def test_partial_directed_coherence():
c = Connectivity(fourier_coefficients=np.empty((1, )))
type(c)._MVAR_Fourier_coefficients = PropertyMock(
return_value=np.arange(1, 5).reshape((2, 2)))
pdc = c.partial_directed_coherence()
assert np.allclose(pdc.sum(axis=-2), 1.0)
assert np.all((pdc >= 0.0) & (pdc <= 1.0))
def test_pairwise_phase_consistency():
'''Test that incoherent signals are set to zero or below
and that differences in power are ignored.'''
np.random.seed(0)
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 200, 5,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
magnitude1 = np.random.uniform(
0.5, 3, (n_time_samples, n_trials, n_tapers, n_fft_samples))
angles1 = np.random.uniform(
0, 2 * np.pi, (n_time_samples, n_trials, n_tapers, n_fft_samples))
magnitude2 = np.random.uniform(
0.5, 3, (n_time_samples, n_trials, n_tapers, n_fft_samples))
angles2 = np.random.uniform(
0, 2 * np.pi, (n_time_samples, n_trials, n_tapers, n_fft_samples))
fourier_coefficients[..., 0] = magnitude1 * np.exp(1j * angles1)
fourier_coefficients[..., 1] = magnitude2 * np.exp(1j * angles2)
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
ppc = this_Conn.pairwise_phase_consistency()
# set diagonal to zero because its always 1
diagonal_ind = np.arange(0, n_signals)
ppc[..., diagonal_ind, diagonal_ind] = 0
assert np.all(ppc < np.finfo(float).eps)
def test_directed_transfer_function():
c = Connectivity(fourier_coefficients=np.empty((1, )))
type(c)._transfer_function = PropertyMock(
return_value=np.arange(1, 5).reshape((2, 2)))
dtf = c.directed_transfer_function()
assert np.allclose(dtf.sum(axis=-1), 1.0)
assert np.all((dtf >= 0.0) & (dtf <= 1.0))
def test_weighted_phase_lag_index_sets_zero_phase_signals_to_zero():
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., :] = [2 * np.exp(1j * 0), 3 * np.exp(1j * 0)]
expected_phase_lag_index = np.zeros((2, 2))
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(this_Conn.weighted_phase_lag_index().squeeze(),
expected_phase_lag_index)
def test_weighted_phase_lag_index_is_same_as_phase_lag_index():
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., :] = [
1 * np.exp(1j * 3 * np.pi / 4), 1 * np.exp(1j * 5 * np.pi / 4)
]
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(this_Conn.phase_lag_index(),
this_Conn.weighted_phase_lag_index())
def test_imaginary_coherence():
'''Test that imaginary coherence sets signals with the same phase
to zero.'''
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., :] = [2 * np.exp(1j * 0), 3 * np.exp(1j * 0)]
expected_imaginary_coherence = np.zeros((2, 2))
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(this_Conn.imaginary_coherence().squeeze(),
expected_imaginary_coherence)
def test_phase_locking_value():
'''Make sure phase locking value ignores magnitudes.'''
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = (np.random.uniform(
0, 2, (n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals)) *
np.exp(1j * np.pi / 2))
expected_phase_locking_value_magnitude = np.ones(
fourier_coefficients.shape)
expected_phase_locking_value_angle = np.zeros(fourier_coefficients.shape)
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(np.abs(this_Conn.phase_locking_value()),
expected_phase_locking_value_magnitude)
assert np.allclose(np.angle(this_Conn.phase_locking_value()),
expected_phase_locking_value_angle)
def test_cross_spectrum(axis):
'''Test that the cross spectrum is correct for each dimension.'''
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (2, 2, 2, 2,
2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
signal_fourier_coefficient = [
2 * np.exp(1j * np.pi / 2), 3 * np.exp(1j * -np.pi / 2)
]
fourier_ind = [slice(0, 4)] * 5
fourier_ind[-1] = slice(None)
fourier_ind[axis] = slice(1, 2)
fourier_coefficients[fourier_ind] = signal_fourier_coefficient
expected_cross_spectral_matrix = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals,
n_signals),
dtype=np.complex)
expected_slice = np.array([[4, -6], [-6, 9]], dtype=np.complex)
expected_ind = [slice(0, 5)] * 6
expected_ind[-1] = slice(None)
expected_ind[-2] = slice(None)
expected_ind[axis] = slice(1, 2)
expected_cross_spectral_matrix[expected_ind] = expected_slice
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(expected_cross_spectral_matrix,
this_Conn._cross_spectral_matrix)
def test_power():
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 1, 1, 1,
2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., :] = [
2 * np.exp(1j * np.pi / 2), 3 * np.exp(1j * -np.pi / 2)
]
expected_power = np.zeros((n_time_samples, n_fft_samples, n_signals))
expected_power[..., :] = [4, 9]
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(expected_power, this_Conn.power())
def connectivity_to_xarray(m, method='coherence_magnitude', signal_names=None,
squeeze=False, **kwargs):
"""
calculate connectivity using `method`. Returns an xarray
with dimensions of ['Time', 'Frequency', 'Source', 'Target']
or ['Time', 'Frequency'] if squeeze=True
Parameters
-----------
signal_names : iterable of strings
Sames of time series used to name the 'Source' and 'Target' axes of
xarray.
squeeze : bool
Whether to only take the first and last source and target time series.
