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test_spectral.py
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import os
import numpy as np
from numpy.testing import (assert_allclose, assert_array_almost_equal,
assert_array_less)
import pandas as pd
import pytest
from mne import (EpochsArray, SourceEstimate, create_info)
from mne.filter import filter_data
from mne_connectivity import (
SpectralConnectivity, spectral_connectivity_epochs,
read_connectivity, spectral_connectivity_time)
from mne_connectivity.spectral.epochs import _CohEst, _get_n_epochs
from mne_connectivity.spectral.epochs import (
_compute_freq_mask, _compute_freqs)
def create_test_dataset(sfreq, n_signals, n_epochs, n_times, tmin, tmax,
fstart, fend, trans_bandwidth=2.):
"""Create test dataset with no spurious correlations.
Parameters
----------
sfreq : float
The simulated data sampling rate.
n_signals : int
The number of channels/signals to simulate.
n_epochs : int
The number of Epochs to simulate.
n_times : int
The number of time points at which the Epoch data is "sampled".
tmin : int
The start time of the Epoch data.
tmax : int
The end time of the Epoch data.
fstart : int
The frequency at which connectivity starts. The lower end of the
spectral connectivity.
fend : int
The frequency at which connectivity ends. The upper end of the
spectral connectivity.
trans_bandwidth : int, optional
The bandwidth of the filtering operation, by default 2.
Returns
-------
data : np.ndarray of shape (n_epochs, n_signals, n_times)
The epoched dataset.
times_data : np.ndarray of shape (n_times, )
The times at which each sample of the ``data`` occurs at.
"""
# Use a case known to have no spurious correlations (it would bad if
# tests could randomly fail):
rng = np.random.RandomState(0)
data = rng.randn(n_signals, n_epochs * n_times)
times_data = np.linspace(tmin, tmax, n_times)
# simulate connectivity from fstart to fend
data[1, :] = filter_data(data[0, :], sfreq, fstart, fend,
filter_length='auto', fir_design='firwin2',
l_trans_bandwidth=trans_bandwidth,
h_trans_bandwidth=trans_bandwidth)
# add some noise, so the spectrum is not exactly zero
data[1, :] += 1e-2 * rng.randn(n_times * n_epochs)
data = data.reshape(n_signals, n_epochs, n_times)
data = np.transpose(data, [1, 0, 2])
return data, times_data
def _stc_gen(data, sfreq, tmin, combo=False):
"""Simulate a SourceEstimate generator."""
vertices = [np.arange(data.shape[1]), np.empty(0)]
for d in data:
if not combo:
stc = SourceEstimate(data=d, vertices=vertices,
tmin=tmin, tstep=1 / float(sfreq))
yield stc
else:
# simulate a combination of array and source estimate
arr = d[0]
stc = SourceEstimate(data=d[1:], vertices=vertices,
tmin=tmin, tstep=1 / float(sfreq))
yield (arr, stc)
@pytest.mark.parametrize('method', ['coh', 'cohy', 'imcoh', 'plv'])
@pytest.mark.parametrize('mode', ['multitaper', 'fourier', 'cwt_morlet'])
def test_spectral_connectivity_parallel(method, mode, tmp_path):
"""Test saving spectral connectivity with parallel functions."""
# Use a case known to have no spurious correlations (it would bad if
# tests could randomly fail):
rng = np.random.RandomState(0)
trans_bandwidth = 2.
sfreq = 50.
n_signals = 3
n_epochs = 8
n_times = 256
n_jobs = 2 # test with parallelization
data = rng.randn(n_signals, n_epochs * n_times)
# simulate connectivity from 5Hz..15Hz
fstart, fend = 5.0, 15.0
data[1, :] = filter_data(data[0, :], sfreq, fstart, fend,
filter_length='auto', fir_design='firwin2',
l_trans_bandwidth=trans_bandwidth,
h_trans_bandwidth=trans_bandwidth)
# add some noise, so the spectrum is not exactly zero
data[1, :] += 1e-2 * rng.randn(n_times * n_epochs)
data = data.reshape(n_signals, n_epochs, n_times)
data = np.transpose(data, [1, 0, 2])
# define some frequencies for cwt
cwt_freqs = np.arange(3, 24.5, 1)
if method == 'coh' and mode == 'multitaper':
# only check adaptive estimation for coh to reduce test time
check_adaptive = [False, True]
else:
check_adaptive = [False]
if method == 'coh' and mode == 'cwt_morlet':
# so we also test using an array for num cycles
cwt_n_cycles = 7. * np.ones(len(cwt_freqs))
else:
cwt_n_cycles = 7.
for adaptive in check_adaptive:
if adaptive:
mt_bandwidth = 1.
