def test_set_dig_montage():
"""Test applying DigMontage to inst."""
# Extensive testing of applying `dig` to info is done in test_meas_info
# with `test_make_dig_points`.
names = ["nasion", "lpa", "rpa", "1", "2", "3", "4", "5"]
hsp_points = _read_dig_points(hsp)
elp_points = _read_dig_points(elp)
nasion, lpa, rpa = elp_points[:3]
nm_trans = get_ras_to_neuromag_trans(nasion, lpa, rpa)
elp_points = apply_trans(nm_trans, elp_points)
nasion, lpa, rpa = elp_points[:3]
hsp_points = apply_trans(nm_trans, hsp_points)
montage = read_dig_montage(hsp, hpi, elp, names, transform=True, dev_head_t=True)
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, "test.fif")
montage.save(fname_temp)
montage_read = read_dig_montage(fif=fname_temp)
for use_mon in (montage, montage_read):
info = create_info(["Test Ch"], 1e3, ["eeg"])
with warnings.catch_warnings(record=True) as w: # test ch pos not set
_set_montage(info, use_mon)
assert_true(all("not set" in str(ww.message) for ww in w))
hs = np.array([p["r"] for i, p in enumerate(info["dig"]) if p["kind"] == FIFF.FIFFV_POINT_EXTRA])
nasion_dig = np.array(
[
p["r"]
for p in info["dig"]
if all([p["ident"] == FIFF.FIFFV_POINT_NASION, p["kind"] == FIFF.FIFFV_POINT_CARDINAL])
]
)
lpa_dig = np.array(
[
p["r"]
for p in info["dig"]
if all([p["ident"] == FIFF.FIFFV_POINT_LPA, p["kind"] == FIFF.FIFFV_POINT_CARDINAL])
]
)
rpa_dig = np.array(
[
p["r"]
for p in info["dig"]
if all([p["ident"] == FIFF.FIFFV_POINT_RPA, p["kind"] == FIFF.FIFFV_POINT_CARDINAL])
]
)
hpi_dig = np.array([p["r"] for p in info["dig"] if p["kind"] == FIFF.FIFFV_POINT_HPI])
assert_allclose(hs, hsp_points, atol=1e-7)
assert_allclose(nasion_dig.ravel(), nasion, atol=1e-7)
assert_allclose(lpa_dig.ravel(), lpa, atol=1e-7)
assert_allclose(rpa_dig.ravel(), rpa, atol=1e-7)
assert_allclose(hpi_dig, elp_points[3:], atol=1e-7)
def test_fit_matched_points():
"""Test fit_matched_points: fitting two matching sets of points"""
tgt_pts = np.random.uniform(size=(6, 3))
# rotation only
trans = rotation(2, 6, 3)
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, translate=False, out="trans")
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation")
# rotation & scaling
trans = np.dot(rotation(2, 6, 3), scaling(0.5, 0.5, 0.5))
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, translate=False, scale=1, out="trans")
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation and scaling.")
# rotation & translation
trans = np.dot(translation(2, -6, 3), rotation(2, 6, 3))
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, out="trans")
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation and translation.")
# rotation & translation & scaling
trans = reduce(np.dot, (translation(2, -6, 3), rotation(1.5, 0.3, 1.4), scaling(0.5, 0.5, 0.5)))
src_pts = apply_trans(trans, tgt_pts)
trans_est = fit_matched_points(src_pts, tgt_pts, scale=1, out="trans")
est_pts = apply_trans(trans_est, src_pts)
assert_array_almost_equal(tgt_pts, est_pts, 2, "fit_matched_points with " "rotation, translation and scaling.")
# test exceeding tolerance
tgt_pts[0, :] += 20
assert_raises(RuntimeError, fit_matched_points, tgt_pts, src_pts, tol=10)
def plot_coregistration(subject, subjects_dir, hcp_path, recordings_path,
info_from=(('data_type', 'rest'), ('run_index', 0)),
view_init=(('azim', 0), ('elev', 0))):
"""A diagnostic plot to show the HCP coregistration
Parameters
----------
subject : str
The subject
subjects_dir : str
The path corresponding to MNE/freesurfer SUBJECTS_DIR (to be created)
hcp_path : str
The path where the HCP files can be found.
recordings_path : str
The path to converted data (including the head<->device transform).
info_from : tuple of tuples | dict
The reader info concerning the data from which sensor positions
should be read.
Must not be empty room as sensor positions are in head
coordinates for 4D systems, hence not available in that case.
Note that differences between the sensor positions across runs
are smaller than 12 digits, hence negligible.
view_init : tuple of tuples | dict
The initival view, defaults to azimuth and elevation of 0,
a simple lateral view
Returns
-------
fig : matplotlib.figure.Figure
The figure object.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
if isinstance(info_from, tuple):
info_from = dict(info_from)
if isinstance(view_init, tuple):
view_init = dict(view_init)
head_mri_t = read_trans(
op.join(recordings_path, subject,
'{}-head_mri-trans.fif'.format(subject)))
info = read_info(subject=subject, hcp_path=hcp_path, **info_from)
info = pick_info(info, _pick_data_channels(info, with_ref_meg=False))
sens_pnts = np.array([c['loc'][:3] for c in info['chs']])
sens_pnts = apply_trans(head_mri_t, sens_pnts)
sens_pnts *= 1e3 # put in mm scale
pnts, tris = read_surface(
op.join(subjects_dir, subject, 'bem', 'inner_skull.surf'))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(*sens_pnts.T, color='purple', marker='o')
ax.scatter(*pnts.T, color='green', alpha=0.3)
ax.view_init(**view_init)
fig.tight_layout()
return fig
def _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir):
"""Compute dipole depth."""
trans = _get_trans(fname_trans)[0]
bem = read_bem_solution(fname_bem)
surf = _bem_find_surface(bem, 'inner_skull')
points = surf['rr']
points = apply_trans(trans['trans'], points)
depth = _compute_nearest(points, dip.pos, return_dists=True)[1][0]
return np.ravel(depth)
def test_fit_point_cloud():
"""Test fit_point_cloud: fitting a set of points to a point cloud"""
# evenly spaced target points on a sphere
u = np.linspace(0, np.pi, 150)
v = np.linspace(0, np.pi, 150)
x = np.outer(np.cos(u), np.sin(v)).reshape((-1, 1))
y = np.outer(np.sin(u), np.sin(v)).reshape((-1, 1))
z = np.outer(np.ones(np.size(u)), np.cos(v)).reshape((-1, 1)) * 3
tgt_pts = np.hstack((x, y, z))
tgt_pts = _decimate_points(tgt_pts, .05)
# pick some points to fit
some_tgt_pts = tgt_pts[::362]
# rotation only
trans = rotation(1.5, .3, -0.4)
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation.")
# rotation and translation
trans = np.dot(rotation(0.5, .3, -0.4), translation(.3, .2, -.2))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=True,
scale=0, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and "
"translation.")
# rotation and 1 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.5, 1.5))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=1, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 1 scaling "
"parameter.")
# rotation and 3 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.7, 1.1))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts, tgt_pts, rotate=True, translate=False,
scale=3, out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and 3 scaling "
"parameters.")
def test_hsp_elp():
"""Test KIT usage of *.elp and *.hsp files against *.txt files."""
raw_txt = read_raw_kit(sqd_path, mrk_path, elp_txt_path, hsp_txt_path)
raw_elp = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
# head points
pts_txt = np.array([dig_point['r'] for dig_point in raw_txt.info['dig']])
pts_elp = np.array([dig_point['r'] for dig_point in raw_elp.info['dig']])
assert_array_almost_equal(pts_elp, pts_txt, decimal=5)
# transforms
trans_txt = raw_txt.info['dev_head_t']['trans']
trans_elp = raw_elp.info['dev_head_t']['trans']
assert_array_almost_equal(trans_elp, trans_txt, decimal=5)
# head points in device space
pts_txt_in_dev = apply_trans(linalg.inv(trans_txt), pts_txt)
pts_elp_in_dev = apply_trans(linalg.inv(trans_elp), pts_elp)
assert_array_almost_equal(pts_elp_in_dev, pts_txt_in_dev, decimal=5)
def test_set_dig_montage():
"""Test applying DigMontage to inst."""
# Extensive testing of applying `dig` to info is done in test_meas_info
# with `test_make_dig_points`.
names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']
hsp_points = _read_dig_points(hsp)
elp_points = _read_dig_points(elp)
nasion, lpa, rpa = elp_points[:3]
nm_trans = get_ras_to_neuromag_trans(nasion, lpa, rpa)
elp_points = apply_trans(nm_trans, elp_points)
nasion, lpa, rpa = elp_points[:3]
hsp_points = apply_trans(nm_trans, hsp_points)
montage = read_dig_montage(hsp, hpi, elp, names, transform=True,
dev_head_t=True)
temp_dir = _TempDir()
fname_temp = op.join(temp_dir, 'test.fif')
montage.save(fname_temp)
montage_read = read_dig_montage(fif=fname_temp)
for use_mon in (montage, montage_read):
info = create_info(['Test Ch'], 1e3, ['eeg'])
with pytest.warns(None): # warns on one run about not all positions
_set_montage(info, use_mon)
hs = np.array([p['r'] for i, p in enumerate(info['dig'])
if p['kind'] == FIFF.FIFFV_POINT_EXTRA])
nasion_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_NASION,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])
])
lpa_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_LPA,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])
rpa_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_RPA,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])
hpi_dig = np.array([p['r'] for p in info['dig']
if p['kind'] == FIFF.FIFFV_POINT_HPI])
assert_allclose(hs, hsp_points, atol=1e-7)
assert_allclose(nasion_dig.ravel(), nasion, atol=1e-7)
assert_allclose(lpa_dig.ravel(), lpa, atol=1e-7)
assert_allclose(rpa_dig.ravel(), rpa, atol=1e-7)
assert_allclose(hpi_dig, elp_points[3:], atol=1e-7)
def test_get_ras_to_neuromag_trans():
"""Test the coordinate transformation from ras to neuromag"""
# create model points in neuromag-like space
anterior = [0, 1, 0]
left = [-1, 0, 0]
right = [0.8, 0, 0]
up = [0, 0, 1]
rand_pts = np.random.uniform(-1, 1, (3, 3))
pts = np.vstack((anterior, left, right, up, rand_pts))
# change coord system
rx, ry, rz, tx, ty, tz = np.random.uniform(-2 * np.pi, 2 * np.pi, 6)
trans = np.dot(translation(tx, ty, tz), rotation(rx, ry, rz))
pts_changed = apply_trans(trans, pts)
# transform back into original space
nas, lpa, rpa = pts_changed[:3]
hsp_trans = get_ras_to_neuromag_trans(nas, lpa, rpa)
pts_restored = apply_trans(hsp_trans, pts_changed)
err = "Neuromag transformation failed"
assert_array_almost_equal(pts_restored, pts, 6, err)
def test_set_dig_montage():
"""Test applying DigMontage to inst
Extensive testing of applying `dig` to info is done in test_meas_info
with `test_make_dig_points`.
"""
names = ['nasion', 'lpa', 'rpa', '1', '2', '3', '4', '5']
hsp_points = _read_dig_points(hsp)
elp_points = _read_dig_points(elp)
hpi_points = read_mrk(hpi)
p0, p1, p2 = elp_points[:3]
nm_trans = get_ras_to_neuromag_trans(p0, p1, p2)
elp_points = apply_trans(nm_trans, elp_points)
nasion_point, lpa_point, rpa_point = elp_points[:3]
hsp_points = apply_trans(nm_trans, hsp_points)
montage = read_dig_montage(hsp, hpi, elp, names, unit='m', transform=True)
info = create_info(['Test Ch'], 1e3, ['eeg'])
_set_montage(info, montage)
hs = np.array([p['r'] for i, p in enumerate(info['dig'])
if p['kind'] == FIFF.FIFFV_POINT_EXTRA])
nasion_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_NASION,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])
lpa_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_LPA,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])
rpa_dig = np.array([p['r'] for p in info['dig']
if all([p['ident'] == FIFF.FIFFV_POINT_RPA,
p['kind'] == FIFF.FIFFV_POINT_CARDINAL])])
hpi_dig = np.array([p['r'] for p in info['dig']
if p['kind'] == FIFF.FIFFV_POINT_HPI])
assert_array_equal(hs, hsp_points)
assert_array_equal(nasion_dig.ravel(), nasion_point)
assert_array_equal(lpa_dig.ravel(), lpa_point)
assert_array_equal(rpa_dig.ravel(), rpa_point)
assert_array_equal(hpi_dig, hpi_points)
assert_array_equal(montage.dev_head_t, info['dev_head_t']['trans'])
def test_head_to_mni():
"""Test conversion of aseg vertices to MNI coordinates."""
# obtained using freeview
coords = np.array([[22.52, 11.24, 17.72], [22.52, 5.46, 21.58],
[16.10, 5.46, 22.23], [21.24, 8.36, 22.23]])
xfm = _read_talxfm('sample', subjects_dir)
coords_MNI = apply_trans(xfm['trans'], coords)
trans = read_trans(trans_fname) # head->MRI (surface RAS)
mri_head_t = invert_transform(trans) # MRI (surface RAS)->head matrix
# obtained from sample_audvis-meg-oct-6-mixed-fwd.fif
coo_right_amygdala = np.array([[0.01745682, 0.02665809, 0.03281873],
[0.01014125, 0.02496262, 0.04233755],
[0.01713642, 0.02505193, 0.04258181],
[0.01720631, 0.03073877, 0.03850075]])
coords_MNI_2 = head_to_mni(coo_right_amygdala, 'sample', mri_head_t,
subjects_dir)
# less than 1mm error
assert_allclose(coords_MNI, coords_MNI_2, atol=10.0)
def test_coregister_fiducials():
"""Test coreg.coregister_fiducials()."""
# prepare head and MRI fiducials
trans = Transform('head', 'mri',
rotation(.4, .1, 0).dot(translation(.1, -.1, .1)))
coords_orig = np.array([[-0.08061612, -0.02908875, -0.04131077],
[0.00146763, 0.08506715, -0.03483611],
[0.08436285, -0.02850276, -0.04127743]])
coords_trans = apply_trans(trans, coords_orig)
def make_dig(coords, cf):
return ({'coord_frame': cf, 'ident': 1, 'kind': 1, 'r': coords[0]},
{'coord_frame': cf, 'ident': 2, 'kind': 1, 'r': coords[1]},
{'coord_frame': cf, 'ident': 3, 'kind': 1, 'r': coords[2]})
mri_fiducials = make_dig(coords_trans, FIFF.FIFFV_COORD_MRI)
info = {'dig': make_dig(coords_orig, FIFF.FIFFV_COORD_HEAD)}
# test coregister_fiducials()
trans_est = coregister_fiducials(info, mri_fiducials)
assert trans_est.from_str == trans.from_str
assert trans_est.to_str == trans.to_str
assert_array_almost_equal(trans_est['trans'], trans['trans'])
def _interpolate_bads_meg(inst, mode='accurate', origin=None, verbose=None):
"""Interpolate bad channels from data in good channels.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
mode : str
Either `'accurate'` or `'fast'`, determines the quality of the
Legendre polynomial expansion used for interpolation. `'fast'` should
be sufficient for most applications.
origin : None | list
If None, origin is set to sensor center of mass, otherwise use the
coordinates provided as origin. The old standard value is (0., 0., 0.04)
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
"""
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
meg_ch_names = [inst.info['ch_names'][p] for p in picks_meg]
bads_meg = [ch for ch in inst.info['bads'] if ch in meg_ch_names]
# select the bad meg channel to be interpolated
if len(bads_meg) == 0:
picks_bad = []
else:
picks_bad = pick_channels(inst.info['ch_names'], bads_meg, exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
info_from = pick_info(inst.info, picks_good)
info_to = pick_info(inst.info, picks_bad)
if origin is None:
posvec = np.array([inst.info['chs'][p]['loc'][0:3] for p in picks_meg])
norvec = np.array(
[inst.info['chs'][p]['loc'][9:12] for p in picks_meg])
cogpos = np.mean(posvec, axis=0)
norsum = np.mean(norvec, axis=0)
anorm = np.sqrt(np.dot(norsum, norsum.T))
ndir = norsum / anorm
# push the position slightly (4cm) away from the helmet:
altpos = cogpos - 0.04 * ndir
print(">_interpolate_bads_meg\\DBG> cog(sens) = [%8.5f %8.5f %8.5f]" % \
(cogpos[0], cogpos[1], cogpos[2]))
print(">_interpolate_bads_meg\\DBG> alt(sens) = [%8.5f %8.5f %8.5f]" % \
(altpos[0], altpos[1], altpos[2]))
cogposhd = apply_trans(inst.info['dev_head_t']['trans'],
cogpos,
move=True)
altposhd = apply_trans(inst.info['dev_head_t']['trans'],
altpos,
move=True)
print(">_interpolate_bads_meg\\DBG> cog(hdcs) = [%8.5f %8.5f %8.5f]" % \
(cogposhd[0], cogposhd[1], cogposhd[2]))
print(">_interpolate_bads_meg\\DBG> alt(hdcs) = [%8.5f %8.5f %8.5f]" % \
(altposhd[0], altposhd[1], altposhd[2]))
print(">_interpolate_bads_meg\\DBG> calling _map_meg_channels(..., origin=(%8.5f %8.5f %8.5f))" % \
(altposhd[0], altposhd[1], altposhd[2]))
origin = (altposhd[0], altposhd[1], altposhd[2])
else:
origin = origin
mapping = _map_meg_channels(info_from, info_to, mode=mode, origin=origin)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
def test_scale_mri():
"""Test creating fsaverage and scaling it."""
# create fsaverage using the testing "fsaverage" instead of the FreeSurfer
# one
tempdir = _TempDir()
fake_home = testing.data_path()
create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
verbose=True)
assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed"
fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif')
os.remove(fid_path)
create_default_subject(update=True, subjects_dir=tempdir,
fs_home=fake_home)
assert op.exists(fid_path), "Updating fsaverage"
# copy MRI file from sample data (shouldn't matter that it's incorrect,
# so here choose a small one)
path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri',
'T1.mgz')
path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
copyfile(path_from, path_to)
# remove redundant label files
label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label')
label_paths = glob(label_temp)
for label_path in label_paths[1:]:
os.remove(label_path)
# create source space
print('Creating surface source space')
path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
add_dist=False)
write_source_spaces(path % 'ico-0', src)
mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
print('Creating volume source space')
vsrc = mne.setup_volume_source_space(
'fsaverage', pos=50, mri=mri, subjects_dir=tempdir,
add_interpolator=False)
write_source_spaces(path % 'vol-50', vsrc)
# scale fsaverage
for scale in (.9, [1, .2, .8]):
os.environ['_MNE_FEW_SURFACES'] = 'true'
scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir,
verbose='debug')
del os.environ['_MNE_FEW_SURFACES']
assert _is_mri_subject('flachkopf', tempdir), "Scaling failed"
spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')
assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled"
assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf',
'lh.sphere.reg'))
vsrc_s = mne.read_source_spaces(spath % 'vol-50')
pt = np.array([0.12, 0.41, -0.22])
assert_array_almost_equal(
apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)),
apply_trans(vsrc[0]['src_mri_t'], pt))
scale_labels('flachkopf', subjects_dir=tempdir)
# add distances to source space
mne.add_source_space_distances(src)
src.save(path % 'ico-0', overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert ssrc[0]['dist'] is not None
def transform_voxels_to_RAS(aseg_hdr, pts):
# Transform data to RAS coordinates
trans = aseg_hdr.get_vox2ras_tkr()
pts = apply_trans(trans, pts)
return pts
def make_mne_anatomy(subject, subjects_dir, recordings_path=None,
hcp_path=op.curdir, outputs=('label', 'mri', 'surf')):
"""Extract relevant anatomy and create MNE friendly directory layout
The function will create the following outputs by default:
$subjects_dir/$subject/bem/inner_skull.surf
$subjects_dir/$subject/label/*
$subjects_dir/$subject/mri/*
$subjects_dir/$subject/surf/*
$recordings_path/$subject/$subject-head_mri-trans.fif
These can then be set as $SUBJECTS_DIR and as MEG directory, consistent
with MNE examples.
