def test_spectral_connectivity():
"""Test frequency-domain connectivity methods."""
# 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
tmin = 0.
tmax = (n_times - 1) / sfreq
data = rng.randn(n_signals, n_epochs * n_times)
times_data = np.linspace(tmin, tmax, 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])
# First we test some invalid parameters:
pytest.raises(ValueError, spectral_connectivity, data, method='notamethod')
pytest.raises(ValueError, spectral_connectivity, data,
mode='notamode')
# test invalid fmin fmax settings
pytest.raises(ValueError, spectral_connectivity, data, fmin=10,
fmax=10 + 0.5 * (sfreq / float(n_times)))
pytest.raises(ValueError, spectral_connectivity, data, fmin=10, fmax=5)
pytest.raises(ValueError, spectral_connectivity, data, fmin=(0, 11),
fmax=(5, 10))
pytest.raises(ValueError, spectral_connectivity, data, fmin=(11,),
fmax=(12, 15))
methods = ['coh', 'cohy', 'imcoh', ['plv', 'ppc', 'pli', 'pli2_unbiased',
'wpli', 'wpli2_debiased', 'coh']]
modes = ['multitaper', 'fourier', 'cwt_morlet']
# define some frequencies for cwt
cwt_freqs = 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_freqs))
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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
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[1, 0, gidx[0]:gidx[1]] > upper_t), \
con[1, 0, gidx[0]:gidx[1]].min()
# we see something for zero-lag
assert (np.all(con[1, 0, :bidx[0]] < lower_t))
assert np.all(con[1, 0, bidx[1]:] < lower_t), \
con[1, 0, bidx[1:]].max()
elif method == 'cohy':
# imaginary coh will be zero
check = np.imag(con[1, 0, gidx[0]:gidx[1]])
assert np.all(check < lower_t), check.max()
# we see something for zero-lag
assert np.all(np.abs(con[1, 0, gidx[0]:gidx[1]]) > upper_t)
assert np.all(np.abs(con[1, 0, :bidx[0]]) < lower_t)
assert np.all(np.abs(con[1, 0, bidx[1]:]) < lower_t)
elif method == 'imcoh':
# imaginary coh will be zero
assert np.all(con[1, 0, gidx[0]:gidx[1]] < lower_t)
assert np.all(con[1, 0, :bidx[0]] < lower_t)
assert np.all(con[1, 0, bidx[1]:] < lower_t), \
con[1, 0, bidx[1]:].max()
# compute same connections using indices and 2 jobs
indices = np.tril_indices(n_signals, -1)
if not isinstance(method, list):
test_methods = (method, _CohEst)
else:
test_methods = method
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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles, n_jobs=2)
assert (isinstance(con2, list))
assert (len(con2) == len(test_methods))
if method == 'coh':
assert_array_almost_equal(con2[0], con2[1])
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)
assert_array_almost_equal(con[indices], con2)
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[indices], c2)
assert (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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
assert (isinstance(freqs3, list))
assert (len(freqs3) == len(fmin))
for i in range(len(freqs3)):
assert np.all((freqs3[i] >= fmin[i]) &
(freqs3[i] <= fmax[i]))
# average con2 "manually" and we get the same result
if not isinstance(method, list):
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])
else:
for j in range(len(con2)):
for i in range(len(freqs3)):
freq_idx = np.searchsorted(freqs2, freqs3[i])
con2_avg = np.mean(con2[j][:, freq_idx], axis=1)
assert_array_almost_equal(con2_avg, con3[j][:, 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)
def test_spectral_connectivity(method, mode):
"""Test frequency-domain connectivity methods."""
# 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
tmin = 0.
tmax = (n_times - 1) / sfreq
data = rng.randn(n_signals, n_epochs * n_times)
times_data = np.linspace(tmin, tmax, 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])
# First we test some invalid parameters:
pytest.raises(ValueError, spectral_connectivity, data, method='notamethod')
pytest.raises(ValueError, spectral_connectivity, data,
mode='notamode')
# test invalid fmin fmax settings
pytest.raises(ValueError, spectral_connectivity, data, fmin=10,
fmax=10 + 0.5 * (sfreq / float(n_times)))
pytest.raises(ValueError, spectral_connectivity, data, fmin=10, fmax=5)
pytest.raises(ValueError, spectral_connectivity, data, fmin=(0, 11),
fmax=(5, 10))
pytest.raises(ValueError, spectral_connectivity, 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, 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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
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[1, 0, gidx[0]:gidx[1]] > upper_t), \
con[1, 0, gidx[0]:gidx[1]].min()
# we see something for zero-lag
assert (np.all(con[1, 0, :bidx[0]] < lower_t))
assert np.all(con[1, 0, bidx[1]:] < lower_t), \
con[1, 0, bidx[1:]].max()
elif method == 'cohy':
# imaginary coh will be zero
check = np.imag(con[1, 0, gidx[0]:gidx[1]])
assert np.all(check < lower_t), check.max()
# we see something for zero-lag
assert np.all(np.abs(con[1, 0, gidx[0]:gidx[1]]) > upper_t)
assert np.all(np.abs(con[1, 0, :bidx[0]]) < lower_t)
assert np.all(np.abs(con[1, 0, bidx[1]:]) < lower_t)
elif method == 'imcoh':
# imaginary coh will be zero
assert np.all(con[1, 0, gidx[0]:gidx[1]] < lower_t)
assert np.all(con[1, 0, :bidx[0]] < lower_t)
assert np.all(con[1, 0, bidx[1]:] < lower_t), \
con[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, 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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles, n_jobs=2)
assert isinstance(con2, list)
assert len(con2) == len(test_methods)
if method == 'coh':
assert_array_almost_equal(con2[0], con2[1])
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)
assert_array_almost_equal(con[indices], con2)
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[indices], c2)
assert (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_freqs=cwt_freqs,
cwt_n_cycles=cwt_n_cycles)
assert (isinstance(freqs3, list))
assert (len(freqs3) == len(fmin))
for i in range(len(freqs3)):
assert np.all((freqs3[i] >= fmin[i]) &
(freqs3[i] <= fmax[i]))
# average con2 "manually" and we get the same result
if not isinstance(method, list):
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])
else:
for j in range(len(con2)):
for i in range(len(freqs3)):
freq_idx = np.searchsorted(freqs2, freqs3[i])
con2_avg = np.mean(con2[j][:, freq_idx], axis=1)
assert_array_almost_equal(con2_avg, con3[j][:, 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)