def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model"""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, "sample-oct-2-src.fif")
src = setup_source_space("sample", fname_src_small, "oct2", subjects_dir=subjects_dir, add_dist=False)
out_name = op.join(temp_dir, "tmp-fwd.fif")
run_subprocess(
[
"mne_forward_solution",
"--meg",
"--eeg",
"--meas",
fname_raw,
"--src",
fname_src_small,
"--mri",
fname_trans,
"--fwd",
out_name,
]
)
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_["sol"]["data"].ravel(), fwd_py_["sol"]["data"].ravel())[0, 1], 1.0, rtol=1e-3)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess(['mne_forward_solution', '--meg', '--eeg',
'--meas', fname_raw, '--src', fname_src_small,
'--mri', fname_trans, '--fwd', out_name])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108,
meg_rtol=5e-1, meg_atol=1e-6,
eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0, rtol=1e-3)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess([
'mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src',
fname_src_small, '--mri', fname_trans, '--fwd', out_name
])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=True,
verbose=True)
_compare_forwards(fwd,
fwd_py,
366,
108,
meg_rtol=5e-1,
meg_atol=1e-6,
eeg_rtol=5e-1,
eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0,
rtol=1e-3)
# Number of layers in the sphere model doesn't matter for MEG
# (as long as no sources are omitted due to distance)
assert len(sphere['layers']) == 4
fwd = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=False)
sphere_1 = make_sphere_model(head_radius=None)
assert len(sphere_1['layers']) == 0
assert_array_equal(sphere['r0'], sphere_1['r0'])
fwd_1 = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=False)
_compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
def test_make_forward_solution_discrete(tmpdir):
"""Test making and converting a forward solution with discrete src."""
# smoke test for depth weighting and discrete source spaces
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
src = src + setup_volume_source_space(
pos=dict(rr=src[0]['rr'][src[0]['vertno'][:3]].copy(),
nn=src[0]['nn'][src[0]['vertno'][:3]].copy()))
sphere = make_sphere_model()
fwd = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=False)
convert_forward_solution(fwd, surf_ori=True)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model."""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess(['mne_forward_solution', '--meg', '--eeg',
'--meas', fname_raw, '--src', fname_src_small,
'--mri', fname_trans, '--fwd', out_name])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=True, verbose=True)
_compare_forwards(fwd, fwd_py, 366, 108,
meg_rtol=5e-1, meg_atol=1e-6,
eeg_rtol=5e-1, eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0, rtol=1e-3)
# Number of layers in the sphere model doesn't matter for MEG
# (as long as no sources are omitted due to distance)
assert len(sphere['layers']) == 4
fwd = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=False)
sphere_1 = make_sphere_model(head_radius=None)
assert len(sphere_1['layers']) == 0
assert_array_equal(sphere['r0'], sphere_1['r0'])
fwd_1 = make_forward_solution(fname_raw, fname_trans, src, sphere,
meg=True, eeg=False)
_compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
# Homogeneous model
sphere = make_sphere_model(head_radius=None)
with pytest.raises(RuntimeError, match='zero shells.*EEG'):
make_forward_solution(fname_raw, fname_trans, src, sphere)
def test_make_forward_solution_sphere():
"""Test making a forward solution with a sphere model"""
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
fname_src_small,
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
out_name = op.join(temp_dir, 'tmp-fwd.fif')
run_subprocess([
'mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src',
fname_src_small, '--mri', fname_trans, '--fwd', out_name
])
fwd = read_forward_solution(out_name)
sphere = make_sphere_model(verbose=True)
fwd_py = make_forward_solution(fname_raw,
fname_trans,
src,
sphere,
meg=True,
eeg=True,
verbose=True)
_compare_forwards(fwd,
fwd_py,
366,
108,
meg_rtol=5e-1,
meg_atol=1e-6,
eeg_rtol=5e-1,
