml for this. \n university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'\n\n n = datetime.datetime.now()\n return JsonResponse(\n {\n 'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))\n }\n )","def json_format(data):\n return {\n 'Title': data[\"title\"],\n 'Publication date': data['pubDate'],\n 'News link': data['link'],\n 'Image link': data['media'],\n }","def GET(self, *args):\n all_news= self.get_all_news()\n all_news.sort( key=lambda n : n['date'], reverse=True)\n if len(args):\n n_last=int(args[0])\n all_news = all_news[:n_last]\n\n return json.dumps(all_news)","def home_feeds(request):\n result = {}\n \n result['feeds'] = []\n\n u = request.user\n\n\n # get other people's feeds, filter by friends if in social group\n feeds = Feed.objects.exclude(actor=u).order_by('-timestamp')\n result['feeds'] = [ f.get_json(me=u, android=True) for f in feeds ]\n\n return JSONHttpResponse(result)","def show_news_list():\r\n\tnews_list = Page.objects.filter(tags='news').order_by('-created')\r\n\treturn {'news_list': news_list}","def get_all_feeds(request):\n feed = Feeds.objects.all()\n\n\n\n\n serializer = FeedsSerializer(feed,many=True)\n return JsonResponse({\"FeedList\":serializer.data,\"ResponseCode\": \"200\",\"ResponseMessage\":\"Successfully\"}, safe=False)","def newsfeed(request):\n article_list = Article.objects.order_by('published_date')\n context = {'article_list': article_list}\n return render(request, 'sacms/newsfeed.html', context)","def to_json(self):\n return {\n \"item_name\": self.item_name,\n \"summary\": self.summary,\n \"content\": self.content,\n \"date_published\": self.date_published,\n \"item_slug\": self.item_slug,\n \"category_name\": self.category_name,\n }","def get_feed():\n return jsonify(dict({\n \"result\": mongo.get_hpfeed(),\n \"code\": 200\n }))","def json(self):\n if self.valid:\n return {\n 'articleID': self._id,\n 'ticker_symbol': self.ticker,\n 'published_date': self.pub_date,\n 'author_name': self.author,\n 'title': self.title,\n 'text': self.text,\n 'num_likes': 0,\n 'includes_symbols': self.includes\n }\n\n return {}","def news(request):\n articles = News.objects.all()\n return render(request, 'news.html', {\"articles\": articles})","async def news(self):\n url = f\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\"\n async with ClientSession() as session:\n async with session.get(url) as response:\n r = await response.json()\n firstArticle = r[\"articles\"][0]\n nSource = firstArticle[\"source\"][\"name\"]\n nTitle = firstArticle[\"title\"]\n nTimestamp = firstArticle[\"publishedAt\"]\n embed = discord.Embed(\n title=f\"News Title: {nTitle}\", description=f\"News Source: {nSource}\"\n )\n embed.add_field(name=\"News Content\", value=firstArticle[\"description\"])\n embed.set_image(url=firstArticle[\"urlToImage\"])\n embed.set_footer(text=f\"News Timestamp: {nTimestamp}\")\n\n channel = self.bot.get_channel(self.bot.main_channel_id)\n await channel.send(embed=embed)","def news_feed(request):\n\n all_friends = get_all_friends(request)\n news_feed = get_news_feed(request)\n user_profile = get_users_profile(request.user.id)\n\n context = {\n 'news_feed': news_feed,\n 'user_profile': user_profile,\n 'status_form': StatusForm,\n }\n\n return render(request, 'status/news_feed.html', context)","def data_json(request):\n json_data = []\n for resource in Resource.objects.all():\n record = {} \n record['title'] = resource.name\n record['description'] = resource.description\n record['keyword'] = resource.csw_keywords.split(',')\n record['modified'] = resource.last_updated\n record['publisher'] = resource.organization\n record['contactPoint'] = resource.metadata_contact\n record['mbox'] = resource.contact_email\n record['identifier'] = resource.csw_identifier\n if resource.is_published:\n record['accessLevel'] = 'public'\n else:\n record['accessLevel'] = 'non-public'\n\n json_data.append(record)\n\n return HttpResponse(json.dumps(json_data), 'application/json')","def render(self):\n with open(self.config[\"home.newssource\"]) as fp:\n stories = json.load(fp)\n\n filename = self.config[\"home.template\"]\n parts = dict(stories=stories)\n\n return self.render_template(filename, parts)","def news_handle(news_json):\n #Sets all the variables to global space to be used later.\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\n #Handles data from API and assigns data to variables.\n news_data = news_json['articles']\n article_1 = news_data[0]\n article_2 = news_data[1]\n title_1 = article_1['title']\n author_1 = article_1['author']\n source_1 = article_1['source']['name']\n url_1 = article_1['url']\n url_1_final = '<a href=\"{}\">Read More</a>'.format(url_1)\n title_2 = article_2['title']\n author_2 = article_2['author']\n source_2 = article_2['source']['name']\n url_2 = article_2['url']\n url_2_final = '<a href=\"{}\">Read More</a>'.format(url_2)\n return","def GET(self):\n web.header(\"Content-Type\",\"application/json; charset=utf-8\")\n\n data = web.input(module=\"module\", start=\"start\", num=\"num\", type=\"type\")\n module = data[\"module\"]\n start = data[\"start\"]\n num = data[\"num\"]\n type_ = data[\"type\"]\n\n module = (1 if module == \"module\" else module)\n start = (1 if start == \"start\" else start)\n num = (1 if num == \"num\" else num)\n\n news = api.get_news_fromDB(int(module), int(start), int(num))\n\n if type_ != \"html\":\n return json.dumps(news)\n else:\n web.header(\"Content-Type\",\"text/html; charset=utf-8\")\n html = \"\"\n for item in news:\n html = html + item[\"maindiv\"]\n return html","def json(self):\n return {\n 'author': self.author,\n 'email': self.email,\n 'display_email': self.display_email,\n 'title': self.title,\n 'trailer_path': self.trailer_path,\n 'date': self.date,\n 'link': self.link,\n '_id': self._id\n }","def tweet_list_view_pure_django(request, *args, **kwargs):\n\n qs = Tweet.objects.all()\n # tweets_list = [\n # {\"id\": x.id, \"content\": x.content, \"likes\": random.randint(0, 129)} for x in qs\n # ]\n\n # @Anyi use this line of code instead of the above line\n tweets_list = [x.serialize() for x in qs]\n\n data = {\"isUser\": False, \"tweet_list_response\": tweets_list}\n # @Anyi this is a new way of returning our data\n # instead of HttpResponse() or render()\n # and since we want to make our page as dynamic as possible\n return JsonResponse(data)","def data_json(self, extra_context=None, publish=False):\n if not self.project.CREATE_JSON:\n # nothing to see here, but the right mimetype\n return jsonify()\n\n if not self.data:\n # this sets site.data by spreadsheet or gdoc\n self.get_context(publish)\n\n return jsonify(self.data)","def get(self):\n\n return {\"message\": \"Welcome to the news API. \"}","def json_posts_latest():\n posts = posts_base.order_by(Post.pubdate.desc())[:app.config['FEEDITEMS']]\n out = {'posts': []}\n for post_result in posts:\n post_dict = get_public_post_dict(post_result[0], post_result[2])\n out['posts'].append(post_dict)\n\n return jsonify(out)","def display(request):\n data = {}\n about = About.objects.all()\n if about:\n data['success']=1\n data['message']=\"Comments available\"\n data['about']=[]\n for i in range(len(about)):\n data['about'].append(\n {'about':about[i].about,\n 'about_id':about[i].id,\n })\n return JsonResponse(data)\n else:\n data['success']=0\n data['message']='no about available'\n return JsonResponse(data)","def newsList(request):\n\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\n news_list = New.objects.all().order_by(\"date\") # Sort by date ... and only part of list\n # misto vsech zaznamu ziskat jen ty v intervalu start - stop -> API\n\n pictureOfWeek = PhotoOfWeek.objects.last()\n context = {'news_list': news_list, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek}\n return render(request, 'news/newsList.html', context)","def format(self, *args):\n\t\tweb.header('Content-Type', 'application/json; charset=utf-8')\n\t\treturn json.dumps(self.content)","def get_recent_news_items():\n news_item_count = request.args.get('newsItemCount') or 3\n try:\n animal_news = AnimalNews.get_printable_news_items_all_animals(news_item_count)\n return jsonify(message=animal_news), 200\n except Exception as e:\n print(e)\n return jsonify(message='{}'.format(e)), 501","def news_fetch(region,news_key):\n #Allows for customizable API key and weather location.\n url = (f\"http://newsapi.org/v2/top-headlines?country={region}&apiKey={news_key}\")\n #Gets API with requests and convert to .json\n news_api = requests.get(url)\n news_json = news_api.json()\n return news_json","def news():\r\n with open('config.json', 'r') as cfile:\r\n config = json.load(cfile)\r\n news_api_key = config[\"news_api_key\"]\r\n response = requests.get(\"https://newsapi.org/v2/top-headlines?\"\r\n \"sources=bbc-news&apiKey=\" + news_api_key)\r\n resp_json = response.json()\r\n with open(\"news.json\", 'w') as file:\r\n json.dump(resp_json, file)\r\n file.close()","def render_json(object):\r\n return HttpResponse(jsonify(object), content_type='application/json')","def tweet_list_view(request, *args, **kwargs):\n\n objs = Tweet.objects.all()\n\n tweets_list = [{\"id\": obj.id, \"content\": obj.content} for obj in objs]\n\n data = {\n\n \"isUser\":False,\n\n \"tweets_list\": tweets_list\n }\n\n return JsonResponse(data)","def newsfeed_en(request):\n article_list = Article.objects.order_by('published_date')\n context = {'article_list': article_list}\n return render(request, 'sacms/newsfeed_en.html', context)","def get_news(request):\n return get_all_posts(request, PostType.NEWS)","def get_feed(request):\n user = request.user\n\n # Get feed data and paginate it.\n feed = Post.objects.filter(community__communitymember__user=user).order_by(\n \"-created_at\"\n )\n\n # Check for filter phrase.\n phrase = request.GET.get(\"phrase\")\n if phrase is not None:\n if phrase != \"\" and not phrase.isspace():\n for term in phrase.split():\n feed = feed.filter(Q(title__icontains=term))\n else:\n feed = feed.none()\n\n return JsonResponse(\n json_paginator(request, feed, lambda d: d.serialize(request)),\n status=200,\n )","def blog():\n return jsonify(\n {\n 'entries': [\n {\n 'title': 'A blog post about things',\n 'date': '7/3/12',\n 'id': 1,\n 'lead': \"\"\"once upon a time, there was a cool dude who did\n cool things. This is his story.\"\"\",\n 'body': \"More content for the blog post\",\n 'more_url': 'http://blog.tobywaite.net',\n },\n {\n 'title': 'Cool projects, ftw',\n 'date': '6/3/12',\n 'id': 2,\n 'lead': \"\"\"I did a really cool project once, this is all\n about it.\"\"\",\n 'body': \"More content for the blog post\",\n 'more_url': 'http://blog.tobywaite.net',\n },\n ]\n }\n )","def __repr__(self):\r\n\r\n return f\"<News: id = {self.news_id}, title = {self.title} summary = {self.summary}>\"","def select_news(self):\n data = self.soup.findAll('item')\n for item in data:\n news_data = dict()\n for tag in ['title', 'link']:\n news_data[tag] = item.find(tag).get_text()\n\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\n media = item.find('media:content')\n\n if media:\n news_data['media'] = media.get('url')\n else:\n news_data['media'] = None\n\n yield news_data","def news()->str:#return array[news desc,news link]\n event_log(\"retrieve news data....\",\"\")\n c = 0\n location = read_json(\"news_api\")[0]\n main_url = \"https://newsapi.org/v2/top-headlines?country=\"+location+\"&apiKey=\"+read_json(\"news_api\")[1]+\"\"#add a country selection optin via json\n page = requests.get(main_url).json()\n article = page[\"articles\"]\n news_result = []\n for data in article:\n news_result.append([data[\"title\"],str(data[\"url\"]).replace('\"',\" \")])#exctracts the wanted data from api\n if c == 5:#add this to json file so scalibility\n break\n c+=1\n return news_result","def post_list_view(request, *args, **kwargs):\n qs = Post.objects.all()\n posts_list = [{\"id\": x.id, \"content\": x.content, \"likes\": random.randint(0, 120), \"reposts\": random.randint(0, 10)} for x in qs] #{\"id\": x.id, \"content\": x.content, \"likes\": random.randint(0, 120), \"reposts\": random.randint(0, 10) }/x.serialize()\n data = {\n \"isUser\": False,\n \"response\": posts_list\n }\n return JsonResponse(data) #, save=False","def get(self):\n return GlobalNews.retrieve()","def guidebook_news_feed(request):\n current_site = Site.objects.get_current()\n\n feed_title = 'DjangoCon US News Updates'\n feed_description = 'The latest updates and additions for DjangoCon US.'\n feed_mimetype = 'application/rss+xml'\n feed_template = 'pinax/blog/rss_feed.xml'\n\n blog_url = 'http://%s%s' % (current_site.domain, reverse('blog'))\n # feed_url = 'http://%s%s' % (current_site.domain, reverse(url_name, kwargs=kwargs))\n\n posts = Post.objects.published().exclude(title__endswith='Sponsor')\\\n .order_by('-published')\n\n if posts:\n feed_updated = posts[0].updated\n else:\n feed_updated = datetime(2009, 8, 1, 0, 0, 0)\n\n feed = render_to_string(feed_template, {\n # 'feed_id': feed_url,\n 'feed_title': feed_title,\n 'feed_description': feed_description,\n 'blog_url': blog_url,\n # 'feed_url': feed_url,\n 'feed_updated': feed_updated,\n 'entries': posts,\n 'current_site': current_site,\n })\n\n return HttpResponse(feed, content_type=feed_mimetype)","def get_featured_articles(request):\n try:\n count = 1\n if 'count' in request.POST and int(request.POST['count']):\n count = int(request.POST['count'])\n\n newest_list = []\n for article in Article.objects.order_by('-modified')[:count]:\n newest_list.append(article.dump_to_dict())\n\n popular_list = []\n for article in Article.objects.order_by('-views')[:count]:\n popular_list.append(article.dump_to_dict())\n\n return format_ajax_response(True, \"Featured articles retrieved successfully.\", {'newest': newest_list,'popular': popular_list})\n except Exception as ex:\n logger.error(\"Failed to get_featured_articles: %s\" % ex)\n return format_ajax_response(False, \"There was an error retrieving the featured articles.\")","def json_view(self, recursive=False):\n\n context = self.context.aq_inner\n data = self.export(context, recursive=recursive)\n pretty = json.dumps(data, sort_keys=True, indent=4)\n self.request.response.setHeader(\"Content-type\", \"application/json\")\n return pretty","def all_news(request):\n\n all_news = News.objects.all().order_by(\"-date_added\")\n context = {\n 'news': all_news,\n 'show_without_bag': True\n }\n return render(request, 'news/news.html', context)","def get_articles():\n _, articles = base_query(db_session)\n return jsonify([p.serialize for p in articles])","def news(self):\n\n # Get articles with search term, if available, from each News API source\n news_api_articles = pd.DataFrame()\n\n q = urllib.parse.quote(\" OR \".join(self.search_terms), safe='')\n\n response = requests.get(\"https://newsapi.org/v2/everything?q=\" + q + \"&from=\" + datetime.now().strftime(\n \"%Y-%m-%d\") + \"&sortBy=popularity&pageSize=100&apiKey=\" + self.__news_api_key)\n\n if response.status_code == 200:\n data = json.loads(response.text)\n\n source_articles = []\n\n for article in data['articles']:\n source_articles.append([article['title'],\n article['description'],\n article['url'],\n article['publishedAt']])\n\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\n news_api_articles = pd.concat([news_api_articles, source_articles])\n\n news_api_articles = news_api_articles.reset_index(drop='True')\n\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\n\n news_api_articles = news_api_articles.fillna(' ')\n\n term_in_title = news_api_articles['title'].apply(self.any_term)\n\n news_api_articles = news_api_articles[term_in_title]\n\n if (len(news_api_articles) > 10):\n news_api_articles = news_api_articles[0:10]\n\n else:\n print(\"News API failed to return any items\")\n\n # Create shortened links using bitly if access token is provided\n if self.__bitly_access_token != '':\n\n bitly_urls = []\n\n for index, article in news_api_articles.iterrows():\n url = article['url']\n bitly_response = requests.get(\"https://api-ssl.bitly.com/v3/shorten\",\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\n\n if bitly_response.status_code == 200:\n data = json.loads(bitly_response.text)\n bitly_urls.append(data['data']['url'])\n\n news_api_articles['url'] = bitly_urls\n\n # Store final list to TwitterBot object\n self.list = news_api_articles\n\n return","def as_json(self):","def list(self, request):\n return HttpResponse(request.user.series_list, content_type=\"application/json\")","def storiesOnMap(request):\n requestDict = request.POST\n zoom = int(requestDict['zoom'])\n north = Decimal(requestDict['north'])\n south = Decimal(requestDict['south'])\n east = Decimal(requestDict['east'])\n west = Decimal(requestDict['west'])\n # The \"old\" parameter contains a commma-separated list of story ids that are already displayed by the browser\n currentlyDisplayedStoryIds = requestDict['old'].split(',')\n\n storiesToDisplay = models.Story.objects.filter(latitude__range=(south, north)).filter(longitude__range=(west, east)).order_by('-created')\n\n # Filter the amount of story markers to display based on the current zoom level\n if len(storiesToDisplay) >= (zoom-1)*5:\n storiesToDisplay = storiesToDisplay[(zoom-1)*5:zoom*5]\n\n response = []\n for story in storiesToDisplay:\n if str(story.id) not in currentlyDisplayedStoryIds:\n\n if story.contributor:\n\t\tcontribName = story.contributor.name\n else: \n contribName = None\n langName = story.language.name if story.language else None\n response.append({'id': story.id,\n 'title': story.title,\n 'summary': story.summary,\n 'link': story.link,\n 'has_flv':story.has_flv(),\n 'flv_id':story.flv_id(),\n 'media_type':story.media_type,\n 'imageRef': story.contributor.imageRef,\n 'contributor': contribName,\n 'language': langName,\n 'created': story.created.strftime('%d %B %Y'),\n 'lat': float(story.latitude),\n 'long': float(story.longitude)})\n return JsonResponse(response)","def all_bugs_chart(request):\n labels = []\n data = []\n\n queryset = Bug.objects.values('title', 'id').order_by('-created').exclude(status='Resolved').annotate(\n bug_votes=Count('votes'))[:5]\n for entry in queryset:\n labels.append(entry['title'])\n data.append(entry['bug_votes'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n })","def NewsArticles():\n health_articles = get_articles('health')\n education_articles = get_articles('technology')\n return render_template('articles.html',health=health_articles, tech =education_articles)","def main(feed=None):\n feed_processor = core.FeedProcessor()\n feed_processor(feed_type=feed)\n return feed_processor.feed_json","def search_news(request):\n try:\n query_string = ''\n if request.GET['search_text'].strip() != '':\n query_string = '&title='+request.GET['search_text']\n response = requests.get(APIURL + '/articles/?format=json'+query_string)\n parser = json.loads(response.content)\n return render_to_response('article/search_result.html', {'articlelist':parser})\n except:\n raise Http404(\"Search Item error\")","def weblinks_view(request):\n\n\trecords = WebLinks.objects.filter(user_id=request.user.id)\n\tjson_response = [dict(title=record.title, url=record.url, description=record.description) for record in records]\n\treturn JsonResponse(json_response, safe=False)","def fetch_news(n):\n\n # This is the list we will use the pass back the news information.\n data = []\n\n # Get news stories from the MEN RSS feed.\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\n\n # Loop through the news items, and the pull out the data we need.\n for news in response.entries[:n]:\n data.append({\n 'headline': news.title,\n 'content': news.description,\n })\n\n return data","def all_features_chart(request):\n labels = []\n data = []\n\n queryset = Feature.objects.values('title').order_by('-created').exclude(status='Implemented').annotate(\n feature_purchases=Sum('purchases'))[:5]\n for entry in queryset:\n labels.append(entry['title'])\n data.append(entry['feature_purchases'])\n\n return JsonResponse(data={\n 'labels': labels,\n 'data': data,\n })","def news(self) -> List[News]:\n return self._news","def run(self,dispatcher,tracker,domain): \n data=newsapi(\"us\")\n leng=len(data)\n for i in range(leng):\t\n gt = {\n \"attachment\": {\n \"type\": \"template\",\n \"payload\": {\n \"template_type\": \"generic\",\n \"elements\": [\n {\n \"title\": data['articles'][i]['title'],\n \"image_url\":data['articles'][i]['urlToImage'],\n \"subtitle\": data['articles'][i]['description'],\n \"buttons\": [\n {\n \"type\": \"web_url\",\n \"url\": data['articles'][i]['url'],\n \"title\": \"Read More\"\n },\n ]\n },\n ]\n }\n }\n } \n dispatcher.utter_custom_json(gt)\n return []","def get(self, request):\n \n posts = post.objects.filter(unlisted=False,visibility=\"PUBLIC\")\n\n page = self.request.GET.get('page', 1)\n page_num = self.request.GET.get('size', 1000)\n \n paginator = custom()\n results = paginator.paginate_queryset(posts,request)\n serializer = postSerializer(results, many=True)\n #return JsonResponse(serializer.data,safe=False)\n #http://stackoverflow.com/questions/34798703/creating-utf-8-jsonresponse-in-django\n #return JsonResponse(json.dumps(serializer.data, ensure_ascii=False),\n #safe=False)\n\n\n return JsonResponse(OrderedDict([('count', paginator.count),\n ('current', page),\n ('next', paginator.get_next_link()),\n ('previous', paginator.get_previous_link()),\n ('size', page_num),\n ('posts', serializer.data)]))\n\n \n\n\n\n\n\n #data = JSONParser().parse(request)\n #serializer = postSerializer(data=data)\n #if serializer.is_valid():\n # serializer.save()\n # return JsonResponse(serializer.data, status=201)\n #return JsonResponse(serializer.errors, status=400)","def json(self):\n return {'id': self.id, 'name': self.name, 'description': self.description}","def articleList():\n articles = get_news(\n 5, since=news.YESTERDAY.strftime(\"%yyyy-%mm-%dd\"), query=\"covid\"\n )\n title_list = []\n desc_list = []\n url_list = []\n image_list = []\n source_list = []\n for art in articles:\n image_list.append(art.image)\n title_list.append(art.title)\n source_list.append(art.source)\n desc_list.append(art.description)\n url_list.append(art.url)\n socketio.emit(\n ARTICLE,\n {\n \"title\": title_list,\n \"desc\": desc_list,\n \"url\": url_list,\n \"img\": image_list,\n \"sources\": source_list,\n },\n )\n return True","def render_to_json_response(self, context, **response_kwargs):\n return JsonResponse(self.get_data(context))","def index_json():\n\n response = views.get_feature_collection_metadata(config)\n\n return make_response(jsonify(response))","def json_sluglist_latest():\n posts = posts_base.order_by(Post.pubdate.desc())[:app.config['FEEDITEMS']]\n out = {'posts': []}\n for post in posts:\n out['posts'].append([post[0].pubdate.strftime(app.config['POST_DATETIME_FORMAT']), post[0].slug])\n\n return jsonify(out)","def getchart(request):\n data = ast.literal_eval(request.body.decode('utf-8'))\n data['user'] = request.user\n data = newchart(data)\n return JsonResponse(data)","def Topic_index(request):\n \"\"\"return path,date of images of topic to javascript\"\"\"\n fileID=get_sorted_img_list()\n week_img_list = simplejson.dumps(fileID)\n #latest\n path=fileID[0][0]\n date=fileID[0][1]\n print date\n return render(request, 'WebPtt/Gossip_topic_model.html',{'pic_path':path,'week_img_list':week_img_list,'date':date})","def get(self, request, *args, **kwargs):\n if self.request.is_ajax():\n data = self.get_context_data(**kwargs)\n return JsonResponse({\n 'has_more': data['has_more'],\n 'lastPublished': data['last_published'],\n 'content': render_to_string(\"partials/posts_list.html\", data, request=self.request)\n })\n\n return super().get(request, *args, **kwargs)","def json(self):\n return {\n 'uri': self.view_uri,\n 'created': time.strftime('%c', time.gmtime(self.created)),\n 'created_timestamp': self.created,\n 'exception_type': str(self.exc_type),\n 'exception': str(self.exc_value),\n }","def print_json_content(self, rss_feed):\n\n self.print_if_verbose(\n f\"Method 'print_json_content' is working: \\n\"\n f\"RSS feed will be printed in JSON format: \\n\"\n )\n\n json_content = json.dumps(rss_feed, indent=3)\n print(json_content)\n\n self.print_if_verbose(f\"Method 'print_json_content' is finished. \\n\")\n\n return \"Content is printed in JSON format\"","def api_get_post(request, post_id):\n\n post = get_object_or_404(Post, id=post_id)\n\n json = serializers.serialize(\"json\", [post], fields=(\n \"pub_time\", \"_text_rendered\", \"title\", \"text\", \"image\",\n \"image_width\", \"image_height\", \"replies\", \"tags\"\n ))\n\n return HttpResponse(content=json)","def serialize(self):\n return {\n 'id': self.id,\n 'title': self.title,\n 'description': self.description,\n 'ranking': self.ranking,\n 'created_date': self.created_date,\n }","def feed(self):\r\n return feed.Feed(self)","def json_response(self, request, *args, **kwargs):\n\n return HttpResponse(self.construct_json(),\n content_type='application/json',\n mimetype='application/json', status=self.status)","def news_view():\n session_id = request.args.get('session-id', None)\n user_id = request.args.get('user-id', None)\n edit_mode_string = request.args.get('edit', None)\n news_list = get_news_list()\n if edit_mode_string == 'true':\n edit_mode = True\n else:\n edit_mode = False\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\n return render_template('news_manager.html', user=user_id, session_id=session_id, edit_mode=edit_mode,\n news_list=news_list)\n else:\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\n preview_length=get_cars_preview().__len__(), del_session_cookie=True)","def run(self,dispatcher,tracker,domain): \n data=newsapi(\"in\")\n leng=len(data)\n for i in range(leng):\t\n gt = {\n \"attachment\": {\n \"type\": \"template\",\n \"payload\": {\n \"template_type\": \"generic\",\n \"elements\": [\n {\n \"title\": data['articles'][i]['title'],\n \"image_url\":data['articles'][i]['urlToImage'],\n \"subtitle\": data['articles'][i]['description'],\n \"buttons\": [\n {\n \"type\": \"web_url\",\n \"url\": data['articles'][i]['url'],\n \"title\": \"Read More\"\n },\n ]\n },\n ]\n }\n }\n } \n dispatcher.utter_custom_json(gt)\n return []","def get_headlines():\n country = request.args.get('country', type=str)\n if country is not None:\n data = te.getNews(country=country).dropna()\n return jsonify(data.to_dict(orient='records'))\n data = te.getNews()\n return jsonify(te.getNews().dropna().to_dict(orient='records'))","def Export(self):\n\n current_time = datetime.datetime.now(tz.UTC)\n\n self.data = np.load(self.cache_path, allow_pickle=True)[()]\n\n news = []\n\n ID = self.data.keys()\n\n for id in ID:\n v = self.data[id]\n if 'address' in v and \"河南\" in v['address'] and v['valid'] == 1\\\n and current_time - parse(v['time']) < datetime.timedelta(hours=12):\n news.append({\"Time\": v['time'], \"address\": v['address'], \"location\": v['location'], \"post\": v['post'],\n \"link\": v[\"link\"]})\n\n with open(self.output_path, \"w\", encoding=\"utf-8\") as fp:\n json.dump(news, fp, ensure_ascii=False, indent=4)\n\n print(\"Export %d info\" % len(news))","def index(self):\r\n shows = Shows.query.order_by(asc(Shows.ShowID)).all()\r\n content = jsonify({\r\n \"shows\": [{\r\n \"date\": get_iso_format(show.ShowDate),\r\n \"countryCode\": show.CountryCode,\r\n \"country\": show.Country,\r\n \"city\": show.City,\r\n \"venue\": show.Venue,\r\n \"setlist\": self.get_setlist(show.ShowID),\r\n \"otherBands\": self.get_other_bands(show.ShowID),\r\n \"people\": self.get_show_people(show.ShowID),\r\n } for show in shows]\r\n })\r\n\r\n return make_response(content, 200)","def latest_content(request):\n latest_articles = Article.published_articles()[:5]\n latest_comments = Comment.objects.all().order_by('-pub_date')[:5]\n tags = Tag.objects.annotate(num_articles=Count('article')).order_by(\n '-num_articles')\n contributors = Contributor.objects.annotate(\n num_articles=Count('article')).order_by('-num_articles')\n return {'latest_articles': latest_articles,\n 'latest_comments': latest_comments,\n 'tags': tags,\n 'contributors': contributors,\n }","def get_remote_news_items(self):\n items = []\n params = {\n \"base_url\": self.osha_json_url,\n \"lang\": api.portal.get_tool(\"portal_languages\").getPreferredLanguage(),\n \"query_tags\": self.remote_news_query_tags,\n }\n qurl = \"{base_url}/{lang}/services/hw/news/{query_tags}\".format(**params)\n result = urlopen(qurl)\n if result.code == 200:\n json = load(result)\n for node in json.get(\"nodes\"):\n item = node.get(\"node\")\n pd = item.get('publication_date', '')\n items.append({\n 'remote_item': True,\n 'Title': item['title'],\n 'Date': (\n pd and DateTime(pd, datefmt=\"international\").strftime(\n \"%Y/%m/%d %H:%M\") or \"\"),\n 'getURL': item.get('path'),\n 'path': item.get('path'),\n 'Description': item.get('summary', '') or item.get('body', ''),\n 'text': item.get('summary', '') and item.get('body', '') or '',\n 'remote_image': item.get('image', ''),\n 'node_id': item.get('nid'),\n })\n return items","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 get_chart_one(request):\r\n json_str = []\r\n \r\n usuarios = Usuario.objects.all()\r\n for usuario in usuarios:\r\n peticiones = Peticion.objects.filter(usuario=usuario)\r\n json_str.append({ \r\n 'name': u'%s %s' % (usuario.persona.nombre,\r\n usuario.persona.apellidos),\r\n 'data': len(peticiones)\r\n }) \r\n json_obj = json.dumps(json_str, sort_keys=True, indent=4)\r\n response = HttpResponse(json_obj, mimetype=\"application/json\") \r\n return response","def projects_JSON(request):\n projects = Project.objects.all()\n for p in projects:\n p.description = bleach.clean(markdown.markdown(p.description, extensions=['markdown.extensions.fenced_code']), strip=True)\n projects_as_json = serializers.serialize(\n 'json',\n projects,\n fields=('title',\n 'posted',\n 'difficulty',\n 'tags',\n 'articles',\n 'user',\n 'description',\n 'pk'),\n use_natural_foreign_keys=True)\n return HttpResponse(json.dumps(projects_as_json), content_type='json')","def run(self,dispatcher,tracker,domain): \n data=newsapi(\"au\")\n leng=len(data)\n for i in range(leng):\t\n gt = {\n \"attachment\": {\n \"type\": \"template\",\n \"payload\": {\n \"template_type\": \"generic\",\n \"elements\": [\n {\n \"title\": data['articles'][i]['title'],\n \"image_url\":data['articles'][i]['urlToImage'],\n \"subtitle\": data['articles'][i]['description'],\n \"buttons\": [\n {\n \"type\": \"web_url\",\n \"url\": data['articles'][i]['url'],\n \"title\": \"Read More\"\n },\n ]\n },\n ]\n }\n }\n } \n dispatcher.utter_custom_json(gt)\n return []","def json_frapp(request):\n from pv.settings import MEDIA_URL\n\n if request.GET.get('date') == None:\n start = datetime.combine(date.today(), time(0, 0))\n else:\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\n\n end = datetime.combine(start, time(23, 59))\n\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\n\n\n '''Generate categories object for output'''\n\n categories = Category.objects.all()\n categories_output = []\n\n for c in categories:\n c_entry = {\n 'id': c.id,\n 'color': c.color.replace('#', '').upper(),\n 'namedisplay': c.category,\n 'description': c.description\n }\n\n categories_output.append(c_entry)\n\n # Get all series for timeslots\n series = set()\n for ts in timeslots:\n series.add(ts.show)\n\n\n '''Generate series object for output'''\n\n series_output = []\n\n for s in series:\n metainfos = []\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\n\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\n url = '' if s.website == None or s.website == '' else s.website\n\n # Get active schedules for the given date\n # But include upcoming single timeslots (with rrule_id=1)\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\n (\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\n Q(rrule_id=1,dstart__gte=start)\n )\n )\n\n broadcastinfos = ''\n\n if not schedules.exists():\n continue\n\n for schedule in schedules:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n if schedules_repetition.exists():\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\n for schedule in schedules_repetition:\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\n\n s_entry = {\n 'id': s.id,\n 'categoryid': s.category.values_list('id', flat=True)[0],\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\n 'namedisplay': s.name,\n 'description': s.description,\n 'url': url,\n 'image': image,\n 'broadcastinfos': broadcastinfos,\n 'metainfos': metainfos\n }\n\n series_output.append(s_entry)\n\n\n '''Generate shows object for output'''\n\n shows_output = []\n\n for ts in timeslots:\n\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\n namedisplay = ts.show.name + is_repetition\n description = ts.show.description\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\n urlmp3 = ''\n\n # If there's a note to the timeslot use its title, description and url\n try:\n note = Note.objects.get(timeslot=ts.id)\n namedisplay = note.title + is_repetition\n description = note.content\n url = str(get_current_site(request)) + '/notes/' + note.slug\n urlmp3 = note.audio_url\n except ObjectDoesNotExist:\n pass\n\n ts_entry = {\n 'id': ts.id,\n 'seriesid': ts.show.id,\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\n 'namedisplay': namedisplay,\n 'description': description,\n 'url': url,\n 'urlmp3': urlmp3,\n }\n\n shows_output.append(ts_entry)\n\n output = {}\n output['categories'] = categories_output\n output['series'] = series_output\n output['shows'] = shows_output\n\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\n content_type=\"application/json; charset=utf-8\")","def get(self, request):\n query = Article.objects.exclude(in_menu=True)\n\n article_filter = Q()\n searchable_paths = {}\n\n search_terms = [term for term in request.GET.get(\"query\").split(\" \")]\n for term in search_terms:\n searchable_paths[term] = Q(headline__icontains=term) | Q(\n teaser__icontains=term\n )\n\n for key in searchable_paths.keys():\n article_filter &= searchable_paths[key]\n\n query = query.filter(article_filter).distinct()\n articles = [\n {\n \"updated\": article.updated,\n \"headline\": article.headline,\n \"teaser\": article.teaser,\n \"tags\": [tag.name for tag in article.tags.all()],\n \"url\": reverse(\n \"article.detail\",\n kwargs={\n \"year\": article.created.year,\n \"month\": article.created.month,\n \"slug\": article.slug,\n },\n ),\n }\n for article in query\n ]\n\n return JsonResponse({\"articles\": articles})","def json(data):\n if isinstance(data, dict):\n data = ujson.encode(data)\n uid = str(uuid.uuid4())\n display(HTML('<div id=\"{0}\" style=\"height: 600px; width:100%;\"></div>'.format(uid)))\n display(Javascript(\"\"\"\n require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"], function() {\n document.getElementById('%s').appendChild(renderjson(%s))\n });\n \"\"\" % (uid, data)))","def json(self, **kwargs):\n return json.loads(self.content, **kwargs)","def as_dict(self):\r\n return {\r\n \"text\": {\r\n \"title\": self.title_text,\r\n \"body\": self.body_text,\r\n },\r\n \"html\": {\r\n \"title\": self.title,\r\n \"body\": self.body,\r\n },\r\n \"seen\": self.seen,\r\n \"sent_at\": self.sent_at,\r\n }","def posts_get():\n \n\n # Get and filter the posts from the database\n songs = session.query(models.Song).all()\n \n # Convert the posts to JSON and return a response\n data = json.dumps([song.as_dictionary() for song in songs])\n return Response(data, 200, mimetype=\"application/json\")","def make_feed_dict(self, data):\r\n raise NotImplementedError","def index(request):\n\n\t#define variables\n\targs = {}\n\tall_teams = []\n\tall_news = []\n\t\n\t#getting data form models\n\tleagues = League.objects.all()\n\t\n\t#in this loop we get all teams in our all leagues to a 'all_teams' list \n\tfor league in leagues:\n\t\tall_teams += league.team_set.all()\n\n\t#in this loop we get all new form every team which is in 'all_teams' list and place that new into 'all_news' list \n\tfor team in all_teams:\n\t\tall_news += team.news_set.all()\n\n\t#sort news by date so that last news pop up first\n\tall_news = reversed(sorted(all_news, key=lambda ne:ne.date))\n\t#taking forst 50 news of all news\n\tall_news = list(all_news)[:50]\n\t#context data initialization into dictionary 'args'\n\targs['news'] = all_news\n\targs['leagues'] = leagues\n\n\treturn render_to_response('news/index.html',args)","def articles():\n \n # Parse through RSS feed of Get Rich Slowly\n feed = feedparser.parse(\"http://www.getrichslowly.org/blog/feed/\")\n \n # Get current username\n username = get_user()\n \n return render_template(\"articles.html\", username=username, feed=feed)","def get_data(self):\n return self.data.to_json()","def showItemsJSON():\n items = db.getAllItems()\n return jsonify(CategoryItems=[i.serialize for i in items])","def to_dict(self):\n return {\n 'title': self.title,\n 'description': self.description,\n 'dec_description': self.dec_description,\n 'link': self.link,\n 'pubDate': self.pubDate.strftime(\"%a, %d %b %Y %H:%M\"),\n 'media': json.loads(self.media),\n 'source': self.source.url,\n 'links': json.loads(self.links),\n 'dec_links': json.loads(self.dec_links),\n }","def index(self):\n \n return self.view.render('index.html', {\"posts\"=posts})","def get_big_feed():\n\n # A list of news items. We need to preserve it in order to create it once\n # during the first call, and to\n # return these items from this already existing list\n big_feed_gen = None\n\n def nested_return_feed():\n nonlocal big_feed_gen\n\n if big_feed_gen:\n try:\n return json.dumps(next(big_feed_gen), indent=4, sort_keys=True,\n separators=(',', ': '), ensure_ascii=False)\n except StopIteration:\n return '', 204\n else:\n big_feed_gen = get_feed_generator()\n return json.dumps(next(big_feed_gen), indent=4, sort_keys=True,\n separators=(',', ': '), ensure_ascii=False)\n return nested_return_feed","def feed(self):\n return feed.Feed(self)"],"string":"[\n \"def top_news():\\n data = get_top_news()\\n return jsonify(data)\",\n \"def news():\\n\\n # ensure parameters are present\\n # geo = request.args.get(\\\"geo\\\")\\n geo = '95060'\\n if not geo:\\n raise RuntimeError(\\\"missing geo\\\")\\n\\n # lookup articles and store them as JSON array\\n article_list = lookup(geo)\\n\\n # TODO\\n print(article_list)\\n news = jsonify(article_list) \\n print(news)\\n # return render_template(\\\"index.html\\\")\\n return article_list\",\n \"def json_events(request):\\n if request.method == 'GET':\\n ttrss_url = request.GET['feed']\\n\\n # need xml for this. \\n university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'\\n\\n n = datetime.datetime.now()\\n return JsonResponse(\\n {\\n 'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))\\n }\\n )\",\n \"def json_format(data):\\n return {\\n 'Title': data[\\\"title\\\"],\\n 'Publication date': data['pubDate'],\\n 'News link': data['link'],\\n 'Image link': data['media'],\\n }\",\n \"def GET(self, *args):\\n all_news= self.get_all_news()\\n all_news.sort( key=lambda n : n['date'], reverse=True)\\n if len(args):\\n n_last=int(args[0])\\n all_news = all_news[:n_last]\\n\\n return json.dumps(all_news)\",\n \"def home_feeds(request):\\n result = {}\\n \\n result['feeds'] = []\\n\\n u = request.user\\n\\n\\n # get other people's feeds, filter by friends if in social group\\n feeds = Feed.objects.exclude(actor=u).order_by('-timestamp')\\n result['feeds'] = [ f.get_json(me=u, android=True) for f in feeds ]\\n\\n return JSONHttpResponse(result)\",\n \"def show_news_list():\\r\\n\\tnews_list = Page.objects.filter(tags='news').order_by('-created')\\r\\n\\treturn {'news_list': news_list}\",\n \"def get_all_feeds(request):\\n feed = Feeds.objects.all()\\n\\n\\n\\n\\n serializer = FeedsSerializer(feed,many=True)\\n return JsonResponse({\\\"FeedList\\\":serializer.data,\\\"ResponseCode\\\": \\\"200\\\",\\\"ResponseMessage\\\":\\\"Successfully\\\"}, safe=False)\",\n \"def newsfeed(request):\\n article_list = Article.objects.order_by('published_date')\\n context = {'article_list': article_list}\\n return render(request, 'sacms/newsfeed.html', context)\",\n \"def to_json(self):\\n return {\\n \\\"item_name\\\": self.item_name,\\n \\\"summary\\\": self.summary,\\n \\\"content\\\": self.content,\\n \\\"date_published\\\": self.date_published,\\n \\\"item_slug\\\": self.item_slug,\\n \\\"category_name\\\": self.category_name,\\n }\",\n \"def get_feed():\\n return jsonify(dict({\\n \\\"result\\\": mongo.get_hpfeed(),\\n \\\"code\\\": 200\\n }))\",\n \"def json(self):\\n if self.valid:\\n return {\\n 'articleID': self._id,\\n 'ticker_symbol': self.ticker,\\n 'published_date': self.pub_date,\\n 'author_name': self.author,\\n 'title': self.title,\\n 'text': self.text,\\n 'num_likes': 0,\\n 'includes_symbols': self.includes\\n }\\n\\n return {}\",\n \"def news(request):\\n articles = News.objects.all()\\n return render(request, 'news.html', {\\\"articles\\\": articles})\",\n \"async def news(self):\\n url = f\\\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\\\"\\n async with ClientSession() as session:\\n async with session.get(url) as response:\\n r = await response.json()\\n firstArticle = r[\\\"articles\\\"][0]\\n nSource = firstArticle[\\\"source\\\"][\\\"name\\\"]\\n nTitle = firstArticle[\\\"title\\\"]\\n nTimestamp = firstArticle[\\\"publishedAt\\\"]\\n embed = discord.Embed(\\n title=f\\\"News Title: {nTitle}\\\", description=f\\\"News Source: {nSource}\\\"\\n )\\n embed.add_field(name=\\\"News Content\\\", value=firstArticle[\\\"description\\\"])\\n embed.set_image(url=firstArticle[\\\"urlToImage\\\"])\\n embed.set_footer(text=f\\\"News Timestamp: {nTimestamp}\\\")\\n\\n channel = self.bot.get_channel(self.bot.main_channel_id)\\n await channel.send(embed=embed)\",\n \"def news_feed(request):\\n\\n all_friends = get_all_friends(request)\\n news_feed = get_news_feed(request)\\n user_profile = get_users_profile(request.user.id)\\n\\n context = {\\n 'news_feed': news_feed,\\n 'user_profile': user_profile,\\n 'status_form': StatusForm,\\n }\\n\\n return render(request, 'status/news_feed.html', context)\",\n \"def data_json(request):\\n json_data = []\\n for resource in Resource.objects.all():\\n record = {} \\n record['title'] = resource.name\\n record['description'] = resource.description\\n record['keyword'] = resource.csw_keywords.split(',')\\n record['modified'] = resource.last_updated\\n record['publisher'] = resource.organization\\n record['contactPoint'] = resource.metadata_contact\\n record['mbox'] = resource.contact_email\\n record['identifier'] = resource.csw_identifier\\n if resource.is_published:\\n record['accessLevel'] = 'public'\\n else:\\n record['accessLevel'] = 'non-public'\\n\\n json_data.append(record)\\n\\n return HttpResponse(json.dumps(json_data), 'application/json')\",\n \"def render(self):\\n with open(self.config[\\\"home.newssource\\\"]) as fp:\\n stories = json.load(fp)\\n\\n filename = self.config[\\\"home.template\\\"]\\n parts = dict(stories=stories)\\n\\n return self.render_template(filename, parts)\",\n \"def news_handle(news_json):\\n #Sets all the variables to global space to be used later.