Parameters
----------
subject : str
The subject name.
subjects_dir : str
The path corresponding to MNE/freesurfer SUBJECTS_DIR (to be created)
hcp_path : str
The path where the HCP files can be found.
outputs : {'label', 'mri', 'stats', 'surf', 'touch'}
The outputs of the freesrufer pipeline shipped by HCP. Defaults to
('mri', 'surf'), the minimum needed to extract MNE-friendly anatomy
files and data.
"""
if hcp_path == op.curdir:
hcp_path = op.realpath(hcp_path)
if not op.isabs(subjects_dir):
subjects_dir = op.realpath(subjects_dir)
this_subjects_dir = op.join(subjects_dir, subject)
if not op.isabs(recordings_path):
recordings_path = op.realpath(recordings_path)
this_recordings_path = op.join(recordings_path, subject)
if not op.exists(this_recordings_path):
os.makedirs(this_recordings_path)
for output in outputs:
if not op.exists(op.join(this_subjects_dir, output)):
os.makedirs(op.join(this_subjects_dir, output))
if output == 'mri':
for suboutput in ['orig', 'transforms']:
if not op.exists(
op.join(this_subjects_dir, output, suboutput)):
os.makedirs(op.join(this_subjects_dir, output, suboutput))
files = get_file_paths(
subject=subject, data_type='freesurfer', output=output,
hcp_path=hcp_path)
for source in files:
match = [match for match in re.finditer(subject, source)][-1]
split_path = source[:match.span()[1] + 1]
target = op.join(this_subjects_dir, source.split(split_path)[-1])
if (not op.isfile(target) and not op.islink(target) and
op.exists(source)): # don't link if it's not there.
if sys.platform != 'win32':
os.symlink(source, target)
else:
shutil.copyfile(source, target)
logger.info('reading extended structural processing ...')
# Step 1 #################################################################
# transform head models to expected coordinate system
# make hcp trans
transforms_fname = get_file_paths(
subject=subject, data_type='meg_anatomy', output='transforms',
hcp_path=hcp_path)
transforms_fname = [k for k in transforms_fname if
k.endswith('transform.txt')][0]
hcp_trans = _read_trans_hcp(fname=transforms_fname, convert_to_meter=False)
# get RAS freesurfer trans
c_ras_trans_fname = get_file_paths(
subject=subject, data_type='freesurfer', output='mri',
hcp_path=hcp_path)
c_ras_trans_fname = [k for k in c_ras_trans_fname if
k.endswith('c_ras.mat')][0]
logger.info('reading RAS freesurfer transform')
# ceci n'est pas un .mat file ...
with open(op.join(subjects_dir, c_ras_trans_fname)) as fid:
ras_trans = np.array([
r.split() for r in fid.read().split('\n') if r],
dtype=np.float64)
logger.info('Combining RAS transform and coregistration')
ras_trans_m = linalg.inv(ras_trans) # and the inversion
logger.info('extracting head model')
head_model_fname = get_file_paths(
subject=subject, data_type='meg_anatomy', output='head_model',
hcp_path=hcp_path)[0]
pnts, faces = _get_head_model(head_model_fname=head_model_fname)
logger.info('coregistring head model to MNE-HCP coordinates')
pnts = apply_trans(ras_trans_m.dot(hcp_trans['bti2spm']), pnts)
tri_fname = op.join(this_subjects_dir, 'bem', 'inner_skull.surf')
if not op.exists(op.dirname(tri_fname)):
os.makedirs(op.dirname(tri_fname))
write_surface(tri_fname, pnts, faces)
# Step 2 #################################################################
# write corresponding device to MRI transform
logger.info('extracting coregistration')
# now convert to everything meter too here
ras_trans_m[:3, 3] *= 1e-3
bti2spm = hcp_trans['bti2spm']
bti2spm[:3, 3] *= 1e-3
head_mri_t = Transform( # we're lying here for a good purpose
'head', 'mri', np.dot(ras_trans_m, bti2spm)) # it should be 'ctf_head'
write_trans(op.join(this_recordings_path,
'%s-head_mri-trans.fif' % subject), head_mri_t)
def test_nirx_15_2_short():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_2_short, preload=True)
# Test data import
assert raw._data.shape == (26, 145)
assert raw.info['sfreq'] == 12.5
assert raw.info['meas_date'] == dt.datetime(2019,
8,
23,
7,
37,
4,
540000,
tzinfo=dt.timezone.utc)
# Test channel naming
assert raw.info['ch_names'][:4] == [
"S1_D1 760", "S1_D1 850", "S1_D9 760", "S1_D9 850"
]
assert raw.info['ch_names'][24:26] == ["S5_D13 760", "S5_D13 850"]
# Test frequency encoding
assert raw.info['chs'][0]['loc'][9] == 760
assert raw.info['chs'][1]['loc'][9] == 850
# Test info import
assert raw.info['subject_info'] == dict(sex=1,
first_name="MNE",
middle_name="Test",
last_name="Recording",
birthday=(2014, 8, 23))
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/51
# step 4 figure 2
allowed_distance_error = 0.0002
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0304, 0.0078, 0.0310, 0.0086, 0.0416, 0.0072, 0.0389, 0.0075, 0.0558,
0.0562, 0.0561, 0.0565, 0.0077
],
atol=allowed_distance_error)
# Test which channels are short
# These are the ones marked as red at
# https://github.com/mne-tools/mne-testing-data/pull/51 step 4 figure 2
is_short = short_channels(raw.info)
assert_array_equal(is_short[:9:2], [False, True, False, True, False])
is_short = short_channels(raw.info, threshold=0.003)
assert_array_equal(is_short[:3:2], [False, False])
is_short = short_channels(raw.info, threshold=50)
assert_array_equal(is_short[:3:2], [True, True])
# Test trigger events
assert_array_equal(raw.annotations.description, ['3.0', '2.0', '1.0'])
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/51
# And have been manually copied below
# These values were reported in mm, but according to this page...
# https://mne.tools/stable/auto_tutorials/intro/plot_40_sensor_locations.html
# 3d locations should be specified in meters, so that's what's tested below
# Detector locations are stored in the third three loc values
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][4][3:5] == 'D3'
assert_allclose(mni_locs[4], [0.0846, -0.0142, -0.0156],
atol=allowed_dist_error)
assert raw.info['ch_names'][8][3:5] == 'D2'
assert_allclose(mni_locs[8], [0.0207, -0.1062, 0.0484],
atol=allowed_dist_error)
assert raw.info['ch_names'][12][3:5] == 'D4'
assert_allclose(mni_locs[12], [-0.0196, 0.0821, 0.0275],
atol=allowed_dist_error)
assert raw.info['ch_names'][16][3:5] == 'D5'
assert_allclose(mni_locs[16], [-0.0360, 0.0276, 0.0778],
atol=allowed_dist_error)
assert raw.info['ch_names'][19][3:5] == 'D6'
assert_allclose(mni_locs[19], [0.0352, 0.0283, 0.0780],
atol=allowed_dist_error)
assert raw.info['ch_names'][21][3:5] == 'D7'
assert_allclose(mni_locs[21], [0.0388, -0.0477, 0.0932],
atol=allowed_dist_error)
def test_snirf_nirsport2_w_positions():
"""Test reading SNIRF files with known positions."""
raw = read_raw_snirf(nirx_nirsport2_103_2, preload=True,
optode_frame="mri")
# Test data import
assert raw._data.shape == (40, 128)
assert_almost_equal(raw.info['sfreq'], 10.2, decimal=1)
# Test channel naming
assert raw.info['ch_names'][:4] == ['S1_D1 760', 'S1_D1 850',
'S1_D6 760', 'S1_D6 850']
assert raw.info['ch_names'][24:26] == ['S6_D4 760', 'S6_D4 850']
# Test frequency encoding
assert raw.info['chs'][0]['loc'][9] == 760
assert raw.info['chs'][1]['loc'][9] == 850
assert sum(short_channels(raw.info)) == 16
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/86
# figure 3
allowed_distance_error = 0.005
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2][:14],
[0.0304, 0.0411, 0.008, 0.0400, 0.008, 0.0310, 0.0411,
0.008, 0.0299, 0.008, 0.0370, 0.008, 0.0404, 0.008],
atol=allowed_distance_error)
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(
mni_locs[0], [-0.0841, -0.0464, -0.0129], atol=allowed_dist_error)
assert raw.info['ch_names'][2][3:5] == 'D6'
assert_allclose(
mni_locs[2], [-0.0841, -0.0138, 0.0248], atol=allowed_dist_error)
assert raw.info['ch_names'][34][3:5] == 'D5'
assert_allclose(
mni_locs[34], [0.0845, -0.0451, -0.0123], atol=allowed_dist_error)
# Test location of sensors
# The locations of sensors can be seen in the second
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][3:6] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][:2] == 'S1'
assert_allclose(
mni_locs[0], [-0.0848, -0.0162, -0.0163], atol=allowed_dist_error)
assert raw.info['ch_names'][9][:2] == 'S2'
assert_allclose(
mni_locs[9], [-0.0, -0.1195, 0.0142], atol=allowed_dist_error)
assert raw.info['ch_names'][34][:2] == 'S8'
assert_allclose(
mni_locs[34], [0.0828, -0.046, 0.0285], atol=allowed_dist_error)
def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep, subject,
subjects_dir):
"""
Plot the MFT sources at time point of peak.
Parameters
----------
fwdmag: forward solution
stcdata: stc with ||cdv|| (point sequence as in fwdmag['source_rr'])
tmin, tstep, subject: passed to mne.SourceEstimate()
"""
print("##### Attempting to plot:")
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [
fwdmag['src'][0]['vertno']
[fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][
fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]
]
elif len(vertices) > 2:
warnings.warn(
'plot_visualize_mft_sources(): Cannot handle more than two sources spaces'
)
return
stc_feat = SourceEstimate(stcdata,
vertices=vertices,
tmin=tmin,
tstep=tstep,
subject=subject)
itmaxsum = np.argmax(np.sum(stcdata, axis=0))
twmin = tmin + tstep * float(itmaxsum - stcdata.shape[1] / 20)
twmax = tmin + tstep * float(itmaxsum + stcdata.shape[1] / 20)
for ihemi, hemi in enumerate(['lh', 'rh', 'both']):
brain = stc_feat.plot(surface='white',
hemi=hemi,
subjects_dir=subjects_dir,
transparent=True,
clim='auto')
# use peak getter to move visualization to the time point of the peak
print("Restricting peak search to [%fs, %fs]" % (twmin, twmax))
if hemi == 'both':
brain.show_view('parietal')
vertno_max, time_idx = stc_feat.get_peak(hemi=None,
time_as_index=True,
tmin=twmin,
tmax=twmax)
else:
brain.show_view('lateral')
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi,
time_as_index=True,
tmin=twmin,
tmax=twmax)
print("hemi=%s: setting time_idx=%d" % (hemi, time_idx))
brain.set_data_time_index(time_idx)
if hemi == 'lh' or hemi == 'rh':
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.6)
if len(fwdmag['src']) > ihemi:
fwds = fwdmag['src'][ihemi]
comax = fwds['rr'][vertno_max]
print("hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][%d]['rr'][vertno_max] = " % \
(hemi, vertno_max, time_idx, ihemi), comax)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [
np.nonzero([
stcdata[0:offsets[1], time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
elif len(fwdmag['src']) > 1:
ifoci = [
np.nonzero([
stcdata[offsets[1]:, time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwds['vertno'][ifoci[0][0]]
cfoci = fwds['rr'][vfoci]
print("Coords of %d sel. vfoci: " % cfoci.shape[0])
print(cfoci)
print("vfoci: ")
print(vfoci)
print("brain.geo[%s].coords[vfoci] : " % hemi)
print(brain.geo[hemi].coords[vfoci])
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'], cfoci, move=True)
print("mrfoci: ")
print(mrfoci)
# Just some blops along the coordinate axis:
# This will not yield reasonable results w an inflated brain.
# bloblist = np.zeros((300,3))
# for i in xrange(100):
# bloblist[i,0] = float(i)
# bloblist[i+100,1] = float(i)
# bloblist[i+200,2] = float(i)
# mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
# brain.add_foci(mrblobs, coords_as_verts=False, hemi=hemi, color='yellow', scale_factor=0.3)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
def write_anat(bids_root,
subject,
t1w,
session=None,
acquisition=None,
raw=None,
trans=None,
deface=False,
overwrite=False,
verbose=False):
"""Put anatomical MRI data into a BIDS format.
Given a BIDS directory and a T1 weighted MRI scan for a certain subject,
format the MRI scan to be in BIDS format and put it into the correct
location in the bids_dir. If a transformation matrix is supplied, a
sidecar JSON file will be written for the T1 weighted data.
Parameters
----------
bids_root : str
Path to root of the BIDS folder
subject : str
Subject label as in 'sub-<label>', for example: '01'
t1w : str | nibabel image object
Path to a T1 weighted MRI scan of the subject. Can be in any format
readable by nibabel. Can also be a nibabel image object of a T1
weighted MRI scan. Will be written as a .nii.gz file.
session : str | None
The session for `t1w`. Corresponds to "ses"
acquisition: str | None
The acquisition parameters for `t1w`. Corresponds to "acq"
raw : instance of Raw | None
The raw data of `subject` corresponding to `t1w`. If `raw` is None,
`trans` has to be None as well
trans : instance of mne.transforms.Transform | str | None
The transformation matrix from head coordinates to MRI coordinates. Can
also be a string pointing to a .trans file containing the
transformation matrix. If None, no sidecar JSON file will be written
for `t1w`
deface : bool | dict
If False, no defacing is performed.
If True, deface with default parameters.
`trans` and `raw` must not be `None` if True.
If dict, accepts the following keys:
`inset`: how far back in millimeters to start defacing
relative to the nasion (default 20)
`theta`: is the angle of the defacing shear in degrees relative
to the normal to the plane passing through the anatomical
landmarks (default 35).
overwrite : bool
Whether to overwrite existing files or data in files.
Defaults to False.
If overwrite is True, any existing files with the same BIDS parameters
will be overwritten with the exception of the `participants.tsv` and
`scans.tsv` files. For these files, parts of pre-existing data that
match the current data will be replaced.
If overwrite is False, no existing data will be overwritten or
replaced.
verbose : bool
If verbose is True, this will print a snippet of the sidecar files. If
False, no content will be printed.
Returns
-------
anat_dir : str
Path to the anatomical scan in the `bids_dir`
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
if deface and (trans is None or raw is None):
raise ValueError('The raw object, trans and raw must be provided to '
'deface the T1')
# Make directory for anatomical data
anat_dir = op.join(bids_root, 'sub-{}'.format(subject))
# Session is optional
if session is not None:
anat_dir = op.join(anat_dir, 'ses-{}'.format(session))
anat_dir = op.join(anat_dir, 'anat')
if not op.exists(anat_dir):
os.makedirs(anat_dir)
# Try to read our T1 file and convert to MGH representation
if isinstance(t1w, str):
t1w = nib.load(t1w)
elif type(t1w) not in nib.all_image_classes:
raise ValueError('`t1w` must be a path to a T1 weighted MRI data file '
', or a nibabel image object, but it is of type '
'"{}"'.format(type(t1w)))
t1w = nib.Nifti1Image(t1w.dataobj, t1w.affine)
# XYZT_UNITS = NIFT_UNITS_MM (10 in binary or 2 in decimal)
# seems to be the default for Nifti files
# https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/xyzt_units.html
if t1w.header['xyzt_units'] == 0:
t1w.header['xyzt_units'] = np.array(10, dtype='uint8')
# Now give the NIfTI file a BIDS name and write it to the BIDS location
t1w_basename = make_bids_basename(subject=subject,
session=session,
acquisition=acquisition,
prefix=anat_dir,
suffix='T1w.nii.gz')
# Check if we have necessary conditions for writing a sidecar JSON
if trans is not None:
# get trans and ensure it is from head to MRI
trans, _ = _get_trans(trans, fro='head', to='mri')
if not isinstance(raw, BaseRaw):
raise ValueError('`raw` must be specified if `trans` is not None')
# Prepare to write the sidecar JSON
# extract MEG landmarks
coords_dict = _extract_landmarks(raw.info['dig'])
meg_landmarks = np.asarray(
(coords_dict['LPA'], coords_dict['NAS'], coords_dict['RPA']))
# Transform MEG landmarks into MRI space, adjust units by * 1e3
mri_landmarks = apply_trans(trans, meg_landmarks, move=True) * 1e3
# Get landmarks in voxel space, using the mgh version of our T1 data
t1_mgh = nib.MGHImage(t1w.dataobj, t1w.affine)
vox2ras_tkr = t1_mgh.header.get_vox2ras_tkr()
ras2vox_tkr = np.linalg.inv(vox2ras_tkr)
mri_landmarks = apply_trans(ras2vox_tkr, mri_landmarks) # in vox
# Write sidecar.json
t1w_json = dict()
t1w_json['AnatomicalLandmarkCoordinates'] = \
{'LPA': list(mri_landmarks[0, :]),
'NAS': list(mri_landmarks[1, :]),
'RPA': list(mri_landmarks[2, :])}
fname = t1w_basename.replace('.nii.gz', '.json')
if op.isfile(fname) and not overwrite:
raise IOError(
'Wanted to write a file but it already exists and '
'`overwrite` is set to False. File: "{}"'.format(fname))
_write_json(fname, t1w_json, overwrite, verbose)
if deface:
t1w = _deface(t1w, mri_landmarks, deface, trans, raw)
# Save anatomical data
if op.exists(t1w_basename):
if overwrite:
os.remove(t1w_basename)
else:
raise IOError(
'Wanted to write a file but it already exists and '
'`overwrite` is set to False. File: "{}"'.format(t1w_basename))
nib.save(t1w, t1w_basename)
return anat_dir
def test_ieeg_elec_locate_gui_display(_locate_ieeg, _fake_CT_coords):
"""Test that the intracranial location GUI displays properly."""
