def get_aligned_artifacts(info=None, trans=None, subject=None, subjects_dir=None,
coord_frame='mri', head_surf=None):
head_mri_t, _ = _get_trans(trans, 'head', 'mri')
dev_head_t, _ = _get_trans(info['dev_head_t'], 'meg', 'head')
head_trans = head_mri_t
mri_trans = Transform('mri', 'mri')
mri_fiducials = mne.coreg.get_mni_fiducials(subject, subjects_dir)
fid_loc = _fiducial_coords(mri_fiducials, FIFF.FIFFV_COORD_MRI)
fid_loc = apply_trans(mri_trans, fid_loc)
fid_loc = pd.DataFrame(fid_loc, index=[fid["ident"]._name.split("_")[-1] for fid in mri_fiducials],
columns=["x", "y", "z"])
if head_surf is None:
subject_dir = Path(get_subjects_dir(subjects_dir, raise_error=True)) / subject
fname = subject_dir / 'bem' / 'sample-head.fif'
head_surf = read_bem_surfaces(fname)[0]
head_surf = transform_surface_to(head_surf, coord_frame, [mri_trans, head_trans], copy=True)
eeg_picks = mne.pick_types(info, meg=False, eeg=True, ref_meg=False)
eeg_loc = np.array([info['chs'][k]['loc'][:3] for k in eeg_picks])
eeg_loc = apply_trans(head_trans, eeg_loc)
eegp_loc = _project_onto_surface(eeg_loc, head_surf, project_rrs=True, return_nn=True)[2]
eegp_loc = pd.DataFrame(eegp_loc, index=[ch["ch_name"] for ch in info['chs']], columns=["x", "y", "z"])
return eegp_loc, fid_loc, head_surf
def test_helmet():
"""Test loading helmet surfaces."""
base_dir = op.join(op.dirname(__file__), '..', 'io')
fname_raw = op.join(base_dir, 'tests', 'data', 'test_raw.fif')
fname_kit_raw = op.join(base_dir, 'kit', 'tests', 'data',
'test_bin_raw.fif')
fname_bti_raw = op.join(base_dir, 'bti', 'tests', 'data',
'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(base_dir, 'tests', 'data', 'test_ctf_raw.fif')
fname_trans = op.join(base_dir, 'tests', 'data',
'sample-audvis-raw-trans.txt')
trans = _get_trans(fname_trans)[0]
new_info = read_info(fname_raw)
artemis_info = new_info.copy()
for pick in pick_types(new_info):
new_info['chs'][pick]['coil_type'] = 9999
artemis_info['chs'][pick]['coil_type'] = \
FIFF.FIFFV_COIL_ARTEMIS123_GRAD
for info, n, name in [(read_info(fname_raw), 304, '306m'),
(read_info(fname_kit_raw), 304, 'KIT'),
(read_info(fname_bti_raw), 304, 'Magnes'),
(read_info(fname_ctf_raw), 342, 'CTF'),
(new_info, 102, 'unknown'),
(artemis_info, 102, 'ARTEMIS123')
]:
with catch_logging() as log:
helmet = get_meg_helmet_surf(info, trans, verbose=True)
log = log.getvalue()
assert name in log
assert_equal(len(helmet['rr']), n)
assert_equal(len(helmet['rr']), len(helmet['nn']))
def _compute_coreg_dist(subject, trans_fname, info_fname, subjects_dir):
"""Assess quality of coregistration."""
trans = mne.read_trans(trans_fname)
high_res_surf = subjects_dir + "/%s/surf/lh.seghead" % subject
low_res_surf = subjects_dir + "/%s/bem/%s-outer_skull.surf" % (subject,
subject)
low_res_surf_2 = subjects_dir + "/%s/bem/outer_skull.surf" % subject
if os.path.exists(high_res_surf):
pts, _ = mne.read_surface(high_res_surf, verbose=False)
pts /= 1e3 # convert to mm
elif os.path.exists(low_res_surf):
warnings.warn("""Using low resolution head surface,
the average distance will be potentially
overestimated""")
pts, _ = mne.read_surface(low_res_surf, verbose=False)
pts /= 1e3 # convert to mm
elif os.path.exists(low_res_surf_2):
warnings.warn("""Using low resolution head surface,
the average distance will be potentially
overestimated""")
pts, _ = mne.read_surface(low_res_surf_2, verbose=False)
pts /= 1e3 # convert to mm
else:
raise FileNotFoundError("No MRI surface was found!")
