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 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 +
'cc700/mri/pipeline/release004/BIDSsep/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, True)
cm.fit_hsp_points()
with open(camcan_root + 'processed/coreg_logs/' + subj + '_hs.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 +
'cc700/mri/pipeline/release004/BIDSsep/megraw/' + subj +
'/meg/' + subj + '-new-hs-AR-trans.fif')
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_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_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_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_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)