else:
mt_bandwidth = None
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=None, sfreq=sfreq,
mt_adaptive=adaptive, mt_low_bias=True,
mt_bandwidth=mt_bandwidth, cwt_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles, n_jobs=n_jobs)
tmp_file = tmp_path / 'temp_file.nc'
con.save(tmp_file)
read_con = read_connectivity(tmp_file)
assert_array_almost_equal(con.get_data(), read_con.get_data())
# split `repr` before the file size (`~23 kB` for example)
a = repr(con).split('~')[0]
b = repr(read_con).split('~')[0]
assert a == b
@pytest.mark.parametrize('method', ['coh', 'cohy', 'imcoh', 'plv',
['ciplv', 'ppc', 'pli', 'pli2_unbiased',
'dpli', 'wpli', 'wpli2_debiased', 'coh']])
@pytest.mark.parametrize('mode', ['multitaper', 'fourier', 'cwt_morlet'])
def test_spectral_connectivity(method, mode):
"""Test frequency-domain connectivity methods."""
sfreq = 50.
n_signals = 3
n_epochs = 8
n_times = 256
trans_bandwidth = 2.
tmin = 0.
tmax = (n_times - 1) / sfreq
# 5Hz..15Hz
fstart, fend = 5.0, 15.0
data, times_data = create_test_dataset(
sfreq, n_signals=n_signals, n_epochs=n_epochs, n_times=n_times,
tmin=tmin, tmax=tmax,
fstart=fstart, fend=fend, trans_bandwidth=trans_bandwidth)
# First we test some invalid parameters:
pytest.raises(ValueError, spectral_connectivity_epochs,
data, method='notamethod')
pytest.raises(ValueError, spectral_connectivity_epochs, data,
mode='notamode')
# test invalid fmin fmax settings
pytest.raises(ValueError, spectral_connectivity_epochs, data, fmin=10,
fmax=10 + 0.5 * (sfreq / float(n_times)))
pytest.raises(ValueError, spectral_connectivity_epochs,
data, fmin=10, fmax=5)
pytest.raises(ValueError, spectral_connectivity_epochs, data, fmin=(0, 11),
fmax=(5, 10))
pytest.raises(ValueError, spectral_connectivity_epochs, data, fmin=(11,),
fmax=(12, 15))
# define some frequencies for cwt
cwt_freqs = np.arange(3, 24.5, 1)
if method == 'coh' and mode == 'multitaper':
# only check adaptive estimation for coh to reduce test time
check_adaptive = [False, True]
else:
check_adaptive = [False]
if method == 'coh' and mode == 'cwt_morlet':
# so we also test using an array for num cycles
cwt_n_cycles = 7. * np.ones(len(cwt_freqs))
else:
cwt_n_cycles = 7.
for adaptive in check_adaptive:
if adaptive:
mt_bandwidth = 1.
else:
mt_bandwidth = None
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=None, sfreq=sfreq,
mt_adaptive=adaptive, mt_low_bias=True,
mt_bandwidth=mt_bandwidth, cwt_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
if isinstance(method, list):
this_con = con[0]
else:
this_con = con
freqs = this_con.attrs.get('freqs_used')
n = this_con.n_epochs_used
if isinstance(this_con, SpectralConnectivity):
times = this_con.attrs.get('times_used')
else:
times = this_con.times
assert (n == n_epochs)
assert_array_almost_equal(times_data, times)
if mode == 'multitaper':
upper_t = 0.95
lower_t = 0.5
else: # mode == 'fourier' or mode == 'cwt_morlet'
# other estimates have higher variance
upper_t = 0.8
lower_t = 0.75
# test the simulated signal
gidx = np.searchsorted(freqs, (fstart, fend))
bidx = np.searchsorted(freqs,
(fstart - trans_bandwidth * 2,
fend + trans_bandwidth * 2))
if method == 'coh':
assert np.all(
con.get_data(output='dense')[
1, 0, gidx[0]:gidx[1]
] > upper_t), \
con.get_data()[
1, 0, gidx[0]:gidx[1]].min()
# we see something for zero-lag
assert_array_less(
con.get_data(output='dense')
[1, 0, :bidx[0]],
lower_t)
assert np.all(
con.get_data(output='dense')[1, 0, bidx[1]:] < lower_t), \
con.get_data()[1, 0, bidx[1:]].max()
elif method == 'cohy':
# imaginary coh will be zero
check = np.imag(con.get_data(output='dense')
[1, 0, gidx[0]:gidx[1]])
assert np.all(check < lower_t), check.max()
# we see something for zero-lag
assert_array_less(
upper_t,
np.abs(con.get_data(output='dense')[
1, 0, gidx[0]:gidx[1]
]))
assert_array_less(
np.abs(con.get_data(output='dense')[1, 0, :bidx[0]]),
lower_t)
assert_array_less(
np.abs(con.get_data(output='dense')[1, 0, bidx[1]:]),
lower_t)
elif method == 'imcoh':
# imaginary coh will be zero
assert_array_less(
con.get_data(output='dense')[1, 0, gidx[0]:gidx[1]],
lower_t)
assert_array_less(
con.get_data(output='dense')[1, 0, :bidx[0]],
lower_t)
assert_array_less(
con.get_data(output='dense')[1, 0, bidx[1]:], lower_t),
assert np.all(
con.get_data(output='dense')[1, 0, bidx[1]:] < lower_t), \
