# Author: Mark Wronkiewicz # # License: BSD (3-clause) import os.path as op import warnings import numpy as np import sys import scipy from numpy.testing import assert_equal, assert_allclose from nose.tools import assert_true, assert_raises from nose.plugins.skip import SkipTest from distutils.version import LooseVersion from mne import compute_raw_covariance, pick_types from mne.chpi import read_head_pos, filter_chpi from mne.forward import _prep_meg_channels from mne.cov import _estimate_rank_meeg_cov from mne.datasets import testing from mne.io import (read_raw_fif, proc_history, read_info, read_raw_bti, read_raw_kit, _BaseRaw) from mne.preprocessing.maxwell import ( maxwell_filter, _get_n_moments, _sss_basis_basic, _sh_complex_to_real, _sh_real_to_complex, _sh_negate, _bases_complex_to_real, _trans_sss_basis, _bases_real_to_complex, _sph_harm, _prep_mf_coils) from mne.tests.common import assert_meg_snr from mne.utils import (_TempDir, run_tests_if_main, slow_test, catch_logging, requires_version, object_diff, buggy_mkl_svd) from mne.externals.six import PY3 warnings.simplefilter('always') # Always throw warnings data_path = testing.data_path(download=False) sss_path = op.join(data_path, 'SSS') pre = op.join(sss_path, 'test_move_anon_') raw_fname = pre + 'raw.fif' sss_std_fname = pre + 'stdOrigin_raw_sss.fif' sss_nonstd_fname = pre + 'nonStdOrigin_raw_sss.fif' sss_bad_recon_fname = pre + 'badRecon_raw_sss.fif' sss_reg_in_fname = pre + 'regIn_raw_sss.fif' sss_fine_cal_fname = pre + 'fineCal_raw_sss.fif' sss_ctc_fname = pre + 'crossTalk_raw_sss.fif' sss_trans_default_fname = pre + 'transDefault_raw_sss.fif' sss_trans_sample_fname = pre + 'transSample_raw_sss.fif' sss_st1FineCalCrossTalkRegIn_fname = \ pre + 'st1FineCalCrossTalkRegIn_raw_sss.fif' sss_st1FineCalCrossTalkRegInTransSample_fname = \ pre + 'st1FineCalCrossTalkRegInTransSample_raw_sss.fif' sss_movecomp_fname = pre + 'movecomp_raw_sss.fif' sss_movecomp_reg_in_fname = pre + 'movecomp_regIn_raw_sss.fif' sss_movecomp_reg_in_st4s_fname = pre + 'movecomp_regIn_st4s_raw_sss.fif' erm_fname = pre + 'erm_raw.fif' sss_erm_std_fname = pre + 'erm_devOrigin_raw_sss.fif' sss_erm_reg_in_fname = pre + 'erm_regIn_raw_sss.fif' sss_erm_fine_cal_fname = pre + 'erm_fineCal_raw_sss.fif' sss_erm_ctc_fname = pre + 'erm_crossTalk_raw_sss.fif' sss_erm_st_fname = pre + 'erm_st1_raw_sss.fif' sss_erm_st1FineCalCrossTalk_fname = pre + 'erm_st1FineCalCrossTalk_raw_sss.fif' sss_erm_st1FineCalCrossTalkRegIn_fname = \ pre + 'erm_st1FineCalCrossTalkRegIn_raw_sss.fif' sample_fname = op.join(data_path, 'MEG', 'sample_audvis_trunc_raw.fif') sss_samp_reg_in_fname = op.join(data_path, 'SSS', 'sample_audvis_trunc_regIn_raw_sss.fif') sss_samp_fname = op.join(data_path, 'SSS', 'sample_audvis_trunc_raw_sss.fif') pos_fname = op.join(data_path, 'SSS', 'test_move_anon_raw.pos') bases_fname = op.join(sss_path, 'sss_data.mat') fine_cal_fname = op.join(sss_path, 'sss_cal_3053.dat') fine_cal_fname_3d = op.join(sss_path, 'sss_cal_3053_3d.dat') ctc_fname = op.join(sss_path, 'ct_sparse.fif') fine_cal_mgh_fname = op.join(sss_path, 'sss_cal_mgh.dat') ctc_mgh_fname = op.join(sss_path, 