\\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\\n #Handles data from API and assigns data to variables.\\n news_data = news_json['articles']\\n article_1 = news_data[0]\\n article_2 = news_data[1]\\n title_1 = article_1['title']\\n author_1 = article_1['author']\\n source_1 = article_1['source']['name']\\n url_1 = article_1['url']\\n url_1_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_1)\\n title_2 = article_2['title']\\n author_2 = article_2['author']\\n source_2 = article_2['source']['name']\\n url_2 = article_2['url']\\n url_2_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_2)\\n return\",\n \"def GET(self):\\n web.header(\\\"Content-Type\\\",\\\"application/json; charset=utf-8\\\")\\n\\n data = web.input(module=\\\"module\\\", start=\\\"start\\\", num=\\\"num\\\", type=\\\"type\\\")\\n module = data[\\\"module\\\"]\\n start = data[\\\"start\\\"]\\n num = data[\\\"num\\\"]\\n type_ = data[\\\"type\\\"]\\n\\n module = (1 if module == \\\"module\\\" else module)\\n start = (1 if start == \\\"start\\\" else start)\\n num = (1 if num == \\\"num\\\" else num)\\n\\n news = api.get_news_fromDB(int(module), int(start), int(num))\\n\\n if type_ != \\\"html\\\":\\n return json.dumps(news)\\n else:\\n web.header(\\\"Content-Type\\\",\\\"text/html; charset=utf-8\\\")\\n html = \\\"\\\"\\n for item in news:\\n html = html + item[\\\"maindiv\\\"]\\n return html\",\n \"def json(self):\\n return {\\n 'author': self.author,\\n 'email': self.email,\\n 'display_email': self.display_email,\\n 'title': self.title,\\n 'trailer_path': self.trailer_path,\\n 'date': self.date,\\n 'link': self.link,\\n '_id': self._id\\n }\",\n \"def tweet_list_view_pure_django(request, *args, **kwargs):\\n\\n qs = Tweet.objects.all()\\n # tweets_list = [\\n # {\\\"id\\\": x.id, \\\"content\\\": x.content, \\\"likes\\\": random.randint(0, 129)} for x in qs\\n # ]\\n\\n # @Anyi use this line of code instead of the above line\\n tweets_list = [x.serialize() for x in qs]\\n\\n data = {\\\"isUser\\\": False, \\\"tweet_list_response\\\": tweets_list}\\n # @Anyi this is a new way of returning our data\\n # instead of HttpResponse() or render()\\n # and since we want to make our page as dynamic as possible\\n return JsonResponse(data)\",\n \"def data_json(self, extra_context=None, publish=False):\\n if not self.project.CREATE_JSON:\\n # nothing to see here, but the right mimetype\\n return jsonify()\\n\\n if not self.data:\\n # this sets site.data by spreadsheet or gdoc\\n self.get_context(publish)\\n\\n return jsonify(self.data)\",\n \"def get(self):\\n\\n return {\\\"message\\\": \\\"Welcome to the news API. \\\"}\",\n \"def json_posts_latest():\\n posts = posts_base.order_by(Post.pubdate.desc())[:app.config['FEEDITEMS']]\\n out = {'posts': []}\\n for post_result in posts:\\n post_dict = get_public_post_dict(post_result[0], post_result[2])\\n out['posts'].append(post_dict)\\n\\n return jsonify(out)\",\n \"def display(request):\\n data = {}\\n about = About.objects.all()\\n if about:\\n data['success']=1\\n data['message']=\\\"Comments available\\\"\\n data['about']=[]\\n for i in range(len(about)):\\n data['about'].append(\\n {'about':about[i].about,\\n 'about_id':about[i].id,\\n })\\n return JsonResponse(data)\\n else:\\n data['success']=0\\n data['message']='no about available'\\n return JsonResponse(data)\",\n \"def newsList(request):\\n\\n news_count = New.objects.count() # Pocet vsech zaznamu novinek\\n news_list = New.objects.all().order_by(\\\"date\\\") # Sort by date ... and only part of list\\n # misto vsech zaznamu ziskat jen ty v intervalu start - stop -> API\\n\\n pictureOfWeek = PhotoOfWeek.objects.last()\\n context = {'news_list': news_list, 'news_count': news_count, 'pictureOfWeek': pictureOfWeek}\\n return render(request, 'news/newsList.html', context)\",\n \"def format(self, *args):\\n\\t\\tweb.header('Content-Type', 'application/json; charset=utf-8')\\n\\t\\treturn json.dumps(self.content)\",\n \"def get_recent_news_items():\\n news_item_count = request.args.get('newsItemCount') or 3\\n try:\\n animal_news = AnimalNews.get_printable_news_items_all_animals(news_item_count)\\n return jsonify(message=animal_news), 200\\n except Exception as e:\\n print(e)\\n return jsonify(message='{}'.format(e)), 501\",\n \"def news_fetch(region,news_key):\\n #Allows for customizable API key and weather location.\\n url = (f\\\"http://newsapi.org/v2/top-headlines?country={region}&apiKey={news_key}\\\")\\n #Gets API with requests and convert to .json\\n news_api = requests.get(url)\\n news_json = news_api.json()\\n return news_json\",\n \"def news():\\r\\n with open('config.json', 'r') as cfile:\\r\\n config = json.load(cfile)\\r\\n news_api_key = config[\\\"news_api_key\\\"]\\r\\n response = requests.get(\\\"https://newsapi.org/v2/top-headlines?\\\"\\r\\n \\\"sources=bbc-news&apiKey=\\\" + news_api_key)\\r\\n resp_json = response.json()\\r\\n with open(\\\"news.json\\\", 'w') as file:\\r\\n json.dump(resp_json, file)\\r\\n file.close()\",\n \"def render_json(object):\\r\\n return HttpResponse(jsonify(object), content_type='application/json')\",\n \"def tweet_list_view(request, *args, **kwargs):\\n\\n objs = Tweet.objects.all()\\n\\n tweets_list = [{\\\"id\\\": obj.id, \\\"content\\\": obj.content} for obj in objs]\\n\\n data = {\\n\\n \\\"isUser\\\":False,\\n\\n \\\"tweets_list\\\": tweets_list\\n }\\n\\n return JsonResponse(data)\",\n \"def newsfeed_en(request):\\n article_list = Article.objects.order_by('published_date')\\n context = {'article_list': article_list}\\n return render(request, 'sacms/newsfeed_en.html', context)\",\n \"def get_news(request):\\n return get_all_posts(request, PostType.NEWS)\",\n \"def get_feed(request):\\n user = request.user\\n\\n # Get feed data and paginate it.\\n feed = Post.objects.filter(community__communitymember__user=user).order_by(\\n \\\"-created_at\\\"\\n )\\n\\n # Check for filter phrase.\\n phrase = request.GET.get(\\\"phrase\\\")\\n if phrase is not None:\\n if phrase != \\\"\\\" and not phrase.isspace():\\n for term in phrase.split():\\n feed = feed.filter(Q(title__icontains=term))\\n else:\\n feed = feed.none()\\n\\n return JsonResponse(\\n json_paginator(request, feed, lambda d: d.serialize(request)),\\n status=200,\\n )\",\n \"def blog():\\n return jsonify(\\n {\\n 'entries': [\\n {\\n 'title': 'A blog post about things',\\n 'date': '7/3/12',\\n 'id': 1,\\n 'lead': \\\"\\\"\\\"once upon a time, there was a cool dude who did\\n cool things. This is his story.\\\"\\\"\\\",\\n 'body': \\\"More content for the blog post\\\",\\n 'more_url': 'http://blog.tobywaite.net',\\n },\\n {\\n 'title': 'Cool projects, ftw',\\n 'date': '6/3/12',\\n 'id': 2,\\n 'lead': \\\"\\\"\\\"I did a really cool project once, this is all\\n about it.\\\"\\\"\\\",\\n 'body': \\\"More content for the blog post\\\",\\n 'more_url': 'http://blog.tobywaite.net',\\n },\\n ]\\n }\\n )\",\n \"def __repr__(self):\\r\\n\\r\\n return f\\\"<News: id = {self.news_id}, title = {self.title} summary = {self.summary}>\\\"\",\n \"def select_news(self):\\n data = self.soup.findAll('item')\\n for item in data:\\n news_data = dict()\\n for tag in ['title', 'link']:\\n news_data[tag] = item.find(tag).get_text()\\n\\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\\n media = item.find('media:content')\\n\\n if media:\\n news_data['media'] = media.get('url')\\n else:\\n news_data['media'] = None\\n\\n yield news_data\",\n \"def news()->str:#return array[news desc,news link]\\n event_log(\\\"retrieve news data....\\\",\\\"\\\")\\n c = 0\\n location = read_json(\\\"news_api\\\")[0]\\n main_url = \\\"https://newsapi.org/v2/top-headlines?country=\\\"+location+\\\"&apiKey=\\\"+read_json(\\\"news_api\\\")[1]+\\\"\\\"#add a country selection optin via json\\n page = requests.get(main_url).json()\\n article = page[\\\"articles\\\"]\\n news_result = []\\n for data in article:\\n news_result.append([data[\\\"title\\\"],str(data[\\\"url\\\"]).replace('\\\"',\\\" \\\")])#exctracts the wanted data from api\\n if c == 5:#add this to json file so scalibility\\n break\\n c+=1\\n return news_result\",\n \"def post_list_view(request, *args, **kwargs):\\n qs = Post.objects.all()\\n posts_list = [{\\\"id\\\": x.id, \\\"content\\\": x.content, \\\"likes\\\": random.randint(0, 120), \\\"reposts\\\": random.randint(0, 10)} for x in qs] #{\\\"id\\\": x.id, \\\"content\\\": x.content, \\\"likes\\\": random.randint(0, 120), \\\"reposts\\\": random.randint(0, 10) }/x.serialize()\\n data = {\\n \\\"isUser\\\": False,\\n \\\"response\\\": posts_list\\n }\\n return JsonResponse(data) #, save=False\",\n \"def get(self):\\n return GlobalNews.retrieve()\",\n \"def guidebook_news_feed(request):\\n current_site = Site.objects.get_current()\\n\\n feed_title = 'DjangoCon US News Updates'\\n feed_description = 'The latest updates and additions for DjangoCon US.'\\n feed_mimetype = 'application/rss+xml'\\n feed_template = 'pinax/blog/rss_feed.xml'\\n\\n blog_url = 'http://%s%s' % (current_site.domain, reverse('blog'))\\n # feed_url = 'http://%s%s' % (current_site.domain, reverse(url_name, kwargs=kwargs))\\n\\n posts = Post.objects.published().exclude(title__endswith='Sponsor')\\\\\\n .order_by('-published')\\n\\n if posts:\\n feed_updated = posts[0].updated\\n else:\\n feed_updated = datetime(2009, 8, 1, 0, 0, 0)\\n\\n feed = render_to_string(feed_template, {\\n # 'feed_id': feed_url,\\n 'feed_title': feed_title,\\n 'feed_description': feed_description,\\n 'blog_url': blog_url,\\n # 'feed_url': feed_url,\\n 'feed_updated': feed_updated,\\n 'entries': posts,\\n 'current_site': current_site,\\n })\\n\\n return HttpResponse(feed, content_type=feed_mimetype)\",\n \"def get_featured_articles(request):\\n try:\\n count = 1\\n if 'count' in request.POST and int(request.POST['count']):\\n count = int(request.POST['count'])\\n\\n newest_list = []\\n for article in Article.objects.order_by('-modified')[:count]:\\n newest_list.append(article.dump_to_dict())\\n\\n popular_list = []\\n for article in Article.objects.order_by('-views')[:count]:\\n popular_list.append(article.dump_to_dict())\\n\\n return format_ajax_response(True, \\\"Featured articles retrieved successfully.\\\", {'newest': newest_list,'popular': popular_list})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_featured_articles: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retrieving the featured articles.\\\")\",\n \"def json_view(self, recursive=False):\\n\\n context = self.context.aq_inner\\n data = self.export(context, recursive=recursive)\\n pretty = json.dumps(data, sort_keys=True, indent=4)\\n self.request.response.setHeader(\\\"Content-type\\\", \\\"application/json\\\")\\n return pretty\",\n \"def all_news(request):\\n\\n all_news = News.objects.all().order_by(\\\"-date_added\\\")\\n context = {\\n 'news': all_news,\\n 'show_without_bag': True\\n }\\n return render(request, 'news/news.html', context)\",\n \"def get_articles():\\n _, articles = base_query(db_session)\\n return jsonify([p.serialize for p in articles])\",\n \"def news(self):\\n\\n # Get articles with search term, if available, from each News API source\\n news_api_articles = pd.DataFrame()\\n\\n q = urllib.parse.quote(\\\" OR \\\".join(self.search_terms), safe='')\\n\\n response = requests.get(\\\"https://newsapi.org/v2/everything?q=\\\" + q + \\\"&from=\\\" + datetime.now().strftime(\\n \\\"%Y-%m-%d\\\") + \\\"&sortBy=popularity&pageSize=100&apiKey=\\\" + self.__news_api_key)\\n\\n if response.status_code == 200:\\n data = json.loads(response.text)\\n\\n source_articles = []\\n\\n for article in data['articles']:\\n source_articles.append([article['title'],\\n article['description'],\\n article['url'],\\n article['publishedAt']])\\n\\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\\n news_api_articles = pd.concat([news_api_articles, source_articles])\\n\\n news_api_articles = news_api_articles.reset_index(drop='True')\\n\\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\\n\\n news_api_articles = news_api_articles.fillna(' ')\\n\\n term_in_title = news_api_articles['title'].apply(self.any_term)\\n\\n news_api_articles = news_api_articles[term_in_title]\\n\\n if (len(news_api_articles) > 10):\\n news_api_articles = news_api_articles[0:10]\\n\\n else:\\n print(\\\"News API failed to return any items\\\")\\n\\n # Create shortened links using bitly if access token is provided\\n if self.__bitly_access_token != '':\\n\\n bitly_urls = []\\n\\n for index, article in news_api_articles.iterrows():\\n url = article['url']\\n bitly_response = requests.get(\\\"https://api-ssl.bitly.com/v3/shorten\\\",\\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\\n\\n if bitly_response.status_code == 200:\\n data = json.loads(bitly_response.text)\\n bitly_urls.append(data['data']['url'])\\n\\n news_api_articles['url'] = bitly_urls\\n\\n # Store final list to TwitterBot object\\n self.list = news_api_articles\\n\\n return\",\n \"def as_json(self):\",\n \"def list(self, request):\\n return HttpResponse(request.user.series_list, content_type=\\\"application/json\\\")\",\n \"def storiesOnMap(request):\\n requestDict = request.POST\\n zoom = int(requestDict['zoom'])\\n north = Decimal(requestDict['north'])\\n south = Decimal(requestDict['south'])\\n east = Decimal(requestDict['east'])\\n west = Decimal(requestDict['west'])\\n # The \\\"old\\\" parameter contains a commma-separated list of story ids that are already displayed by the browser\\n currentlyDisplayedStoryIds = requestDict['old'].split(',')\\n\\n storiesToDisplay = models.Story.objects.filter(latitude__range=(south, north)).filter(longitude__range=(west, east)).order_by('-created')\\n\\n # Filter the amount of story markers to display based on the current zoom level\\n if len(storiesToDisplay) >= (zoom-1)*5:\\n storiesToDisplay = storiesToDisplay[(zoom-1)*5:zoom*5]\\n\\n response = []\\n for story in storiesToDisplay:\\n if str(story.id) not in currentlyDisplayedStoryIds:\\n\\n if story.contributor:\\n\\t\\tcontribName = story.contributor.name\\n else: \\n contribName = None\\n langName = story.language.name if story.language else None\\n response.append({'id': story.id,\\n 'title': story.title,\\n 'summary': story.summary,\\n 'link': story.link,\\n 'has_flv':story.has_flv(),\\n 'flv_id':story.flv_id(),\\n 'media_type':story.media_type,\\n 'imageRef': story.contributor.imageRef,\\n 'contributor': contribName,\\n 'language': langName,\\n 'created': story.created.strftime('%d %B %Y'),\\n 'lat': float(story.latitude),\\n 'long': float(story.longitude)})\\n return JsonResponse(response)\",\n \"def all_bugs_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Bug.objects.values('title', 'id').order_by('-created').exclude(status='Resolved').annotate(\\n bug_votes=Count('votes'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['bug_votes'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def NewsArticles():\\n health_articles = get_articles('health')\\n education_articles = get_articles('technology')\\n return render_template('articles.html',health=health_articles, tech =education_articles)\",\n \"def main(feed=None):\\n feed_processor = core.FeedProcessor()\\n feed_processor(feed_type=feed)\\n return feed_processor.feed_json\",\n \"def search_news(request):\\n try:\\n query_string = ''\\n if request.GET['search_text'].strip() != '':\\n query_string = '&title='+request.GET['search_text']\\n response = requests.get(APIURL + '/articles/?format=json'+query_string)\\n parser = json.loads(response.content)\\n return render_to_response('article/search_result.html', {'articlelist':parser})\\n except:\\n raise Http404(\\\"Search Item error\\\")\",\n \"def weblinks_view(request):\\n\\n\\trecords = WebLinks.objects.filter(user_id=request.user.id)\\n\\tjson_response = [dict(title=record.title, url=record.url, description=record.description) for record in records]\\n\\treturn JsonResponse(json_response, safe=False)\",\n \"def fetch_news(n):\\n\\n # This is the list we will use the pass back the news information.\\n data = []\\n\\n # Get news stories from the MEN RSS feed.\\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\\n\\n # Loop through the news items, and the pull out the data we need.\\n for news in response.entries[:n]:\\n data.append({\\n 'headline': news.title,\\n 'content': news.description,\\n })\\n\\n return data\",\n \"def all_features_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Feature.objects.values('title').order_by('-created').exclude(status='Implemented').annotate(\\n feature_purchases=Sum('purchases'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['feature_purchases'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def news(self) -> List[News]:\\n return self._news\",\n \"def run(self,dispatcher,tracker,domain): \\n data=newsapi(\\\"us\\\")\\n leng=len(data)\\n for i in range(leng):\\t\\n gt = {\\n \\\"attachment\\\": {\\n \\\"type\\\": \\\"template\\\",\\n \\\"payload\\\": {\\n \\\"template_type\\\": \\\"generic\\\",\\n \\\"elements\\\": [\\n {\\n \\\"title\\\": data['articles'][i]['title'],\\n \\\"image_url\\\":data['articles'][i]['urlToImage'],\\n \\\"subtitle\\\": data['articles'][i]['description'],\\n \\\"buttons\\\": [\\n {\\n \\\"type\\\": \\\"web_url\\\",\\n \\\"url\\\": data['articles'][i]['url'],\\n \\\"title\\\": \\\"Read More\\\"\\n },\\n ]\\n },\\n ]\\n }\\n }\\n } \\n dispatcher.utter_custom_json(gt)\\n return []\",\n \"def get(self, request):\\n \\n posts = post.objects.filter(unlisted=False,visibility=\\\"PUBLIC\\\")\\n\\n page = self.request.GET.get('page', 1)\\n page_num = self.request.GET.get('size', 1000)\\n \\n paginator = custom()\\n results = paginator.paginate_queryset(posts,request)\\n serializer = postSerializer(results, many=True)\\n #return JsonResponse(serializer.data,safe=False)\\n #http://stackoverflow.com/questions/34798703/creating-utf-8-jsonresponse-in-django\\n #return JsonResponse(json.dumps(serializer.data, ensure_ascii=False),\\n #safe=False)\\n\\n\\n return JsonResponse(OrderedDict([('count', paginator.count),\\n ('current', page),\\n ('next', paginator.get_next_link()),\\n ('previous', paginator.get_previous_link()),\\n ('size', page_num),\\n ('posts', serializer.data)]))\\n\\n \\n\\n\\n\\n\\n\\n #data = JSONParser().parse(request)\\n #serializer = postSerializer(data=data)\\n #if serializer.is_valid():\\n # serializer.save()\\n # return JsonResponse(serializer.data, status=201)\\n #return JsonResponse(serializer.errors, status=400)\",\n \"def json(self):\\n return {'id': self.id, 'name': self.name, 'description': self.description}\",\n \"def articleList():\\n articles = get_news(\\n 5, since=news.YESTERDAY.strftime(\\\"%yyyy-%mm-%dd\\\"), query=\\\"covid\\\"\\n )\\n title_list = []\\n desc_list = []\\n url_list = []\\n image_list = []\\n source_list = []\\n for art in articles:\\n image_list.append(art.image)\\n title_list.append(art.title)\\n source_list.append(art.source)\\n desc_list.append(art.description)\\n url_list.append(art.url)\\n socketio.emit(\\n ARTICLE,\\n {\\n \\\"title\\\": title_list,\\n \\\"desc\\\": desc_list,\\n \\\"url\\\": url_list,\\n \\\"img\\\": image_list,\\n \\\"sources\\\": source_list,\\n },\\n )\\n return True\",\n \"def render_to_json_response(self, context, **response_kwargs):\\n return JsonResponse(self.get_data(context))\",\n \"def index_json():\\n\\n response = views.get_feature_collection_metadata(config)\\n\\n return make_response(jsonify(response))\",\n \"def json_sluglist_latest():\\n posts = posts_base.order_by(Post.pubdate.desc())[:app.config['FEEDITEMS']]\\n out = {'posts': []}\\n for post in posts:\\n out['posts'].append([post[0].pubdate.strftime(app.config['POST_DATETIME_FORMAT']), post[0].slug])\\n\\n return jsonify(out)\",\n \"def getchart(request):\\n data = ast.literal_eval(request.body.decode('utf-8'))\\n data['user'] = request.user\\n data = newchart(data)\\n return JsonResponse(data)\",\n \"def Topic_index(request):\\n \\\"\\\"\\\"return path,date of images of topic to javascript\\\"\\\"\\\"\\n fileID=get_sorted_img_list()\\n week_img_list = simplejson.dumps(fileID)\\n #latest\\n path=fileID[0][0]\\n date=fileID[0][1]\\n print date\\n return render(request, 'WebPtt/Gossip_topic_model.html',{'pic_path':path,'week_img_list':week_img_list,'date':date})\",\n \"def get(self, request, *args, **kwargs):\\n if self.request.is_ajax():\\n data = self.get_context_data(**kwargs)\\n return JsonResponse({\\n 'has_more': data['has_more'],\\n 'lastPublished': data['last_published'],\\n 'content': render_to_string(\\\"partials/posts_list.html\\\", data, request=self.request)\\n })\\n\\n return super().get(request, *args, **kwargs)\",\n \"def json(self):\\n return {\\n 'uri': self.view_uri,\\n 'created': time.strftime('%c', time.gmtime(self.created)),\\n 'created_timestamp': self.created,\\n 'exception_type': str(self.exc_type),\\n 'exception': str(self.exc_value),\\n }\",\n \"def print_json_content(self, rss_feed):\\n\\n self.print_if_verbose(\\n f\\\"Method 'print_json_content' is working: \\\\n\\\"\\n f\\\"RSS feed will be printed in JSON format: \\\\n\\\"\\n )\\n\\n json_content = json.dumps(rss_feed, indent=3)\\n print(json_content)\\n\\n self.print_if_verbose(f\\\"Method 'print_json_content' is finished. \\\\n\\\")\\n\\n return \\\"Content is printed in JSON format\\\"\",\n \"def api_get_post(request, post_id):\\n\\n post = get_object_or_404(Post, id=post_id)\\n\\n json = serializers.serialize(\\\"json\\\", [post], fields=(\\n \\\"pub_time\\\", \\\"_text_rendered\\\", \\\"title\\\", \\\"text\\\", \\\"image\\\",\\n \\\"image_width\\\", \\\"image_height\\\", \\\"replies\\\", \\\"tags\\\"\\n ))\\n\\n return HttpResponse(content=json)\",\n \"def serialize(self):\\n return {\\n 'id': self.id,\\n 'title': self.title,\\n 'description': self.description,\\n 'ranking': self.ranking,\\n 'created_date': self.created_date,\\n }\",\n \"def feed(self):\\r\\n return feed.Feed(self)\",\n \"def json_response(self, request, *args, **kwargs):\\n\\n return HttpResponse(self.construct_json(),\\n content_type='application/json',\\n mimetype='application/json', status=self.status)\",\n \"def news_view():\\n session_id = request.args.get('session-id', None)\\n user_id = request.args.get('user-id', None)\\n edit_mode_string = request.args.get('edit', None)\\n news_list = get_news_list()\\n if edit_mode_string == 'true':\\n edit_mode = True\\n else:\\n edit_mode = False\\n if check_authentication(session_id, user_id) and is_admin_user(user_id):\\n return render_template('news_manager.html', user=user_id, session_id=session_id, edit_mode=edit_mode,\\n news_list=news_list)\\n else:\\n return render_template('home.html', cars_list=get_cars_preview(), news_list=get_news_list(), authjs=False,\\n preview_length=get_cars_preview().__len__(), del_session_cookie=True)\",\n \"def run(self,dispatcher,tracker,domain): \\n data=newsapi(\\\"in\\\")\\n leng=len(data)\\n for i in range(leng):\\t\\n gt = {\\n \\\"attachment\\\": {\\n \\\"type\\\": \\\"template\\\",\\n \\\"payload\\\": {\\n \\\"template_type\\\": \\\"generic\\\",\\n \\\"elements\\\": [\\n {\\n \\\"title\\\": data['articles'][i]['title'],\\n \\\"image_url\\\":data['articles'][i]['urlToImage'],\\n \\\"subtitle\\\": data['articles'][i]['description'],\\n \\\"buttons\\\": [\\n {\\n \\\"type\\\": \\\"web_url\\\",\\n \\\"url\\\": data['articles'][i]['url'],\\n \\\"title\\\": \\\"Read More\\\"\\n },\\n ]\\n },\\n ]\\n }\\n }\\n } \\n dispatcher.utter_custom_json(gt)\\n return []\",\n \"def get_headlines():\\n country = request.args.get('country', type=str)\\n if country is not None:\\n data = te.getNews(country=country).dropna()\\n return jsonify(data.to_dict(orient='records'))\\n data = te.getNews()\\n return jsonify(te.getNews().dropna().to_dict(orient='records'))\",\n \"def Export(self):\\n\\n current_time = datetime.datetime.now(tz.UTC)\\n\\n self.data = np.load(self.cache_path, allow_pickle=True)[()]\\n\\n news = []\\n\\n ID = self.data.keys()\\n\\n for id in ID:\\n v = self.data[id]\\n if 'address' in v and \\\"河南\\\" in v['address'] and v['valid'] == 1\\\\\\n and current_time - parse(v['time']) < datetime.timedelta(hours=12):\\n news.append({\\\"Time\\\": v['time'], \\\"address\\\": v['address'], \\\"location\\\": v['location'], \\\"post\\\": v['post'],\\n \\\"link\\\": v[\\\"link\\\"]})\\n\\n with open(self.output_path, \\\"w\\\", encoding=\\\"utf-8\\\") as fp:\\n json.dump(news, fp, ensure_ascii=False, indent=4)\\n\\n print(\\\"Export %d info\\\" % len(news))\",\n \"def index(self):\\r\\n shows = Shows.query.order_by(asc(Shows.ShowID)).all()\\r\\n content = jsonify({\\r\\n \\\"shows\\\": [{\\r\\n \\\"date\\\": get_iso_format(show.ShowDate),\\r\\n \\\"countryCode\\\": show.CountryCode,\\r\\n \\\"country\\\": show.Country,\\r\\n \\\"city\\\": show.City,\\r\\n \\\"venue\\\": show.Venue,\\r\\n \\\"setlist\\\": self.get_setlist(show.ShowID),\\r\\n \\\"otherBands\\\": self.get_other_bands(show.ShowID),\\r\\n \\\"people\\\": self.get_show_people(show.ShowID),\\r\\n } for show in shows]\\r\\n })\\r\\n\\r\\n return make_response(content, 200)\",\n \"def latest_content(request):\\n latest_articles = Article.published_articles()[:5]\\n latest_comments = Comment.objects.all().order_by('-pub_date')[:5]\\n tags = Tag.objects.annotate(num_articles=Count('article')).order_by(\\n '-num_articles')\\n contributors = Contributor.objects.annotate(\\n num_articles=Count('article')).order_by('-num_articles')\\n return {'latest_articles': latest_articles,\\n 'latest_comments': latest_comments,\\n 'tags': tags,\\n 'contributors': contributors,\\n }\",\n \"def get_remote_news_items(self):\\n items = []\\n params = {\\n \\\"base_url\\\": self.osha_json_url,\\n \\\"lang\\\": api.portal.get_tool(\\\"portal_languages\\\").getPreferredLanguage(),\\n \\\"query_tags\\\": self.remote_news_query_tags,\\n }\\n qurl = \\\"{base_url}/{lang}/services/hw/news/{query_tags}\\\".format(**params)\\n result = urlopen(qurl)\\n if result.code == 200:\\n json = load(result)\\n for node in json.get(\\\"nodes\\\"):\\n item = node.get(\\\"node\\\")\\n pd = item.get('publication_date', '')\\n items.append({\\n 'remote_item': True,\\n 'Title': item['title'],\\n 'Date': (\\n pd and DateTime(pd, datefmt=\\\"international\\\").strftime(\\n \\\"%Y/%m/%d %H:%M\\\") or \\\"\\\"),\\n 'getURL': item.get('path'),\\n 'path': item.get('path'),\\n 'Description': item.get('summary', '') or item.get('body', ''),\\n 'text': item.get('summary', '') and item.get('body', '') or '',\\n 'remote_image': item.get('image', ''),\\n 'node_id': item.get('nid'),\\n })\\n return items\",\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 get_chart_one(request):\\r\\n json_str = []\\r\\n \\r\\n usuarios = Usuario.objects.all()\\r\\n for usuario in usuarios:\\r\\n peticiones = Peticion.objects.filter(usuario=usuario)\\r\\n json_str.append({ \\r\\n 'name': u'%s %s' % (usuario.persona.nombre,\\r\\n usuario.persona.apellidos),\\r\\n 'data': len(peticiones)\\r\\n }) \\r\\n json_obj = json.dumps(json_str, sort_keys=True, indent=4)\\r\\n response = HttpResponse(json_obj, mimetype=\\\"application/json\\\") \\r\\n return response\",\n \"def projects_JSON(request):\\n projects = Project.objects.all()\\n for p in projects:\\n p.description = bleach.clean(markdown.markdown(p.description, extensions=['markdown.extensions.fenced_code']), strip=True)\\n projects_as_json = serializers.serialize(\\n 'json',\\n projects,\\n fields=('title',\\n 'posted',\\n 'difficulty',\\n 'tags',\\n 'articles',\\n 'user',\\n 'description',\\n 'pk'),\\n use_natural_foreign_keys=True)\\n return HttpResponse(json.dumps(projects_as_json), content_type='json')\",\n \"def run(self,dispatcher,tracker,domain): \\n data=newsapi(\\\"au\\\")\\n leng=len(data)\\n for i in range(leng):\\t\\n gt = {\\n \\\"attachment\\\": {\\n \\\"type\\\": \\\"template\\\",\\n \\\"payload\\\": {\\n \\\"template_type\\\": \\\"generic\\\",\\n \\\"elements\\\": [\\n {\\n \\\"title\\\": data['articles'][i]['title'],\\n \\\"image_url\\\":data['articles'][i]['urlToImage'],\\n \\\"subtitle\\\": data['articles'][i]['description'],\\n \\\"buttons\\\": [\\n {\\n \\\"type\\\": \\\"web_url\\\",\\n \\\"url\\\": data['articles'][i]['url'],\\n \\\"title\\\": \\\"Read More\\\"\\n },\\n ]\\n },\\n ]\\n }\\n }\\n } \\n dispatcher.utter_custom_json(gt)\\n return []\",\n \"def json_frapp(request):\\n from pv.settings import MEDIA_URL\\n\\n if request.GET.get('date') == None:\\n start = datetime.combine(date.today(), time(0, 0))\\n else:\\n start = datetime.combine( datetime.strptime(request.GET.get('date'), '%Y-%m-%d').date(), time(0, 0))\\n\\n end = datetime.combine(start, time(23, 59))\\n\\n timeslots = TimeSlot.objects.filter(start__gte=start,start__lte=end).select_related('show').order_by('start')\\n\\n\\n '''Generate categories object for output'''\\n\\n categories = Category.objects.all()\\n categories_output = []\\n\\n for c in categories:\\n c_entry = {\\n 'id': c.id,\\n 'color': c.color.replace('#', '').upper(),\\n 'namedisplay': c.category,\\n 'description': c.description\\n }\\n\\n categories_output.append(c_entry)\\n\\n # Get all series for timeslots\\n series = set()\\n for ts in timeslots:\\n series.add(ts.show)\\n\\n\\n '''Generate series object for output'''\\n\\n series_output = []\\n\\n for s in series:\\n metainfos = []\\n metainfos.append({ 'key': 'ProduzentIn', 'value': ', '.join(ts.show.hosts.values_list('name', flat=True)) })\\n metainfos.append({ 'key': 'E-Mail', 'value': ', '.join(ts.show.hosts.values_list('email', flat=True)) })\\n\\n image = '' if s.image.name == None or s.image.name == '' else str(get_current_site(request)) + MEDIA_URL + s.image.name\\n url = '' if s.website == None or s.website == '' else s.website\\n\\n # Get active schedules for the given date\\n # But include upcoming single timeslots (with rrule_id=1)\\n schedules = Schedule.objects.filter( Q(show=s.id,is_repetition=False) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n schedules_repetition = Schedule.objects.filter( Q(show=s.id,is_repetition=True) &\\n (\\n Q(rrule_id__gt=1,dstart__lte=start,until__gte=start) |\\n Q(rrule_id=1,dstart__gte=start)\\n )\\n )\\n\\n broadcastinfos = ''\\n\\n if not schedules.exists():\\n continue\\n\\n for schedule in schedules:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n if schedules_repetition.exists():\\n broadcastinfos = broadcastinfos + 'Wiederholung jeweils:'\\n for schedule in schedules_repetition:\\n broadcastinfos = broadcastinfos + generate_frapp_broadcastinfos(schedule)\\n\\n s_entry = {\\n 'id': s.id,\\n 'categoryid': s.category.values_list('id', flat=True)[0],\\n 'color': s.category.values_list('color', flat=True)[0].replace('#', '').upper(),\\n 'namedisplay': s.name,\\n 'description': s.description,\\n 'url': url,\\n 'image': image,\\n 'broadcastinfos': broadcastinfos,\\n 'metainfos': metainfos\\n }\\n\\n series_output.append(s_entry)\\n\\n\\n '''Generate shows object for output'''\\n\\n shows_output = []\\n\\n for ts in timeslots:\\n\\n is_repetition = ' ' + _('REP') if ts.schedule.is_repetition is 1 else ''\\n namedisplay = ts.show.name + is_repetition\\n description = ts.show.description\\n url = str(get_current_site(request)) + '/shows/' + ts.show.slug\\n urlmp3 = ''\\n\\n # If there's a note to the timeslot use its title, description and url\\n try:\\n note = Note.objects.get(timeslot=ts.id)\\n namedisplay = note.title + is_repetition\\n description = note.content\\n url = str(get_current_site(request)) + '/notes/' + note.slug\\n urlmp3 = note.audio_url\\n except ObjectDoesNotExist:\\n pass\\n\\n ts_entry = {\\n 'id': ts.id,\\n 'seriesid': ts.show.id,\\n 'datetimestart': ts.start.strftime('%d.%m.%Y %H:%M:%S'),\\n 'datetimeend': ts.end.strftime('%d.%m.%Y %H:%M:%S'),\\n 'namedisplay': namedisplay,\\n 'description': description,\\n 'url': url,\\n 'urlmp3': urlmp3,\\n }\\n\\n shows_output.append(ts_entry)\\n\\n output = {}\\n output['categories'] = categories_output\\n output['series'] = series_output\\n output['shows'] = shows_output\\n\\n return HttpResponse(json.dumps(output, ensure_ascii=False).encode('utf8'),\\n content_type=\\\"application/json; charset=utf-8\\\")\",\n \"def get(self, request):\\n query = Article.objects.exclude(in_menu=True)\\n\\n article_filter = Q()\\n searchable_paths = {}\\n\\n search_terms = [term for term in request.GET.get(\\\"query\\\").split(\\\" \\\")]\\n for term in search_terms:\\n searchable_paths[term] = Q(headline__icontains=term) | Q(\\n teaser__icontains=term\\n )\\n\\n for key in searchable_paths.keys():\\n article_filter &= searchable_paths[key]\\n\\n query = query.filter(article_filter).distinct()\\n articles = [\\n {\\n \\\"updated\\\": article.updated,\\n \\\"headline\\\": article.headline,\\n \\\"teaser\\\": article.teaser,\\n \\\"tags\\\": [tag.name for tag in article.tags.all()],\\n \\\"url\\\": reverse(\\n \\\"article.detail\\\",\\n kwargs={\\n \\\"year\\\": article.created.year,\\n \\\"month\\\": article.created.month,\\n \\\"slug\\\": article.slug,\\n },\\n ),\\n }\\n for article in query\\n ]\\n\\n return JsonResponse({\\\"articles\\\": articles})\",\n \"def json(data):\\n if isinstance(data, dict):\\n data = ujson.encode(data)\\n uid = str(uuid.uuid4())\\n display(HTML('<div id=\\\"{0}\\\" style=\\\"height: 600px; width:100%;\\\"></div>'.format(uid)))\\n display(Javascript(\\\"\\\"\\\"\\n require([\\\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\\\"], function() {\\n document.getElementById('%s').appendChild(renderjson(%s))\\n });\\n \\\"\\\"\\\" % (uid, data)))\",\n \"def json(self, **kwargs):\\n return json.loads(self.content, **kwargs)\",\n \"def as_dict(self):\\r\\n return {\\r\\n \\\"text\\\": {\\r\\n \\\"title\\\": self.title_text,\\r\\n \\\"body\\\": self.body_text,\\r\\n },\\r\\n \\\"html\\\": {\\r\\n \\\"title\\\": self.title,\\r\\n \\\"body\\\": self.body,\\r\\n },\\r\\n \\\"seen\\\": self.seen,\\r\\n \\\"sent_at\\\": self.sent_at,\\r\\n }\",\n \"def posts_get():\\n \\n\\n # Get and filter the posts from the database\\n songs = session.query(models.Song).all()\\n \\n # Convert the posts to JSON and return a response\\n data = json.dumps([song.as_dictionary() for song in songs])\\n return Response(data, 200, mimetype=\\\"application/json\\\")\",\n \"def make_feed_dict(self, data):\\r\\n raise NotImplementedError\",\n \"def index(request):\\n\\n\\t#define variables\\n\\targs = {}\\n\\tall_teams = []\\n\\tall_news = []\\n\\t\\n\\t#getting data form models\\n\\tleagues = League.objects.all()\\n\\t\\n\\t#in this loop we get all teams in our all leagues to a 'all_teams' list \\n\\tfor league in leagues:\\n\\t\\tall_teams += league.team_set.all()\\n\\n\\t#in this loop we get all new form every team which is in 'all_teams' list and place that new into 'all_news' list \\n\\tfor team in all_teams:\\n\\t\\tall_news += team.news_set.all()\\n\\n\\t#sort news by date so that last news pop up first\\n\\tall_news = reversed(sorted(all_news, key=lambda ne:ne.date))\\n\\t#taking forst 50 news of all news\\n\\tall_news = list(all_news)[:50]\\n\\t#context data initialization into dictionary 'args'\\n\\targs['news'] = all_news\\n\\targs['leagues'] = leagues\\n\\n\\treturn render_to_response('news/index.html',args)\",\n \"def articles():\\n \\n # Parse through RSS feed of Get Rich Slowly\\n feed = feedparser.parse(\\\"http://www.getrichslowly.org/blog/feed/\\\")\\n \\n # Get current username\\n username = get_user()\\n \\n return render_template(\\\"articles.html\\\", username=username, feed=feed)\",\n \"def get_data(self):\\n return self.data.to_json()\",\n \"def showItemsJSON():\\n items = db.getAllItems()\\n return jsonify(CategoryItems=[i.serialize for i in items])\",\n \"def to_dict(self):\\n return {\\n 'title': self.title,\\n 'description': self.description,\\n 'dec_description': self.dec_description,\\n 'link': self.link,\\n 'pubDate': self.pubDate.strftime(\\\"%a, %d %b %Y %H:%M\\\"),\\n 'media': json.loads(self.media),\\n 'source': self.source.url,\\n 'links': json.loads(self.links),\\n 'dec_links': json.loads(self.dec_links),\\n }\",\n \"def index(self):\\n \\n return self.view.render('index.html', {\\\"posts\\\"=posts})\",\n \"def get_big_feed():\\n\\n # A list of news items. We need to preserve it in order to create it once\\n # during the first call, and to\\n # return these items from this already existing list\\n big_feed_gen = None\\n\\n def nested_return_feed():\\n nonlocal big_feed_gen\\n\\n if big_feed_gen:\\n try:\\n return json.dumps(next(big_feed_gen), indent=4, sort_keys=True,\\n separators=(',', ': '), ensure_ascii=False)\\n except StopIteration:\\n return '', 204\\n else:\\n big_feed_gen = get_feed_generator()\\n return json.dumps(next(big_feed_gen), indent=4, sort_keys=True,\\n separators=(',', ': '), ensure_ascii=False)\\n return nested_return_feed\",\n \"def feed(self):\\n return feed.Feed(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.673006","0.66394603","0.6454153","0.6373204","0.6363174","0.63411576","0.6298449","0.61722803","0.61526084","0.60082966","0.59839356","0.5969505","0.5918859","0.5916364","0.5914569","0.5899843","0.58803356","0.5869351","0.58558553","0.5835319","0.58160734","0.5789556","0.578666","0.5769338","0.57673436","0.575199","0.57514006","0.5728741","0.5715693","0.57057583","0.5700944","0.56653327","0.56580675","0.5637963","0.56135064","0.5610844","0.5608263","0.55990064","0.55826885","0.5582132","0.55744374","0.55644864","0.5562998","0.5554155","0.5544765","0.5528018","0.5527508","0.5527318","0.55093616","0.5498074","0.5492856","0.548424","0.547956","0.547839","0.54766935","0.546302","0.5453283","0.54493934","0.54376054","0.5421959","0.54196537","0.54152215","0.54131967","0.5412921","0.5412663","0.54109","0.5410872","0.54093087","0.54083955","0.5405362","0.54034257","0.54026306","0.54010636","0.5396703","0.53824735","0.5367753","0.5364813","0.5353915","0.53502494","0.5347915","0.53455484","0.5335862","0.5330128","0.5324862","0.532466","0.53071433","0.5301614","0.52930593","0.5290631","0.5287332","0.5286725","0.52773803","0.5272741","0.52688426","0.526833","0.5262902","0.52608186","0.52591294","0.52547234","0.52532595"],"string":"[\n \"0.673006\",\n \"0.66394603\",\n \"0.6454153\",\n \"0.6373204\",\n \"0.6363174\",\n \"0.63411576\",\n \"0.6298449\",\n \"0.61722803\",\n \"0.61526084\",\n \"0.60082966\",\n \"0.59839356\",\n \"0.5969505\",\n \"0.5918859\",\n \"0.5916364\",\n \"0.5914569\",\n \"0.5899843\",\n \"0.58803356\",\n \"0.5869351\",\n \"0.58558553\",\n \"0.5835319\",\n \"0.58160734\",\n \"0.5789556\",\n \"0.578666\",\n \"0.5769338\",\n \"0.57673436\",\n \"0.575199\",\n \"0.57514006\",\n \"0.5728741\",\n \"0.5715693\",\n \"0.57057583\",\n \"0.5700944\",\n \"0.56653327\",\n \"0.56580675\",\n \"0.5637963\",\n \"0.56135064\",\n \"0.5610844\",\n \"0.5608263\",\n \"0.55990064\",\n \"0.55826885\",\n \"0.5582132\",\n \"0.55744374\",\n \"0.55644864\",\n \"0.5562998\",\n \"0.5554155\",\n \"0.5544765\",\n \"0.5528018\",\n \"0.5527508\",\n \"0.5527318\",\n \"0.55093616\",\n \"0.5498074\",\n \"0.5492856\",\n \"0.548424\",\n \"0.547956\",\n \"0.547839\",\n \"0.54766935\",\n \"0.546302\",\n \"0.5453283\",\n \"0.54493934\",\n \"0.54376054\",\n \"0.5421959\",\n \"0.54196537\",\n \"0.54152215\",\n \"0.54131967\",\n \"0.5412921\",\n \"0.5412663\",\n \"0.54109\",\n \"0.5410872\",\n \"0.54093087\",\n \"0.54083955\",\n \"0.5405362\",\n \"0.54034257\",\n \"0.54026306\",\n \"0.54010636\",\n \"0.5396703\",\n \"0.53824735\",\n \"0.5367753\",\n \"0.5364813\",\n \"0.5353915\",\n \"0.53502494\",\n \"0.5347915\",\n \"0.53455484\",\n \"0.5335862\",\n \"0.5330128\",\n \"0.5324862\",\n \"0.532466\",\n \"0.53071433\",\n \"0.5301614\",\n \"0.52930593\",\n \"0.5290631\",\n \"0.5287332\",\n \"0.5286725\",\n \"0.52773803\",\n \"0.5272741\",\n \"0.52688426\",\n \"0.526833\",\n \"0.5262902\",\n \"0.52608186\",\n \"0.52591294\",\n \"0.52547234\",\n \"0.52532595\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78444064"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7498,"cells":{"query":{"kind":"string","value":"View for retreiving the chat status for Ask a Librarian pages. Returns json."