con.get_data()[1, 0, bidx[1]:].max()
# compute a subset of connections using indices and 2 jobs
indices = (np.array([2, 1]), np.array([0, 0]))
if not isinstance(method, list):
test_methods = (method, _CohEst)
else:
test_methods = method
stc_data = _stc_gen(data, sfreq, tmin)
con2 = spectral_connectivity_epochs(
stc_data, method=test_methods, mode=mode, indices=indices,
sfreq=sfreq, mt_adaptive=adaptive, mt_low_bias=True,
mt_bandwidth=mt_bandwidth, tmin=tmin, tmax=tmax,
cwt_freqs=cwt_freqs, cwt_n_cycles=cwt_n_cycles)
assert isinstance(con2, list)
assert len(con2) == len(test_methods)
freqs2 = con2[0].attrs.get('freqs_used')
if 'times' in con2[0].dims:
times2 = con2[0].times
else:
times2 = con2[0].attrs.get('times_used')
n2 = con2[0].n_epochs_used
if method == 'coh':
assert_array_almost_equal(con2[0].get_data(), con2[1].get_data())
if not isinstance(method, list):
con2 = con2[0] # only keep the first method
# we get the same result for the probed connections
assert_array_almost_equal(freqs, freqs2)
# "con2" is a raveled array already, so
# simulate setting indices on the full output in "con"
assert_array_almost_equal(con.get_data(output='dense')[indices],
con2.get_data())
assert (n == n2)
assert_array_almost_equal(times_data, times2)
else:
# we get the same result for the probed connections
assert (len(con) == len(con2))
for c, c2 in zip(con, con2):
assert_array_almost_equal(freqs, freqs2)
assert_array_almost_equal(c.get_data(output='dense')[indices],
c2.get_data())
assert (n == n2)
assert_array_almost_equal(times_data, times2)
# Test with faverage
# compute same connections for two bands, fskip=1, and f. avg.
fmin = (5., 15.)
fmax = (15., 30.)
con3 = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices,
sfreq=sfreq, fmin=fmin, fmax=fmax, fskip=1, faverage=True,
mt_adaptive=adaptive, mt_low_bias=True,
mt_bandwidth=mt_bandwidth, cwt_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
if isinstance(method, list):
freqs3 = con3[0].attrs.get('freqs_used')
else:
freqs3 = con3.attrs.get('freqs_used')
assert (isinstance(freqs3, list))
assert (len(freqs3) == len(fmin))
for i in range(len(freqs3)):
_fmin = max(fmin[i], min(cwt_freqs))
_fmax = min(fmax[i], max(cwt_freqs))
assert_allclose(freqs3[i][0], _fmin, atol=1)
assert_allclose(freqs3[i][1], _fmax, atol=1)
# average con2 "manually" and we get the same result
fskip = 1
if not isinstance(method, list):
for i in range(len(freqs3)):
# now we want to get the frequency indices
# create a frequency mask for all bands
n_times = len(con2.attrs.get('times_used'))
# compute frequencies to analyze based on number of samples,
# sampling rate, specified wavelet frequencies and mode
freqs = _compute_freqs(n_times, sfreq, cwt_freqs, mode)
# compute the mask based on specified min/max and decim factor
freq_mask = _compute_freq_mask(
freqs, [fmin[i]], [fmax[i]], fskip)
freqs = freqs[freq_mask]
freqs_idx = np.searchsorted(freqs2, freqs)
con2_avg = np.mean(con2.get_data()[:, freqs_idx], axis=1)
assert_array_almost_equal(con2_avg, con3.get_data()[:, i])
else:
for j in range(len(con2)):
for i in range(len(freqs3)):
# now we want to get the frequency indices
# create a frequency mask for all bands
n_times = len(con2[0].attrs.get('times_used'))
# compute frequencies to analyze based on number of
# samples, sampling rate, specified wavelet frequencies
# and mode
freqs = _compute_freqs(n_times, sfreq, cwt_freqs, mode)
# compute the mask based on specified min/max and
# decim factor
freq_mask = _compute_freq_mask(
freqs, [fmin[i]], [fmax[i]], fskip)
freqs = freqs[freq_mask]
freqs_idx = np.searchsorted(freqs2, freqs)
con2_avg = np.mean(con2[j].get_data()[
:, freqs_idx], axis=1)
assert_array_almost_equal(
con2_avg, con3[j].get_data()[:, i])
# test _get_n_epochs
full_list = list(range(10))
out_lens = np.array([len(x) for x in _get_n_epochs(full_list, 4)])
assert ((out_lens == np.array([4, 4, 2])).all())
out_lens = np.array([len(x) for x in _get_n_epochs(full_list, 11)])
assert (len(out_lens) > 0)
assert (out_lens[0] == 10)
@pytest.mark.parametrize('method', ['mic', 'mim', 'gc'])
def test_spectral_connectivity_epochs_multivariate(method):
"""Test over-epoch multivariate connectivity methods."""
mode = 'multitaper' # stick with single mode in interest of time
sfreq = 100.0 # Hz
n_signals = 4 # should be even!
