How to use load_source method in localstack

Best Python code snippet using localstack_python

base.py

Source:base.py

...10from relationGraph import Relation, RelationGraph, MatrixOfRelationGraph11import numpy as np12from skfusion.fusion import ObjectType, Relation, FusionGraph13__all__ = ['load_dicty', 'load_pharma', 'load_movielens']14def load_source(source_path, delimiter=',', filling_value='0'):15    """Load and return a data source.16    Parameters17    ----------18    delimiter : str, optional (default=',')19        The string used to separate values. By default, comma acts as delimiter.20    filling_value : variable, optional (default='0')21        The value to be used as default when the data are missing.22    Returns23    -------24    data : DataSource25        Dictionary-like object, the interesting attributes are:26        'data', the data to learn, 'obj1_names', the meaning of row objects,27        'obj2_names', the meaning of column objects.28    """29    module_path = dirname(__file__)30    data_file = gzip.open(join(module_path, 'data', source_path))31    row_names = np.array(next(data_file).decode('utf-8').strip().replace(', ', ' ').replace('.,', '.').split(delimiter))32    col_names = np.array(next(data_file).decode('utf-8').strip().replace(', ', ' ').replace('.,', '.').split(delimiter))33    data = np.genfromtxt(data_file, delimiter=delimiter, missing_values=[''],34                         filling_values=filling_value)35    return data, row_names, col_names36def load_dicty():37    """Construct fusion graph from molecular biology of Dictyostelium."""38    gene = ObjectType('Gene', 50)39    go_term = ObjectType('GO term', 15)40    exprc = ObjectType('Experimental condition', 5)41    data, rn, cn = load_source(join('dicty', 'dicty.gene_annnotations.csv.gz'))42    ann = Relation(data=data, row_type=gene, col_type=go_term, name='ann',43                   row_names=rn, col_names=cn)44    data, rn, cn = load_source(join('dicty', 'dicty.gene_expression.csv.gz'))45    expr = Relation(data=data, row_type=gene, col_type=exprc, name='expr',46                    row_names=rn, col_names=cn)47    expr.data = np.log(np.maximum(expr.data, np.finfo(np.float).eps))48    data, rn, cn = load_source(join('dicty', 'dicty.ppi.csv.gz'))49    ppi = Relation(data=data, row_type=gene, col_type=gene, name='ppi',50                   row_names=rn, col_names=cn)51    return FusionGraph([ann, expr, ppi])52def load_pharma():53    """Construct fusion graph from the pharmacology domain."""54    action=ObjectType('Action', 5)55    pmid=ObjectType('PMID', 5)56    depositor=ObjectType('Depositor', 5)57    fingerprint=ObjectType('Fingerprint', 20)58    depo_cat=ObjectType('Depositor category', 5)59    chemical=ObjectType('Chemical', 10)60    data, rn, cn = load_source(join('pharma', 'pharma.actions.csv.gz'))61    actions = Relation(data=data, row_type=chemical, col_type=action,62                       row_names=rn, col_names=cn)63    data, rn, cn = load_source(join('pharma', 'pharma.pubmed.csv.gz'))64    pubmed = Relation(data=data, row_type=chemical, col_type=pmid,65                      row_names=rn, col_names=cn)66    data, rn, cn = load_source(join('pharma', 'pharma.depositors.csv.gz'))67    depositors = Relation(data=data, row_type=chemical, col_type=depositor,68                          row_names=rn, col_names=cn)69    data, rn, cn = load_source(join('pharma', 'pharma.fingerprints.csv.gz'))70    fingerprints = Relation(data=data, row_type=chemical, col_type=fingerprint,71                            row_names=rn, col_names=cn)72    data, rn, cn = load_source(join('pharma', 'pharma.depo_cats.csv.gz'))73    depo_cats = Relation(data=data, row_type=depositor, col_type=depo_cat,74                         row_names=rn, col_names=cn)75    data, rn, cn = load_source(join('pharma', 'pharma.tanimoto.csv.gz'))76    tanimoto = Relation(data=data, row_type=chemical, col_type=chemical,77                        row_names=rn, col_names=cn)78    return FusionGraph([actions, pubmed, depositors, fingerprints, depo_cats, tanimoto])79def