def test_make_forward_solution_sphere(): """Test making a forward solution with a sphere model.""" temp_dir = _TempDir() fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif') src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C out_name = op.join(temp_dir, 'tmp-fwd.fif') run_subprocess(['mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src', fname_src_small, '--mri', fname_trans, '--fwd', out_name]) fwd = read_forward_solution(out_name) sphere = make_sphere_model(verbose=True) fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True) _compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1) # Since the above is pretty lax, let's check a different way for meg, eeg in zip([True, False], [False, True]): fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg) fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg) assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(), fwd_py_['sol']['data'].ravel())[0, 1], 1.0, rtol=1e-3)
def test_scale_mri(): """Test creating fsaverage and scaling it""" # create fsaverage tempdir = _TempDir() create_default_subject(subjects_dir=tempdir) is_mri = _is_mri_subject('fsaverage', tempdir) assert_true(is_mri, "Creating fsaverage failed") fid_path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir) assert_true(os.path.exists(fid_path), "Updating fsaverage") # remove redundant label files label_temp = os.path.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-ico-0-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) src_path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-ico-0-src.fif') write_source_spaces(src_path, src) # scale fsaverage os.environ['_MNE_FEW_SURFACES'] = 'true' scale_mri('fsaverage', 'flachkopf', [1, .2, .8], True, subjects_dir=tempdir) del os.environ['_MNE_FEW_SURFACES'] is_mri = _is_mri_subject('flachkopf', tempdir) assert_true(is_mri, "Scaling fsaverage failed") src_path = os.path.join(tempdir, 'flachkopf', 'bem', 'flachkopf-ico-0-src.fif') assert_true(os.path.exists(src_path), "Source space was not scaled") scale_labels('flachkopf', subjects_dir=tempdir) # scale source space separately os.remove(src_path) scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) assert_true(os.path.exists(src_path), "Source space was not scaled") # add distances to source space src = mne.read_source_spaces(path) mne.add_source_space_distances(src) src.save(path, overwrite=True) # scale with distances os.remove(src_path) scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) assert_true(os.path.exists(src_path), "Source space was not scaled")
def test_make_forward_solution_sphere(): """Test making a forward solution with a sphere model.""" temp_dir = _TempDir() fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif') src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C out_name = op.join(temp_dir, 'tmp-fwd.fif') run_subprocess([ 'mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src', fname_src_small, '--mri', fname_trans, '--fwd', out_name ]) fwd = read_forward_solution(out_name) sphere = make_sphere_model(verbose=True) fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True) _compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1) # Since the above is pretty lax, let's check a different way for meg, eeg in zip([True, False], [False, True]): fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg) fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg) assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(), fwd_py_['sol']['data'].ravel())[0, 1], 1.0, rtol=1e-3) # Number of layers in the sphere model doesn't matter for MEG # (as long as no sources are omitted due to distance) assert len(sphere['layers']) == 4 fwd = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=False) sphere_1 = make_sphere_model(head_radius=None) assert len(sphere_1['layers']) == 0 assert_array_equal(sphere['r0'], sphere_1['r0']) fwd_1 = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=False) _compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12)
