def test_sns():
"""Test against NoiseTools."""
mat = loadmat('./tests/data/snsdata.mat')
x = mat['x']
y_sns = mat['y_sns']
r_sns0 = mat['y_sns0']
cx = mat['cx']
r = sns.sns0(cx, n_neighbors=4)
assert_allclose(r, r_sns0) # assert our results match Matlab's
y, _ = sns.sns(x, n_neighbors=4)
assert_allclose(y, y_sns) # assert our results match Matlab's
def test_tspca_sns_dss(): # TODO
"""Test TSPCA, SNS, DSS.
Requires data stored in a time X channels X trials matrix.
Remove environmental noise with TSPCA (shifts=-50:50).
Remove sensor noise with SNS.
Remove non-repeatable components with DSS.
"""
# Random data (time*chans*trials)
data = np.random.random((800, 102, 200))
ref = np.random.random((800, 3, 200))
# remove means
noisy_data = demean(data)
noisy_ref = demean(ref)
# Apply TSPCA
# -------------------------------------------------------------------------
# shifts = np.arange(-50, 51)
# print('TSPCA...')
# y_tspca, idx = tspca.tsr(noisy_data, noisy_ref, shifts)[0:2]
# print('\b OK!')
y_tspca = noisy_data
# Apply SNS
# -------------------------------------------------------------------------
nneighbors = 10
print('SNS...')
y_tspca_sns, r = sns.sns(y_tspca, nneighbors)
print('\b OK!')
# apply DSS
# -------------------------------------------------------------------------
print('DSS...')
# Keep all PC components
y_tspca_sns = demean(y_tspca_sns)
print(y_tspca_sns.shape)
todss, fromdss, _, _ = dss.dss1(y_tspca_sns)
print('\b OK!')
# c3 = DSS components
y_tspca_sns_dss = fold(np.dot(unfold(y_tspca_sns), todss),
y_tspca_sns.shape[0])
return y_tspca, y_tspca_sns, y_tspca_sns_dss