trans = _get_trans(trans, fro="mri", to="head")[0]
pts = apply_trans(trans, pts)
info = mne.io.read_info(info_fname, verbose=False)
info_dig = np.stack([list(x["r"]) for x in info["dig"]], axis=0)
M = euclidean_distances(info_dig, pts)
idx = np.argmin(M, axis=1)
dist = M[np.arange(len(info_dig)), idx].mean()
return dist
def test_nirx_15_2():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_2, preload=True)
# Test data import
assert raw._data.shape == (64, 67)
assert raw.info['sfreq'] == 3.90625
# Test channel naming
assert raw.info['ch_names'][:4] == [
"S1_D1 760", "S1_D1 850", "S1_D10 760", "S1_D10 850"
]
# Test info import
assert raw.info['subject_info'] == dict(sex=1, first_name="TestRecording")
# Test trigger events
assert_array_equal(raw.annotations.description, ['4.0', '6.0', '2.0'])
# Test location of detectors
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], [-0.0292, 0.0852, -0.0142],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D4'
assert_allclose(mni_locs[15], [-0.0739, -0.0756, -0.0075],
atol=allowed_dist_error)
def _find_closest_standard_location(position, reference, *, out='label'):
"""Return closest montage label to coordinates.
Parameters
----------
position : array, shape (3,)
Coordinates.
reference : dataframe
As generated by _generate_montage_locations.
trans_pos : str
Apply a transformation to positions to specified frame.
Use None for no transformation.
"""
from scipy.spatial.distance import cdist
p0 = np.array(position)
p0.shape = (-1, 3)
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
p0 = apply_trans(head_mri_t, p0)
dists = cdist(p0, np.asarray(reference[['x', 'y', 'z']], float))
if out == 'label':
min_idx = np.argmin(dists)
return reference["label"][min_idx]
else:
assert out == 'dists'
return dists
def test_helmet():
"""Test loading helmet surfaces."""
base_dir = op.join(op.dirname(__file__), '..', 'io')
fname_raw = op.join(base_dir, 'tests', 'data', 'test_raw.fif')
fname_kit_raw = op.join(base_dir, 'kit', 'tests', 'data',
'test_bin_raw.fif')
fname_bti_raw = op.join(base_dir, 'bti', 'tests', 'data',
'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(base_dir, 'tests', 'data', 'test_ctf_raw.fif')
fname_trans = op.join(base_dir, 'tests', 'data',
'sample-audvis-raw-trans.txt')
trans = _get_trans(fname_trans)[0]
new_info = read_info(fname_raw)
artemis_info = new_info.copy()
for pick in pick_types(new_info, meg=True):
new_info['chs'][pick]['coil_type'] = 9999
artemis_info['chs'][pick]['coil_type'] = \
FIFF.FIFFV_COIL_ARTEMIS123_GRAD
for info, n, name in [(read_info(fname_raw), 304, '306m'),
(read_info(fname_kit_raw), 304, 'KIT'),
(read_info(fname_bti_raw), 304, 'Magnes'),
(read_info(fname_ctf_raw), 342, 'CTF'),
(new_info, 102, 'unknown'),
(artemis_info, 102, 'ARTEMIS123')]:
with catch_logging() as log:
helmet = get_meg_helmet_surf(info, trans, verbose=True)
log = log.getvalue()
assert name in log
assert_equal(len(helmet['rr']), n)
assert_equal(len(helmet['rr']), len(helmet['nn']))
def test_get_trans():
"""Test converting '-trans.txt' to '-trans.fif'"""
trans = read_trans(fname)
trans = invert_transform(trans) # starts out as head->MRI, so invert
trans_2 = _get_trans(fname_trans)[0]
assert_equal(trans['from'], trans_2['from'])
assert_equal(trans['to'], trans_2['to'])
assert_allclose(trans['trans'], trans_2['trans'], rtol=1e-5, atol=1e-5)
def test_get_trans():
"""Test converting '-trans.txt' to '-trans.fif'"""
trans = read_trans(fname)
trans = invert_transform(trans) # starts out as head->MRI, so invert
trans_2 = _get_trans(fname_trans)[0]