raw = mne.io.read_raw_fif(raw_path, preload=True)
raw.pick_types(eeg=True)
ch_dict = {
'EEG 001': 'LAMY 1',
'EEG 002': 'LAMY 2',
'EEG 003': 'LSTN 1',
'EEG 004': 'LSTN 2'
}
raw.pick_channels(list(ch_dict.keys()))
raw.rename_channels(ch_dict)
raw.set_eeg_reference('average')
raw.set_channel_types({name: 'seeg' for name in raw.ch_names})
raw.set_montage(None)
aligned_ct, coords = _fake_CT_coords
trans = mne.read_trans(fname_trans)
with pytest.warns(RuntimeWarning, match='`pial` surface not found'):
gui = _locate_ieeg(raw.info,
trans,
aligned_ct,
subject=subject,
subjects_dir=subjects_dir,
verbose=True)
with pytest.raises(ValueError, match='read-only'):
gui._ras[:] = coords[0] # start in the right position
gui._set_ras(coords[0])
gui._mark_ch()
assert not gui._lines and not gui._lines_2D # no lines for one contact
for ci, coord in enumerate(coords[1:], 1):
coord_vox = apply_trans(gui._ras_vox_t, coord)
with use_log_level('debug'):
_fake_click(gui._figs[2],
gui._figs[2].axes[0],
coord_vox[:-1],
xform='data',
kind='release')
assert_allclose(coord[:2],
gui._ras[:2],
atol=0.1,
err_msg=f'coords[{ci}][:2]')
assert_allclose(coord[2],
gui._ras[2],
atol=2,
err_msg=f'coords[{ci}][2]')
gui._mark_ch()
# ensure a 3D line was made for each group
assert len(gui._lines) == 2
# test snap to center
gui._ch_index = 0
gui._set_ras(coords[0]) # move to first position
gui._mark_ch()
assert_allclose(coords[0], gui._chs['LAMY 1'], atol=0.2)
gui._snap_button.click()
assert gui._snap_button.text() == 'Off'
# now make sure no snap happens
gui._ch_index = 0
gui._set_ras(coords[1] + 1)
gui._mark_ch()
assert_allclose(coords[1] + 1, gui._chs['LAMY 1'], atol=0.01)
# check that it turns back on
gui._snap_button.click()
assert gui._snap_button.text() == 'On'
# test remove
gui._ch_index = 1
gui._update_ch_selection()
gui._remove_ch()
assert np.isnan(gui._chs['LAMY 2']).all()
# check that raw object saved
assert not np.isnan(raw.info['chs'][0]['loc'][:3]).any() # LAMY 1
assert np.isnan(raw.info['chs'][1]['loc'][:3]).all() # LAMY 2 (removed)
# move sliders
gui._alpha_slider.setValue(75)
assert gui._ch_alpha == 0.75
gui._radius_slider.setValue(5)
assert gui._radius == 5
ct_sum_before = np.nansum(gui._images['ct'][0].get_array().data)
gui._ct_min_slider.setValue(500)
assert np.nansum(gui._images['ct'][0].get_array().data) < ct_sum_before
# test buttons
gui._toggle_show_brain()
assert 'mri' in gui._images
assert 'local_max' not in gui._images
gui._toggle_show_max()
assert 'local_max' in gui._images
assert 'mip' not in gui._images
gui._toggle_show_mip()
assert 'mip' in gui._images
assert 'mip_chs' in gui._images
assert len(gui._lines_2D) == 1 # LAMY only has one contact
def create_volume(subjects_dir, subject, src_space, corr_file, corr_vol):
set_num_threads(4)
sources = []
src_space = mne.read_source_spaces(src_space)
# lh_surf_coord = src_space[0]['rr'] # Triangle Mesh coordinates
# lh_triangle_idx = src_space[0]['tris'] # traingular mesh face of 3 vertices
t1_fname = os.path.join(subjects_dir, subject, 'mri', 'T1.mgz')
t1 = nib.load(t1_fname)
vox_mri_t = t1.header.get_vox2ras_tkr()
mri_vox_t = np.linalg.inv(vox_mri_t)
for src_ in src_space:
points = src_['rr'][src_['inuse'].astype(bool)]
sources.append(apply_trans(mri_vox_t, points * 1e3))
sources = np.concatenate(sources, axis=0)
sources = np.round(sources)
img = np.zeros([256, 256, 256])
corr_data = np.load(corr_file)
# print(len(sources), corr_data.shape)
for idx, val in enumerate(sources):
i, j, k = int(val[0]), int(val[1]), int(val[2])
img[i][j][k] = corr_data[idx]
img[img < 0.0] = sys.float_info.epsilon
# Fill neiboring voxels
fill_neighbor = True
if fill_neighbor:
found = np.argwhere(img > 0.0)
for a1, b1, c1 in found:
neighbor = list(neighbors((a1, b1, c1)))
for i, j, k in neighbor:
img[i][j][k] = img[a1][b1][c1]
found = np.argwhere(img > 0.0)
for a1, b1, c1 in found:
neighbor = list(neighbors((a1, b1, c1)))
for i, j, k in neighbor:
img[i][j][k] = img[a1][b1][c1]
found = np.argwhere(img > 0.0)
for a1, b1, c1 in found:
neighbor = list(neighbors((a1, b1, c1)))
for i, j, k in neighbor:
img[i][j][k] = img[a1][b1][c1]
found = np.argwhere(img > 0.0)
for a1, b1, c1 in found:
neighbor = list(neighbors((a1, b1, c1)))
for i, j, k in neighbor:
img[i][j][k] = img[a1][b1][c1]
#---------------------- Convolution Nearest Neighbor-------------------------#
convolution = True
if convolution:
print(f'Convolve 2-dimensional array')
final_list = [] # Axial
for dim_0_slice in img:
output = eight_neighbor_average_convolve2d(dim_0_slice)
final_list.append(output)
img = np.dstack(final_list)
final_list = [] # Coronal
img = np.swapaxes(img, 0, 1)
for dim_0_slice in img:
output = eight_neighbor_average_convolve2d(dim_0_slice)
final_list.append(output)
img = np.dstack(final_list)
final_list = [] # Sagittal
img = np.swapaxes(img, 0, 2)
for dim_0_slice in img:
output = eight_neighbor_average_convolve2d(dim_0_slice)
final_list.append(output)
img = np.dstack(final_list)
affine = t1.affine
hdr = t1.header
result_img = nib.Nifti1Image(img, affine, header=hdr)
result_img.to_filename(corr_vol)
print(corr_vol)
def plot_coregistration(subject,
subjects_dir,
hcp_path,
recordings_path,
info_from=(('data_type', 'rest'), ('run_index', 0)),
view_init=(('azim', 0), ('elev', 0))):
"""A diagnostic plot to show the HCP coregistration
Parameters
----------
subject : str
The subject
subjects_dir : str
The path corresponding to MNE/freesurfer SUBJECTS_DIR (to be created)
hcp_path : str
The path where the HCP files can be found.
recordings_path : str
The path to converted data (including the head<->device transform).
info_from : tuple of tuples | dict
The reader info concerning the data from which sensor positions
should be read.
Must not be empty room as sensor positions are in head
coordinates for 4D systems, hence not available in that case.
Note that differences between the sensor positions across runs
are smaller than 12 digits, hence negligible.
view_init : tuple of tuples | dict
The initival view, defaults to azimuth and elevation of 0,
a simple lateral view
Returns
-------
fig : matplotlib.figure.Figure
The figure object.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
if isinstance(info_from, tuple):
info_from = dict(info_from)
if isinstance(view_init, tuple):
view_init = dict(view_init)
head_mri_t = read_trans(
op.join(recordings_path, subject,
'{}-head_mri-trans.fif'.format(subject)))
info = read_info(subject=subject, hcp_path=hcp_path, **info_from)
info = pick_info(info, _pick_data_channels(info, with_ref_meg=False))
sens_pnts = np.array([c['loc'][:3] for c in info['chs']])
sens_pnts = apply_trans(head_mri_t, sens_pnts)
sens_pnts *= 1e3 # put in mm scale
pnts, tris = read_surface(
op.join(subjects_dir, subject, 'bem', 'inner_skull.surf'))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(*sens_pnts.T, color='purple', marker='o')
ax.scatter(*pnts.T, color='green', alpha=0.3)
ax.view_init(**view_init)
fig.tight_layout()
return fig
def boxy2mne(*, boxy_file=None, mtg_file=None, coord_file=None):
# =============================================================================
# ready raw data from boxy file
# =============================================================================
###this keeps track of the line we're on###
###mostly to know the start and stop of data (probably an easier way)###
line_num = 0
###load and read data to get some meta information###
###there is alot of information at the beginning of a file###
###but this only grabs some of it###
with open(boxy_file, 'r') as data:
for i_line in data:
line_num += 1
if '#DATA ENDS' in i_line:
end_line = line_num - 1
break
if 'Detector Channels' in i_line:
detect_num = int(i_line.rsplit(' ')[0])
elif 'External MUX Channels' in i_line:
source_num = int(i_line.rsplit(' ')[0])
elif 'Auxiliary Channels' in i_line:
aux_num = int(i_line.rsplit(' ')[0])
elif 'Waveform (CCF) Frequency (Hz)' in i_line:
ccf_ha = float(i_line.rsplit(' ')[0])
elif 'Update Rate (Hz)' in i_line:
srate = float(i_line.rsplit(' ')[0])
elif 'Updata Rate (Hz)' in i_line:
srate = float(i_line.rsplit(' ')[0])
elif '#DATA BEGINS' in i_line:
start_line = line_num
###now let's go through and parse our raw data###
raw_data = pd.read_csv(boxy_file, skiprows=start_line, sep='\t')
###detectors, sources, and data types###
detectors = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N',
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
]
data_types = ['AC', 'DC', 'Ph']
sources = np.arange(1, source_num + 1, 1)
###since we can save boxy files in two different styles###
###this will check to see which style the data is saved###
###seems to also work with older boxy files###
if 'exmux' in raw_data.columns:
filetype = 'non-parsed'
###drop the last line as this is just '#DATA ENDS'###
raw_data = raw_data.drop([len(raw_data) - 1])
###store some extra info###
record = raw_data['record'].to_numpy()
exmux = raw_data['exmux'].to_numpy()
###make some empty variables to store our data###
raw_ac = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
raw_dc = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
raw_ph = np.zeros(
(detect_num * source_num, int(len(raw_data) / source_num)))
else:
filetype = 'parsed'
###drop the last line as this is just '#DATA ENDS'###
###also drop the first line since this is empty###
raw_data = raw_data.drop([0, len(raw_data) - 1])
###make some empty variables to store our data###
raw_ac = np.zeros(((detect_num * source_num), len(raw_data)))
raw_dc = np.zeros(((detect_num * source_num), len(raw_data)))
raw_ph = np.zeros(((detect_num * source_num), len(raw_data)))
###store some extra data, might not need these though###
time = raw_data['time'].to_numpy() if 'time' in raw_data.columns else []
time = raw_data['time'].to_numpy() if 'time' in raw_data.columns else []
group = raw_data['group'].to_numpy() if 'group' in raw_data.columns else []
step = raw_data['step'].to_numpy() if 'step' in raw_data.columns else []
mark = raw_data['mark'].to_numpy() if 'mark' in raw_data.columns else []
flag = raw_data['flag'].to_numpy() if 'flag' in raw_data.columns else []
aux1 = raw_data['aux-1'].to_numpy() if 'aux-1' in raw_data.columns else []
digaux = raw_data['digaux'].to_numpy(
) if 'digaux' in raw_data.columns else []
bias = np.zeros((detect_num, len(raw_data)))
###loop through detectors###
for i_detect in detectors[0:detect_num]:
###older boxy files don't seem to keep track of detector bias###
###probably due to specific boxy settings actually###
if 'bias-A' in raw_data.columns:
bias[detectors.index(i_detect), :] = raw_data['bias-' +
i_detect].to_numpy()
###loop through data types###
for i_data in data_types:
###loop through sources###
for i_source in sources:
###where to store our data###
index_loc = detectors.index(i_detect) * source_num + (
i_source - 1)
###need to treat our filetypes differently###
if filetype == 'non-parsed':
###filetype saves timepoints in groups###
###this should account for that###
time_points = np.arange(i_source - 1,
int(record[-1]) * source_num,
source_num)
###determine which channel to look for###
channel = i_detect + '-' + i_data
###save our data based on data type###
if data_types.index(i_data) == 0:
raw_ac[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif data_types.index(i_data) == 1:
raw_dc[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif data_types.index(i_data) == 2:
raw_ph[index_loc, :] = raw_data[channel][
time_points].to_numpy()
elif filetype == 'parsed':
###determine which channel to look for###
channel = i_detect + '-' + i_data + str(i_source)
###save our data based on data type###
if data_types.index(i_data) == 0:
raw_ac[index_loc, :] = raw_data[channel].to_numpy()
elif data_types.index(i_data) == 1:
raw_dc[index_loc, :] = raw_data[channel].to_numpy()
elif data_types.index(i_data) == 2:
raw_ph[index_loc, :] = raw_data[channel].to_numpy()
# =============================================================================
# read source and electrode locations from .mtg and .tol/.elp files
# =============================================================================
###set up some variables###
chan_num = []
source_label = []
detect_label = []
chan_wavelength = []
chan_modulation = []
###load and read each line of the .mtg file###
with open(mtg_file, 'r') as data:
for i_ignore in range(2):
next(data)
for i_line in data:
chan1, chan2, source, detector, wavelength, modulation = i_line.split(
)
chan_num.append(chan1)
source_label.append(source)
detect_label.append(detector)
chan_wavelength.append(wavelength)
chan_modulation.append(modulation)
###check if we are given a .tol or .elp file###
all_labels = []
all_coords = []
fiducial_coords = []
if coord_file[-3:].lower() == 'elp'.lower():
get_label = 0
get_coords = 0
###load and read .elp file###
with open(coord_file, 'r') as data:
for i_line in data:
###first let's get our fiducial coordinates###
if '%F' in i_line:
fiducial_coords.append(i_line.split()[1:])
###check where sensor info starts###
if '//Sensor name' in i_line:
get_label = 1
elif get_label == 1:
###grab the part after '%N' for the label###
label = i_line.split()[1]
all_labels.append(label)
get_label = 0
get_coords = 1
elif get_coords == 1:
X, Y, Z = i_line.split()
all_coords.append([float(X), float(Y), float(Z)])
get_coords = 0
for i_index in range(3):
fiducial_coords[i_index] = np.asarray(
[float(x) for x in fiducial_coords[i_index]])
elif coord_file[-3:] == 'tol':
###load and read .tol file###
with open(coord_file, 'r') as data:
for i_line in data:
label, X, Y, Z = i_line.split()
all_labels.append(label)
###convert coordinates from mm to m##
all_coords.append([(float(X) * 0.001), (float(Y) * 0.001),
(float(Z) * 0.001)])
###get coordinates for sources###
source_coords = []
for i_chan in source_label:
if i_chan in all_labels:
chan_index = all_labels.index(i_chan)
source_coords.append(all_coords[chan_index])
###get coordinates for detectors###
detect_coords = []
for i_chan in detect_label:
if i_chan in all_labels:
chan_index = all_labels.index(i_chan)
detect_coords.append(all_coords[chan_index])
###need to rename labels to make other functions happy###
###get our unique labels for sources and detectors###
unique_source_labels = []
unique_detect_labels = []
[
unique_source_labels.append(label) for label in source_label
if label not in unique_source_labels
]
[
unique_detect_labels.append(label) for label in detect_label
if label not in unique_detect_labels
]
###now let's label each channel in our data###
###data is channels X timepoint where the first source_num rows correspond to###
###the first detector, and each row within that group is a different source###
###should note that current .mtg files contain channels for multiple data files###
###going to move to have a single .mtg file per participant, condition, and montage###
###combine coordinates and label our channels###
###will label them based on ac, dc, and ph data###
boxy_coords = []
boxy_labels = []
data_types = ['AC', 'DC', 'Ph']
total_chans = detect_num * source_num
for i_type in data_types:
for i_coord in range(len(source_coords[0:total_chans])):
boxy_coords.append(
np.mean(np.vstack((source_coords[i_coord],
detect_coords[i_coord])),
axis=0).tolist() + source_coords[i_coord] +
detect_coords[i_coord] + [chan_wavelength[i_coord]] + [0] +
[0])
boxy_labels.append(
'S' +
str(unique_source_labels.index(source_label[i_coord]) + 1) +
'_D' +
str(unique_detect_labels.index(detect_label[i_coord]) + 1) +
' ' + chan_wavelength[i_coord] + ' ' + i_type)
###montage only wants channel coords, so need to grab those, convert to###
###array, then make a dict with labels###
for i_chan in range(len(boxy_coords)):
boxy_coords[i_chan] = np.asarray(boxy_coords[i_chan], dtype=np.float64)
for i_chan in range(len(all_coords)):
all_coords[i_chan] = np.asarray(all_coords[i_chan], dtype=np.float64)
all_chan_dict = dict(zip(all_labels, all_coords))
###make our montage###
montage_orig = mne.channels.make_dig_montage(ch_pos=all_chan_dict,
coord_frame='head',
nasion=fiducial_coords[0],
lpa=fiducial_coords[1],
rpa=fiducial_coords[2])
###for some reason make_dig_montage put our channels in a different order than what we input###
###let's fix that. should be fine to just change coords and ch_names###
for i_chan in range(len(all_coords)):
montage_orig.dig[i_chan + 3]['r'] = all_coords[i_chan]
montage_orig.ch_names[i_chan] = all_labels[i_chan]
###add an extra channel for our triggers###
boxy_labels.append('Markers')
###create info structure###
info = mne.create_info(boxy_labels, srate, ch_types='fnirs_raw')
info.update(dig=montage_orig.dig)
###get our fiducials and transform matrix from fsaverage###
subjects_dir = op.dirname(fetch_fsaverage())
fid_path = op.join(subjects_dir, 'fsaverage', 'bem',
'fsaverage-fiducials.fif')
fiducials = read_fiducials(fid_path)
trans = coregister_fiducials(info, fiducials[0], tol=0.02)
###remake montage using the transformed coordinates###
all_coords_trans = apply_trans(trans, all_coords)
all_chan_dict_trans = dict(zip(all_labels, all_coords_trans))
fiducial_coords_trans = apply_trans(trans, fiducial_coords)
###make our montage###
montage_trans = mne.channels.make_dig_montage(
ch_pos=all_chan_dict_trans,
coord_frame='head',
nasion=fiducial_coords_trans[0],
lpa=fiducial_coords_trans[1],
rpa=fiducial_coords_trans[2])
###let's fix montage order again###
for i_chan in range(len(all_coords_trans)):
montage_trans.dig[i_chan + 3]['r'] = all_coords_trans[i_chan]
montage_trans.ch_names[i_chan] = all_labels[i_chan]
###add data type and channel wavelength to info###
info.update(dig=montage_trans.dig, trans=trans)
###place our coordinates and wavelengths for each channel###
for i_chan in range(len(boxy_labels) - 1):
temp_chn = apply_trans(trans, boxy_coords[i_chan][0:3])
temp_src = apply_trans(trans, boxy_coords[i_chan][3:6])
temp_det = apply_trans(trans, boxy_coords[i_chan][6:9])
temp_other = np.asarray(boxy_coords[i_chan][9:], dtype=np.float64)
info['chs'][i_chan]['loc'] = test = np.concatenate(
(temp_chn, temp_src, temp_det, temp_other), axis=0)
info['chs'][-1]['loc'] = np.zeros((12, ))
###now combine our data types into a single array###
all_data = np.append(raw_ac, np.append(raw_dc, raw_ph, axis=0), axis=0)
###add our markers to the data array based on filetype###
if filetype == 'non-parsed':
if type(digaux) is list and digaux != []:
markers = digaux[np.arange(0, len(digaux), source_num)]
else:
markers = np.zeros(np.size(all_data, axis=1))
elif filetype == 'parsed':
markers = digaux
all_data = np.vstack((all_data, markers))
###create our raw data object###
raw_data_obj = mne.io.RawArray(all_data, info)
return raw_data_obj
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd=
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc=
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = [
"F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3", "CPZ",
"CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"
]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
fname = op.join(tempdir, 'test_fid.hpts')
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)),
info=info,
tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def get_head_mri_trans(bids_path,
extra_params=None,
t1_bids_path=None,
fs_subject=None,
fs_subjects_dir=None,
verbose=None):
"""Produce transformation matrix from MEG and MRI landmark points.
