def test_ssd_epoched_data():
"""Test Common Spatial Patterns algorithm on epoched data.
Compare the outputs when raw data is used.
"""
X, A, S = simulate_data(n_trials=100, n_channels=20, n_samples=500)
sf = 250
n_channels = X.shape[0]
info = create_info(ch_names=n_channels, sfreq=sf, ch_types='eeg')
n_components_true = 5
# Build epochs as sliding windows over the continuous raw file
# Epoch length is 1 second
X_e = np.reshape(X, (100, 20, 500))
# Fit
filt_params_signal = dict(l_freq=freqs_sig[0], h_freq=freqs_sig[1],
l_trans_bandwidth=4, h_trans_bandwidth=4)
filt_params_noise = dict(l_freq=freqs_noise[0], h_freq=freqs_noise[1],
l_trans_bandwidth=4, h_trans_bandwidth=4)
# ssd on epochs
ssd_e = SSD(info, filt_params_signal, filt_params_noise)
ssd_e.fit(X_e)
# ssd on raw
ssd = SSD(info, filt_params_signal, filt_params_noise)
ssd.fit(X)
# Check if the 5 first 5 components are the same for both
_, sorter_spec_e = ssd_e.get_spectral_ratio(ssd_e.transform(X_e))
_, sorter_spec = ssd.get_spectral_ratio(ssd.transform(X))
assert_array_equal(sorter_spec_e[:n_components_true],
sorter_spec[:n_components_true])
def test_sorting():
"""Test sorting learning during training."""
X, _, _ = simulate_data(n_trials=100, n_channels=20, n_samples=500)
# Epoch length is 1 second
X = np.reshape(X, (100, 20, 500))
# split data
Xtr, Xte = X[:80], X[80:]
sf = 250
n_channels = Xtr.shape[1]
info = create_info(ch_names=n_channels, sfreq=sf, ch_types='eeg')
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=4,
h_trans_bandwidth=4)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=4,
h_trans_bandwidth=4)
# check sort_by_spectral_ratio set to False
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=None,
sort_by_spectral_ratio=False)
ssd.fit(Xtr)
_, sorter_tr = ssd.get_spectral_ratio(ssd.transform(Xtr))
_, sorter_te = ssd.get_spectral_ratio(ssd.transform(Xte))
assert any(sorter_tr != sorter_te)
# check sort_by_spectral_ratio set to True
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=None,
sort_by_spectral_ratio=True)
ssd.fit(Xtr)
# check sorters
sorter_in = ssd.sorter_spec
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=None,
sort_by_spectral_ratio=False)
ssd.fit(Xtr)
_, sorter_out = ssd.get_spectral_ratio(ssd.transform(Xtr))
assert all(sorter_in == sorter_out)
def test_ssd():
"""Test Common Spatial Patterns algorithm on raw data."""
X, A, S = simulate_data()
sf = 250
n_channels = X.shape[0]
info = create_info(ch_names=n_channels, sfreq=sf, ch_types='eeg')
n_components_true = 5
# Init
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info, filt_params_signal, filt_params_noise)
# freq no int
freq = 'foo'
filt_params_signal = dict(l_freq=freq,
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
with pytest.raises(TypeError, match='must be an instance '):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# Wrongly specified noise band
freq = 2
filt_params_signal = dict(l_freq=freq,
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
with pytest.raises(ValueError, match='Wrongly specified '):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# filt param no dict
filt_params_signal = freqs_sig
filt_params_noise = freqs_noise
with pytest.raises(ValueError, match='must be defined'):
ssd = SSD(info, filt_params_signal, filt_params_noise)
# Data type
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info, filt_params_signal, filt_params_noise)
raw = io.RawArray(X, info)
pytest.raises(TypeError, ssd.fit, raw)
# More than 1 channel type
ch_types = np.reshape([['mag'] * 10, ['eeg'] * 10], n_channels)
info_2 = create_info(ch_names=n_channels, sfreq=sf, ch_types=ch_types)
with pytest.raises(ValueError, match='At this point SSD'):
ssd = SSD(info_2, filt_params_signal, filt_params_noise)
# Number of channels
info_3 = create_info(ch_names=n_channels + 1, sfreq=sf, ch_types='eeg')
ssd = SSD(info_3, filt_params_signal, filt_params_noise)
pytest.raises(ValueError, ssd.fit, X)
# Fit
n_components = 10
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=n_components)
# Call transform before fit
pytest.raises(AttributeError, ssd.transform, X)
# Check outputs
ssd.fit(X)
assert (ssd.filters_.shape == (n_channels, n_channels))
assert (ssd.patterns_.shape == (n_channels, n_channels))
# Transform
X_ssd = ssd.fit_transform(X)
assert (X_ssd.shape[0] == n_components)
# back and forward
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=None,
sort_by_spectral_ratio=False)
ssd.fit(X)
X_denoised = ssd.inverse_transform(X)
assert_array_almost_equal(X_denoised, X)
# Power ratio ordering
spec_ratio, _ = ssd.get_spectral_ratio(ssd.transform(X))
# since we now that the number of true components is 5, the relative
# difference should be low for the first 5 components and then increases
index_diff = np.argmax(-np.diff(spec_ratio))
assert index_diff == n_components_true - 1
# Check detected peaks
# fit ssd
n_components = n_components_true
filt_params_signal = dict(l_freq=freqs_sig[0],
h_freq=freqs_sig[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
filt_params_noise = dict(l_freq=freqs_noise[0],
h_freq=freqs_noise[1],
l_trans_bandwidth=1,
h_trans_bandwidth=1)
ssd = SSD(info,
filt_params_signal,
filt_params_noise,
n_components=n_components,
sort_by_spectral_ratio=False)
ssd.fit(X)
out = ssd.transform(X)
psd_out, _ = psd_array_welch(out[0], sfreq=250, n_fft=250)
psd_S, _ = psd_array_welch(S[0], sfreq=250, n_fft=250)
corr = np.abs(np.corrcoef((psd_out, psd_S))[0, 1])
assert np.abs(corr) > 0.95
# Check pattern estimation
# Since there is no exact ordering of the recovered patterns
# a pair-wise greedy search will be done
error = list()
for ii in range(n_channels):
corr = np.abs(np.corrcoef(ssd.patterns_[ii, :].T, A[:, 0])[0, 1])
error.append(1 - corr)
min_err = np.min(error)
assert min_err < 0.3 # threshold taken from SSD original paper