eeg_atol=5e-1)
# Since the above is pretty lax, let's check a different way
for meg, eeg in zip([True, False], [False, True]):
fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg)
assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(),
fwd_py_['sol']['data'].ravel())[0, 1],
1.0,
rtol=1e-3)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files."""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit', 'tests',
'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
trans_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti', 'tests',
'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample',
'oct2',
subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution('sample',
fname_kit_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw,
trans_path,
src,
fname_bem_meg,
eeg=False,
meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(NotImplementedError,
make_forward_solution,
raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info,
pick_types(raw_py.info, meg=True, eeg=False))
fwd_py = make_forward_solution(meg_only_info,
src=src,
meg=True,
eeg=True,
bem=fname_bem_meg,
trans=trans_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution('sample',
fname_bti_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
with warnings.catch_warnings(record=True): # weight tables
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw,
fname_trans,
src,
fname_bem_meg,
eeg=False,
meg=True)
fwd = _do_forward_solution('sample',
fname_ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = read_raw_fif(fname_ctf_raw)
ctf_raw.apply_gradient_compensation(2)
fwd_py = make_forward_solution(ctf_raw.info,
fname_trans,
src,
fname_bem_meg,
eeg=False,
meg=True)
with warnings.catch_warnings(record=True):
fwd = _do_forward_solution('sample',
ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files."""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit',
'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
trans_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti',
'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir,
add_dist=False)
write_source_spaces(fname_src_small, src) # to enable working with MNE-C
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution('sample', fname_kit_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
assert (isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, trans_path, src,
fname_bem_meg, eeg=False, meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert (isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
pytest.raises(NotImplementedError, make_forward_solution, raw_py.info,
src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True,
eeg=False))
fwd_py = make_forward_solution(meg_only_info, src=src, meg=True, eeg=True,
bem=fname_bem_meg, trans=trans_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src,
meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution('sample', fname_bti_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution('sample', fname_ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True, subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = read_raw_fif(fname_ctf_raw)
ctf_raw.info['bads'] = ['MRO24-2908'] # test that it works with some bads
ctf_raw.apply_gradient_compensation(2)
fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution('sample', ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'test-ctf-fwd.fif')
write_forward_solution(fname_temp, fwd_py)
fwd_py2 = read_forward_solution(fname_temp)
_compare_forwards(fwd_py, fwd_py2, 274, n_src)
repr(fwd_py)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files
"""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit',
'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
trans_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti',
'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', fname_src_small, 'oct2',
subjects_dir=subjects_dir, add_dist=False)
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = do_forward_solution('sample', fname_kit_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, trans_path, src,
fname_bem_meg, eeg=False, meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(NotImplementedError, make_forward_solution, raw_py.info,