n_seeds = n_signals // 2
n_epochs = 10
n_times = 200 # samples
trans_bandwidth = 2.0 # Hz
delay = 10 # samples (non-zero delay needed for ImCoh and GC to be >> 0)
indices = (np.arange(n_seeds)[np.newaxis, :],
np.arange(n_seeds)[np.newaxis, :] + n_seeds)
n_targets = n_seeds
# 15-25 Hz connectivity
fstart, fend = 15.0, 25.0
rng = np.random.RandomState(0)
data = rng.randn(n_signals, n_epochs * n_times + delay)
# simulate connectivity from fstart to fend
data[n_seeds:, :] = filter_data(
data[:n_seeds, :], sfreq, fstart, fend, filter_length='auto',
fir_design='firwin2', l_trans_bandwidth=trans_bandwidth,
h_trans_bandwidth=trans_bandwidth)
# add some noise, so the spectrum is not exactly zero
data[n_seeds:, :] += 1e-2 * rng.randn(n_seeds, n_times * n_epochs + delay)
# shift the seeds to that the targets are a delayed version of them
data[:n_seeds, :n_epochs * n_times] = data[:n_seeds, delay:]
data = data[:, :n_times * n_epochs]
data = data.reshape(n_signals, n_epochs, n_times)
data = np.transpose(data, [1, 0, 2])
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices, sfreq=sfreq,
gc_n_lags=20)
freqs = con.freqs
gidx = (freqs.index(fstart), freqs.index(fend) + 1)
bidx = (freqs.index(fstart - trans_bandwidth * 2),
freqs.index(fend + trans_bandwidth * 2) + 1)
if method in ['mic', 'mim']:
lower_t = 0.2
upper_t = 0.5
assert np.abs(con.get_data())[0, gidx[0]:gidx[1]].mean() > upper_t
assert np.abs(con.get_data())[0, :bidx[0]].mean() < lower_t
assert np.abs(con.get_data())[0, bidx[1]:].mean() < lower_t
elif method == 'gc':
lower_t = 0.2
upper_t = 0.8
assert con.get_data()[0, gidx[0]:gidx[1]].mean() > upper_t
assert con.get_data()[0, :bidx[0]].mean() < lower_t
assert con.get_data()[0, bidx[1]:].mean() < lower_t
# check that target -> seed connectivity is low
indices_ts = (indices[1], indices[0])
con_ts = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices_ts, sfreq=sfreq,
gc_n_lags=20)
assert con_ts.get_data()[0, gidx[0]:gidx[1]].mean() < lower_t
# check that TRGC is positive (i.e. net seed -> target connectivity not
# due to noise)
con_tr = spectral_connectivity_epochs(
data, method='gc_tr', mode=mode, indices=indices, sfreq=sfreq,
gc_n_lags=20)
con_ts_tr = spectral_connectivity_epochs(
data, method='gc_tr', mode=mode, indices=indices_ts, sfreq=sfreq,
gc_n_lags=20)
trgc = ((con.get_data() - con_ts.get_data()) -
(con_tr.get_data() - con_ts_tr.get_data()))
# checks that TRGC is positive and >> 0 (for 15-25 Hz)
assert np.all(trgc[0, gidx[0]:gidx[1]] > 0)
assert np.all(trgc[0, gidx[0]:gidx[1]] > upper_t)
# checks that TRGC is ~ 0 for other frequencies
assert np.allclose(trgc[0, :bidx[0]].mean(), 0, atol=lower_t)
assert np.allclose(trgc[0, bidx[1]:].mean(), 0, atol=lower_t)
# check all-to-all conn. computed for MIC/MIM when no indices given
if method in ['mic', 'mim']:
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=None, sfreq=sfreq)
assert con.indices is None
assert con.n_nodes == n_signals
if method == 'mic':
assert np.array(con.attrs['patterns']).shape[2] == n_signals
# check ragged indices padded correctly
ragged_indices = (np.array([[0]]), np.array([[1, 2]]))
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=ragged_indices, sfreq=sfreq)
assert np.all(np.array(con.indices) ==
np.array([np.array([[0, -1]]), np.array([[1, 2]])]))
# check shape of MIC patterns
if method == 'mic':
for mode in ['multitaper', 'cwt_morlet']:
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices, sfreq=sfreq,
fmin=10, fmax=25, cwt_freqs=np.arange(10, 25),
faverage=True)
if mode == 'cwt_morlet':
patterns_shape = (
(n_seeds, len(con.freqs), len(con.times)),
(n_targets, len(con.freqs), len(con.times)))
else:
patterns_shape = (
(n_seeds, len(con.freqs)),
(n_targets, len(con.freqs)))
assert np.shape(con.attrs["patterns"][0][0]) == patterns_shape[0]
assert np.shape(con.attrs["patterns"][1][0]) == patterns_shape[1]
# only check these once for speed
if mode == 'multitaper':
# check patterns averaged over freqs
fmin = (5., 15.)
fmax = (15., 30.)