def test_make_forward_solution_sphere(): """Test making a forward solution with a sphere model.""" temp_dir = _TempDir() fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif') src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C out_name = op.join(temp_dir, 'tmp-fwd.fif') run_subprocess(['mne_forward_solution', '--meg', '--eeg', '--meas', fname_raw, '--src', fname_src_small, '--mri', fname_trans, '--fwd', out_name]) fwd = read_forward_solution(out_name) sphere = make_sphere_model(verbose=True) fwd_py = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=True, verbose=True) _compare_forwards(fwd, fwd_py, 366, 108, meg_rtol=5e-1, meg_atol=1e-6, eeg_rtol=5e-1, eeg_atol=5e-1) # Since the above is pretty lax, let's check a different way for meg, eeg in zip([True, False], [False, True]): fwd_ = pick_types_forward(fwd, meg=meg, eeg=eeg) fwd_py_ = pick_types_forward(fwd, meg=meg, eeg=eeg) assert_allclose(np.corrcoef(fwd_['sol']['data'].ravel(), fwd_py_['sol']['data'].ravel())[0, 1], 1.0, rtol=1e-3) # Number of layers in the sphere model doesn't matter for MEG # (as long as no sources are omitted due to distance) assert len(sphere['layers']) == 4 fwd = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=False) sphere_1 = make_sphere_model(head_radius=None) assert len(sphere_1['layers']) == 0 assert_array_equal(sphere['r0'], sphere_1['r0']) fwd_1 = make_forward_solution(fname_raw, fname_trans, src, sphere, meg=True, eeg=False) _compare_forwards(fwd, fwd_1, 306, 108, meg_rtol=1e-12, meg_atol=1e-12) # Homogeneous model sphere = make_sphere_model(head_radius=None) with pytest.raises(RuntimeError, match='zero shells.*EEG'): make_forward_solution(fname_raw, fname_trans, src, sphere)
def test_make_forward_solution_kit(): """Test making fwd using KIT, BTI, and CTF (compensated) files.""" kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit', 'tests', 'data') sqd_path = op.join(kit_dir, 'test.sqd') mrk_path = op.join(kit_dir, 'test_mrk.sqd') elp_path = op.join(kit_dir, 'test_elp.txt') hsp_path = op.join(kit_dir, 'test_hsp.txt') trans_path = op.join(kit_dir, 'trans-sample.fif') fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif') bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti', 'tests', 'data') bti_pdf = op.join(bti_dir, 'test_pdf_linux') bti_config = op.join(bti_dir, 'test_config_linux') bti_hs = op.join(bti_dir, 'test_hs_linux') fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif') fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', 'test_ctf_comp_raw.fif') # first set up a small testing source space temp_dir = _TempDir() fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif') src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C n_src = 108 # this is the resulting # of verts in fwd # first use mne-C: convert file, make forward solution fwd = _do_forward_solution('sample', fname_kit_raw, src=fname_src_small, bem=fname_bem_meg, mri=trans_path, eeg=False, meg=True, subjects_dir=subjects_dir) assert_true(isinstance(fwd, Forward)) # now let's use python with the same raw file fwd_py = make_forward_solution(fname_kit_raw, trans_path, src, fname_bem_meg, eeg=False, meg=True) _compare_forwards(fwd, fwd_py, 157, n_src) assert_true(isinstance(fwd_py, Forward)) # now let's use mne-python all the way raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path) # without ignore_ref=True, this should throw an error: assert_raises(NotImplementedError, make_forward_solution, raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path) # check that asking for eeg channels (even if they don't exist) is handled meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True, eeg=False)) fwd_py = make_forward_solution(meg_only_info, src=src, meg=True, eeg=True, bem=fname_bem_meg, trans=trans_path, ignore_ref=True) _compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7) # BTI python end-to-end versus C fwd = _do_forward_solution('sample', fname_bti_raw, src=fname_src_small, bem=fname_bem_meg, mri=trans_path, eeg=False, meg=True, subjects_dir=subjects_dir) with warnings.catch_warnings(record=True): # weight tables raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False) fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path) _compare_forwards(fwd, fwd_py, 248, n_src) # now let's test CTF w/compensation fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src, fname_bem_meg, eeg=False, meg=True) fwd = _do_forward_solution('sample', fname_ctf_raw, mri=fname_trans, src=fname_src_small, bem=fname_bem_meg, eeg=False, meg=True, subjects_dir=subjects_dir) _compare_forwards(fwd, fwd_py, 274, n_src) # CTF with compensation changed in python ctf_raw = read_raw_fif(fname_ctf_raw) ctf_raw.apply_gradient_compensation(2) fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src, fname_bem_meg, eeg=False, meg=True) with warnings.catch_warnings(record=True): fwd = _do_forward_solution('sample', ctf_raw, mri=fname_trans, src=fname_src_small, bem=fname_bem_meg, eeg=False, meg=True, subjects_dir=subjects_dir) _compare_forwards(fwd, fwd_py, 274, n_src)
