def test_star1():
"""Test STAR 1."""
# N channels, 1 sinusoidal target, N-3 noise sources, temporally local
# artifacts on each channel.
n_samples = 1000
n_chans = 10
f = 2
def sim_data(n_samples, n_chans, f, SNR):
target = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)
target = target[:, np.newaxis]
noise = np.random.randn(n_samples, n_chans - 3)
x0 = (
normcol(np.dot(noise, np.random.randn(noise.shape[1], n_chans))) +
SNR * target * np.random.randn(1, n_chans))
x0 = demean(x0)
artifact = np.zeros(x0.shape)
for k in np.arange(n_chans):
artifact[k * 100 + np.arange(20), k] = 1
x = x0 + 20 * artifact
return x, x0
# Test SNR=1
x, x0 = sim_data(n_samples, n_chans, f, SNR=np.sqrt(1))
y, w, _ = star(x, 2, verbose='debug')
assert_allclose(demean(y), x0) # check that denoised signal ~ x0
# Test more unfavourable SNR
x, x0 = sim_data(n_samples, n_chans, f, SNR=np.sqrt(1e-7))
y, w, _ = star(x, 2)
assert_allclose(demean(y), x0) # check that denoised signal ~ x0
# Test an all-zero channel is properly handled
x = x - x[:, 0][:, np.newaxis]
y, w, _ = star(x, 2)
assert_allclose(demean(y)[:, 0], x[:, 0])
SNR * target * np.random.randn(1, nchans))
x0 = demean(x0)
artifact = np.zeros(x0.shape)
for k in np.arange(nchans):
artifact[k * 100 + np.arange(20), k] = 1
x = x0 + 10 * artifact
# This is to compare with matlab numerically
# from scipy.io import loadmat
# mat = loadmat('/Users/nicolas/Toolboxes/NoiseTools/TEST/X.mat')
# x = mat['x']
# x0 = mat['x0']
###############################################################################
# Apply STAR
# -----------------------------------------------------------------------------
y, w, _ = star.star(x, 2)
###############################################################################
# Plot results
# -----------------------------------------------------------------------------
f, (ax1, ax2, ax3) = plt.subplots(3, 1)
ax1.plot(x, lw=.5)
ax1.set_title('Signal + Artifacts (SNR = {})'.format(SNR))
ax2.plot(y, lw=.5)
ax2.set_title('Denoised')
ax3.plot(demean(y) - x0, lw=.5)
ax3.set_title('Residual')
f.set_tight_layout(True)
plt.show()