def test_average_forward_solution():
"""Test averaging forward solutions
"""
fwd = read_forward_solution(fname)
# input not a list
assert_raises(TypeError, average_forward_solutions, 1)
# list is too short
assert_raises(ValueError, average_forward_solutions, [])
# negative weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [-1, 0])
# all zero weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0])
# weights not same length
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0, 0])
# list does not only have all dict()
assert_raises(TypeError, average_forward_solutions, [1, fwd])
# try an easy case
fwd_copy = average_forward_solutions([fwd])
assert_array_equal(fwd['sol']['data'], fwd_copy['sol']['data'])
# modify a fwd solution, save it, use MNE to average with old one
fwd_copy['sol']['data'] *= 0.5
fname_copy = op.join(temp_dir, 'fwd.fif')
write_forward_solution(fname_copy, fwd_copy, overwrite=True)
cmd = ('mne_average_forward_solutions', '--fwd', fname, '--fwd',
fname_copy, '--out', fname_copy)
run_subprocess(cmd)
# now let's actually do it, with one filename and one fwd
fwd_ave = average_forward_solutions([fwd, fwd_copy])
assert_array_equal(0.75 * fwd['sol']['data'], fwd_ave['sol']['data'])
def test_average_forward_solution():
"""Test averaging forward solutions
"""
fwd = read_forward_solution(fname)
# input not a list
assert_raises(TypeError, average_forward_solutions, 1)
# list is too short
assert_raises(ValueError, average_forward_solutions, [])
# negative weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [-1, 0])
# all zero weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0])
# weights not same length
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0, 0])
# list does not only have all dict()
assert_raises(TypeError, average_forward_solutions, [1, fwd])
# try an easy case
fwd_copy = average_forward_solutions([fwd])
assert_array_equal(fwd['sol']['data'], fwd_copy['sol']['data'])
# modify a fwd solution, save it, use MNE to average with old one
fwd_copy['sol']['data'] *= 0.5
fname_copy = op.join(temp_dir, 'fwd.fif')
write_forward_solution(fname_copy, fwd_copy, overwrite=True)
cmd = ('mne_average_forward_solutions', '--fwd', fname, '--fwd',
fname_copy, '--out', fname_copy)
run_subprocess(cmd)
# now let's actually do it, with one filename and one fwd
fwd_ave = average_forward_solutions([fwd, fwd_copy])
assert_array_equal(0.75 * fwd['sol']['data'], fwd_ave['sol']['data'])
def test_average_forward_solution():
"""Test averaging forward solutions
"""
temp_dir = _TempDir()
fwd = read_forward_solution(fname_meeg)
# input not a list
assert_raises(TypeError, average_forward_solutions, 1)
# list is too short
assert_raises(ValueError, average_forward_solutions, [])
# negative weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [-1, 0])
# all zero weights
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0])
# weights not same length
assert_raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0, 0])
# list does not only have all dict()
assert_raises(TypeError, average_forward_solutions, [1, fwd])
# try an easy case
fwd_copy = average_forward_solutions([fwd])
assert_true(isinstance(fwd_copy, Forward))
assert_array_equal(fwd["sol"]["data"], fwd_copy["sol"]["data"])
# modify a fwd solution, save it, use MNE to average with old one
fwd_copy["sol"]["data"] *= 0.5
fname_copy = op.join(temp_dir, "copy-fwd.fif")
write_forward_solution(fname_copy, fwd_copy, overwrite=True)
cmd = ("mne_average_forward_solutions", "--fwd", fname_meeg, "--fwd", fname_copy, "--out", fname_copy)
run_subprocess(cmd)
# now let's actually do it, with one filename and one fwd
fwd_ave = average_forward_solutions([fwd, fwd_copy])
assert_array_equal(0.75 * fwd["sol"]["data"], fwd_ave["sol"]["data"])
# fwd_ave_mne = read_forward_solution(fname_copy)
# assert_array_equal(fwd_ave_mne['sol']['data'], fwd_ave['sol']['data'])
# with gradient
fwd = read_forward_solution(fname_meeg_grad)
fwd_ave = average_forward_solutions([fwd, fwd])
compare_forwards(fwd, fwd_ave)
def test_average_forward_solution():
"""Test averaging forward solutions."""