def test_scale_mri(): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one tempdir = _TempDir() fake_home = testing.data_path() create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir, fs_home=fake_home) assert op.exists(fid_path), "Updating fsaverage" # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) # remove redundant label files label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space print('Creating surface source space') path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') print('Creating volume source space') vsrc = mne.setup_volume_source_space( 'fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage for scale in (.9, [1, .2, .8]): write_source_spaces(path % 'ico-0', src, overwrite=True) os.environ['_MNE_FEW_SURFACES'] = 'true' with pytest.warns(None): # sometimes missing nibabel scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') del os.environ['_MNE_FEW_SURFACES'] assert _is_mri_subject('flachkopf', tempdir), "Scaling failed" spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled" assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg')) vsrc_s = mne.read_source_spaces(spath % 'vol-50') pt = np.array([0.12, 0.41, -0.22]) assert_array_almost_equal( apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)), apply_trans(vsrc[0]['src_mri_t'], pt)) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space after hacking the properties to make # it run *much* faster src_dist = src.copy() for s in src_dist: s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']], tris=s['use_tris']) s.update(np=len(s['rr']), ntri=len(s['tris']), vertno=np.arange(len(s['rr'])), inuse=np.ones(len(s['rr']), int)) mne.add_source_space_distances(src_dist) write_source_spaces(path % 'ico-0', src_dist, overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is not None
def test_scale_mri_xfm(): """Test scale_mri transforms and MRI scaling.""" # scale fsaverage tempdir = _TempDir() os.environ['_MNE_FEW_SURFACES'] = 'true' fake_home = testing.data_path() # add fsaverage create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) # add sample (with few files) sample_dir = op.join(tempdir, 'sample') os.mkdir(sample_dir) os.mkdir(op.join(sample_dir, 'bem')) for dirname in ('mri', 'surf'): copytree(op.join(fake_home, 'subjects', 'sample', dirname), op.join(sample_dir, dirname)) subject_to = 'flachkopf' spacing = 'oct2' for subject_from in ('fsaverage', 'sample'): if subject_from == 'fsaverage': scale = 1. # single dim else: scale = [0.9, 2, .8] # separate src_from_fname = op.join(tempdir, subject_from, 'bem', '%s-%s-src.fif' % (subject_from, spacing)) src_from = mne.setup_source_space( subject_from, spacing, subjects_dir=tempdir, add_dist=False) write_source_spaces(src_from_fname, src_from) print(src_from_fname) vertices_from = np.concatenate([s['vertno'] for s in src_from]) assert len(vertices_from) == 36 hemis = ([0] * len(src_from[0]['vertno']) + [1] * len(src_from[0]['vertno'])) mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from, subjects_dir=tempdir) if subject_from == 'fsaverage': # identity transform source_rr = np.concatenate([s['rr'][s['vertno']] for s in src_from]) * 1e3 assert_allclose(mni_from, source_rr) if subject_from == 'fsaverage': overwrite = skip_fiducials = False else: with pytest.raises(IOError, match='No fiducials file'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir) skip_fiducials = True with pytest.raises(IOError, match='already exists'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, skip_fiducials=skip_fiducials) overwrite = True scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, verbose='debug', overwrite=overwrite, skip_fiducials=skip_fiducials) if subject_from == 'fsaverage': assert _is_mri_subject(subject_to, tempdir), "Scaling failed" src_to_fname = op.join(tempdir, subject_to, 'bem', '%s-%s-src.fif' % (subject_to, spacing)) assert op.exists(src_to_fname), "Source space was not scaled" # Check MRI scaling fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz') assert op.exists(fname_mri), "MRI was not scaled" # Check MNI transform src = mne.read_source_spaces(src_to_fname) vertices = np.concatenate([s['vertno'] for s in src]) assert_array_equal(vertices, vertices_from) mni = mne.vertex_to_mni(vertices, hemis, subject_to, subjects_dir=tempdir) assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm del os.environ['_MNE_FEW_SURFACES']
