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""" 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""" 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.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, labels, annot, bemsol = \ model.get_scaling_job('scaled', False) 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', ))) 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_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)