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices,
sfreq=sfreq, fmin=fmin, fmax=fmax, faverage=True)
assert np.shape(con.attrs["patterns"][0][0])[1] == len(fmin)
assert np.shape(con.attrs["patterns"][1][0])[1] == len(fmin)
# check patterns shape matches input data, not rank
rank = (np.array([1]), np.array([1]))
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices,
sfreq=sfreq, rank=rank)
assert (np.shape(con.attrs["patterns"][0][0])[0] == n_seeds)
assert (np.shape(con.attrs["patterns"][1][0])[0] == n_targets)
# check patterns padded correctly
ragged_indices = (np.array([[0]]), np.array([[1, 2]]))
con = spectral_connectivity_epochs(
data, method=method, mode=mode, indices=ragged_indices,
sfreq=sfreq)
patterns = np.array(con.attrs["patterns"])
patterns_shape = (
(n_seeds, len(con.freqs)), (n_targets, len(con.freqs)))
assert patterns[0, 0].shape == patterns_shape[0]
assert patterns[1, 0].shape == patterns_shape[1]
assert not np.any(np.isnan(patterns[0, 0, 0]))
assert np.all(np.isnan(patterns[0, 0, 1]))
assert not np.any(np.isnan(patterns[1, 0]))
def test_multivariate_spectral_connectivity_epochs_regression():
"""Test multivar. spectral connectivity over epochs for regression.
The multivariate methods were originally implemented in MATLAB by their
respective authors. To show that this Python implementation is identical
and to avoid any future regressions, we compare the results of the Python
and MATLAB implementations on some example data (randomly generated).
As the MNE code for computing the cross-spectral density matrix is not
available in MATLAB, the CSD matrix was computed using MNE and then loaded
into MATLAB to compute the connectivity from the original implementations
using the same processing settings in MATLAB and Python.
It is therefore important that no changes are made to the settings for
computing the CSD or the final connectivity scores!
"""
fpath = os.path.dirname(os.path.realpath(__file__))
data = pd.read_pickle(
os.path.join(fpath, 'data', 'example_multivariate_data.pkl'))
sfreq = 100
indices = (np.array([[0, 1]]), np.array([[2, 3]]))
methods = ['mic', 'mim', 'gc', 'gc_tr']
con = spectral_connectivity_epochs(
data, method=methods, indices=indices, mode='multitaper', sfreq=sfreq,
fskip=0, faverage=False, tmin=0, tmax=None, mt_bandwidth=4,
mt_low_bias=True, mt_adaptive=False, gc_n_lags=20,
rank=tuple([[2], [2]]), n_jobs=1)
# should take the absolute of the MIC scores, as the MATLAB implementation
# returns the absolute values.
mne_results = {this_con.method: np.abs(this_con.get_data())
for this_con in con}
matlab_results = pd.read_pickle(
os.path.join(fpath, 'data', 'example_multivariate_matlab_results.pkl'))
for method in methods:
assert_allclose(matlab_results[method], mne_results[method], 1e-5)
@pytest.mark.parametrize(
'method', ['mic', 'mim', 'gc', 'gc_tr', ['mic', 'mim', 'gc', 'gc_tr']])
@pytest.mark.parametrize('mode', ['multitaper', 'fourier', 'cwt_morlet'])
def test_multivar_spectral_connectivity_epochs_error_catch(method, mode):
"""Test error catching for multivar. freq.-domain connectivity methods."""
sfreq = 50.
n_signals = 4 # Do not change!
n_epochs = 8
n_times = 256
rng = np.random.RandomState(0)
data = rng.randn(n_epochs, n_signals, n_times)
indices = (np.array([[0, 1]]), np.array([[2, 3]]))
cwt_freqs = np.arange(10, 25 + 1)
# check bad indices without nested array caught
with pytest.raises(TypeError,
match='multivariate indices must contain array-likes'):
non_nested_indices = (np.array([0, 1]), np.array([2, 3]))
spectral_connectivity_epochs(
data, method=method, mode=mode, indices=non_nested_indices,
sfreq=sfreq, gc_n_lags=10)
# check bad indices with repeated channels caught
with pytest.raises(ValueError,
match='multivariate indices cannot contain repeated'):
repeated_indices = (np.array([[0, 1, 1]]), np.array([[2, 2, 3]]))
spectral_connectivity_epochs(
data, method=method, mode=mode, indices=repeated_indices,
sfreq=sfreq, gc_n_lags=10)
# check mixed methods caught
with pytest.raises(ValueError,
match='bivariate and multivariate connectivity'):
if isinstance(method, str):
mixed_methods = [method, 'coh']
elif isinstance(method, list):
mixed_methods = [*method, 'coh']
spectral_connectivity_epochs(data, method=mixed_methods, mode=mode,
indices=indices, sfreq=sfreq,
cwt_freqs=cwt_freqs)
# check bad rank args caught
too_low_rank = (np.array([0]), np.array([0]))
with pytest.raises(ValueError,
match='ranks for seeds and targets must be'):
spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices,
sfreq=sfreq, rank=too_low_rank, cwt_freqs=cwt_freqs)
too_high_rank = (np.array([3]), np.array([3]))
with pytest.raises(ValueError,
match='ranks for seeds and targets must be'):
spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices,
sfreq=sfreq, rank=too_high_rank, cwt_freqs=cwt_freqs)
# check rank-deficient data caught
bad_data = data.copy()
