def test_ar_raw(): """Test fitting AR model on raw data.""" raw = io.read_raw_fif(raw_fname) # pick MEG gradiometers picks = pick_types(raw.info, meg='grad', exclude='bads') picks = picks[:2] tmin, tmax, order = 0, 10, 2 coefs = fit_iir_model_raw(raw, order, picks, tmin, tmax)[1][1:] assert_equal(coefs.shape, (order,)) assert_true(0.9 < -coefs[0] < 1.1)
def test_ar_raw(): """Test fitting AR model on raw data.""" raw = io.read_raw_fif(raw_fname, add_eeg_ref=False) # pick MEG gradiometers picks = pick_types(raw.info, meg='grad', exclude='bads') picks = picks[:2] tmin, tmax, order = 0, 10, 2 coefs = fit_iir_model_raw(raw, order, picks, tmin, tmax)[1][1:] assert_equal(coefs.shape, (order,)) assert_true(0.9 < -coefs[0] < 1.1)
def test_ar_raw(): """Test fitting AR model on raw data.""" raw = io.read_raw_fif(raw_fname).crop(0, 2).load_data() raw.pick_types(meg='grad') # pick MEG gradiometers for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1][1:] assert coeffs.shape == (order, ) assert_allclose(-coeffs[0], 1., atol=0.5) # let's make sure we're doing something reasonable: first, white noise rng = np.random.RandomState(0) raw._data = rng.randn(*raw._data.shape) raw._data *= 1e-15 for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1] assert_allclose(coeffs, [1.] + [0.] * order, atol=2e-2) # Now let's try pink noise iir = [1, -1, 0.2] raw._data = lfilter([1.], iir, raw._data) for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1] assert_allclose(coeffs, iir + [0.] * (order - 2), atol=5e-2)
def test_ar_raw(): """Test fitting AR model on raw data.""" raw = io.read_raw_fif(raw_fname).crop(0, 2).load_data() raw.pick_types(meg='grad') # pick MEG gradiometers for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1][1:] assert_equal(coeffs.shape, (order,)) assert_allclose(-coeffs[0], 1., atol=0.5) # let's make sure we're doing something reasonable: first, white noise rng = np.random.RandomState(0) raw._data = rng.randn(*raw._data.shape) raw._data *= 1e-15 for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1] assert_allclose(coeffs, [1.] + [0.] * order, atol=2e-2) # Now let's try pink noise iir = [1, -1, 0.2] raw._data = lfilter([1.], iir, raw._data) for order in (2, 5, 10): coeffs = fit_iir_model_raw(raw, order)[1] assert_allclose(coeffs, iir + [0.] * (order - 2), atol=5e-2)