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import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true
from mne import io, pick_types
from mne.time_frequency import yule_walker, ar_raw
from mne.utils import requires_statsmodels, requires_patsy
raw_fname = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data',
'test_raw.fif')
@requires_patsy
@requires_statsmodels
def test_yule_walker():
"""Test Yule-Walker against statsmodels
"""
from statsmodels.regression.linear_model import yule_walker as sm_yw
d = np.random.randn(100)
sm_rho, sm_sigma = sm_yw(d, order=2)
rho, sigma = yule_walker(d, order=2)
assert_array_almost_equal(sm_sigma, sigma)
assert_array_almost_equal(sm_rho, rho)
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)
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