def test_write_csv(self):
ratios = dm.DataMatrix(2, 2, ['Gene 1', 'Gene 2'], ['Cond 1', 'Cond 2'],
np.array([[1, 2], [3, 4]]))
ratios.write_tsv_file('/tmp/simple_ratios.tsv', compressed=False)
# check the written file
ratios = dm.create_from_csv('/tmp/simple_ratios.tsv')
self.assertEquals(ratios.row_names, ['Gene 1', 'Gene 2'])
self.assertEquals(ratios.column_names, ['Cond 1', 'Cond 2'])
self.assertAlmostEquals(ratios.values[0][0], 1.0)
def read_ratios(params, args_in):
"""reading ratios matrix"""
if params['normalize_ratios']:
if test_data_change(params, args_in) == True:
#Turn off the nochange_filter if you're resuming a run an have changed the data matrix
ratio_filters = [dm.center_scale_filter]
else :
ratio_filters = [dm.nochange_filter, dm.center_scale_filter]
else:
ratio_filters = []
matrix_filename = args_in.ratios
case_sensitive = params['case_sensitive'] or args_in.case_sensitive
return dm.create_from_csv(matrix_filename, filters=ratio_filters, case_sensitive=case_sensitive)
def setUp(self): # pylint; disable-msg=C0103
"""test fixture"""
self.search_distances = {'upstream': (-20, 150)}
self.scan_distances = {'upstream': (-30, 250)}
self.ratio_matrix = dm.create_from_csv('example_data/hal/halo_ratios5.tsv',
filters=[dm.nochange_filter, dm.center_scale_filter])
self.organism = testutil.make_halo(self.search_distances, self.scan_distances,
self.ratio_matrix)
self.config_params = {'memb.min_cluster_rows_allowed': 3,
'memb.max_cluster_rows_allowed': 70,
'multiprocessing': False,
'memb.clusters_per_row': 2,
'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
'num_clusters': 43,
'num_iterations': 2000}
self.membership = self.__read_members() # relies on config_params
self.iteration_result = { 'iteration': 51 }
def setUp(self):
with open('testdata/hsa_mir_thesaurus.json') as infile:
self.synonyms = json.load(infile)
self.ratios = dm.create_from_csv('testdata/acc_rnaseq.tsv.gz', sep='\t')
self.input_genes = self.ratios.row_names
self.config_params = {'SetEnrichment': {'set_types': 'tfbs'},
'SetEnrichment-tfbs': {'set_file': 'testdata/test_sets.json', 'weight': 1.0} }
self.set_types = se.read_set_types(self.config_params, self.synonyms, self.input_genes)
self.canonical_rownames = set(map(lambda n: self.synonyms[n] if n in self.synonyms else n,
self.ratios.row_names))
self.canonical_row_indexes = {}
for index, row in enumerate(self.ratios.row_names):
if row in self.synonyms:
self.canonical_row_indexes[self.synonyms[row]] = index
else:
self.canonical_row_indexes[row] = index
def setUp(self): # pylint; disable-msg=C0103
"""test fixture"""
self.search_distances = {'upstream': (-20, 150)}
self.scan_distances = {'upstream': (-30, 250)}
self.ratio_matrix = dm.create_from_csv('example_data/hal/halo_ratios5.tsv',
filters=[dm.nochange_filter, dm.center_scale_filter])
self.ratios = self.ratio_matrix
self.organism = testutil.make_halo(self.search_distances, self.scan_distances,
self.ratio_matrix)
self.config_params = {'memb.min_cluster_rows_allowed': 3,
'memb.max_cluster_rows_allowed': 70,
'multiprocessing': False,
'num_cores': None,
'memb.clusters_per_row': 2,
'memb.clusters_per_col': int(round(43 * 2.0 / 3.0)),
'num_clusters': 43,
'output_dir': 'out',
'remap_network_nodes': False,
'use_BSCM': False,
'num_iterations': 2000,
'debug': {},
'search_distances': {'upstream': (-20, 150)},
'Columns': {'schedule': lambda i: True },
'Rows': {'schedule': lambda i: True, 'scaling': ('scaling_const', 6.0) },
'Motifs': {'schedule': lambda i: True,
'scaling': ('scaling_rvec', 'seq(0, 1, length=num_iterations*3/4)')},
'MEME': {'version': '4.3.0',
'global_background': False,
'schedule': lambda i: True,
'nmotifs_rvec': 'c(rep(1, num_iterations/3), rep(2, num_iterations/3))',
'max_width': 24, 'arg_mod': 'zoops',
'background_order': 3, 'use_revcomp': 'True'},
'Networks': {'schedule': lambda i: True, 'scaling': ('scaling_rvec', 'seq(1e-5, 0.5, length=num_iterations*3/4)')}}
self.membership = self.__read_members() # relies on config_params
self.iteration_result = { 'iteration': 51, 'score_means': {} }
def read_matrix(filename):
"""reads a matrix file"""
return dm.create_from_csv(filename, filters=[])
def __read_colscores_refresult(self):
return dm.create_from_csv('testdata/column_scores_refresult.tsv', filters=[], case_sensitive=True)
def __read_ratios(self):
return dm.create_from_csv('testdata/row_scores_testratios.tsv', filters=[], case_sensitive=True)
def test_read_csv_numeric_rownames(self):
ratios = dm.create_from_csv('testdata/simple_ratios_numeric_rownames.tsv')
self.assertEquals(['12231245', '12344542'], ratios.row_names)
self.assertEquals(['Cond 1', 'Cond 2'], ratios.column_names)
print(ratios.row_names)
self.assertAlmostEquals(ratios.values[0][0], 54.1)
def test_read_csv(self):
ratios = dm.create_from_csv('testdata/simple_ratios.tsv')
self.assertEquals(ratios.row_names, ['Gene 1', 'Gene 2'])
self.assertEquals(ratios.column_names, ['Cond 1', 'Cond 2'])
self.assertAlmostEquals(ratios.values[0][0], 23.1)
def read_matrix(filename):
"""reads a matrix file"""
return dm.create_from_csv(filename, filters=[])
def read_matrix(filename):
"""reads a matrix file"""
return dm.create_from_csv(filename, filters=[]).sorted_by_row_name()