Will attempt to read the landmarks of Nasion, LPA, and RPA from the sidecar
files of (i) the MEG and (ii) the T1-weighted MRI data. The two sets of
points will then be used to calculate a transformation matrix from head
coordinates to MRI coordinates.
.. note:: The MEG and MRI data need **not** necessarily be stored in the
same session or even in the same BIDS dataset. See the
``t1_bids_path`` parameter for details.
Parameters
----------
bids_path : mne_bids.BIDSPath
The path of the electrophysiology recording.
extra_params : None | dict
Extra parameters to be passed to :func:`mne.io.read_raw` when reading
the MEG file.
t1_bids_path : mne_bids.BIDSPath | None
If ``None`` (default), will try to discover the T1-weighted MRI file
based on the name and location of the MEG recording specified via the
``bids_path`` parameter. Alternatively, you explicitly specify which
T1-weighted MRI scan to use for extraction of MRI landmarks. To do
that, pass a :class:`mne_bids.BIDSPath` pointing to the scan.
Use this parameter e.g. if the T1 scan was recorded during a different
session than the MEG. It is even possible to point to a T1 image stored
in an entirely different BIDS dataset than the MEG data.
fs_subject : str | None
The subject identifier used for FreeSurfer. If ``None``, defaults to
the ``subject`` entity in ``bids_path``.
fs_subjects_dir : str | pathlib.Path | None
The FreeSurfer subjects directory. If ``None``, defaults to the
``SUBJECTS_DIR`` environment variable.
.. versionadded:: 0.8
%(verbose)s
Returns
-------
trans : mne.transforms.Transform
The data transformation matrix from head to MRI coordinates.
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
if not isinstance(bids_path, BIDSPath):
raise RuntimeError('"bids_path" must be a BIDSPath object. Please '
'instantiate using mne_bids.BIDSPath().')
# check root available
meg_bids_path = bids_path.copy()
del bids_path
if meg_bids_path.root is None:
raise ValueError('The root of the "bids_path" must be set. '
'Please use `bids_path.update(root="<root>")` '
'to set the root of the BIDS folder to read.')
# only get this for MEG data
meg_bids_path.update(datatype='meg', suffix='meg')
# Get the sidecar file for MRI landmarks
match_bids_path = meg_bids_path if t1_bids_path is None else t1_bids_path
t1w_path = _find_matching_sidecar(match_bids_path,
suffix='T1w',
extension='.nii.gz')
t1w_json_path = _find_matching_sidecar(match_bids_path,
suffix='T1w',
extension='.json')
# Get MRI landmarks from the JSON sidecar
with open(t1w_json_path, 'r', encoding='utf-8') as f:
t1w_json = json.load(f)
mri_coords_dict = t1w_json.get('AnatomicalLandmarkCoordinates', dict())
# landmarks array: rows: [LPA, NAS, RPA]; columns: [x, y, z]
mri_landmarks = np.full((3, 3), np.nan)
for landmark_name, coords in mri_coords_dict.items():
if landmark_name.upper() == 'LPA':
mri_landmarks[0, :] = coords
elif landmark_name.upper() == 'RPA':
mri_landmarks[2, :] = coords
elif (landmark_name.upper() == 'NAS'
or landmark_name.lower() == 'nasion'):
mri_landmarks[1, :] = coords
else:
continue
if np.isnan(mri_landmarks).any():
raise RuntimeError(
f'Could not extract fiducial points from T1w sidecar file: '
f'{t1w_json_path}\n\n'
f'The sidecar file SHOULD contain a key '
f'"AnatomicalLandmarkCoordinates" pointing to an '
f'object with the keys "LPA", "NAS", and "RPA". '
f'Yet, the following structure was found:\n\n'
f'{mri_coords_dict}')
# The MRI landmarks are in "voxels". We need to convert the to the
# neuromag RAS coordinate system in order to compare the with MEG landmarks
# see also: `mne_bids.write.write_anat`
if fs_subject is None:
fs_subject = f'sub-{meg_bids_path.subject}'
fs_subjects_dir = get_subjects_dir(fs_subjects_dir, raise_error=False)
fs_t1_fname = Path(fs_subjects_dir) / fs_subject / 'mri' / 'T1.mgz'
if not fs_t1_fname.exists():
raise ValueError(
f"Could not find {fs_t1_fname}. Consider running FreeSurfer's "
f"'recon-all` for subject {fs_subject}.")
fs_t1_mgh = nib.load(str(fs_t1_fname))
t1_nifti = nib.load(str(t1w_path))
# Convert to MGH format to access vox2ras method
t1_mgh = nib.MGHImage(t1_nifti.dataobj, t1_nifti.affine)
# convert to scanner RAS
mri_landmarks = apply_trans(t1_mgh.header.get_vox2ras(), mri_landmarks)
# convert to FreeSurfer T1 voxels (same scanner RAS as T1)
mri_landmarks = apply_trans(fs_t1_mgh.header.get_ras2vox(), mri_landmarks)
# now extract transformation matrix and put back to RAS coordinates of MRI
vox2ras_tkr = fs_t1_mgh.header.get_vox2ras_tkr()
mri_landmarks = apply_trans(vox2ras_tkr, mri_landmarks)
mri_landmarks = mri_landmarks * 1e-3
# Get MEG landmarks from the raw file
_, ext = _parse_ext(meg_bids_path)
if extra_params is None:
extra_params = dict()
if ext == '.fif':
extra_params['allow_maxshield'] = True
raw = read_raw_bids(bids_path=meg_bids_path, extra_params=extra_params)
meg_coords_dict = _extract_landmarks(raw.info['dig'])
meg_landmarks = np.asarray((meg_coords_dict['LPA'], meg_coords_dict['NAS'],
meg_coords_dict['RPA']))
# Given the two sets of points, fit the transform
trans_fitted = fit_matched_points(src_pts=meg_landmarks,
tgt_pts=mri_landmarks)
trans = mne.transforms.Transform(fro='head', to='mri', trans=trans_fitted)
return trans
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = ["""FidNz 0.00000 10.56381 -2.05108
FidT9 -7.82694 0.45386 -3.76056
FidT10 7.82694 0.45386 -3.76056""",
"""// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000""", # noqa
"""# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
Positions
-86.0761 -19.9897 -47.9860
85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
Labels
LPA
RPA
Nz
""",
"""Site Theta Phi
Fp1 -92 -72
Fp2 92 72
F3 -60 -51
""",
"""346
EEG F3 -62.027 -50.053 85
EEG Fz 45.608 90 85
EEG F4 62.01 50.103 85
""",
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
"""]
kinds = ['test.sfp', 'test.csd', 'test.elc', 'test.txt', 'test.elp',
'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal nasion -91 0 -42
cardinal lpa 0 -91 -42
cardinal rpa 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('nasion')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('lpa')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('rpa')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
apply_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
pos3 = np.array([c['eeg_loc'][:, 0] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd='// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = ["""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
nasion -91 0 -42
lpa 0 -91 -42
rpa 0 91 -42
"""]
all_fiducials = [['2', '1', '3'], ['nasion', 'lpa', 'rpa']]
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, fiducials, input_str in zip(kinds, all_fiducials, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index(fiducials[0])
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index(fiducials[1])
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index(fiducials[2])
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
# Channel names can be treated case insensitive
with warnings.catch_warnings(record=True) as w:
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
assert_true(len(w) == 0)
# Unless there is a collision in names
with warnings.catch_warnings(record=True) as w:
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
assert_true(len(w) == 1)
with warnings.catch_warnings(record=True) as w:
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
_set_montage(info, montage)
assert_true(len(w) == 1)
def test_nirsport_v2():
"""Test NIRSport2 file."""
raw = read_raw_nirx(nirsport2, preload=True)
assert raw._data.shape == (40, 128)
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/86
# figure 3
allowed_distance_error = 0.005
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2][:14],
[0.0304, 0.0411, 0.008, 0.0400, 0.008, 0.0310, 0.0411,
0.008, 0.0299, 0.008, 0.0370, 0.008, 0.0404, 0.008],
atol=allowed_distance_error)
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(
mni_locs[0], [-0.0841, -0.0464, -0.0129], atol=allowed_dist_error)
assert raw.info['ch_names'][2][3:5] == 'D6'
assert_allclose(
mni_locs[2], [-0.0841, -0.0138, 0.0248], atol=allowed_dist_error)
assert raw.info['ch_names'][34][3:5] == 'D5'
assert_allclose(
mni_locs[34], [0.0845, -0.0451, -0.0123], atol=allowed_dist_error)
# Test location of sensors
# The locations of sensors can be seen in the second
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
locs = [ch['loc'][3:6] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][:2] == 'S1'
assert_allclose(
mni_locs[0], [-0.0848, -0.0162, -0.0163], atol=allowed_dist_error)
assert raw.info['ch_names'][9][:2] == 'S2'
assert_allclose(
mni_locs[9], [-0.0, -0.1195, 0.0142], atol=allowed_dist_error)
assert raw.info['ch_names'][34][:2] == 'S8'
assert_allclose(
mni_locs[34], [0.0828, -0.046, 0.0285], atol=allowed_dist_error)
assert len(raw.annotations) == 3
assert raw.annotations.description[0] == '1.0'
assert raw.annotations.description[2] == '6.0'
# Lose tolerance as I am eyeballing the time differences on screen
assert_allclose(
np.diff(raw.annotations.onset), [2.3, 3.1], atol=0.1)
mon = raw.get_montage()
assert len(mon.dig) == 43
def make_mne_anatomy(subject, anatomy_path, recordings_path=None,
hcp_path=op.curdir, mode='minimal', outputs=(
'label', 'mri', 'surf')):
"""Extract relevant anatomy and create MNE friendly directory layout
The function will create the following outputs by default:
$anatomy_path/$subject/bem/inner_skull.surf
$anatomy_path/$subject/label/*
$anatomy_path/$subject/mri/*
$anatomy_path/$subject/surf/*
$recordings_path/$subject/$subject-head_mri-trans.fif
These can then be set as $SUBJECTS_DIR and as MEG directory, consistent
with MNE examples.
Parameters
----------
subject : str
The subject name.
anatomy_path : str
The path corresponding to MNE/freesurfer SUBJECTS_DIR (to be created)
hcp_path : str
The path where the HCP files can be found.
mode : {'minimal', 'full'}
If 'minimal', only the directory structure is created. If 'full' the
freesurfer outputs shipped withm HCP (see `outputs`) are symbolically
linked.
outputs : {'label', 'mri', 'stats', 'surf', 'touch'}
The outputs of the freesrufer pipeline shipped by HCP. Defaults to
('mri', 'surf'), the minimum needed to extract MNE-friendly anatomy
files and data.
"""
if mode not in ('full', 'minimal'):
raise ValueError('`mode` must either be "minimal" or "full"')
if hcp_path == op.curdir:
hcp_path = op.realpath(hcp_path)
if not op.isabs(anatomy_path):
anatomy_path = op.realpath(anatomy_path)
this_anatomy_path = op.join(anatomy_path, subject)
if not op.isabs(recordings_path):
recordings_path = op.realpath(recordings_path)
this_recordings_path = op.join(recordings_path, subject)
if not op.exists(this_recordings_path):
os.makedirs(this_recordings_path)
for output in outputs:
if not op.exists(op.join(this_anatomy_path, output)):
os.makedirs(op.join(this_anatomy_path, output))
if output == 'mri':
for suboutput in ['orig', 'transforms']:
if not op.exists(
op.join(this_anatomy_path, output, suboutput)):
os.makedirs(op.join(this_anatomy_path, output, suboutput))
files = get_file_paths(
subject=subject, data_type='freesurfer', output=output,
mode=mode,
processing='preprocessed', hcp_path=hcp_path)
for source in files:
match = [match for match in re.finditer(subject, source)][-1]
split_path = source[:match.span()[1] + 1]
target = op.join(this_anatomy_path, source.split(split_path)[-1])
if not op.isfile(target) and not op.islink(target):
os.symlink(source, target)
logger.info('reading extended structural processing ...')
# Step 1 #################################################################
# transform head models to expected coordinate system
# make hcp trans
transforms_fname = get_file_paths(
subject=subject, data_type='meg_anatomy', output='transforms',
processing='preprocessed', hcp_path=hcp_path)
transforms_fname = [k for k in transforms_fname if
k.endswith('transform.txt')][0]
hcp_trans = _read_trans_hcp(fname=transforms_fname, convert_to_meter=False)
# get RAS freesurfer trans
c_ras_trans_fname = get_file_paths(
subject=subject, data_type='freesurfer', output='mri',
processing='preprocessed', hcp_path=hcp_path)
c_ras_trans_fname = [k for k in c_ras_trans_fname if
k.endswith('c_ras.mat')][0]
logger.info('reading RAS freesurfer transform')
# ceci n'est pas un .mat file ...
with open(op.join(anatomy_path, c_ras_trans_fname)) as fid:
ras_trans = np.array([
r.split() for r in fid.read().split('\n') if r],
dtype=np.float64)
logger.info('Combining RAS transform and coregistration')
ras_trans_m = linalg.inv(ras_trans) # and the inversion
logger.info('extracting head model')
head_model_fname = get_file_paths(
subject=subject, data_type='meg_anatomy', output='head_model',
processing='preprocessed', hcp_path=hcp_path)[0]
pnts, faces = _get_head_model(head_model_fname=head_model_fname)
logger.info('coregistring head model to MNE-HCP coordinates')
pnts = apply_trans(ras_trans_m.dot(hcp_trans['bti2spm']), pnts)
tri_fname = op.join(this_anatomy_path, 'bem', 'inner_skull.surf')
if not op.exists(op.dirname(tri_fname)):
os.makedirs(op.dirname(tri_fname))
write_surface(tri_fname, pnts, faces)
# Step 2 #################################################################
# write corresponding device to MRI transform
logger.info('extracting coregistration')
# now convert to everything meter too here
ras_trans_m[:3, 3] *= 1e-3
bti2spm = hcp_trans['bti2spm']
bti2spm[:3, 3] *= 1e-3
head_mri_t = Transform( # we're lying here for a good purpose
'head', 'mri', np.dot(ras_trans_m, bti2spm)) # it should be 'ctf_head'
write_trans(
op.join(this_recordings_path, '%s-head_mri-trans.fif') % subject,
head_mri_t)
def test_scale_mri(tmpdir, few_surfaces, scale):
"""Test creating fsaverage and scaling it."""
# create fsaverage using the testing "fsaverage" instead of the FreeSurfer
# one
tempdir = str(tmpdir)
fake_home = testing.data_path()
create_default_subject(subjects_dir=tempdir,
fs_home=fake_home,
verbose=True)
assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed"
fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif')
os.remove(fid_path)
create_default_subject(update=True,
subjects_dir=tempdir,
fs_home=fake_home)
assert op.exists(fid_path), "Updating fsaverage"
# copy MRI file from sample data (shouldn't matter that it's incorrect,
# so here choose a small one)
path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri',
'T1.mgz')
path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
copyfile(path_from, path_to)
# remove redundant label files
label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label')
label_paths = glob(label_temp)
for label_path in label_paths[1:]:
os.remove(label_path)
# create source space
print('Creating surface source space')
path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
src = mne.setup_source_space('fsaverage',
'ico0',
subjects_dir=tempdir,
add_dist=False)
mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
print('Creating volume source space')
vsrc = mne.setup_volume_source_space('fsaverage',
pos=50,
mri=mri,
subjects_dir=tempdir,
add_interpolator=False)
write_source_spaces(path % 'vol-50', vsrc)
# scale fsaverage
write_source_spaces(path % 'ico-0', src, overwrite=True)
with pytest.warns(None): # sometimes missing nibabel
scale_mri('fsaverage',
'flachkopf',
scale,
True,
subjects_dir=tempdir,
verbose='debug')
assert _is_mri_subject('flachkopf', tempdir), "Scaling failed"
spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')
assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled"
assert os.path.isfile(
os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg'))
vsrc_s = mne.read_source_spaces(spath % 'vol-50')
for vox in ([0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 2, 3]):
idx = np.ravel_multi_index(vox, vsrc[0]['shape'], order='F')
err_msg = f'idx={idx} @ {vox}, scale={scale}'
assert_allclose(apply_trans(vsrc[0]['src_mri_t'], vox),
vsrc[0]['rr'][idx],
err_msg=err_msg)
assert_allclose(apply_trans(vsrc_s[0]['src_mri_t'], vox),
vsrc_s[0]['rr'][idx],
err_msg=err_msg)
scale_labels('flachkopf', subjects_dir=tempdir)
# add distances to source space after hacking the properties to make
# it run *much* faster
src_dist = src.copy()
for s in src_dist:
s.update(rr=s['rr'][s['vertno']],
nn=s['nn'][s['vertno']],
tris=s['use_tris'])
s.update(np=len(s['rr']),
ntri=len(s['tris']),
vertno=np.arange(len(s['rr'])),
inuse=np.ones(len(s['rr']), int))
mne.add_source_space_distances(src_dist)
write_source_spaces(path % 'ico-0', src_dist, overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert ssrc[0]['dist'] is not None
assert ssrc[0]['nearest'] is not None
# check patch info computation (only if SciPy is new enough to be fast)
if check_version('scipy', '1.3'):
for s in src_dist:
for key in ('dist', 'dist_limit'):
s[key] = None
write_source_spaces(path % 'ico-0', src_dist, overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert ssrc[0]['dist'] is None
assert ssrc[0]['nearest'] is not None
def test_nirx_15_3_short():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_3_short, preload=True)
# Test data import
assert raw._data.shape == (26, 220)
assert raw.info['sfreq'] == 12.5
# Test channel naming
assert raw.info['ch_names'][:4] == [
"S1_D2 760", "S1_D2 850", "S1_D9 760", "S1_D9 850"
]
assert raw.info['ch_names'][24:26] == ["S5_D13 760", "S5_D13 850"]
# Test frequency encoding
assert raw.info['chs'][0]['loc'][9] == 760
assert raw.info['chs'][1]['loc'][9] == 850
# Test info import
assert raw.info['subject_info'] == dict(birthday=(2020, 8, 18),
sex=0,
first_name="testMontage\\0A"
"TestMontage")
# Test distance between optodes matches values from
# https://github.com/mne-tools/mne-testing-data/pull/72
allowed_distance_error = 0.001
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0304, 0.0078, 0.0310, 0.0086, 0.0416, 0.0072, 0.0389, 0.0075, 0.0558,
0.0562, 0.0561, 0.0565, 0.0077
],
atol=allowed_distance_error)
# Test which channels are short
# These are the ones marked as red at
# https://github.com/mne-tools/mne-testing-data/pull/72
is_short = short_channels(raw.info)
assert_array_equal(is_short[:9:2], [False, True, False, True, False])
is_short = short_channels(raw.info, threshold=0.003)
assert_array_equal(is_short[:3:2], [False, False])
is_short = short_channels(raw.info, threshold=50)
assert_array_equal(is_short[:3:2], [True, True])
# Test trigger events
assert_array_equal(raw.annotations.description, ['4.0', '2.0', '1.0'])
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/72
# And have been manually copied below
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D2'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][4][3:5] == 'D1'
assert_allclose(mni_locs[4], [0.0846, -0.0142, -0.0156],
atol=allowed_dist_error)
assert raw.info['ch_names'][8][3:5] == 'D3'
assert_allclose(mni_locs[8], [0.0207, -0.1062, 0.0484],
atol=allowed_dist_error)
assert raw.info['ch_names'][12][3:5] == 'D4'
assert_allclose(mni_locs[12], [-0.0196, 0.0821, 0.0275],
atol=allowed_dist_error)
assert raw.info['ch_names'][16][3:5] == 'D5'
assert_allclose(mni_locs[16], [-0.0360, 0.0276, 0.0778],
atol=allowed_dist_error)
assert raw.info['ch_names'][19][3:5] == 'D6'
assert_allclose(mni_locs[19], [0.0388, -0.0477, 0.0932],
atol=allowed_dist_error)
assert raw.info['ch_names'][21][3:5] == 'D7'
assert_allclose(mni_locs[21], [-0.0394, -0.0483, 0.0928],
atol=allowed_dist_error)
def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep, subject,
subjects_dir):
'''
Plot the MFT sources at time point of peak.
