def test_fetch_megatrawls_netmats(tmp_path):
# smoke test to see that files are fetched and read properly
# since we are loading data present in it
files_dir = str(tmp_path / 'Megatrawls' /
'3T_Q1-Q6related468_MSMsulc_d100_ts3')
os.makedirs(files_dir)
with open(os.path.join(files_dir, 'Znet2.txt'), 'w') as net_file:
net_file.write("1")
files_dir2 = str(tmp_path / 'Megatrawls' /
'3T_Q1-Q6related468_MSMsulc_d300_ts2')
os.makedirs(files_dir2)
with open(os.path.join(files_dir2, 'Znet1.txt'), 'w') as net_file2:
net_file2.write("1")
megatrawl_netmats_data = func.fetch_megatrawls_netmats(
data_dir=str(tmp_path))
# expected number of returns in output name should be equal
assert len(megatrawl_netmats_data) == 5
# check if returned bunch should not be empty
# dimensions
assert megatrawl_netmats_data.dimensions != ''
# timeseries
assert megatrawl_netmats_data.timeseries != ''
# matrices
assert megatrawl_netmats_data.matrices != ''
# correlation matrices
assert megatrawl_netmats_data.correlation_matrices != ''
# description
assert megatrawl_netmats_data.description != ''
# check if input provided for dimensions, timeseries, matrices to be same
# to user settings
netmats_data = func.fetch_megatrawls_netmats(
data_dir=str(tmp_path),
dimensionality=300,
timeseries='multiple_spatial_regression',
matrices='full_correlation')
assert netmats_data.dimensions == 300
assert netmats_data.timeseries == 'multiple_spatial_regression'
assert netmats_data.matrices == 'full_correlation'
def test_fetch_megatrawls_netmats():
# smoke test to see that files are fetched and read properly
# since we are loading data present in it
files_dir = os.path.join(tst.tmpdir, 'Megatrawls', '3T_Q1-Q6related468_MSMsulc_d100_ts3')
os.makedirs(files_dir)
with open(os.path.join(files_dir, 'Znet2.txt'), 'w') as net_file:
net_file.write("1")
files_dir2 = os.path.join(tst.tmpdir, 'Megatrawls', '3T_Q1-Q6related468_MSMsulc_d300_ts2')
os.makedirs(files_dir2)
with open(os.path.join(files_dir2, 'Znet1.txt'), 'w') as net_file2:
net_file2.write("1")
megatrawl_netmats_data = func.fetch_megatrawls_netmats(data_dir=tst.tmpdir)
# expected number of returns in output name should be equal
assert_equal(len(megatrawl_netmats_data), 5)
# check if returned bunch should not be empty
# dimensions
assert_not_equal(megatrawl_netmats_data.dimensions, '')
# timeseries
assert_not_equal(megatrawl_netmats_data.timeseries, '')
# matrices
assert_not_equal(megatrawl_netmats_data.matrices, '')
# correlation matrices
assert_not_equal(megatrawl_netmats_data.correlation_matrices, '')
# description
assert_not_equal(megatrawl_netmats_data.description, '')
# check if input provided for dimensions, timeseries, matrices to be same
# to user settings
netmats_data = func.fetch_megatrawls_netmats(data_dir=tst.tmpdir,
dimensionality=300,
timeseries='multiple_spatial_regression',
matrices='full_correlation')
assert_equal(netmats_data.dimensions, 300)
assert_equal(netmats_data.timeseries, 'multiple_spatial_regression')
assert_equal(netmats_data.matrices, 'full_correlation')
def test_check_parameters_megatrawls_datasets(request_mocker):
# testing whether the function raises the same error message
# if invalid input parameters are provided
message = "Invalid {0} input is provided: {1}."
for invalid_input_dim in [1, 5, 30]:
with pytest.raises(ValueError,
match=message.format('dimensionality',
invalid_input_dim)):
func.fetch_megatrawls_netmats(dimensionality=invalid_input_dim)
for invalid_input_timeserie in ['asdf', 'time', 'st2']:
with pytest.raises(ValueError,
match=message.format('timeseries',
invalid_input_timeserie)):
func.fetch_megatrawls_netmats(timeseries=invalid_input_timeserie)
for invalid_output_name in ['net1', 'net2']:
with pytest.raises(ValueError,
match=message.format('matrices',
invalid_output_name)):
func.fetch_megatrawls_netmats(matrices=invalid_output_name)