assert_equal(trans['from'], trans_2['from'])
assert_equal(trans['to'], trans_2['to'])
assert_allclose(trans['trans'], trans_2['trans'], rtol=1e-5, atol=1e-5)
def test_nirx_15_0():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_0, preload=True)
# Test data import
assert raw._data.shape == (20, 92)
assert raw.info['sfreq'] == 6.25
assert raw.info['meas_date'] == dt.datetime(2019,
10,
27,
13,
53,
34,
209000,
tzinfo=dt.timezone.utc)
# Test channel naming
assert raw.info['ch_names'][:12] == [
"S1_D1 760", "S1_D1 850", "S2_D2 760", "S2_D2 850", "S3_D3 760",
"S3_D3 850", "S4_D4 760", "S4_D4 850", "S5_D5 760", "S5_D5 850",
"S6_D6 760", "S6_D6 850"
]
# Test info import
assert raw.info['subject_info'] == {
'birthday': (2004, 10, 27),
'first_name': 'NIRX',
'last_name': 'Test',
'sex': FIFF.FIFFV_SUBJ_SEX_UNKNOWN,
'his_id': "NIRX_Test"
}
# Test trigger events
assert_array_equal(raw.annotations.description, ['1.0', '2.0', '2.0'])
# Test location of detectors
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], [0.0287, -0.1143, -0.0332],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D8'
assert_allclose(mni_locs[15], [-0.0693, -0.0480, 0.0657],
atol=allowed_dist_error)
# Test distance between optodes matches values from
allowed_distance_error = 0.0002
assert_allclose(source_detector_distances(
raw.copy().pick("S1_D1 760").info), [0.0300],
atol=allowed_distance_error)
assert_allclose(source_detector_distances(
raw.copy().pick("S7_D7 760").info), [0.0392],
atol=allowed_distance_error)
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 _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_nirx_15_2():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_2, preload=True)
# Test data import
assert raw._data.shape == (64, 67)
assert raw.info['sfreq'] == 3.90625
assert raw.info['meas_date'] == dt.datetime(2019,
10,
2,
9,
8,
47,
511000,
tzinfo=dt.timezone.utc)
# Test channel naming
assert raw.info['ch_names'][:4] == [
"S1_D1 760", "S1_D1 850", "S1_D10 760", "S1_D10 850"
]
# Test info import
assert raw.info['subject_info'] == dict(sex=1,
first_name="TestRecording",
birthday=(1989, 10, 2))
# Test trigger events
assert_array_equal(raw.annotations.description, ['4.0', '6.0', '2.0'])
print(raw.annotations.onset)
# Test location of detectors
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], [-0.0292, 0.0852, -0.0142],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D4'
assert_allclose(mni_locs[15], [-0.0739, -0.0756, -0.0075],
atol=allowed_dist_error)
# Old name aliases for backward compat
assert 'fnirs_cw_amplitude' in raw
with pytest.deprecated_call():
assert 'fnirs_raw' in raw
assert 'fnirs_od' not in raw
picks = pick_types(raw.info, fnirs='fnirs_cw_amplitude')
with pytest.deprecated_call():
picks_alias = pick_types(raw.info, fnirs='fnirs_raw')
assert_array_equal(picks, picks_alias)
def test_set_montage_artinis_fsaverage(kind):
"""Test that artinis montages match fsaverage's head<->MRI transform."""
# Compare OctaMon and Brite23 to fsaverage
trans_fs, _ = _get_trans('fsaverage')
montage = make_standard_montage(f'artinis-{kind}')
trans = compute_native_head_t(montage)
assert trans['to'] == trans_fs['to']
assert trans['from'] == trans_fs['from']
translation = 1000 * np.linalg.norm(trans['trans'][:3, 3] -
trans_fs['trans'][:3, 3])
assert 0 < translation < 1 # mm
rotation = np.rad2deg(
_angle_between_quats(rot_to_quat(trans['trans'][:3, :3]),
rot_to_quat(trans_fs['trans'][:3, :3])))
assert 0 < rotation < 1 # degrees
def test_helmet():
"""Test loading helmet surfaces."""