src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src,
meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = do_forward_solution('sample', fname_bti_raw, src=fname_src_small,
bem=fname_bem_meg, mri=trans_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
fwd = do_forward_solution('sample', fname_ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True, subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = Raw(fname_ctf_raw, compensation=2)
fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src,
fname_bem_meg, eeg=False, meg=True)
with warnings.catch_warnings(record=True):
fwd = do_forward_solution('sample', ctf_raw, mri=fname_trans,
src=fname_src_small, bem=fname_bem_meg,
eeg=False, meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files
"""
kit_dir = op.join(op.dirname(__file__), "..", "..", "io", "kit", "tests", "data")
sqd_path = op.join(kit_dir, "test.sqd")
mrk_path = op.join(kit_dir, "test_mrk.sqd")
elp_path = op.join(kit_dir, "test_elp.txt")
hsp_path = op.join(kit_dir, "test_hsp.txt")
trans_path = op.join(kit_dir, "trans-sample.fif")
fname_kit_raw = op.join(kit_dir, "test_bin_raw.fif")
bti_dir = op.join(op.dirname(__file__), "..", "..", "io", "bti", "tests", "data")
bti_pdf = op.join(bti_dir, "test_pdf_linux")
bti_config = op.join(bti_dir, "test_config_linux")
bti_hs = op.join(bti_dir, "test_hs_linux")
fname_bti_raw = op.join(bti_dir, "exported4D_linux_raw.fif")
fname_ctf_raw = op.join(op.dirname(__file__), "..", "..", "io", "tests", "data", "test_ctf_comp_raw.fif")
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, "sample-oct-2-src.fif")
src = setup_source_space("sample", fname_src_small, "oct2", subjects_dir=subjects_dir, add_dist=False)
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = _do_forward_solution(
"sample",
fname_kit_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, trans_path, src, fname_bem_meg, eeg=False, meg=True)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(
NotImplementedError,
make_forward_solution,
raw_py.info,
src=src,
eeg=False,
meg=True,
bem=fname_bem_meg,
trans=trans_path,
)
# check that asking for eeg channels (even if they don't exist) is handled
meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True, eeg=False))
fwd_py = make_forward_solution(
meg_only_info, src=src, meg=True, eeg=True, bem=fname_bem_meg, trans=trans_path, ignore_ref=True
)
_compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = _do_forward_solution(
"sample",
fname_bti_raw,
src=fname_src_small,
bem=fname_bem_meg,
mri=trans_path,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
with warnings.catch_warnings(record=True): # weight tables
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src, fname_bem_meg, eeg=False, meg=True)
fwd = _do_forward_solution(
"sample",
fname_ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = Raw(fname_ctf_raw, compensation=2)
fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src, fname_bem_meg, eeg=False, meg=True)
with warnings.catch_warnings(record=True):
fwd = _do_forward_solution(
"sample",
ctf_raw,
mri=fname_trans,
src=fname_src_small,
bem=fname_bem_meg,
eeg=False,
meg=True,
subjects_dir=subjects_dir,
)
_compare_forwards(fwd, fwd_py, 274, n_src)
def run():
args = sys.argv
if len(args) <= 1:
print 'Usage: run_meg_tutorial.sh <sample data directory>'
return
sample_dir = args[1]
subjects_dir = join(sample_dir, 'subjects')
meg_dir = join(sample_dir, 'MEG', 'sample')
os.environ['SUBJECTS_DIR'] = subjects_dir
os.environ['MEG_DIR'] = meg_dir
subject = 'sample'
src = setup_source_space(subject,
fname=True,
spacing='oct6',
n_jobs=2,
overwrite=True)
# If one wanted to use other source spaces, these types of options are
# available
src_fsaverage = setup_source_space('fsaverage',
fname=True,
spacing='ico5',
n_jobs=2,
overwrite=True,
add_dist=False)
morph_source_spaces(src_fsaverage, subject_to='sample')
setup_source_space(subject,
fname=True,
spacing='all',
overwrite=True,
n_jobs=2,
add_dist=False)
# Add distances to source space (if desired, takes a long time)
bem_dir = join(subjects_dir, join('sample', 'bem'))
