def test_idxiter():
n_channels = data.shape[0]
# Upper-triangular part, including diag
idx0, idx1 = np.triu_indices(n_channels)
triu_indices = np.array([np.arange(idx0.size), idx0, idx1])
triu_indices2 = np.array(list(_idxiter(n_channels, include_diag=True)))
# Upper-triangular part, without diag
idx2, idx3 = np.triu_indices(n_channels, 1)
triu_indices_nodiag = np.array([np.arange(idx2.size), idx2, idx3])
triu_indices2_nodiag = np.array(list(_idxiter(n_channels,
include_diag=False)))
assert_almost_equal(triu_indices, triu_indices2.transpose())
assert_almost_equal(triu_indices_nodiag, triu_indices2_nodiag.transpose())
# Upper and lower-triangular parts, without diag
expected = [(i, j) for _, (i, j) in
enumerate(np.ndindex((n_channels, n_channels))) if i != j]
assert_equal(np.array([(i, j) for _, i, j in _idxiter(n_channels,
triu=False)]),
expected)
def test_channel_naming_bivariate(selected_func, include_diag):
ch_names = ['CHANNEL%s' % i for i in range(n_channels)]
ch_names[:4] = ['Cz', 'FCz', 'P1', 'CP1']
func_params = {selected_func + '__include_diag': include_diag}
df = extract_features(
data, sfreq, [selected_func], func_params, ch_names=ch_names,
return_as_df=True)
expected_col_names = [
(selected_func, ch_names[i] + '-' + ch_names[j])
for s, i, j in _idxiter(n_channels, include_diag=include_diag)]
assert df.columns.values.tolist() == expected_col_names
def test_channel_naming_pow_freq_bands():
ch_names = ['CHANNEL%s' % i for i in range(n_channels)]
ch_names[:4] = ['Cz', 'FCz', 'P1', 'CP1']
selected_funcs = ['pow_freq_bands']
func_params = {
'pow_freq_bands__freq_bands': np.array([[0, 2], [10, 20]]),
'pow_freq_bands__ratios': 'only'
}
df = extract_features(
data, sfreq, selected_funcs, func_params, ch_names=ch_names,
return_as_df=True)
expected_col_names = [
('pow_freq_bands', f'{ch_name}_band{i}/band{j}')
for ch_name in ch_names for _, i, j in _idxiter(2, triu=False)]
assert df.columns.values.tolist() == expected_col_names