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# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.testing import assert_allclose, assert_equal
from mne import Epochs, read_evokeds, pick_types
from mne.io.compensator import make_compensator, get_current_comp
from mne.io import read_raw_fif
from mne.utils import _TempDir, requires_mne, run_subprocess, run_tests_if_main
base_dir = op.join(op.dirname(__file__), 'data')
ctf_comp_fname = op.join(base_dir, 'test_ctf_comp_raw.fif')
def test_compensation():
"""Test compensation."""
tempdir = _TempDir()
raw = read_raw_fif(ctf_comp_fname)
assert_equal(get_current_comp(raw.info), 3)
comp1 = make_compensator(raw.info, 3, 1, exclude_comp_chs=False)
assert comp1.shape == (340, 340)
comp2 = make_compensator(raw.info, 3, 1, exclude_comp_chs=True)
assert comp2.shape == (311, 340)
# round-trip
desired = np.eye(340)
for from_ in range(3):
for to in range(3):
if from_ == to:
continue
comp1 = make_compensator(raw.info, from_, to)
comp2 = make_compensator(raw.info, to, from_)
# To get 1e-12 here (instead of 1e-6) we must use the linalg.inv
# method mentioned in compensator.py
assert_allclose(np.dot(comp1, comp2), desired, atol=1e-12)
assert_allclose(np.dot(comp2, comp1), desired, atol=1e-12)
# make sure that changing the comp doesn't modify the original data
raw2 = read_raw_fif(ctf_comp_fname)
raw2.apply_gradient_compensation(2)
assert_equal(get_current_comp(raw2.info), 2)
fname = op.join(tempdir, 'ctf-raw.fif')
raw2.save(fname)
raw2 = read_raw_fif(fname)
assert_equal(raw2.compensation_grade, 2)
raw2.apply_gradient_compensation(3)
assert_equal(raw2.compensation_grade, 3)
data, _ = raw[:, :]
data2, _ = raw2[:, :]
# channels have norm ~1e-12
assert_allclose(data, data2, rtol=1e-9, atol=1e-18)
for ch1, ch2 in zip(raw.info['chs'], raw2.info['chs']):
assert ch1['coil_type'] == ch2['coil_type']
@requires_mne
def test_compensation_mne():
"""Test comensation by comparing with MNE."""
tempdir = _TempDir()
def make_evoked(fname, comp):
"""Make evoked data."""
raw = read_raw_fif(fname)
if comp is not None:
raw.apply_gradient_compensation(comp)
picks = pick_types(raw.info, meg=True, ref_meg=True)
events = np.array([[0, 0, 1]], dtype=np.int)
evoked = Epochs(raw, events, 1, 0, 20e-3, picks=picks).average()
return evoked
def compensate_mne(fname, comp):
"""Compensate using MNE-C."""
tmp_fname = '%s-%d-ave.fif' % (fname[:-4], comp)
cmd = ['mne_compensate_data', '--in', fname,
'--out', tmp_fname, '--grad', str(comp)]
run_subprocess(cmd)
return read_evokeds(tmp_fname)[0]
# save evoked response with default compensation
fname_default = op.join(tempdir, 'ctf_default-ave.fif')
make_evoked(ctf_comp_fname, None).save(fname_default)
for comp in [0, 1, 2, 3]:
evoked_py = make_evoked(ctf_comp_fname, comp)
evoked_c = compensate_mne(fname_default, comp)
picks_py = pick_types(evoked_py.info, meg=True, ref_meg=True)
picks_c = pick_types(evoked_c.info, meg=True, ref_meg=True)
assert_allclose(evoked_py.data[picks_py], evoked_c.data[picks_c],
rtol=1e-3, atol=1e-17)
chs_py = [evoked_py.info['chs'][ii] for ii in picks_py]
chs_c = [evoked_c.info['chs'][ii] for ii in picks_c]
for ch_py, ch_c in zip(chs_py, chs_c):
assert_equal(ch_py['coil_type'], ch_c['coil_type'])
run_tests_if_main()
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