1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
|
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from mne.connectivity import spectral_connectivity
from mne.connectivity.spectral import _CohEst, _get_n_epochs
from mne import SourceEstimate
from mne.utils import run_tests_if_main
from mne.filter import filter_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.slowtest
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)
run_tests_if_main()
|
