A = np.random.rand(sig.shape[0] + 1, sig.shape[0])
x = A @ sig
# x += 0.1*np.random.randn(*x.shape)
xm = x - x.mean(axis=1)[:, None]
# #### Perform PCA
u, s, v = np.linalg.svd(xm, full_matrices=False)
nm = np.sum(s > 1e-6)
s_pca = v[:nm, :].T * s[:nm]
# #### Apply ICA
xw = (u / s).T[:sig.shape[0]] @ xm
xw *= np.sqrt(xm.shape[1])
ica = FastICA(algorithm='deflation', tol=1e-12)
ica.whiten = True
ica.n_components = sig.shape[0]
s_ica = ica.fit_transform(xm.T)
# ica.whiten = False
# s_ica = ica.fit_transform(xw.T)
s_ica *= s[:nm].mean()
# #### Plot data
f = mplt.figure(figsize=(10, 10))
gs = mgs.GridSpec(4, 3)
gs.update(top=0.96,
bottom=0.05,
left=0.08,
right=0.98,
hspace=0.3,
wspace=0.35)