},"document":{"kind":"string","value":"def chat_status(request):\n if request.method == 'GET':\n ask_name = request.GET['name']\n status = get_chat_status_and_css(ask_name)\n return JsonResponse(\n {\n 'chat_status': status[0],\n 'chat_css': status[1],\n }\n )"},"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 chat_status(request):\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\n if request.method == 'GET' and request.is_ajax():\n if(team is None):\n return render(request, 'access_error.html', {'reason': \"team\"})\n status = team.num_waiting_messages\n return HttpResponse(json.dumps({\"num_messages\": status}))\n else:\n return HttpResponseNotFound()","def get_conversation(request):\n collected_values = {}\n\n # Only allow GET requests for this endpoint\n if request.method != 'GET':\n collected_values[\"success\"] = False\n collected_values[\"errmsg\"] = \"Wrong HTTP verb\"\n return JsonResponse(collected_values, status=400)\n\n # Extract and form params\n uid = request.GET['uid']\n oid = request.GET['oid']\n token = request.GET['token']\n ts_query = request.GET['ts']\n time_user_seen = request.GET.get('tus')\n limit = int(request.GET['limit'])\n\n if ts_query == \"\":\n ts_query = timezone.now()\n\n change_user_seen = False\n if time_user_seen == \"true\":\n change_user_seen = True\n\n # Check if token is valid\n is_valid, collected_values[\"token\"] = check_auth(uid, token, timezone.now())\n if not is_valid:\n collected_values[\"success\"] = False\n collected_values[\"errmsg\"] = \"Invalid Token\"\n return JsonResponse(collected_values, status=400)\n\n # Collect all messages sent by two users in question listed by created at time\n message_query_set = Messages.objects.filter(\n Q(user_id=uid, other_id=oid) |\n Q(other_id=uid, user_id=oid)).order_by('-created_at')[:limit]\n\n # Collect all messages from query\n test_list = []\n for message in message_query_set:\n if change_user_seen:\n message.time_user_seen = timezone.now()\n message.save()\n test_list.append(message.get_map())\n\n # Collect return values\n collected_values[\"messages\"] = test_list\n collected_values[\"success\"] = True\n\n LOGGER.info(\"Get Conversation Result: %s\", collected_values)\n return JsonResponse(collected_values, status=200)","def load_messages(request):\n thread = models.MessageThread.objects.get(hash_id=request.GET['id'])\n \n # check if user is a part of this chat\n if not request.user in thread.clients.all():\n return HttpResponse(status=403)\n\n # query for messages filter\n q = [Q(thread=thread)]\n if 'before' in request.GET:\n q.append(Q(date__lt=int(request.GET['before'])))\n\n # query messages matching filter\n messages = models.Message.objects.filter(*q).order_by('-id')\n messages_data = serializers.MessageListSerializer(messages[:30]).data\n\n # mark any unread messages in chat as read\n thread.mark_read(request.user)\n return JsonResponse({\"messages\":messages_data,\"end\":messages.count() <= 30})","def get_conversation_list(request):\n collected_values = {}\n\n # Only accept GET requests for this endpoint\n if request.method != 'GET':\n collected_values[\"success\"] = False\n collected_values[\"errmsg\"] = \"Wrong HTTP verb\"\n return JsonResponse(collected_values, status=400)\n\n # Extract params\n uid = request.GET['uid']\n token = request.GET['token']\n limit = int(request.GET['limit']) # Force a limiter to see how many users to get\n\n # Check if the token is valid\n is_valid, collected_values[\"token\"] = check_auth(uid, token, timezone.now())\n if not is_valid:\n collected_values[\"success\"] = False\n collected_values[\"errmsg\"] = \"Invalid Token\"\n return JsonResponse(collected_values, status=400)\n\n # Maybe cache or find better way of getting most recent id's messaged\n # Do a walkthrough of all messages and count totals\n # Potential Improvement is to keep a mapping of all messages sent from users to users\n users = {}\n msg_sent = Messages.objects.filter(user_id=uid).order_by('-created_at')[:limit]\n msg_recieved = Messages.objects.filter(other_id=uid).order_by('-created_at')[:limit]\n for msg in msg_sent:\n if users.get(msg.other_id) is None:\n users[msg.other_id] = 1\n else:\n users[msg.other_id] += 1\n for msg in msg_recieved:\n if users.get(msg.user_id) is None:\n users[msg.user_id] = 1\n else:\n users[msg.user_id] += 1\n\n # Collect return values\n collected_values[\"users\"] = users\n collected_values[\"success\"] = True\n\n LOGGER.info(\"Get Conversation List Result: %s\", collected_values)\n return JsonResponse(collected_values, status=200)","def load_inbox(request):\n threads = models.MessageThread.objects.filter(clients=request.user).annotate(\n unread_count=Count('receipts',filter=Q(receipts__recipient=request.user))\n )\n thread_data = serializers.MessageThreadListSerializer(threads).data\n #user = userauth_models.User.objects.filter(username=request.user.username)\n #print(user.username)\n #print(get_channel_layer())\n #print(request.session['channel_name'])\n return JsonResponse({'threads':thread_data})","def chatlist(request):\n\n chats = get_chat_list()\n chat_list = pagination(request, chats, CHATS_PER_PAGE)\n\n dic = {'chatlist': chat_list}\n return render_to_response('whatsapp/chatlist.html', dic, context_instance=RequestContext(request))","async def status(self):\n cmd = subprocess.check_output([\"birdc\", \"show\", \"proto\"])\n for page in chat_formatting.pagify(cmd.decode(), ['\\n', ' '], shorten_by=12):\n await self.bot.say(chat_formatting.box(page))","def ajax_status(request):\r\n if not request.user.is_authenticated():\r\n raise PermissionDenied\r\n\r\n\r\n qs = UserPreference.objects.filter(\r\n user=request.user,\r\n key=NOTIFICATION_PREF_KEY\r\n )\r\n\r\n return HttpResponse(json.dumps({\"status\":len(qs)}), content_type=\"application/json\")","def test_get_project_chat_messages_passes(self):\n response = self.client.get(self.endpoint_url)\n response_body = response.get_json()\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response_body, {\"chat\": [], \"pagination\": None})\n # to do: add comments and test again","def list(self, request, *args, **kwargs):\n return super(PublicChatViewSet, self).list(request, *args, **kwargs)","def chat(request):\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\n if request.method == 'POST':\n # There is data in the post request, but we don't need anything but\n # the message because normal users can't send as staff or other teams\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\n is_response=False, team=team)\n team.num_waiting_messages = 0\n messages = [m]\n else:\n if(team is None):\n return render(request, 'access_error.html', {'reason': \"team\"})\n if(team.hunt.is_locked and not team.is_playtester_team):\n return render(request, 'access_error.html', {'reason': \"hunt\"})\n if request.is_ajax():\n messages = Message.objects.filter(pk__gt=request.GET.get(\"last_pk\"))\n else:\n messages = Message.objects\n messages = messages.filter(team=team).order_by('time')\n\n # The whole message_dict format is for ajax/template uniformity\n rendered_messages = render_to_string('chat_messages.html',\n {'messages': messages, 'team_name': team.team_name})\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\n try:\n last_pk = Message.objects.latest('id').id\n except Message.DoesNotExist:\n last_pk = 0\n team.num_waiting_messages = 0\n\n team.save() # Save last_*_message vars\n context = {'message_dict': message_dict, 'last_pk': last_pk}\n if request.is_ajax() or request.method == 'POST':\n return HttpResponse(json.dumps(context))\n else:\n context['team'] = team\n return render(request, 'chat.html', context)","def get_status_messages(self):\n return self.data[\"allMessagesForFrontend\"][\"messages\"]","def get_messages(self):\n other_user_email = request.args.get('other_user_email')\n page = request.args.get('page')\n per_page = request.args.get('per_page')\n if not other_user_email or not page or not per_page:\n self.logger.debug(messages.MISSING_FIELDS_ERROR % \"query params\")\n return messages.ERROR_JSON % messages.MISSING_FIELDS_ERROR % \"query params\", 400\n email_token = auth.current_user()[0]\n page = int(page)\n per_page = int(per_page)\n # App sends starting with 1 but we start at 0\n page -= 1\n try:\n message_list, pages = self.friend_database.get_conversation(email_token, other_user_email, per_page, page)\n except NoMoreMessagesError:\n self.logger.debug(messages.NO_MORE_PAGES_ERROR)\n return messages.NO_MORE_PAGES_ERROR, 404\n message_list = [{k:v for k,v in m._asdict().items() if k != \"hidden_to\"} for m in message_list]\n for i in range(len(message_list)):\n message_list[i][\"timestamp\"] = message_list[i][\"timestamp\"].isoformat()\n return json.dumps({\"messages\": message_list, \"pages\": pages}), 200","def list_messages(chat_id):\n response = jsonify({\"messages\": list_messages_for_chat(chat_id)})\n return response","def get_unread_messages():\n mark_seen = request.args.get('mark_seen', True)\n unread_msg = g.driver.get_unread()\n\n if mark_seen:\n for msg in unread_msg:\n msg.chat.send_seen()\n\n return jsonify(unread_msg)","def single_chat(request, key):\n \n if not Messages.objects.using('msgstore').filter(key_remote_jid=key).count() > 0:\n return render_to_response('whatsapp/errors/404.html', context_instance=RequestContext(request)) \n\n msgs = get_chat_messages(key)\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\n\n dic = {\n 'peer': key,\n 'chatmessages': msgs_list,\n 'gps': Messages.objects.using('msgstore').exclude((Q(longitude='0.0') | Q(latitude='0.0'))),\n 'media': Messages.objects.using('msgstore').exclude(media_url__isnull=True),\n 'PAG_TITLE': 'Conversation'\n }\n\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))","def status_api(request):\n if request.method == 'GET':\n return JsonResponse({\n 'status': 'OK',\n 'version': __version__\n }, status=200)","def list(self, request, *args, **kwargs):\n return super(ConversationViewSet, self).list(request, *args, **kwargs)","def msgStatus():\n return jsonify({\"status\": \"OK\"})","def get_messages_json(self, limit=10):\n params = self.params\n params[\"limit\"] = limit\n response = requests.get(self.url + \"conversations.history\", params=params)\n return response.json()[\"messages\"]","def get_status(self):\n r = requests.get(self.base_url + '/status')\n return r.json()","def list(request, format=None):\r\n user_messages = request.user.profile.recent_messages()\r\n if format and format == '.json':\r\n data = {\r\n 'messages': [msg.to_json() for msg in user_messages],\r\n }\r\n return HttpResponse(json.dumps(data), mimetype='application/json')\r\n d = {\r\n 'objects': user_messages,\r\n 'title': 'Messages',\r\n }\r\n return render(request, 'usermessages/list.html', d)","def retrieve_conversation_history(username: Text) -> Text:\n history = inmemory_storage[username]\n if history:\n return jsonify(history)\n else:\n return jsonify(history), 404","def GET(self, request):\n timer = Timer()\n try:\n myaccount = get_account_info_for_current_user()\n\n resp_json = myaccount.json_data(\n use_abbr_week_month_day_format=True)\n log_success_response(logger, timer)\n return HttpResponse(json.dumps(resp_json))\n except Exception:\n return handle_exception(logger, timer, traceback)","def notification_list(request):\n try:\n validator = NotificationListValidator(request.GET)\n valid = validator.validate() # Validate the request\n if valid:\n current_user_id = request.user_id\n page_limit = int(request.GET['page_limit'])\n page_offset = int(request.GET['page_offset'])\n\n # notification listing\n notification_list = Notification.objects.filter(user_id=current_user_id).all().order_by('-created_on')[page_offset:page_limit+page_offset]\n serializer = NoitifcationListSerializer(notification_list, many=True)\n\n # set is_read = 1\n Notification.objects.filter(user_id=current_user_id).update(\n is_read=1\n )\n \n return Response({'data':serializer.data}, status=status.HTTP_200_OK)\n else:\n return Response({'error':requestErrorMessagesFormate(validator.get_message())}, status=status.HTTP_200_OK)\n except Exception as exception:\n logerror('notifications/views.py/notification_list', str(exception))\n return Response({'error':str(exception)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)","def get(self):\n self.write(\n '{\"error\": \"cryptochat-server main page, '\n 'please refer to /api/message/new or /api/message/updates\"}')","def get_messages(request, uuid):\n if request.is_ajax():\n user = request.user\n room = Room.objects.get(pk=uuid)\n if room.members.filter(pk=user.pk).exists():\n messages_qs = room.message_set.all()\n page = request.GET.get('page')\n paginator = Paginator(messages_qs, 20)\n try:\n selected = paginator.page(page)\n except PageNotAnInteger:\n selected = paginator.page(1)\n except EmptyPage:\n selected = []\n messages = []\n for message in selected:\n data = {\n 'sender': {\n 'name': str(message.sender),\n 'id': message.sender.pk\n },\n 'message': message.text,\n 'received_room_id': uuid,\n 'date_created': message.date_created.strftime(\"%d %b %Y %H:%M:%S %Z\")\n }\n messages.append(data)\n return JsonResponse(messages, safe=False)\n\n else:\n return Http404(_(\"Sorry! We can't find what you're looking for.\"))\n else:\n return Http404(_(\"Sorry! We can't find what you're looking for.\"))","def get_languages():\n\n api = (api_name, 'languages')\n\n response = make_request(api=api, action='get', **{})\n status_code = response.status_code\n content = response.text\n\n msg = str(status_code) + ' : ' + content\n \n logger.debug(\"response from spanglish languages: {}\".format(response))\n logger.debug(\"response statuscode from spanglish languages: {}\".format(status_code))\n\n click.echo(\"response message: %s \" % msg)","def count(request):\r\n n = request.user.profile.unread_message_count()\r\n data = {\r\n 'count': n,\r\n }\r\n return HttpResponse(json.dumps(data), mimetype='application/json')","def show_status():\n return jsonify({\"status\": \"OK\"})","def list_messages(self):","def get_messages(chat_id):\n\n mark_seen = request.args.get('mark_seen', True)\n\n chat = g.driver.get_chat_from_id(chat_id)\n msgs = list(g.driver.get_all_messages_in_chat(chat))\n\n for msg in msgs:\n print(msg.id)\n\n if mark_seen:\n for msg in msgs:\n try:\n msg.chat.send_seen()\n except:\n pass\n \n return jsonify(msgs)","def getPanchayats(request):\n if request.method == 'GET':\n inName=request.GET.get('panchayat', '')\n ptid=request.GET.get('ptid', '')\n blockName=request.GET.get('block', '')\n bid = request.GET.get('bid', '')\n districtName = request.GET.get('district', '')\n stateName = request.GET.get('state', '')\n limit=request.GET.get('limit', '')\n if limit == '':\n limit=50\n else:\n limit=int(limit)\n if ptid == '':\n if bid != '':\n panchayats=Panchayat.objects.filter(name__icontains=inName, block__id=bid)\n else:\n panchayats=Panchayat.objects.filter(name__icontains=inName, block__name__icontains=blockName, block__id__icontains=bid, block__district__name__icontains = districtName, block__district__state__name__icontains = stateName)\n else:\n panchayats=Panchayat.objects.filter(id = ptid)\n \n\n # if inCode != '':\n # panchayats=\n panchayats = panchayats[:limit]\n serializer = PanchayatSerializer(panchayats, many=True)\n return JsonResponse(serializer.data, safe=False)","def polls_list(request):\n\n polls = Poll.objects.all()[:MAX_OBJECTS]\n data = {\n 'result': list(polls.values('question', 'created_by__username', 'pub_date'))\n }\n return JsonResponse(data)","def my_channels(_response=Response, _db=Depends(get_db), Authorization=Header(None)):\n\n stat, auth_data = verification_details(Authorization)\n\n if stat != 200:\n _response.status_code = 500\n return {\"data\": \"something happened\"}\n\n res_status, _data = ChatController(_db).get_my_channels(\n auth_data[\"data\"][\"user\"][\"username\"]\n )\n\n _response.status_code = res_status\n\n return {\"data\": _data}","def get(self) -> List[Conversation]:\n return get_all_conversations(), 200","def get_status(request):\n if \"liveness\" in request.query_params:\n return Response({\"alive\": True})\n\n app_status = ApplicationStatus()\n celery_param = request.query_params.get(\"celery\", \"false\").lower()\n if celery_param == \"true\":\n return Response(app_status.celery_task_status)\n\n response = {\n \"api_version\": app_status.api_version,\n \"celery_status\": app_status.celery_status,\n \"commit\": app_status.commit,\n \"current_datetime\": app_status.current_datetime,\n \"database_status\": app_status.database_status,\n \"debug\": app_status.debug,\n \"modules\": app_status.modules,\n \"platform_info\": app_status.platform_info,\n \"python_version\": app_status.python_version,\n }\n return Response(response)","def irrigation_status():\n try:\n response_status = garden_controller.branch_status()\n\n arr = form_responce_for_branches(response_status)\n send_branch_status_message('branch_status', arr)\n return jsonify(branches=arr)\n except Exception as e:\n logging.error(e)\n logging.error(\"Can't get Raspberri Pi pin status. Exception occured\")\n abort(500)","def get(self, request):\n activities = (\n activitystreams.streams[\"local\"]\n .get_activity_stream(request.user)\n .filter(\n Q(comment__isnull=False)\n | Q(review__isnull=False)\n | Q(quotation__isnull=False)\n | Q(mention_books__isnull=False)\n )\n )\n\n large_activities = Paginator(\n activities.filter(mention_books__isnull=True)\n .exclude(content=None, quotation__quote=None)\n .exclude(content=\"\"),\n 6,\n )\n small_activities = Paginator(\n activities.filter(\n Q(mention_books__isnull=False) | Q(content=None) | Q(content=\"\")\n ),\n 4,\n )\n\n page = request.GET.get(\"page\")\n data = {\n \"large_activities\": large_activities.get_page(page),\n \"small_activities\": small_activities.get_page(page),\n }\n return TemplateResponse(request, \"discover/discover.html\", data)","def home_view(request):\n status_list = Status.objects.filter(reply_to=None).order_by('-created')\n paginator = Paginator(status_list, 20)\n\n page = request.GET.get('page')\n try:\n statuses = paginator.page(page)\n except PageNotAnInteger:\n statuses = paginator.page(1)\n except EmptyPage:\n statuses = paginator.page(paginator.num_pages)\n\n return render(request, 'status/index.html', {\n 'statuses': statuses,\n })","def conference_status(request, call_sid):\n resp = VoiceResponse()\n call_status = None\n if request.method == 'POST':\n call_status = request.POST.get('CallStatus', None)\n if request.method == 'GET':\n call_status = request.GET.get('CallStatus', None)\n if call_status == 'completed':\n client.calls.hangup(call_sid)\n if call_status == 'no-answer' or call_status == 'busy' or call_status == 'failed':\n call = client.calls(call_sid).update(\n method=\"POST\",\n url=BASE_URL + \"/NoAnswer?myCallSid=\"+call_sid\n )\n print(call.to)\n return HttpResponse(str(resp))","def get(self, request, format=None):\n mess = Booking.objects.filter(renter=request.user).order_by('-pk')\n if mess:\n serializer = self.serializer_class(mess, many=True)\n return Response(serializer.data, status=200)\n return Response([], status=200)","def chat(request):\n return jingo.render(request, 'chat/chat.html')","def display(request):\n data = {}\n about = About.objects.all()\n if about:\n data['success']=1\n data['message']=\"Comments available\"\n data['about']=[]\n for i in range(len(about)):\n data['about'].append(\n {'about':about[i].about,\n 'about_id':about[i].id,\n })\n return JsonResponse(data)\n else:\n data['success']=0\n data['message']='no about available'\n return JsonResponse(data)","def get_localizations(request):\n return JsonResponse(get_all_objects(Localization, LocalizationSerializer), safe=False)","def load_messages():\n # rows = db(db.bds.author == auth.user_id).select()\n rows = db(db.contacts).select()\n d = {r.contact_id: {'name': r.name}\n for r in rows}\n return response.json(dict(board_dict=d))","def get(self, request, format = None):\n goalProgress = GoalProgress.objects.all()\n serializer = GoalProgressSerializer(goalProgress, many=True)\n return Response(serializer.data)","def index(request):\n messages = SESSION.get_messages_sent_list(request.session)\n feedbacks = SESSION.get_messages_received_list(request.session)\n \n # initially display the first 20 messages/feedback chronologically\n messages.sort(key=lambda r: r.createdAt, reverse=True)\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\n \n # prepare our template context variables use in our messages template\n data = {\n 'messages_nav': True,\n 'messages': messages[:PAGINATION_THRESHOLD],\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\n 'pag_threshold': PAGINATION_THRESHOLD,\n 'pag_page': 1,\n 'sent_count': len(messages),\n 'feedback_count': len(feedbacks),\n 'tab_feedback': request.GET.get('tab_feedback'),\n }\n \n if SESSION.get_patronStore_count(request.session):\n data['has_patrons'] = True\n \n # inserting this success and error message into the template\n # should be done in a cleaner way - this was done by the first guy\n # I just didn't bother changing it.\n if request.GET.get(\"success\"):\n data['success'] = request.GET.get(\"success\")\n if request.GET.get(\"error\"):\n data['error'] = request.GET.get(\"error\")\n \n return render(request, 'manage/messages.djhtml', data)","def display_messenger_status(self):\n caller = self.caller\n unread = caller.messages.pending_messengers\n read = caller.messages.messenger_history\n if not (read or unread):\n caller.msg(\n \"You have no messengers waiting for you, and have never received any messengers.\"\n + \" {wEver{n. At all. Not {rone{n.\"\n )\n if read:\n caller.msg(\"You have {w%s{n old messages you can re-read.\" % len(read))\n if unread:\n caller.msg(\n \"{mYou have {w%s{m new messengers waiting to be received.\" % len(unread)\n )","def get(self):\n all_questions = get_all_questions()\n if all_questions:\n return jsonify({\"message\": \"All questions viewed successfully\", \"questions\": all_questions}), 200\n return jsonify({\"message\": \"No questions posted yet\"}), 404","def list(self, request, graph_type=None):\n user = {}\n if request.authenticated_userid:\n account = Account.one(request, request.authenticated_userid)\n user['account_id'] = account.id\n if account.check_admin(request, user):\n cleaned_data = {}\n raw_data = NLTKOutput.all(request)\n for record in raw_data:\n if record.account_id in cleaned_data:\n cleaned_data[record.account_id].append(record.nltk_result)\n else:\n cleaned_data[record.account_id] = [record.nltk_result]\n if graph_type == 'stacked_bar':\n return_obj = stacked_bar_for_all(cleaned_data)\n if graph_type == 'pie':\n return_obj = pie_for_all(cleaned_data)\n if graph_type == 'compound_bar':\n return_obj = compound_for_all(cleaned_data) \n return Response(return_obj.encode(), status=200)","def index(request):\n # Get a list of rooms, ordered alphabetically\n import json\n rooms = Room.objects.order_by(\"title\")\n roomslist = []\n chat_participants = []\n participant_count=1\n user = User.objects.get(username=request.user.username)\n #print(user)\n tuj_list =TaskUserJunction.objects.filter(worker_id = user)\n #role = request.user.profile.role\n role=request.session['role']\n #print(role)\n participants = Profile.objects.filter(user_id=request.user.profile.user_id)\n mentee_task_list = {};\n workername = None\n #mentor_boolean = 'false';\n for p in participants:\n # if(p.is_Mentor):\n # mentor_boolean = 'true';\n # else:\n # mentor_boolean = 'false';\n if(role == 'mentor'):\n try:\n mentee_id = p.get_mentees();\n for m in mentee_id:\n user = User.objects.get(id=m);\n participant_count=1\n workername=user.username\n tuj_list = TaskUserJunction.objects.filter(worker_id = user)\n chat_participants.append(user.username);\n task_list = [];\n for tuj in tuj_list:\n task_list.append((str(tuj),tuj.room_id));\n mentee_task_list[user.username] = task_list;\n except:\n mentee_task_list={}\n else:\n #Get Mentors list\n try:\n workername=p.user\n mentor_id = p.get_mentors()[0];\n participant_count=len(p.get_mentors())\n if(participant_count==1):\n user = User.objects.get(id=mentor_id);\n chat_participants.append(user.username);\n else:\n chat_participants.append(\"Sam\")\n except:\n tuj_list=tuj_list\n \n # Render that in the index template\n return render(request, \"messages.html\", {\n \"tuj_list\": tuj_list,\n \"chat_participants\" : chat_participants,\n \"participant_count\" : participant_count,\n \"mentee_task_list\" : mentee_task_list,\n \"workername\" : workername,\n \"role\" : role,\n \"curr\" : user\n })","def get_status():\n data = {\n 'status': 'up',\n }\n jsn = json.dumps(data)\n\n resp = Response(jsn, status=200, mimetype='application/json')\n\n return resp","def __chat_id_response(self) -> int:\n try:\n fetch_updates = self.__get_updates()\n return fetch_updates[0]['message']['chat']['id']\n except TimeoutError as tm_err:\n print(tm_err)\n sys.exit(1)","def status():\n statuses = get_all_statuses()\n return json.dumps(statuses, indent=4)","async def statusinfo(self, astable):\n cmd = subprocess.check_output([\"birdc\", \"show\", \"proto\", \"all\", str(astable)])\n for page in chat_formatting.pagify(cmd.decode(), ['\\n', ' '], shorten_by=12):\n await self.bot.say(chat_formatting.box(page))","def getinformation(request):\n all_information_objects = information.objects.all()\n serializer = information_serializer(all_information_objects, many=True)\n return JsonResponse ({'message':serializer.data}, status=status.HTTP_200_OK)","def status(self):\n r = requests.get('/'.join([self.base_url, self.ENDPOINT_STATUS]))\n return r.json()","def home(request):\n response_status = {'text': \"API is up\"}\n return JsonResponse(response_status, status=200)","def get_messages(request):\n import urllib.parse\n if request.user.is_authenticated():\n msgs = BroadcastMessage.objects.current().for_auth_users()\n else:\n msgs = BroadcastMessage.objects.current().for_unauth_users()\n\n # exclude by those seen\n excluded_session = decode_excluded(request.session.get(\"excluded_broadcasts\", \"\"))\n excluded_cookie = decode_excluded(request.COOKIES.get(\"excluded_broadcasts\", \"\"))\n excluded = excluded_session | excluded_cookie\n msgs = msgs.exclude(pk__in=list(excluded))\n\n # filter them by the HTTP_REFERER\n url_parts = urllib.parse.urlparse(request.META.get('HTTP_REFERER', '/'))\n path = url_parts.path\n valid_messages = [msg for msg in msgs if re.match(msg.url_target, path)]\n msg_list = []\n for msg in valid_messages:\n msg_list.append(msg.msg_info())\n if msg.show_frequency == BroadcastMessage.SHOW_ONCE:\n excluded_cookie.add(msg.pk)\n elif msg.show_frequency == BroadcastMessage.SHOW_ONCE_SESSION:\n excluded_session.add(msg.pk)\n request.session['excluded_broadcasts'] = encode_excluded(excluded_session)\n response = HttpResponse(json.dumps(msg_list),\n content_type=\"application/json\")\n response.set_cookie('excluded_broadcasts', encode_excluded(excluded_cookie))\n return response","def health_check(request):\n response = {\"Status\": True}\n return JsonResponse(response, safe=False)","def chat(self):\n return self._get(\"chat\")","def messages_gps(request):\n\n msgs = get_messages_gps()\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\n dic = {\n 'chatmessages': msgs_list,\n 'PAG_TITLE': 'Messages with GPS Data'\n }\n\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))","def get_status_messages(self):\n\n try:\n subContext = conf.EHST_MESSAGES\n connHandler = self._tap._TapPlus__getconnhandler()\n response = connHandler.execute_tapget(subContext, verbose=False)\n if response.status == 200:\n for line in response:\n string_message = line.decode(\"utf-8\")\n print(string_message[string_message.index('=') + 1:])\n except OSError:\n print(\"Status messages could not be retrieved\")","def get(self, request):\n followers = request.GET.get('followers')\n followings = request.GET.get('followings')\n\n if followers:\n follower_obj = UserFollower.objects.filter(user=request.user.id).first()\n\n follower_serialized = UserFollowerShowSerializer(follower_obj)\n\n response_json = {\n 'status': True,\n 'message': 'successful',\n 'data': follower_serialized.data\n }\n\n return Response(response_json, status=200)\n\n if followings:\n following_obj = UserFollowing.objects.filter(user=request.user.id).first()\n\n following_serialized = UserFollowingShowSerializer(following_obj)\n\n response_json = {\n 'status': True,\n 'message': 'successful',\n 'data': following_serialized.data\n }\n\n return Response(response_json, status=200)\n\n response_json = {\n 'status': False,\n 'message': 'unsuccessful',\n 'data': {}\n }\n\n return Response(response_json, status=200)","def index(self):\n try:\n query = h.eagerload_morpheme_language_model(Session.query(MorphemeLanguageModel))\n query = h.add_order_by(query, dict(request.GET), self.query_builder)\n return h.add_pagination(query, dict(request.GET))\n except Invalid, e:\n response.status_int = 400\n return {'errors': e.unpack_errors()}","def list(self, request):\n # Get transaction\n transaction_id = self.request.query_params.get('transaction_id')\n transaction = Transaction.objects.get(id=transaction_id)\n\n return JsonResponse({\"status\": transaction.status, \"transaction_error\": transaction.error_type})","def handle_state(token: str = \"\"):\n helper = SlackHelper(token)\n\n response = paginated_api_call(\n api_method=helper.client.conversations_list,\n response_objects_name=\"channels\",\n exclude_archived=0,\n types=\"public_channel, private_channel\",\n )\n return {\"channels\": response}","def message_list(request, sender=None, receiver=None):\n if request.method == 'GET':\n messages = Message.objects.filter(sender_id=sender, receiver_id=receiver, is_read=False)\n serializer = MessageSerializer(messages, many=True, context={'request': request})\n for message in messages:\n message.is_read = True\n message.save()\n return JsonResponse(serializer.data, safe=False)\n\n elif request.method == 'POST':\n data = JSONParser().parse(request)\n # print(data)\n serializer = MessageSerializer(data=data)\n if serializer.is_valid():\n serializer.save()\n return JsonResponse(serializer.data, status=201)\n return JsonResponse(serializer.errors, status=400)","def chat():\n kwargs = {\"title\": u\"chat channel\", \"entries\": log.getLogEntries()}\n return render_template(\"chat.html\", **kwargs)","def test_chat_page(self):\n c_code = 'new-chat'\n rsp = self.client.get('/{}'.format(c_code))\n html = rsp.data\n\n self.assertEqual(200, rsp.status_code)\n # HTML is returned.\n self.assertIn('<html>', html)\n # HTML for chat is retrieved.\n self.assertIn(c_code, html)","def index(self, req):\n context = req.environ['manila.context']\n\n search_opts = {}\n search_opts.update(req.GET)\n\n # Remove keys that are not related to message attrs\n search_opts.pop('limit', None)\n search_opts.pop('marker', None)\n sort_key = search_opts.pop('sort_key', 'created_at')\n sort_dir = search_opts.pop('sort_dir', 'desc')\n\n messages = self.message_api.get_all(\n context, search_opts=search_opts, sort_dir=sort_dir,\n sort_key=sort_key)\n limited_list = common.limited(messages, req)\n\n return self._view_builder.index(req, limited_list)","def getPanchayatsAccurateData(request):\n if request.method == 'GET':\n panchayat=request.GET.get('panchayat', '')\n ptid=request.GET.get('ptid', '')\n block = request.GET.get('block', '')\n bid = request.GET.get('bid', '')\n district = request.GET.get('district', '')\n state = request.GET.get('state', '')\n limit=request.GET.get('limit', '')\n if limit == '':\n limit=500\n else:\n limit=int(limit)\n #PTID we need to make it exact. Finyaer make it current finyear\n panchayats = PanchayatStat.objects.filter(panchayat__name__icontains = panchayat, panchayat__id__icontains = ptid, panchayat__block__id__icontains = bid, panchayat__block__name__icontains=block, panchayat__block__district__name__icontains = district, panchayat__block__district__state__name__icontains = state, workDaysAccuracyIndex__gte = 90, finyear = '17')\n\n\n panchayats = panchayats[:limit]\n serializer = PanchayatStatSerializer(panchayats, many=True)\n return JsonResponse(serializer.data, safe=False)","def blocked(self, request, *args, **kwargs):\n conversation = self.get_object()\n blocked = User.objects.filter(id__in=conversation.blocked)\n self.pagination_class = ShoutitPageNumberPagination\n page = self.paginate_queryset(blocked)\n serializer = ProfileSerializer(page, many=True, context={'request': request})\n return self.get_paginated_response(serializer.data)","def get_response(self):\n return self.messages","def msgs(request):\r\n results = list(PFASMsgRecord.objects.values())\r\n for result in results:\r\n # add a field that's not in the DB\r\n result['valid'] = 'literally' not in result['message'].lower()\r\n # change to text so JSON can save it\r\n result['timestamp'] = str(result['timestamp'])\r\n\r\n return HttpResponse(\r\n json.dumps(results, indent=2, sort_keys=True),\r\n content_type='application/json',\r\n )","def get_homework_statuses(current_timestamp: int) -> dict:\n data = {\"from_date\": current_timestamp}\n headers = {\"Authorization\": f\"OAuth {PRAKTIKUM_TOKEN}\"}\n try:\n homework_statuses = requests.get(\n PRAKTIKUM_API_URL,\n params=data,\n headers=headers,\n )\n # Заккоментриовал, т.к. валятся тесты\n # homework_statuses.raise_for_status()\n except requests.exceptions.RequestException as e:\n logger.exception(e)\n send_message(str(e))\n raise\n return homework_statuses.json()","def get_all(request):\n\treturn JsonResponse(todo)","def polls_details(request, pk):\n poll = get_object_or_404(Poll, pk=pk)\n data = {\n 'result': {\n 'question': poll.question,\n 'created_by': poll.created_by.username,\n 'pub_date': poll.pub_date\n }\n }\n return JsonResponse(data)","def get(self, request):\n return Response(\"Dashboard Listing Page\", status=status.HTTP_200_OK)","def read_home():\n return {'message': 'API live!'}","def get_languages(request):\n if request.is_ajax():\n publications = Publication.objects.all()\n language_list = []\n for pub in publications:\n languages = pub.languages.all()\n for lang in languages:\n language_list.append({\"pub\": pub.pk,\"pub_name\":pub.name,\"lang\": u\"%s\" % lang.pk,\"name_lang\":lang.alias})\n data = simplejson.dumps(language_list)\n return HttpResponse(data)","def librarian_list(request):\n librarian = User.objects.filter(is_student=False, is_lecturer=False, is_parent=False, is_superuser=False)\n user_type = \"Librarian\"\n context = {\n \"librarian\": librarian,\n \"user_type\": user_type,\n }\n return render(request, 'library/librarians_list.html', context)","def check_status(bot, chat_id, query):\n\n remain = remain_time(chat_id)\n query_id = query.id\n\n message = bot_collection[chat_id].get_remained_message(remain)\n\n bot.answer_callback_query(callback_query_id=query_id, text=message)\n\n pass","def test_get_unread_status_count(self):\n request = self.factory.get(\"\")\n request.user = self.local_user\n\n with patch(\"bookwyrm.activitystreams.ActivityStream.get_unread_count\") as mock:\n mock.return_value = 3\n result = views.get_unread_status_count(request, \"home\")\n\n self.assertIsInstance(result, JsonResponse)\n data = json.loads(result.getvalue())\n self.assertEqual(data[\"count\"], 3)","def index():\n #Have to create LOGIN\n #serverxmpp.init()\n return dict()","def chat(self) -> \"api.Chat\":\n raise NotImplementedError","def get(self):\n\n return {\"message\": \"Welcome to the news API. \"}","def show_all(message, bot):\n reports_status = str(message.text)\n try:\n reports = requests.get(\n url=f'http://127.0.0.1:8000/api/v1/report/?status={reports_status}',\n headers={\n 'Authorization': f'Bearer {jwt_token}',\n }\n )\n if reports.status_code == 200:\n result = reports.json()\n output = format_result(result)\n bot.send_message(\n message.from_user.id,\n output,\n parse_mode='HTML',\n )\n elif reports.status_code == 401:\n bot.send_message(\n message.from_user.id,\n 'Ошибка авторизации, проверьте токен',\n )\n except requests.exceptions.ConnectionError:\n bot.send_message(\n message.from_user.id,\n 'Ошибка соединения с сервером',\n )","def get(self, request):\n announcement_id = request.GET.get(\"id\")\n if announcement_id:\n try:\n announcement = Announcement.objects.get(id=announcement_id)\n return self.success(AnnouncementSerializer(announcement).data)\n except Announcement.DoesNotExist:\n return self.error(\"Announcement does not exist\")\n announcement = Announcement.objects.all().order_by(\"-create_time\")\n if request.GET.get(\"visible\") == \"true\":\n announcement = announcement.filter(visible=True)\n return self.success(self.paginate_data(request, announcement, AnnouncementSerializer))","def outgoing_messages_view(request):\n try:\n data = JSONParser().parse(request)\n user_id = data['sender_id'].encode('utf-8')\n messages_list = Message.objects.filter(sender_id=user_id)\n serializer = MessageSerializer(messages_list, many=True)\n except Exception as e:\n return Response({})\n return Response(serializer.data)","def annotation_all_stats(request):\n\n id_report = request.GET.get('report',None)\n language = request.GET.get('language',None)\n\n json_dict = get_annotations_count(id_report,language)\n\n # print('annotations',json_dict)\n return JsonResponse(json_dict)","def render_GET(self, request):\n request.setHeader(b'content-type', b'application/json; charset=utf-8')\n set_cors(request, 'GET')\n\n timestamp = None\n if b'timestamp' in request.args:\n try:\n timestamp = int(request.args[b'timestamp'][0])\n except ValueError:\n return json.dumps({\n 'success': False,\n 'message': 'Invalid timestamp parameter, expecting an integer'\n }).encode('utf-8')\n\n tx_tips = self.manager.tx_storage.get_tx_tips(timestamp)\n ret = {'success': True, 'tips': [tip.data.hex() for tip in tx_tips]}\n return json.dumps(ret).encode('utf-8')","def index():\n text_list = ['노인복지']\n fulfillment = {'text':''}\n dialogflow_response = detect_intent_texts('', '', text_list, 'ko')\n fulfillment['text'] = dialogflow_response\n json_fulfillment = json.dumps(fulfillment, ensure_ascii=False)\n\n return jsonify(fulfillment)","async def get(self):\n await self.handle_request(self.chats_api, 1)","def view_meetups():\n\n return make_response(jsonify({\n \"status\": 200,\n \"data\": meetups.view_meetups()\n })), 200","def queue_status(request):\n\n xml = cache.get(settings.CHAT_CACHE_KEY)\n status = 200\n if not xml:\n xml = ''\n status = 503\n return HttpResponse(xml, mimetype='application/xml', status=status)","def view_status(request, pk):\n\n status = Status.objects.get(pk=pk)\n user_profile = get_users_profile(request.user.id)\n\n context = {\n 'news_feed': [status],\n 'user_profile': user_profile,\n }\n\n return render(request, 'status/view_status.html', context)","def get_report_translations(request):\n\n id_report = request.GET.get('id_report',None)\n if id_report is not None:\n languages = []\n lang = Report.objects.filter(id_report = id_report)\n for el in lang:\n if el.language not in languages:\n languages.append(el.language)\n\n json_resp = {}\n # print(languages)\n json_resp['languages'] = languages\n return JsonResponse(json_resp)","def GET(self):\n web.header(\"Content-Type\",\"application/json; charset=utf-8\")\n data = web.input(module=\"module\")\n module = data[\"module\"]\n count = db_module.get_module_newsNum(module)\n result = json.dumps({\"count\": count})\n return result"],"string":"[\n \"def chat_status(request):\\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\\n if request.method == 'GET' and request.is_ajax():\\n if(team is None):\\n return render(request, 'access_error.html', {'reason': \\\"team\\\"})\\n status = team.num_waiting_messages\\n return HttpResponse(json.dumps({\\\"num_messages\\\": status}))\\n else:\\n return HttpResponseNotFound()\",\n \"def get_conversation(request):\\n collected_values = {}\\n\\n # Only allow GET requests for this endpoint\\n if request.method != 'GET':\\n collected_values[\\\"success\\\"] = False\\n collected_values[\\\"errmsg\\\"] = \\\"Wrong HTTP verb\\\"\\n return JsonResponse(collected_values, status=400)\\n\\n # Extract and form params\\n uid = request.GET['uid']\\n oid = request.GET['oid']\\n token = request.GET['token']\\n ts_query = request.GET['ts']\\n time_user_seen = request.GET.get('tus')\\n limit = int(request.GET['limit'])\\n\\n if ts_query == \\\"\\\":\\n ts_query = timezone.now()\\n\\n change_user_seen = False\\n if time_user_seen == \\\"true\\\":\\n change_user_seen = True\\n\\n # Check if token is valid\\n is_valid, collected_values[\\\"token\\\"] = check_auth(uid, token, timezone.now())\\n if not is_valid:\\n collected_values[\\\"success\\\"] = False\\n collected_values[\\\"errmsg\\\"] = \\\"Invalid Token\\\"\\n return JsonResponse(collected_values, status=400)\\n\\n # Collect all messages sent by two users in question listed by created at time\\n message_query_set = Messages.objects.filter(\\n Q(user_id=uid, other_id=oid) |\\n