'''
print "##### Attempting to plot:"
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [
fwdmag['src'][0]['vertno']
[fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][
fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]
]
stc_feat = SourceEstimate(stcdata,
vertices=vertices,
tmin=-0.2,
tstep=tstep,
subject=subject)
for hemi in ['lh', 'rh']:
brain = stc_feat.plot(surface='white',
hemi=hemi,
subjects_dir=subjects_dir,
transparent=True,
clim='auto')
brain.show_view('lateral')
# use peak getter to move visualization to the time point of the peak
tmin = 0.095
tmax = 0.10
print "Restricting peak search to [%fs, %fs]" % (tmin, tmax)
if hemi == 'both':
vertno_max, time_idx = stc_feat.get_peak(hemi='rh',
time_as_index=True,
tmin=tmin,
tmax=tmax)
else:
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi,
time_as_index=True,
tmin=tmin,
tmax=tmax)
if hemi == 'lh':
comax = fwdmag['src'][0]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][0]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
elif len(fwdmag['src']) > 1:
comax = fwdmag['src'][1]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][1]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
print "hemi=%s: setting time_idx=%d" % (hemi, time_idx)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max,
coords_as_verts=True,
hemi=hemi,
color='blue',
scale_factor=0.6)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [
np.nonzero([
stcdata[0:offsets[1], time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwdmag['src'][0]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][0]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['lh'].coords[vfoci] : "
print brain.geo['lh'].coords[vfoci]
elif len(fwdmag['src']) > 1:
ifoci = [
np.nonzero([
stcdata[offsets[1]:, time_idx] >=
0.25 * np.max(stcdata[:, time_idx])
][0])
]
vfoci = fwdmag['src'][1]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][1]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['rh'].coords[vfoci] : "
print brain.geo['rh'].coords[vfoci]
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'], cfoci, move=True)
print "mrfoci: "
print mrfoci
# Just some blops:
bloblist = np.zeros((300, 3))
for i in xrange(100):
bloblist[i, 0] = float(i)
bloblist[i + 100, 1] = float(i)
bloblist[i + 200, 2] = float(i)
mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
def plot_visualize_mft_sources(fwdmag, stcdata, tmin, tstep,
subject, subjects_dir):
'''
Plot the MFT sources at time point of peak.
'''
print "##### Attempting to plot:"
# cf. decoding/plot_decoding_spatio_temporal_source.py
vertices = [s['vertno'] for s in fwdmag['src']]
if len(vertices) == 1:
vertices = [fwdmag['src'][0]['vertno'][fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] <= -0.],
fwdmag['src'][0]['vertno'][fwdmag['src'][0]['rr'][fwdmag['src'][0]['vertno']][:, 0] > -0.]]
stc_feat = SourceEstimate(stcdata, vertices=vertices,
tmin=-0.2, tstep=tstep, subject=subject)
for hemi in ['lh', 'rh']:
brain = stc_feat.plot(surface='white', hemi=hemi, subjects_dir=subjects_dir,
transparent=True, clim='auto')
brain.show_view('lateral')
# use peak getter to move visualization to the time point of the peak
tmin = 0.095
tmax = 0.10
print "Restricting peak search to [%fs, %fs]" % (tmin, tmax)
if hemi == 'both':
vertno_max, time_idx = stc_feat.get_peak(hemi='rh', time_as_index=True,
tmin=tmin, tmax=tmax)
else:
vertno_max, time_idx = stc_feat.get_peak(hemi=hemi, time_as_index=True,
tmin=tmin, tmax=tmax)
if hemi == 'lh':
comax = fwdmag['src'][0]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][0]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
elif len(fwdmag['src']) > 1:
comax = fwdmag['src'][1]['rr'][vertno_max]
print "hemi=%s: vertno_max=%d, time_idx=%d fwdmag['src'][1]['rr'][vertno_max] = " %\
(hemi, vertno_max, time_idx), comax
print "hemi=%s: setting time_idx=%d" % (hemi, time_idx)
brain.set_data_time_index(time_idx)
# draw marker at maximum peaking vertex
brain.add_foci(vertno_max, coords_as_verts=True, hemi=hemi, color='blue',
scale_factor=0.6)
offsets = np.append([0], [s['nuse'] for s in fwdmag['src']])
if hemi == 'lh':
ifoci = [np.nonzero([stcdata[0:offsets[1],time_idx]>=0.25*np.max(stcdata[:,time_idx])][0])]
vfoci = fwdmag['src'][0]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][0]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['lh'].coords[vfoci] : "
print brain.geo['lh'].coords[vfoci]
elif len(fwdmag['src']) > 1:
ifoci = [np.nonzero([stcdata[offsets[1]:,time_idx]>=0.25*np.max(stcdata[:,time_idx])][0])]
vfoci = fwdmag['src'][1]['vertno'][ifoci[0][0]]
cfoci = fwdmag['src'][1]['rr'][vfoci]
print "Coords of %d sel. vfoci: " % cfoci.shape[0]
print cfoci
print "vfoci: "
print vfoci
print "brain.geo['rh'].coords[vfoci] : "
print brain.geo['rh'].coords[vfoci]
mrfoci = np.zeros(cfoci.shape)
invmri_head_t = invert_transform(fwdmag['info']['mri_head_t'])
mrfoci = apply_trans(invmri_head_t['trans'],cfoci, move=True)
print "mrfoci: "
print mrfoci
# Just some blops:
bloblist = np.zeros((300,3))
for i in xrange(100):
bloblist[i,0] = float(i)
bloblist[i+100,1] = float(i)
bloblist[i+200,2] = float(i)
mrblobs = apply_trans(invmri_head_t['trans'], bloblist, move=True)
brain.save_image('testfig_map_%s.png' % hemi)
brain.close()
def select_vertices_in_sensor_range(inst,
dist,
info=None,
picks=None,
trans=None,
indices=False,
verbose=None):
"""Find vertices within given distance to a sensor.
Parameters
----------
inst : instance of Forward | instance of SourceSpaces
The object to select vertices from.
dist : float
The minimum distance between a vertex and the nearest sensor. All
vertices for which the distance to the nearest sensor exceeds this
limit are discarded.
info : instance of Info | None
The info structure that contains information about the channels. Only
needs to be specified if the object to select vertices from does is
an instance of SourceSpaces.
picks : array-like of int | None
Indices of sensors to include in the search for the nearest sensor. If
``None``, the default, only MEG channels are used.
trans : str | instance of Transform | None
Either the full path to the head<->MRI transform ``*-trans.fif`` file
produced during coregistration, or the Transformation itself. If trans
is None, an identity matrix is assumed. Only needed when ``inst`` is a
source space in MRI coordinates.
indices: False | True
If ``True``, return vertex indices instead of vertex numbers. Defaults
to ``False``.
verbose : bool | str | int | None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
vertices : pair of lists | list of int
Either a list of vertex numbers for the left and right hemisphere (if
``indices==False``) or a single list with vertex indices.
See Also
--------
restrict_forward_to_vertices : restrict Forward to the given vertices
restrict_src_to_vertices : restrict SourceSpaces to the given vertices
"""
if isinstance(inst, Forward):
info = inst['info']
src = inst['src']
elif isinstance(inst, SourceSpaces):
src = inst
if info is None:
raise ValueError('You need to specify an Info object with '
'information about the channels.')
# Load the head<->MRI transform if necessary
if src[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI:
if trans is None:
raise ValueError('Source space is in MRI coordinates, but no '
'head<->MRI transform was given. Please specify '
'the full path to the appropriate *-trans.fif '
'file as the "trans" parameter.')
if isinstance(trans, string_types):
trans = read_trans(trans, return_all=True)
for trans in trans: # we got at least 1
try:
trans = _ensure_trans(trans, 'head', 'mri')
except Exception as exp:
pass
else:
break
else:
raise exp
src_trans = invert_transform(_ensure_trans(trans, 'head', 'mri'))
print('Transform!')
else:
src_trans = Transform('head', 'head') # Identity transform
dev_to_head = _ensure_trans(info['dev_head_t'], 'meg', 'head')
if picks is None:
picks = pick_types(info, meg=True)
if len(picks) > 0:
logger.info('Using MEG channels')
else:
logger.info('Using EEG channels')
picks = pick_types(info, eeg=True)
src_pos = np.vstack([
apply_trans(src_trans, s['rr'][s['inuse'].astype(np.bool)])
for s in src
])
sensor_pos = []
for ch in picks:
# MEG channels are in device coordinates, translate them to head
if channel_type(info, ch) in ['mag', 'grad']:
sensor_pos.append(
apply_trans(dev_to_head, info['chs'][ch]['loc'][:3]))
else:
sensor_pos.append(info['chs'][ch]['loc'][:3])
sensor_pos = np.array(sensor_pos)
# Find vertices that are within range of a sensor. We use a KD-tree for
# speed.
logger.info('Finding vertices within sensor range...')
tree = cKDTree(sensor_pos)
distances, _ = tree.query(src_pos, distance_upper_bound=dist)
# Vertices out of range are flagged as np.inf
src_sel = np.isfinite(distances)
logger.info('[done]')
if indices:
return np.flatnonzero(src_sel)
else:
n_lh_verts = src[0]['nuse']
lh_sel, rh_sel = src_sel[:n_lh_verts], src_sel[n_lh_verts:]
vert_lh = src[0]['vertno'][lh_sel]
vert_rh = src[1]['vertno'][rh_sel]
return [vert_lh, vert_rh]
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = [
'FidNz 0.00000 10.56381 -2.05108\nFidT9 -7.82694 0.45386 -3.76056\n'
'very_very_very_long_name 7.82694 0.45386 -3.76056',
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa
'E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000\n' # noqa
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000', # noqa
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n'
'NumberPositions= 68\nPositions\n-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n0.0083 86.8110 -39.9830\n'
'Labels\nLPA\nRPA\nNz\n',
'Site Theta Phi\nFp1 -92 -72\nFp2 92 72\n'
'very_very_very_long_name -60 -51\n',
'346\n'
'EEG F3 -62.027 -50.053 85\n'
'EEG Fz 45.608 90 85\n'
'EEG F4 62.01 50.103 85\n',
'eeg Fp1 -95.0 -31.0 -3.0\neeg AF7 -81 -59 -3\neeg AF3 -87 -41 28\n'
]
kinds = ['test.sfp', 'test.csd', 'test.elc', 'test.txt', 'test.elp',
'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if ".sfp" in kind or ".txt" in kind:
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def test_read_ctf(tmpdir):
"""Test CTF reader."""
temp_dir = str(tmpdir)
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
pytest.raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (
str(ch_num + 1),
raw.ch_names[ch_num],
) + tuple('%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos),
pos_read,
rtol=1e-5,
atol=1e-5)
assert (pos_read == pos_read_old).mean() < 0.1
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with pytest.warns(RuntimeWarning, match='MISC channel'):
pytest.raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = read_raw_fif(fname + '_raw.fif', preload=True)
with pytest.warns(None): # sometimes matches "MISC channel"
raw = read_raw_ctf(fname)
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
assert (raw.info['meas_id']['version'] ==
raw_c.info['meas_id']['version'] + 1)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'],
raw_c.info[t]['trans'],
rtol=1e-4,
atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads', 'ch_names',
'custom_ref_applied', 'description', 'events',
'experimenter', 'highpass', 'line_freq', 'lowpass',
'nchan', 'proj_id', 'proj_name', 'projs', 'sfreq',
'subject_info'):
assert raw.info[key] == raw_c.info[key], key
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert raw.buffer_size_sec == raw_c.buffer_size_sec
assert len(raw.info['comps']) == len(raw_c.info['comps'])
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert c1['save_calibrated'] == c2['save_calibrated']
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'],
c2['data']['data'],
atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5,
atol=1e-7)
assert len(raw.info['chs']) == len(raw_c.info['chs'])
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
if key == 'coil_type' and c1[key] == FIFF.FIFFV_COIL_EEG:
# XXX MNE-C bug that this is not set
assert c2[key] == FIFF.FIFFV_COIL_NONE
continue
assert c1[key] == c2[key], key
for key in ('cal', ):
assert_allclose(c1[key],
c2[key],
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc', ):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
if (c2[key][:3] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][:3]
assert_allclose(c1[key][:3],
check,
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if (c2[key][3:] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][9:12]
assert_allclose(c1[key][9:12],
check,
atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# Make sure all digitization points are in the MNE head coord frame
for p in raw.info['dig']:
assert p['coord_frame'] == FIFF.FIFFV_COORD_HEAD, \
'dig points must be in FIFF.FIFFV_COORD_HEAD'
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
# XXX: Next test would fail because c-tools assign the fiducials from
# CTF data as HPI. Should eventually clarify/unify with Matti.
# assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = read_raw_fif(out_fname)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.buffer_size_sec))
assert bnd == raw._raw_extras[0]['block_size']
assert bnd == block_sizes[op.basename(fname)]
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(
bnd, bnd + 1), slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0], raw_c[pick_ch,
sl_time][0])
# all data / preload
with pytest.warns(None): # sometimes MISC
raw = read_raw_ctf(fname, preload=True)
assert_allclose(raw[:][0], raw_c[:][0], atol=1e-15)
# test bad segment annotations
if 'testdata_ctf_short.ds' in fname:
assert 'bad' in raw.annotations.description[0]
assert_allclose(raw.annotations.onset, [2.15])
assert_allclose(raw.annotations.duration, [0.0225])
pytest.raises(TypeError, read_raw_ctf, 1)
pytest.raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
pytest.raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def plot_3d_montage(info,
view_map,
*,
src_det_names='auto',
ch_names='numbered',
subject='fsaverage',
trans='fsaverage',
surface='pial',
subjects_dir=None,
verbose=None):
"""
Plot a 3D sensor montage.
Parameters
----------
info : instance of Info
Measurement info.
view_map : dict
Dict of view (key) to channel-pair-numbers (value) to use when
plotting. Note that, because these get plotted as 1-based channel
*numbers*, the values should be 1-based rather than 0-based.
The keys are of the form:
``'{side}-{view}'``
For views like ``'left-lat'`` or ``'right-frontal'`` where the side
matters.
``'{view}'``
For views like ``'caudal'`` that are along the midline.
See :meth:`mne.viz.Brain.show_view` for ``view`` options, and the
Examples section below for usage examples.
src_det_names : None | dict | str
Source and detector names to use. "auto" (default) will see if the
channel locations correspond to standard 10-20 locations and will
use those if they do (otherwise will act like None). None will use
S1, S2, ..., D1, D2, ..., etc. Can also be an explicit dict mapping,
for example::
src_det_names=dict(S1='Fz', D1='FCz', ...)
ch_names : str | dict | None
If ``'numbered'`` (default), use ``['1', '2', ...]`` for the channel
names, or ``None`` to use ``['S1_D2', 'S2_D1', ...]``. Can also be a
dict to provide a mapping from the ``'S1_D2'``-style names (keys) to
other names, e.g., ``defaultdict(lambda: '')`` will prevent showing
the names altogether.
.. versionadded:: 0.3
subject : str
The subject.
trans : str | Transform
The subjects head<->MRI transform.
surface : str
The FreeSurfer surface name (e.g., 'pial', 'white').
subjects_dir : str
The subjects directory.
%(verbose)s
Returns
-------
figure : matplotlib.figure.Figure
The matplotlib figimage.
Examples
--------
For a Hitachi system with two sets of 12 source-detector arrangements,
one on each side of the head, showing 1-12 on the left and 13-24 on the
right can be accomplished using the following ``view_map``::
>>> view_map = {
... 'left-lat': np.arange(1, 13),
... 'right-lat': np.arange(13, 25),
... }
NIRx typically involves more complicated arrangements. See
:ref:`the 3D tutorial <tut-fnirs-vis-brain-plot-3d-montage>` for
an advanced example that incorporates the ``'caudal'`` view as well.