base_dir = op.join(op.dirname(__file__), '..', 'io')
fname_raw = op.join(base_dir, 'tests', 'data', 'test_raw.fif')
fname_kit_raw = op.join(base_dir, 'kit', 'tests', 'data',
'test_bin_raw.fif')
fname_bti_raw = op.join(base_dir, 'bti', 'tests', 'data',
'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(base_dir, 'tests', 'data', 'test_ctf_raw.fif')
fname_trans = op.join(base_dir, 'tests', 'data',
'sample-audvis-raw-trans.txt')
trans = _get_trans(fname_trans)[0]
for fname in [fname_raw, fname_kit_raw, fname_bti_raw, fname_ctf_raw]:
helmet = get_meg_helmet_surf(read_info(fname), trans)
assert_equal(len(helmet['rr']), 304) # they all have 304 verts
assert_equal(len(helmet['rr']), len(helmet['nn']))
def test_helmet():
"""Test loading helmet surfaces."""
base_dir = op.join(op.dirname(__file__), '..', 'io')
fname_raw = op.join(base_dir, 'tests', 'data', 'test_raw.fif')
fname_kit_raw = op.join(base_dir, 'kit', 'tests', 'data',
'test_bin_raw.fif')
fname_bti_raw = op.join(base_dir, 'bti', 'tests', 'data',
'exported4D_linux_raw.fif')
fname_ctf_raw = op.join(base_dir, 'tests', 'data', 'test_ctf_raw.fif')
fname_trans = op.join(base_dir, 'tests', 'data',
'sample-audvis-raw-trans.txt')
trans = _get_trans(fname_trans)[0]
for fname in [fname_raw, fname_kit_raw, fname_bti_raw, fname_ctf_raw]:
helmet = get_meg_helmet_surf(read_info(fname), trans)
assert_equal(len(helmet['rr']), 304) # they all have 304 verts
assert_equal(len(helmet['rr']), len(helmet['nn']))
def test_nirx_15_0():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_0, preload=True)
# Test data import
assert raw._data.shape == (20, 92)
assert raw.info['sfreq'] == 6.25
# Test channel naming
assert raw.info['ch_names'][:12] == [
"S1_D1 760", "S1_D1 850", "S2_D2 760", "S2_D2 850", "S3_D3 760",
"S3_D3 850", "S4_D4 760", "S4_D4 850", "S5_D5 760", "S5_D5 850",
"S6_D6 760", "S6_D6 850"
]
# Test info import
assert raw.info['subject_info'] == {
'first_name': 'NIRX',
'last_name': 'Test',
'sex': '0'
}
# Test trigger events
assert_array_equal(raw.annotations.description, ['1.0', '2.0', '2.0'])
# Test location of detectors
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], [0.0287, -0.1143, -0.0332],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D8'
assert_allclose(mni_locs[15], [-0.0693, -0.0480, 0.0657],
atol=allowed_dist_error)
# Test distance between optodes matches values from
allowed_distance_error = 0.0002
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0301, 0.0315, 0.0343, 0.0368, 0.0408, 0.0399, 0.0393, 0.0367, 0.0336,
0.0447
],
atol=allowed_distance_error)
def test_project_sensors_onto_brain(tmp_path):
"""Test projecting sensors onto the brain surface."""
tempdir = str(tmp_path)
raw = mne.io.read_raw_fif(fname_raw)
trans = _get_trans(fname_trans)[0]
# test informative error for no surface first
with pytest.raises(RuntimeError, match='requires generating a BEM'):
project_sensors_onto_brain(raw.info,
trans,
'sample',
subjects_dir=tempdir)
brain_surf_fname = op.join(tempdir, 'sample', 'bem', 'brain.surf')
if not op.isdir(op.dirname(brain_surf_fname)):
os.makedirs(op.dirname(brain_surf_fname))
if not op.isfile(brain_surf_fname):
copyfile(op.join(subjects_dir, 'sample', 'bem', 'inner_skull.surf'),
brain_surf_fname)
# now make realistic ECoG grid
raw.pick_types(meg=False, eeg=True)
raw.load_data()
raw.set_eeg_reference([])
raw.set_channel_types({ch: 'ecog' for ch in raw.ch_names})
pos = np.zeros((49, 3))
pos[:, :2] = np.array(
np.meshgrid(np.linspace(0, 0.02, 7), np.linspace(0, 0.02,
7))).reshape(2, -1).T
pos[:, 2] = 0.12
raw.drop_channels(raw.ch_names[49:])
raw.set_montage(
mne.channels.make_dig_montage(ch_pos=dict(zip(raw.ch_names[:49], pos)),
coord_frame='head'))
raw.info = project_sensors_onto_brain(raw.info,
trans,
'sample',
subjects_dir=tempdir)
# plot to check, should be projected down onto inner skull
# brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, alpha=0.5,
# surf='white')
# brain.add_sensors(raw.info, trans=trans)
test_locs = [[0.00149, -0.001588, 0.133029],
[0.004302, 0.001959, 0.133922], [0.008602, 0.00116, 0.133723]]
for ch, test_loc in zip(raw.info['chs'][:3], test_locs):
assert_allclose(ch['loc'][:3], test_loc, rtol=0.01)
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]]) / 1000.