Q(other_id=uid, user_id=oid)).order_by('-created_at')[:limit]\\n\\n # Collect all messages from query\\n test_list = []\\n for message in message_query_set:\\n if change_user_seen:\\n message.time_user_seen = timezone.now()\\n message.save()\\n test_list.append(message.get_map())\\n\\n # Collect return values\\n collected_values[\\\"messages\\\"] = test_list\\n collected_values[\\\"success\\\"] = True\\n\\n LOGGER.info(\\\"Get Conversation Result: %s\\\", collected_values)\\n return JsonResponse(collected_values, status=200)\",\n \"def load_messages(request):\\n thread = models.MessageThread.objects.get(hash_id=request.GET['id'])\\n \\n # check if user is a part of this chat\\n if not request.user in thread.clients.all():\\n return HttpResponse(status=403)\\n\\n # query for messages filter\\n q = [Q(thread=thread)]\\n if 'before' in request.GET:\\n q.append(Q(date__lt=int(request.GET['before'])))\\n\\n # query messages matching filter\\n messages = models.Message.objects.filter(*q).order_by('-id')\\n messages_data = serializers.MessageListSerializer(messages[:30]).data\\n\\n # mark any unread messages in chat as read\\n thread.mark_read(request.user)\\n return JsonResponse({\\\"messages\\\":messages_data,\\\"end\\\":messages.count() <= 30})\",\n \"def get_conversation_list(request):\\n collected_values = {}\\n\\n # Only accept GET requests for this endpoint\\n if request.method != 'GET':\\n collected_values[\\\"success\\\"] = False\\n collected_values[\\\"errmsg\\\"] = \\\"Wrong HTTP verb\\\"\\n return JsonResponse(collected_values, status=400)\\n\\n # Extract params\\n uid = request.GET['uid']\\n token = request.GET['token']\\n limit = int(request.GET['limit']) # Force a limiter to see how many users to get\\n\\n # Check if the token is valid\\n is_valid, collected_values[\\\"token\\\"] = check_auth(uid, token, timezone.now())\\n if not is_valid:\\n collected_values[\\\"success\\\"] = False\\n collected_values[\\\"errmsg\\\"] = \\\"Invalid Token\\\"\\n return JsonResponse(collected_values, status=400)\\n\\n # Maybe cache or find better way of getting most recent id's messaged\\n # Do a walkthrough of all messages and count totals\\n # Potential Improvement is to keep a mapping of all messages sent from users to users\\n users = {}\\n msg_sent = Messages.objects.filter(user_id=uid).order_by('-created_at')[:limit]\\n msg_recieved = Messages.objects.filter(other_id=uid).order_by('-created_at')[:limit]\\n for msg in msg_sent:\\n if users.get(msg.other_id) is None:\\n users[msg.other_id] = 1\\n else:\\n users[msg.other_id] += 1\\n for msg in msg_recieved:\\n if users.get(msg.user_id) is None:\\n users[msg.user_id] = 1\\n else:\\n users[msg.user_id] += 1\\n\\n # Collect return values\\n collected_values[\\\"users\\\"] = users\\n collected_values[\\\"success\\\"] = True\\n\\n LOGGER.info(\\\"Get Conversation List Result: %s\\\", collected_values)\\n return JsonResponse(collected_values, status=200)\",\n \"def load_inbox(request):\\n threads = models.MessageThread.objects.filter(clients=request.user).annotate(\\n unread_count=Count('receipts',filter=Q(receipts__recipient=request.user))\\n )\\n thread_data = serializers.MessageThreadListSerializer(threads).data\\n #user = userauth_models.User.objects.filter(username=request.user.username)\\n #print(user.username)\\n #print(get_channel_layer())\\n #print(request.session['channel_name'])\\n return JsonResponse({'threads':thread_data})\",\n \"def chatlist(request):\\n\\n chats = get_chat_list()\\n chat_list = pagination(request, chats, CHATS_PER_PAGE)\\n\\n dic = {'chatlist': chat_list}\\n return render_to_response('whatsapp/chatlist.html', dic, context_instance=RequestContext(request))\",\n \"async def status(self):\\n cmd = subprocess.check_output([\\\"birdc\\\", \\\"show\\\", \\\"proto\\\"])\\n for page in chat_formatting.pagify(cmd.decode(), ['\\\\n', ' '], shorten_by=12):\\n await self.bot.say(chat_formatting.box(page))\",\n \"def ajax_status(request):\\r\\n if not request.user.is_authenticated():\\r\\n raise PermissionDenied\\r\\n\\r\\n\\r\\n qs = UserPreference.objects.filter(\\r\\n user=request.user,\\r\\n key=NOTIFICATION_PREF_KEY\\r\\n )\\r\\n\\r\\n return HttpResponse(json.dumps({\\\"status\\\":len(qs)}), content_type=\\\"application/json\\\")\",\n \"def test_get_project_chat_messages_passes(self):\\n response = self.client.get(self.endpoint_url)\\n response_body = response.get_json()\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response_body, {\\\"chat\\\": [], \\\"pagination\\\": None})\\n # to do: add comments and test again\",\n \"def list(self, request, *args, **kwargs):\\n return super(PublicChatViewSet, self).list(request, *args, **kwargs)\",\n \"def chat(request):\\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\\n if request.method == 'POST':\\n # There is data in the post request, but we don't need anything but\\n # the message because normal users can't send as staff or other teams\\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\\n is_response=False, team=team)\\n team.num_waiting_messages = 0\\n messages = [m]\\n else:\\n if(team is None):\\n return render(request, 'access_error.html', {'reason': \\\"team\\\"})\\n if(team.hunt.is_locked and not team.is_playtester_team):\\n return render(request, 'access_error.html', {'reason': \\\"hunt\\\"})\\n if request.is_ajax():\\n messages = Message.objects.filter(pk__gt=request.GET.get(\\\"last_pk\\\"))\\n else:\\n messages = Message.objects\\n messages = messages.filter(team=team).order_by('time')\\n\\n # The whole message_dict format is for ajax/template uniformity\\n rendered_messages = render_to_string('chat_messages.html',\\n {'messages': messages, 'team_name': team.team_name})\\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\\n try:\\n last_pk = Message.objects.latest('id').id\\n except Message.DoesNotExist:\\n last_pk = 0\\n team.num_waiting_messages = 0\\n\\n team.save() # Save last_*_message vars\\n context = {'message_dict': message_dict, 'last_pk': last_pk}\\n if request.is_ajax() or request.method == 'POST':\\n return HttpResponse(json.dumps(context))\\n else:\\n context['team'] = team\\n return render(request, 'chat.html', context)\",\n \"def get_status_messages(self):\\n return self.data[\\\"allMessagesForFrontend\\\"][\\\"messages\\\"]\",\n \"def get_messages(self):\\n other_user_email = request.args.get('other_user_email')\\n page = request.args.get('page')\\n per_page = request.args.get('per_page')\\n if not other_user_email or not page or not per_page:\\n self.logger.debug(messages.MISSING_FIELDS_ERROR % \\\"query params\\\")\\n return messages.ERROR_JSON % messages.MISSING_FIELDS_ERROR % \\\"query params\\\", 400\\n email_token = auth.current_user()[0]\\n page = int(page)\\n per_page = int(per_page)\\n # App sends starting with 1 but we start at 0\\n page -= 1\\n try:\\n message_list, pages = self.friend_database.get_conversation(email_token, other_user_email, per_page, page)\\n except NoMoreMessagesError:\\n self.logger.debug(messages.NO_MORE_PAGES_ERROR)\\n return messages.NO_MORE_PAGES_ERROR, 404\\n message_list = [{k:v for k,v in m._asdict().items() if k != \\\"hidden_to\\\"} for m in message_list]\\n for i in range(len(message_list)):\\n message_list[i][\\\"timestamp\\\"] = message_list[i][\\\"timestamp\\\"].isoformat()\\n return json.dumps({\\\"messages\\\": message_list, \\\"pages\\\": pages}), 200\",\n \"def list_messages(chat_id):\\n response = jsonify({\\\"messages\\\": list_messages_for_chat(chat_id)})\\n return response\",\n \"def get_unread_messages():\\n mark_seen = request.args.get('mark_seen', True)\\n unread_msg = g.driver.get_unread()\\n\\n if mark_seen:\\n for msg in unread_msg:\\n msg.chat.send_seen()\\n\\n return jsonify(unread_msg)\",\n \"def single_chat(request, key):\\n \\n if not Messages.objects.using('msgstore').filter(key_remote_jid=key).count() > 0:\\n return render_to_response('whatsapp/errors/404.html', context_instance=RequestContext(request)) \\n\\n msgs = get_chat_messages(key)\\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\\n\\n dic = {\\n 'peer': key,\\n 'chatmessages': msgs_list,\\n 'gps': Messages.objects.using('msgstore').exclude((Q(longitude='0.0') | Q(latitude='0.0'))),\\n 'media': Messages.objects.using('msgstore').exclude(media_url__isnull=True),\\n 'PAG_TITLE': 'Conversation'\\n }\\n\\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))\",\n \"def status_api(request):\\n if request.method == 'GET':\\n return JsonResponse({\\n 'status': 'OK',\\n 'version': __version__\\n }, status=200)\",\n \"def list(self, request, *args, **kwargs):\\n return super(ConversationViewSet, self).list(request, *args, **kwargs)\",\n \"def msgStatus():\\n return jsonify({\\\"status\\\": \\\"OK\\\"})\",\n \"def get_messages_json(self, limit=10):\\n params = self.params\\n params[\\\"limit\\\"] = limit\\n response = requests.get(self.url + \\\"conversations.history\\\", params=params)\\n return response.json()[\\\"messages\\\"]\",\n \"def get_status(self):\\n r = requests.get(self.base_url + '/status')\\n return r.json()\",\n \"def list(request, format=None):\\r\\n user_messages = request.user.profile.recent_messages()\\r\\n if format and format == '.json':\\r\\n data = {\\r\\n 'messages': [msg.to_json() for msg in user_messages],\\r\\n }\\r\\n return HttpResponse(json.dumps(data), mimetype='application/json')\\r\\n d = {\\r\\n 'objects': user_messages,\\r\\n 'title': 'Messages',\\r\\n }\\r\\n return render(request, 'usermessages/list.html', d)\",\n \"def retrieve_conversation_history(username: Text) -> Text:\\n history = inmemory_storage[username]\\n if history:\\n return jsonify(history)\\n else:\\n return jsonify(history), 404\",\n \"def GET(self, request):\\n timer = Timer()\\n try:\\n myaccount = get_account_info_for_current_user()\\n\\n resp_json = myaccount.json_data(\\n use_abbr_week_month_day_format=True)\\n log_success_response(logger, timer)\\n return HttpResponse(json.dumps(resp_json))\\n except Exception:\\n return handle_exception(logger, timer, traceback)\",\n \"def notification_list(request):\\n try:\\n validator = NotificationListValidator(request.GET)\\n valid = validator.validate() # Validate the request\\n if valid:\\n current_user_id = request.user_id\\n page_limit = int(request.GET['page_limit'])\\n page_offset = int(request.GET['page_offset'])\\n\\n # notification listing\\n notification_list = Notification.objects.filter(user_id=current_user_id).all().order_by('-created_on')[page_offset:page_limit+page_offset]\\n serializer = NoitifcationListSerializer(notification_list, many=True)\\n\\n # set is_read = 1\\n Notification.objects.filter(user_id=current_user_id).update(\\n is_read=1\\n )\\n \\n return Response({'data':serializer.data}, status=status.HTTP_200_OK)\\n else:\\n return Response({'error':requestErrorMessagesFormate(validator.get_message())}, status=status.HTTP_200_OK)\\n except Exception as exception:\\n logerror('notifications/views.py/notification_list', str(exception))\\n return Response({'error':str(exception)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\",\n \"def get(self):\\n self.write(\\n '{\\\"error\\\": \\\"cryptochat-server main page, '\\n 'please refer to /api/message/new or /api/message/updates\\\"}')\",\n \"def get_messages(request, uuid):\\n if request.is_ajax():\\n user = request.user\\n room = Room.objects.get(pk=uuid)\\n if room.members.filter(pk=user.pk).exists():\\n messages_qs = room.message_set.all()\\n page = request.GET.get('page')\\n paginator = Paginator(messages_qs, 20)\\n try:\\n selected = paginator.page(page)\\n except PageNotAnInteger:\\n selected = paginator.page(1)\\n except EmptyPage:\\n selected = []\\n messages = []\\n for message in selected:\\n data = {\\n 'sender': {\\n 'name': str(message.sender),\\n 'id': message.sender.pk\\n },\\n 'message': message.text,\\n 'received_room_id': uuid,\\n 'date_created': message.date_created.strftime(\\\"%d %b %Y %H:%M:%S %Z\\\")\\n }\\n messages.append(data)\\n return JsonResponse(messages, safe=False)\\n\\n else:\\n return Http404(_(\\\"Sorry! We can't find what you're looking for.\\\"))\\n else:\\n return Http404(_(\\\"Sorry! We can't find what you're looking for.\\\"))\",\n \"def get_languages():\\n\\n api = (api_name, 'languages')\\n\\n response = make_request(api=api, action='get', **{})\\n status_code = response.status_code\\n content = response.text\\n\\n msg = str(status_code) + ' : ' + content\\n \\n logger.debug(\\\"response from spanglish languages: {}\\\".format(response))\\n logger.debug(\\\"response statuscode from spanglish languages: {}\\\".format(status_code))\\n\\n click.echo(\\\"response message: %s \\\" % msg)\",\n \"def count(request):\\r\\n n = request.user.profile.unread_message_count()\\r\\n data = {\\r\\n 'count': n,\\r\\n }\\r\\n return HttpResponse(json.dumps(data), mimetype='application/json')\",\n \"def show_status():\\n return jsonify({\\\"status\\\": \\\"OK\\\"})\",\n \"def list_messages(self):\",\n \"def get_messages(chat_id):\\n\\n mark_seen = request.args.get('mark_seen', True)\\n\\n chat = g.driver.get_chat_from_id(chat_id)\\n msgs = list(g.driver.get_all_messages_in_chat(chat))\\n\\n for msg in msgs:\\n print(msg.id)\\n\\n if mark_seen:\\n for msg in msgs:\\n try:\\n msg.chat.send_seen()\\n except:\\n pass\\n \\n return jsonify(msgs)\",\n \"def getPanchayats(request):\\n if request.method == 'GET':\\n inName=request.GET.get('panchayat', '')\\n ptid=request.GET.get('ptid', '')\\n blockName=request.GET.get('block', '')\\n bid = request.GET.get('bid', '')\\n districtName = request.GET.get('district', '')\\n stateName = request.GET.get('state', '')\\n limit=request.GET.get('limit', '')\\n if limit == '':\\n limit=50\\n else:\\n limit=int(limit)\\n if ptid == '':\\n if bid != '':\\n panchayats=Panchayat.objects.filter(name__icontains=inName, block__id=bid)\\n else:\\n panchayats=Panchayat.objects.filter(name__icontains=inName, block__name__icontains=blockName, block__id__icontains=bid, block__district__name__icontains = districtName, block__district__state__name__icontains = stateName)\\n else:\\n panchayats=Panchayat.objects.filter(id = ptid)\\n \\n\\n # if inCode != '':\\n # panchayats=\\n panchayats = panchayats[:limit]\\n serializer = PanchayatSerializer(panchayats, many=True)\\n return JsonResponse(serializer.data, safe=False)\",\n \"def polls_list(request):\\n\\n polls = Poll.objects.all()[:MAX_OBJECTS]\\n data = {\\n 'result': list(polls.values('question', 'created_by__username', 'pub_date'))\\n }\\n return JsonResponse(data)\",\n \"def my_channels(_response=Response, _db=Depends(get_db), Authorization=Header(None)):\\n\\n stat, auth_data = verification_details(Authorization)\\n\\n if stat != 200:\\n _response.status_code = 500\\n return {\\\"data\\\": \\\"something happened\\\"}\\n\\n res_status, _data = ChatController(_db).get_my_channels(\\n auth_data[\\\"data\\\"][\\\"user\\\"][\\\"username\\\"]\\n )\\n\\n _response.status_code = res_status\\n\\n return {\\\"data\\\": _data}\",\n \"def get(self) -> List[Conversation]:\\n return get_all_conversations(), 200\",\n \"def get_status(request):\\n if \\\"liveness\\\" in request.query_params:\\n return Response({\\\"alive\\\": True})\\n\\n app_status = ApplicationStatus()\\n celery_param = request.query_params.get(\\\"celery\\\", \\\"false\\\").lower()\\n if celery_param == \\\"true\\\":\\n return Response(app_status.celery_task_status)\\n\\n response = {\\n \\\"api_version\\\": app_status.api_version,\\n \\\"celery_status\\\": app_status.celery_status,\\n \\\"commit\\\": app_status.commit,\\n \\\"current_datetime\\\": app_status.current_datetime,\\n \\\"database_status\\\": app_status.database_status,\\n \\\"debug\\\": app_status.debug,\\n \\\"modules\\\": app_status.modules,\\n \\\"platform_info\\\": app_status.platform_info,\\n \\\"python_version\\\": app_status.python_version,\\n }\\n return Response(response)\",\n \"def irrigation_status():\\n try:\\n response_status = garden_controller.branch_status()\\n\\n arr = form_responce_for_branches(response_status)\\n send_branch_status_message('branch_status', arr)\\n return jsonify(branches=arr)\\n except Exception as e:\\n logging.error(e)\\n logging.error(\\\"Can't get Raspberri Pi pin status. Exception occured\\\")\\n abort(500)\",\n \"def get(self, request):\\n activities = (\\n activitystreams.streams[\\\"local\\\"]\\n .get_activity_stream(request.user)\\n .filter(\\n Q(comment__isnull=False)\\n | Q(review__isnull=False)\\n | Q(quotation__isnull=False)\\n | Q(mention_books__isnull=False)\\n )\\n )\\n\\n large_activities = Paginator(\\n activities.filter(mention_books__isnull=True)\\n .exclude(content=None, quotation__quote=None)\\n .exclude(content=\\\"\\\"),\\n 6,\\n )\\n small_activities = Paginator(\\n activities.filter(\\n Q(mention_books__isnull=False) | Q(content=None) | Q(content=\\\"\\\")\\n ),\\n 4,\\n )\\n\\n page = request.GET.get(\\\"page\\\")\\n data = {\\n \\\"large_activities\\\": large_activities.get_page(page),\\n \\\"small_activities\\\": small_activities.get_page(page),\\n }\\n return TemplateResponse(request, \\\"discover/discover.html\\\", data)\",\n \"def home_view(request):\\n status_list = Status.objects.filter(reply_to=None).order_by('-created')\\n paginator = Paginator(status_list, 20)\\n\\n page = request.GET.get('page')\\n try:\\n statuses = paginator.page(page)\\n except PageNotAnInteger:\\n statuses = paginator.page(1)\\n except EmptyPage:\\n statuses = paginator.page(paginator.num_pages)\\n\\n return render(request, 'status/index.html', {\\n 'statuses': statuses,\\n })\",\n \"def conference_status(request, call_sid):\\n resp = VoiceResponse()\\n call_status = None\\n if request.method == 'POST':\\n call_status = request.POST.get('CallStatus', None)\\n if request.method == 'GET':\\n call_status = request.GET.get('CallStatus', None)\\n if call_status == 'completed':\\n client.calls.hangup(call_sid)\\n if call_status == 'no-answer' or call_status == 'busy' or call_status == 'failed':\\n call = client.calls(call_sid).update(\\n method=\\\"POST\\\",\\n url=BASE_URL + \\\"/NoAnswer?myCallSid=\\\"+call_sid\\n )\\n print(call.to)\\n return HttpResponse(str(resp))\",\n \"def get(self, request, format=None):\\n mess = Booking.objects.filter(renter=request.user).order_by('-pk')\\n if mess:\\n serializer = self.serializer_class(mess, many=True)\\n return Response(serializer.data, status=200)\\n return Response([], status=200)\",\n \"def chat(request):\\n return jingo.render(request, 'chat/chat.html')\",\n \"def display(request):\\n data = {}\\n about = About.objects.all()\\n if about:\\n data['success']=1\\n data['message']=\\\"Comments available\\\"\\n data['about']=[]\\n for i in range(len(about)):\\n data['about'].append(\\n {'about':about[i].about,\\n 'about_id':about[i].id,\\n })\\n return JsonResponse(data)\\n else:\\n data['success']=0\\n data['message']='no about available'\\n return JsonResponse(data)\",\n \"def get_localizations(request):\\n return JsonResponse(get_all_objects(Localization, LocalizationSerializer), safe=False)\",\n \"def load_messages():\\n # rows = db(db.bds.author == auth.user_id).select()\\n rows = db(db.contacts).select()\\n d = {r.contact_id: {'name': r.name}\\n for r in rows}\\n return response.json(dict(board_dict=d))\",\n \"def get(self, request, format = None):\\n goalProgress = GoalProgress.objects.all()\\n serializer = GoalProgressSerializer(goalProgress, many=True)\\n return Response(serializer.data)\",\n \"def index(request):\\n messages = SESSION.get_messages_sent_list(request.session)\\n feedbacks = SESSION.get_messages_received_list(request.session)\\n \\n # initially display the first 20 messages/feedback chronologically\\n messages.sort(key=lambda r: r.createdAt, reverse=True)\\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\\n \\n # prepare our template context variables use in our messages template\\n data = {\\n 'messages_nav': True,\\n 'messages': messages[:PAGINATION_THRESHOLD],\\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\\n 'pag_threshold': PAGINATION_THRESHOLD,\\n 'pag_page': 1,\\n 'sent_count': len(messages),\\n 'feedback_count': len(feedbacks),\\n 'tab_feedback': request.GET.get('tab_feedback'),\\n }\\n \\n if SESSION.get_patronStore_count(request.session):\\n data['has_patrons'] = True\\n \\n # inserting this success and error message into the template\\n # should be done in a cleaner way - this was done by the first guy\\n # I just didn't bother changing it.\\n if request.GET.get(\\\"success\\\"):\\n data['success'] = request.GET.get(\\\"success\\\")\\n if request.GET.get(\\\"error\\\"):\\n data['error'] = request.GET.get(\\\"error\\\")\\n \\n return render(request, 'manage/messages.djhtml', data)\",\n \"def display_messenger_status(self):\\n caller = self.caller\\n unread = caller.messages.pending_messengers\\n read = caller.messages.messenger_history\\n if not (read or unread):\\n caller.msg(\\n \\\"You have no messengers waiting for you, and have never received any messengers.\\\"\\n + \\\" {wEver{n. At all. Not {rone{n.\\\"\\n )\\n if read:\\n caller.msg(\\\"You have {w%s{n old messages you can re-read.\\\" % len(read))\\n if unread:\\n caller.msg(\\n \\\"{mYou have {w%s{m new messengers waiting to be received.\\\" % len(unread)\\n )\",\n \"def get(self):\\n all_questions = get_all_questions()\\n if all_questions:\\n return jsonify({\\\"message\\\": \\\"All questions viewed successfully\\\", \\\"questions\\\": all_questions}), 200\\n return jsonify({\\\"message\\\": \\\"No questions posted yet\\\"}), 404\",\n \"def list(self, request, graph_type=None):\\n user = {}\\n if request.authenticated_userid:\\n account = Account.one(request, request.authenticated_userid)\\n user['account_id'] = account.id\\n if account.check_admin(request, user):\\n cleaned_data = {}\\n raw_data = NLTKOutput.all(request)\\n for record in raw_data:\\n if record.account_id in cleaned_data:\\n cleaned_data[record.account_id].append(record.nltk_result)\\n else:\\n cleaned_data[record.account_id] = [record.nltk_result]\\n if graph_type == 'stacked_bar':\\n return_obj = stacked_bar_for_all(cleaned_data)\\n if graph_type == 'pie':\\n return_obj = pie_for_all(cleaned_data)\\n if graph_type == 'compound_bar':\\n return_obj = compound_for_all(cleaned_data) \\n return Response(return_obj.encode(), status=200)\",\n \"def index(request):\\n # Get a list of rooms, ordered alphabetically\\n import json\\n rooms = Room.objects.order_by(\\\"title\\\")\\n roomslist = []\\n chat_participants = []\\n participant_count=1\\n user = User.objects.get(username=request.user.username)\\n #print(user)\\n tuj_list =TaskUserJunction.objects.filter(worker_id = user)\\n #role = request.user.profile.role\\n role=request.session['role']\\n #print(role)\\n participants = Profile.objects.filter(user_id=request.user.profile.user_id)\\n mentee_task_list = {};\\n workername = None\\n #mentor_boolean = 'false';\\n for p in participants:\\n # if(p.is_Mentor):\\n # mentor_boolean = 'true';\\n # else:\\n # mentor_boolean = 'false';\\n if(role == 'mentor'):\\n try:\\n mentee_id = p.get_mentees();\\n for m in mentee_id:\\n user = User.objects.get(id=m);\\n participant_count=1\\n workername=user.username\\n tuj_list = TaskUserJunction.objects.filter(worker_id = user)\\n chat_participants.append(user.username);\\n task_list = [];\\n for tuj in tuj_list:\\n task_list.append((str(tuj),tuj.room_id));\\n mentee_task_list[user.username] = task_list;\\n except:\\n mentee_task_list={}\\n else:\\n #Get Mentors list\\n try:\\n workername=p.user\\n mentor_id = p.get_mentors()[0];\\n participant_count=len(p.get_mentors())\\n if(participant_count==1):\\n user = User.objects.get(id=mentor_id);\\n chat_participants.append(user.username);\\n else:\\n chat_participants.append(\\\"Sam\\\")\\n except:\\n tuj_list=tuj_list\\n \\n # Render that in the index template\\n return render(request, \\\"messages.html\\\", {\\n \\\"tuj_list\\\": tuj_list,\\n \\\"chat_participants\\\" : chat_participants,\\n \\\"participant_count\\\" : participant_count,\\n \\\"mentee_task_list\\\" : mentee_task_list,\\n \\\"workername\\\" : workername,\\n \\\"role\\\" : role,\\n \\\"curr\\\" : user\\n })\",\n \"def get_status():\\n data = {\\n 'status': 'up',\\n }\\n jsn = json.dumps(data)\\n\\n resp = Response(jsn, status=200, mimetype='application/json')\\n\\n return resp\",\n \"def __chat_id_response(self) -> int:\\n try:\\n fetch_updates = self.__get_updates()\\n return fetch_updates[0]['message']['chat']['id']\\n except TimeoutError as tm_err:\\n print(tm_err)\\n sys.exit(1)\",\n \"def status():\\n statuses = get_all_statuses()\\n return json.dumps(statuses, indent=4)\",\n \"async def statusinfo(self, astable):\\n cmd = subprocess.check_output([\\\"birdc\\\", \\\"show\\\", \\\"proto\\\", \\\"all\\\", str(astable)])\\n for page in chat_formatting.pagify(cmd.decode(), ['\\\\n', ' '], shorten_by=12):\\n await self.bot.say(chat_formatting.box(page))\",\n \"def getinformation(request):\\n all_information_objects = information.objects.all()\\n serializer = information_serializer(all_information_objects, many=True)\\n return JsonResponse ({'message':serializer.data}, status=status.HTTP_200_OK)\",\n \"def status(self):\\n r = requests.get('/'.join([self.base_url, self.ENDPOINT_STATUS]))\\n return r.json()\",\n \"def home(request):\\n response_status = {'text': \\\"API is up\\\"}\\n return JsonResponse(response_status, status=200)\",\n \"def get_messages(request):\\n import urllib.parse\\n if request.user.is_authenticated():\\n msgs = BroadcastMessage.objects.current().for_auth_users()\\n else:\\n msgs = BroadcastMessage.objects.current().for_unauth_users()\\n\\n # exclude by those seen\\n excluded_session = decode_excluded(request.session.get(\\\"excluded_broadcasts\\\", \\\"\\\"))\\n excluded_cookie = decode_excluded(request.COOKIES.get(\\\"excluded_broadcasts\\\", \\\"\\\"))\\n excluded = excluded_session | excluded_cookie\\n msgs = msgs.exclude(pk__in=list(excluded))\\n\\n # filter them by the HTTP_REFERER\\n url_parts = urllib.parse.urlparse(request.META.get('HTTP_REFERER', '/'))\\n path = url_parts.path\\n valid_messages = [msg for msg in msgs if re.match(msg.url_target, path)]\\n msg_list = []\\n for msg in valid_messages:\\n msg_list.append(msg.msg_info())\\n if msg.show_frequency == BroadcastMessage.SHOW_ONCE:\\n excluded_cookie.add(msg.pk)\\n elif msg.show_frequency == BroadcastMessage.SHOW_ONCE_SESSION:\\n excluded_session.add(msg.pk)\\n request.session['excluded_broadcasts'] = encode_excluded(excluded_session)\\n response = HttpResponse(json.dumps(msg_list),\\n content_type=\\\"application/json\\\")\\n response.set_cookie('excluded_broadcasts', encode_excluded(excluded_cookie))\\n return response\",\n \"def health_check(request):\\n response = {\\\"Status\\\": True}\\n return JsonResponse(response, safe=False)\",\n \"def chat(self):\\n return self._get(\\\"chat\\\")\",\n \"def messages_gps(request):\\n\\n msgs = get_messages_gps()\\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\\n dic = {\\n 'chatmessages': msgs_list,\\n 'PAG_TITLE': 'Messages with GPS Data'\\n }\\n\\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))\",\n \"def get_status_messages(self):\\n\\n try:\\n subContext = conf.EHST_MESSAGES\\n connHandler = self._tap._TapPlus__getconnhandler()\\n response = connHandler.execute_tapget(subContext, verbose=False)\\n if response.status == 200:\\n for line in response:\\n string_message = line.decode(\\\"utf-8\\\")\\n print(string_message[string_message.index('=') + 1:])\\n except OSError:\\n print(\\\"Status messages could not be retrieved\\\")\",\n \"def get(self, request):\\n followers = request.GET.get('followers')\\n followings = request.GET.get('followings')\\n\\n if followers:\\n follower_obj = UserFollower.objects.filter(user=request.user.id).first()\\n\\n follower_serialized = UserFollowerShowSerializer(follower_obj)\\n\\n response_json = {\\n 'status': True,\\n 'message': 'successful',\\n 'data': follower_serialized.data\\n }\\n\\n return Response(response_json, status=200)\\n\\n if followings:\\n following_obj = UserFollowing.objects.filter(user=request.user.id).first()\\n\\n following_serialized = UserFollowingShowSerializer(following_obj)\\n\\n response_json = {\\n 'status': True,\\n 'message': 'successful',\\n 'data': following_serialized.data\\n }\\n\\n return Response(response_json, status=200)\\n\\n response_json = {\\n 'status': False,\\n 'message': 'unsuccessful',\\n 'data': {}\\n }\\n\\n return Response(response_json, status=200)\",\n \"def index(self):\\n try:\\n query = h.eagerload_morpheme_language_model(Session.query(MorphemeLanguageModel))\\n query = h.add_order_by(query, dict(request.GET), self.query_builder)\\n return h.add_pagination(query, dict(request.GET))\\n except Invalid, e:\\n response.status_int = 400\\n return {'errors': e.unpack_errors()}\",\n \"def list(self, request):\\n # Get transaction\\n transaction_id = self.request.query_params.get('transaction_id')\\n transaction = Transaction.objects.get(id=transaction_id)\\n\\n return JsonResponse({\\\"status\\\": transaction.status, \\\"transaction_error\\\": transaction.error_type})\",\n \"def handle_state(token: str = \\\"\\\"):\\n helper = SlackHelper(token)\\n\\n response = paginated_api_call(\\n api_method=helper.client.conversations_list,\\n response_objects_name=\\\"channels\\\",\\n exclude_archived=0,\\n types=\\\"public_channel, private_channel\\\",\\n )\\n return {\\\"channels\\\": response}\",\n \"def message_list(request, sender=None, receiver=None):\\n if request.method == 'GET':\\n messages = Message.objects.filter(sender_id=sender, receiver_id=receiver, is_read=False)\\n serializer = MessageSerializer(messages, many=True, context={'request': request})\\n for message in messages:\\n message.is_read = True\\n message.save()\\n return JsonResponse(serializer.data, safe=False)\\n\\n elif request.method == 'POST':\\n data = JSONParser().parse(request)\\n # print(data)\\n serializer = MessageSerializer(data=data)\\n if serializer.is_valid():\\n serializer.save()\\n return JsonResponse(serializer.data, status=201)\\n return JsonResponse(serializer.errors, status=400)\",\n \"def chat():\\n kwargs = {\\\"title\\\": u\\\"chat channel\\\", \\\"entries\\\": log.getLogEntries()}\\n return render_template(\\\"chat.html\\\", **kwargs)\",\n \"def test_chat_page(self):\\n c_code = 'new-chat'\\n rsp = self.client.get('/{}'.format(c_code))\\n html = rsp.data\\n\\n self.assertEqual(200, rsp.status_code)\\n # HTML is returned.\\n self.assertIn('<html>', html)\\n # HTML for chat is retrieved.\\n self.assertIn(c_code, html)\",\n \"def index(self, req):\\n context = req.environ['manila.context']\\n\\n search_opts = {}\\n search_opts.update(req.GET)\\n\\n # Remove keys that are not related to message attrs\\n search_opts.pop('limit', None)\\n search_opts.pop('marker', None)\\n sort_key = search_opts.pop('sort_key', 'created_at')\\n sort_dir = search_opts.pop('sort_dir', 'desc')\\n\\n messages = self.message_api.get_all(\\n context, search_opts=search_opts, sort_dir=sort_dir,\\n sort_key=sort_key)\\n limited_list = common.limited(messages, req)\\n\\n return self._view_builder.index(req, limited_list)\",\n \"def getPanchayatsAccurateData(request):\\n if request.method == 'GET':\\n panchayat=request.GET.get('panchayat', '')\\n ptid=request.GET.get('ptid', '')\\n block = request.GET.get('block', '')\\n bid = request.GET.get('bid', '')\\n district = request.GET.get('district', '')\\n state = request.GET.get('state', '')\\n limit=request.GET.get('limit', '')\\n if limit == '':\\n limit=500\\n else:\\n limit=int(limit)\\n #PTID we need to make it exact. Finyaer make it current finyear\\n panchayats = PanchayatStat.objects.filter(panchayat__name__icontains = panchayat, panchayat__id__icontains = ptid, panchayat__block__id__icontains = bid, panchayat__block__name__icontains=block, panchayat__block__district__name__icontains = district, panchayat__block__district__state__name__icontains = state, workDaysAccuracyIndex__gte = 90, finyear = '17')\\n\\n\\n panchayats = panchayats[:limit]\\n serializer = PanchayatStatSerializer(panchayats, many=True)\\n return JsonResponse(serializer.data, safe=False)\",\n \"def blocked(self, request, *args, **kwargs):\\n conversation = self.get_object()\\n blocked = User.objects.filter(id__in=conversation.blocked)\\n self.pagination_class = ShoutitPageNumberPagination\\n page = self.paginate_queryset(blocked)\\n serializer = ProfileSerializer(page, many=True, context={'request': request})\\n return self.get_paginated_response(serializer.data)\",\n \"def get_response(self):\\n return self.messages\",\n \"def msgs(request):\\r\\n results = list(PFASMsgRecord.objects.values())\\r\\n for result in results:\\r\\n # add a field that's not in the DB\\r\\n result['valid'] = 'literally' not in result['message'].lower()\\r\\n # change to text so JSON can save it\\r\\n result['timestamp'] = str(result['timestamp'])\\r\\n\\r\\n return HttpResponse(\\r\\n json.dumps(results, indent=2, sort_keys=True),\\r\\n content_type='application/json',\\r\\n )\",\n \"def get_homework_statuses(current_timestamp: int) -> dict:\\n data = {\\\"from_date\\\": current_timestamp}\\n headers = {\\\"Authorization\\\": f\\\"OAuth {PRAKTIKUM_TOKEN}\\\"}\\n try:\\n homework_statuses = requests.get(\\n PRAKTIKUM_API_URL,\\n params=data,\\n headers=headers,\\n )\\n # Заккоментриовал, т.к. валятся тесты\\n # homework_statuses.raise_for_status()\\n except requests.exceptions.RequestException as e:\\n logger.exception(e)\\n send_message(str(e))\\n raise\\n return homework_statuses.json()\",\n \"def get_all(request):\\n\\treturn JsonResponse(todo)\",\n \"def polls_details(request, pk):\\n poll = get_object_or_404(Poll, pk=pk)\\n data = {\\n 'result': {\\n 'question': poll.question,\\n 'created_by': poll.created_by.username,\\n 'pub_date': poll.pub_date\\n }\\n }\\n return JsonResponse(data)\",\n \"def get(self, request):\\n return Response(\\\"Dashboard Listing Page\\\", status=status.HTTP_200_OK)\",\n \"def read_home():\\n return {'message': 'API live!'}\",\n \"def get_languages(request):\\n if request.is_ajax():\\n publications = Publication.objects.all()\\n language_list = []\\n for pub in publications:\\n languages = pub.languages.all()\\n for lang in languages:\\n language_list.append({\\\"pub\\\": pub.pk,\\\"pub_name\\\":pub.name,\\\"lang\\\": u\\\"%s\\\" % lang.pk,\\\"name_lang\\\":lang.alias})\\n data = simplejson.dumps(language_list)\\n return HttpResponse(data)\",\n \"def librarian_list(request):\\n librarian = User.objects.filter(is_student=False, is_lecturer=False, is_parent=False, is_superuser=False)\\n user_type = \\\"Librarian\\\"\\n context = {\\n \\\"librarian\\\": librarian,\\n \\\"user_type\\\": user_type,\\n }\\n return render(request, 'library/librarians_list.html', context)\",\n \"def check_status(bot, chat_id, query):\\n\\n remain = remain_time(chat_id)\\n query_id = query.id\\n\\n message = bot_collection[chat_id].get_remained_message(remain)\\n\\n bot.answer_callback_query(callback_query_id=query_id, text=message)\\n\\n pass\",\n \"def test_get_unread_status_count(self):\\n request = self.factory.get(\\\"\\\")\\n request.user = self.local_user\\n\\n with patch(\\\"bookwyrm.activitystreams.ActivityStream.get_unread_count\\\") as mock:\\n mock.return_value = 3\\n result = views.get_unread_status_count(request, \\\"home\\\")\\n\\n self.assertIsInstance(result, JsonResponse)\\n data = json.loads(result.getvalue())\\n self.assertEqual(data[\\\"count\\\"], 3)\",\n \"def index():\\n #Have to create LOGIN\\n #serverxmpp.init()\\n return dict()\",\n \"def chat(self) -> \\\"api.Chat\\\":\\n raise NotImplementedError\",\n \"def get(self):\\n\\n return {\\\"message\\\": \\\"Welcome to the news API. \\\"}\",\n \"def show_all(message, bot):\\n reports_status = str(message.text)\\n try:\\n reports = requests.get(\\n url=f'http://127.0.0.1:8000/api/v1/report/?status={reports_status}',\\n headers={\\n 'Authorization': f'Bearer {jwt_token}',\\n }\\n )\\n if reports.status_code == 200:\\n result = reports.json()\\n output = format_result(result)\\n bot.send_message(\\n message.from_user.id,\\n output,\\n parse_mode='HTML',\\n )\\n elif reports.status_code == 401:\\n bot.send_message(\\n message.from_user.id,\\n 'Ошибка авторизации, проверьте токен',\\n )\\n except requests.exceptions.ConnectionError:\\n bot.send_message(\\n message.from_user.id,\\n 'Ошибка соединения с сервером',\\n )\",\n \"def get(self, request):\\n announcement_id = request.GET.get(\\\"id\\\")\\n if announcement_id:\\n try:\\n announcement = Announcement.objects.get(id=announcement_id)\\n return self.success(AnnouncementSerializer(announcement).data)\\n except Announcement.DoesNotExist:\\n return self.error(\\\"Announcement does not exist\\\")\\n announcement = Announcement.objects.all().order_by(\\\"-create_time\\\")\\n if request.GET.get(\\\"visible\\\") == \\\"true\\\":\\n announcement = announcement.filter(visible=True)\\n return self.success(self.paginate_data(request, announcement, AnnouncementSerializer))\",\n \"def outgoing_messages_view(request):\\n try:\\n data = JSONParser().parse(request)\\n user_id = data['sender_id'].encode('utf-8')\\n messages_list = Message.objects.filter(sender_id=user_id)\\n serializer = MessageSerializer(messages_list, many=True)\\n except Exception as e:\\n return Response({})\\n return Response(serializer.data)\",\n \"def annotation_all_stats(request):\\n\\n id_report = request.GET.get('report',None)\\n language = request.GET.get('language',None)\\n\\n json_dict = get_annotations_count(id_report,language)\\n\\n # print('annotations',json_dict)\\n return JsonResponse(json_dict)\",\n \"def render_GET(self, request):\\n request.setHeader(b'content-type', b'application/json; charset=utf-8')\\n set_cors(request, 'GET')\\n\\n timestamp = None\\n if b'timestamp' in request.args:\\n try:\\n timestamp = int(request.args[b'timestamp'][0])\\n except ValueError:\\n return json.dumps({\\n 'success': False,\\n 'message': 'Invalid timestamp parameter, expecting an integer'\\n }).encode('utf-8')\\n\\n tx_tips = self.manager.tx_storage.get_tx_tips(timestamp)\\n ret = {'success': True, 'tips': [tip.data.hex() for tip in tx_tips]}\\n return json.dumps(ret).encode('utf-8')\",\n \"def index():\\n text_list = ['노인복지']\\n fulfillment = {'text':''}\\n dialogflow_response = detect_intent_texts('', '', text_list, 'ko')\\n fulfillment['text'] = dialogflow_response\\n json_fulfillment = json.dumps(fulfillment, ensure_ascii=False)\\n\\n return jsonify(fulfillment)\",\n \"async def get(self):\\n await self.handle_request(self.chats_api, 1)\",\n \"def view_meetups():\\n\\n return make_response(jsonify({\\n \\\"status\\\": 200,\\n \\\"data\\\": meetups.view_meetups()\\n })), 200\",\n \"def queue_status(request):\\n\\n xml = cache.get(settings.CHAT_CACHE_KEY)\\n status = 200\\n if not xml:\\n xml = ''\\n status = 503\\n return HttpResponse(xml, mimetype='application/xml', status=status)\",\n \"def view_status(request, pk):\\n\\n status = Status.objects.get(pk=pk)\\n user_profile = get_users_profile(request.user.id)\\n\\n context = {\\n 'news_feed': [status],\\n 'user_profile': user_profile,\\n }\\n\\n return render(request, 'status/view_status.html', context)\",\n \"def get_report_translations(request):\\n\\n id_report = request.GET.get('id_report',None)\\n if id_report is not None:\\n languages = []\\n lang = Report.objects.filter(id_report = id_report)\\n for el in lang:\\n if el.language not in languages:\\n languages.append(el.language)\\n\\n json_resp = {}\\n # print(languages)\\n json_resp['languages'] = languages\\n return JsonResponse(json_resp)\",\n \"def GET(self):\\n web.header(\\\"Content-Type\\\",\\\"application/json; charset=utf-8\\\")\\n data = web.input(module=\\\"module\\\")\\n module = data[\\\"module\\\"]\\n count = db_module.get_module_newsNum(module)\\n result = json.dumps({\\\"count\\\": count})\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7056218","0.58047336","0.57761455","0.56156147","0.56094897","0.5595043","0.55575705","0.5555067","0.5535522","0.553365","0.5530453","0.55122775","0.540345","0.53645366","0.53573716","0.53215814","0.53145057","0.52810264","0.5262246","0.5239236","0.52295315","0.5229372","0.52216506","0.5219727","0.5216611","0.52143365","0.5213971","0.5187544","0.51789004","0.5177824","0.5176105","0.5157143","0.5154037","0.51500577","0.51406693","0.51380485","0.5134545","0.5114321","0.5094981","0.50934637","0.5089629","0.50571316","0.50527954","0.5043728","0.50434357","0.50343096","0.5032079","0.5032062","0.5026707","0.50208706","0.50157106","0.50086814","0.5007704","0.5002635","0.49898568","0.4988651","0.49841982","0.4980139","0.49798295","0.49785084","0.4971758","0.495879","0.49498272","0.49442643","0.49430567","0.49418598","0.49394518","0.49368566","0.49331415","0.49319774","0.49296612","0.4929069","0.4924208","0.49227092","0.49137104","0.49105138","0.49091852","0.48928583","0.4878677","0.4870548","0.4863661","0.48606655","0.485837","0.4852743","0.48527423","0.48517093","0.48449242","0.48353043","0.48187932","0.48186865","0.4817548","0.48160022","0.48152146","0.4813951","0.4807799","0.4806316","0.48054883","0.48015216","0.47994596","0.47973204"],"string":"[\n \"0.7056218\",\n \"0.58047336\",\n \"0.57761455\",\n \"0.56156147\",\n \"0.56094897\",\n \"0.5595043\",\n \"0.55575705\",\n \"0.5555067\",\n \"0.5535522\",\n \"0.553365\",\n \"0.5530453\",\n \"0.55122775\",\n \"0.540345\",\n \"0.53645366\",\n \"0.53573716\",\n \"0.53215814\",\n \"0.53145057\",\n \"0.52810264\",\n \"0.5262246\",\n \"0.5239236\",\n \"0.52295315\",\n \"0.5229372\",\n \"0.52216506\",\n \"0.5219727\",\n \"0.5216611\",\n \"0.52143365\",\n \"0.5213971\",\n \"0.5187544\",\n \"0.51789004\",\n \"0.5177824\",\n \"0.5176105\",\n \"0.5157143\",\n \"0.5154037\",\n \"0.51500577\",\n \"0.51406693\",\n \"0.51380485\",\n \"0.5134545\",\n \"0.5114321\",\n \"0.5094981\",\n \"0.50934637\",\n \"0.5089629\",\n \"0.50571316\",\n \"0.50527954\",\n \"0.5043728\",\n \"0.50434357\",\n \"0.50343096\",\n \"0.5032079\",\n \"0.5032062\",\n \"0.5026707\",\n \"0.50208706\",\n \"0.50157106\",\n \"0.50086814\",\n \"0.5007704\",\n \"0.5002635\",\n \"0.49898568\",\n \"0.4988651\",\n \"0.49841982\",\n \"0.4980139\",\n \"0.49798295\",\n \"0.49785084\",\n \"0.4971758\",\n \"0.495879\",\n \"0.49498272\",\n \"0.49442643\",\n \"0.49430567\",\n \"0.49418598\",\n \"0.49394518\",\n \"0.49368566\",\n \"0.49331415\",\n \"0.49319774\",\n \"0.49296612\",\n \"0.4929069\",\n \"0.4924208\",\n \"0.49227092\",\n \"0.49137104\",\n \"0.49105138\",\n \"0.49091852\",\n \"0.48928583\",\n \"0.4878677\",\n \"0.4870548\",\n \"0.4863661\",\n \"0.48606655\",\n \"0.485837\",\n \"0.4852743\",\n \"0.48527423\",\n \"0.48517093\",\n \"0.48449242\",\n \"0.48353043\",\n \"0.48187932\",\n \"0.48186865\",\n \"0.4817548\",\n \"0.48160022\",\n \"0.48152146\",\n \"0.4813951\",\n \"0.4807799\",\n \"0.4806316\",\n \"0.48054883\",\n \"0.48015216\",\n \"0.47994596\",\n \"0.47973204\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76308614"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":7499,"cells":{"query":{"kind":"string","value":"Fonction pour placer un disque de couleur 'col' a la coordonnee (xa,ya)"},"document":{"kind":"string","value":"def create_point(xa,ya,col):\n disque = canvas.create_oval(xa-(rayon),ya-(rayon),xa+(rayon),ya+(rayon),fill=\"white\",outline=col)\n return disque"},"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_xy_position(row, col):\n spacing_x = 86 + 11\n spacing_y = 98 + 8\n top_y = 50\n left_x = 50\n return left_x + col * spacing_x, top_y + row * spacing_y","def colWithTile(self, pos):\n\n\n return self.colWithBox(pos, [2.0,2.0,2.0])","def em_coord_turtle(lin, col, dim, tam_celula):\n meio = dim // 2\n x = (col - meio) * tam_celula\n y = (meio - lin) * tam_celula\n return x, y","def _coord(self, x, y):\n gridEdge = 7 # originally 5\n y = gridEdge - y\n cx = 100 * (x - 1) + 50\n cy = 100 * (y - 1) + 50\n r = 20\n return (cx - r, cy - r, cx + r, cy + r)","def collocation_points(self, **kw):\n pass","def _get_x_y_from_pos(self, col,row): \r\n return (self.margin_left+(self.text_width*col),\r\n self.margin_top+(self.text_height*row))","def _position_x_to_column(self, x, y):\n col = -1\n if y>self.padding_top and y<self.padding_top+self.len_y_cercles:\n for i in range(self.n_columns):\n if x>self.padding_left+i*63 and x<self.padding_left+i*63+self.diam_cercles:\n col = i+1\n break\n return col","def coords_to_node(self,row,col):\n return row*self.cols + col + 1","def coord (i, j):\r\n return j, i","def rowcol2XY(row,col,CCD):\n pixscale = 0.015 #mm/pix\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\n return X,Y","def get_cell_coords(self, pt):\n\n\t return int(pt[0] // self.a), int(pt[1] // self.a)","def get_coords_for_col(self, i):\n X = N.zeros((self.rows,2),dtype=config.floatX)\n X[:,0] = self.xmin + float(i) * self.delta_x\n X[:,1] = self.ymin + N.cast[config.floatX](N.asarray(range(self.rows-1,-1,-1))) * self.delta_y\n\n\n return X","def col(self, col: tuple) -> list:\n return self.grid[col::9]","def location_to_pos(self,row, col):\r\n\r\n pos_row = str(row + 1)\r\n pos_col = chr(col + 97)\r\n return pos_col + pos_row","def _add_coordinate_data(self, df, geom_col):\n x = df.apply(self._get_coords,\n geom_col=geom_col,\n coord_type='x',\n axis=1)\n\n y = df.apply(self._get_coords,\n geom_col=geom_col,\n coord_type='y',\n axis=1)\n return x, y","def xy_to_rowcol(self, x, y):\n col = int((x - self.board_lft_x) / self.next_square)\n row = int((self.board_top_y - y) / self.next_square)\n return [row, col]","def drawcolumn(self, colnum: int, epc: str, dst: float) -> None:\n svg = self.svg\n wscr, hscr = svg.get_WH()\n w_rect = 50\n colsep = 20\n w_column = w_rect + colsep\n\n xleft = colnum * w_column\n TOP_MARGIN = 20\n MAXH = hscr - TOP_MARGIN\n MAXDIST = 5.0\n h_rect = (dst*MAXH)/MAXDIST\n print(\"CALC dist {}, maxh {}, maxdist {} h_rect {} \".format(dst, MAXH, MAXDIST, h_rect))\n if h_rect > MAXH:\n h_rect = MAXH\n h_rect = int(h_rect)\n ytop = TOP_MARGIN + (MAXH - h_rect)\n print(\"DRAW {}: {} {} {} {}\".format(colnum, xleft, ytop, w_rect, h_rect))\n red_colorstr = '#ff0066'\n blu_colorstr = '#6600ff'\n svg.rect(xleft, ytop, w_rect, h_rect, red_colorstr)\n svg.text(xleft, ytop, blu_colorstr, epc)","def get_id_from_coor(self, x, y):\n x_coor = x // self._cell_dim\n y_coor = y // self._cell_dim\n return (x_coor, y_coor)","def _modify_columns(self, cols, X, y=None):","def col_for_cell(self, coords, include_self=False):\n row, col = coords\n return ((r, col) for r in self.rows if include_self or r != row)","def set_col( self, col ):\n self.ix_col = col","def positionColour(row, col):\n if (row + col) % 2 == 0:\n return BLACK\n else:\n return WHITE","def draw_pixel(x, y, col):\n unicornhathd.set_pixel(x, 12 - y, col[0], col[1], col[2])","def get_xy(self, x, y):\r\n\t\treturn self.grid[y, x]","def set_collocation_points(self, X_f):\n self.t = self.tensor(X_f[:,0:1])\n self.x = self.tensor(X_f[:,1:2])\n self.y = self.tensor(X_f[:,2:3])","def spot_coords(self,spot):\n if spot == '1':\n return (330 - 60 ,335 - 15)\n if spot == '2':\n return (419 - 60, 335 - 15)\n if spot == '3':\n return (591 - 60, 159 - 15)\n if spot == '4':\n return (588 - 60, 248 - 15)","def drawColorColumn(x, yseq, zseq):\n dislin.curvy3(x, yseq, zseq, len(yseq))","def seg_row_col(sp) : \n return src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)","def coord_char(coord, matrix):\n row_index, column_index = coord\n\n return matrix[row_index][column_index]","def coordinates(self):","def redraw_col(cls, col):\n # TODO: It's wasteful to make draw_block get block all over again here!\n for _, coord in MapModel.get_column(col, cls.depth):\n cls.draw_block(coord)","def grid_to_mouse( pos ):\n ix,iy=pos\n px= ix*CELLSIZE + H_CELLSIZE + ix*CELLGAP\n py= iy*CELLSIZE + H_CELLSIZE + iy*CELLGAP\n return (px,py)","def schcoords(self, canx, cany):\n # Coordinates of scheme (0,0) corner as canvas coords.\n # Scheme (0,0) is the left, bottom corner (like in mathematic,\n # not like in canvas)\n x0 = (self._cw - self.width)/2\n y0 = (self._ch - self.height)/2 + self.height\n return (canx - x0, y0 - cany)","def Pos(row, col):\n return ESC + str(row) + ';' + str(col) + 'H'","def set_2d_location(self, x, y):\r\n self.unif[42:44] = [x, y]","def _havannah_coord_to_canvas_coord(self, coord):\n col, slant = cubic_to_axial(*coord)\n canvas_x, canvas_y = self.CANVAS_CENTER\n\n canvas_x += col * self.HEX_WIDTH // 4 * 3\n canvas_y += (col * self.HEX_WIDTH // 2) + (slant * self.HEX_WIDTH)\n\n return (canvas_x, canvas_y)","def click(self, x, y):\n row = int((x - self.x)/self.cell_size)\n col = int((y - self.y)/self.cell_size)\n if 0 <= row < ROWS and 0 <= col < COLS:\n return row, col\n return None","def node_to_coords(self,node_num):\n row = (node_num - 1) / self.cols\n col = (node_num - 1) % self.cols\n return (row,col)","def to_coords(self, px, py):\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\n raise IndexError\n return (px // self.SIZE, py // self.SIZE,\n px % self.SIZE, py % self.SIZE)","def compute_row_col(self, x, y):\n if self.is_in_small_cell_area(x, y):\n x = int(math.fabs(x - self.xoffset))\n col = x / self.small.width\n row = (self.small_yoffset - y) / self.small.height\n else:\n col = (x - self.xoffset) / self.large.width\n row = (self.large_yoffset - y) / self.large.height\n return row, col","def __check_col(self, x: int, y: int) -> bool:\n return not any([self.__maze[x + i, y] for i in (-1, 0, 1)])","def col_data_mover_at(row, col):\n if col == 0:\n return NAME_SCHEME[\"memory move\"].format(prefix=f\"l{row}\")\n else:\n return NAME_SCHEME[\"register move right\"].format(pe=f\"pe_{row}_{col - 1}\")","def getCellpos(self, event):\n e = event.widget\n cx, cy = cart(e.canvasx(event.x), e.canvasy(event.y))\n cellx = int(cx) // self.cell_width\n celly = int(cy) // self.cell_height\n return cellx, celly","def get_cell_coords(pt):\n\n return int(pt[0] // a), int(pt[1] // a)","def get_pos(self):\n return [self.row, self.col]","def map_loc_to_pixel((x, y), xc = 17.25, yc = 630, run = 17.25):\n xp, yp = xc + x*run, yc - y*run\n return (xp, yp)","def convertFrom1Dto2D(coord, num_cols):\n y = int(np.floor(coord/num_cols))\n x = coord % num_cols\n return (y,x)","def _get_grid_coord(self, point):\n return tuple([int(point[i] / self._cell_length) for i in range(self._dim)])","def _goto_piece_xy(self, row, col, adjustment_x=0, adjustment_y=0):\n self.pen.up()\n x = (self.board_lft_x + col * (self.next_square) +\n self.square_side_size * .05) + adjustment_x * self.square_side_size\n y = (self.board_top_y - row * (self.next_square) -\n self.square_side_size * .8) - adjustment_y * self.square_side_size\n self.pen.goto(x, y)","def cell_from_xy(self,x,y):\n return self.cell_array.item((x,y))","def coord(self, x, y):\n origin_x = self._raster_meta['transform'][3]\n origin_y = self._raster_meta['transform'][0]\n pixel_x = self._raster_meta['transform'][5]\n pixel_y = self._raster_meta['transform'][1]\n\n x = int((x - origin_x) / pixel_x)\n y = int((y - origin_y) / pixel_y)\n return self[x, y]","def coord(self):\n fmt = \"{min_col}{min_row}:{max_col}{max_row}\"\n if (self.min_col == self.max_col\n and self.min_row == self.max_row):\n fmt = \"{min_col}{min_row}\"\n\n return fmt.format(\n min_col=get_column_letter(self.min_col),\n min_row=self.min_row,\n max_col=get_column_letter(self.max_col),\n max_row=self.max_row\n )","def cols(self, col):\n self.col += col","def _get_grid_coord(wl, bl):\n row = None\n col = None\n\n for i, (l, h) in QLDbEntry.wl_map.items():\n if wl >= l and wl <= h:\n row = i\n break\n\n for i, (l, h) in QLDbEntry.bl_map.items():\n if bl >= l and bl <= h:\n col = i\n break\n\n return col, row","def ind2coord(self, index):\n\n # assert (index >= 0)\n # assert(index < self.n - 1)\n\n col = index // self.rows\n row = index % self.rows\n\n return [row, col]","def locate(x, y):\n position(x * 6, y)","def get_origin(col, row, pattern_size, margin):\n\tw,h = pattern_size\n\torigin = col*(w+margin), row*(h+margin)\n\treturn origin","def set(self, row: int, col: int, color: Color) -> None:\n super(ColorGrid, self).set(row, col, color)","def genRowColCell(self, args):\n if len(args) == 2:\n x = int(args[0])\n y = int(args[1])\n if x == 0 and y == 0:\n return self.genMatrix()\n elif x == 0:\n return self.genRow(y - 1)\n elif y == 0:\n return self.genCol(x - 1)\n else:\n return self.genCell(x - 1, y - 1)\n else:\n return self.genMatrix()","def xy_to_cxcy(xy):\n return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2, # c_x, c_y\n xy[:, 2:] - xy[:, :2]], 1) # w, h","def getCoordinateAt(self,colIndex,rowIndex):\n gridval = self.grid.get(self.createKey(colIndex,rowIndex),self.defaultVal)\n retvals = [self.currentCol,self.currentRow,gridval]\n return retvals","def __init__(self, row = 0, col = 0):\n self.row = row\n self.col = col","def get_index(self, row, col):\n return (row * self.cols) + col","def test_board_coordinates_toXY():\r\n m = Move()\r\n for col_num, col_name in enumerate(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']):\r\n for row in range(1, 9):\r\n assert m.translate_to_xy(col_name + str(row)) == (Board.SIZE - row, col_num)","def get_coord(self,x,y,z):\n a = 0\n b = 0\n c = 0\n \n distance = 0\n \n while (distance <= x):\n distance += SQUARE_SIZE\n if ( (x - distance) > - (SQUARE_SIZE / 2) ):\n a += 1\n distance = 0\n \n while (distance <= y):\n distance += SQUARE_SIZE\n if ( (y - distance) > - (SQUARE_SIZE / 2) ):\n b += 1\n distance = 0\n \n while (distance <= z):\n distance += SQUARE_SIZE\n if ( (z - distance) > - (SQUARE_SIZE / 2) ):\n c += 1\n distance = 0\n \n return(a,b,c)","def chr_coords(s):\n return max_y - (max_y - min_y)*s","def test_if_row_col_well_retrieved_from_mouse_pos(self):\n ui = UIRender(TestUI.image_path)\n row, col = ui.get_row_col_from_mouse((10,25))\n self.assertEqual(row, 0)\n self.assertEqual(col, 0)","def mark(board, player, row, col):\r\n pass","def map_grid_loc_to_pixel((grid, x, y), panel_dimensions = bm_panel_dimensions, xc = 17.25, yc = 630, run = 17.25):\n x_offset = 0\n for panel_index, panel_dim in panel_dimensions.iteritems():\n if panel_index < grid:\n width, height = panel_dim\n x_offset += width*xc\n xp, yp = xc + x*run + x_offset, yc - y*run\n return (xp, yp)","def indexToPosition(self, col, row):\n columns = \"ABCDEFGH\"\n return columns[col] + str(row + 1)","def get_row_col(mouse_x, mouse_y):\n # Note: the top row is row=0 (bottom row=2), left col is col=0 (right col=2)\n spacing_x = 86 + 8\n spacing_y = 98 + 5\n top_y = 50\n left_x = 50\n return (mouse_y - top_y) // spacing_y, (mouse_x - left_x) // spacing_x","def mouse_to_grid( pos ):\n mx,my=pos\n # account for window border and gap between cells\n ix = int((mx-H_CELLSIZE)/(CELLSIZE+CELLGAP))\n iy = int((my-H_CELLSIZE)/(CELLSIZE+CELLGAP))\n # force respect window borders\n if ix<0 or ix>=GRID_X or iy<0 or iy>=GRID_Y:\n return None\n else:\n return (ix,iy)","def cell_value(self, x, y):\n if x == 8 and y == 0:\n return \"--\"\n (r, g) = self[(x, y)]\n return \"%s%s\" % (r, g)","def get_xy(self):\r\n return self.board.get_xy()","def putpixel(self, col, row, color=GREEN):\n if col < 0 or row < 0:\n return\n try:\n self.vram[row][col] = color\n except IndexError:\n pass","def pixel(self, x: int, y: int, colour: int, /) -> None:","def cell_coord(id, Nx):\n nx = id // (Nx**2)\n ny = (id - nx * Nx**2) // Nx\n nz = id - nx * Nx**2 - ny * Nx\n return np.array([nx, ny, nz])","def piece_at(self, row, col):\n return self.board[row + PADDING][col + PADDING]","def to_position(self, x, y, i, j):\n return (x * self.SIZE + i, y * self.SIZE + j)","def __convert_position(self, row_position: int = None, col_position: int = None) -> int:\n if row_position is None or col_position is None:\n return self.__row_position * len(self.__labyrinth[0]) + self.__col_position\n\n return row_position * len(self.__labyrinth[0]) + col_position","def print_seg_row_col(sp) : \n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))","def getCol(self, n, offset=0):\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]","def placeCrate (self, crates_char, row, column):","def room_xy(room, x, y, value=None):\n return room[x][y]","def _seat_id(col: int, row: int):\n return row * 8 + col","def get_column(self, coords, direction):\n column = []\n x = coords[0]\n y = coords[1]\n if direction == \"down\":\n # x, 0-y\n for i in range(y):\n column.append((x, i))\n elif direction == \"right\":\n # 0-x, y\n for i in range(x):\n column.append((i, y))\n elif direction == \"up\":\n # x, y-last\n for i in range(y + 1, self.size):\n column.append((x, i))\n elif direction == \"left\":\n # x-last, y\n for i in range(x + 1, self.size):\n column.append((i, y))\n\n return column","def get_pos(self, cx, cy):\n x = self.min_x + cx*(self.size+0.5)\n y = self.min_y + cy*(self.size+0.5)\n return (x,y)","def get_coordinates():\n\tallowed_range = [0,1,2]\n\trow = int(input(\"Enter row: \")) - 1\n\tcol = int(input(\"Enter column: \")) - 1","def _to_maze_coord(self, x, y):\n maze = self._get_maze()\n x = int(x / _MAZE_CELL_SIZE)\n y = int(y / _MAZE_CELL_SIZE)\n y = maze.shape[1] - y - 1\n return x, y","def set_tile(self, row, col, value):\n # replace with your code\n self.grid[row][col] = value","def mark(board, player, row, col):\n pass","def place_at(self, row, col, piece):\n self.board[row + PADDING][col + PADDING] = piece","def Haut():\r\n X1, Y1, X2, Y2 = canvas.coords(boule)\r\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)","def __init__(self, x, y):\n self.x = x\n self.y = y\n self.x1 = self.x + 30 # largeur et hauteur fixees\n self.y1 = self.y + 30","def set_xy(self, x, y, val):\r\n\t\tself.grid[y, x] = val","def cie_xyz(self):\n return tuple(matrix_mult(\n ((0.4124564, 0.3575761, 0.1804375),\n (0.2126729, 0.7151522, 0.0721750),\n (0.0193339, 0.1191920, 0.9503041),\n ),\n (from_srgb(self.red),\n from_srgb(self.green),\n from_srgb(self.blue)\n )\n ))","def col2vex(self, sele=None, lelem=None, laty=None):\n if sele is None:\n ncol = self.ncolors\n col = self.colors.astype(np.int)\n nbonds = self.nbonds\n ba = self.blist[:,0]\n bb = self.blist[:,1]\n else:\n try:\n indexcol = np.where(sum([self.colors == i for i in sele]))[0] ###since bool arrays, here \"sum\" means \"or\"\n except ValueError:\n indexcol = []\n col = self.colors[indexcol]\n ncol = len(sele)\n nbonds = len(col)\n ba = self.blist[:,0][indexcol]\n bb = self.blist[:,1][indexcol]\n xyz_a = self.xyz[ba]\n xyz_c = []\n for i in range(nbonds):\n bci = self.conn[ba[i]].index(bb[i])\n xyz_ic = self.get_neighb_coords(ba[i], bci)\n xyz_c.append(xyz_ic)\n xyz_c = np.array(xyz_c) + xyz_a\n xyz_c *= .5\n m = mol.fromArray(xyz_c)\n ### DEFAULT ASSIGNMENT\n if lelem is None and laty is None:\n laty = range(ncol)\n lowercase = list('kbabcdefghijklmnopqrstuvwxyz')\n lelem = [lowercase[i] for i in laty]\n laty = [str(i) for i in laty]\n elif lelem is None or laty is None:\n raise TypeError(\"lelem and laty must be both either None or ndarrays\")\n elif len(lelem) != ncol or len(laty) != ncol:\n raise ValueError(\"len of sele, lelem and laty must be the same!\\nsele:\\t%s\\nlelem:\\t%s\\nlaty:\\t%s\" % (sele, lelem, laty))\n lelem = np.array(lelem)\n laty = np.array(laty)\n m.set_elems(lelem[col])\n m.set_atypes(laty[col])\n if hasattr(self,'cell'): m.set_cell(self.cell)\n if hasattr(self,'supercell'): m.supercell = self.supercell[:]\n m.colors = self.colors\n return m","def __init__(self, grid, x, y, cols):\n self.grid = grid\n self.x = x\n self.y = y\n self.cols = cols","def neighbours_of_position(coords):\n row = coords[0]\n col = coords[1]\n \n #assign each of neighbours corrds\n #top left to top rigt\n top_left = (row - 1, col - 1)\n top_center = (row - 1, col)\n top_right = (row - 1, col + 1)\n \n # left to right (center)\n left = (row, col - 1)\n # the (row, col) cordinates passed into this function are situated here\n right = (row, col + 1)\n \n #bottom-left to bottom-right\n bottom_left = (row +1, col -1)\n bottom_center = (row +1, col)\n bottom_right = (row +1, col +1)\n \n return [top_left, top_center, top_right,\n left , right ,\n bottom_left, bottom_center, bottom_right]","def cancoords(self, schx, schy):\n # Coordinates of scheme (0,0) corner as canvas coords\n x0 = (self._cw - self.width)/2\n y0 = (self._ch - self.height)/2 + self.height\n return (schx + x0, y0 - schy)"],"string":"[\n \"def get_xy_position(row, col):\\n spacing_x = 86 + 11\\n spacing_y = 98 + 8\\n top_y = 50\\n left_x = 50\\n return left_x + col * spacing_x, top_y + row * spacing_y\",\n \"def colWithTile(self, pos):\\n\\n\\n return self.colWithBox(pos, [2.0,2.0,2.0])\",\n \"def em_coord_turtle(lin, col, dim, tam_celula):\\n meio = dim // 2\\n x = (col - meio) * tam_celula\\n y = (meio - lin) * tam_celula\\n return x, y\",\n \"def _coord(self, x, y):\\n gridEdge = 7 # originally 5\\n y = gridEdge - y\\n cx = 100 * (x - 1) + 50\\n cy = 100 * (y - 1) + 50\\n r = 20\\n return (cx - r, cy - r, cx + r, cy + r)\",\n \"def collocation_points(self, **kw):\\n pass\",\n \"def _get_x_y_from_pos(self, col,row): \\r\\n return (self.margin_left+(self.text_width*col),\\r\\n self.margin_top+(self.text_height*row))\",\n \"def _position_x_to_column(self, x, y):\\n col = -1\\n if y>self.padding_top and y<self.padding_top+self.len_y_cercles:\\n for i in range(self.n_columns):\\n if x>self.padding_left+i*63 and x<self.padding_left+i*63+self.diam_cercles:\\n col = i+1\\n break\\n return col\",\n \"def coords_to_node(self,row,col):\\n return row*self.cols + col + 1\",\n \"def coord (i, j):\\r\\n return j, i\",\n \"def rowcol2XY(row,col,CCD):\\n pixscale = 0.015 #mm/pix\\n X = CCD[1]+1024*pixscale-(col*pixscale+pixscale/2.)\\n Y = CCD[2]+2048*pixscale-(row*pixscale+pixscale/2.)\\n return X,Y\",\n \"def get_cell_coords(self, pt):\\n\\n\\t return int(pt[0] // self.a), int(pt[1] // self.a)\",\n \"def get_coords_for_col(self, i):\\n X = N.zeros((self.rows,2),dtype=config.floatX)\\n X[:,0] = self.xmin + float(i) * self.delta_x\\n X[:,1] = self.ymin + N.cast[config.floatX](N.asarray(range(self.rows-1,-1,-1))) * self.delta_y\\n\\n\\n return X\",\n \"def col(self, col: tuple) -> list:\\n return self.grid[col::9]\",\n \"def location_to_pos(self,row, col):\\r\\n\\r\\n pos_row = str(row + 1)\\r\\n pos_col = chr(col + 97)\\r\\n return pos_col + pos_row\",\n \"def _add_coordinate_data(self, df, geom_col):\\n x = df.apply(self._get_coords,\\n geom_col=geom_col,\\n coord_type='x',\\n axis=1)\\n\\n y = df.apply(self._get_coords,\\n geom_col=geom_col,\\n coord_type='y',\\n axis=1)\\n return x, y\",\n \"def xy_to_rowcol(self, x, y):\\n col = int((x - self.board_lft_x) / self.next_square)\\n row = int((self.board_top_y - y) / self.next_square)\\n return [row, col]\",\n \"def drawcolumn(self, colnum: int, epc: str, dst: float) -> None:\\n svg = self.svg\\n wscr, hscr = svg.get_WH()\\n w_rect = 50\\n colsep = 20\\n w_column = w_rect + colsep\\n\\n xleft = colnum * w_column\\n TOP_MARGIN = 20\\n MAXH = hscr - TOP_MARGIN\\n MAXDIST = 5.0\\n h_rect = (dst*MAXH)/MAXDIST\\n print(\\\"CALC dist {}, maxh {}, maxdist {} h_rect {} \\\".format(dst, MAXH, MAXDIST, h_rect))\\n if h_rect > MAXH:\\n h_rect = MAXH\\n h_rect = int(h_rect)\\n ytop = TOP_MARGIN + (MAXH - h_rect)\\n print(\\\"DRAW {}: {} {} {} {}\\\".format(colnum, xleft, ytop, w_rect, h_rect))\\n red_colorstr = '#ff0066'\\n blu_colorstr = '#6600ff'\\n svg.rect(xleft, ytop, w_rect, h_rect, red_colorstr)\\n svg.text(xleft, ytop, blu_colorstr, epc)\",\n \"def get_id_from_coor(self, x, y):\\n x_coor = x // self._cell_dim\\n y_coor = y // self._cell_dim\\n return (x_coor, y_coor)\",\n \"def _modify_columns(self, cols, X, y=None):\",\n \"def col_for_cell(self, coords, include_self=False):\\n row, col = coords\\n return ((r, col) for r in self.rows if include_self or r != row)\",\n \"def set_col( self, col ):\\n self.ix_col = col\",\n \"def positionColour(row, col):\\n if (row + col) % 2 == 0:\\n return BLACK\\n else:\\n return WHITE\",\n \"def draw_pixel(x, y, col):\\n unicornhathd.set_pixel(x, 12 - y, col[0], col[1], col[2])\",\n \"def get_xy(self, x, y):\\r\\n\\t\\treturn self.grid[y, x]\",\n \"def set_collocation_points(self, X_f):\\n self.t = self.tensor(X_f[:,0:1])\\n self.x = self.tensor(X_f[:,1:2])\\n self.y = self.tensor(X_f[:,2:3])\",\n \"def spot_coords(self,spot):\\n if spot == '1':\\n return (330 - 60 ,335 - 15)\\n if spot == '2':\\n return (419 - 60, 335 - 15)\\n if spot == '3':\\n return (591 - 60, 159 - 15)\\n if spot == '4':\\n return (588 - 60, 248 - 15)\",\n \"def drawColorColumn(x, yseq, zseq):\\n dislin.curvy3(x, yseq, zseq, len(yseq))\",\n \"def seg_row_col(sp) : \\n return src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\",\n \"def coord_char(coord, matrix):\\n row_index, column_index = coord\\n\\n return matrix[row_index][column_index]\",\n \"def coordinates(self):\",\n \"def redraw_col(cls, col):\\n # TODO: It's wasteful to make draw_block get block all over again here!\\n for _, coord in MapModel.get_column(col, cls.depth):\\n cls.draw_block(coord)\",\n \"def grid_to_mouse( pos ):\\n ix,iy=pos\\n px= ix*CELLSIZE + H_CELLSIZE + ix*CELLGAP\\n py= iy*CELLSIZE + H_CELLSIZE + iy*CELLGAP\\n return (px,py)\",\n \"def schcoords(self, canx, cany):\\n # Coordinates of scheme (0,0) corner as canvas coords.\\n # Scheme (0,0) is the left, bottom corner (like in mathematic,\\n # not like in canvas)\\n x0 = (self._cw - self.width)/2\\n y0 = (self._ch - self.height)/2 + self.height\\n return (canx - x0, y0 - cany)\",\n \"def Pos(row, col):\\n return ESC + str(row) + ';' + str(col) + 'H'\",\n \"def set_2d_location(self, x, y):\\r\\n self.unif[42:44] = [x, y]\",\n \"def _havannah_coord_to_canvas_coord(self, coord):\\n col, slant = cubic_to_axial(*coord)\\n canvas_x, canvas_y = self.CANVAS_CENTER\\n\\n canvas_x += col * self.HEX_WIDTH // 4 * 3\\n canvas_y += (col * self.HEX_WIDTH // 2) + (slant * self.HEX_WIDTH)\\n\\n return (canvas_x, canvas_y)\",\n \"def click(self, x, y):\\n row = int((x - self.x)/self.cell_size)\\n col = int((y - self.y)/self.cell_size)\\n if 0 <= row < ROWS and 0 <= col < COLS:\\n return row, col\\n return None\",\n \"def node_to_coords(self,node_num):\\n row = (node_num - 1) / self.cols\\n col = (node_num - 1) % self.cols\\n return (row,col)\",\n \"def to_coords(self, px, py):\\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\\n raise IndexError\\n return (px // self.SIZE, py // self.SIZE,\\n px % self.SIZE, py % self.SIZE)\",\n \"def compute_row_col(self, x, y):\\n if self.is_in_small_cell_area(x, y):\\n x = int(math.fabs(x - self.xoffset))\\n col = x / self.small.width\\n row = (self.small_yoffset - y) / self.small.height\\n else:\\n col = (x - self.xoffset) / self.large.width\\n row = (self.large_yoffset - y) / self.large.height\\n return row, col\",\n \"def __check_col(self, x: int, y: int) -> bool:\\n return not any([self.__maze[x + i, y] for i in (-1, 0, 1)])\",\n \"def col_data_mover_at(row, col):\\n if col == 0:\\n return NAME_SCHEME[\\\"memory move\\\"].format(prefix=f\\\"l{row}\\\")\\n else:\\n return NAME_SCHEME[\\\"register move right\\\"].format(pe=f\\\"pe_{row}_{col - 1}\\\")\",\n \"def getCellpos(self, event):\\n e = event.widget\\n cx, cy = cart(e.canvasx(event.x), e.canvasy(event.y))\\n cellx = int(cx) // self.cell_width\\n celly = int(cy) // self.cell_height\\n return cellx, celly\",\n \"def get_cell_coords(pt):\\n\\n return int(pt[0] // a), int(pt[1] // a)\",\n \"def get_pos(self):\\n return [self.row, self.col]\",\n \"def map_loc_to_pixel((x, y), xc = 17.25, yc = 630, run = 17.25):\\n xp, yp = xc + x*run, yc - y*run\\n return (xp, yp)\",\n \"def convertFrom1Dto2D(coord, num_cols):\\n y = int(np.floor(coord/num_cols))\\n x = coord % num_cols\\n return (y,x)\",\n \"def _get_grid_coord(self, point):\\n return tuple([int(point[i] / self._cell_length) for i in range(self._dim)])\",\n \"def _goto_piece_xy(self, row, col, adjustment_x=0, adjustment_y=0):\\n self.pen.up()\\n x = (self.board_lft_x + col * (self.next_square) +\\n self.square_side_size * .05) + adjustment_x * self.square_side_size\\n y = (self.board_top_y - row * (self.next_square) -\\n self.square_side_size * .8) - adjustment_y * self.square_side_size\\n self.pen.goto(x, y)\",\n \"def cell_from_xy(self,x,y):\\n return self.cell_array.item((x,y))\",\n \"def coord(self, x, y):\\n origin_x = self._raster_meta['transform'][3]\\n origin_y = self._raster_meta['transform'][0]\\n pixel_x = self._raster_meta['transform'][5]\\n pixel_y = self._raster_meta['transform'][1]\\n\\n x = int((x - origin_x) / pixel_x)\\n y = int((y - origin_y) / pixel_y)\\n return self[x, y]\",\n \"def coord(self):\\n fmt = \\\"{min_col}{min_row}:{max_col}{max_row}\\\"\\n if (self.min_col == self.max_col\\n and self.min_row == self.max_row):\\n fmt = \\\"{min_col}{min_row}\\\"\\n\\n return fmt.format(\\n min_col=get_column_letter(self.min_col),\\n min_row=self.min_row,\\n max_col=get_column_letter(self.max_col),\\n max_row=self.max_row\\n )\",\n \"def cols(self, col):\\n self.col += col\",\n \"def _get_grid_coord(wl, bl):\\n row = None\\n col = None\\n\\n for i, (l, h) in QLDbEntry.wl_map.items():\\n if wl >= l and wl <= h:\\n row = i\\n break\\n\\n for i, (l, h) in QLDbEntry.bl_map.items():\\n if bl >= l and bl <= h:\\n col = i\\n break\\n\\n return col, row\",\n \"def ind2coord(self, index):\\n\\n # assert (index >= 0)\\n # assert(index < self.n - 1)\\n\\n col = index // self.rows\\n row = index % self.rows\\n\\n return [row, col]\",\n \"def locate(x, y):\\n position(x * 6, y)\",\n \"def get_origin(col, row, pattern_size, margin):\\n\\tw,h = pattern_size\\n\\torigin = col*(w+margin), row*(h+margin)\\n\\treturn origin\",\n \"def set(self, row: int, col: int, color: Color) -> None:\\n super(ColorGrid, self).set(row, col, color)\",\n \"def genRowColCell(self, args):\\n if len(args) == 2:\\n x = int(args[0])\\n y = int(args[1])\\n if x == 0 and y == 0:\\n return self.genMatrix()\\n elif x == 0:\\n return self.genRow(y - 1)\\n elif y == 0:\\n return self.genCol(x - 1)\\n else:\\n return self.genCell(x - 1, y - 1)\\n else:\\n return self.genMatrix()\",\n \"def xy_to_cxcy(xy):\\n return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2, # c_x, c_y\\n xy[:, 2:] - xy[:, :2]], 1) # w, h\",\n \"def getCoordinateAt(self,colIndex,rowIndex):\\n gridval = self.grid.get(self.createKey(colIndex,rowIndex),self.defaultVal)\\n retvals = [self.currentCol,self.currentRow,gridval]\\n return retvals\",\n \"def __init__(self, row = 0, col = 0):\\n self.row = row\\n self.col = col\",\n \"def get_index(self, row, col):\\n return (row * self.cols) + col\",\n \"def test_board_coordinates_toXY():\\r\\n m = Move()\\r\\n for col_num, col_name in enumerate(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']):\\r\\n for row in range(1, 9):\\r\\n assert m.translate_to_xy(col_name + str(row)) == (Board.SIZE - row, col_num)\",\n \"def get_coord(self,x,y,z):\\n a = 0\\n b = 0\\n c = 0\\n \\n distance = 0\\n \\n while (distance <= x):\\n distance += SQUARE_SIZE\\n if ( (x - distance) > - (SQUARE_SIZE / 2) ):\\n a += 1\\n distance = 0\\n \\n while (distance <= y):\\n distance += SQUARE_SIZE\\n if ( (y - distance) > - (SQUARE_SIZE / 2) ):\\n b += 1\\n distance = 0\\n \\n while (distance <= z):\\n distance += SQUARE_SIZE\\n if ( (z - distance) > - (SQUARE_SIZE / 2) ):\\n c += 1\\n distance = 0\\n \\n return(a,b,c)\",\n \"def chr_coords(s):\\n return max_y - (max_y - min_y)*s\",\n \"def test_if_row_col_well_retrieved_from_mouse_pos(self):\\n ui = UIRender(TestUI.image_path)\\n row, col = ui.get_row_col_from_mouse((10,25))\\n self.assertEqual(row, 0)\\n self.assertEqual(col, 0)\",\n \"def mark(board, player, row, col):\\r\\n pass\",\n \"def map_grid_loc_to_pixel((grid, x, y), panel_dimensions = bm_panel_dimensions, xc = 17.25, yc = 630, run = 17.25):\\n x_offset = 0\\n for panel_index, panel_dim in panel_dimensions.iteritems():\\n if panel_index < grid:\\n width, height = panel_dim\\n x_offset += width*xc\\n xp, yp = xc + x*run + x_offset, yc - y*run\\n return (xp, yp)\",\n \"def indexToPosition(self, col, row):\\n columns = \\\"ABCDEFGH\\\"\\n return columns[col] + str(row + 1)\",\n \"def get_row_col(mouse_x, mouse_y):\\n # Note: the top row is row=0 (bottom row=2), left col is col=0 (right col=2)\\n spacing_x = 86 + 8\\n spacing_y = 98 + 5\\n top_y = 50\\n left_x = 50\\n return (mouse_y - top_y) // spacing_y, (mouse_x - left_x) // spacing_x\",\n \"def mouse_to_grid( pos ):\\n mx,my=pos\\n # account for window border and gap between cells\\n ix = int((mx-H_CELLSIZE)/(CELLSIZE+CELLGAP))\\n iy = int((my-H_CELLSIZE)/(CELLSIZE+CELLGAP))\\n # force respect window borders\\n if ix<0 or ix>=GRID_X or iy<0 or iy>=GRID_Y:\\n return None\\n else:\\n return (ix,iy)\",\n \"def cell_value(self, x, y):\\n if x == 8 and y == 0:\\n return \\\"--\\\"\\n (r, g) = self[(x, y)]\\n return \\\"%s%s\\\" % (r, g)\",\n \"def get_xy(self):\\r\\n return self.board.get_xy()\",\n \"def putpixel(self, col, row, color=GREEN):\\n if col < 0 or row < 0:\\n return\\n try:\\n self.vram[row][col] = color\\n except IndexError:\\n pass\",\n \"def pixel(self, x: int, y: int, colour: int, /) -> None:\",\n \"def cell_coord(id, Nx):\\n nx = id // (Nx**2)\\n ny = (id - nx * Nx**2) // Nx\\n nz = id - nx * Nx**2 - ny * Nx\\n return np.array([nx, ny, nz])\",\n \"def piece_at(self, row, col):\\n return self.board[row + PADDING][col + PADDING]\",\n \"def to_position(self, x, y, i, j):\\n return (x * self.SIZE + i, y * self.SIZE + j)\",\n \"def __convert_position(self, row_position: int = None, col_position: int = None) -> int:\\n if row_position is None or col_position is None:\\n return self.__row_position * len(self.__labyrinth[0]) + self.__col_position\\n\\n return row_position * len(self.__labyrinth[0]) + col_position\",\n \"def print_seg_row_col(sp) : \\n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))\",\n \"def getCol(self, n, offset=0):\\n return self._c[(n*self.__height + offset):((n+1) * self.__height)]\",\n \"def placeCrate (self, crates_char, row, column):\",\n \"def room_xy(room, x, y, value=None):\\n return room[x][y]\",\n \"def _seat_id(col: int, row: int):\\n return row * 8 + col\",\n \"def get_column(self, coords, direction):\\n column = []\\n x = coords[0]\\n y = coords[1]\\n if direction == \\\"down\\\":\\n # x, 0-y\\n for i in range(y):\\n column.append((x, i))\\n elif direction == \\\"right\\\":\\n # 0-x, y\\n for i in range(x):\\n column.append((i, y))\\n elif direction == \\\"up\\\":\\n # x, y-last\\n for i in range(y + 1, self.size):\\n column.append((x, i))\\n elif direction == \\\"left\\\":\\n # x-last, y\\n for i in range(x + 1, self.size):\\n column.append((i, y))\\n\\n return column\",\n \"def get_pos(self, cx, cy):\\n x = self.min_x + cx*(self.size+0.5)\\n y = self.min_y + cy*(self.size+0.5)\\n return (x,y)\",\n \"def get_coordinates():\\n\\tallowed_range = [0,1,2]\\n\\trow = int(input(\\\"Enter row: \\\")) - 1\\n\\tcol = int(input(\\\"Enter column: \\\")) - 1\",\n \"def _to_maze_coord(self, x, y):\\n maze = self._get_maze()\\n x = int(x / _MAZE_CELL_SIZE)\\n y = int(y / _MAZE_CELL_SIZE)\\n y = maze.shape[1] - y - 1\\n return x, y\",\n \"def set_tile(self, row, col, value):\\n # replace with your code\\n self.grid[row][col] = value\",\n \"def mark(board, player, row, col):\\n pass\",\n \"def place_at(self, row, col, piece):\\n self.board[row + PADDING][col + PADDING] = piece\",\n \"def Haut():\\r\\n X1, Y1, X2, Y2 = canvas.coords(boule)\\r\\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)\",\n \"def __init__(self, x, y):\\n self.x = x\\n self.y = y\\n self.x1 = self.x + 30 # largeur et hauteur fixees\\n self.y1 = self.y + 30\",\n \"def set_xy(self, x, y, val):\\r\\n\\t\\tself.grid[y, x] = val\",\n \"def cie_xyz(self):\\n return tuple(matrix_mult(\\n ((0.4124564, 0.3575761, 0.1804375),\\n (0.2126729, 0.7151522, 0.0721750),\\n (0.0193339, 0.1191920, 0.9503041),\\n ),\\n (from_srgb(self.red),\\n from_srgb(self.green),\\n from_srgb(self.blue)\\n )\\n ))\",\n \"def col2vex(self, sele=None, lelem=None, laty=None):\\n if sele is None:\\n ncol = self.ncolors\\n col = self.colors.astype(np.int)\\n nbonds = self.nbonds\\n ba = self.blist[:,0]\\n bb = self.blist[:,1]\\n else:\\n try:\\n indexcol = np.where(sum([self.colors == i for i in sele]))[0] ###since bool arrays, here \\\"sum\\\" means \\\"or\\\"\\n except ValueError:\\n indexcol = []\\n col = self.colors[indexcol]\\n ncol = len(sele)\\n nbonds = len(col)\\n ba = self.blist[:,0][indexcol]\\n bb = self.blist[:,1][indexcol]\\n xyz_a = self.xyz[ba]\\n xyz_c = []\\n for i in range(nbonds):\\n bci = self.conn[ba[i]].index(bb[i])\\n xyz_ic = self.get_neighb_coords(ba[i], bci)\\n xyz_c.append(xyz_ic)\\n xyz_c = np.array(xyz_c) + xyz_a\\n xyz_c *= .5\\n m = mol.fromArray(xyz_c)\\n ### DEFAULT ASSIGNMENT\\n if lelem is None and laty is None:\\n laty = range(ncol)\\n lowercase = list('kbabcdefghijklmnopqrstuvwxyz')\\n lelem = [lowercase[i] for i in laty]\\n laty = [str(i) for i in laty]\\n elif lelem is None or laty is None:\\n raise TypeError(\\\"lelem and laty must be both either None or ndarrays\\\")\\n elif len(lelem) != ncol or len(laty) != ncol:\\n raise ValueError(\\\"len of sele, lelem and laty must be the same!\\\\nsele:\\\\t%s\\\\nlelem:\\\\t%s\\\\nlaty:\\\\t%s\\\" % (sele, lelem, laty))\\n lelem = np.array(lelem)\\n laty = np.array(laty)\\n m.set_elems(lelem[col])\\n m.set_atypes(laty[col])\\n if hasattr(self,'cell'): m.set_cell(self.cell)\\n if hasattr(self,'supercell'): m.supercell = self.supercell[:]\\n m.colors = self.colors\\n return m\",\n \"def __init__(self, grid, x, y, cols):\\n self.grid = grid\\n self.x = x\\n self.y = y\\n self.cols = cols\",\n \"def neighbours_of_position(coords):\\n row = coords[0]\\n col = coords[1]\\n \\n #assign each of neighbours corrds\\n #top left to top rigt\\n top_left = (row - 1, col - 1)\\n top_center = (row - 1, col)\\n top_right = (row - 1, col + 1)\\n \\n # left to right (center)\\n left = (row, col - 1)\\n # the (row, col) cordinates passed into this function are situated here\\n right = (row, col + 1)\\n \\n #bottom-left to bottom-right\\n bottom_left = (row +1, col -1)\\n bottom_center = (row +1, col)\\n bottom_right = (row +1, col +1)\\n \\n return [top_left, top_center, top_right,\\n left , right ,\\n bottom_left, bottom_center, bottom_right]\",\n \"def cancoords(self, schx, schy):\\n # Coordinates of scheme (0,0) corner as canvas coords\\n x0 = (self._cw - self.width)/2\\n y0 = (self._ch - self.height)/2 + self.height\\n return (schx + x0, y0 - schy)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68489397","0.67793953","0.6498352","0.63620645","0.6283588","0.62691915","0.6266249","0.62381095","0.61900526","0.6178052","0.6152861","0.6122054","0.60530484","0.6049112","0.60243577","0.59963053","0.59574944","0.5950011","0.59291357","0.59247184","0.5914957","0.5896849","0.58949035","0.58637714","0.5840288","0.5827327","0.58185786","0.58174324","0.5812368","0.5806068","0.5796294","0.579251","0.5774421","0.57538503","0.57185054","0.571724","0.5679961","0.56725025","0.56600493","0.5649528","0.56480396","0.5642581","0.563824","0.5636128","0.56336004","0.5633588","0.5624049","0.5621011","0.5618241","0.561225","0.5603748","0.55994093","0.5596659","0.55941355","0.5590205","0.55883616","0.55831695","0.5574649","0.556294","0.55606025","0.5556829","0.55556166","0.55546534","0.555066","0.5549394","0.55475426","0.55434424","0.5531558","0.55284536","0.5527784","0.55275196","0.55268997","0.55246115","0.5524097","0.54964954","0.54930806","0.5492445","0.54832745","0.54778814","0.54757875","0.54666084","0.54636425","0.5456116","0.54537106","0.5451957","0.54496753","0.5440616","0.54366595","0.5434764","0.5431005","0.5421391","0.5416332","0.5415555","0.5414096","0.54135466","0.54133296","0.541064","0.5408142","0.54063296","0.54051304"],"string":"[\n \"0.68489397\",\n \"0.67793953\",\n \"0.6498352\",\n \"0.63620645\",\n \"0.6283588\",\n \"0.62691915\",\n \"0.6266249\",\n \"0.62381095\",\n \"0.61900526\",\n \"0.6178052\",\n \"0.6152861\",\n \"0.6122054\",\n \"0.60530484\",\n \"0.6049112\",\n \"0.60243577\",\n \"0.59963053\",\n \"0.59574944\",\n \"0.5950011\",\n \"0.59291357\",\n \"0.59247184\",\n \"0.5914957\",\n \"0.5896849\",\n \"0.58949035\",\n \"0.58637714\",\n \"0.5840288\",\n \"0.5827327\",\n \"0.58185786\",\n \"0.58174324\",\n \"0.5812368\",\n \"0.5806068\",\n \"0.5796294\",\n \"0.579251\",\n \"0.5774421\",\n \"0.57538503\",\n \"0.57185054\",\n \"0.571724\",\n \"0.5679961\",\n \"0.56725025\",\n \"0.56600493\",\n \"0.5649528\",\n \"0.56480396\",\n \"0.5642581\",\n \"0.563824\",\n \"0.5636128\",\n \"0.56336004\",\n \"0.5633588\",\n \"0.5624049\",\n \"0.5621011\",\n \"0.5618241\",\n \"0.561225\",\n \"0.5603748\",\n \"0.55994093\",\n \"0.5596659\",\n \"0.55941355\",\n \"0.5590205\",\n \"0.55883616\",\n \"0.55831695\",\n \"0.5574649\",\n \"0.556294\",\n \"0.55606025\",\n \"0.5556829\",\n \"0.55556166\",\n \"0.55546534\",\n \"0.555066\",\n \"0.5549394\",\n \"0.55475426\",\n \"0.55434424\",\n \"0.5531558\",\n \"0.55284536\",\n \"0.5527784\",\n \"0.55275196\",\n \"0.55268997\",\n \"0.55246115\",\n \"0.5524097\",\n \"0.54964954\",\n \"0.54930806\",\n \"0.5492445\",\n \"0.54832745\",\n \"0.54778814\",\n \"0.54757875\",\n \"0.54666084\",\n \"0.54636425\",\n \"0.5456116\",\n \"0.54537106\",\n \"0.5451957\",\n \"0.54496753\",\n \"0.5440616\",\n \"0.54366595\",\n \"0.5434764\",\n \"0.5431005\",\n \"0.5421391\",\n \"0.5416332\",\n \"0.5415555\",\n \"0.5414096\",\n \"0.54135466\",\n \"0.54133296\",\n \"0.541064\",\n \"0.5408142\",\n \"0.54063296\",\n \"0.54051304\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60508746"},"document_rank":{"kind":"string","value":"13"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":74,"numItemsPerPage":100,"numTotalItems":95772,"offset":7400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDAyMzY5NSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzQwMjcyOTUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.adw6X3ivdf-etT312xTVLJ4H_5_8RrkLOZmIZUVmeto70GHs13zA8aFAVuWGUv_rWgWEKb3uBv13Q92tBikgCg","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 |
|---|---|---|---|---|---|---|
Evaluate function for embedding network. | def evaluate(embed_net, trials_loader, mode="norm2",
eval_L=300, num_eval=6, device=None,
batch_size=30, embed_norm=True, trials_feat=None, load_wav=None):
def _prefeat(embed_net, trials_loader, L, num_eval, batch_size,
embed_norm, device, load_wav=None):
if load_wav is None:
def load_trial_wav(path): return loadWAV(
path, L=L, evalmode=True, num_eval=num_eval)
else:
load_trial_wav = load_wav
wav_file = [[trials_loader.dataset[i]["enroll"],
trials_loader.dataset[i]["test"]]
for i in range(len(trials_loader.dataset))]
wav_file = sorted(list(set(np.concatenate(wav_file).tolist())))
trials = waveform(wav_file, load_trial_wav)
def collate_fn(batch):
"""collect from a batch of VoxWave Dataset."""