bad_data[:, 1] = bad_data[:, 0]
bad_data[:, 3] = bad_data[:, 2]
assert np.all(np.linalg.matrix_rank(bad_data[:, (0, 1), :]) == 1)
assert np.all(np.linalg.matrix_rank(bad_data[:, (2, 3), :]) == 1)
if isinstance(method, str):
rank_con = spectral_connectivity_epochs(
bad_data, method=method, mode=mode, indices=indices, sfreq=sfreq,
gc_n_lags=10, cwt_freqs=cwt_freqs)
assert rank_con.attrs["rank"] == (np.array([1]), np.array([1]))
if method in ['mic', 'mim']:
# check rank-deficient transformation matrix caught
with pytest.raises(RuntimeError,
match='the transformation matrix'):
spectral_connectivity_epochs(
bad_data, method=method, mode=mode, indices=indices,
sfreq=sfreq, rank=(np.array([2]), np.array([2])),
cwt_freqs=cwt_freqs)
# only check these once (e.g. only with multitaper) for speed
if method == 'gc' and mode == 'multitaper':
# check bad n_lags caught
frange = (5, 10)
n_lags = 200 # will be far too high
with pytest.raises(ValueError, match='the number of lags'):
spectral_connectivity_epochs(
data, method=method, mode=mode, indices=indices, sfreq=sfreq,
fmin=frange[0], fmax=frange[1], gc_n_lags=n_lags,
cwt_freqs=cwt_freqs)
# check no indices caught
with pytest.raises(ValueError, match='indices must be specified'):
spectral_connectivity_epochs(data, method=method, mode=mode,
indices=None, sfreq=sfreq,
cwt_freqs=cwt_freqs)
# check intersecting indices caught
bad_indices = (np.array([[0, 1]]), np.array([[0, 2]]))
with pytest.raises(ValueError,
match='seed and target indices must not intersect'):
spectral_connectivity_epochs(data, method=method, mode=mode,
indices=bad_indices, sfreq=sfreq,
cwt_freqs=cwt_freqs)
# check bad fmin/fmax caught
with pytest.raises(ValueError,
match='computing Granger causality on multiple'):
spectral_connectivity_epochs(data, method=method, mode=mode,
indices=indices, sfreq=sfreq,
fmin=(10., 15.), fmax=(15., 20.),
cwt_freqs=cwt_freqs)
# check rank-deficient autocovariance caught
with pytest.raises(RuntimeError,
match='the autocovariance matrix is singular'):
spectral_connectivity_epochs(
bad_data, method=method, mode=mode, indices=indices,
sfreq=sfreq, rank=(np.array([2]), np.array([2])),
cwt_freqs=cwt_freqs)
@pytest.mark.parametrize('method', ['mic', 'mim', 'gc', 'gc_tr'])
def test_multivar_spectral_connectivity_parallel(method):
"""Test multivar. freq.-domain connectivity methods run in parallel."""
sfreq = 50.
n_signals = 4 # Do not change!
n_epochs = 8
n_times = 256
rng = np.random.RandomState(0)
data = rng.randn(n_epochs, n_signals, n_times)
indices = (np.array([[0, 1]]), np.array([[2, 3]]))
spectral_connectivity_epochs(
data, method=method, mode="multitaper", indices=indices, sfreq=sfreq,
gc_n_lags=10, n_jobs=2)
spectral_connectivity_time(
data, freqs=np.arange(10, 25), method=method, mode="multitaper",
indices=indices, sfreq=sfreq, gc_n_lags=10, n_jobs=2)
def test_multivar_spectral_connectivity_flipped_indices():
"""Test multivar. indices structure maintained by connectivity methods."""
sfreq = 50.
n_signals = 4
n_epochs = 8
n_times = 256
rng = np.random.RandomState(0)
data = rng.randn(n_epochs, n_signals, n_times)
freqs = np.arange(10, 20)
# if we're not careful, when finding the channels we need to compute the
# CSD for, we might accidentally reorder the connectivity indices
indices = (np.array([[0, 1]]),
np.array([[2, 3]]))
flipped_indices = (np.array([[2, 3]]),
np.array([[0, 1]]))
concat_indices = (np.array([[0, 1], [2, 3]]),
np.array([[2, 3], [0, 1]]))
# we test on GC since this is a directed connectivity measure
method = 'gc'
con_st = spectral_connectivity_epochs( # seed -> target
data, method=method, indices=indices, sfreq=sfreq, gc_n_lags=10)
con_ts = spectral_connectivity_epochs( # target -> seed
data, method=method, indices=flipped_indices, sfreq=sfreq,
gc_n_lags=10)
con_st_ts = spectral_connectivity_epochs( # seed -> target; target -> seed
data, method=method, indices=concat_indices, sfreq=sfreq, gc_n_lags=10)
assert not np.all(con_st.get_data() == con_ts.get_data())
assert np.all(con_st.get_data()[0] == con_st_ts.get_data()[0])
assert np.all(con_ts.get_data()[0] == con_st_ts.get_data()[1])
con_st = spectral_connectivity_time( # seed -> target
data, freqs, method=method, indices=indices, sfreq=sfreq,
gc_n_lags=10)
con_ts = spectral_connectivity_time( # target -> seed
data, freqs, method=method, indices=flipped_indices, sfreq=sfreq,
gc_n_lags=10)
con_st_ts = spectral_connectivity_time( # seed -> target; target -> seed
data, freqs, method=method, indices=concat_indices, sfreq=sfreq,
gc_n_lags=10)
assert not np.all(con_st.get_data() == con_ts.get_data())
assert np.all(con_st.get_data()[:, 0] == con_st_ts.get_data()[:, 0])
assert np.all(con_ts.get_data()[:, 0] == con_st_ts.get_data()[:, 1])
@ pytest.mark.parametrize('kind', ('epochs', 'ndarray', 'stc', 'combo'))
def test_epochs_tmin_tmax(kind):
"""Test spectral.spectral_connectivity_epochs with epochs and arrays."""