temp_dir = _TempDir()
fwd = read_forward_solution(fname_meeg)
# input not a list
pytest.raises(TypeError, average_forward_solutions, 1)
# list is too short
pytest.raises(ValueError, average_forward_solutions, [])
# negative weights
pytest.raises(ValueError, average_forward_solutions, [fwd, fwd], [-1, 0])
# all zero weights
pytest.raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0])
# weights not same length
pytest.raises(ValueError, average_forward_solutions, [fwd, fwd], [0, 0, 0])
# list does not only have all dict()
pytest.raises(TypeError, average_forward_solutions, [1, fwd])
# try an easy case
fwd_copy = average_forward_solutions([fwd])
assert (isinstance(fwd_copy, Forward))
assert_array_equal(fwd['sol']['data'], fwd_copy['sol']['data'])
# modify a fwd solution, save it, use MNE to average with old one
fwd_copy['sol']['data'] *= 0.5
fname_copy = op.join(temp_dir, 'copy-fwd.fif')
write_forward_solution(fname_copy, fwd_copy, overwrite=True)
cmd = ('mne_average_forward_solutions', '--fwd', fname_meeg, '--fwd',
fname_copy, '--out', fname_copy)
run_subprocess(cmd)
# now let's actually do it, with one filename and one fwd
fwd_ave = average_forward_solutions([fwd, fwd_copy])
assert_array_equal(0.75 * fwd['sol']['data'], fwd_ave['sol']['data'])
# fwd_ave_mne = read_forward_solution(fname_copy)
# assert_array_equal(fwd_ave_mne['sol']['data'], fwd_ave['sol']['data'])
# with gradient
fwd = read_forward_solution(fname_meeg_grad)
fwd_ave = average_forward_solutions([fwd, fwd])
compare_forwards(fwd, fwd_ave)
mne.write_forward_solution("{dir}nc_{meg}_limb_mix_3-fwd.fif".format(
dir=meg_dir, meg=meg),
fwd_a,
overwrite=True)
fwd_b = mne.make_forward_solution(epo_b.info,
trans=trans,
src=src,
bem=bem,
meg=True,
mindist=5.0,
n_jobs=4)
mne.write_forward_solution("{dir}nc_{meg}_limb_mix_4-fwd.fif".format(
dir=meg_dir, meg=meg),
fwd_b,
overwrite=True)
# clear up memory
del src, rest, ton, epo_a, epo_b
# build averaged forward models for baseline comps (rest vs. ton) and experimental analyses (ton,exp blocks a+b)
fwd_base = mne.average_forward_solutions([fwd_rest, fwd_ton], weights=None)
mne.write_forward_solution("{dir}nc_{meg}_limb_mix_base-fwd.fif".format(
dir=meg_dir, meg=meg),
fwd_base,
overwrite=True)
del fwd_rest
fwd_exp = mne.average_forward_solutions([fwd_ton, fwd_a, fwd_b],
weights=None)
mne.write_forward_solution("{dir}nc_{meg}_limb_mix_exp-fwd.fif".format(
dir=meg_dir, meg=meg),
fwd_exp,
overwrite=True)
n_jobs=n_jobs)
mne.write_forward_solution(
"{dir}nc_{sub}_{run}_{spacing}-fwd.fif".format(dir=proc_dir,
sub=v,
run=run,
spacing=spacing),
fwd,
overwrite=True)
del src, bem, fwd
fwds = []
for run in runs:
fwds.append(
mne.read_forward_solution(
"{dir}nc_{sub}_{run}_{spacing}-fwd.fif".format(
dir=proc_dir, sub=v, run=run, spacing=spacing)))
avg_fwd = mne.average_forward_solutions(fwds)
del fwds
mne.write_forward_solution("{dir}nc_{sub}_{spacing}-fwd.fif".format(
dir=proc_dir, sub=v, spacing=spacing),
avg_fwd,
overwrite=True)
avg_fwd = mne.read_forward_solution(
"{dir}nc_{sub}_{spacing}-fwd.fif".format(dir=proc_dir,
sub=v,
spacing=spacing))
sen = mne.sensitivity_map(avg_fwd, ch_type="mag", mode="fixed")
m_to_fs = mne.compute_source_morph(sen,
subject_from=k,
subject_to="fsaverage",
spacing=int(spacing[-1]),
subjects_dir=subjects_dir,