file = [item["file"] for item in batch]
wave = torch.cat([item["wave"] for item in batch], dim=0)
return {"file": file, "wave": wave}
trialoader = DataLoader(trials, batch_size=batch_size,
num_workers=5, collate_fn=collate_fn,
shuffle=False)
trials_feat = {}
with torch.no_grad():
embed_net.to(device)
embed_net.eval()
for data in tqdm(trialoader, total=len(trialoader)):
file = data["file"]
wave = data["wave"].to(device)
feat = embed_net(wave)
if embed_norm is True:
feat = F.normalize(feat, p=2, dim=1).detach().cpu()
for i, j in enumerate(range(0, feat.shape[0], num_eval)):
trials_feat[file[i]] = feat[j: j + num_eval].clone()
return trials_feat
if trials_feat is None:
trials_feat = _prefeat(embed_net, trials_loader, eval_L, num_eval,
batch_size, embed_norm, device, load_wav)
all_scores = []
all_labels = []
all_trials = []
for trial in tqdm(trials_loader, total=len(trials_loader)):
label = trial["label"]
enroll = trial["enroll"]
test = trial["test"]
for i in range(len(label)):
enroll_embed = trials_feat[enroll[i]]
test_embed = trials_feat[test[i]]
score = calculate_score(enroll_embed.to(device),
test_embed.to(device),
mode=mode, num_eval=num_eval)
all_scores.append(score)
all_trials.append([enroll[i], test[i]])
all_labels.extend(label.numpy().tolist())
all_scores = np.array(all_scores)
all_labels = np.array(all_labels)
eer, thresh = calculate_eer(all_labels, all_scores, 1)
return eer, thresh, all_scores, all_labels, all_trials, trials_feat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, prediction_fn):\n pass",
"def evaluate():\n model.eval()\n with torch.no_grad():\n loss, n = 0, 0\n for xb, yb in valid_dl:\n n += len(xb)\n loss += loss_func(model(xb), yb) * len(xb)\n\n return loss/n",
"def eva... | [
"0.65809673",
"0.65288246",
"0.63682526",
"0.631708",
"0.6305707",
"0.63028127",
"0.6297462",
"0.6287825",
"0.62783986",
"0.627571",
"0.6149087",
"0.604402",
"0.6024852",
"0.60223997",
"0.60162747",
"0.6010527",
"0.5999741",
"0.5989639",
"0.5989639",
"0.5984649",
"0.5969839",... | 0.5704392 | 49 |
collect from a batch of VoxWave Dataset. | def collate_fn(batch):
file = [item["file"] for item in batch]
wave = torch.cat([item["wave"] for item in batch], dim=0)
return {"file": file, "wave": wave} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data(self, train=True, batch_size=2):\n if train:\n elements = self.prepare_batch(self.training_albums)\n else:\n elements = self.prepare_batch(self.validation_albums)\n\n while len(elements) > 0:\n # Collect the batch\n batch = []\n f... | [
"0.61692625",
"0.6092687",
"0.60661197",
"0.60572034",
"0.6030449",
"0.59338397",
"0.59050703",
"0.5902262",
"0.57682383",
"0.57110006",
"0.5708979",
"0.5663044",
"0.565785",
"0.56574595",
"0.5642957",
"0.5642957",
"0.56295526",
"0.56236845",
"0.5613696",
"0.55893534",
"0.557... | 0.0 | -1 |
collect from a batch of VoxWave Dataset. | def collate_fn(batch):
file = [item["file"] for item in batch]
wave = torch.cat([item["wave"] for item in batch], dim=0)
return {"file": file, "wave": wave} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data(self, train=True, batch_size=2):\n if train:\n elements = self.prepare_batch(self.training_albums)\n else:\n elements = self.prepare_batch(self.validation_albums)\n\n while len(elements) > 0:\n # Collect the batch\n batch = []\n f... | [
"0.61714315",
"0.60954803",
"0.60674745",
"0.6059908",
"0.60306466",
"0.59371",
"0.5907216",
"0.5905398",
"0.57684493",
"0.5712042",
"0.5711107",
"0.56662107",
"0.56599396",
"0.56588864",
"0.56463087",
"0.56463087",
"0.5631652",
"0.56258637",
"0.56173337",
"0.5593329",
"0.557... | 0.0 | -1 |
Returns the single qubit gate Sqrt(X) | def sqrtx():
return Operator([[(1.+1.j)/2,(1.-1.j)/2],[(1.-1.j)/2,(1.+1.j)/2]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sq(self, x):\n\t\treturn x * x",
"def qrst_tm(x):\n return 0.2228*x - 0.6685",
"def sq(x):\n\n return x ** x",
"def isqrt(n): # newton (from stackoverflow)\n if n < 0:\n print(f\"segur que vols fer l'arrel de {n}?\")\n n = -n\n x = n\n y = (x + 1) // 2\n while y < x:\n ... | [
"0.6638449",
"0.66072994",
"0.65911305",
"0.63476807",
"0.6344686",
"0.62796086",
"0.6270373",
"0.62620115",
"0.62110555",
"0.614344",
"0.61330664",
"0.61031693",
"0.6101553",
"0.6096317",
"0.608822",
"0.6087951",
"0.6056629",
"0.60234123",
"0.59946364",
"0.5992759",
"0.59535... | 0.6088627 | 14 |
Returns the single qubit gate Sqrt(Y) | def sqrty():
return Operator([[(1.+1.j)/2,(-1-1.j)/2],[(1.+1.j)/2,(1.+1.j)/2]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qrst_tm(x):\n return 0.2228*x - 0.6685",
"def Y(t, p, q):\n \n if t <= 0:\n return float('inf')\n \n if q == 1:\n return (t**(p+1) - 1) / (p * (p+1)) - np.log(t) / q + (p - 1) / p * (t-1)\n else:\n return (t**(p+1) - 1) / (p * (p+1)) + (t**(1-q) - 1) / (q*(q-1)) + (p - ... | [
"0.6507779",
"0.62822163",
"0.62746733",
"0.6241614",
"0.61534065",
"0.61482567",
"0.61480504",
"0.6128632",
"0.6057746",
"0.6013973",
"0.59892964",
"0.5979296",
"0.5942681",
"0.592979",
"0.59215605",
"0.59135765",
"0.5904457",
"0.59001505",
"0.58625853",
"0.58580345",
"0.581... | 0.61223656 | 8 |
Returns the single qubit gate Sqrt(W) | def sqrtw():
return Operator([[(1.+1.j)/2,-1.j/np.sqrt(2)],[1./np.sqrt(2),(1.+1.j)/2]]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def qrst_tm(x):\n return 0.2228*x - 0.6685",
"def W(self):\n if not self.isVaild():\n pass\n return self.Wq() + 1.0/self.muy",
"def sq(x):\n\n return x ** x",
"def calc_q_square(self):\n return self._q_x()**2 + self._q_z()**2",
"def sqrty():\n return Operator([[(1.+1.j)... | [
"0.6371148",
"0.63678783",
"0.627648",
"0.6267146",
"0.6152248",
"0.61319304",
"0.6127212",
"0.60720533",
"0.6038319",
"0.603076",
"0.6025137",
"0.5983519",
"0.5961708",
"0.58924997",
"0.58151203",
"0.5788785",
"0.57599264",
"0.5748499",
"0.57308507",
"0.571389",
"0.57128286"... | 0.54470795 | 40 |
Constructor parent reference to the parent widget QWidget | def __init__(self, parent=None):
super(ProgressDlg, self).__init__(parent)
self.setupUi(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self, parent):\n self.widget = QtCore.QObject(parent)",
"def __init__(self, parent=None):\n super(Dialog, self).__init__(parent)\n self.setupUi(self)",
"def __init__(self, parent=None):\n super(Form, self).__init__(parent)\n self.setupUi(self)",
"def __init__(sel... | [
"0.80446535",
"0.790637",
"0.7829028",
"0.7829028",
"0.7818371",
"0.770407",
"0.770407",
"0.770407",
"0.770407",
"0.770407",
"0.76782995",
"0.76558757",
"0.76455885",
"0.7594716",
"0.7536031",
"0.75227404",
"0.751839",
"0.7513868",
"0.7468696",
"0.7376986",
"0.736306",
"0.7... | 0.74049985 | 19 |
Construct a new Player | def __init__(self):
self.loop = asyncio.get_event_loop()
self.aiohttp = web.Application(
loop=self.loop,
middlewares=[unhandled_route],
)
self.client = ClientSession()
self.ws = WebSocketHandler(self)
self.cert = self._load_ssl_certificate()
self.config() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createPlayer(self):\n sw, ne = self.playerCreationRectangle\n x = self.random.randrange(sw.x, ne.x)\n y = 1.0\n z = self.random.randrange(sw.y, ne.y)\n player = Player(Vector(x, y, z), 2, self.seconds)\n for observer in self.observers:\n observer.playerCreat... | [
"0.8133294",
"0.73655224",
"0.7338226",
"0.7169045",
"0.7107056",
"0.71011436",
"0.7019268",
"0.70154893",
"0.7000745",
"0.6951531",
"0.6851873",
"0.68201184",
"0.6619382",
"0.6601898",
"0.6583949",
"0.65359473",
"0.65358025",
"0.6531014",
"0.6509729",
"0.6432442",
"0.639431"... | 0.0 | -1 |
Load all quest handlers here | def load_quests(self):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_handlers(self):\n\t\tself.handlers = []\n\t\tfor mod in os.listdir('classes/handlers'):\n\t\t\tif mod == '__init__.py' or mod[-3:] != '.py':\n\t\t\t\tcontinue\n\t\t\tlib = __import__(mod[:-3], locals(), globals())\n\t\t\tself.handlers.append(lib)\n\t\t#\n\t\tself.handlers.sort(key=lambda x: x.order, rever... | [
"0.6526081",
"0.6394275",
"0.60561556",
"0.59975696",
"0.596935",
"0.5927591",
"0.59075165",
"0.5878692",
"0.569096",
"0.5556501",
"0.5527142",
"0.5512644",
"0.55046976",
"0.54805523",
"0.54044414",
"0.5394727",
"0.5393246",
"0.5391057",
"0.5354453",
"0.5347178",
"0.53144395"... | 0.71654475 | 0 |
Add a quest handler to the aiohttp app | def add_quest(self, method: str, route: str, handler):
self.aiohttp.router.add_route(method, route, handler) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def _response_handler(self):",
"async def startup_handler(app):\n\n spotify_client_id = os.environ.get(SPOTIFY_CLIENT_ID)\n spotify_client_secret = os.environ.get(SPOTIFY_CLIENT_SECRET)\n\n # Save dependencies in the HTTP app.\n http.register_dependency(app, SPOTIFY_CLIENT_ID, s... | [
"0.5878627",
"0.5799518",
"0.57429504",
"0.57207054",
"0.5717911",
"0.5691735",
"0.5681638",
"0.5614569",
"0.55652535",
"0.5559621",
"0.5534234",
"0.5512763",
"0.5455151",
"0.5423512",
"0.5397953",
"0.5394697",
"0.5374808",
"0.5370889",
"0.53686166",
"0.53405356",
"0.5336915"... | 0.6682835 | 0 |
Start the application and connect to the server | def connect(self):
Log.info(f'Connecting to Kodeventure server at {SERVER_HOST}')
web.run_app(
self.aiohttp,
host=PLAYER_HOST,
port=PLAYER_PORT,
ssl_context=self.cert
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n\t\t\n\t\tself.connect(self.config[\"server\"])",
"def start_server(self):\n app.run(host=str(self.__constants.host),\n port=int(self.__constants.port),\n debug=bool(self.__constants.runindebug))",
"def start(self):\n run(self.app, host=self.host, por... | [
"0.83166367",
"0.80134946",
"0.79343057",
"0.7532442",
"0.74999815",
"0.74902725",
"0.7488411",
"0.7407796",
"0.7407796",
"0.7342273",
"0.73058623",
"0.72899777",
"0.7254523",
"0.72319925",
"0.72092813",
"0.71867055",
"0.7134933",
"0.71249473",
"0.7120971",
"0.7115196",
"0.71... | 0.75330657 | 3 |
Configure the Player object by attaching some event handlers, adding default route and loading quests | def config(self):
# Set up on_startup listener for connecting to the server
self.aiohttp.on_startup.append(self.ws.connect)
# Await websocket and client session termination
async def shutdown(app):
await self.ws.close()
await self.client.close()
# Set up on_shutdown listeners for graceful shutdown
self.aiohttp.on_shutdown.append(shutdown)
# Add a default route
self.aiohttp.router.add_route('*', '/', lambda request: web.json_response({ "msg": "I'm alive" }))
# Load user defined quests
self.load_quests() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _handler_init(self):\r\n\t\tself._handlers[\"player-join\"] = FunctionDelegate()\r\n\t\tself._handlers[\"player-quit\"] = FunctionDelegate()\r\n\t\tself._handlers[\"game-start\"] = FunctionDelegate()\r\n\t\tself._handlers[\"game-stop\"] = FunctionDelegate()",
"def start(self):\n self.__init__()\n ... | [
"0.6703167",
"0.6290751",
"0.6279966",
"0.6227849",
"0.6143075",
"0.6136186",
"0.6016518",
"0.60164726",
"0.5974453",
"0.5964595",
"0.5954566",
"0.5888326",
"0.5855741",
"0.5851218",
"0.58509254",
"0.5828278",
"0.5817811",
"0.5815114",
"0.5780424",
"0.57746065",
"0.574305",
... | 0.57329845 | 22 |
Private helper to load SSL certificate from disk | def _load_ssl_certificate(self) -> ssl.SSLContext:
sslcontext = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)
sslcontext.load_cert_chain(
path.join(path.dirname(__file__), '..', '..', 'player.crt'),
path.join(path.dirname(__file__), '..', '..', 'player.key')
)
return sslcontext | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ssl_certificate():",
"def load_cert_chain(self, certfile, keyfile: Optional[Any] = ...):\n ...",
"def _load_ssl(self, ssl_options: tuple):\n try:\n self._ssl.load_cert_chain(certfile=ssl_options[0], keyfile=ssl_options[1], password=ssl_options[2])\n except IOError as e:\... | [
"0.7039763",
"0.70248497",
"0.69557095",
"0.69070625",
"0.6604512",
"0.6576468",
"0.6486132",
"0.6481465",
"0.6431241",
"0.6423316",
"0.6407451",
"0.63593185",
"0.6340354",
"0.61137533",
"0.60513645",
"0.6047259",
"0.59898525",
"0.59736806",
"0.59400916",
"0.5930687",
"0.5902... | 0.67338187 | 4 |
Constructor with data and next element | def __init__(self, data):
self.data = data
self.next = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, data, next=None):\n self.data = data\n self.next = next",
"def __init__(self, data, next = None):\n\t\tself.data = data\n\t\tself.next = next",
"def __init__(self, data=None, next=None):\n self.data = data\n self.next = next",
"def __init__(self, data, next_node... | [
"0.8599574",
"0.85121983",
"0.84967536",
"0.8359982",
"0.8315263",
"0.8315263",
"0.81818867",
"0.8087428",
"0.80368114",
"0.8025065",
"0.8019018",
"0.7919946",
"0.76897675",
"0.7610863",
"0.7575822",
"0.75584215",
"0.7526036",
"0.7419128",
"0.72142625",
"0.7198809",
"0.708651... | 0.8276122 | 7 |
Representation of the linked list | def __repr__(self):
return "LinkedList([{}],{}/{})".format(self.cur_node, self.cur_pos, self.length) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __repr__(self):\n\n return \"LinkedList created\"",
"def __repr__(self):\r\n return \"ListNode({})\".format(self.data)",
"def __repr__(self):\n return 'LinkedList({!r})'.format(self.items())",
"def __repr__(self):\n return \"{}\".format(self._head)",
"def __init__(se... | [
"0.77007776",
"0.76717454",
"0.7663137",
"0.7242428",
"0.72094405",
"0.7121683",
"0.7095792",
"0.6991777",
"0.6974106",
"0.69680715",
"0.69224",
"0.68925244",
"0.6854505",
"0.6827989",
"0.6827989",
"0.6827989",
"0.6827989",
"0.6827989",
"0.6827928",
"0.67615014",
"0.6742917",... | 0.76858366 | 1 |
Insert a new node into the linked list | def add_node(self, data):
new_node = Node(data)
if self.cur_node is not None:
new_node.next, self.cur_node.next = self.cur_node.next, new_node
self.cur_node = new_node
self.length += 1
self.cur_pos += 1
if self.start_node is None:
self.start_node = self.cur_node
# print("Node({}) added to {}".format(new_node.data, self.cur_pos-1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_node(self, head, node):\n prev, curr = None, head\n while curr.val < node.val:\n prev, curr = curr, curr.next\n if not prev:\n head = node\n else:\n prev.next = node\n node.next = curr\n return head",
"def insert(self, data):\n... | [
"0.8028355",
"0.8022881",
"0.78825825",
"0.7828899",
"0.78155625",
"0.78046495",
"0.7687818",
"0.76444274",
"0.76059026",
"0.76059026",
"0.75250113",
"0.75250113",
"0.7509364",
"0.7460348",
"0.74056655",
"0.73841995",
"0.73401856",
"0.7332098",
"0.7327415",
"0.7317284",
"0.72... | 0.6689157 | 74 |
Print the linked list | def list_print(self):
node = self.cur_node # cant point to ll!
while node:
print(node.data)
node = node.next | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_list(self) -> None:\n cur_node = self.head\n while cur_node:\n print(cur_node.data)\n cur_node = cur_node.next",
"def print_list(self):\n\n current = self.head\n\n while current is not None:\n print current.data\n current = current... | [
"0.8673001",
"0.86067754",
"0.85906494",
"0.8569578",
"0.85571915",
"0.8512224",
"0.84492445",
"0.8400473",
"0.82561886",
"0.8178204",
"0.8120253",
"0.8110577",
"0.809658",
"0.7893961",
"0.7872987",
"0.7842627",
"0.78114355",
"0.77788997",
"0.77637863",
"0.7760473",
"0.774224... | 0.88183177 | 0 |
Move in the linked list n places, if reached the end start from the start | def move_circular(self, count):
planned_move = self.cur_pos + count
if count + planned_move < self.length:
move_count = count
self.cur_pos += move_count
# print("Move less from {} to {}({})".format(
# self.cur_pos,
# self.cur_pos + move_count,
# planned_move
# ))
else:
self.cur_node = self.start_node
move_count = max(0, (planned_move % (self.length)))
# print("Move circ from {} to {}({})".format(self.cur_pos, move_count, planned_move))
self.cur_pos = move_count
for _ in range(move_count):
self.cur_node = self.cur_node.next | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self, n: int) -> \"Linked[T]\":\n out = self\n if n >= 0:\n for _ in range(n):\n out = out.forward\n else:\n for _ in range(-n):\n out = out.backward\n return out",
"def move_stack(n, start, end):\n assert 1 <= start <= 3... | [
"0.70314944",
"0.6992453",
"0.68459344",
"0.66120696",
"0.66120696",
"0.6502313",
"0.64976275",
"0.6037973",
"0.59619963",
"0.592741",
"0.5908573",
"0.5853676",
"0.58296",
"0.58181906",
"0.5809934",
"0.5785236",
"0.5776495",
"0.57708544",
"0.57155955",
"0.5683604",
"0.5644946... | 0.64244884 | 7 |
Get the data of the next node | def get_next(self):
return self.cur_node.next.data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_next(node):\n return node['next']",
"def data(self):\n return self.first_node.data",
"def get_data(node):\n return node['data']",
"def node_data(self):\n return self.node_data_",
"def _next(self):\n node = self.head\n while node != None:\n yield node.dat... | [
"0.7340955",
"0.72560537",
"0.71013474",
"0.70286286",
"0.70277715",
"0.6898941",
"0.67155683",
"0.67155683",
"0.67155683",
"0.6683656",
"0.6547143",
"0.65334487",
"0.65334487",
"0.64254665",
"0.6382568",
"0.63816816",
"0.63672584",
"0.63516414",
"0.630895",
"0.6291807",
"0.6... | 0.8156502 | 0 |
Constructor for the twister | def __init__(self, init):
self.stepforward = int(init)
self.data = Linkedlist() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, user_ctor, seed=0):\n self._user_ctor = user_ctor\n self._seed = seed\n self.reset_sampler()",
"def __init__(self, seed):\n self.mt = MT19937(seed & 0xFFFF)\n self.keystream = []",
"def __init__(self, seed=__default):\n\n seed = self.__default if seed == 0 else ... | [
"0.65222347",
"0.63090914",
"0.62000424",
"0.6192566",
"0.614152",
"0.612248",
"0.6101128",
"0.60043067",
"0.59858197",
"0.59820634",
"0.5972612",
"0.5955955",
"0.595311",
"0.59528494",
"0.5922501",
"0.5905344",
"0.5901768",
"0.58944374",
"0.5858806",
"0.58463246",
"0.5831957... | 0.0 | -1 |
Representation of the spinlock | def __repr__(self):
return "Spinlock({})".format(self.stepforward) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lock_control(self):\n raise NotImplementedError('PlatformService: Implementation incomplete')",
"def spinlocks(self):\n return self._spinlocks",
"def lock(self):\n raise NotImplementedError",
"def lock(self):\r\n return self._lock",
"def f_lock(self):\n self._locked = Tru... | [
"0.6281956",
"0.61268324",
"0.60570246",
"0.60079235",
"0.5999252",
"0.5991468",
"0.5932551",
"0.5889748",
"0.57721347",
"0.5762535",
"0.5744118",
"0.57419753",
"0.5740945",
"0.5726452",
"0.57194465",
"0.56717205",
"0.56714046",
"0.56600225",
"0.5617723",
"0.5606084",
"0.5601... | 0.71650106 | 0 |
Move n steps then insert a new value | def process(self, count):
self.data.add_node(0)
for index in range(1, count + 1):
# print("{}.: {}".format(index, self.data))
self.data.move_circular(self.stepforward)
self.data.add_node(index)
return self.data.get_next() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def increment_step(self, n_steps: int) -> None:\n self.step = self.policy.increment_step(n_steps)",
"def increment_step(self, n_steps: int) -> None:\n self.step = self.policy.increment_step(n_steps)",
"def increment_steps(self):\n self.num_steps += 1",
"def make_step(self):\n self... | [
"0.6520088",
"0.6520088",
"0.61909217",
"0.6150601",
"0.61039525",
"0.6089643",
"0.60866725",
"0.6062686",
"0.6013688",
"0.59797156",
"0.59569323",
"0.58549404",
"0.58222437",
"0.5809769",
"0.5773611",
"0.5759914",
"0.57056457",
"0.56964505",
"0.5682795",
"0.5681156",
"0.5659... | 0.5482471 | 38 |
Solution to the problem | def solution(data):
lock = Spinlock(data)
return lock.process(2017) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve(self):",
"def solve(self):\n pass",
"def solve(self):\n pass",
"def solve(self):\n ...",
"def problem_298():\n pass",
"def solvate(self):\n\n pass",
"def solution(s):",
"def get_sol(self):",
"def test_get_solution(self):\n pass",
"def solution(self)... | [
"0.7423277",
"0.7013315",
"0.7013315",
"0.69836867",
"0.6830721",
"0.6752851",
"0.64645183",
"0.6391518",
"0.63800526",
"0.63478065",
"0.61165357",
"0.6100315",
"0.6074178",
"0.60716605",
"0.60713553",
"0.60587716",
"0.60174394",
"0.5986508",
"0.59821045",
"0.59797627",
"0.59... | 0.0 | -1 |
Given the tile location (x,y) and zoom level z, fetch the corresponding tile from the server and save it to the location specfied in fpath. Note, this saves just one tile; usually, want to use `positive_dataset` instead. | def save_tile(x,y,z,fpath):
UA = "Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:47.0) Gecko/20100101 Firefox/77.0"
tile_url = f"https://{random.choice('abc')}.tile.openstreetmap.org/{z}/{x}/{y}.png"
# cmd = f"wget --user-agent='please download' -O {fpath} {url}"
if os.path.exists(fpath):
print(f"Already have tile {fpath}!")
return 0
if os.path.isdir(fpath):
raise ValueError(f"requested path {fpath} exists and is a directory!")
try:
res = rq.get(
url=tile_url,
headers={'User-Agent': UA}
)
status = res.status_code
if status == 200:
with open(fpath,'wb') as of:
of.write(res.content)
return 0
else:
print(f"Error: response {status} from server:\n{res.reason}")
return status
except Exception as e:
print(f"Error getting tile: {e}")
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_tile(map_layer, zoom, x, y):\n try:\n tile_url = map_layer.get_tile_url(zoom, x, y)\n tmp_file, headers = urllib.request.urlretrieve(tile_url)\n return (x, y), tmp_file\n except URLError as e:\n app.logger.info(\"Error downloading tile x={}, y={}, z={} for layer {}: {... | [
"0.7046244",
"0.6987471",
"0.6816313",
"0.6353406",
"0.6337127",
"0.63041425",
"0.6291108",
"0.6263484",
"0.6197",
"0.6188584",
"0.6176481",
"0.6158082",
"0.60673577",
"0.60509396",
"0.60040617",
"0.58420604",
"0.5838847",
"0.58381325",
"0.5794229",
"0.5779506",
"0.57553935",... | 0.7838271 | 0 |
Save the tiles whose coordinates are in the input DataFrame, defined by columns x, y, and z | def save_tiles(df,output_dir,namefunc = None):
if not isinstance(df,pd.core.frame.DataFrame):
raise TypeError("df must be a pandas DataFrame!")
if any(e not in df.columns for e in ('z','x','y')):
raise ValueError("df must have columns x, y, and z")
if namefunc is None:
def namefunc(x,y,z):
return f'{z}_{x}_{y}.png'
opath = os.path.abspath(os.path.expanduser(output_dir))
Path(opath).mkdir(parents=True, exist_ok=True)
L = df.shape[0]
flocs = [''] * L
for i,xyz in enumerate(zip(df['x'],df['y'],df['z'])):
x,y,z = xyz
print(f"({i+1} of {L})...")
sleep(0.75)
outloc = os.path.join(opath,namefunc(x,y,z))
if save_tile(x,y,z,outloc) == 0:
flocs[i] = outloc
df = df.assign(file_loc = flocs)
return df[df['file_loc'] != ''] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_coords(self, coords, output_dir):\n new_rows = []\n for i, (lat, lon) in enumerate(coords):\n row = {\n 'tile_id': i,\n 'lat':lat,\n 'long':lon,\n 'side_length': self.side_len \n }\n\n new_rows.appen... | [
"0.6151705",
"0.60504067",
"0.5954003",
"0.576589",
"0.5702577",
"0.5660171",
"0.56481516",
"0.5625405",
"0.55865616",
"0.5577034",
"0.5532618",
"0.55072516",
"0.5442371",
"0.542948",
"0.5415182",
"0.5410632",
"0.54070646",
"0.53605324",
"0.5332602",
"0.5332529",
"0.5324701",... | 0.7190305 | 0 |
add latitude/longitude values to a dataframe | def add_latlon(df):
LLs = [num2deg(x,y,z) for x,y,z in zip(df['x'],df['y'],df['z'])]
LLdf = pd.DataFrame.from_records(LLs,columns = ['latitude','longitude'])
return pd.concat([df.reset_index(drop=True),LLdf],axis = 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_demo_location_history() -> geopandas.GeoDataFrame:\n np.random.seed(123)\n\n time = pd.date_range(start=datetime.fromtimestamp(1624241116), end=datetime.now(), freq=\"1min\").values\n\n center_point = (-36.875990410695394, 174.76398830024274)\n lat = np.random.normal(loc=center_point[0], sca... | [
"0.63746387",
"0.63202995",
"0.60851926",
"0.6078423",
"0.6073065",
"0.60551715",
"0.60244256",
"0.59730685",
"0.59513515",
"0.5890462",
"0.5884484",
"0.58564293",
"0.58458704",
"0.58379817",
"0.58235085",
"0.5796834",
"0.5791018",
"0.5752113",
"0.5700064",
"0.5698395",
"0.56... | 0.79349303 | 0 |
This function creates outputs (x,y,z) tile coordinate files which can be fed into download_tiles.sh or the save_tiles function to get tiles from the OSM server. | def basic_tileset(geo_dict, zooms, buffer = 0,n_neg = None):
if not len(geo_dict['elements']):
raise ValueError("The query is empty - cannot continue!")
if type(zooms) is int:
zooms = [zooms]
if any(z < 2 or z > 19 for z in zooms):
raise ValueError("all zoom levels must be between 2 and 19")
nodes = atomize_features(geo_dict)
points_list = [(node['lat'],node['lon']) for node in nodes]
pos_DFs, neg_DFs = [], []
for zoom in zooms:
zxy = [(zoom,*deg2num(x,y,zoom)) for x,y in points_list]
pos_df = pd.DataFrame.from_records(zxy,columns = ['z','x','y'])\
.drop_duplicates(subset = ['x','y'])
num_neg = pos_df.shape[0] if n_neg is None else int(n_neg)
neg_x, neg_y = sample_complement(pos_df['x'],pos_df['y'],num_neg,buffer)
neg_df = pd.DataFrame({'z': zoom,'x': neg_x,'y': neg_y}).sort_values(by = ['z','x','y'])
pos_DFs.append(pos_df)
neg_DFs.append(neg_df)
out_pos = add_latlon(pd.concat(pos_DFs,axis = 0))
out_neg = add_latlon(pd.concat(neg_DFs,axis = 0))
common_row = pd.merge(out_pos,out_neg,on = ['z','x','y']).shape[0]
if common_row > 0:
raise RuntimeError(f"Somehow there are {common_row} common rows!")