os.rename(join(bem_dir, 'sample-oct-6-src.fif'),
join(bem_dir, 'sample-oct-6-orig-src.fif'))
new_src = add_source_space_distances(src, dist_limit=0.007)
new_src.save(join(bem_dir, 'sample-oct-6-src.fif'))
# Preprocessing
raw = mne.io.Raw(join(meg_dir, 'sample_audvis_raw.fif'), preload=True)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
reject = dict(grad=3000e-13, mag=4000e-15, eeg=100e-6)
ecg_proj, _ = mne.preprocessing.compute_proj_ecg(raw,
l_freq=1,
h_freq=100,
ch_name='MEG 1531',
reject=reject)
eog_proj, _ = mne.preprocessing.compute_proj_eog(raw,
l_freq=1,
h_freq=35,
reject=reject,
no_proj=True)
events = mne.find_events(raw)
mne.write_events(join(meg_dir, 'sample_audvis_raw-eve.fif'), events)
event_id = [1, 2, 3, 4]
tmin, tmax = -0.2, 0.5
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=True, eog=True)
# Average with no filter
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks)
evoked = epochs.average()
evoked.save(join(meg_dir, 'sample_audvis-no-filter-ave.fif'))
raw.filter(l_freq=None, h_freq=40)
raw_resampled = raw.resample(150)
raw_resampled.save(join(meg_dir, 'sample_audvis_filt-0-40_raw.fif'),
overwrite=True)
raw.add_proj(ecg_proj)
raw.add_proj(eog_proj)
resampled_events = mne.find_events(raw_resampled)
mne.write_events(join(meg_dir, 'sample_audvis_filt-0-40_raw-eve.fif'),
resampled_events)
# Average with filter
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks)
evoked = epochs.average()
evoked.save(join(meg_dir, 'sample_audvis-ave.fif'))
# Compute the noise covariance matrix
noise_cov = mne.compute_raw_data_covariance(raw, picks=picks)
noise_cov.save(join(meg_dir, 'audvis.cov'))
# Compute the empty-room noise covariance matrix
ernoise_raw = mne.io.Raw(join(meg_dir, 'ernoise_raw.fif'), preload=True)
ernoise_raw.info['bads'] = ['MEG 2443']
ernoise_raw.filter(l_freq=None, h_freq=40)
picks = mne.pick_types(ernoise_raw.info,
meg=True,
eeg=True,
stim=True,
eog=True)
ernoise_cov = mne.compute_raw_data_covariance(ernoise_raw, picks=picks)
ernoise_cov.save(join(meg_dir, 'ernoise.cov'))
###############################################################################
# Compute forward solution a.k.a. lead field
trans = join(meg_dir, 'sample_audvis_raw-trans.fif')
bem = join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
# for MEG only
fname = join(meg_dir, 'sample_audvis-meg-oct-6-fwd.fif')
fwd_meg = mne.make_forward_solution(raw.info,
trans,
src,
bem,
fname=fname,
meg=True,
eeg=False,
mindist=5.0,
n_jobs=2,
overwrite=True)
# for EEG only
bem = join(subjects_dir, 'sample', 'bem',
'sample-5120-5120-5120-bem-sol.fif')
fname = join(meg_dir, 'sample_audvis-eeg-oct-6-fwd.fif')
fwd_eeg = mne.make_forward_solution(raw.info,
trans,
src,
bem,
fname=fname,
meg=False,
eeg=True,
mindist=5.0,
n_jobs=2,
overwrite=True)
# for both EEG and MEG
fname = join(meg_dir, 'sample_audvis-meg-eeg-oct-6-fwd.fif')
fwd = mne.make_forward_solution(raw.info,
trans,
src,
bem,
fname=fname,
meg=True,
eeg=True,
mindist=5.0,
n_jobs=2,
overwrite=True)
# Create various sensitivity maps
grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='free')
grad_map.save(join(meg_dir, 'sample_audvis-grad-oct-6-fwd-sensmap'),
ftype='w')
mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='free')
mag_map.save(join(meg_dir, 'sample_audvis-mag-oct-6-fwd-sensmap'),
ftype='w')
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='free')
eeg_map.save(join(meg_dir, 'sample_audvis-eeg-oct-6-fwd-sensmap'),
ftype='w')
grad_map2 = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')
grad_map2.save(join(meg_dir, 'sample_audvis-grad-oct-6-fwd-sensmap-2'),
ftype='w')
mag_map2 = mne.sensitivity_map(fwd, ch_type='mag', mode='ratio')
mag_map2.save(join(meg_dir, 'sample_audvis-mag-oct-6-fwd-sensmap-3'),
ftype='w')
# Compute some with the EOG + ECG projectors
projs = ecg_proj + eog_proj + raw.info['projs']
for map_type in ['radiality', 'angle', 'remaining', 'dampening']:
eeg_map = mne.sensitivity_map(fwd,
projs=projs,
ch_type='eeg',
mode=map_type)
eeg_map.save(
join(meg_dir, 'sample_audvis-eeg-oct-6-fwd-sensmap-' + map_type))
###############################################################################