rng = np.random.RandomState(0)
n_epochs, n_chs, n_times, sfreq, f = 10, 2, 2000, 1000., 20.
data = rng.randn(n_epochs, n_chs, n_times)
sig = np.sin(2 * np.pi * f * np.arange(1000) / sfreq) * np.hanning(1000)
data[:, :, 500:1500] += sig
info = create_info(n_chs, sfreq, 'eeg')
if kind == 'epochs':
tmin = -1
X = EpochsArray(data, info, tmin=tmin)
elif kind == 'stc':
tmin = -1
X = [SourceEstimate(d, [[0], [0]], tmin, 1. / sfreq) for d in data]
elif kind == 'combo':
tmin = -1
X = [(d[[0]], SourceEstimate(d[[1]], [[0], []], tmin, 1. / sfreq))
for d in data]
else:
assert kind == 'ndarray'
tmin = 0
X = data
want_times = np.arange(n_times) / sfreq + tmin
# Parameters for computing connectivity
fmin, fmax = f - 2, f + 2
kwargs = {'method': 'coh', 'mode': 'multitaper', 'sfreq': sfreq,
'fmin': fmin, 'fmax': fmax, 'faverage': True,
'mt_adaptive': False, 'n_jobs': 1}
# Check the entire interval
conn = spectral_connectivity_epochs(X, **kwargs)
assert 0.89 < conn.get_data(output='dense')[1, 0] < 0.91
assert_allclose(conn.attrs.get('times_used'), want_times)
# Check a time interval before the sinusoid
conn = spectral_connectivity_epochs(X, tmax=tmin + 0.5, **kwargs)
assert 0 < conn.get_data(output='dense')[1, 0] < 0.15
# Check a time during the sinusoid
conn = spectral_connectivity_epochs(
X, tmin=tmin + 0.5, tmax=tmin + 1.5, **kwargs)
assert 0.93 < conn.get_data(output='dense')[1, 0] <= 0.94
# Check a time interval after the sinusoid
conn = spectral_connectivity_epochs(
X, tmin=tmin + 1.5, tmax=tmin + 1.9, **kwargs)
assert 0 < conn.get_data(output='dense')[1, 0] < 0.15
# Check for warning if tmin, tmax is outside of the time limits of data
with pytest.warns(RuntimeWarning, match='start time tmin'):
spectral_connectivity_epochs(X, **kwargs, tmin=tmin - 0.1)
with pytest.warns(RuntimeWarning, match='stop time tmax'):
spectral_connectivity_epochs(X, **kwargs, tmax=tmin + 2.5)
# make one with mismatched times
if kind != 'combo':
return
X = [(SourceEstimate(d[[0]], [[0], []], tmin - 1, 1. / sfreq),
SourceEstimate(d[[1]], [[0], []], tmin, 1. / sfreq)) for d in data]
with pytest.warns(RuntimeWarning, match='time scales of input') as w:
spectral_connectivity_epochs(X, **kwargs)
assert len(w) == 1 # just one even though there were multiple epochs
@pytest.mark.parametrize(
'method', ['coh', 'mic', 'mim', 'plv', 'pli', 'wpli', 'ciplv'])
@pytest.mark.parametrize('mode', ['cwt_morlet', 'multitaper'])
@pytest.mark.parametrize('data_option', ['sync', 'random'])
def test_spectral_connectivity_time_phaselocked(method, mode, data_option):
"""Test time-resolved spectral connectivity with simulated phase-locked
data."""
rng = np.random.default_rng(0)
n_epochs = 5
n_channels = 3
n_times = 1000
sfreq = 250
data = np.zeros((n_epochs, n_channels, n_times))
if data_option == 'random':
# Data is random, there should be no consistent phase differences.
data = rng.random((n_epochs, n_channels, n_times))
if data_option == 'sync':
# Data consists of phase-locked 10Hz sine waves with constant phase
# difference within each epoch.
wave_freq = 10
epoch_length = n_times / sfreq
for i in range(n_epochs):
for c in range(n_channels):
phase = rng.random() * 10
x = np.linspace(-wave_freq * epoch_length * np.pi + phase,
wave_freq * epoch_length * np.pi + phase,
n_times)
data[i, c] = np.squeeze(np.sin(x))
multivar_methods = ['mic', 'mim']
# the frequency band should contain the frequency at which there is a
# hypothesized "connection"
freq_band_low_limit = (8.)
freq_band_high_limit = (13.)
