def test_unsupervised_spatial_filter():
"""Test unsupervised spatial filter."""
from sklearn.decomposition import PCA
from sklearn.kernel_ridge import KernelRidge
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
preload=True, baseline=None, verbose=False)
# Test estimator
assert_raises(ValueError, UnsupervisedSpatialFilter, KernelRidge(2))
# Test fit
X = epochs.get_data()
n_components = 4
usf = UnsupervisedSpatialFilter(PCA(n_components))
usf.fit(X)
usf1 = UnsupervisedSpatialFilter(PCA(n_components))
# test transform
assert_equal(usf.transform(X).ndim, 3)
# test fit_transform
assert_array_almost_equal(usf.transform(X), usf1.fit_transform(X))
assert_equal(usf.transform(X).shape[1], n_components)
assert_array_almost_equal(usf.inverse_transform(usf.transform(X)), X)
# Test with average param
usf = UnsupervisedSpatialFilter(PCA(4), average=True)
usf.fit_transform(X)
assert_raises(ValueError, UnsupervisedSpatialFilter, PCA(4), 2)
ax2 = fig2.add_subplot(11, 2, i + 1)
ax2.grid()
ax2.set_ylabel('ICA{}'.format(i))
ax2.plot(tiempo, data_ICA[150][i])
ax2.set_xlim([0, 2])
ax2.tick_params(axis='both', which='major', labelsize=8)
if (i < len(data_ICA[0]) - 2):
ax2.set_xticklabels([])
else:
ax2.set_xlabel('time[s]')
fig2.suptitle('Independent components')
#EXCLUDE ARTIFACTS AND PLOT FILTERED AND RAW DATA
exclude_channels = [1, 3, 5, 15]
data_ICA[:, exclude_channels, :] = 0
reconstructed_data = ICA_transformer.inverse_transform(data_ICA)
fig1 = plt.figure(figsize=(10, 20))
plt.subplots_adjust(left=0.07,
bottom=0.08,
right=0.95,
top=0.93,
wspace=0.17,
hspace=0.3)
for i in range(len(reconstructed_data[0])):
ax1 = fig1.add_subplot(11, 2, (i + 1))
ax1.grid()
ax1.set_ylabel('{}'.format(channel_names[i]))
ax1.plot(tiempo, reconstructed_data[0][i], label='filtered data')
ax1.plot(tiempo, datos[0][i], label='raw data')
ax1.set_xlim([0, 2])