# Compute MNE inverse operators
#
# Note: The MEG/EEG forward solution could be used for all
#
inv_meg = make_inverse_operator(raw.info, fwd_meg, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-inv.fif')
write_inverse_operator(fname, inv_meg)
inv_eeg = make_inverse_operator(raw.info, fwd_eeg, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-eeg-oct-6-eeg-inv.fif')
write_inverse_operator(fname, inv_eeg)
inv = make_inverse_operator(raw.info, fwd, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-eeg-oct-6-meg-eeg-inv.fif')
write_inverse_operator(fname, inv)
# inverse operator with fixed orientation (for testing). Not implemented
#inv_fixed = make_inverse_operator(raw.info, fwd_meg, noise_cov,
# depth=None, fixed=True)
#fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-nodepth-fixed-inv.fif')
#write_inverse_operator(fname, inv_fixed)
# produce two with diagonal noise (for testing)
diag = noise_cov.as_diag()
inv_meg_diag = make_inverse_operator(raw.info, fwd_meg, diag, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-diagnoise-inv.fif')
write_inverse_operator(fname, inv_meg_diag)
inv_eeg_diag = make_inverse_operator(raw.info, fwd, diag, loose=0.2)
fname = join(meg_dir,
'sample_audvis-meg-eeg-oct-6-meg-eeg-diagnoise-inv.fif')
write_inverse_operator(fname, inv_eeg_diag)
# Produce stc files
evoked.crop(0, 0.25)
stc_meg = apply_inverse(evoked, inv_meg, method='MNE')
stc_meg.save(join(meg_dir, 'sample_audvis-meg'))
stc_eeg = apply_inverse(evoked, inv_eeg, method='MNE')
stc_eeg.save(join(meg_dir, 'sample_audvis-eeg'))
stc = apply_inverse(evoked, inv, method='MNE')
stc.save(join(meg_dir, 'sample_audvis-meg-eeg'))
# let's also morph to fsaverage
stc_to = stc_meg.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-meg'))
stc_to = stc_eeg.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-eeg'))
stc_to = stc.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-meg-eeg'))
###############################################################################
# Do one dipole fitting
evoked = evoked.pick_types(meg=True, eeg=False)
evoked.crop(0.04, 0.095)
bem = join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
dip, _ = mne.fit_dipole(evoked, noise_cov, bem, trans)
dip.save(join(meg_dir, 'sample_audvis_set1.dip'))
def test_make_forward_solution_kit():
"""Test making fwd using KIT, BTI, and CTF (compensated) files
"""
kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit',
'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
mri_path = op.join(kit_dir, 'trans-sample.fif')
fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')
bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti',
'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
'data', 'test_ctf_comp_raw.fif')
# first set up a small testing source space
temp_dir = _TempDir()
fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif')
src = setup_source_space('sample', fname_src_small, 'oct2',
subjects_dir=subjects_dir, add_dist=False)
n_src = 108 # this is the resulting # of verts in fwd
# first use mne-C: convert file, make forward solution
fwd = do_forward_solution('sample', fname_kit_raw, src=fname_src_small,
bem=fname_bem_meg, mri=mri_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
assert_true(isinstance(fwd, Forward))
# now let's use python with the same raw file
fwd_py = make_forward_solution(fname_kit_raw, src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=mri_path)
_compare_forwards(fwd, fwd_py, 157, n_src)
assert_true(isinstance(fwd_py, Forward))
# now let's use mne-python all the way
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# without ignore_ref=True, this should throw an error:
assert_raises(NotImplementedError, make_forward_solution, raw_py.info,
src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=mri_path)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=mri_path,
ignore_ref=True)
_compare_forwards(fwd, fwd_py, 157, n_src,
meg_rtol=1e-3, meg_atol=1e-7)
# BTI python end-to-end versus C
fwd = do_forward_solution('sample', fname_bti_raw, src=fname_src_small,
bem=fname_bem_meg, mri=mri_path,
eeg=False, meg=True, subjects_dir=subjects_dir)
raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs)
fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=mri_path)
_compare_forwards(fwd, fwd_py, 248, n_src)
# now let's test CTF w/compensation
fwd_py = make_forward_solution(fname_ctf_raw, src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=fname_mri)
fwd = do_forward_solution('sample', fname_ctf_raw, src=fname_src_small,
bem=fname_bem_meg, mri=fname_mri,
eeg=False, meg=True, subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
# CTF with compensation changed in python
ctf_raw = Raw(fname_ctf_raw, compensation=2)
fwd_py = make_forward_solution(ctf_raw.info, src=src, eeg=False, meg=True,
bem=fname_bem_meg, mri=fname_mri)
with warnings.catch_warnings(record=True):
fwd = do_forward_solution('sample', ctf_raw, src=fname_src_small,
bem=fname_bem_meg,
mri=fname_mri, eeg=False, meg=True,
subjects_dir=subjects_dir)
_compare_forwards(fwd, fwd_py, 274, n_src)
def run():
args = sys.argv
if len(args) <= 1:
print 'Usage: run_meg_tutorial.sh <sample data directory>'
return
sample_dir = args[1]
subjects_dir = join(sample_dir, 'subjects')
meg_dir = join(sample_dir, 'MEG', 'sample')
os.environ['SUBJECTS_DIR'] = subjects_dir
os.environ['MEG_DIR'] = meg_dir
subject = 'sample'
src = setup_source_space(subject, fname=True, spacing='oct6', n_jobs=2,
overwrite=True)
# If one wanted to use other source spaces, these types of options are
# available
src_fsaverage = setup_source_space('fsaverage', fname=True, spacing='ico5',
n_jobs=2, overwrite=True,
add_dist=False)
morph_source_spaces(src_fsaverage, subject_to='sample')
setup_source_space(subject, fname=True, spacing='all', overwrite=True,
n_jobs=2, add_dist=False)
# Add distances to source space (if desired, takes a long time)
bem_dir = join(subjects_dir, join('sample', 'bem'))
os.rename(join(bem_dir, 'sample-oct-6-src.fif'),
join(bem_dir, 'sample-oct-6-orig-src.fif'))
new_src = add_source_space_distances(src, dist_limit=0.007)
new_src.save(join(bem_dir, 'sample-oct-6-src.fif'))
# Preprocessing
raw = mne.io.Raw(join(meg_dir, 'sample_audvis_raw.fif'), preload=True)
raw.info['bads'] = ['MEG 2443', 'EEG 053']
reject = dict(grad=3000e-13, mag=4000e-15, eeg=100e-6)
ecg_proj, _ = mne.preprocessing.compute_proj_ecg(raw, l_freq=1, h_freq=100,
ch_name='MEG 1531',
reject=reject)
eog_proj, _ = mne.preprocessing.compute_proj_eog(raw, l_freq=1, h_freq=35,
reject=reject,
no_proj=True)
events = mne.find_events(raw)
mne.write_events(join(meg_dir, 'sample_audvis_raw-eve.fif'), events)
event_id = [1, 2, 3, 4]
tmin, tmax = -0.2, 0.5
picks = mne.pick_types(raw.info, meg=True, eeg=True, stim=True, eog=True)
# Average with no filter
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks)
evoked = epochs.average()
evoked.save(join(meg_dir, 'sample_audvis-no-filter-ave.fif'))
raw.filter(l_freq=None, h_freq=40)
raw_resampled = raw.resample(150)
raw_resampled.save(join(meg_dir, 'sample_audvis_filt-0-40_raw.fif'),
overwrite=True)
raw.add_proj(ecg_proj)
raw.add_proj(eog_proj)
resampled_events = mne.find_events(raw_resampled)
mne.write_events(join(meg_dir, 'sample_audvis_filt-0-40_raw-eve.fif'),
resampled_events)
# Average with filter
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks)
evoked = epochs.average()
evoked.save(join(meg_dir, 'sample_audvis-ave.fif'))
# Compute the noise covariance matrix
noise_cov = mne.compute_raw_data_covariance(raw, picks=picks)
noise_cov.save(join(meg_dir, 'audvis.cov'))
# Compute the empty-room noise covariance matrix
ernoise_raw = mne.io.Raw(join(meg_dir, 'ernoise_raw.fif'), preload=True)
ernoise_raw.info['bads'] = ['MEG 2443']
ernoise_raw.filter(l_freq=None, h_freq=40)
picks = mne.pick_types(ernoise_raw.info, meg=True, eeg=True, stim=True,
eog=True)
ernoise_cov = mne.compute_raw_data_covariance(ernoise_raw, picks=picks)
ernoise_cov.save(join(meg_dir, 'ernoise.cov'))
###############################################################################
# Compute forward solution a.k.a. lead field
trans = join(meg_dir, 'sample_audvis_raw-trans.fif')
bem = join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
# for MEG only
fname = join(meg_dir, 'sample_audvis-meg-oct-6-fwd.fif')