def test_scale_mri(tmp_path, few_surfaces, scale): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one tempdir = str(tmp_path) fake_home = testing.data_path() create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir, fs_home=fake_home) assert op.exists(fid_path), "Updating fsaverage" # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) # remove redundant label files label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space print('Creating surface source space') path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') print('Creating volume source space') vsrc = mne.setup_volume_source_space('fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage write_source_spaces(path % 'ico-0', src, overwrite=True) with pytest.warns(None): # sometimes missing nibabel scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') assert _is_mri_subject('flachkopf', tempdir), "Scaling failed" spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled" assert os.path.isfile( os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg')) vsrc_s = mne.read_source_spaces(spath % 'vol-50') for vox in ([0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 2, 3]): idx = np.ravel_multi_index(vox, vsrc[0]['shape'], order='F') err_msg = f'idx={idx} @ {vox}, scale={scale}' assert_allclose(apply_trans(vsrc[0]['src_mri_t'], vox), vsrc[0]['rr'][idx], err_msg=err_msg) assert_allclose(apply_trans(vsrc_s[0]['src_mri_t'], vox), vsrc_s[0]['rr'][idx], err_msg=err_msg) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space after hacking the properties to make # it run *much* faster src_dist = src.copy() for s in src_dist: s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']], tris=s['use_tris']) s.update(np=len(s['rr']), ntri=len(s['tris']), vertno=np.arange(len(s['rr'])), inuse=np.ones(len(s['rr']), int)) mne.add_source_space_distances(src_dist) write_source_spaces(path % 'ico-0', src_dist, overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is not None assert ssrc[0]['nearest'] is not None # check patch info computation (only if SciPy is new enough to be fast) if check_version('scipy', '1.3'): for s in src_dist: for key in ('dist', 'dist_limit'): s[key] = None write_source_spaces(path % 'ico-0', src_dist, overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is None assert ssrc[0]['nearest'] is not None
def test_scale_mri_xfm(tmp_path, few_surfaces): """Test scale_mri transforms and MRI scaling.""" # scale fsaverage tempdir = str(tmp_path) fake_home = testing.data_path() # add fsaverage create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) # add sample (with few files) sample_dir = op.join(tempdir, 'sample') os.mkdir(sample_dir) os.mkdir(op.join(sample_dir, 'bem')) for dirname in ('mri', 'surf'): copytree(op.join(fake_home, 'subjects', 'sample', dirname), op.join(sample_dir, dirname)) subject_to = 'flachkopf' spacing = 'oct2' for subject_from in ('fsaverage', 'sample'): if subject_from == 'fsaverage': scale = 1. # single dim else: scale = [0.9, 2, .8] # separate src_from_fname = op.join(tempdir, subject_from, 'bem', '%s-%s-src.fif' % (subject_from, spacing)) src_from = mne.setup_source_space(subject_from, spacing, subjects_dir=tempdir, add_dist=False) write_source_spaces(src_from_fname, src_from) vertices_from = np.concatenate([s['vertno'] for s in src_from]) assert len(vertices_from) == 36 hemis = ([0] * len(src_from[0]['vertno']) + [1] * len(src_from[0]['vertno'])) mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from, subjects_dir=tempdir) if subject_from == 'fsaverage': # identity transform source_rr = np.concatenate( [s['rr'][s['vertno']] for s in src_from]) * 1e3 assert_allclose(mni_from, source_rr) if subject_from == 'fsaverage': overwrite = skip_fiducials = False else: with pytest.raises(IOError, match='No fiducials file'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir) skip_fiducials = True with pytest.raises(IOError, match='already exists'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, skip_fiducials=skip_fiducials) overwrite = True if subject_from == 'sample': # support for not needing all surf files os.remove(op.join(sample_dir, 'surf', 'lh.curv')) scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, verbose='debug', overwrite=overwrite, skip_fiducials=skip_fiducials) if subject_from == 'fsaverage': assert _is_mri_subject(subject_to, tempdir), "Scaling failed" src_to_fname = op.join(tempdir, subject_to, 'bem', '%s-%s-src.fif' % (subject_to, spacing)) assert op.exists(src_to_fname), "Source space was not scaled" # Check MRI scaling fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz') assert op.exists(fname_mri), "MRI was not scaled" # Check MNI transform src = mne.read_source_spaces(src_to_fname) vertices = np.concatenate([s['vertno'] for s in src]) assert_array_equal(vertices, vertices_from) mni = mne.vertex_to_mni(vertices, hemis, subject_to, subjects_dir=tempdir) assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm
def test_make_forward_solution_kit(): """Test making fwd using KIT, BTI, and CTF (compensated) files.""" kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'kit', 'tests', 'data') sqd_path = op.join(kit_dir, 'test.sqd') mrk_path = op.join(kit_dir, 'test_mrk.sqd') elp_path = op.join(kit_dir, 'test_elp.txt') hsp_path = op.join(kit_dir, 'test_hsp.txt') trans_path = op.join(kit_dir, 'trans-sample.fif') fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif') bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'bti', 'tests', 'data') bti_pdf = op.join(bti_dir, 'test_pdf_linux') bti_config = op.join(bti_dir, 'test_config_linux') bti_hs = op.join(bti_dir, 'test_hs_linux') fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif') fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data', 'test_ctf_comp_raw.fif') # first set up a small testing source space temp_dir = _TempDir() fname_src_small = op.join(temp_dir, 'sample-oct-2-src.fif') src = setup_source_space('sample', 'oct2', subjects_dir=subjects_dir, add_dist=False) write_source_spaces(fname_src_small, src) # to enable working with MNE-C n_src = 108 # this is the resulting # of verts in fwd # first use mne-C: convert file, make forward solution fwd = _do_forward_solution('sample', fname_kit_raw, src=fname_src_small, bem=fname_bem_meg, mri=trans_path, eeg=False, meg=True, subjects_dir=subjects_dir) assert (isinstance(fwd, Forward)) # now let's use python with the same raw file fwd_py = make_forward_solution(fname_kit_raw, trans_path, src, fname_bem_meg, eeg=False, meg=True) _compare_forwards(fwd, fwd_py, 157, n_src) assert (isinstance(fwd_py, Forward)) # now let's use mne-python all the way raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path) # without ignore_ref=True, this should throw an error: pytest.raises(NotImplementedError, make_forward_solution, raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path) # check that asking for eeg channels (even if they don't exist) is handled meg_only_info = pick_info(raw_py.info, pick_types(raw_py.info, meg=True, eeg=False)) fwd_py = make_forward_solution(meg_only_info, src=src, meg=True, eeg=True, bem=fname_bem_meg, trans=trans_path, ignore_ref=True) _compare_forwards(fwd, fwd_py, 157, n_src, meg_rtol=1e-3, meg_atol=1e-7) # BTI python end-to-end versus C fwd = _do_forward_solution('sample', fname_bti_raw, src=fname_src_small, bem=fname_bem_meg, mri=trans_path, eeg=False, meg=True, subjects_dir=subjects_dir) raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False) fwd_py = make_forward_solution(raw_py.info, src=src, eeg=False, meg=True, bem=fname_bem_meg, trans=trans_path) _compare_forwards(fwd, fwd_py, 248, n_src) # now let's test CTF w/compensation fwd_py = make_forward_solution(fname_ctf_raw, fname_trans, src, fname_bem_meg, eeg=False, meg=True) fwd = _do_forward_solution('sample', fname_ctf_raw, mri=fname_trans, src=fname_src_small, bem=fname_bem_meg, eeg=False, meg=True, subjects_dir=subjects_dir) _compare_forwards(fwd, fwd_py, 274, n_src) # CTF with compensation changed in python ctf_raw = read_raw_fif(fname_ctf_raw) ctf_raw.info['bads'] = ['MRO24-2908'] # test that it works with some bads ctf_raw.apply_gradient_compensation(2) fwd_py = make_forward_solution(ctf_raw.info, fname_trans, src, fname_bem_meg, eeg=False, meg=True) fwd = _do_forward_solution('sample', ctf_raw, mri=fname_trans, src=fname_src_small, bem=fname_bem_meg, eeg=False, meg=True, subjects_dir=subjects_dir) _compare_forwards(fwd, fwd_py, 274, n_src) temp_dir = _TempDir() fname_temp = op.join(temp_dir, 'test-ctf-fwd.fif') write_forward_solution(fname_temp, fwd_py) fwd_py2 = read_forward_solution(fname_temp) _compare_forwards(fwd_py, fwd_py2, 274, n_src) repr(fwd_py)