xfm = read_talxfm('sample', subjects_dir)
coords_MNI = apply_trans(xfm['trans'], coords) * 1000.
mri_head_t, _ = _get_trans(trans_fname, 'mri', 'head', allow_none=False)
# 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 dip_depth(dip, fname_trans, subject, subjects_dir):
trans = read_trans(fname_trans)
trans = _get_trans(trans)[0]
subjects_dir = get_subjects_dir(subjects_dir=subjects_dir)
fname = os.path.join(subjects_dir, subject, 'bem', 'inner_skull.surf')
points, faces = read_surface(fname)
points = apply_trans(trans['trans'], points * 1e-3)
pos = dip.pos
ori = dip.ori
from sklearn.neighbors import NearestNeighbors
nn = NearestNeighbors()
nn.fit(points)
depth, idx = nn.kneighbors(pos, 1, return_distance=True)
idx = np.ravel(idx)
direction = pos - points[idx]
direction /= np.sqrt(np.sum(direction**2, axis=1))[:, None]
ori /= np.sqrt(np.sum(ori**2, axis=1))[:, None]
radiality = np.abs(np.sum(ori * direction, axis=1))
return np.ravel(depth), radiality
def test_get_trans():
"""Test converting '-trans.txt' to '-trans.fif'"""
trans = read_trans(fname)
trans = invert_transform(trans) # starts out as head->MRI, so invert
trans_2 = _get_trans(fname_trans)[0]
assert trans.__eq__(trans_2, atol=1e-5)
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)
mon = raw.get_montage()
assert len(mon.dig) == 43
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_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 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",
his_id="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 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),
his_id="MNE_Test_Recording")
# 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 make_pert_forward_solution(info,
trans,
src,
bem,
perts,
meg=True,
eeg=True,
mindist=0.0,
ignore_ref=False,
n_jobs=1,
verbose=None):
"""Calculate a forward solution for a subject.
Parameters
----------
info : instance of mne.Info | str
If str, then it should be a filename to a Raw, Epochs, or Evoked
file with measurement information. If dict, should be an info
dict (such as one from Raw, Epochs, or Evoked).
trans : dict | str | None
Either a transformation filename (usually made using mne_analyze)
or an info dict (usually opened using read_trans()).
If string, an ending of `.fif` or `.fif.gz` will be assumed to
be in FIF format, any other ending will be assumed to be a text
file with a 4x4 transformation matrix (like the `--trans` MNE-C
option). Can be None to use the identity transform.
src : str | instance of SourceSpaces
If string, should be a source space filename. Can also be an
instance of loaded or generated SourceSpaces.
bem : dict | str
Filename of the BEM (e.g., "sample-5120-5120-5120-bem-sol.fif") to
use, or a loaded sphere model (dict).
meg : bool
If True (Default), include MEG computations.
eeg : bool
If True (Default), include EEG computations.
mindist : float
Minimum distance of sources from inner skull surface (in mm).
ignore_ref : bool
If True, do not include reference channels in compensation. This
option should be True for KIT files, since forward computation
with reference channels is not currently supported.
n_jobs : int
Number of jobs to run in parallel.
perts : dict
A dictionary containing perturbation parameters for gradiometer
imbalance, sensor miscalibration, and misalignment
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).
Returns
-------
fwd : instance of Forward
The forward solution.
See Also
--------
convert_forward_solution
Notes
-----
The ``--grad`` option from MNE-C (to compute gradients) is not implemented
here.
