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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'])...

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