load_movielens(ratings=True, movie_genres=True, movie_actors=True):80    module_path = join(dirname(__file__), 'data', 'movielens')81    if ratings:82        ratings_data = defaultdict(dict)83        with gzip.open(join(module_path, 'ratings.csv.gz'), 'rt', encoding='utf-8') as f:84            f.readline()85            for line in f:86                line = line.strip().split(',')87                ratings_data[int(line[0])][int(line[1])] = float(line[2])88    else:89        ratings_data = None90    if movie_genres:91        movie_genres_data = {}92        with gzip.open(join(module_path, 'movies.csv.gz'), 'rt', encoding='utf-8') as f:93            f.readline()94            lines = csv.reader(f)95            for line in lines:96                movie_genres_data[int(line[0])] = line[2].split('|')97    else:98        movie_genres_data = None99    if movie_actors:100        movie_actors_data = {}101        with gzip.open(join(module_path, 'actors.csv.gz'), 'rt', encoding='utf-8') as f:102            f.readline()103            lines = csv.reader(f)104            for line in lines:105                movie_actors_data[int(line[0])] = line[2].split('|')106    else:107        movie_actors_data = None108    return ratings_data, movie_genres_data, movie_actors_data109def show_data_dicty():110    data1, rn1, cn1 = load_source(join('dicty', 'dicty.gene_annnotations.csv.gz'))111    data2, rn2, cn2 = load_source(join('dicty', 'dicty.gene_expression.csv.gz'))112    data3, rn3, cn3 = load_source(join('dicty', 'dicty.ppi.csv.gz'))113    print('Gene - GO term')114    print('Data1: ' + str(data1.shape))115    print('Gene - Experiment conditions')116    print('Data2: ' + str(data2.shape))117    print('Gene - Gene')118    print('Data3: ' + str(data3.shape))119    print()120    print('Subset (data1 - data2): ' + str(len(set(rn1) & set(rn2))))121    print('Subset (data1 - data3): ' + str(len(set(rn1) & set(rn3))))122    print('Subset (data2 - data3): ' + str(len(set(rn2) & set(rn3))))123    print('Subset (data1 - data2 - data3): ' + str(len(set(rn1) & set(rn2) & set(rn3))))124def show_data_pharma():125    data1, rn1, cn1 = load_source(join('pharma', 'pharma.actions.csv.gz'))126    data2, rn2, cn2 = load_source(join('pharma', 'pharma.pubmed.csv.gz'))127    data3, rn3, cn3 = load_source(join('pharma', 'pharma.depositors.csv.gz'))128    data4, rn4, cn4 = load_source(join('pharma', 'pharma.fingerprints.csv.gz'))129    data5, rn5, cn5 = load_source(join('pharma', 'pharma.depo_cats.csv.gz'))130    data6, rn6, cn6 = load_source(join('pharma', 'pharma.tanimoto.csv.gz'))131    print("Chemical - Action")132    print('Data1: ' + str(data1.shape))133    print('Chemical - PMID')134    print('Data2: ' + str(data2.shape))135    print('Chemical - Depositor')136    print('Data3: ' + str(data3.shape))137    print('Chemical - Fingerprint')138    print('Data4: ' + str(data4.shape))139    print('Depositor - Depositor cateoory')140    print('Data5: ' + str(data5.shape))141    print('Chemical - chemical')142    print('Data6: ' + str(data6.shape))143    print()144    print('Subset(data1 - data2): ' + str(len(set(rn1) & set(rn2))))...

test_conf.py

Source:test_conf.py

...3import os4from mock import Mock5from thefuck import const6@pytest.fixture7def load_source(mocker):8    return mocker.patch('thefuck.conf.load_source')9def test_settings_defaults(load_source, settings):10    load_source.return_value = object()11    settings.init()12    for key, val in const.DEFAULT_SETTINGS.items():13        assert getattr(settings, key) == val14class TestSettingsFromFile(object):15    def test_from_file(self, load_source, settings):16        load_source.return_value = Mock(rules=['test'],17                                        wait_command=10,18                                        require_confirmation=True,19                                        no_colors=True,20                                        priority={'vim': 100},21                                        exclude_rules=['git'])...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.