fwd_meg = mne.make_forward_solution(raw.info, trans, src, bem,
fname=fname, meg=True, eeg=False,
mindist=5.0, n_jobs=2, overwrite=True)
# for EEG only
bem = join(subjects_dir, 'sample', 'bem',
'sample-5120-5120-5120-bem-sol.fif')
fname = join(meg_dir, 'sample_audvis-eeg-oct-6-fwd.fif')
fwd_eeg = mne.make_forward_solution(raw.info, trans, src, bem,
fname=fname, meg=False, eeg=True,
mindist=5.0, n_jobs=2, overwrite=True)
# for both EEG and MEG
fname = join(meg_dir, 'sample_audvis-meg-eeg-oct-6-fwd.fif')
fwd = mne.make_forward_solution(raw.info, trans, src, bem,
fname=fname, meg=True, eeg=True,
mindist=5.0, n_jobs=2, overwrite=True)
# Create various sensitivity maps
grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='free')
grad_map.save(join(meg_dir, 'sample_audvis-grad-oct-6-fwd-sensmap'),
ftype='w')
mag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='free')
mag_map.save(join(meg_dir, 'sample_audvis-mag-oct-6-fwd-sensmap'),
ftype='w')
eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='free')
eeg_map.save(join(meg_dir, 'sample_audvis-eeg-oct-6-fwd-sensmap'),
ftype='w')
grad_map2 = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')
grad_map2.save(join(meg_dir, 'sample_audvis-grad-oct-6-fwd-sensmap-2'),
ftype='w')
mag_map2 = mne.sensitivity_map(fwd, ch_type='mag', mode='ratio')
mag_map2.save(join(meg_dir, 'sample_audvis-mag-oct-6-fwd-sensmap-3'),
ftype='w')
# Compute some with the EOG + ECG projectors
projs = ecg_proj + eog_proj + raw.info['projs']
for map_type in ['radiality', 'angle', 'remaining', 'dampening']:
eeg_map = mne.sensitivity_map(fwd, projs=projs, ch_type='eeg',
mode=map_type)
eeg_map.save(join(meg_dir,
'sample_audvis-eeg-oct-6-fwd-sensmap-' + map_type))
###############################################################################
# Compute MNE inverse operators
#
# Note: The MEG/EEG forward solution could be used for all
#
inv_meg = make_inverse_operator(raw.info, fwd_meg, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-inv.fif')
write_inverse_operator(fname, inv_meg)
inv_eeg = make_inverse_operator(raw.info, fwd_eeg, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-eeg-oct-6-eeg-inv.fif')
write_inverse_operator(fname, inv_eeg)
inv = make_inverse_operator(raw.info, fwd, noise_cov, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-eeg-oct-6-meg-eeg-inv.fif')
write_inverse_operator(fname, inv)
# inverse operator with fixed orientation (for testing). Not implemented
#inv_fixed = make_inverse_operator(raw.info, fwd_meg, noise_cov,
# depth=None, fixed=True)
#fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-nodepth-fixed-inv.fif')
#write_inverse_operator(fname, inv_fixed)
# produce two with diagonal noise (for testing)
diag = noise_cov.as_diag()
inv_meg_diag = make_inverse_operator(raw.info, fwd_meg, diag, loose=0.2)
fname = join(meg_dir, 'sample_audvis-meg-oct-6-meg-diagnoise-inv.fif')
write_inverse_operator(fname, inv_meg_diag)
inv_eeg_diag = make_inverse_operator(raw.info, fwd, diag, loose=0.2)
fname = join(meg_dir,
'sample_audvis-meg-eeg-oct-6-meg-eeg-diagnoise-inv.fif')
write_inverse_operator(fname, inv_eeg_diag)
# Produce stc files
evoked.crop(0, 0.25)
stc_meg = apply_inverse(evoked, inv_meg, method='MNE')
stc_meg.save(join(meg_dir, 'sample_audvis-meg'))
stc_eeg = apply_inverse(evoked, inv_eeg, method='MNE')
stc_eeg.save(join(meg_dir, 'sample_audvis-eeg'))
stc = apply_inverse(evoked, inv, method='MNE')
stc.save(join(meg_dir, 'sample_audvis-meg-eeg'))
# let's also morph to fsaverage
stc_to = stc_meg.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-meg'))
stc_to = stc_eeg.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-eeg'))
stc_to = stc.morph('fsaverage', grade=3, smooth=12)
stc_to.save(join(meg_dir, 'fsaverage_audvis-meg-eeg'))
###############################################################################
# Do one dipole fitting
evoked = evoked.pick_types(meg=True, eeg=False)
evoked.crop(0.04, 0.095)
bem = join(subjects_dir, 'sample', 'bem', 'sample-5120-bem-sol.fif')
dip, _ = mne.fit_dipole(evoked, noise_cov, bem, trans)
dip.save(join(meg_dir, 'sample_audvis_set1.dip'))
