def calcTrans(subj):
camcan_root = os.environ['CAMCAN_ROOT']
if camcan_root == '':
print('No camcan root directory!, exiting!')
sys.exit()
cm = CoregModel()
#cm.mri.subject_source.set(use_high_res_head=False)
cm.mri.subject_source.set(subjects_dir=camcan_root + 'subjects_s3/')
cm.hsp.trait_set(file=camcan_root + 'megraw/' + subj + '/meg/rest_raw.fif')
cm.mri.fid.trait_set(file=camcan_root + 'subjects_s3/' + subj + '/bem/' +
subj + '-fiducials.fif')
cm.fit_fiducials()
cm.omit_hsp_points(0.020)
#cm.fit_icp()
with open(camcan_root + 'processed/coreg_logs/' + subj + '_hs3.csv',
'w') as f:
cm.print_traits()
f.write(str(cm.lpa_distance) + '\n')
f.write(str(cm.rpa_distance) + '\n')
f.write(str(cm.nasion_distance) + '\n')
f.write(str(cm.point_distance) + '\n')
cm.save_trans(camcan_root + 'megraw/' + subj + '/meg/' + subj +
'-new-fid-PR-trans.fif')
def coreg_head2mri(subjects_dir, subject, native_fid, raw_path, raw_NosePtsOut, trans_dst, flag_fid=False):
import scipy.io
import numpy as np
from mne.gui._coreg_gui import CoregModel
model = CoregModel()
model.mri.subjects_dir = subjects_dir
model.mri.subject = subject
# Remove Polhemus points around the nose (y>0, z<0)
model.hsp.file = raw_path
head_pts = model.hsp.points
raw = read_raw_fif(raw_path, preload=True)
pos = np.where((head_pts[:,1] <= 0) | (head_pts[:,2] >= 0))
dig = raw.info['dig']
dig2 = dig[0:8]
dig3 = [dig[p+7] for p in pos[0]]
dig_yeah = dig2+dig3
raw.info['dig'] = dig_yeah
raw.save(raw_NosePtsOut, overwrite=True)
model.hsp.file = raw_NosePtsOut
# Load CamCAN fiducials from matlab file
if flag_fid:
fid = scipy.io.loadmat(native_fid, struct_as_record=False, squeeze_me=True)
fid = fid['fid']
model.mri.lpa = np.reshape(fid.native.mm.lpa*0.001,(1,3))
model.mri.nasion = np.reshape(fid.native.mm.nas*0.001,(1,3))
model.mri.rpa = np.reshape(fid.native.mm.rpa*0.001,(1,3))
assert (model.mri.fid_ok)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
model.nasion_weight = 1.
model.fit_fiducials(0)
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert new_x < old_x
avg_point_distance = np.mean(model.point_distance)
while True:
model.fit_icp(0)
new_dist = np.mean(model.point_distance)
assert new_dist < avg_point_distance
if model.status_text.endswith('converged)'):
break
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
np.testing.assert_allclose(trans['trans'], model.head_mri_t)
def test_coreg_model_decimation(subjects_dir_tmp):
"""Test CoregModel decimation of high-res to low-res head."""