freqs = np.arange(freq_band_low_limit, freq_band_high_limit + 1)
con = spectral_connectivity_time(
data, freqs, method=method, mode=mode, sfreq=sfreq,
fmin=freq_band_low_limit, fmax=freq_band_high_limit, n_jobs=1,
faverage=True if method != 'mic' else False,
average=True if method != 'mic' else False, sm_times=0)
con_matrix = con.get_data()
# MIC values can be pos. and neg., so must be averaged after taking the
# absolute values for the test to work
if method in multivar_methods:
if method == 'mic':
con_matrix = np.mean(np.abs(con_matrix), axis=(0, 2))
assert con.shape == (n_epochs, 1, len(con.freqs))
else:
assert con.shape == (1, len(con.freqs))
else:
assert con.shape == (n_channels ** 2, len(con.freqs))
con_matrix = np.reshape(con_matrix, (n_channels, n_channels))[
np.tril_indices(n_channels, -1)]
if data_option == 'sync':
# signals are perfectly phase-locked, connectivity matrix should be
# a matrix of ones
assert np.allclose(con_matrix, np.ones(con_matrix.shape), atol=0.01)
if data_option == 'random':
# signals are random, all connectivity values should be small
# 0.5 is picked rather arbitrarily such that the obsolete wrong
# implementation fails
assert np.all(con_matrix <= 0.5)
def test_spectral_connectivity_time_delayed():
"""Test per-epoch Granger causality with time-delayed data.
N.B.: the spectral_connectivity_time method seems to be more unstable than
spectral_connectivity_epochs for GC estimation. Accordingly, we assess
Granger scores only in the context of the noise-corrected TRGC metric,
where the true directionality of the connections seems to identified.
"""
mode = 'multitaper' # stick with single mode in interest of time
sfreq = 100.0 # Hz
n_signals = 4 # should be even!
n_seeds = n_signals // 2
n_epochs = 10
n_times = 200 # samples
trans_bandwidth = 2.0 # Hz
delay = 5 # samples (non-zero delay needed for GC to be >> 0)
indices = (np.array([[0, 1]]), np.array([[2, 3]]))
# 20-30 Hz connectivity
fstart, fend = 20.0, 30.0
rng = np.random.RandomState(0)
data = rng.randn(n_signals, n_epochs * n_times + delay)
# simulate connectivity from fstart to fend
data[n_seeds:, :] = filter_data(
data[:n_seeds, :], sfreq, fstart, fend, filter_length='auto',
fir_design='firwin2', l_trans_bandwidth=trans_bandwidth,
h_trans_bandwidth=trans_bandwidth)
# add some noise, so the spectrum is not exactly zero
data[n_seeds:, :] += 1e-2 * rng.randn(n_seeds, n_times * n_epochs + delay)
# shift the seeds to that the targets are a delayed version of them
data[:n_seeds, :n_epochs * n_times] = data[:n_seeds, delay:]
data = data[:, :n_times * n_epochs]
data = data.reshape(n_signals, n_epochs, n_times)
data = np.transpose(data, [1, 0, 2])
freqs = np.arange(2.5, 50, 0.5)
con_st = spectral_connectivity_time(
data, freqs, method=['gc', 'gc_tr'], indices=indices, mode=mode,
sfreq=sfreq, n_jobs=1, gc_n_lags=20, n_cycles=5, average=True)
con_ts = spectral_connectivity_time(
data, freqs, method=['gc', 'gc_tr'], indices=(indices[1], indices[0]),
mode=mode, sfreq=sfreq, n_jobs=1, gc_n_lags=20, n_cycles=5,
average=True)
st = con_st[0].get_data()
st_tr = con_st[1].get_data()
ts = con_ts[0].get_data()
ts_tr = con_ts[1].get_data()
trgc = (st - ts) - (st_tr - ts_tr)
freqs = con_st[0].freqs
gidx = (freqs.index(fstart), freqs.index(fend) + 1)
bidx = (freqs.index(fstart - trans_bandwidth * 2),
freqs.index(fend + trans_bandwidth * 2) + 1)
# assert that TRGC (i.e. net, noise-corrected connectivity) is positive and
# >> 0 (i.e. that there is indeed a flow of info. from seeds to targets,
# as simulated)
assert np.all(trgc[:, gidx[0]:gidx[1]] > 0)
assert trgc[:, gidx[0]:gidx[1]].mean() > 0.4
# check that non-interacting freqs. have close to zero connectivity
assert np.allclose(trgc[0, :bidx[0]].mean(), 0, atol=0.1)
assert np.allclose(trgc[0, bidx[1]:].mean(), 0, atol=0.1)
@pytest.mark.parametrize('method', ['coh', 'plv', 'pli', 'wpli', 'ciplv'])
@pytest.mark.parametrize(
'freqs', [[8., 10.], [8, 10], 10., 10])
@pytest.mark.parametrize('mode', ['cwt_morlet', 'multitaper'])
def test_spectral_connectivity_time_freqs(method, freqs, mode):
"""Test time-resolved spectral connectivity with int and float values for
freqs."""
rng = np.random.default_rng(0)
n_epochs = 5
n_channels = 3
n_times = 1000
sfreq = 250
data = np.zeros((n_epochs, n_channels, n_times))
# Data consists of phase-locked 10Hz sine waves with constant phase