return {'positive': out_pos, 'negative': out_neg } | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_metadata(self):\n if self.options.mbtiles:\n return\n if not os.path.exists(self.output):\n os.makedirs(self.output)\n\n if self.options.profile == 'mercator':\n\n south, west = self.mercator.MetersToLatLon( self.ominx, self.ominy)\n north, east = self.mercator.MetersToLatLon( self.omaxx, s... | [
"0.65990293",
"0.64210165",
"0.6350539",
"0.6310586",
"0.6223159",
"0.6142363",
"0.6141133",
"0.61198944",
"0.60979927",
"0.60793155",
"0.605626",
"0.6051542",
"0.6041125",
"0.5997875",
"0.59818053",
"0.5976445",
"0.5960446",
"0.5947027",
"0.5920985",
"0.5904805",
"0.58695346... | 0.0 | -1 |
Create a DataFrame containing information on all tiles identified as downloadable | def shapely_tileset(processed_query,min_ovp = 0,max_ovp = 1,
n_neg = None,buffer = 0):
types, xx, yy, qual, tags = [],[],[],[],[]
z = processed_query['zoom']
for elem in processed_query['elements']:
for tile in elem['tiles']:
qq = tile[1]
if qq >= min_ovp and qq <= max_ovp:
x,y,_ = find_tile_coords(tile[0],z)
xx.append(x)
yy.append(y)
qual.append(tile[1])
tags.append(json.dumps(elem['tags']))
types.append(elem['type'])
pos_df = pd.DataFrame({
'z': z, 'x' : xx, 'y': yy,
'entity': types,
'overlap': qual,'tags': tags,
'placename': processed_query['query_info']['placename']
}) \
.drop_duplicates(subset = ['x','y']) \
.sort_values(by = ['x','y'])
if n_neg is None: n_neg = pos_df.shape[0]
negt = sample_complement(pos_df['x'],pos_df['y'],n_neg,buffer)
neg_df = pd.DataFrame({'z': z,'x': negt[0],'y': negt[1]}) \
.sort_values(by = ['x','y'])
return {
'positive': add_latlon(pos_df),
'negative': add_latlon(neg_df)
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_tiles(x_index = None, y_index = None):\n db_cursor2 = self.db_connection.cursor()\n\n sql = \"\"\"-- Check for any existing tiles\nselect\n tile_id,\n x_index,\n y_index,\n tile_type_id,\n tile_pathname,\n dataset_id,\n tile_class_id,\n tile_size\nfrom tile_footprint\... | [
"0.5520219",
"0.55059403",
"0.5477932",
"0.5469635",
"0.5457995",
"0.54150224",
"0.5368179",
"0.534763",
"0.5336239",
"0.53228396",
"0.53197813",
"0.5276907",
"0.5260043",
"0.52545166",
"0.52139163",
"0.521263",
"0.5197093",
"0.5197093",
"0.5183362",
"0.51803267",
"0.5175006"... | 0.0 | -1 |
Infinite sequence of integers. | def integers():
i = 1
while True:
yield i
i = i + 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_sequence_infinite(seq):\n while True:\n for item in seq:\n yield item",
"def infinite_increment():\n i = 0\n while 1:\n yield i\n i += 1",
"def int_to_seq(i):\n\ts = []\n\tprime = xprimes()\n\twhile i != 1:\n\t\ts.append(0)\n\t\tp = next(prime)\n\t\twhile i % p == 0:\n\t\t... | [
"0.7470997",
"0.7343358",
"0.6424951",
"0.6344632",
"0.63421345",
"0.62673986",
"0.60785764",
"0.60572314",
"0.60339946",
"0.6026884",
"0.5996939",
"0.59359825",
"0.5839094",
"0.58256906",
"0.58099836",
"0.5782609",
"0.57588905",
"0.5748371",
"0.5706836",
"0.5637029",
"0.5626... | 0.7472032 | 1 |
Returns first n values from the given sequence. | def take(n, seq):
seq = iter(seq)
result = []
try:
for i in range(n):
result.append(next(seq))
except StopIteration:
pass
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take(n, seq):\n return itertools.islice(seq, n)",
"def nth(n, seq):\n try:\n return seq[n]\n except TypeError:\n return next(itertools.islice(seq, n, None))",
"def take(iterable, n):\n return list(itertools.islice(iterable, n))",
"def lookahead(n, iterable):\n for value in is... | [
"0.7429517",
"0.6876003",
"0.6727532",
"0.67118037",
"0.66985476",
"0.66985476",
"0.66961426",
"0.6665714",
"0.6665714",
"0.66652995",
"0.6647308",
"0.64990807",
"0.6490624",
"0.6476307",
"0.6448948",
"0.6416278",
"0.6414325",
"0.64115244",
"0.6388718",
"0.63765794",
"0.63302... | 0.7366886 | 1 |
Report Method to Get Work Order Details. | def get_work_order_detail(self, date_range):
work_order_obj = self.env["task.line"]
start = datetime.strptime(date_range.get("date_from"), "%Y-%m-%d")
end = datetime.strptime(date_range.get("date_to"), "%Y-%m-%d")
step = timedelta(days=1)
workorder_detail = []
while start <= end:
sdate = str(
datetime.strptime(
str(start.date()) + " 00:00:00", DEFAULT_SERVER_DATETIME_FORMAT
)
)
edate = str(
datetime.strptime(
str(start.date()) + " 23:59:59", DEFAULT_SERVER_DATETIME_FORMAT
)
)
work_order_ids = work_order_obj.search(
[("date_issued", ">=", sdate), ("date_issued", "<=", edate)]
)
if work_order_ids:
parts_data = {}
parts_value = []
for parts_line in work_order_ids:
if (
parts_line.fleet_service_id
and parts_line.fleet_service_id.state == "done"
):
parts_dict = {
"wo_name": parts_line.fleet_service_id
and parts_line.fleet_service_id.name
or "",
"vehicle_id": parts_line.fleet_service_id
and parts_line.fleet_service_id.vehicle_id
and parts_line.fleet_service_id.vehicle_id.name
or "",
"part_no": parts_line.product_id
and parts_line.product_id.default_code
or "",
"part_name": parts_line.product_id
and parts_line.product_id.name
or "",
"vehicle_make": parts_line.vehicle_make_id
and parts_line.vehicle_make_id.name
or "",
"qty": parts_line.qty or 0.0,
"uom": parts_line.product_uom
and parts_line.product_uom.name
or "",
"old_part_return": parts_line.old_part_return
and "Yes"
or "No",
"issued_by": parts_line.issued_by
and parts_line.issued_by.name
or "",
"remarks": parts_line.fleet_service_id
and parts_line.fleet_service_id.note
or "",
}
parts_value.append(parts_dict)
if parts_value:
parts_value = sorted(parts_value, key=lambda k: k["wo_name"])
parts_data = {"date": start.date(), "value": parts_value}
workorder_detail.append(parts_data)
start += step
return workorder_detail | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def order_report():",
"def work_order_receipt_retrieve(self, work_order_id, id=None):\n pass",
"def get_order_detail(orderid): \n data = order_obj.get_order_detail(orderid)\n return data",
"def open_workorders(self, cr, uid, ids, context=None):\n context = context or {}\n models_da... | [
"0.68440074",
"0.6475144",
"0.6159926",
"0.6083596",
"0.608028",
"0.59921384",
"0.5808397",
"0.58080214",
"0.5693121",
"0.5688188",
"0.56651086",
"0.5659879",
"0.5623415",
"0.56180996",
"0.5615256",
"0.5562105",
"0.55497396",
"0.55432594",
"0.55332184",
"0.5496018",
"0.546495... | 0.70237696 | 0 |
Generate xlsx format print report. | def generate_xlsx_report(self, workbook, data, parts_data):
worksheet = workbook.add_worksheet("daily_parts_issuance_wizard")
worksheet.set_column(0, 0, 10)
worksheet.set_column(1, 1, 15)
worksheet.set_column(2, 2, 20)
worksheet.set_column(3, 3, 15)
worksheet.set_column(4, 4, 10)
worksheet.set_column(5, 5, 12)
worksheet.set_column(6, 6, 10)
worksheet.set_column(7, 7, 10)
worksheet.set_column(8, 8, 15)
worksheet.set_column(9, 9, 10)
worksheet.set_column(10, 10, 15)
worksheet.set_column(11, 11, 10)
worksheet.set_column(12, 12, 20)
worksheet.set_column(13, 13, 5)
worksheet.set_column(14, 14, 5)
worksheet.set_column(15, 15, 5)
bold = workbook.add_format(
{"bold": True, "font_name": "Arial", "font_size": "10"}
)
tot = workbook.add_format(
{"border": 2, "bold": True, "font_name": "Arial", "font_size": "10"}
)
border = workbook.add_format(
{"border": 2, "font_name": "Arial", "font_size": "10"}
)
merge_format = workbook.add_format({"border": 2, "align": "center"})
format1 = workbook.add_format(
{"border": 2, "bold": True, "font_name": "Arial", "font_size": "10"}
)
format1.set_bg_color("gray")
date = workbook.add_format({"num_format": "dd/mm/yy"})
worksheet.merge_range("C3:F3", "Merged Cells", merge_format)
row = 0
row += 1
row += 1
worksheet.write(row, 2, "DAILY PARTS ISSUANCE", tot)
row += 1
worksheet.write(row, 2, "Date From:", tot)
worksheet.write(row, 3, data["form"]["date_from"] or "", border)
worksheet.write(row, 4, "To:", tot)
worksheet.write(row, 5, data["form"]["date_to"] or "", border)
row += 2
worksheet.write(row, 0, "CMF", bold)
row = 3
for objec in self.get_work_order_detail(data["form"]):
row += 3
worksheet.write(row, 0, "DATE ISSUED :", bold)
worksheet.write(row, 1, objec.get("date") or "", date)
row += 2
worksheet.write(row, 0, "NO.", format1)
worksheet.write(row, 1, "WO NO.", format1)
worksheet.write(row, 2, "VEHICLE ID", format1)
worksheet.write(row, 3, "PART NO.", format1)
worksheet.write(row, 4, "PART NAME", format1)
worksheet.write(row, 5, "VEHICLE MAKE", format1)
worksheet.write(row, 6, "USED", format1)
worksheet.write(row, 7, "UNIT TYPE", format1)
worksheet.write(row, 8, "OLD PART RETURND", format1)
worksheet.write(row, 9, "ISSUED BY", format1)
worksheet.write(row, 10, "REMARKS", format1)
line_row = row + 1
line_col = 0
counter = 1
for obj in objec.get("value"):
worksheet.write(line_row, line_col, counter, border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("wo_name") or "", border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("vehicle_id") or "", border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("part_no") or "", border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("part_name") or "", border)
line_col += 1
worksheet.write(
line_row, line_col, obj.get("vehicle_make") or "", border
)
line_col += 1
worksheet.write(line_row, line_col, obj.get("qty") or "", border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("uom") or "", border)
line_col += 1
worksheet.write(
line_row, line_col, obj.get("old_part_return") or "", border
)
line_col += 1
worksheet.write(line_row, line_col, obj.get("issued_by") or "", border)
line_col += 1
worksheet.write(line_row, line_col, obj.get("remarks") or "", border)
line_col = 0
line_row += 1
counter += 1
worksheet.write(line_row, line_col, "********", border) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_xlsx(self):\n if self.date_from and self.date_to:\n if self.date_from > self.date_to:\n raise ValidationError(\"Date From must be less than Date To\")\n\n # active_record = self._context['id']\n # record = self.env['room.accommodation'].browse(active_record)... | [
"0.7330527",
"0.6696778",
"0.64272696",
"0.6423999",
"0.640377",
"0.63337004",
"0.625461",
"0.621144",
"0.61932975",
"0.61916244",
"0.6103704",
"0.59963775",
"0.5941088",
"0.5938474",
"0.5917404",
"0.58896947",
"0.58573407",
"0.5852752",
"0.5852723",
"0.58489084",
"0.5818077"... | 0.69838953 | 1 |
Sample pure Lambda function | def lambda_handler(event, context):
# try:
# ip = requests.get("http://checkip.amazonaws.com/")
# except requests.RequestException as e:
# # Send some context about this error to Lambda Logs
# print(e)
# raise e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_lambda(n):\n for i in range(n):\n yield lambda : i",
"def test_lambda(n):\n return [lambda v=i: v for i in range(n)]",
"def lambda_method(self,t): \n return 5*math.sin(2*math.pi*1*t) # I don't see the value of 1 here but this is how lamda is defined in the exercise.",
"def... | [
"0.71773744",
"0.69108605",
"0.6901355",
"0.65597636",
"0.6500524",
"0.62655693",
"0.62334967",
"0.6138908",
"0.6111436",
"0.6015638",
"0.5991434",
"0.5974308",
"0.5957245",
"0.5953069",
"0.59432507",
"0.59294564",
"0.5925585",
"0.59229636",
"0.5896368",
"0.58732843",
"0.5852... | 0.0 | -1 |
Checks num for primality. Returns bool. | def isPrime(num):
if num == 2:
return True
elif num < 2 or not num % 2: # even numbers > 2 not prime
return False
# factor can be no larger than the square root of num
for i in range(3, int(num ** .5 + 1), 2):
if not num % i: return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_prime(self, num):\n if num == 2:\n return True\n if num < 2 or num % 2 == 0:\n return False\n for n in range(3, int(num ** 0.5) + 2, 2):\n if num % n == 0:\n return False\n return True",
"def is_prime(num: int) -> bool:\n retu... | [
"0.818409",
"0.8014647",
"0.7901733",
"0.7833093",
"0.782687",
"0.7789034",
"0.7774602",
"0.77519137",
"0.773821",
"0.77361023",
"0.77142495",
"0.77123564",
"0.77093136",
"0.7696949",
"0.76745045",
"0.76480395",
"0.7635072",
"0.760836",
"0.7582893",
"0.7576612",
"0.75485444",... | 0.7704085 | 13 |
Here we define the configuration settings needed for all ingestion plugins with reasonable defaults. | def vdk_configure(self, config_builder: ConfigurationBuilder) -> None:
# Plugin-related configurations
config_builder.add(
key="INGEST_METHOD_DEFAULT",
default_value=None,
description="Default Ingestion method to be used.",
)
config_builder.add(
key="INGEST_TARGET_DEFAULT",
default_value=None,
description="Default Ingestion target to be used.",
)
# Configure ingestion specific environment variables
ingester_configuration.add_definitions(config_builder=config_builder) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configuration():",
"def init_config(self):\n pass",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def configure(self):",
"def configure(self):\n\n pass",
"def get_default_config(self):\n config = super(SignalfxHandler, self).get_default_config()\n\n... | [
"0.6318215",
"0.6309264",
"0.62523276",
"0.62523276",
"0.62523276",
"0.62523276",
"0.62494737",
"0.62309724",
"0.6225098",
"0.6225098",
"0.6183282",
"0.6180916",
"0.6179421",
"0.6114876",
"0.6098103",
"0.60826004",
"0.60826004",
"0.6071773",
"0.6046305",
"0.60166687",
"0.6011... | 0.6801265 | 0 |
Save a piece of data in the configuration directory. | def save_data(data: str, data_name: str):
with open(config_path / data_name, "w") as f:
f.write(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self):\n try:\n with open(self._filename, 'w') as conf_file:\n conf_file.write(json.dumps(self._data))\n except OSError:\n _LOGGER.exception(\"Can't store config in %s\", self._filename)",
"def save(self):\n file = open(self.path, 'w')\n s... | [
"0.78885645",
"0.7684677",
"0.76479673",
"0.76162696",
"0.75278085",
"0.7526425",
"0.7504001",
"0.74552506",
"0.74546015",
"0.72604036",
"0.72053385",
"0.7191787",
"0.71898353",
"0.7184517",
"0.7153047",
"0.7149685",
"0.71368116",
"0.71217936",
"0.7120903",
"0.7091909",
"0.70... | 0.7392408 | 13 |
Load a piece of data from the configuration directory. | def load_data(data_name) -> str:
with open(config_path / data_name, "r") as f:
data = f.readline().strip().split()[0]
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load():\n # get (or create) config path\n p = initialize()\n return load_config(open(p['config']))",
"def load_data_from_config(self):\n\n config_file_name = \"cicada/config/config.yaml\"\n config_dict = None\n self.labels = []\n self.to_add_labels = []\n if os.pat... | [
"0.7237997",
"0.71998596",
"0.70477736",
"0.701441",
"0.69010216",
"0.6879877",
"0.6821252",
"0.6756391",
"0.6728431",
"0.66508085",
"0.66283035",
"0.65852034",
"0.6569168",
"0.6547239",
"0.64764965",
"0.6467039",
"0.64538944",
"0.64244694",
"0.64153147",
"0.6405894",
"0.6381... | 0.59277076 | 85 |
Get a single TW task as an Albert Item. | def get_tw_item(task: taskw.task.Task) -> v0.Item: # type: ignore
field = get_as_subtext_field
task_id = tw_side.get_task_id(task)
actions = [
FuncAction(
"Complete task",
lambda args_list=["done", task_id]: run_tw_action(args_list),
),
FuncAction(
"Delete task",
lambda args_list=["delete", task_id]: run_tw_action(args_list),
),
FuncAction(
"Start task",
lambda args_list=["start", task_id]: run_tw_action(args_list),
),
FuncAction(
"Stop task",
lambda args_list=["stop", task_id]: run_tw_action(args_list),
),
FuncAction(
"Edit task interactively",
lambda args_list=["edit", task_id]: run_tw_action(args_list, need_pty=True),
),
FuncAction(
"Fail task",
lambda task_id=task_id: fail_task(task_id=task_id),
),
ClipAction("Copy task UUID", f"{task_id}"),
]
found_urls = url_re.findall(task["description"])
if "annotations" in task.keys():
found_urls.extend(url_re.findall(" ".join(task["annotations"])))
for url in found_urls[-1::-1]:
actions.insert(0, UrlAction(f"Open {url}", url))
if reminders_tag_path.is_file():
global reminders_tag
reminders_tag = load_data(reminders_tag_path)
else:
save_data("remindme", str(reminders_tag_path))
actions.append(
FuncAction(
f"Add to Reminders (+{reminders_tag})",
lambda args_list=[
"modify",
task_id,
f"+{reminders_tag}",
]: run_tw_action(args_list),
)
)
actions.append(
FuncAction(
"Work on next (+next)",
lambda args_list=[
"modify",
task_id,
"+next",
]: run_tw_action(args_list),
)
)
urgency_str, icon = urgency_to_visuals(task.get("urgency"))
text = task["description"]
due = None
if "due" in task:
due = task["due"].astimezone(dateutil.tz.tzlocal()).strftime("%Y-%m-%d %H:%M:%S") # type: ignore
return get_as_item(
text=text,
subtext="{}{}{}{}{}".format(
field(urgency_str),
"ID: {}... | ".format(tw_side.get_task_id(task)[:8]),
field(task["status"]),
field(task.get("tags"), "tags"),
field(due, "due"),
)[:-2],
icon=[str(icon)],
completion=f'{curr_trigger}{task["description"]}',
actions=actions,
urgency=task.get("urgency"),
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTask():\n\tcontent = requests.get(MANAGER_URL+\"task\", params={\"apiKey\": API_KEY}).text\n\tif content == \"null\":\n\t\treturn None\n\telse:\n\t\treturn json.loads(content)",
"def get_task(task_id):\n return db.task.find_one({'_id': ObjectId(task_id)})",
"def get_item(self):\n return self.i... | [
"0.6219642",
"0.6196074",
"0.6165974",
"0.6165974",
"0.61451584",
"0.61449784",
"0.61061776",
"0.6070151",
"0.6032105",
"0.6015247",
"0.6007213",
"0.59836924",
"0.59652644",
"0.59573513",
"0.59348106",
"0.5902991",
"0.5900254",
"0.58807874",
"0.58392173",
"0.58383256",
"0.583... | 0.676394 | 0 |
Query for a specific subcommand. | def __init__(self, subcommand: Subcommand, query: str):
self.command = subcommand
self.query = query | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_subcommand(self, name):\n try:\n subcommand_class = self.subcommands[name]\n except KeyError:\n self.print_command_unkown_error(name)\n sys.exit(1)\n return subcommand_class(self.prog, name, self.argv[2:], self.stdout)",
"def fetch_command(self, glo... | [
"0.6903667",
"0.68024147",
"0.66922116",
"0.667356",
"0.659423",
"0.65638745",
"0.63775086",
"0.6367834",
"0.636292",
"0.6309504",
"0.623588",
"0.62239105",
"0.6186224",
"0.6177531",
"0.61727655",
"0.6118774",
"0.6098485",
"0.6085753",
"0.6055098",
"0.6037985",
"0.6026761",
... | 0.6384904 | 6 |
Get a subcommand with the indicated name. | def get_subcommand_for_name(name: str) -> Optional[Subcommand]:
matching = [s for s in subcommands if s.name.lower() == name.lower()]
if matching:
return matching[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_subcommand(self, name):\n try:\n subcommand_class = self.subcommands[name]\n except KeyError:\n self.print_command_unkown_error(name)\n sys.exit(1)\n return subcommand_class(self.prog, name, self.argv[2:], self.stdout)",
"def get_subcmd(self, name: ... | [
"0.8886178",
"0.8527717",
"0.79279536",
"0.7438187",
"0.735898",
"0.7330646",
"0.72542876",
"0.7133576",
"0.70916045",
"0.70674455",
"0.7017528",
"0.6924694",
"0.6864823",
"0.6756936",
"0.6737042",
"0.6726676",
"0.6696921",
"0.6614363",
"0.66121435",
"0.66044974",
"0.65040296... | 0.8167717 | 2 |
Determine whether current query is of a subcommand. If so first returned the corresponding SubcommandQeury object. | def get_subcommand_query(query_str: str) -> Optional[SubcommandQuery]:
if not query_str:
return None
# spilt:
# "subcommand_name rest of query" -> ["subcommand_name", "rest of query""]
query_parts = query_str.strip().split(None, maxsplit=1)
if len(query_parts) < 2:
query_str = ""
else:
query_str = query_parts[1]
subcommand = get_subcommand_for_name(query_parts[0])
if subcommand:
return SubcommandQuery(subcommand=subcommand, query=query_str) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_sub_commands(self) -> bool:\n if self.__dict__.get(\"sub_commands\"):\n return True\n\n return False",
"def _subcommand_for_name(self, name):\n for subcommand in self.subcommands:\n if name == subcommand.name or \\\n name in subcommand.... | [
"0.65202355",
"0.6357731",
"0.63516015",
"0.6259924",
"0.60126",
"0.59781826",
"0.5967946",
"0.5843146",
"0.5827129",
"0.5752442",
"0.5749248",
"0.57485867",
"0.5696898",
"0.5505429",
"0.5458622",
"0.53960603",
"0.5385143",
"0.53838754",
"0.5379334",
"0.53359336",
"0.5302235"... | 0.6729017 | 0 |
Establishes a upper limit for the forloop that parses the SWMM input file | def countsubcatchments(inputfilename=FileSettings.settingsdict['inputfilename']):
global count
with open(inputfilename, 'r') as swmmput:
contents = swmmput.readlines()
count = len(contents)
return(count) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _uppLim(self):\n if self.getResult(param='TS value')[0] >= self.tsmin:\n print(\"\\t=== TS value {} is above TSmin {}, no need to compute an upperlimit ===\"\n .format(self.getResult(param='TS value')[0], self.tsmin))\n return\n\n from UpperLimits import Uppe... | [
"0.6134237",
"0.6107441",
"0.5806846",
"0.5753623",
"0.56226695",
"0.5602243",
"0.5540518",
"0.54625756",
"0.5389855",
"0.5387457",
"0.53571594",
"0.53309345",
"0.53300595",
"0.53155893",
"0.5291036",
"0.5275519",
"0.52754134",
"0.523921",
"0.5234351",
"0.52126867",
"0.520834... | 0.0 | -1 |
Opens and reads the parameters in the [SUBCATCHMENT] and [SUBAREA] headers within the SWMM input file. Adds these parameters (as strings) to a numpy array | def read_initial_parameters(inputfilename):
subc_params = []
subarea_params = []
global subc_names
subc_names = []
subcatchment_parameters = []
inputfile = open(inputfilename, 'r')
for line in inputfile:
if(line.find("[SUBCATCHMENTS]") != -1):
line = inputfile.readline()
for i in range(count):
templine = list(line)
if templine[0] == ";" or templine[0] == " " or len(templine) < 10:
line = inputfile.readline()
continue
elif (line.find("[") != -1):
break
else:
linesplit = line.split()
subc_params.append(linesplit[4:7])
subc_names.append(linesplit[0])
line = inputfile.readline()
if (line.find("[SUBAREAS]") != -1):
line = inputfile.readline()
for i in range(count):
templine = list(line)
if templine[0] == ";" or templine[0] == " " or len(templine) < 10:
line = inputfile.readline()
continue
elif (line.find("[") != -1):
break
else:
linesplit = line.split()
subarea_params.append(linesplit[1:6])
line = inputfile.readline()
inputfile.close()
#Part of the function that experiments with np array. Potentially removes the need for the list transformation
# functions that chew up a lot of time. Each subcatchment has a row, each parameter type has a column.
global subcatchment_parameters_np
subcatchment_parameters_np = np.empty((len(subc_params[0]) + len(subarea_params[0]), len(subc_params)), dtype=float)
for row in range(len(subc_params)):
for col in range(len(subc_params[0])):
subcatchment_parameters_np[row, col] = float(subc_params[row][col])
for row in range(len(subarea_params)):
for col in range(len(subarea_params[0])):
subcatchment_parameters_np[row, col + len(subc_params[0])] = float(subarea_params[row][col])
#Old string code
# for i in range(len(subc_params)):
# for j in range(len(subarea_params[i])):
# subc_params[i].append(subarea_params[i][j])
# subcatchment_parameters.append(subc_params[i])
return(np_subcatchment_parameters) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subcatch(ini_file='subcatch.ini'):\n config.read(ini_file)\n print 'Read the file ', ini_file\n\n file_in = config.get('file_in', 'file_in')\n\n file_out = config.get('file_out', 'file_out')\n\n picture_out = config.get('picture_out', 'picture_out')\n\n Xoutlet = config.getfloat('coord_outlet... | [
"0.58298117",
"0.5741129",
"0.5640352",
"0.54980344",
"0.5496068",
"0.5432148",
"0.5394989",
"0.53380096",
"0.53087157",
"0.5273573",
"0.52647877",
"0.5246655",
"0.5224779",
"0.5212376",
"0.52076614",
"0.5198927",
"0.5182285",
"0.5177914",
"0.5172997",
"0.5172781",
"0.516248"... | 0.7251039 | 0 |
Old string code that takes any 2D list and maps it to a 1D list by adding each subsequent row to the end of the first row | def transformation_flatten(twoDlistinput):
oneDlistoutput = []
for i in range(len(twoDlistinput)):
for j in range(len(twoDlistinput[i])):
oneDlistoutput.append(twoDlistinput[i][j])
return(oneDlistoutput) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rebuild_row(lst, is_collocation):\n split_list = lst[0].split(\"\\t\")\n if is_collocation:\n return [split_list[0] + \" \" + split_list[1], \"1\"]\n return [split_list[0] + \" \" + split_list[1], \"0\"]",
"def transpose(str_list):\n # takes list of strings of dimensions n x m\n if (n :... | [
"0.66831225",
"0.6467971",
"0.6433592",
"0.62176746",
"0.61118764",
"0.59531605",
"0.59194076",
"0.5904703",
"0.5842135",
"0.58245844",
"0.5760106",
"0.57579666",
"0.573827",
"0.5714012",
"0.5692371",
"0.569116",
"0.56879634",
"0.56724775",
"0.56658834",
"0.5663291",
"0.56433... | 0.576872 | 10 |
Converts Penn Treebank tags to WordNet. | def penn2morphy(penntag):
morphy_tag = {'NN':'n', 'JJ':'a',
'VB':'v', 'RB':'r'}
try:
return morphy_tag[penntag[:2]]
except:
return 'n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def penn_to_wn(self,tag):\n if tag.startswith('N'):\n return 'n'\n \n if tag.startswith('V'):\n return 'v'\n \n if tag.startswith('J'):\n return 'a'\n \n if tag.startswith('R'):\n return 'r'\n \n return None",
"def pen... | [
"0.6572314",
"0.65697056",
"0.65697056",
"0.64920396",
"0.6471394",
"0.64376074",
"0.64255834",
"0.64045995",
"0.6399852",
"0.6393718",
"0.6372048",
"0.6361967",
"0.63461703",
"0.6333776",
"0.6136483",
"0.60728174",
"0.60711044",
"0.6015081",
"0.60002613",
"0.58672816",
"0.57... | 0.5299345 | 36 |
Make API request with given params. Signs the request with secret key. | def request(self, params):
params["public_key"] = self.__PUBLIC_KEY
post_data = urllib.parse.urlencode(params)
hmac_ = hmac.new(self.__SECRET_KEY, post_data.encode("utf-8"), hashlib.sha256).hexdigest()
curl = pycurl.Curl()
curl.setopt(pycurl.URL, self.__URL)
curl.setopt(pycurl.HTTPHEADER, ['HMAC: ' + str(hmac_)])
curl.setopt(pycurl.POST, True)
curl.setopt(pycurl.POSTFIELDS, post_data)
curl.setopt(pycurl.CONNECTTIMEOUT, 10)
curl.setopt(pycurl.TIMEOUT, 5)
buf = io.BytesIO()
curl.setopt(pycurl.WRITEFUNCTION, buf.write)
curl.perform()
response = buf.getvalue()
# Uncomment to debug raw JSON response
# self.__log("< " + response)
http_code = curl.getinfo(pycurl.HTTP_CODE)
curl.close()
result = json.loads(response.decode('utf-8'))
if http_code != 200:
if result["error"]:
# 404 with some valid JSON
self.__log("ERROR: HTTP " + str(http_code) + ": " + json.dumps(result["error"]))
else:
self.__log("ERROR: HTTP " + str(http_code) + ": " + response)
else:
if result is None:
self.__log("ERROR: Unparsable JSON " + response)
else:
if 'error' in result: # || !$result["result"]
self.__log("ERROR: " + json_encode(result["error"]))
else:
result = result["result"]
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def http_signed_call(self, api_endpoint, params):\r\n if (not self.secret) or (not self.secret.know_secret()):\r\n self.debug(\"### don't know secret, cannot call %s\" % api_endpoint)\r\n return\r\n\r\n key = self.secret.key\r\n sec = self.secret.secret\r\n\r\n if ... | [
"0.7493004",
"0.69957143",
"0.6800038",
"0.6760122",
"0.67544",
"0.672769",
"0.67007923",
"0.6548487",
"0.6517478",
"0.649527",
"0.63868725",
"0.6383492",
"0.6381418",
"0.6370356",
"0.63326025",
"0.6278101",
"0.62663436",
"0.62406445",
"0.6220862",
"0.6220461",
"0.6168506",
... | 0.69033766 | 2 |
Monitor stats for all the rigs | def getCurrentStats(self):
params = {
'method': 'getCurrentStats'
}
stats = self.request(params)
if 'error' in stats:
return False
return stats | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def monitor(self, rms):\n pass",
"def monitor(self):",
"def _calc_stats(self):\n\n for res in self.rsts:\n _LOG.info(\"Calculate statistics for '%s'\", res.reportid)\n res.calc_stats(regexs=self._stats_colnames, funcnames=self._stats_funcs)",
"def refresh_stats(self) -> No... | [
"0.6406339",
"0.63248503",
"0.615353",
"0.60156983",
"0.5939783",
"0.58072263",
"0.57075703",
"0.56916606",
"0.5660787",
"0.56502295",
"0.56461763",
"0.5633014",
"0.55788845",
"0.54534495",
"0.5422858",
"0.54086626",
"0.54054075",
"0.53678644",
"0.5366866",
"0.53413457",
"0.5... | 0.0 | -1 |
Sets parameters for rigs rig_ids_str coma separated string with rig ids "1,2,3,4" miner Miner to set. Leave it null if you do not want to change. "claymore", "claymorez", "ewbf", ... miner2 Second miner to set. Leave it null if you do not want to change. "0" if you want to unset it. id_wal ID of wallet. Leave it null if you do not want to change. id_oc ID of OC profile. Leave it null if you do not want to change. bool|mixed | def multiRocket(self, rig_ids_str, miner, miner2, id_wal, id_oc):
if rig_ids_str is not None:
self.log("Rigs ids required")
exit()
params = {
'method': 'multiRocket',
'rig_ids_str': rig_ids_str,
'miner': miner,
'miner2': miner2,
'id_wal': id_wal,
'id_oc': id_oc
}
result = self.request(params)
if 'error' in result:
return False
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_sids(self, sids):\n self._sids = sids\n # encode sids in RGB\n r = sids // 256**2\n rem = sids % 256**2 # remainder\n g = rem // 256\n b = rem % 256\n self.rgbsids = np.zeros((self.npoints, 3), dtype=np.uint8)\n self.rgbsids[:, 0] = r\n self.rg... | [
"0.50190747",
"0.50190747",
"0.4670787",
"0.45393595",
"0.45045197",
"0.44899312",
"0.44867754",
"0.4224754",
"0.42206508",
"0.4202021",
"0.4202021",
"0.41952497",
"0.41925597",
"0.41226864",
"0.41184327",
"0.40634874",
"0.40553337",
"0.40470064",
"0.40200716",
"0.40155357",
... | 0.6751865 | 0 |
Dump utils image template.py as a Dict. The key is like "simnet/lndbtc" | def _dump_template(self, utils_image) -> Dict[str, str]:
cmd = f"docker run -i --rm --entrypoint python {utils_image}"
p = Popen(cmd, shell=True, stdin=PIPE, stdout=PIPE, stderr=STDOUT)
out, _ = p.communicate(input=SCRIPT.encode())
output = out.decode()
if p.returncode != 0:
self._logger.error("Failed to dump %s template.py\n%s", utils_image, output)
raise RuntimeError("Failed to dump %s template.py" % utils_image)
lines = output.splitlines()
result = {}
for line in lines:
key, value = line.split()
result[key] = value
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _driver_template_data(self):\n return {\n 'driver_module': self.driver_modulename(),\n 'file': self.driver_relative_path(),\n 'author': self.metadata.author,\n 'driver_name': self.metadata.driver_name,\n 'driver_path': self.metadata.driver_path,\n ... | [
"0.5737873",
"0.55475605",
"0.5534782",
"0.5529993",
"0.55225337",
"0.55086523",
"0.5487717",
"0.54603654",
"0.545359",
"0.54374003",
"0.53441983",
"0.53371257",
"0.5325836",
"0.5310178",
"0.530841",
"0.5289318",
"0.5286247",
"0.52432984",
"0.5220684",
"0.5216042",
"0.5184362... | 0.7714567 | 0 |
Update the doubleQ method, i.e. make sure to select actions a' using self.Q but evaluate the Qvalues using the target network (see slides). In other words, > self.target(s) is a Qfunction network which evaluates | def experience_replay(self):
s,a,r,sp,done = self.memory.sample(self.batch_size)
# TODO: 5 lines missing.
raise NotImplementedError("")
self.Q.fit(s, target=target) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def learn(self):\n if self.learn_step_counter % self.target_q_update_step == 0:\n self.target_net.load_state_dict(self.eval_net.state_dict()) #update target_net's parameters\n logging.info(\"updtate target q\")\n self.learn_step_counter += 1\n\n rgbs,depths, rgbs_1, dept... | [
"0.6463897",
"0.6316078",
"0.61735415",
"0.61302334",
"0.611619",
"0.6111125",
"0.60791606",
"0.605653",
"0.60473",
"0.60191995",
"0.6005732",
"0.59470886",
"0.59439164",
"0.59313065",
"0.5930287",
"0.59225607",
"0.5906226",
"0.5905362",
"0.5883166",
"0.58092326",
"0.5794577"... | 0.0 | -1 |
Sending data through a queue | def send_message(self, data):
self.agent_msg_queue.put(data)
self._send_counter += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_msg(self, my_queue, my_msg):",
"def send_data(queue, data):\n for obj in data:\n queue.put(dumps(obj, protocol=-1))",
"def add_to_send_queue(self, data):\n if self.socket is not None:\n self.send_queue.put(data)",
"def send_message(data):\n if data is not None:\n ... | [
"0.7874383",
"0.7698461",
"0.7406423",
"0.7342643",
"0.7194812",
"0.7130858",
"0.7115332",
"0.7114572",
"0.7103772",
"0.70748067",
"0.70712423",
"0.70644194",
"0.69962597",
"0.6987004",
"0.6939428",
"0.6920469",
"0.6905456",
"0.68996507",
"0.68771124",
"0.68152535",
"0.680416... | 0.6878409 | 18 |
Send detection data and return status | def send_detection_data(self, image_width, image_height,
image, detection_result):
if self._send_buffer.full() is True:
log_error("Send detection data failed for buffer is full")
return False
image_data = None
if isinstance(image, AclImage):
image_data = DataBuf(image.data(), image.size).copy_to_local()
elif isinstance(image, np.ndarray):
image_data = image
else:
log_error("Invalid data to send")
return False
request_msg = pm.image_frame_request(image_width, image_height,
image_data.tobytes(),
detection_result)
self.send_message(request_msg)
self._send_buffer.put(image_data)
self._release_send_success_data()
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_image(self, image_width, image_height, image):\n detection_result = []\n return self.send_detection_data(image_width, image_height, image, detection_result)",
"def test_http_classifier(self):\n \n msg = \"\"\n \n files = 0\n tp = 0\n fp = 0\n ... | [
"0.6287213",
"0.6152359",
"0.60468817",
"0.57658076",
"0.5763883",
"0.56264436",
"0.5618451",
"0.56042737",
"0.5574418",
"0.5567113",
"0.5565702",
"0.55625457",
"0.55605626",
"0.553324",
"0.5526417",
"0.5485286",
"0.5476039",
"0.54632705",
"0.5459137",
"0.54455686",
"0.543737... | 0.6724717 | 0 |
send detection image data | def send_image(self, image_width, image_height, image):
detection_result = []
return self.send_detection_data(image_width, image_height, image, detection_result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_detection_data(self, image_width, image_height,\n image, detection_result):\n if self._send_buffer.full() is True:\n log_error(\"Send detection data failed for buffer is full\")\n return False\n\n image_data = None\n if isinstance(image... | [
"0.7168172",
"0.6857813",
"0.6741724",
"0.65148985",
"0.64819443",
"0.64530796",
"0.64069283",
"0.6382538",
"0.6379917",
"0.633284",
"0.6293329",
"0.6292463",
"0.6283481",
"0.6194607",
"0.6180911",
"0.617084",
"0.61642075",
"0.61596763",
"0.615218",
"0.61351126",
"0.6130977",... | 0.7531366 | 0 |
get channel presenter_server_ip, port, channel_name, content_type | def get_channel_config(config_file):
config = configparser.ConfigParser()
config.read(config_file)
presenter_server_ip = config['baseconf']['presenter_server_ip']
port = int(config['baseconf']['presenter_server_port'])
channel_name = config['baseconf']['channel_name']
content_type = int(config['baseconf']['content_type'])
log_info("presenter server ip %s, port %d, channel name %s, "
"type %d " % (presenter_server_ip, port, channel_name, content_type))
return presenter_server_ip, port, channel_name, content_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getChannel(self):\r\n return self.channel",
"def get_channel(channel_id):\r\n if channel_id[0] == 'C':\r\n type = \"channel\"\r\n elif channel_id[0] == 'G':\r\n type = \"group\"\r\n elif channel_id[0] == 'D':\r\n return False\r\n else:\r\n return False\r\n da... | [
"0.65134233",
"0.643602",
"0.6210229",
"0.619519",
"0.6009834",
"0.5932588",
"0.58862126",
"0.58620787",
"0.5847913",
"0.5847913",
"0.584146",
"0.58371985",
"0.58296996",
"0.5799957",
"0.57254803",
"0.57109845",
"0.57049286",
"0.5669257",
"0.5649315",
"0.5622001",
"0.5602927"... | 0.74030155 | 0 |
according to config open channel | def open_channel(config_file):
server_ip, port, channel_name, content_type = get_channel_config(config_file)
channel = PresenterChannel(server_ip, port, channel_name, content_type)
ret = channel.startup()
if ret:
log_error("ERROR:Open channel failed")
return None
return channel | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default_channel(self) -> int:\r\n ...",
"def open_channel(self):\n # LOGGER.info('Creating a new channel')\n self._connection.channel(on_open_callback=self.on_channel_task_open)\n self._connection.channel(on_open_callback=self.on_channel_ctrl_open)",
"def channel_open(self):\n ... | [
"0.6495685",
"0.6411523",
"0.6357903",
"0.6315835",
"0.6212122",
"0.6187699",
"0.6173837",
"0.61588097",
"0.61560345",
"0.6084364",
"0.60564893",
"0.60467905",
"0.60155",
"0.6004395",
"0.5985936",
"0.59202945",
"0.58966035",
"0.58927876",
"0.5869291",
"0.5857404",
"0.5834471"... | 0.5856543 | 20 |
Dial a model defined in Swagger | def __init__(self, caller=None, dialstatus=None, dialstring=None, forward=None, forwarded=None, peer=None): # noqa: E501 # noqa: E501
self._caller = None
self._dialstatus = None
self._dialstring = None
self._forward = None
self._forwarded = None
self._peer = None
self.discriminator = None
if caller is not None:
self.caller = caller
self.dialstatus = dialstatus
if dialstring is not None:
self.dialstring = dialstring
if forward is not None:
self.forward = forward
if forwarded is not None:
self.forwarded = forwarded
self.peer = peer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def opt_model_create_rest_api():\n request_json = request.get_json()\n OptimModelRequestAPI(request_json).validate()\n return create_model_data(request_json)",
"def openapi(self) -> api.OpenAPISpec:\n return self._get_model(model=api.OpenAPISpec)",
"def swagger_definition(self, base_path=None, ... | [
"0.6751859",
"0.633995",
"0.63148445",
"0.6260051",
"0.6195077",
"0.6152982",
"0.5982219",
"0.59721094",
"0.59506464",
"0.5930559",
"0.5904223",
"0.5891059",
"0.5891059",
"0.5891059",
"0.5891059",
"0.5891059",
"0.58744615",
"0.5842159",
"0.58389103",
"0.5817477",
"0.5785608",... | 0.0 | -1 |
Sets the caller of this Dial. | def caller(self, caller):
self._caller = caller | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_scripts_caller(self, caller):\n self._scripts_caller = caller",
"def sender(self, sender):\n\n self._sender = sender",
"def sender(self, sender):\n\n self._sender = sender",
"def sender(self, sender):\n\n self._sender = sender",
"def sender(self, sender):\n\n self... | [
"0.5862553",
"0.55642617",
"0.55642617",
"0.55642617",
"0.55642617",
"0.55642617",
"0.55458647",
"0.52504754",
"0.52443874",
"0.5207483",
"0.5065583",
"0.5034338",
"0.50276506",
"0.50047404",
"0.5002639",
"0.49828595",
"0.49691632",
"0.49691632",
"0.49691632",
"0.49691632",
"... | 0.74337715 | 0 |
Sets the dialstatus of this Dial. | def dialstatus(self, dialstatus):
if dialstatus is None:
raise ValueError("Invalid value for `dialstatus`, must not be `None`") # noqa: E501
self._dialstatus = dialstatus | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_status(self, status):\n self.status = status",
"def set_status(self, status):\n self.status = status",
"def set_status(self, status):\n self.status = status",
"def setstatus(self, status):\n with self.lock:\n self.status = status",
"def SetStatus(self, status)... | [
"0.6324947",
"0.6324947",
"0.6324947",
"0.62151617",
"0.6139082",
"0.6040312",
"0.6012847",
"0.60080004",
"0.5966004",
"0.59111637",
"0.5868122",
"0.5861913",
"0.58358765",
"0.58328503",
"0.58328503",
"0.58328503",
"0.58328503",
"0.58328503",
"0.58328503",
"0.58328503",
"0.58... | 0.8169267 | 0 |
Sets the dialstring of this Dial. | def dialstring(self, dialstring):
self._dialstring = dialstring | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def string_value(self, string_value):\n\n self._string_value = string_value",
"def setString(self, name: unicode, value: unicode) -> None:\n ...",
"def setstring(self):\n self._str = 's '+' '.join([self.src, self.start, self.size,\n self.strand, self.srcSi... | [
"0.5384727",
"0.5367918",
"0.52513164",
"0.5229012",
"0.5229012",
"0.51429296",
"0.4930052",
"0.49152938",
"0.48876902",
"0.48603123",
"0.48325157",
"0.47601306",
"0.47166997",
"0.46935034",
"0.46845663",
"0.46628696",
"0.46405885",
"0.4633442",
"0.4625754",
"0.46073928",
"0.... | 0.8753864 | 0 |
Sets the forward of this Dial. | def forward(self, forward):
self._forward = forward | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward( self ):\n self._has_change = True\n print( \"Forward\" )",
"def fastforward(self):\n self.run_command('fastforward')",
"def forward(self):\n self.cursor.forward()",
"def forward(self):\n pass",
"def forward(self):\n pass",
"def go_forward(self):\n ... | [
"0.6834646",
"0.67426616",
"0.6636892",
"0.64269316",
"0.64269316",
"0.6326287",
"0.62784827",
"0.62357396",
"0.62272525",
"0.62225056",
"0.6159911",
"0.6145522",
"0.6091365",
"0.6059019",
"0.6059019",
"0.6059019",
"0.60449684",
"0.60174173",
"0.5994386",
"0.5994386",
"0.5978... | 0.7644515 | 0 |
Sets the forwarded of this Dial. | def forwarded(self, forwarded):
self._forwarded = forwarded | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, forward):\n\n self._forward = forward",
"def forwarder(self, forwarder: ICNForwarder):\n self._forwarder = forwarder",
"def forward(self):\n pass",
"def forward(self):\n pass",
"def forward( self ):\n self._has_change = True\n print( \"Forward\" )... | [
"0.6887666",
"0.6606515",
"0.6383124",
"0.6383124",
"0.62122256",
"0.60811234",
"0.594021",
"0.5932288",
"0.5932288",
"0.5932288",
"0.581016",
"0.5741199",
"0.57171863",
"0.56688905",
"0.5637646",
"0.5620836",
"0.5603269",
"0.5560376",
"0.5535223",
"0.5532661",
"0.5506793",
... | 0.81721765 | 0 |
Sets the peer of this Dial. | def peer(self, peer):
if peer is None:
raise ValueError("Invalid value for `peer`, must not be `None`") # noqa: E501
self._peer = peer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPeer (self, peer):\n\t\tself.peer = peer",
"def setPeerToPeerNetwork(self, peerToPeerNetwork):\r\n raise NotImplementedError()",
"def peer(self, value: Optional[MicrobitPeer]) -> None:\n if self.__peer is not None:\n self.__peer.remove_listener(self.__execute)\n\n if valu... | [
"0.83918417",
"0.67424357",
"0.6415004",
"0.60654163",
"0.5945206",
"0.5834176",
"0.5686866",
"0.5547047",
"0.54729116",
"0.54373324",
"0.54167396",
"0.53934383",
"0.5380653",
"0.5360441",
"0.5354226",
"0.53020024",
"0.5296971",
"0.5267418",
"0.52219296",
"0.52138495",
"0.517... | 0.7684957 | 1 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(Dial, dict):
for key, value in self.items():
result[key] = value
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.85849506",
"0.78144926",
"0.7789918",
"0.77506983",
"0.77506983",
"0.7712724",
"0.7699126",
"0.76694965",
"0.7650795",
"0.7600532",
"0.7582587",
"0.75700307",
"0.75406003",
"0.7523009",
"0.7515774",
"0.75006944",
"0.74870557",
"0.74870557",
"0.7468478",
"0.74514323",
"0.74... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.75577617",
"0.73375154",
"0.6986672",
"0.698475",
"0.6944995",
"0.692333",
"0.6899106",
"0.6898902",
"0.68146646",
"0.6806209",
"0.6753795",
"0.67497987",
"0.6744008",
"0.6700308",
"0.6691256",
"0.6674591",
"0.6658083",
"0.66091245",
"0.6606931",
"0.6601862",
"0.6563738",
... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, Dial):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.808826",
"0.808826",
"0.8054592",
"0.79827315",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"0.79666996",
"... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Provide video codec vcodec = "h264" acodec = "copy" extra = "" split_cmd = "ffmpeg i '%s' vcodec %s acodec %s y %s" % (file_path, vcodec, acodec, extra) s_cmd = " i '%s' vcodec %s acodec %s"%(file_path, vcodec, acodec) | def split_video_random(file_path, start_pos, split_length, out_path):
s_cmd = " -i '%s'"%(file_path) #use default CODEC
try:
fileext = file_path.split(".")[-1]
except IndexError as e:
raise IndexError("No ext. in filename. Error: " + str(e))
split_start = start_pos
split_length = split_length
head, tail = os.path.split(file_path)
name, ext = tail.split('.')