from mne.gui._coreg_gui import CoregModel
# This makes the test much faster
subject_dir = op.join(subjects_dir_tmp, 'sample')
shutil.move(op.join(subject_dir, 'bem', 'outer_skin.surf'),
op.join(subject_dir, 'surf', 'lh.seghead'))
for fname in ('sample-head.fif', 'sample-head-dense.fif'):
os.remove(op.join(subject_dir, 'bem', fname))
model = CoregModel(guess_mri_subject=False)
with pytest.warns(RuntimeWarning, match='No low-resolution'):
model.mri.subjects_dir = op.dirname(subject_dir)
assert model.mri.subject == 'sample' # already set by setting subjects_dir
assert model.mri.bem_low_res.file == ''
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 2562 # because we moved it
def test_coreg_model_decimation():
"""Test CoregModel decimation of high-res to low-res head."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
subject_dir = op.join(tempdir, 'sample')
shutil.copytree(op.join(subjects_dir, 'sample'), subject_dir)
# This makes the test much faster
shutil.move(op.join(subject_dir, 'bem', 'outer_skin.surf'),
op.join(subject_dir, 'surf', 'lh.seghead'))
for fname in ('sample-head.fif', 'sample-head-dense.fif'):
os.remove(op.join(subject_dir, 'bem', fname))
model = CoregModel(guess_mri_subject=False)
with warnings.catch_warnings(record=True) as w:
model.mri.subjects_dir = tempdir
assert model.mri.subject == 'sample' # already set by setting subjects_dir
assert any('No low-resolution' in str(ww.message) for ww in w)
assert model.mri.bem_low_res.file == ''
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 2562 # because we moved it
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
pytest.raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert not model.mri.fid_ok
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert (model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert model.has_lpa_data
assert model.has_nasion_data
assert model.has_rpa_data
assert len(model.hsp.eeg_points) > 1
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 267122
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.nasion_weight = 1.
model.fit_fiducials(0)
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert new_x < old_x
model.fit_icp(0)
new_dist = np.mean(model.point_distance)
assert new_dist < avg_point_distance
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
# test restoring trans
x, y, z = 100, 200, 50
rot_x, rot_y, rot_z = np.rad2deg([1.5, 0.1, -1.2])
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.mri_head_t
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_array_almost_equal(model.trans_x, x)
assert_array_almost_equal(model.trans_y, y)
assert_array_almost_equal(model.trans_z, z)
assert_array_almost_equal(model.rot_x, rot_x)
assert_array_almost_equal(model.rot_y, rot_y)
assert_array_almost_equal(model.rot_z, rot_z)
# info
assert (isinstance(model.fid_eval_str, str))
assert (isinstance(model.points_eval_str, str))
# scaling job
assert not model.can_prepare_bem_model
model.n_scale_params = 1
assert (model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_allclose(scale, model.parameters[6:9])
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert (skip_fiducials)
model.load_trans(fname_trans)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
assert_allclose(invert_transform(trans)['trans'][:3, 3] * 1000.,
[model.trans_x, model.trans_y, model.trans_z])
def test_coreg_model_with_fsaverage():
"""Test CoregModel with the fsaverage brain data."""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir,
fs_home=op.join(subjects_dir, '..'))
model = CoregModel()
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert (model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.nasion_weight = 1.
model.trans_y = -0.008
model.fit_fiducials(0)
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
model.omit_hsp_points(np.inf)
assert model.hsp.n_omitted == 0
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
model.omit_hsp_points(0.01)
assert model.hsp.n_omitted == 4
model.omit_hsp_points(0.005)
assert model.hsp.n_omitted == 40
model.omit_hsp_points(0.01)
assert model.hsp.n_omitted == 4
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
# scale with 1 parameter
model.n_scale_params = 1
model.fit_fiducials(1)
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert (new_x < old_x)
model.fit_icp(1)
avg_point_distance_1param = np.mean(model.point_distance)
assert (avg_point_distance_1param < avg_point_distance)