""" # noqa: E501
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
_validate_type(info, Info, 'info')
_validate_type(view_map, dict, 'views')
_validate_type(src_det_names, (None, dict, str), 'src_det_names')
_validate_type(ch_names, (dict, str, None), 'ch_names')
info = pick_info(info, pick_types(info, fnirs=True, exclude=())[::2])
if isinstance(ch_names, str):
_check_option('ch_names', ch_names, ('numbered', ), extra='when str')
ch_names = {
name.split()[0]: str(ni)
for ni, name in enumerate(info['ch_names'], 1)
}
info['bads'] = []
if isinstance(src_det_names, str):
_check_option('src_det_names',
src_det_names, ('auto', ),
extra='when str')
# Decide if we can map to 10-20 locations
names, pos = zip(
*transform_to_head(make_standard_montage(
'standard_1020')).get_positions()['ch_pos'].items())
pos = np.array(pos, float)
locs = dict()
bad = False
for ch in info['chs']:
name = ch['ch_name']
s_name, d_name = name.split()[0].split('_')
for name, loc in [(s_name, ch['loc'][3:6]),
(d_name, ch['loc'][6:9])]:
if name in locs:
continue
# see if it's close enough
idx = np.where(cdist(loc[np.newaxis], pos)[0] < 1e-3)[0]
if len(idx) < 1:
bad = True
break
# Some are duplicated (e.g., T7+T3) but we can rely on the
# first one being the canonical one
locs[name] = names[idx[0]]
if bad:
break
if bad:
src_det_names = None
logger.info('Could not automatically map source/detector names to '
'10-20 locations.')
else:
src_det_names = locs
logger.info('Source-detector names automatically mapped to 10-20 '
'locations')
head_mri_t = _get_trans(trans, 'head', 'mri')[0]
del trans
views = list()
for key, num in view_map.items():
_validate_type(key, str, f'view_map key {repr(key)}')
_validate_type(num, np.ndarray, f'view_map[{repr(key)}]')
if '-' in key:
hemi, v = key.split('-', maxsplit=1)
hemi = dict(left='lh', right='rh')[hemi]
views.append((hemi, v, num))
else:
views.append(('lh', key, num))
del view_map
size = (400 * len(views), 400)
brain = Brain(subject,
'both',
surface,
views=['lat'] * len(views),
size=size,
background='w',
units='m',
view_layout='horizontal',
subjects_dir=subjects_dir)
with _safe_brain_close(brain):
brain.add_head(dense=False, alpha=0.1)
brain.add_sensors(info,
trans=head_mri_t,
fnirs=['channels', 'pairs', 'sources', 'detectors'])
add_text_kwargs = dict()
if 'render' in _get_args(brain.plotter.add_text):
add_text_kwargs['render'] = False
for col, view in enumerate(views):
plotted = set()
brain.show_view(view[1],
hemi=view[0],
focalpoint=(0, -0.02, 0.02),
distance=0.4,
row=0,
col=col)
brain.plotter.subplot(0, col)
vp = brain.plotter.renderer
for ci in view[2]: # figure out what we need to plot
this_ch = info['chs'][ci - 1]
ch_name = this_ch['ch_name'].split()[0]
s_name, d_name = ch_name.split('_')
needed = [
(ch_names, 'ch_names', ch_name, this_ch['loc'][:3], 12,
'Centered'),
(src_det_names, 'src_det_names', s_name,
this_ch['loc'][3:6], 8, 'Bottom'),
(src_det_names, 'src_det_names', d_name,
this_ch['loc'][6:9], 8, 'Bottom'),
]
for lookup, lname, name, ch_pos, font_size, va in needed:
if name in plotted:
continue
plotted.add(name)
orig_name = name
if lookup is not None:
name = lookup[name]
_validate_type(name, str, f'{lname}[{repr(orig_name)}]')
ch_pos = apply_trans(head_mri_t, ch_pos)
vp.SetWorldPoint(np.r_[ch_pos, 1.])
vp.WorldToDisplay()
ch_pos = (np.array(vp.GetDisplayPoint()[:2]) -
np.array(vp.GetOrigin()))
actor = brain.plotter.add_text(name,
ch_pos,
font_size=font_size,
color=(0., 0., 0.),
**add_text_kwargs)
prop = actor.GetTextProperty()
getattr(prop, f'SetVerticalJustificationTo{va}')()
prop.SetJustificationToCentered()
actor.SetTextProperty(prop)
prop.SetBold(True)
img = brain.screenshot()
return plt.figimage(img, resize=True).figure
def test_warp_montage_volume():
"""Test warping an montage based on intracranial electrode positions."""
import nibabel as nib
subject_brain = nib.load(
op.join(subjects_dir, 'sample', 'mri', 'brain.mgz'))
template_brain = nib.load(
op.join(subjects_dir, 'fsaverage', 'mri', 'brain.mgz'))
zooms = dict(translation=10, rigid=10, sdr=10)
reg_affine, sdr_morph = compute_volume_registration(
subject_brain,
template_brain,
zooms=zooms,
niter=[3, 3, 3],
pipeline=('translation', 'rigid', 'sdr'))
# make an info object with three channels with positions
ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017],
[-14.03007764, 19.69978401, 12.07236939],
[-21.1130506, 21.98310911, 13.25658887]])
ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m
lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir)
montage = make_dig_montage(ch_pos,
lpa=lpa['r'],
nasion=nasion['r'],
rpa=rpa['r'],
coord_frame='mri')
# make fake image based on the info
CT_data = np.zeros(subject_brain.shape)
# convert to voxels
ch_coords_vox = apply_trans(
np.linalg.inv(subject_brain.header.get_vox2ras_tkr()), ch_coords)
for (x, y, z) in ch_coords_vox.round().astype(int):
# make electrode contact hyperintensities
# first, make the surrounding voxels high intensity
CT_data[x - 1:x + 2, y - 1:y + 2, z - 1:z + 2] = 500
# then, make the center even higher intensity
CT_data[x, y, z] = 1000
CT = nib.Nifti1Image(CT_data, subject_brain.affine)
ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017],
[-14.03007764, 19.69978401, 12.07236939],
[-21.1130506, 21.98310911, 13.25658887]])
ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m
lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir)
montage = make_dig_montage(ch_pos,
lpa=lpa['r'],
nasion=nasion['r'],
rpa=rpa['r'],
coord_frame='mri')
montage_warped, image_from, image_to = warp_montage_volume(
montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir_from=subjects_dir,
thresh=0.99)
# checked with nilearn plot from `tut-ieeg-localize`
# check montage in surface RAS
ground_truth_warped = np.array([[-0.009, -0.00133333, -0.033],
[-0.01445455, 0.00127273, -0.03163636],
[-0.022, 0.00285714, -0.031]])
for i, d in enumerate(montage_warped.dig):
assert np.linalg.norm( # off by less than 1.5 cm
d['r'] - ground_truth_warped[i]) < 0.015
# check image_from
for idx, contact in enumerate(range(1, len(ch_pos) + 1)):
voxels = np.array(np.where(np.array(image_from.dataobj) == contact)).T
assert ch_coords_vox.round()[idx] in voxels
assert ch_coords_vox.round()[idx] + 5 not in voxels
# check image_to, too many, just check center
ground_truth_warped_voxels = np.array(
[[135.5959596, 161.97979798, 123.83838384],
[143.11111111, 159.71428571, 125.61904762],
[150.53982301, 158.38053097, 127.31858407]])
for i in range(len(montage.ch_names)):
assert np.linalg.norm(
np.array(np.where(np.array(image_to.dataobj) == i + 1)).mean(
axis=1) - ground_truth_warped_voxels[i]) < 8
# test inputs
with pytest.raises(ValueError, match='`thresh` must be between 0 and 1'):
warp_montage_volume(montage,
CT,
reg_affine,
sdr_morph,
'sample',
thresh=11.)
with pytest.raises(ValueError, match='subject folder is incorrect'):
warp_montage_volume(montage,
CT,
reg_affine,
sdr_morph,
subject_from='foo')
CT_unaligned = nib.Nifti1Image(CT_data, template_brain.affine)
with pytest.raises(RuntimeError, match='not aligned to Freesurfer'):
warp_montage_volume(montage,
CT_unaligned,
reg_affine,
sdr_morph,
'sample',
subjects_dir_from=subjects_dir)
bad_montage = montage.copy()
for d in bad_montage.dig:
d['coord_frame'] = 99
with pytest.raises(RuntimeError, match='Coordinate frame not supported'):
warp_montage_volume(bad_montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir_from=subjects_dir)
# check channel not warped
ch_pos_doubled = ch_pos.copy()
ch_pos_doubled.update(zip(['4', '5', '6'], ch_coords / 1000))
doubled_montage = make_dig_montage(ch_pos_doubled,
lpa=lpa['r'],
nasion=nasion['r'],
rpa=rpa['r'],
coord_frame='mri')
with pytest.warns(RuntimeWarning, match='not assigned'):
warp_montage_volume(doubled_montage,
CT,
reg_affine,
None,
'sample',
subjects_dir_from=subjects_dir)
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = Raw(fname + '_raw.fif', add_eeg_ref=False, preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3], c2[key][:3], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12], c2[key][9:12], atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = Raw(out_fname, add_eeg_ref=False)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def get_head_mri_trans(bids_path, extra_params=None):
"""Produce transformation matrix from MEG and MRI landmark points.
Will attempt to read the landmarks of Nasion, LPA, and RPA from the sidecar
files of (i) the MEG and (ii) the T1 weighted MRI data. The two sets of
points will then be used to calculate a transformation matrix from head
coordinates to MRI coordinates.
Parameters
----------
bids_path : mne_bids.BIDSPath
The path of the recording for which to retrieve the transformation. The
:class:`mne_bids.BIDSPath` instance passed here **must** have the
``.root`` attribute set.
extra_params : None | dict
Extra parameters to be passed to MNE read_raw_* functions when reading
the lankmarks from the MEG file.
If a dict, for example: ``extra_params=dict(allow_maxshield=True)``.
Returns
-------
trans : mne.transforms.Transform
The data transformation matrix from head to MRI coordinates
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
if not isinstance(bids_path, BIDSPath):
raise RuntimeError('"bids_path" must be a BIDSPath object. Please '
'instantiate using mne_bids.BIDSPath().')
# check root available
bids_path = bids_path.copy()
bids_root = bids_path.root
if bids_root is None:
raise ValueError('The root of the "bids_path" must be set. '
'Please use `bids_path.update(root="<root>")` '
'to set the root of the BIDS folder to read.')
# only get this for MEG data
bids_path.update(datatype='meg')
# Get the sidecar file for MRI landmarks
bids_fname = bids_path.update(suffix='meg', root=bids_root)
t1w_json_path = _find_matching_sidecar(bids_fname,
suffix='T1w',
extension='.json')
# Get MRI landmarks from the JSON sidecar
with open(t1w_json_path, 'r', encoding='utf-8-sig') as f:
t1w_json = json.load(f)
mri_coords_dict = t1w_json.get('AnatomicalLandmarkCoordinates', dict())
mri_landmarks = np.asarray(
(mri_coords_dict.get('LPA',
np.nan), mri_coords_dict.get('NAS', np.nan),
mri_coords_dict.get('RPA', np.nan)))
if np.isnan(mri_landmarks).any():
raise RuntimeError(
'Could not parse T1w sidecar file: "{}"\n\n'
'The sidecar file MUST contain a key '
'"AnatomicalLandmarkCoordinates" pointing to a '
'dict with keys "LPA", "NAS", "RPA". '
'Yet, the following structure was found:\n\n"{}"'.format(
t1w_json_path, t1w_json))
# The MRI landmarks are in "voxels". We need to convert the to the
# neuromag RAS coordinate system in order to compare the with MEG landmarks
# see also: `mne_bids.write.write_anat`
t1w_path = t1w_json_path.replace('.json', '.nii')
if not op.exists(t1w_path):
t1w_path += '.gz' # perhaps it is .nii.gz? ... else raise an error
if not op.exists(t1w_path):
raise RuntimeError(
'Could not find the T1 weighted MRI associated '
'with "{}". Tried: "{}" but it does not exist.'.format(
t1w_json_path, t1w_path))
t1_nifti = nib.load(t1w_path)
# Convert to MGH format to access vox2ras method
t1_mgh = nib.MGHImage(t1_nifti.dataobj, t1_nifti.affine)
# now extract transformation matrix and put back to RAS coordinates of MRI
vox2ras_tkr = t1_mgh.header.get_vox2ras_tkr()
mri_landmarks = apply_trans(vox2ras_tkr, mri_landmarks)
mri_landmarks = mri_landmarks * 1e-3
# Get MEG landmarks from the raw file
_, ext = _parse_ext(bids_fname)
if extra_params is None:
extra_params = dict()
if ext == '.fif':
extra_params = dict(allow_maxshield=True)
raw = read_raw_bids(bids_path=bids_path, extra_params=extra_params)
meg_coords_dict = _extract_landmarks(raw.info['dig'])
meg_landmarks = np.asarray((meg_coords_dict['LPA'], meg_coords_dict['NAS'],
meg_coords_dict['RPA']))
# Given the two sets of points, fit the transform
trans_fitted = fit_matched_points(src_pts=meg_landmarks,
tgt_pts=mri_landmarks)
trans = mne.transforms.Transform(fro='head', to='mri', trans=trans_fitted)
return trans
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp="FidNz 0 9.071585155 -2.359754454\n"
"FidT9 -6.711765 0.040402876 -3.251600355\n"
"very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n"
"Cz 0 0 8.899186843",
csd="// MatLab Sphere coordinates [degrees] Cartesian coordinates\n" # noqa: E501
"// Label Theta Phi Radius X Y Z off sphere surface\n" # noqa: E501
"E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n" # noqa: E501
"E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n" # noqa: E501
"E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n" # noqa: E501
"E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022", # noqa: E501
mm_elc="# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n" # noqa:E501
"NumberPositions= 68\n"
"Positions\n"
"-86.0761 -19.9897 -47.9860\n"
"85.7939 -20.0093 -48.0310\n"
"0.0083 86.8110 -39.9830\n"
"-86.0761 -24.9897 -67.9860\n"
"Labels\nLPA\nRPA\nNz\nDummy\n",
m_elc="# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n"
"NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n" # noqa:E501
".0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n"
".08 -.02 -.04\n"
"Labels\nLPA\nRPA\nNz\nDummy\n",
txt="Site Theta Phi\n"
"Fp1 -92 -72\n"
"Fp2 92 72\n"
"very_very_very_long_name -92 72\n"
"O2 92 -90\n",
elp="346\n"
"EEG\t F3\t -62.027\t -50.053\t 85\n"
"EEG\t Fz\t 45.608\t 90\t 85\n"
"EEG\t F4\t 62.01\t 50.103\t 85\n"
"EEG\t FCz\t 68.01\t 58.103\t 85\n",
hpts="eeg Fp1 -95.0 -3. -3.\n" "eeg AF7 -1 -1 -3\n" "eeg A3 -2 -2 2\n" "eeg A 0 0 0",
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516], [-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[
[-0.08608, -0.01999, -0.04799],
[0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998],
[-0.08608, -0.02499, -0.06799],
],
m_elc=[
[-0.08608, -0.01999, -0.04799],
[0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998],
[0.08, -0.02, -0.04],
],
txt=[
[-26.25044, 80.79056, -2.96646],
[26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646],
[0.0, -84.94822, -2.96646],
],
elp=[
[-48.20043, 57.55106, 39.86971],
[0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198],
[41.64599, 66.91489, 31.8278],
],
hpts=[[-95, -3, -3], [-1, -1.0, -3.0], [-2, -2, 2.0], [0, 0, 0]],
)
for key, text in inputs.items():
kind = key.split("_")[-1]
fname = op.join(tempdir, "test." + kind)
with open(fname, "w") as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ("sfp", "txt"):
assert_true("very_very_very_long_name" in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, "test")
if kind == "csd":
dtype = [
("label", "S4"),
("theta", "f8"),
("phi", "f8"),
("radius", "f8"),
("x", "f8"),
("y", "f8"),
("z", "f8"),
("off_sph", "f8"),
]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss["csd"] = np.c_[table["x"], table["y"], table["z"]]
if kind == "elc":
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = [
"F3",
"FZ",
"F4",
"FC3",
"FCz",
"FC4",
"C3",
"CZ",
"C4",
"CP3",
"CPZ",
"CP4",
"P3",
"PZ",
"P4",
"O1",
"OZ",
"O2",
]
montage = read_montage("standard_1020", ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
fname = op.join(tempdir, "test_fid.hpts")
with open(fname, "w") as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, "test_fid.hpts"), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index("2")
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index("1")
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index("3")
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, "test_fid.hpts")
montage = read_montage(montage_fname, unit="mm")
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ["eeg"] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c["loc"][:3] for c in info["chs"]])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info["ch_names"])
info = create_info(montage.ch_names, 1e3, ["eeg"] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c["loc"][:3] for c in evoked.info["chs"]])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info["ch_names"])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ["foo", "bar"], 1e3, ["eeg"] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def get_head_mri_trans(bids_fname, bids_root):
"""Produce transformation matrix from MEG and MRI landmark points.
Will attempt to read the landmarks of Nasion, LPA, and RPA from the sidecar
files of (i) the MEG and (ii) the T1 weighted MRI data. The two sets of
points will then be used to calculate a transformation matrix from head
coordinates to MRI coordinates.
