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
# Create simulated data
# -----------------------------------------------------------------------------
# Simulated data consist of N channels, 1 sinusoidal target, N-3 noise sources,
# with temporally local artifacts on each channel.
# Create simulated data
nchans = 10
n_samples = 1000
f = 2
target = np.sin(np.arange(n_samples) / n_samples * 2 * np.pi * f)
target = target[:, np.newaxis]
noise = np.random.randn(n_samples, nchans - 3)
# Create artifact signal
SNR = np.sqrt(1)
x0 = (normcol(np.dot(noise, np.random.randn(noise.shape[1], nchans))) +
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
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from meegkit import star, dss # noqa:E402
from meegkit.utils import demean, normcol, tscov # noqa:E402
# Create simulated data
np.random.seed(9)
n_chans, n_samples = 10, 1000
f = 2
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)
# Create artifact signal
SNR = np.sqrt(1)
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 + 10 * artifact
# Basic function to fit a sinusoidal trend for DSS
def _sine_fit(x):
guess_mean = np.mean(x)
guess_std = np.std(x)
guess_phase = 0
t = np.linspace(0, 4 * np.pi, x.shape[0])