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,
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"""
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)