def test_scale_mri(): """Test creating fsaverage and scaling it""" # create fsaverage tempdir = _TempDir() create_default_subject(subjects_dir=tempdir) assert_true(_is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed") fid_path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir) assert_true(os.path.exists(fid_path), "Updating fsaverage") # copy MRI file from sample data path = os.path.join('%s', 'fsaverage', 'mri', 'orig.mgz') sample_sdir = os.path.join(mne.datasets.sample.data_path(), 'subjects') copyfile(path % sample_sdir, path % tempdir) # remove redundant label files label_temp = os.path.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space path = os.path.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) write_source_spaces(path % 'ico-0', src) mri = os.path.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') vsrc = mne.setup_volume_source_space('fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage os.environ['_MNE_FEW_SURFACES'] = 'true' scale = np.array([1, .2, .8]) scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir) del os.environ['_MNE_FEW_SURFACES'] assert_true(_is_mri_subject('flachkopf', tempdir), "Scaling fsaverage failed") spath = os.path.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert_true(os.path.exists(spath % 'ico-0'), "Source space ico-0 was not scaled") vsrc_s = mne.read_source_spaces(spath % 'vol-50') pt = np.array([0.12, 0.41, -0.22]) assert_array_almost_equal(apply_trans(vsrc_s[0]['src_mri_t'], pt * scale), apply_trans(vsrc[0]['src_mri_t'], pt)) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space mne.add_source_space_distances(src) src.save(path % 'ico-0', overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert_is_not(ssrc[0]['dist'], None)
def test_scale_mri(): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one tempdir = _TempDir() fake_home = testing.data_path() create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir, fs_home=fake_home) assert op.exists(fid_path), "Updating fsaverage" # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) # remove redundant label files label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space print('Creating surface source space') path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) write_source_spaces(path % 'ico-0', src) mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') print('Creating volume source space') vsrc = mne.setup_volume_source_space( 'fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage os.environ['_MNE_FEW_SURFACES'] = 'true' scale = np.array([1, .2, .8]) scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') del os.environ['_MNE_FEW_SURFACES'] assert _is_mri_subject('flachkopf', tempdir), "Scaling fsaverage failed" spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled" assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg')) vsrc_s = mne.read_source_spaces(spath % 'vol-50') pt = np.array([0.12, 0.41, -0.22]) assert_array_almost_equal(apply_trans(vsrc_s[0]['src_mri_t'], pt * scale), apply_trans(vsrc[0]['src_mri_t'], pt)) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space mne.add_source_space_distances(src) src.save(path % 'ico-0', overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is not None
def test_scale_mri_xfm(tmp_path, few_surfaces, subjects_dir_tmp_few): """Test scale_mri transforms and MRI scaling.""" # scale fsaverage tempdir = str(subjects_dir_tmp_few) sample_dir = subjects_dir_tmp_few / 'sample' subject_to = 'flachkopf' spacing = 'oct2' for subject_from in ('fsaverage', 'sample'): if subject_from == 'fsaverage': scale = 1. # single dim else: scale = [0.9, 2, .8] # separate src_from_fname = op.join(tempdir, subject_from, 'bem', '%s-%s-src.fif' % (subject_from, spacing)) src_from = mne.setup_source_space(subject_from, spacing, subjects_dir=tempdir, add_dist=False) write_source_spaces(src_from_fname, src_from) vertices_from = np.concatenate([s['vertno'] for s in src_from]) assert len(vertices_from) == 36 hemis = ([0] * len(src_from[0]['vertno']) + [1] * len(src_from[0]['vertno'])) mni_from = mne.vertex_to_mni(vertices_from, hemis, subject_from, subjects_dir=tempdir) if subject_from == 'fsaverage': # identity transform source_rr = np.concatenate( [s['rr'][s['vertno']] for s in src_from]) * 1e3 assert_allclose(mni_from, source_rr) if subject_from == 'fsaverage': overwrite = skip_fiducials = False else: with pytest.raises(IOError, match='No fiducials file'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir) skip_fiducials = True with pytest.raises(IOError, match='already exists'): scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, skip_fiducials=skip_fiducials) overwrite = True if subject_from == 'sample': # support for not needing all surf files os.remove(op.join(sample_dir, 'surf', 'lh.curv')) scale_mri(subject_from, subject_to, scale, subjects_dir=tempdir, verbose='debug', overwrite=overwrite, skip_fiducials=skip_fiducials) if subject_from == 'fsaverage': assert _is_mri_subject(subject_to, tempdir), "Scaling failed" src_to_fname = op.join(tempdir, subject_to, 'bem', '%s-%s-src.fif' % (subject_to, spacing)) assert op.exists(src_to_fname), "Source space was not scaled" # Check MRI scaling fname_mri = op.join(tempdir, subject_to, 'mri', 'T1.mgz') assert op.exists(fname_mri), "MRI was not scaled" # Check MNI transform src = mne.read_source_spaces(src_to_fname) vertices = np.concatenate([s['vertno'] for s in src]) assert_array_equal(vertices, vertices_from) mni = mne.vertex_to_mni(vertices, hemis, subject_to, subjects_dir=tempdir) assert_allclose(mni, mni_from, atol=1e-3) # 0.001 mm # Check head_to_mni (the `trans` here does not really matter) trans = rotation(0.001, 0.002, 0.003) @ translation(0.01, 0.02, 0.03) trans = Transform('head', 'mri', trans) pos_head_from = np.random.RandomState(0).randn(4, 3) pos_mni_from = mne.head_to_mni(pos_head_from, subject_from, trans, tempdir) pos_mri_from = apply_trans(trans, pos_head_from) pos_mri = pos_mri_from * scale pos_head = apply_trans(invert_transform(trans), pos_mri) pos_mni = mne.head_to_mni(pos_head, subject_to, trans, tempdir) assert_allclose(pos_mni, pos_mni_from, atol=1e-3)
def test_scale_mri(tmpdir, few_surfaces): """Test creating fsaverage and scaling it.""" # create fsaverage using the testing "fsaverage" instead of the FreeSurfer # one tempdir = str(tmpdir) fake_home = testing.data_path() create_default_subject(subjects_dir=tempdir, fs_home=fake_home, verbose=True) assert _is_mri_subject('fsaverage', tempdir), "Creating fsaverage failed" fid_path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-fiducials.fif') os.remove(fid_path) create_default_subject(update=True, subjects_dir=tempdir, fs_home=fake_home) assert op.exists(fid_path), "Updating fsaverage" # copy MRI file from sample data (shouldn't matter that it's incorrect, # so here choose a small one) path_from = op.join(testing.data_path(), 'subjects', 'sample', 'mri', 'T1.mgz') path_to = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') copyfile(path_from, path_to) # remove redundant label files label_temp = op.join(tempdir, 'fsaverage', 'label', '*.label') label_paths = glob(label_temp) for label_path in label_paths[1:]: os.remove(label_path) # create source space print('Creating surface source space') path = op.join(tempdir, 'fsaverage', 'bem', 'fsaverage-%s-src.fif') src = mne.setup_source_space('fsaverage', 'ico0', subjects_dir=tempdir, add_dist=False) mri = op.join(tempdir, 'fsaverage', 'mri', 'orig.mgz') print('Creating volume source space') vsrc = mne.setup_volume_source_space( 'fsaverage', pos=50, mri=mri, subjects_dir=tempdir, add_interpolator=False) write_source_spaces(path % 'vol-50', vsrc) # scale fsaverage for scale in (.9, [1, .2, .8]): write_source_spaces(path % 'ico-0', src, overwrite=True) with pytest.warns(None): # sometimes missing nibabel scale_mri('fsaverage', 'flachkopf', scale, True, subjects_dir=tempdir, verbose='debug') assert _is_mri_subject('flachkopf', tempdir), "Scaling failed" spath = op.join(tempdir, 'flachkopf', 'bem', 'flachkopf-%s-src.fif') assert op.exists(spath % 'ico-0'), "Source space ico-0 was not scaled" assert os.path.isfile(os.path.join(tempdir, 'flachkopf', 'surf', 'lh.sphere.reg')) vsrc_s = mne.read_source_spaces(spath % 'vol-50') pt = np.array([0.12, 0.41, -0.22]) assert_array_almost_equal( apply_trans(vsrc_s[0]['src_mri_t'], pt * np.array(scale)), apply_trans(vsrc[0]['src_mri_t'], pt)) scale_labels('flachkopf', subjects_dir=tempdir) # add distances to source space after hacking the properties to make # it run *much* faster src_dist = src.copy() for s in src_dist: s.update(rr=s['rr'][s['vertno']], nn=s['nn'][s['vertno']], tris=s['use_tris']) s.update(np=len(s['rr']), ntri=len(s['tris']), vertno=np.arange(len(s['rr'])), inuse=np.ones(len(s['rr']), int)) mne.add_source_space_distances(src_dist) write_source_spaces(path % 'ico-0', src_dist, overwrite=True) # scale with distances os.remove(spath % 'ico-0') scale_source_space('flachkopf', 'ico-0', subjects_dir=tempdir) ssrc = mne.read_source_spaces(spath % 'ico-0') assert ssrc[0]['dist'] is not None