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('scaled', False)
assert_equal(sdir, tempdir)
assert_equal(sfrom, 'fsaverage')
assert_equal(sto, 'scaled')
assert_allclose(scale, model.parameters[6:9])
assert_equal(set(bemsol), set(('inner_skull-bem',)))
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('scaled', False)
assert_equal(bemsol, [])
# scale with 3 parameters
model.n_scale_params = 3
model.fit_icp(3)
assert (np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.use_high_res_head = False
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('sample2', False, True)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_equal(scale, model.scale)
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(os.path.join(subjects_dir, 'sample', 'bem')):
match = re.match('sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('sample2', True, False)
assert_equal(bemsol, [])
assert_true(skip_fiducials)
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']
def test_coreg_model_with_fsaverage():
"""Test CoregModel with the fsaverage brain data"""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir)
model = CoregModel()
model.mri.use_high_res_head = False
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.trans_y = -0.008
model.fit_auricular_points()
model.omit_hsp_points(0.02)
assert_equal(model.hsp.n_omitted, 1)
model.omit_hsp_points(reset=True)
assert_equal(model.hsp.n_omitted, 0)
model.omit_hsp_points(0.02, reset=True)
assert_equal(model.hsp.n_omitted, 1)
# scale with 1 parameter
model.n_scale_params = 1
model.fit_scale_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_scale_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_scale_hsp_points()
avg_point_distance_1param = np.mean(model.point_distance)
assert_true(avg_point_distance_1param < avg_point_distance)
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('scaled', False, True)
assert_equal(sdir, tempdir)
assert_equal(sfrom, 'fsaverage')
assert_equal(sto, 'scaled')
assert_equal(scale, model.scale)
assert_equal(set(bemsol), set(('inner_skull-bem',)))
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('scaled', False, False)
assert_equal(bemsol, [])
# scale with 3 parameters
model.n_scale_params = 3
model.fit_scale_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
with warnings.catch_warnings(record=True):
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
pytest.raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert not model.mri.fid_ok
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert (model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert model.has_lpa_data
assert model.has_nasion_data
assert model.has_rpa_data
assert len(model.hsp.eeg_points) > 1
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 267122
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.nasion_weight = 1.
model.fit_fiducials(0)
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert new_x < old_x
model.fit_icp(0)
new_dist = np.mean(model.point_distance)
assert new_dist < avg_point_distance
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
# test restoring trans
x, y, z = 100, 200, 50
rot_x, rot_y, rot_z = np.rad2deg([1.5, 0.1, -1.2])
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.mri_head_t
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_array_almost_equal(model.trans_x, x)
assert_array_almost_equal(model.trans_y, y)
assert_array_almost_equal(model.trans_z, z)
assert_array_almost_equal(model.rot_x, rot_x)
assert_array_almost_equal(model.rot_y, rot_y)
assert_array_almost_equal(model.rot_z, rot_z)
# info
assert (isinstance(model.fid_eval_str, str))
assert (isinstance(model.points_eval_str, str))
# scaling job
assert not model.can_prepare_bem_model
model.n_scale_params = 1
assert (model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_allclose(scale, model.parameters[6:9])
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert (skip_fiducials)
model.load_trans(fname_trans)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
assert_allclose(
invert_transform(trans)['trans'][:3, 3] * 1000.,
[model.trans_x, model.trans_y, model.trans_z])
def test_coreg_model_with_fsaverage():
"""Test CoregModel with the fsaverage brain data."""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir,
fs_home=op.join(subjects_dir, '..'))
model = CoregModel()
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert (model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.nasion_weight = 1.