Parameters
----------
bids_fname : str
Full name of the MEG data file
bids_root : str
Path to root of the BIDS folder
Returns
-------
trans : instance of mne.transforms.Transform
The data transformation matrix from head to MRI coordinates
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
# Get the sidecar file for MRI landmarks
bids_fname = op.basename(bids_fname)
t1w_json_path = _find_matching_sidecar(bids_fname, bids_root, 'T1w.json')
# Get MRI landmarks from the JSON sidecar
with open(t1w_json_path, 'r') as f:
t1w_json = json.load(f)
mri_coords_dict = t1w_json.get('AnatomicalLandmarkCoordinates', dict())
mri_landmarks = np.asarray(
(mri_coords_dict.get('LPA',
np.nan), mri_coords_dict.get('NAS', np.nan),
mri_coords_dict.get('RPA', np.nan)))
if np.isnan(mri_landmarks).any():
raise RuntimeError(
'Could not parse T1w sidecar file: "{}"\n\n'
'The sidecar file MUST contain a key '
'"AnatomicalLandmarkCoordinates" pointing to a '
'dict with keys "LPA", "NAS", "RPA". '
'Yet, the following structure was found:\n\n"{}"'.format(
t1w_json_path, t1w_json))
# The MRI landmarks are in "voxels". We need to convert the to the
# neuromag RAS coordinate system in order to compare the with MEG landmarks
# see also: `mne_bids.write.write_anat`
t1w_path = t1w_json_path.replace('.json', '.nii')
if not op.exists(t1w_path):
t1w_path += '.gz' # perhaps it is .nii.gz? ... else raise an error
if not op.exists(t1w_path):
raise RuntimeError(
'Could not find the T1 weighted MRI associated '
'with "{}". Tried: "{}" but it does not exist.'.format(
t1w_json_path, t1w_path))
t1_nifti = nib.load(t1w_path)
# Convert to MGH format to access vox2ras method
t1_mgh = nib.MGHImage(t1_nifti.dataobj, t1_nifti.affine)
# now extract transformation matrix and put back to RAS coordinates of MRI
vox2ras_tkr = t1_mgh.header.get_vox2ras_tkr()
mri_landmarks = apply_trans(vox2ras_tkr, mri_landmarks)
mri_landmarks = mri_landmarks * 1e-3
# Get MEG landmarks from the raw file
raw = read_raw_bids(bids_fname, bids_root)
meg_coords_dict = _extract_landmarks(raw.info['dig'])
meg_landmarks = np.asarray((meg_coords_dict['LPA'], meg_coords_dict['NAS'],
meg_coords_dict['RPA']))
# Given the two sets of points, fit the transform
trans_fitted = fit_matched_points(src_pts=meg_landmarks,
tgt_pts=mri_landmarks)
trans = mne.transforms.Transform(fro='head', to='mri', trans=trans_fitted)
return trans
def test_scale_mri():
"""Test creating fsaverage and scaling it"""
# create fsaverage
tempdir = _TempDir()
create_default_subject(subjects_dir=tempdir)
assert_true(_is_mri_subject('fsaverage', tempdir),
"Creating fsaverage failed")
fid_path = os.path.join(tempdir, 'fsaverage', 'bem',
'fsaverage-fiducials.fif')
os.remove(fid_path)
create_default_subject(update=True, subjects_dir=tempdir)
assert_true(os.path.exists(fid_path), "Updating fsaverage")
# copy MRI file from sample data
path = os.path.join('%s', 'fsaverage', 'mri', 'orig.mgz')
sample_sdir = os.path.join(mne.datasets.sample.data_path(), 'subjects')
copyfile(path % sample_sdir, path % tempdir)
# remove redundant label files
label_temp = os.path.join(tempdir, 'fsaverage', 'label', '*.label')
label_paths = glob(label_temp)
for label_path in label_paths[1:]:
os.remove(label_path)
# create source space
path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
add_dist=False)
write_source_spaces(path % 'ico-0', src)
mri = os.path.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
vsrc = mne.setup_volume_source_space('fsaverage', pos=50, mri=mri,
subjects_dir=tempdir,
add_interpolator=False)
write_source_spaces(path % 'vol-50', vsrc)
# scale fsaverage
os.environ['_MNE_FEW_SURFACES'] = 'true'
scale = np.array([1, .2, .8])
scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir)
del os.environ['_MNE_FEW_SURFACES']
assert_true(_is_mri_subject('flachkopf', tempdir),
"Scaling fsaverage failed")
spath = os.path.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')
assert_true(os.path.exists(spath % 'ico-0'),
"Source space ico-0 was not scaled")
vsrc_s = mne.read_source_spaces(spath % 'vol-50')
pt = np.array([0.12, 0.41, -0.22])
assert_array_almost_equal(apply_trans(vsrc_s[0]['src_mri_t'], pt * scale),
apply_trans(vsrc[0]['src_mri_t'], pt))
scale_labels('flachkopf', subjects_dir=tempdir)
# add distances to source space
mne.add_source_space_distances(src)
src.save(path % 'ico-0', overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert_is_not(ssrc[0]['dist'], None)
def test_read_ctf():
"""Test CTF reader"""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
assert_raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with warnings.catch_warnings(record=True) as w: # reclassified channel
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
assert_true(all('MISC channel' in str(ww.message) for ww in w))
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert_true((pos_read == pos_read_old).mean() < 0.1)
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with warnings.catch_warnings(record=True): # no coord tr
assert_raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = read_raw_fif(fname + '_raw.fif', preload=True)
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
for key in ('version', 'usecs'):
assert_equal(raw.info['meas_id'][key], raw_c.info['meas_id'][key])
py_time = raw.info['meas_id']['secs']
c_time = raw_c.info['meas_id']['secs']
max_offset = 24 * 60 * 60 # probably overkill but covers timezone
assert_true(c_time - max_offset <= py_time <= c_time)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert_equal(raw.info['buffer_size_sec'],
raw_c.info['buffer_size_sec'])
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
assert_allclose(c1[key][:3], c2[key][:3], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
assert_allclose(c1[key][9:12], c2[key][9:12], atol=1e-6,
rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
# XXX: Next test would fail because c-tools assign the fiducials from
# CTF data as HPI. Should eventually clarify/unify with Matti.
# assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = read_raw_fif(out_fname)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.info['buffer_size_sec']))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with warnings.catch_warnings(record=True) as w: # reclassified ch
raw = read_raw_ctf(fname, preload=True)
assert_true(all('MISC channel' in str(ww.message) for ww in w))
assert_allclose(raw[:][0], raw_c[:][0])
assert_raises(TypeError, read_raw_ctf, 1)
assert_raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
assert_raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd='// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
bvef='<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
'<!-- Generated by EasyCap Configurator 19.05.2014 -->\n'
'<Electrodes defaults="false">\n'
' <Electrode>\n'
' <Name>Fp1</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-72</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>1</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>Fz</Name>\n'
' <Theta>45</Theta>\n'
' <Phi>90</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>2</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F3</Name>\n'
' <Theta>-60</Theta>\n'
' <Phi>-51</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>3</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F7</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-36</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>4</Number>\n'
' </Electrode>\n'
'</Electrodes>',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
bvef=[[-26.266444, 80.839803, 5.204748e-15],
[3.680313e-15, 60.104076, 60.104076],
[-46.325632, 57.207392, 42.500000],
[-68.766444, 49.961746, 5.204748e-15]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert ('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = ["""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
FidNz -91 0 -42
FidT9 0 -91 -42
FidT10 0 91 -42
"""]
# sfp files seem to have Nz, T9, and T10 as fiducials:
# https://github.com/mne-tools/mne-python/pull/4482#issuecomment-321980611
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, input_str in zip(kinds, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
assert_array_equal(montage.nasion[[0, 2]], [0, 0])
assert_array_equal(montage.lpa[[1, 2]], [0, 0])
assert_array_equal(montage.rpa[[1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
# test return type as well as set montage
assert (isinstance(evoked.set_montage(montage), type(evoked)))
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
# Channel names can be treated case insensitive
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
# Unless there is a collision in names
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
assert len(info['dig']) == 5 # 2 EEG w/pos, 3 fiducials
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage, set_dig=False)
assert (info['dig'] is None)
# test get_pos2d method
montage = read_montage("standard_1020")
c3 = montage.get_pos2d()[montage.ch_names.index("C3")]
c4 = montage.get_pos2d()[montage.ch_names.index("C4")]
fz = montage.get_pos2d()[montage.ch_names.index("Fz")]
oz = montage.get_pos2d()[montage.ch_names.index("Oz")]
f1 = montage.get_pos2d()[montage.ch_names.index("F1")]
assert (c3[0] < 0) # left hemisphere
assert (c4[0] > 0) # right hemisphere
assert (fz[1] > 0) # frontal
assert (oz[1] < 0) # occipital
assert_allclose(fz[0], 0, atol=1e-2) # midline
assert_allclose(oz[0], 0, atol=1e-2) # midline
assert (f1[0] < 0 and f1[1] > 0) # left frontal
# test get_builtin_montages function
montages = get_builtin_montages()
assert (len(montages) > 0) # MNE should always ship with montages
assert ("standard_1020" in montages) # 10/20 montage
assert ("standard_1005" in montages) # 10/05 montage
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd='// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
bvef='<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
'<!-- Generated by EasyCap Configurator 19.05.2014 -->\n'
'<Electrodes defaults="false">\n'
' <Electrode>\n'
' <Name>Fp1</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-72</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>1</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>Fz</Name>\n'
' <Theta>45</Theta>\n'
' <Phi>90</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>2</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F3</Name>\n'
' <Theta>-60</Theta>\n'
' <Phi>-51</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>3</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F7</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-36</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>4</Number>\n'
' </Electrode>\n'
'</Electrodes>',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
bvef=[[-2.62664445e-02, 8.08398039e-02, 5.20474890e-18],
[3.68031324e-18, 6.01040764e-02, 6.01040764e-02],
[-4.63256329e-02, 5.72073923e-02, 4.25000000e-02],
[-6.87664445e-02, 4.99617464e-02, 5.20474890e-18]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
unit = 'mm' if kind == 'bvef' else 'm'
montage = read_montage(fname, unit=unit)
if kind in ('sfp', 'txt'):
assert ('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Bvef is either auto or mm in terms of "units"
with pytest.raises(ValueError, match='be "auto" or "mm" for .bvef files.'):
bvef_file = op.join(tempdir, 'test.' + 'bvef')
read_montage(bvef_file, unit='m')
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = ["""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
FidNz -91 0 -42
FidT9 0 -91 -42
FidT10 0 91 -42
"""]
# sfp files seem to have Nz, T9, and T10 as fiducials:
# https://github.com/mne-tools/mne-python/pull/4482#issuecomment-321980611
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, input_str in zip(kinds, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
assert_array_equal(montage.nasion[[0, 2]], [0, 0])
assert_array_equal(montage.lpa[[1, 2]], [0, 0])
assert_array_equal(montage.rpa[[1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
# test return type as well as set montage
assert (isinstance(evoked.set_montage(montage), type(evoked)))
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
# Channel names can be treated case insensitive
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
# Unless there is a collision in names
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
assert len(info['dig']) == 5 # 2 EEG w/pos, 3 fiducials
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage, set_dig=False)
assert (info['dig'] is None)
# test get_pos2d method
montage = read_montage("standard_1020")
c3 = montage.get_pos2d()[montage.ch_names.index("C3")]
c4 = montage.get_pos2d()[montage.ch_names.index("C4")]
fz = montage.get_pos2d()[montage.ch_names.index("Fz")]
oz = montage.get_pos2d()[montage.ch_names.index("Oz")]
f1 = montage.get_pos2d()[montage.ch_names.index("F1")]
assert (c3[0] < 0) # left hemisphere
assert (c4[0] > 0) # right hemisphere
assert (fz[1] > 0) # frontal
assert (oz[1] < 0) # occipital
assert_allclose(fz[0], 0, atol=1e-2) # midline
assert_allclose(oz[0], 0, atol=1e-2) # midline
assert (f1[0] < 0 and f1[1] > 0) # left frontal
# test get_builtin_montages function
montages = get_builtin_montages()
assert (len(montages) > 0) # MNE should always ship with montages
assert ("standard_1020" in montages) # 10/20 montage
assert ("standard_1005" in montages) # 10/05 montage
def test_fit_point_cloud():
"""Test fit_point_cloud: fitting a set of points to a point cloud"""
# evenly spaced target points on a sphere
u = np.linspace(0, np.pi, 150)
v = np.linspace(0, np.pi, 150)
x = np.outer(np.cos(u), np.sin(v)).reshape((-1, 1))
y = np.outer(np.sin(u), np.sin(v)).reshape((-1, 1))
z = np.outer(np.ones(np.size(u)), np.cos(v)).reshape((-1, 1)) * 3
tgt_pts = np.hstack((x, y, z))
tgt_pts = _decimate_points(tgt_pts, .05)
# pick some points to fit
some_tgt_pts = tgt_pts[::362]
# rotation only
trans = rotation(1.5, .3, -0.4)
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts,
tgt_pts,
rotate=True,
translate=False,
scale=0,
out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation.")
# rotation and translation
trans = np.dot(rotation(0.5, .3, -0.4), translation(.3, .2, -.2))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts,
tgt_pts,
rotate=True,
translate=True,
scale=0,
out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(err, .1, "fit_point_cloud with rotation and "
"translation.")
# rotation and 1 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.5, 1.5))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts,
tgt_pts,
rotate=True,
translate=False,
scale=1,
out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(
err, .1, "fit_point_cloud with rotation and 1 scaling "
"parameter.")
# rotation and 3 scale parameter
trans = np.dot(rotation(0.5, .3, -0.4), scaling(1.5, 1.7, 1.1))
src_pts = apply_trans(trans, some_tgt_pts)
trans_est = fit_point_cloud(src_pts,
tgt_pts,
rotate=True,
translate=False,
scale=3,
out='trans')
est_pts = apply_trans(trans_est, src_pts)
err = _point_cloud_error(est_pts, tgt_pts)
assert_array_less(
err, .1, "fit_point_cloud with rotation and 3 scaling "
"parameters.")
def get_sensor_pos_from_fwd(inst, info=None, picks=None, trans=None):
from mne import SourceSpaces, Forward
from mne.io.constants import FIFF
from six import string_types
from mne.transforms import read_trans, _ensure_trans, invert_transform, Transform, apply_trans
from mne.io.pick import channel_type, pick_types
if isinstance(inst, Forward):
info = inst['info']
src = inst['src']
elif isinstance(inst, SourceSpaces):
src = inst
if info is None:
raise ValueError('You need to specify an Info object with '
'information about the channels.')
# Load the head<->MRI transform if necessary
if src[0]['coord_frame'] == FIFF.FIFFV_COORD_MRI:
if trans is None:
raise ValueError('Source space is in MRI coordinates, but no '
'head<->MRI transform was given. Please specify '
'the full path to the appropriate *-trans.fif '
'file as the "trans" parameter.')
if isinstance(trans, string_types):
trans = read_trans(trans, return_all=True)
for trans in trans: # we got at least 1
try:
trans = _ensure_trans(trans, 'head', 'mri')
except Exception as exp:
pass
else:
break
else:
raise exp
src_trans = invert_transform(_ensure_trans(trans, 'head', 'mri'))
print('Transform!')
else:
src_trans = Transform('head', 'head') # Identity transform
dev_to_head = _ensure_trans(info['dev_head_t'], 'meg', 'head')
if picks is None:
picks = pick_types(info, meg=True)
if len(picks) > 0:
print('Using MEG channels')
else:
print('Using EEG channels')
picks = pick_types(info, eeg=True)
sensor_pos = []
for ch in picks:
# MEG channels are in device coordinates, translate them to head
if channel_type(info, ch) in ['mag', 'grad']:
sensor_pos.append(
apply_trans(dev_to_head, info['chs'][ch]['loc'][:3]))
else:
sensor_pos.append(info['chs'][ch]['loc'][:3])
sensor_pos = np.array(sensor_pos)
return sensor_pos
def test_read_ctf():
"""Test CTF reader."""
temp_dir = _TempDir()
out_fname = op.join(temp_dir, 'test_py_raw.fif')
# Create a dummy .eeg file so we can test our reading/application of it
os.mkdir(op.join(temp_dir, 'randpos'))
ctf_eeg_fname = op.join(temp_dir, 'randpos', ctf_fname_catch)
shutil.copytree(op.join(ctf_dir, ctf_fname_catch), ctf_eeg_fname)
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = _test_raw_reader(read_raw_ctf, directory=ctf_eeg_fname)
picks = pick_types(raw.info, meg=False, eeg=True)
pos = np.random.RandomState(42).randn(len(picks), 3)
fake_eeg_fname = op.join(ctf_eeg_fname, 'catch-alp-good-f.eeg')
# Create a bad file
with open(fake_eeg_fname, 'wb') as fid:
fid.write('foo\n'.encode('ascii'))
pytest.raises(RuntimeError, read_raw_ctf, ctf_eeg_fname)
# Create a good file
with open(fake_eeg_fname, 'wb') as fid:
for ii, ch_num in enumerate(picks):
args = (str(ch_num + 1), raw.ch_names[ch_num],) + tuple(
'%0.5f' % x for x in 100 * pos[ii]) # convert to cm
fid.write(('\t'.join(args) + '\n').encode('ascii'))
pos_read_old = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
with pytest.warns(RuntimeWarning, match='RMSP .* changed to a MISC ch'):
raw = read_raw_ctf(ctf_eeg_fname) # read modified data
pos_read = np.array([raw.info['chs'][p]['loc'][:3] for p in picks])
assert_allclose(apply_trans(raw.info['ctf_head_t'], pos), pos_read,
rtol=1e-5, atol=1e-5)
assert (pos_read == pos_read_old).mean() < 0.1
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_randpos_raw.fif'),
op.join(temp_dir, 'randpos', 'catch-alp-good-f.ds_raw.fif'))
# Create a version with no hc, starting out *with* EEG pos (error)
os.mkdir(op.join(temp_dir, 'nohc'))
ctf_no_hc_fname = op.join(temp_dir, 'no_hc', ctf_fname_catch)
shutil.copytree(ctf_eeg_fname, ctf_no_hc_fname)
remove_base = op.join(ctf_no_hc_fname, op.basename(ctf_fname_catch[:-3]))
os.remove(remove_base + '.hc')
with pytest.warns(RuntimeWarning, match='MISC channel'):
pytest.raises(RuntimeError, read_raw_ctf, ctf_no_hc_fname)
os.remove(remove_base + '.eeg')
shutil.copy(op.join(ctf_dir, 'catch-alp-good-f.ds_nohc_raw.fif'),
op.join(temp_dir, 'no_hc', 'catch-alp-good-f.ds_raw.fif'))
# All our files
use_fnames = [op.join(ctf_dir, c) for c in ctf_fnames]
for fname in use_fnames:
raw_c = read_raw_fif(fname + '_raw.fif', preload=True)
with pytest.warns(None): # sometimes matches "MISC channel"
raw = read_raw_ctf(fname)
# check info match
assert_array_equal(raw.ch_names, raw_c.ch_names)
assert_allclose(raw.times, raw_c.times)
assert_allclose(raw._cals, raw_c._cals)
assert_equal(raw.info['meas_id']['version'],
raw_c.info['meas_id']['version'] + 1)
for t in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
assert_allclose(raw.info[t]['trans'], raw_c.info[t]['trans'],
rtol=1e-4, atol=1e-7)
for key in ('acq_pars', 'acq_stim', 'bads',
'ch_names', 'custom_ref_applied', 'description',
'events', 'experimenter', 'highpass', 'line_freq',
'lowpass', 'nchan', 'proj_id', 'proj_name',
'projs', 'sfreq', 'subject_info'):
assert_equal(raw.info[key], raw_c.info[key], key)
if op.basename(fname) not in single_trials:
# We don't force buffer size to be smaller like MNE-C
assert raw.buffer_size_sec == raw_c.buffer_size_sec
assert_equal(len(raw.info['comps']), len(raw_c.info['comps']))
for c1, c2 in zip(raw.info['comps'], raw_c.info['comps']):
for key in ('colcals', 'rowcals'):
assert_allclose(c1[key], c2[key])
assert_equal(c1['save_calibrated'], c2['save_calibrated'])
for key in ('row_names', 'col_names', 'nrow', 'ncol'):
assert_array_equal(c1['data'][key], c2['data'][key])
assert_allclose(c1['data']['data'], c2['data']['data'], atol=1e-7,
rtol=1e-5)
assert_allclose(raw.info['hpi_results'][0]['coord_trans']['trans'],
raw_c.info['hpi_results'][0]['coord_trans']['trans'],
rtol=1e-5, atol=1e-7)
assert_equal(len(raw.info['chs']), len(raw_c.info['chs']))
for ii, (c1, c2) in enumerate(zip(raw.info['chs'], raw_c.info['chs'])):
for key in ('kind', 'scanno', 'unit', 'ch_name', 'unit_mul',
'range', 'coord_frame', 'coil_type', 'logno'):
if c1['ch_name'] == 'RMSP' and \
'catch-alp-good-f' in fname and \
key in ('kind', 'unit', 'coord_frame', 'coil_type',
'logno'):
continue # XXX see below...
