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import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true, assert_raises
from mne.fixes import tril_indices
from mne.connectivity import spectral_connectivity
from mne.connectivity.spectral import _CohEst
from mne import SourceEstimate
from mne.filter import band_pass_filter
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)
def test_spectral_connectivity():
"""Test frequency-domain connectivity methods"""
# Use a case known to have no spurious correlations (it would bad if
# nosetests could randomly fail):
np.random.seed(0)
sfreq = 50.
n_signals = 3
n_epochs = 10
n_times = 500
tmin = 0.
tmax = (n_times - 1) / sfreq
data = np.random.randn(n_epochs, n_signals, n_times)
times_data = np.linspace(tmin, tmax, n_times)
# simulate connectivity from 5Hz..15Hz
fstart, fend = 5.0, 15.0
for i in range(n_epochs):
data[i, 1, :] = band_pass_filter(data[i, 0, :], sfreq, fstart, fend)
# add some noise, so the spectrum is not exactly zero
data[i, 1, :] += 1e-2 * np.random.randn(n_times)
# First we test some invalid parameters:
assert_raises(ValueError, spectral_connectivity, data, method='notamethod')
assert_raises(ValueError, spectral_connectivity, data,
mode='notamode')
# test invalid fmin fmax settings
assert_raises(ValueError, spectral_connectivity, data, fmin=10,
fmax=10 + 0.5 * (sfreq / float(n_times)))
assert_raises(ValueError, spectral_connectivity, data, fmin=10, fmax=5)
assert_raises(ValueError, spectral_connectivity, data, fmin=(0, 11),
fmax=(5, 10))
assert_raises(ValueError, spectral_connectivity, data, fmin=(11,),
fmax=(12, 15))
methods = ['coh', 'imcoh', 'cohy', 'plv', 'ppc', 'pli', 'pli2_unbiased',
'wpli', 'wpli2_debiased', 'coh']
modes = ['multitaper', 'fourier', 'cwt_morlet']
# define some frequencies for cwt
cwt_frequencies = np.arange(3, 24.5, 1)
for mode in modes:
for method in methods:
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_frequencies))
else:
cwt_n_cycles = 7.
for adaptive in check_adaptive:
if adaptive:
mt_bandwidth = 1.
else:
mt_bandwidth = None
con, freqs, times, n, _ = spectral_connectivity(data,
method=method, mode=mode,
indices=None, sfreq=sfreq, mt_adaptive=adaptive,
mt_low_bias=True, mt_bandwidth=mt_bandwidth,
cwt_frequencies=cwt_frequencies,
cwt_n_cycles=cwt_n_cycles)
assert_true(n == n_epochs)
assert_array_almost_equal(times_data, times)
if mode == 'multitaper':
upper_t = 0.95
lower_t = 0.5
else:
# other estimates have higher variance
upper_t = 0.8
lower_t = 0.75
# test the simulated signal
if method == 'coh':
idx = np.searchsorted(freqs, (fstart + 1, fend - 1))
# we see something for zero-lag
assert_true(np.all(con[1, 0, idx[0]:idx[1]] > upper_t))
if mode != 'cwt_morlet':
idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
assert_true(np.all(con[1, 0, :idx[0]] < lower_t))
assert_true(np.all(con[1, 0, idx[1]:] < lower_t))
elif method == 'cohy':
idx = np.searchsorted(freqs, (fstart + 1, fend - 1))
# imaginary coh will be zero
assert_true(np.all(np.imag(con[1, 0, idx[0]:idx[1]])
< lower_t))
# we see something for zero-lag
assert_true(np.all(np.abs(con[1, 0, idx[0]:idx[1]])
> upper_t))
idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
if mode != 'cwt_morlet':
assert_true(np.all(np.abs(con[1, 0, :idx[0]])
< lower_t))
assert_true(np.all(np.abs(con[1, 0, idx[1]:])
< lower_t))
elif method == 'imcoh':
idx = np.searchsorted(freqs, (fstart + 1, fend - 1))
# imaginary coh will be zero
assert_true(np.all(con[1, 0, idx[0]:idx[1]] < lower_t))
idx = np.searchsorted(freqs, (fstart - 1, fend + 1))
assert_true(np.all(con[1, 0, :idx[0]] < lower_t))
assert_true(np.all(con[1, 0, idx[1]:] < lower_t))
# compute same connections using indices and 2 jobs,
# also add a second method
indices = tril_indices(n_signals, -1)
test_methods = (method, _CohEst)
combo = True if method == 'coh' else False
stc_data = _stc_gen(data, sfreq, tmin)
con2, freqs2, times2, n2, _ = spectral_connectivity(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_frequencies=cwt_frequencies,
cwt_n_cycles=cwt_n_cycles, n_jobs=2)
assert_true(isinstance(con2, list))
assert_true(len(con2) == 2)
if method == 'coh':
assert_array_almost_equal(con2[0], con2[1])
con2 = con2[0] # only keep the first method
# we get the same result for the probed connections
assert_array_almost_equal(freqs, freqs2)
assert_array_almost_equal(con[indices], con2)
assert_true(n == n2)
assert_array_almost_equal(times_data, times2)
# compute same connections for two bands, fskip=1, and f. avg.
fmin = (5., 15.)
fmax = (15., 30.)
con3, freqs3, times3, n3, _ = spectral_connectivity(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_frequencies=cwt_frequencies,
cwt_n_cycles=cwt_n_cycles)
assert_true(isinstance(freqs3, list))
assert_true(len(freqs3) == len(fmin))
for i in range(len(freqs3)):
assert_true(np.all((freqs3[i] >= fmin[i])
& (freqs3[i] <= fmax[i])))
# average con2 "manually" and we get the same result
for i in range(len(freqs3)):
freq_idx = np.searchsorted(freqs2, freqs3[i])
con2_avg = np.mean(con2[:, freq_idx], axis=1)
assert_array_almost_equal(con2_avg, con3[:, i])
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