model.trans_y = -0.008
model.fit_fiducials(0)
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
model.omit_hsp_points(np.inf)
assert model.hsp.n_omitted == 0
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
model.omit_hsp_points(0.01)
assert model.hsp.n_omitted == 4
model.omit_hsp_points(0.005)
assert model.hsp.n_omitted == 40
model.omit_hsp_points(0.01)
assert model.hsp.n_omitted == 4
model.omit_hsp_points(0.02)
assert model.hsp.n_omitted == 1
# scale with 1 parameter
model.n_scale_params = 1
model.fit_fiducials(1)
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert (new_x < old_x)
model.fit_icp(1)
avg_point_distance_1param = np.mean(model.point_distance)
assert (avg_point_distance_1param < avg_point_distance)
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('scaled', False)
assert_equal(sdir, tempdir)
assert_equal(sfrom, 'fsaverage')
assert_equal(sto, 'scaled')
assert_allclose(scale, model.parameters[6:9])
assert_equal(set(bemsol), set(('inner_skull-bem', )))
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('scaled', False)
assert_equal(bemsol, [])
# scale with 3 parameters
model.n_scale_params = 3
model.fit_icp(3)
assert (np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.use_high_res_head = False
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
assert len(model.hsp.eeg_points) > 1
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
# scaling job
assert_false(model.can_prepare_bem_model)
model.n_scale_params = 1
assert_true(model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_equal(scale, model.scale)
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert_true(skip_fiducials)
model.load_trans(fname_trans)
def test_coreg_model_with_fsaverage():
"""Test CoregModel"""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir)
model = CoregModel()
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.trans_y = -0.008
model.fit_auricular_points()
model.omit_hsp_points(0.02)
assert_equal(model.hsp.n_omitted, 1)
model.omit_hsp_points(reset=True)
assert_equal(model.hsp.n_omitted, 0)
model.omit_hsp_points(0.02, reset=True)
assert_equal(model.hsp.n_omitted, 1)
# scale with 1 parameter
model.n_scale_params = 1
model.fit_scale_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_scale_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2
+ model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_scale_hsp_points()
avg_point_distance_1param = np.mean(model.point_distance)
assert_true(avg_point_distance_1param < avg_point_distance)
desc, func, args, kwargs = model.get_scaling_job('test')
assert_true(isinstance(desc, string_types))
assert_equal(args[0], 'fsaverage')
assert_equal(args[1], 'test')
assert_allclose(args[2], model.scale)
assert_equal(kwargs['subjects_dir'], tempdir)
# scale with 3 parameters
model.n_scale_params = 3
model.fit_scale_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
with warnings.catch_warnings(record=True):
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2
+ model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
def test_coreg_gui_automation():
"""Test that properties get properly updated."""
from mne.gui._file_traits import DigSource
from mne.gui._fiducials_gui import MRIHeadWithFiducialsModel
from mne.gui._coreg_gui import CoregModel
subject = 'sample'
hsp = DigSource()
hsp.file = raw_path
mri = MRIHeadWithFiducialsModel(subjects_dir=subjects_dir, subject=subject)
model = CoregModel(mri=mri, hsp=hsp)
# gh-7254
assert not (model.nearest_transformed_high_res_mri_idx_hsp == 0).all()
model.fit_fiducials()
model.icp_iterations = 2
model.nasion_weight = 2.
model.fit_icp()
model.omit_hsp_points(distance=5e-3)
model.icp_iterations = 2
model.fit_icp()
errs_icp = np.median(model._get_point_distance())
assert 2e-3 < errs_icp < 3e-3
info = mne.io.read_info(raw_path)
errs_nearest = np.median(
dig_mri_distances(info, fname_trans, subject, subjects_dir))
assert 1e-3 < errs_nearest < 2e-3
def test_coreg_model_with_fsaverage():
"""Test CoregModel"""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir)
model = CoregModel()
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.trans_y = -0.008
model.fit_auricular_points()
model.omit_hsp_points(0.02)
assert_equal(model.hsp.n_omitted, 1)
model.omit_hsp_points(reset=True)
assert_equal(model.hsp.n_omitted, 0)
model.omit_hsp_points(0.02, reset=True)
assert_equal(model.hsp.n_omitted, 1)
# scale with 1 parameter
model.n_scale_params = 1
model.fit_scale_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_scale_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_scale_hsp_points()
avg_point_distance_1param = np.mean(model.point_distance)
assert_true(avg_point_distance_1param < avg_point_distance)
desc, func, args, kwargs = model.get_scaling_job('test')
assert_true(isinstance(desc, string_types))
assert_equal(args[0], 'fsaverage')
assert_equal(args[1], 'test')
assert_allclose(args[2], model.scale)
assert_equal(kwargs['subjects_dir'], tempdir)
# scale with 3 parameters
model.n_scale_params = 3
model.fit_scale_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
with warnings.catch_warnings(record=True):
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
model.get_prepare_bem_model_job('sample')
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2
+ model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
model.get_prepare_bem_model_job('sample')
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']