To create a fixed-orientation forward solution, use this function
followed by :func:`mne.convert_forward_solution`.
"""
# Currently not (sup)ported:
# 1. --grad option (gradients of the field, not used much)
# 2. --fixed option (can be computed post-hoc)
# 3. --mricoord option (probably not necessary)
# read the transformation from MRI to HEAD coordinates
# (could also be HEAD to MRI)
mri_head_t, trans = _get_trans(trans)
if isinstance(bem, ConductorModel):
bem_extra = 'instance of ConductorModel'
else:
bem_extra = bem
if not isinstance(info, (Info, string_types)):
raise TypeError('info should be an instance of Info or string')
if isinstance(info, string_types):
info_extra = op.split(info)[1]
info = read_info(info, verbose=False)
else:
info_extra = 'instance of Info'
n_jobs = check_n_jobs(n_jobs)
# Report the setup
logger.info('Source space : %s' % src)
logger.info('MRI -> head transform : %s' % trans)
logger.info('Measurement data : %s' % info_extra)
if isinstance(bem, ConductorModel) and bem['is_sphere']:
logger.info('Sphere model : origin at %s mm' % (bem['r0'], ))
logger.info('Standard field computations')
else:
logger.info('Conductor model : %s' % bem_extra)
logger.info('Accurate field computations')
logger.info('Do computations in %s coordinates',
_coord_frame_name(FIFF.FIFFV_COORD_HEAD))
logger.info('Free source orientations')
megcoils, meg_info, compcoils, megnames, eegels, eegnames, rr, info, \
update_kwargs, bem = _prepare_for_forward(
src, mri_head_t, info, bem, mindist, n_jobs, perts, bem_extra, trans,
info_extra, meg, eeg, ignore_ref)
del (src, mri_head_t, trans, info_extra, bem_extra, mindist, meg, eeg,
ignore_ref)
# Time to do the heavy lifting: MEG first, then EEG
coil_types = ['meg', 'eeg']
coils = [megcoils, eegels]
ccoils = [compcoils, None]
infos = [meg_info, None]
megfwd, eegfwd = _compute_forwards(rr, bem, coils, ccoils, infos,
coil_types, n_jobs)
# merge forwards
fwd = _merge_meg_eeg_fwds(_to_forward_dict(megfwd, megnames),
_to_forward_dict(eegfwd, eegnames),
verbose=False)
logger.info('')
# Don't transform the source spaces back into MRI coordinates (which is
# done in the C code) because mne-python assumes forward solution source
# spaces are in head coords.
fwd.update(**update_kwargs)
logger.info('Finished.')
return fwd
# House funcs here so PertInv is just scripting
import mne
from _make_perturbed_forward import make_pert_forward_solution, make_pert_forward_dipole
from mne.datasets import sample
import numpy as np # noqa
from mne.transforms import (_ensure_trans, transform_surface_to, apply_trans,
_get_trans, invert_transform, _print_coord_trans, _coord_frame_name,
Transform)
# local_data_path = 'C:\MEG\Local_mne_data'
data_path = sample.data_path() # local copy of mne sample data
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
subjects_dir = data_path + '/subjects'
subject = 'sample'
trans = data_path + '\MEG\sample/sample_audvis_raw-trans.fif'
mri_head_t, trans = _get_trans(trans)
head_mri_t = invert_transform(mri_head_t)
cov = mne.read_cov(cov_fname)
info = mne.io.read_info(raw_fname)
def fit_dips(min_rad, max_rad, nn, sphere, perts, sourcenorm):
testsources = dict(rr=[], nn=[])
nsources = max_rad - min_rad + 1
vertices = np.zeros((nsources, 1))
for i in range(min_rad, max_rad + 1):
ex, ey, ez = sourcenorm[0], sourcenorm[1], sourcenorm[2]
source = [.001*i*ex, .001*i*ey, .001*i*ez]
normal = [nn[0], nn[1], nn[2]]
testsources['rr'].append(source)
testsources['nn'].append(normal)
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_get_trans():
"""Test converting '-trans.txt' to '-trans.fif'."""
trans = read_trans(fname)
trans = invert_transform(trans) # starts out as head->MRI, so invert
trans_2 = _get_trans(fname_trans)[0]
assert trans.__eq__(trans_2, atol=1e-5)