Only makes sense for one pair of signals and symmetrical measures
"""
if ((method in ['power', 'group_delay', 'canonical_coherence']) or ('directed' in method)):
raise NotImplementedError(f'{method} is not supported by xarray interface')
connectivity = Connectivity.from_multitaper(m)
if method == 'canonical_coherence':
connectivity_mat, labels = getattr(connectivity, method)(**kwargs)
else:
connectivity_mat = getattr(connectivity, method)(**kwargs)
# Only one couple (only makes sense for symmetrical metrics)
if (m.time_series.shape[-1] == 2) and squeeze:
logger.warning(
f'Squeeze is on, but there are {m.time_series.shape[-1]} pairs!')
connectivity_mat = connectivity_mat[:, :, 0, -1]
xar = xr.DataArray(connectivity_mat,
coords=[connectivity.time,
connectivity.frequencies],
dims=['Time', 'Frequency'])
else: # Name the source and target axes
if signal_names is None:
signal_names = np.arange(m.time_series.shape[-1])
xar = xr.DataArray(connectivity_mat,
coords=[connectivity.time, connectivity.frequencies,
signal_names, signal_names],
dims=['Time', 'Frequency', 'Source', 'Target'])
xar.name = method
for attr in dir(m):
if (attr[0] == '_') or (attr == 'time_series'):
continue
# If we don't add 'mt_', get:
# TypeError: '.dt' accessor only available for DataArray with
# datetime64 timedelta64 dtype
# or for arrays containing cftime datetime objects.
xar.attrs['mt_' + attr] = getattr(m, attr)
return xar
def test_n_observations(expectation_type, expected_n_observations):
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 2, 3, 4,
5)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
this_Conn = Connectivity(
fourier_coefficients=fourier_coefficients,
expectation_type=expectation_type,
)
assert this_Conn.n_observations == expected_n_observations
def test_debiased_squared_phase_lag_index():
'''Test that incoherent signals are set to zero or below.'''
np.random.seed(0)
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 200, 5,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
angles1 = np.random.uniform(
0, 2 * np.pi, (n_time_samples, n_trials, n_tapers, n_fft_samples))
angles2 = np.random.uniform(
0, 2 * np.pi, (n_time_samples, n_trials, n_tapers, n_fft_samples))
fourier_coefficients[..., 0] = np.exp(1j * angles1)
fourier_coefficients[..., 1] = np.exp(1j * angles2)
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.all(
this_Conn.debiased_squared_phase_lag_index() < np.finfo(float).eps)
def test_phase_lag_index_sets_angles_up_to_pi_to_same_value():
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., 0] = (np.random.uniform(
0.1, 2, (n_time_samples, n_trials, n_tapers, n_fft_samples)) *
np.exp(1j * np.pi / 2))
fourier_coefficients[..., 1] = (np.random.uniform(
0.1, 2, (n_time_samples, n_trials, n_tapers, n_fft_samples)) *
np.exp(1j * np.pi / 4))
expected_phase_lag_index = np.zeros((2, 2))
expected_phase_lag_index[0, 1] = 1
expected_phase_lag_index[1, 0] = -1
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
assert np.allclose(this_Conn.phase_lag_index().squeeze(),
expected_phase_lag_index)
def test_coherency():
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 30, 1,
1, 2)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
fourier_coefficients[..., :] = [
2 * np.exp(1j * np.pi / 2), 3 * np.exp(1j * -np.pi / 2)
]
this_Conn = Connectivity(fourier_coefficients=fourier_coefficients)
expected_coherence_magnitude = np.array([[np.nan, 1], [1, np.nan]])
expected_phase = np.zeros((2, 2)) * np.nan
expected_phase[0, 1] = np.pi
expected_phase[1, 0] = -np.pi
assert np.allclose(np.abs(this_Conn.coherency().squeeze()),
expected_coherence_magnitude,
equal_nan=True)
assert np.allclose(np.angle(this_Conn.coherency().squeeze()),
expected_phase,
equal_nan=True)
def test_expectation(expectation_type, expected_shape):
n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals = (1, 2, 3, 4,
5)
fourier_coefficients = np.zeros(
(n_time_samples, n_trials, n_tapers, n_fft_samples, n_signals),
dtype=np.complex)
this_Conn = Connectivity(
fourier_coefficients=fourier_coefficients,
expectation_type=expectation_type,
)
expectation_function = this_Conn._expectation
assert np.allclose(expected_shape,
expectation_function(fourier_coefficients).shape)
