def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(),
dict(window=128, overlap=0.75, fmin=None, fmax=None, fs=None))
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
assert_equal(cov.get_params(), dict(window=128, overlap=0.75, fmin=None,
fmax=None, fs=None))
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=None, preload=True, add_eeg_ref=False, verbose=False)
labels = epochs.events[:, -1] - 2
# get epochs
epochs_data = epochs.get_data()
# compute cospectral covariance matrices
fmin = 2.0
fmax = 40.0
cosp = CospCovariances(window=128, overlap=0.98, fmin=fmin, fmax=fmax,
fs=160.0)
covmats = cosp.fit_transform(epochs_data[:, ::4, :])
fr = np.fft.fftfreq(128)[0:64]*160
fr = fr[(fr >= fmin) & (fr <= fmax)]
pv = []
Fv = []
# For each frequency bin, estimate the stats
for i in range(covmats.shape[3]):
p_test = PermutationTest(5000)
p, F = p_test.test(covmats[:, :, :, i], labels)
print(p_test.summary())
pv.append(p)
Fv.append(F[0])
plot(fr, Fv, lw=2)
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2, 3, 1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
def test_Cospcovariances():
"""Test fit CospCovariances"""
x = np.random.randn(2,3,1000)
cov = CospCovariances()
cov.fit(x)
cov.fit_transform(x)