assert_equal(c1[key], c2[key], err_msg=key)
for key in ('cal',):
assert_allclose(c1[key], c2[key], atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# XXX 2016/02/24: fixed bug with normal computation that used
# to exist, once mne-C tools are updated we should update our FIF
# conversion files, then the slices can go away (and the check
# can be combined with that for "cal")
for key in ('loc',):
if c1['ch_name'] == 'RMSP' and 'catch-alp-good-f' in fname:
continue
if (c2[key][:3] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][:3]
assert_allclose(c1[key][:3], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
if (c2[key][3:] == 0.).all():
check = [np.nan] * 3
else:
check = c2[key][9:12]
assert_allclose(c1[key][9:12], check, atol=1e-6, rtol=1e-4,
err_msg='raw.info["chs"][%d][%s]' % (ii, key))
# Make sure all digitization points are in the MNE head coord frame
for p in raw.info['dig']:
assert_equal(p['coord_frame'], FIFF.FIFFV_COORD_HEAD,
err_msg='dig points must be in FIFF.FIFFV_COORD_HEAD')
if fname.endswith('catch-alp-good-f.ds'): # omit points from .pos file
raw.info['dig'] = raw.info['dig'][:-10]
# XXX: Next test would fail because c-tools assign the fiducials from
# CTF data as HPI. Should eventually clarify/unify with Matti.
# assert_dig_allclose(raw.info, raw_c.info)
# check data match
raw_c.save(out_fname, overwrite=True, buffer_size_sec=1.)
raw_read = read_raw_fif(out_fname)
# so let's check tricky cases based on sample boundaries
rng = np.random.RandomState(0)
pick_ch = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = int(round(raw.info['sfreq'] * raw.buffer_size_sec))
assert_equal(bnd, raw._raw_extras[0]['block_size'])
assert_equal(bnd, block_sizes[op.basename(fname)])
slices = (slice(0, bnd), slice(bnd - 1, bnd), slice(3, bnd),
slice(3, 300), slice(None))
if len(raw.times) >= 2 * bnd: # at least two complete blocks
slices = slices + (slice(bnd, 2 * bnd), slice(bnd, bnd + 1),
slice(0, bnd + 100))
for sl_time in slices:
assert_allclose(raw[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
assert_allclose(raw_read[pick_ch, sl_time][0],
raw_c[pick_ch, sl_time][0])
# all data / preload
with pytest.warns(None): # sometimes MISC
raw = read_raw_ctf(fname, preload=True)
assert_allclose(raw[:][0], raw_c[:][0], atol=1e-15)
# test bad segment annotations
if 'testdata_ctf_short.ds' in fname:
assert 'bad' in raw.annotations.description[0]
assert_allclose(raw.annotations.onset, [2.15])
assert_allclose(raw.annotations.duration, [0.0225])
pytest.raises(TypeError, read_raw_ctf, 1)
pytest.raises(ValueError, read_raw_ctf, ctf_fname_continuous + 'foo.ds')
# test ignoring of system clock
read_raw_ctf(op.join(ctf_dir, ctf_fname_continuous), 'ignore')
pytest.raises(ValueError, read_raw_ctf,
op.join(ctf_dir, ctf_fname_continuous), 'foo')
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
# no pep8
input_str = [
'FidNz 0.00000 10.56381 -2.05108\nFidT9 -7.82694 0.45386 -3.76056\n'
'very_very_very_long_name 7.82694 0.45386 -3.76056\nDmy 7.0 0.0 1.0',
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E4 52.700 12.000 1.000 0.7084 0.7504 0.1508 0.00000000000000000', # noqa: E501; dummy line
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n'
'NumberPositions= 68\nPositions\n-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n0.0083 86.8110 -39.9830\n'
'85 -20 -48\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
'# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n'
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
'Site Theta Phi\nFp1 -92 -72\nFp2 92 72\n'
'very_very_very_long_name -60 -51\n'
'dummy -60 -52\n',
'346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
'eeg Fp1 -95.0 -3. -3.\neeg AF7 -1 -1 -3\neeg A3 -2 -2 2\neeg A 0 0 0'
]
kinds = [
'test.sfp', 'test.csd', 'test_mm.elc', 'test_m.elc', 'test.txt',
'test.elp', 'test.hpts'
]
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if ".sfp" in kind or ".txt" in kind:
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2[:-1, :], montage.pos[:-1, :], 4)
if kind.endswith('elc'):
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
# Test reading in different letter case.
ch_names = [
"F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3", "CPZ",
"CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"
]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)),
info=info,
tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def test_scale_mri():
"""Test creating fsaverage and scaling it."""
# create fsaverage using the testing "fsaverage" instead of the FreeSurfer
# one
tempdir = _TempDir()
fake_home = testing.data_path()
create_default_subject(subjects_dir=tempdir, fs_home=fake_home,
verbose=True)
assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed"
fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif')
os.remove(fid_path)
create_default_subject(update=True, subjects_dir=tempdir,
fs_home=fake_home)
assert op.exists(fid_path), "Updating fsaverage"
# copy MRI file from sample data (shouldn't matter that it's incorrect,
# so here choose a small one)
path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri',
'T1.mgz')
path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
copyfile(path_from, path_to)
# remove redundant label files
label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label')
label_paths = glob(label_temp)
for label_path in label_paths[1:]:
os.remove(label_path)
# create source space
print('Creating surface source space')
path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif')
src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir,
add_dist=False)
mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz')
print('Creating volume source space')
vsrc = mne.setup_volume_source_space(
'fsaverage', pos=50, mri=mri, subjects_dir=tempdir,
add_interpolator=False)
write_source_spaces(path % 'vol-50', vsrc)
# scale fsaverage
for scale in (.9, [1, .2, .8]):
write_source_spaces(path % 'ico-0', src, overwrite=True)
os.environ['_MNE_FEW_SURFACES'] = 'true'
with pytest.warns(None): # sometimes missing nibabel
scale_mri('fsaverage', 'flachkopf', scale, True,
subjects_dir=tempdir, verbose='debug')
del os.environ['_MNE_FEW_SURFACES']
assert _is_mri_subject('flachkopf', tempdir), "Scaling failed"
spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif')
assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled"
assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf',
'lh.sphere.reg'))
vsrc_s = mne.read_source_spaces(spath % 'vol-50')
pt = np.array([0.12, 0.41, -0.22])
assert_array_almost_equal(
apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)),
apply_trans(vsrc[0]['src_mri_t'], pt))
scale_labels('flachkopf', subjects_dir=tempdir)
# add distances to source space after hacking the properties to make
# it run *much* faster
src_dist = src.copy()
for s in src_dist:
s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']],
tris=s['use_tris'])
s.update(np=len(s['rr']), ntri=len(s['tris']),
vertno=np.arange(len(s['rr'])),
inuse=np.ones(len(s['rr']), int))
mne.add_source_space_distances(src_dist)
write_source_spaces(path % 'ico-0', src_dist, overwrite=True)
# scale with distances
os.remove(spath % 'ico-0')
scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir)
ssrc = mne.read_source_spaces(spath % 'ico-0')
assert ssrc[0]['dist'] is not None
def test_montage():
"""Test making montages"""
tempdir = _TempDir()
# no pep8
input_str = ["""FidNz 0.00000 10.56381 -2.05108
FidT9 -7.82694 0.45386 -3.76056
FidT10 7.82694 0.45386 -3.76056""",
"""// MatLab Sphere coordinates [degrees] Cartesian coordinates
// Label Theta Phi Radius X Y Z off sphere surface
E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011
E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000
E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000""", # noqa
"""# ASA electrode file
ReferenceLabel avg
UnitPosition mm
NumberPositions= 68
Positions
-86.0761 -19.9897 -47.9860
85.7939 -20.0093 -48.0310
0.0083 86.8110 -39.9830
Labels
LPA
RPA
Nz
""",
"""Site Theta Phi
Fp1 -92 -72
Fp2 92 72
F3 -60 -51
""",
"""346
EEG F3 -62.027 -50.053 85
EEG Fz 45.608 90 85
EEG F4 62.01 50.103 85
""",
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
"""]
kinds = ['test.sfp', 'test.csd', 'test.elc', 'test.txt', 'test.elp',
'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
assert_equal(len(montage.ch_names), 3)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (3, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2, montage.pos, 4)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)
def get_head_mri_trans(bids_path,
extra_params=None,
t1_bids_path=None,
fs_subject=None,
fs_subjects_dir=None,
*,
kind=None,
verbose=None):
"""Produce transformation matrix from MEG and MRI landmark points.
Will attempt to read the landmarks of Nasion, LPA, and RPA from the sidecar
files of (i) the MEG and (ii) the T1-weighted MRI data. The two sets of
points will then be used to calculate a transformation matrix from head
coordinates to MRI coordinates.
.. note:: The MEG and MRI data need **not** necessarily be stored in the
same session or even in the same BIDS dataset. See the
``t1_bids_path`` parameter for details.
Parameters
----------
bids_path : BIDSPath
The path of the electrophysiology recording. If ``datatype`` and
``suffix`` are not present, they will be set to ``'meg'``, and a
warning will be raised.
.. versionchanged:: 0.10
A warning is raised it ``datatype`` or ``suffix`` are not set.
extra_params : None | dict
Extra parameters to be passed to :func:`mne.io.read_raw` when reading
the MEG file.
t1_bids_path : BIDSPath | None
If ``None`` (default), will try to discover the T1-weighted MRI file
based on the name and location of the MEG recording specified via the
``bids_path`` parameter. Alternatively, you explicitly specify which
T1-weighted MRI scan to use for extraction of MRI landmarks. To do
that, pass a :class:`mne_bids.BIDSPath` pointing to the scan.
Use this parameter e.g. if the T1 scan was recorded during a different
session than the MEG. It is even possible to point to a T1 image stored
in an entirely different BIDS dataset than the MEG data.
fs_subject : str
The subject identifier used for FreeSurfer.
.. versionchanged:: 0.10
Does not default anymore to ``bids_path.subject`` if ``None``.
fs_subjects_dir : path-like | None
The FreeSurfer subjects directory. If ``None``, defaults to the
``SUBJECTS_DIR`` environment variable.
.. versionadded:: 0.8
kind : str | None
The suffix of the anatomical landmark names in the JSON sidecar.
A suffix might be present e.g. to distinguish landmarks between
sessions. If provided, should not include a leading underscore ``_``.
For example, if the landmark names in the JSON sidecar file are
``LPA_ses-1``, ``RPA_ses-1``, ``NAS_ses-1``, you should pass
``'ses-1'`` here.
If ``None``, no suffix is appended, the landmarks named
``Nasion`` (or ``NAS``), ``LPA``, and ``RPA`` will be used.
.. versionadded:: 0.10
%(verbose)s
Returns
-------
trans : mne.transforms.Transform
The data transformation matrix from head to MRI coordinates.
"""
if not has_nibabel(): # pragma: no cover
raise ImportError('This function requires nibabel.')
import nibabel as nib
if not isinstance(bids_path, BIDSPath):
raise RuntimeError('"bids_path" must be a BIDSPath object. Please '
'instantiate using mne_bids.BIDSPath().')
# check root available
meg_bids_path = bids_path.copy()
del bids_path
if meg_bids_path.root is None:
raise ValueError('The root of the "bids_path" must be set. '
'Please use `bids_path.update(root="<root>")` '
'to set the root of the BIDS folder to read.')
# if the bids_path is underspecified, only get info for MEG data
if meg_bids_path.datatype is None:
meg_bids_path.datatype = 'meg'
warn(
'bids_path did not have a datatype set. Assuming "meg". This '
'will raise an exception in the future.',
module='mne_bids',
category=DeprecationWarning)
if meg_bids_path.suffix is None:
meg_bids_path.suffix = 'meg'
warn(
'bids_path did not have a suffix set. Assuming "meg". This '
'will raise an exception in the future.',
module='mne_bids',
category=DeprecationWarning)
# Get the sidecar file for MRI landmarks
t1w_bids_path = ((meg_bids_path if t1_bids_path is None else
t1_bids_path).copy().update(datatype='anat',
suffix='T1w',
task=None))
t1w_json_path = _find_matching_sidecar(bids_path=t1w_bids_path,
extension='.json',
on_error='ignore')
del t1_bids_path
if t1w_json_path is not None:
t1w_json_path = Path(t1w_json_path)
if t1w_json_path is None or not t1w_json_path.exists():
raise FileNotFoundError(
f'Did not find T1w JSON sidecar file, tried location: '
f'{t1w_json_path}')
for extension in ('.nii', '.nii.gz'):
t1w_path_candidate = t1w_json_path.with_suffix(extension)
if t1w_path_candidate.exists():
t1w_bids_path = get_bids_path_from_fname(fname=t1w_path_candidate)
break
if not t1w_bids_path.fpath.exists():
raise FileNotFoundError(
f'Did not find T1w recording file, tried location: '
f'{t1w_path_candidate.name.replace(".nii.gz", "")}[.nii, .nii.gz]')
# Get MRI landmarks from the JSON sidecar
t1w_json = json.loads(t1w_json_path.read_text(encoding='utf-8'))
mri_coords_dict = t1w_json.get('AnatomicalLandmarkCoordinates', dict())
# landmarks array: rows: [LPA, NAS, RPA]; columns: [x, y, z]
suffix = f"_{kind}" if kind is not None else ""
mri_landmarks = np.full((3, 3), np.nan)
for landmark_name, coords in mri_coords_dict.items():
if landmark_name.upper() == ('LPA' + suffix).upper():
mri_landmarks[0, :] = coords
elif landmark_name.upper() == ('RPA' + suffix).upper():
mri_landmarks[2, :] = coords
elif (landmark_name.upper() == ('NAS' + suffix).upper()
or landmark_name.lower() == ('nasion' + suffix).lower()):
mri_landmarks[1, :] = coords
else:
continue
if np.isnan(mri_landmarks).any():
raise RuntimeError(
f'Could not extract fiducial points from T1w sidecar file: '
f'{t1w_json_path}\n\n'
f'The sidecar file SHOULD contain a key '
f'"AnatomicalLandmarkCoordinates" pointing to an '
f'object with the keys "LPA", "NAS", and "RPA". '
f'Yet, the following structure was found:\n\n'
f'{mri_coords_dict}')
# The MRI landmarks are in "voxels". We need to convert them to the
# Neuromag RAS coordinate system in order to compare them with MEG
# landmarks. See also: `mne_bids.write.write_anat`
if fs_subject is None:
warn(
'Passing "fs_subject=None" has been deprecated and will raise '
'an error in future versions. Please explicitly specify the '
'FreeSurfer subject name.', DeprecationWarning)
fs_subject = f'sub-{meg_bids_path.subject}'
fs_subjects_dir = get_subjects_dir(fs_subjects_dir, raise_error=False)
fs_t1_path = Path(fs_subjects_dir) / fs_subject / 'mri' / 'T1.mgz'
if not fs_t1_path.exists():
raise ValueError(
f"Could not find {fs_t1_path}. Consider running FreeSurfer's "
f"'recon-all` for subject {fs_subject}.")
fs_t1_mgh = nib.load(str(fs_t1_path))
t1_nifti = nib.load(str(t1w_bids_path.fpath))
# Convert to MGH format to access vox2ras method
t1_mgh = nib.MGHImage(t1_nifti.dataobj, t1_nifti.affine)
# convert to scanner RAS
mri_landmarks = apply_trans(t1_mgh.header.get_vox2ras(), mri_landmarks)
# convert to FreeSurfer T1 voxels (same scanner RAS as T1)
mri_landmarks = apply_trans(fs_t1_mgh.header.get_ras2vox(), mri_landmarks)
# now extract transformation matrix and put back to RAS coordinates of MRI
vox2ras_tkr = fs_t1_mgh.header.get_vox2ras_tkr()
mri_landmarks = apply_trans(vox2ras_tkr, mri_landmarks)
mri_landmarks = mri_landmarks * 1e-3
# Get MEG landmarks from the raw file
_, ext = _parse_ext(meg_bids_path)
if extra_params is None:
extra_params = dict()
if ext == '.fif':
extra_params['allow_maxshield'] = True
raw = read_raw_bids(bids_path=meg_bids_path, extra_params=extra_params)
if (raw.get_montage() is None or raw.get_montage().get_positions() is None
or any([
raw.get_montage().get_positions()[fid_key] is None
for fid_key in ('nasion', 'lpa', 'rpa')
])):
raise RuntimeError(
f'Could not extract fiducial points from ``raw`` file: '
f'{meg_bids_path}\n\n'
f'The ``raw`` file SHOULD contain digitization points '
'for the nasion and left and right pre-auricular points '
'but none were found')
pos = raw.get_montage().get_positions()
meg_landmarks = np.asarray((pos['lpa'], pos['nasion'], pos['rpa']))
# Given the two sets of points, fit the transform
trans_fitted = fit_matched_points(src_pts=meg_landmarks,
tgt_pts=mri_landmarks)
trans = mne.transforms.Transform(fro='head', to='mri', trans=trans_fitted)
return trans
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
# no pep8
input_str = [
'FidNz 0.00000 10.56381 -2.05108\nFidT9 -7.82694 0.45386 -3.76056\n'
'very_very_very_long_name 7.82694 0.45386 -3.76056\nDmy 7.0 0.0 1.0',
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E2 44.600 -0.880 1.000 0.7119 0.7021 -0.0154 0.00000000000000000\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E4 52.700 12.000 1.000 0.7084 0.7504 0.1508 0.00000000000000000', # noqa: E501; dummy line
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n'
'NumberPositions= 68\nPositions\n-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n0.0083 86.8110 -39.9830\n'
'85 -20 -48\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
'# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n'
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
'Site Theta Phi\nFp1 -92 -72\nFp2 92 72\n'
'very_very_very_long_name -60 -51\n'
'dummy -60 -52\n',
'346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
'eeg Fp1 -95.0 -3. -3.\neeg AF7 -1 -1 -3\neeg A3 -2 -2 2\neeg A 0 0 0'
]
kinds = ['test.sfp', 'test.csd', 'test_mm.elc', 'test_m.elc', 'test.txt',
'test.elp', 'test.hpts']
for kind, text in zip(kinds, input_str):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if ".sfp" in kind or ".txt" in kind:
assert_true('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, op.splitext(kind)[0])
if kind.endswith('csd'):
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
pos2 = np.c_[table['x'], table['y'], table['z']]
assert_array_almost_equal(pos2[:-1, :], montage.pos[:-1, :], 4)
if kind.endswith('elc'):
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1,
montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
# Test reading in different letter case.
ch_names = ["F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3",
"CPZ", "CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_str = """
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
"""
kind = 'test_fid.hpts'
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, 'test_fid.hpts'), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
idx = montage.ch_names.index('2')
assert_array_equal(montage.pos[idx, [0, 2]], [0, 0])
idx = montage.ch_names.index('1')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
idx = montage.ch_names.index('3')
assert_array_equal(montage.pos[idx, [1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, 'test_fid.hpts')
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(
montage.ch_names, 1e3, ['eeg'] * len(montage.ch_names))
evoked = EvokedArray(
data=np.zeros((len(montage.ch_names), 1)), info=info, tmin=0)
evoked.set_montage(montage)
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in the montage
with warnings.catch_warnings(record=True) as w:
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
_set_montage(info, montage)
assert_true(len(w) == 1)