filebase=name+'_'+str(start_pos)+'-'+str(split_length)
dstfilebase = out_path + '/' + filebase # create output file base
#split_str = ""
#split_str += " -ss " + str(split_start) + " -t " + str(split_length) + " '"+ dstfilebase + "." + fileext + "'"
s_str = ""
#s_str += "ffmpeg"+" -ss "+str(split_start)+" -t "+str(split_length) + s_cmd + " '"+dstfilebase + "." + fileext + "'"
s_str += "ffmpeg" + " -ss " + str(split_start) + s_cmd + " -t " + str(split_length) + " '"+ dstfilebase + "." + fileext + "'"
print("########################################################")
#print "About to run: "+split_cmd+split_str
print("About to run: "+s_str)
print("########################################################")
#output = subprocess.Popen(split_cmd+split_str, shell = True, stdout = subprocess.PIPE).stdout.read()
output = subprocess.Popen(s_str, shell=True, stdout=subprocess.PIPE).stdout.read() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_by_manifest(filename, manifest, vcodec=\"copy\", acodec=\"copy\",\n extra=\"\", **kwargs):\n if not os.path.exists(manifest):\n raise SystemExit\n\n with open(manifest) as manifest_file:\n manifest_type = manifest.split(\".\")[-1]\n if manifest_type == \"js... | [
"0.67609626",
"0.6545024",
"0.64391136",
"0.6160051",
"0.6078338",
"0.60496306",
"0.6016545",
"0.6010689",
"0.5968558",
"0.59620136",
"0.5842451",
"0.583345",
"0.5804066",
"0.5784901",
"0.5773912",
"0.5729956",
"0.56486654",
"0.56443024",
"0.5613024",
"0.5551656",
"0.54755616... | 0.6554189 | 1 |
Verifies credentials for username and password. Returns None on success or a string describing the error on failure Adapt to your needs | def check_credentials(username, password):
LDAP_SERVER = 'ldap://172.24.1.102:389'
# fully qualified AD user name
LDAP_USERNAME = '%s' % username
# your password
LDAP_PASSWORD = password
base_dn = 'DC=LGE,DC=NET'
ldap_filter = 'userPrincipalName=%s' % username
attrs = ['memberOf']
#print "entered username : %s " % username
#print "entered password : %s " % password
try:
# build a client
ldap_client = ldap.initialize(LDAP_SERVER)
#print ldap_client
# perform a synchronous bind
ldap_client.set_option(ldap.OPT_REFERRALS,0)
ldap_client.simple_bind_s(LDAP_USERNAME, LDAP_PASSWORD)
except ldap.INVALID_CREDENTIALS:
ldap_client.unbind()
return 'Wrong username or password'
except ldap.SERVER_DOWN:
return 'AD server not available'
# all is well
ldap_client.unbind()
return "success" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_auth_password(self, username, password):\n return AUTH_FAILED",
"def check_credentials(username, password):\n\t\n\tconn = sqlite3.connect('db/user_task.db')\n\tcursor = conn.execute(\"SELECT password from user WHERE username == \\'%s\\'\" % (username))\n\tdata = cursor.fetchall()\n\tconn.close()... | [
"0.7673481",
"0.7532174",
"0.74800336",
"0.7391194",
"0.7332672",
"0.72637135",
"0.72405916",
"0.7238133",
"0.7238133",
"0.7210242",
"0.7194319",
"0.71895075",
"0.7168887",
"0.71352327",
"0.712828",
"0.71126574",
"0.71101016",
"0.7096003",
"0.70950186",
"0.7062774",
"0.705137... | 0.0 | -1 |
Initialize component which does the ligand depiction. If Nones is provided as parameters just the defalt RDKit functionality is going to be used. | def __init__(
self,
pubchem_templates_path: str = "",
general_templates_path: str = config.general_templates,
) -> None:
self.coordgen_params = rdCoordGen.CoordGenParams()
self.coordgen_params.coordgenScaling = 50 / 1.5
self.coordgen_params.templateFileDir = config.coordgen_templates
self.pubchem_templates = (
pubchem_templates_path if os.path.isdir(pubchem_templates_path) else ""
)
self.templates: Dict[str, rdkit.Chem.rdchem.Mol] = OrderedDict()
if os.path.isdir(general_templates_path):
for k in sorted(os.listdir(general_templates_path)):
template = self._load_template(os.path.join(general_templates_path, k))
template_name = k.split(".")[0]
self.templates[template_name] = template | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(self):\n self.ros.enable()\n self.phone_link.enable()",
"def __init__(self, lbda=None, bandname=None, zp=None, \n mjd=None, empty=False,**kwargs):\n self.__build__()\n if empty:\n return\n prop = kwargs_update(dict(lbda=lbda, bandna... | [
"0.56644416",
"0.5561239",
"0.55297834",
"0.54553896",
"0.5450353",
"0.5445349",
"0.5430579",
"0.5426074",
"0.54253256",
"0.5354728",
"0.53483224",
"0.5339778",
"0.5335766",
"0.5325919",
"0.5325185",
"0.5315453",
"0.52921635",
"0.52817094",
"0.52803695",
"0.5268682",
"0.52672... | 0.0 | -1 |
Given input molecule tries to generate its depictions. | def depict_molecule(
self, het_id: str, mol: rdkit.Chem.rdchem.Mol
) -> DepictionResult:
temp_mol = Chem.RWMol(mol)
templateMol = Chem.RWMol(temp_mol).GetMol()
pubchemMol = Chem.RWMol(temp_mol).GetMol()
rdkitMol = Chem.RWMol(temp_mol).GetMol()
results = []
pubchem_res = (
self._get_2D_by_pubchem(het_id, pubchemMol)
if self.pubchem_templates
else None
)
template_res = self._get_2D_by_template(templateMol) if self.templates else []
rdkit_res = self._get_2D_by_rdkit(rdkitMol)
if pubchem_res is not None:
results.append(pubchem_res)
if rdkit_res is not None:
results.append(rdkit_res)
results = results + template_res
results.sort(key=lambda l: (l.score, l.source))
if results:
to_return = results[0]
fix_conformer(to_return.mol.GetConformer(0))
return to_return
return DepictionResult(
source=DepictionSource.Failed, template_name="", mol=None, score=1000
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sc2depictions(chromosome, root_name=\"output\", lot=0):\n mol_structure = sc2mol_structure(chromosome, lot=lot)\n mol2pdb(mol_structure, \"{0}.pdb\".format(root_name))\n mol2xyz(mol_structure, \"{0}.xyz\".format(root_name))\n Draw.MolToFile(mol_structure, \"{0}.png\".format(root_name))\n logger.... | [
"0.6078832",
"0.55234087",
"0.5375857",
"0.53457975",
"0.5281195",
"0.5259086",
"0.5258982",
"0.5205008",
"0.5194316",
"0.5151961",
"0.5147367",
"0.51438403",
"0.51321965",
"0.5130582",
"0.5127317",
"0.51114666",
"0.51019084",
"0.50857204",
"0.50702864",
"0.5066564",
"0.50647... | 0.4942241 | 28 |
Get path to the PubChem template if it exists. | def _get_pubchem_template_path(self, het_id):
path = os.path.join(self.pubchem_templates, f"{het_id}.sdf")
return path if os.path.isfile(path) else "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_template_path(self):\n raise NotImplementedError()",
"def template_path(self) -> str:\n return self._values.get(\"template_path\")",
"def get_template(self, template):\n\n template_path = aj.config.data['email']['templates'].get(template, 'default')\n\n if template_path == '... | [
"0.6953855",
"0.68982375",
"0.68708664",
"0.67688173",
"0.65419143",
"0.64124465",
"0.63890076",
"0.63717955",
"0.6362928",
"0.63347596",
"0.6195259",
"0.61605114",
"0.61545146",
"0.61206657",
"0.6091912",
"0.60857964",
"0.60805887",
"0.6065575",
"0.60610527",
"0.6056612",
"0... | 0.7834179 | 0 |
Loads a template molecule with 2D coordinates | def _load_template(self, path):
mol = Chem.RWMol()
extension = os.path.basename(path).split(".")[1]
if extension == "sdf":
mol = Chem.MolFromMolFile(path, sanitize=True, removeHs=True)
elif extension == "pdb":
mol = Chem.MolFromPDBFile(path, sanitize=True, removeHs=True)
else:
raise ValueError("Unsupported molecule type '{}'".format(extension))
p = Chem.AdjustQueryParameters()
p.makeAtomsGeneric = True
p.makeBondsGeneric = True
mol = Chem.AdjustQueryProperties(mol, p)
return mol | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_xyz(filename):\n periodic = load_periodic()\n #read molecule\n with open(filename) as f:\n size = int(next(f))\n title = next(f).strip()\n molecule = Molecule(title,size)\n for _ in range(size):\n row = next(f).split()\n tag = row[0]\n ... | [
"0.61214197",
"0.57492346",
"0.5670216",
"0.5607972",
"0.5434534",
"0.5393163",
"0.5368674",
"0.53446394",
"0.53130543",
"0.53116614",
"0.52955234",
"0.5252723",
"0.52483314",
"0.5248229",
"0.5230815",
"0.5224649",
"0.5208221",
"0.52067053",
"0.51932734",
"0.51882654",
"0.513... | 0.54654664 | 4 |
Get depiction done using solely the default RDKit functionality. | def _get_2D_by_rdkit(self, mol):
try:
rdCoordGen.AddCoords(mol, self.coordgen_params)
flaws = DepictionValidator(mol).depiction_score()
return DepictionResult(
source=DepictionSource.RDKit, template_name=None, mol=mol, score=flaws
)
except Exception:
return DepictionResult(
source=DepictionSource.Failed, template_name=None, mol=None, score=1000
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_rd(self):\n return self.__rd",
"def dl():\n raise NotImplementedError()",
"def d(self):\n pass",
"def d(self):\n pass",
"def DM(self):",
"def rd_dr(self, rd):\n # add bits for the bypassed devices\n tdi = bits.bits(rd.n + self.ndevs_before + self.ndevs_after)\n self.... | [
"0.56659234",
"0.5606022",
"0.5375053",
"0.5375053",
"0.53526074",
"0.5315714",
"0.52649176",
"0.5223769",
"0.52229035",
"0.5210231",
"0.51464874",
"0.50903726",
"0.5070479",
"0.5032436",
"0.4937754",
"0.4928767",
"0.4920147",
"0.49045533",
"0.48884135",
"0.48746234",
"0.4869... | 0.45862034 | 61 |
Depict ligand using Pubchem templates. | def _get_2D_by_pubchem(self, code, mol):
try:
template_path = self._get_pubchem_template_path(code)
if template_path:
template = self._load_template(template_path)
if mol.HasSubstructMatch(template):
AllChem.GenerateDepictionMatching2DStructure(mol, template)
flaws = DepictionValidator(mol).depiction_score()
return DepictionResult(
source=DepictionSource.PubChem,
template_name=code,
mol=mol,
score=flaws,
)
except Exception as e:
print(str(e), file=sys.stderr)
return DepictionResult(
source=DepictionSource.Failed, template_name=None, mol=None, score=1000
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prepare_ligand_template(self, ligand_template: pd.Series) -> oechem.OEMolBase:\n from openeye import oechem\n\n from ..modeling.OEModeling import read_molecules, select_chain, select_altloc, remove_non_protein\n from ..utils import FileDownloader, LocalFileStorage\n\n logging.debug... | [
"0.5465929",
"0.5270106",
"0.50946057",
"0.5082453",
"0.5056115",
"0.5039706",
"0.5006837",
"0.4973848",
"0.49720326",
"0.49520364",
"0.4933591",
"0.492104",
"0.49198148",
"0.4917505",
"0.4915033",
"0.49145043",
"0.4857332",
"0.4847261",
"0.48379815",
"0.48348796",
"0.4830842... | 0.0 | -1 |
Depict ligand using userprovided templates | def _get_2D_by_template(self, mol):
results = list()
try:
for key, template in self.templates.items():
temp_mol = Chem.RWMol(mol)
if temp_mol.HasSubstructMatch(template):
AllChem.GenerateDepictionMatching2DStructure(temp_mol, template)
flaws = DepictionValidator(temp_mol).depiction_score()
results.append(
DepictionResult(
source=DepictionSource.Template,
template_name=key,
mol=temp_mol,
score=flaws,
)
)
except Exception:
# if it fails it fails, but generally it wont
logging.warning("Depiction generation by template failed")
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data_mrk():\n return render_template(\"l_markers.html\")",
"def get_html(self):\r\n context = {\r\n 'display_name': self.display_name_with_default,\r\n 'instructions_html': self.instructions,\r\n 'annotation_storage': self.annotation_storage_url,\r\n ... | [
"0.5381906",
"0.5355605",
"0.5339362",
"0.5280232",
"0.5247648",
"0.52295977",
"0.52190715",
"0.51928127",
"0.51736635",
"0.5143805",
"0.5141572",
"0.5127012",
"0.50930816",
"0.50833154",
"0.50804555",
"0.5062896",
"0.5048909",
"0.50279593",
"0.50187063",
"0.50133497",
"0.501... | 0.0 | -1 |
True if two bonds collide, false otherwise. Note that false is retrieved even in case the bonds share common atom, as this is not a problem case. Cramer's rule is used for the linear equations system. | def _intersection(self, bondA, bondB):
atoms = [
bondA.GetBeginAtom(),
bondA.GetEndAtom(),
bondB.GetBeginAtom(),
bondB.GetEndAtom(),
]
names = [a.GetProp("name") for a in atoms]
points = [self.conformer.GetAtomPosition(a.GetIdx()) for a in atoms]
vecA = Geometry.Point2D(points[1].x - points[0].x, points[1].y - points[0].y)
vecB = Geometry.Point2D(points[3].x - points[2].x, points[3].y - points[2].y)
# we need to set up directions of the vectors properly in case
# there is a common atom. So we identify angles correctly
# e.g. B -> A; B -> C and not A -> B; C -> B.
if len(set(names)) == 3:
angle = self.__get_angle(names, vecA, vecB)
return angle < 10.0
# Cramer's rule to identify intersection
det = vecA.x * -vecB.y + vecA.y * vecB.x
if round(det, 2) == 0.00:
return False
a = points[2].x - points[0].x
b = points[2].y - points[0].y
detP = (a * -vecB.y) - (b * -vecB.x)
p = round(detP / det, 3)
if p < 0 or p > 1:
return False
detR = (vecA.x * b) - (vecA.y * a)
r = round(detR / det, 3)
if 0 <= r <= 1:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_bond_crossing(self):\n return self.count_bond_collisions() > 0",
"def collide(b1,b2):\n if mag(b1.pos-b2.pos) < (b1.radius + b2.radius - .05):\n return True",
"def accurate_collision(self, other) -> bool:\r\n if self.collide:\r\n if self.bbox_intersect(other):\r\n ... | [
"0.6964965",
"0.6896832",
"0.67123353",
"0.6666151",
"0.6524003",
"0.6504052",
"0.6484017",
"0.6371618",
"0.63714135",
"0.62512064",
"0.6232055",
"0.62242526",
"0.62045306",
"0.6202152",
"0.6201165",
"0.6185181",
"0.61452067",
"0.6140926",
"0.6111029",
"0.60970056",
"0.609508... | 0.65765357 | 4 |
Get the size of the angle formed by two bonds which share common atom. | def __get_angle(self, names, vecA, vecB):
pivot = max(names, key=names.count)
if names[0] != pivot: # Atoms needs to be order to pick vectors correctly
vecA = vecA * -1
if names[2] != pivot:
vecB = vecB * -1
radians = vecA.AngleTo(vecB)
angle = 180 / math.pi * radians
return angle | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_length(self):\n return AtomMath.length(self.atom1.position - self.atom2.position)",
"def _bond_dist(geom, a1, a2):\n if isinstance(geom, np.ndarray):\n geom = geom.flatten().tolist()\n a13 = a1 * 3\n a23 = a2 * 3\n\n xd = (geom[a13] - geom[a23])**2\n yd = (geom[a13 + 1] - ge... | [
"0.6803342",
"0.6031538",
"0.60075694",
"0.58700997",
"0.58499944",
"0.58232963",
"0.58183986",
"0.57610923",
"0.57447034",
"0.57311267",
"0.5706374",
"0.5695668",
"0.5695543",
"0.56807685",
"0.5657946",
"0.5650897",
"0.5618208",
"0.5611929",
"0.5603994",
"0.55947745",
"0.558... | 0.0 | -1 |
Detects whether the structure has a pair or atoms closer to each other than threshold. This can detect structures which may need a template as they can be handled by RDKit correctly. | def has_degenerated_atom_positions(self, threshold):
for i in range(0, len(self.conformer.GetNumAtoms())):
center = self.conformer.GetAtomPosition(i)
point = [center.x, center.y, center.z]
surrounding = self.kd_tree.query_ball_point(point, threshold)
if len(surrounding) > 1:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_closest_threshold(x: float, y: float, rw_info: pd.DataFrame) -> Tuple[int, int, int]:\n\n min_dist = CONTROL_ZONE_RADIUS\n min_ind = (0, 0, 0)\n\n for ind in rw_info.index:\n\n hp_x = rw_info[0][ind]\n hp_y = rw_info[1][ind]\n\n dist1 = np.sqrt((x - hp_x) ** 2 + (y - hp_y) **... | [
"0.5863067",
"0.57756513",
"0.56205285",
"0.55818784",
"0.54911447",
"0.5490294",
"0.5413528",
"0.5393008",
"0.53929734",
"0.5383899",
"0.5383543",
"0.5363858",
"0.533649",
"0.5334817",
"0.53021765",
"0.5235089",
"0.5234669",
"0.52207446",
"0.52092874",
"0.52092874",
"0.52015... | 0.54279095 | 6 |
Detects whether the structure has a pair or atoms in the range meaning that the depiction could be improved. | def count_suboptimal_atom_positions(self, lowerBound, upperBound):
counter = 0
for i in range(self.conformer.GetNumAtoms()):
center = self.conformer.GetAtomPosition(i)
point = [center.x, center.y, center.z]
surroundingLow = self.kd_tree.query_ball_point(point, lowerBound)
surroundingHigh = self.kd_tree.query_ball_point(point, upperBound)
if len(surroundingHigh) - len(surroundingLow) > 0:
counter += 1
return counter / 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isRangeValid(self) -> bool:\n ...",
"def check_unibranch_validity(pair, positions, intersection):\n assert(intersection is not None)\n check = []\n for par in pair:\n b = 0\n pos = positions[par-1]\n for branch in intersection:\n b +=1\n for tup in b... | [
"0.61594635",
"0.59847325",
"0.58420426",
"0.58221036",
"0.58218324",
"0.5720427",
"0.5718489",
"0.57073534",
"0.5707279",
"0.568753",
"0.5665801",
"0.5664754",
"0.56299406",
"0.5600717",
"0.55935943",
"0.55612284",
"0.55557734",
"0.5544614",
"0.5533591",
"0.5522513",
"0.5516... | 0.0 | -1 |
Counts number of collisions among all bonds. Can be used for estimations of how 'wrong' the depiction is. | def count_bond_collisions(self):
errors = 0
for i in range(0, len(self.bonds)):
for a in range(i + 1, len(self.bonds)):
result = self._intersection(self.bonds[i], self.bonds[a])
if result:
errors += 1
return errors | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_bonds(self):\n n = 0\n for bond in self.iter_bonds():\n n += 1\n return n",
"def count_bonds(self):\n n = 0\n for bond in self.iter_bonds():\n n += 1\n return n",
"def get_collisions(self) -> int:\n if len(self.obstacles) == 0:\n ... | [
"0.6844145",
"0.6844145",
"0.68411255",
"0.68388706",
"0.67286265",
"0.66780823",
"0.64739144",
"0.62883806",
"0.6247389",
"0.62287146",
"0.61868566",
"0.617473",
"0.6031441",
"0.60012114",
"0.5952148",
"0.5952148",
"0.5926391",
"0.5919017",
"0.5828075",
"0.5818269",
"0.58110... | 0.7136189 | 0 |
Tells if the structure contains collisions | def has_bond_crossing(self):
return self.count_bond_collisions() > 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collision_check(self):\n return True",
"def check_collisions(self):",
"def validate_collision(self):\n pass",
"def _check_for_collision(self, sample):\n collide=False\n for i in range(len(self.obstacles)):\n collide=collide or self.obstacles[i].in_collision(sample)\... | [
"0.80110776",
"0.7117187",
"0.69468826",
"0.69385827",
"0.69306934",
"0.6928933",
"0.6904321",
"0.6890036",
"0.6859777",
"0.681465",
"0.6788974",
"0.6769276",
"0.67647177",
"0.6714456",
"0.6694013",
"0.66029567",
"0.6584946",
"0.6561978",
"0.65351593",
"0.6516069",
"0.6511501... | 0.590947 | 88 |
Calculate quality of the ligand depiction. The higher the worse. Ideally that should be 0. | def depiction_score(self):
collision_penalty = 1
degenerated_penalty = 0.4
bond_collisions = self.count_bond_collisions()
degenerated_atoms = self.count_suboptimal_atom_positions(0.0, 0.5)
score = (
collision_penalty * bond_collisions
+ degenerated_penalty * degenerated_atoms
)
return round(score, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quality(self) -> float:\n if self.get_cover_size() == 0:\n return 0\n else:\n if self.baseline == Baseline.COMPLEMENT:\n return self.__complement_quality()\n else:\n return self.__population_quality()",
"def quality(self) -> int:\n ... | [
"0.66823983",
"0.6546814",
"0.65287745",
"0.6406672",
"0.6386629",
"0.6259903",
"0.6180249",
"0.6049594",
"0.6018899",
"0.5956084",
"0.58994746",
"0.5833759",
"0.57996166",
"0.5788256",
"0.57742614",
"0.5768054",
"0.57475317",
"0.5742199",
"0.5725896",
"0.57138395",
"0.567269... | 0.0 | -1 |
Get batch generator `batch_generator` must define a `shape` property that returns the shape of generated sequences as a (n_samples, n_features) tuple. `batch_generator` must define a method called `get_steps_per_epoch` with the signature `def get_steps_per_epoch(self, protocol, subset)` that returns the number of batches to generate before ending an epoch. `batch_generator` may optionally define a method called `callbacks` with the signature `def callbacks(self, extract_embedding=None)` that is expected to return a list of Keras callbacks that will be added to the list of callbacks during training. This might come in handy in case the `batch_generator` depends on the internal state of the model currently being trained. | def get_generator(self, file_generator, batch_size=None, **kwargs):
raise NotImplementedError('') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_generator(self, generator: \"DataGenerator\", nb_epochs: int = 20, **kwargs) -> None:\n raise NotImplementedError",
"def gen_batch(self):\n batch_size = self.batch_size\n shuffle = self.shuffle\n data = np.array(self.sentences)\n\n data_size = len(data)\n num_bat... | [
"0.62859094",
"0.627278",
"0.6238411",
"0.6231443",
"0.61449736",
"0.6116078",
"0.6102709",
"0.6071425",
"0.6071153",
"0.60535413",
"0.60365564",
"0.6031602",
"0.60167223",
"0.59738314",
"0.5962088",
"0.59487414",
"0.59423554",
"0.5910736",
"0.5903543",
"0.5894708",
"0.586065... | 0.6673094 | 0 |
Design the model for which the loss is optimized | def build_model(self, input_shape, design_embedding, **kwargs):
return design_embedding(input_shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_model(self):\n self.g12 = G12(conv_dim=self.g_conv_dim)\n init_weights(self.g12, init_type='normal')\n self.g21 = G21(conv_dim=self.g_conv_dim)\n init_weights(self.g21, init_type='normal')\n self.d1 = D1(conv_dim=self.d_conv_dim, use_labels=self.use_labels)\n ini... | [
"0.70582205",
"0.69713557",
"0.68715394",
"0.68682396",
"0.675125",
"0.66826314",
"0.6662389",
"0.6640771",
"0.6632258",
"0.6623443",
"0.662218",
"0.6608138",
"0.66036206",
"0.6591534",
"0.65909654",
"0.65843874",
"0.65824467",
"0.6580098",
"0.657169",
"0.65641236",
"0.656231... | 0.0 | -1 |
Extract embedding from internal Keras model | def extract_embedding(self, from_model):
return from_model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gensim_to_keras(model):\n return model.wv.get_keras_embedding()",
"def get_embedding(self, model):\n embedding = []\n for node in range(len(self.graph.nodes())):\n embedding.append(list(model[str(node)]))\n embedding = np.array(embedding)\n return embedding",
"def ... | [
"0.7617932",
"0.7358305",
"0.7260656",
"0.70529824",
"0.70419127",
"0.6889344",
"0.6849663",
"0.681078",
"0.6796582",
"0.67891294",
"0.6745876",
"0.66203314",
"0.6616796",
"0.659487",
"0.6581204",
"0.65696263",
"0.6557681",
"0.6531176",
"0.649195",
"0.6473366",
"0.6431787",
... | 0.77565044 | 0 |
Get a logger that produces reasonable output. | def _get_logger():
logger = logging.getLogger(__name__)
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
ch.setFormatter(logging.Formatter("%(asctime)s [%(levelname)8s] %(message)s"))
logger.addHandler(ch)
logger.setLevel(logging.DEBUG)
return logger | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_logger(name=\"unknown_logger\"):\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n handler = logging.StreamHandler(sys.stdout)\n handler.setLevel(logging.DEBUG)\n handler.setFormatter(FORMATTER)\n logger.addHandler(handler)\n logger.propagate = False # to avoid pri... | [
"0.76173455",
"0.73903525",
"0.7385359",
"0.73532313",
"0.72620827",
"0.7261054",
"0.7208833",
"0.72087556",
"0.7140604",
"0.7138828",
"0.7128172",
"0.71156687",
"0.7111992",
"0.7104806",
"0.70627075",
"0.70546305",
"0.70511794",
"0.7048386",
"0.70350707",
"0.7021459",
"0.699... | 0.76066315 | 1 |
Find the first time a running sum repeats | def find_repeating_frequency(values):
frequencies = set([0])
index = 0
frequency = 0
while True:
found = False
for value in values:
frequency += value
index += 1
if frequency in frequencies:
found = True
break
frequencies.add(frequency)
if found:
break
return frequency | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def FirstRepeatingFrequency(self):\n prev_freqs = {0}\n freq = 0\n for offset in cycle(self.freq_changes):\n freq += offset\n if freq in prev_freqs:\n return freq\n else:\n prev_freqs.add(freq)",
"def take_first(nums):\n take=... | [
"0.6377952",
"0.6170564",
"0.61421",
"0.5981056",
"0.5942517",
"0.5867513",
"0.5838692",
"0.58114886",
"0.58026123",
"0.5784409",
"0.5751145",
"0.5738505",
"0.56759614",
"0.56725645",
"0.56579083",
"0.5624306",
"0.55687976",
"0.55209714",
"0.5511702",
"0.5495576",
"0.54878193... | 0.57920206 | 9 |
import count or FPKM table | def import_countOrFPKMTable(
self,filename_I):
#import and format the data
io = base_importData();
io.read_tab(filename_I);
countOrFPKMTable = self.format_countOrFPKMTable(io.data);
return countOrFPKMTable; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count(self):\n ans = self.execute(self.commands.table_count(self.name))\n return ans[0][0]",
"def count():",
"def samples(app, args):\n engine = create_engine(args.datafile)\n meta = MetaData()\n meta.reflect(engine)\n print(\"\\t\".join([str(x).replace('counts.', '')\n ... | [
"0.6216667",
"0.59590936",
"0.5957887",
"0.5952389",
"0.5868071",
"0.58158195",
"0.57693267",
"0.57693267",
"0.5755721",
"0.5700496",
"0.56974876",
"0.5680915",
"0.56658834",
"0.5662107",
"0.56482613",
"0.56469244",
"0.5637406",
"0.559692",
"0.5541019",
"0.55354065",
"0.55352... | 0.7532327 | 0 |
reformat attr tables into a dictionary for rapid alignment of attr table with tracking_id | def reformat_attrTable(
self):
#format into a dictionary of rows for quick aligning with the tracking_id
if self.attrTable: attrTable = self.attrTable[:];
else: attrTable = [];
attrTable_dict = {};
for row in attrTable:
attrTable_dict[row['tracking_id']] = row;
return attrTable_dict; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _process_attrs(attrs):\n new_attrs = OrderedDict()\n for attr in attrs:\n col = attr\n if isinstance(attr, tuple):\n col, attr = attr\n # special cases\n if attr == 'class_name':\n attr = '__class__.__name__'\n if attr == 'repr':\n attr ... | [
"0.65014577",
"0.60592574",
"0.60217834",
"0.6018756",
"0.6007916",
"0.5968255",
"0.58305824",
"0.58305734",
"0.57943606",
"0.575547",
"0.57497114",
"0.574563",
"0.5674587",
"0.56742924",
"0.56551945",
"0.565404",
"0.56439865",
"0.5624873",
"0.561691",
"0.55270827",
"0.551784... | 0.8673553 | 0 |
reformat count table into a flattened table of sample_names/values | def reformat_countTable(
self,analysis_id_I=None,sna2experimentID_I=None,
sna2sns_I=None):
if self.countTable: countTable = self.countTable[:];
else: countTable = [];
countTable_flat = self.reformat_countOrFPKMTable(
countOrFPKMTable_I=countTable,
analysis_id_I=analysis_id_I,
sna2experimentID_I=sna2experimentID_I,
sna2sns_I=sna2sns_I,
count_or_FPKM = 'count');
return countTable_flat; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def freq_table(a):\n Detail_freq = a.loc[:, (a.dtypes == object) | (a.dtypes == long) ].columns.get_values().tolist()\n print(Detail_freq)\n for freq in Detail_freq:\n df1 = pd.DataFrame(a[freq].value_counts(dropna=False).astype(float).map('{:20,.0f}'.format).sort_index()).rename(columns={freq:... | [
"0.57405555",
"0.56753397",
"0.56595165",
"0.56088334",
"0.558755",
"0.5580088",
"0.55345875",
"0.55330217",
"0.5509548",
"0.543421",
"0.5379833",
"0.5359173",
"0.53470004",
"0.52842575",
"0.52725303",
"0.5265805",
"0.5256941",
"0.52491903",
"0.52491903",
"0.52352023",
"0.521... | 0.627885 | 0 |
reformat fpkm table into flattened table of sample_names/values | def reformat_fpkmTable(
self,analysis_id_I=None,sna2experimentID_I=None,
sna2sns_I=None):
if self.fpkmTable: fpkmTable = self.fpkmTable[:];
else: fpkmTable = [];
fpkmTable_flat = self.reformat_countOrFPKMTable(
countOrFPKMTable_I=fpkmTable,
analysis_id_I=analysis_id_I,
sna2experimentID_I=sna2experimentID_I,
sna2sns_I=sna2sns_I,
count_or_FPKM = 'fpkm');
return fpkmTable_flat; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_unifrac_sample_mapping(sample_ids, otu_ids, otu_table_array):\r\n out = []\r\n for i, row in enumerate(otu_table_array):\r\n for j, val in enumerate(row):\r\n if val > 0:\r\n line = [otu_ids[i], sample_ids[j], str(val)]\r\n out.append('\\t'.join(line... | [
"0.60229045",
"0.5637049",
"0.5637049",
"0.53095335",
"0.53014034",
"0.52432746",
"0.5227828",
"0.51772594",
"0.51730895",
"0.51567525",
"0.51420665",
"0.51343006",
"0.511792",
"0.511474",
"0.5104135",
"0.5101256",
"0.50933254",
"0.5086502",
"0.5055283",
"0.5043328",
"0.50231... | 0.5961496 | 1 |
reformat count or FPKM tables into flattened table of sample_names/values for rapid alignment of attr table with tracking_id | def reformat_countOrFPKMTable(
self,
countOrFPKMTable_I=None,
analysis_id_I=None,
sna2experimentID_I=None,
sna2sns_I=None,
count_or_FPKM = 'count'):
#format into a dictionary of rows for quick aligning with the tracking_id
countOrFPKMTable_flat = [];
for row in countOrFPKMTable_I:
for k,v in row.items():
if k=='tracking_id':continue;
tmp = {};
tmp['analysis_id'] = analysis_id_I;
tmp['tracking_id'] = row['tracking_id'];
sample_name_lst = k.split('_');
sample_name_base = '_'.join(sample_name_lst[:-1]);
sample_name_rep = eval(sample_name_lst[-1]);
if sna2experimentID_I:
experiment_id = sna2experimentID_I[sample_name_base];
else:
experiment_id=None;
tmp['experiment_id'] = experiment_id;
if sna2sns_I:
sample_name = sna2sns_I[sample_name_base][sample_name_rep];
else:
sample_name=k;
tmp['sample_name'] = sample_name;
tmp['value'] = v;
tmp['value_units'] = count_or_FPKM;
tmp['used_'] = True;
tmp['comment_'] = None;
countOrFPKMTable_flat.append(tmp);
return countOrFPKMTable_flat; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def alignAndReformat_countFPKMattrTables(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n #reformat\n countTable_flat = self.reformat_countTable(\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=... | [
"0.5996871",
"0.5834174",
"0.56089836",
"0.5596423",
"0.5548671",
"0.554599",
"0.5534935",
"0.548897",
"0.5448419",
"0.5394029",
"0.537485",
"0.5362557",
"0.5339601",
"0.53235745",
"0.52827585",
"0.5272961",
"0.5233933",
"0.522556",
"0.52244353",
"0.5220203",
"0.5207982",
"... | 0.63312393 | 0 |
reformat count or FPKM tables into a dictionary for rapid alignment of attr table with tracking_id | def alignAndReformat_countFPKMattrTables(
self,analysis_id_I=None,sna2experimentID_I=None,
sna2sns_I=None):
#reformat
countTable_flat = self.reformat_countTable(
analysis_id_I=analysis_id_I,
sna2experimentID_I=sna2experimentID_I,
sna2sns_I=sna2sns_I,);
fpkmTable_flat = self.reformat_fpkmTable(
analysis_id_I=analysis_id_I,
sna2experimentID_I=sna2experimentID_I,
sna2sns_I=sna2sns_I,);
attrTable_dict = self.reformat_attrTable();
#align
countAndFpkmTable_aligned = [];
for row in countTable_flat[:]:
row.update(attrTable_dict[row['tracking_id']]);
countAndFpkmTable_aligned.append(row);
for row in fpkmTable_flat[:]:
row.update(attrTable_dict[row['tracking_id']]);
countAndFpkmTable_aligned.append(row);
return countAndFpkmTable_aligned; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reformat_attrTable(\n self):\n #format into a dictionary of rows for quick aligning with the tracking_id\n if self.attrTable: attrTable = self.attrTable[:];\n else: attrTable = [];\n\n attrTable_dict = {};\n for row in attrTable:\n attrTable_dict[row['tracki... | [
"0.7227093",
"0.64672595",
"0.63795495",
"0.5744344",
"0.5535398",
"0.5517264",
"0.54640967",
"0.542029",
"0.53676605",
"0.5360893",
"0.5334983",
"0.5295043",
"0.5288153",
"0.5281474",
"0.52813345",
"0.5253252",
"0.52377903",
"0.5232528",
"0.51988035",
"0.51941615",
"0.517988... | 0.63274735 | 3 |
formats raw string input into their appropriate values | def format_countOrFPKMTable(self,fpkmTracking_I):
for fpkmTracking in fpkmTracking_I:
for k,v in fpkmTracking.items():
if k=='tracking_id' and type(fpkmTracking['tracking_id'])==type('string'):
pass;
elif k!='tracking_id' and type(fpkmTracking[k])==type('string'):
fpkmTracking[k] = eval(v);
return fpkmTracking_I; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_raw_input(user_input):\n # Replace silly “ or ” characters with \"\n # TODO: Swap out with regex\n raw_input = user_input.strip().replace(\n '“', '\"').replace(\"”\", '\"').replace(\",\", \"\").replace(\"\\n\", \" \")\n # Break apart the string into each coordinate\n raw_inputs = [... | [
"0.66427",
"0.6007272",
"0.5868643",
"0.58346975",
"0.57762647",
"0.5764655",
"0.57391506",
"0.57352734",
"0.5719354",
"0.5683081",
"0.5664899",
"0.5664475",
"0.5585627",
"0.5576046",
"0.5576046",
"0.55689394",
"0.5552692",
"0.5552692",
"0.5544937",
"0.5495645",
"0.54807526",... | 0.0 | -1 |
View for rendering hours as json. | def json_hours(request):
current_site = Site.find_for_request(request)
if request.method == 'GET':
if request.GET.get('fallback'):
fallback = request.GET['fallback']
return JsonResponse(
{
'llid': get_default_unit().location.libcal_library_id,
}
)
else:
libcalid = request.GET['libcalid']
all_building_hours = json.dumps(get_building_hours_and_lid(current_site))
return JsonResponse(
{
'all_building_hours': all_building_hours,
'current_hours': get_json_hours_by_id(int(libcalid), all_building_hours),
'llid': libcalid,
'llid_fallback': get_default_unit().location.libcal_library_id,
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_hours(employee_id):\n\n if not g.user:\n flash(\"Please Login to continue.\", \"danger\")\n return redirect(\"/\")\n \n employee = Employee.query.get_or_404(employee_id)\n\n labels = json.dumps( [\"Completed\", \"Required\"])\n data = json.dumps([employee.completed, employe... | [
"0.6702672",
"0.63559496",
"0.6134469",
"0.60062844",
"0.58493066",
"0.5845087",
"0.5821502",
"0.58081955",
"0.57617134",
"0.5753844",
"0.5742273",
"0.5733102",
"0.57142144",
"0.56817853",
"0.56517273",
"0.5609031",
"0.558943",
"0.5580721",
"0.5514128",
"0.549668",
"0.5485034... | 0.72261083 | 0 |
View for rendering events feed data as json. | def json_events(request):
if request.method == 'GET':
ttrss_url = request.GET['feed']
# need xml for this.
university_url = 'http://events.uchicago.edu/widgets/rss.php?key=47866f880d62a4f4517a44381f4a990d&id=48'
n = datetime.datetime.now()
return JsonResponse(
{
'events': flatten_events(get_events(university_url, ttrss_url, n, n + relativedelta(years=1), False))
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_events():\n result = get_events_helper(Event)\n return jsonify(result[0]), result[1]",
"def get():\n return jsonify({'events': 'Events API'}), 200",
"def get_event():\n json_data = request.args or {}\n return make_response(jsonify({ \"data\" : Event.get_events(json_data)}))"... | [
"0.76628214",
"0.67185247",
"0.6591272",
"0.6508852",
"0.6443426",
"0.62528884",
"0.6215547",
"0.6096349",
"0.60427344",
"0.604139",
"0.5985696",
"0.5972402",
"0.5937236",
"0.5932184",
"0.59189314",
"0.58994985",
"0.58941627",
"0.5889959",
"0.58717847",
"0.58609265",
"0.58606... | 0.778918 | 0 |
View for rendering news feed data as json. | def json_news(request):
if request.method == 'GET':
feed = request.GET['feed']
return JsonResponse(
{
'news': get_news(feed),
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_news():\n data = get_top_news()\n return jsonify(data)",
"def news():\n\n # ensure parameters are present\n # geo = request.args.get(\"geo\")\n geo = '95060'\n if not geo:\n raise RuntimeError(\"missing geo\")\n\n # lookup articles and store them as JSON array\n article_lis... | [
"0.673006",
"0.66394603",
"0.6454153",
"0.6373204",
"0.6363174",
"0.63411576",
"0.6298449",
"0.61722803",
"0.61526084",
"0.60082966",
"0.59839356",
"0.5969505",
"0.5918859",
"0.5916364",
"0.5914569",
"0.5899843",
"0.58803356",
"0.5869351",
"0.58558553",
"0.5835319",
"0.581607... | 0.78444064 | 0 |
View for retreiving the chat status for Ask a Librarian pages. Returns json. | def chat_status(request):
if request.method == 'GET':
ask_name = request.GET['name']
status = get_chat_status_and_css(ask_name)
return JsonResponse(
{
'chat_status': status[0],
'chat_css': status[1],
}
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chat_status(request):\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\n if request.method == 'GET' and request.is_ajax():\n if(team is None):\n return render(request, 'access_error.html', {'reason': \"team\"})\n status = team.num_waiting_messages\n ... | [
"0.7056218",
"0.58047336",
"0.57761455",
"0.56156147",
"0.56094897",
"0.5595043",
"0.55575705",
"0.5555067",
"0.5535522",
"0.553365",
"0.5530453",
"0.55122775",
"0.540345",
"0.53645366",
"0.53573716",
"0.53215814",
"0.53145057",
"0.52810264",
"0.5262246",
"0.5239236",
"0.5229... | 0.76308614 | 0 |
Fonction pour placer un disque de couleur 'col' a la coordonnee (xa,ya) | def create_point(xa,ya,col):
disque = canvas.create_oval(xa-(rayon),ya-(rayon),xa+(rayon),ya+(rayon),fill="white",outline=col)
return disque | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_xy_position(row, col):\n spacing_x = 86 + 11\n spacing_y = 98 + 8\n top_y = 50\n left_x = 50\n return left_x + col * spacing_x, top_y + row * spacing_y",
"def colWithTile(self, pos):\n\n\n return self.colWithBox(pos, [2.0,2.0,2.0])",
"def em_coord_turtle(lin, col, dim, tam_celula):\n ... | [
"0.68489397",
"0.67793953",
"0.6498352",
"0.63620645",
"0.6283588",
"0.62691915",
"0.6266249",
"0.62381095",
"0.61900526",
"0.6178052",
"0.6152861",
"0.6122054",
"0.60530484",
"0.6049112",
"0.60243577",
"0.59963053",
"0.59574944",
"0.5950011",
"0.59291357",
"0.59247184",
"0.5... | 0.60508746 | 13 |
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