def test_ar_raw():
raw = fiff.Raw(raw_fname)
# picks MEG gradiometers
picks = fiff.pick_types(raw.info, meg='grad')
picks = picks[:2]
tmin, tmax = 0, 10 # use the first s of data
order = 2
coefs = ar_raw(raw, picks=picks, order=order, tmin=tmin, tmax=tmax)
mean_coefs = np.mean(coefs, axis=0)
assert_true(coefs.shape == (len(picks), order))
assert_true(0.9 < mean_coefs[0] < 1.1)
def test_ar_raw():
"""Test fitting AR model on raw data
"""
raw = io.Raw(raw_fname)
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', exclude='bads')
picks = picks[:2]
tmin, tmax = 0, 10 # use the first s of data
order = 2
coefs = ar_raw(raw, picks=picks, order=order, tmin=tmin, tmax=tmax)
mean_coefs = np.mean(coefs, axis=0)
assert_true(coefs.shape == (len(picks), order))
assert_true(0.9 < mean_coefs[0] < 1.1)
def test_ar_raw():
"""Test fitting AR model on raw data
"""
raw = io.Raw(raw_fname)
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', exclude='bads')
picks = picks[:2]
tmin, tmax = 0, 10 # use the first s of data
order = 2
coefs = ar_raw(raw, picks=picks, order=order, tmin=tmin, tmax=tmax)
mean_coefs = np.mean(coefs, axis=0)
assert_true(coefs.shape == (len(picks), order))
assert_true(0.9 < mean_coefs[0] < 1.1)
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
proj_fname = data_path + '/MEG/sample/sample_audvis_ecg_proj.fif'
raw = mne.io.Raw(raw_fname)
proj = mne.read_proj(proj_fname)
raw.info['projs'] += proj
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
# Set up pick list: Gradiometers - bad channels
picks = mne.pick_types(raw.info, meg='grad', exclude='bads')
order = 5 # define model order
picks = picks[:5]
# Estimate AR models on raw data
coefs = ar_raw(raw, order=order, picks=picks, tmin=60, tmax=180)
mean_coefs = np.mean(coefs, axis=0) # mean model across channels
filt = np.r_[1, -mean_coefs] # filter coefficient
d, times = raw[0, 1e4:2e4] # look at one channel from now on
d = d.ravel() # make flat vector
innovation = signal.convolve(d, filt, 'valid')
d_ = signal.lfilter([1], filt, innovation) # regenerate the signal
d_ = np.r_[d_[0] * np.ones(order), d_] # dummy samples to keep signal length
###############################################################################
# Plot the different time series and PSDs
plt.close('all')
plt.figure()
plt.plot(d[:100], label='signal')
plt.plot(d_[:100], label='regenerated signal')