'ct_sparse_mgh.fif') sample_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') triux_path = op.join(data_path, 'SSS', 'TRIUX') tri_fname = op.join(triux_path, 'triux_bmlhus_erm_raw.fif') tri_sss_fname = op.join(triux_path, 'triux_bmlhus_erm_raw_sss.fif') tri_sss_reg_fname = op.join(triux_path, 'triux_bmlhus_erm_regIn_raw_sss.fif') tri_sss_st4_fname = op.join(triux_path, 'triux_bmlhus_erm_st4_raw_sss.fif') tri_sss_ctc_fname = op.join(triux_path, 'triux_bmlhus_erm_ctc_raw_sss.fif') tri_sss_cal_fname = op.join(triux_path, 'triux_bmlhus_erm_cal_raw_sss.fif') tri_sss_ctc_cal_fname = op.join( triux_path, 'triux_bmlhus_erm_ctc_cal_raw_sss.fif') tri_sss_ctc_cal_reg_in_fname = op.join( triux_path, 'triux_bmlhus_erm_ctc_cal_regIn_raw_sss.fif') tri_ctc_fname = op.join(triux_path, 'ct_sparse_BMLHUS.fif') tri_cal_fname = op.join(triux_path, 'sss_cal_BMLHUS.dat') io_dir = op.join(op.dirname(__file__), '..', '..', 'io') fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif') int_order, ext_order = 8, 3 mf_head_origin = (0., 0., 0.04) mf_meg_origin = (0., 0.013, -0.006) # otherwise we can get SVD error requires_svd_convergence = requires_version('scipy', '0.12') # 30 random bad MEG channels (20 grad, 10 mag) that were used in generation bads = ['MEG0912', 'MEG1722', 'MEG2213', 'MEG0132', 'MEG1312', 'MEG0432', 'MEG2433', 'MEG1022', 'MEG0442', 'MEG2332', 'MEG0633', 'MEG1043', 'MEG1713', 'MEG0422', 'MEG0932', 'MEG1622', 'MEG1343', 'MEG0943', 'MEG0643', 'MEG0143', 'MEG2142', 'MEG0813', 'MEG2143', 'MEG1323', 'MEG0522', 'MEG1123', 'MEG0423', 'MEG2122', 'MEG2532', 'MEG0812'] def _assert_n_free(raw_sss, lower, upper=None): """Check the DOF.""" upper = lower if upper is None else upper n_free = raw_sss.info['proc_history'][0]['max_info']['sss_info']['nfree'] assert_true(lower <= n_free <= upper, 'nfree fail: %s <= %s <= %s' % (lower, n_free, upper)) def read_crop(fname, lims=(0, None)): """Read and crop.""" return read_raw_fif(fname, allow_maxshield='yes', add_eeg_ref=False).crop(*lims) @slow_test @testing.requires_testing_data def test_movement_compensation(): """Test movement compensation.""" temp_dir = _TempDir() lims = (0, 4, False) raw = read_crop(raw_fname, lims).load_data() head_pos = read_head_pos(pos_fname) # # Movement compensation, no regularization, no tSSS # raw_sss = maxwell_filter(raw, head_pos=head_pos, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, read_crop(sss_movecomp_fname, lims), 4.6, 12.4, chpi_med_tol=58) # IO temp_fname = op.join(temp_dir, 'test_raw_sss.fif') raw_sss.save(temp_fname) raw_sss = read_crop(temp_fname) assert_meg_snr(raw_sss, read_crop(sss_movecomp_fname, lims), 4.6, 12.4, chpi_med_tol=58) # # Movement compensation, regularization, no tSSS # raw_sss = maxwell_filter(raw, head_pos=head_pos, origin=mf_head_origin) assert_meg_snr(raw_sss, read_crop(sss_movecomp_reg_in_fname, lims), 0.5, 1.9, chpi_med_tol=121) # # Movement compensation, regularization, tSSS at the end # raw_nohpi = filter_chpi(raw.copy()) with warnings.catch_warnings(record=True) as w: # untested feature raw_sss_mv = maxwell_filter(raw_nohpi, head_pos=head_pos, st_duration=4., origin=mf_head_origin, st_fixed=False) assert_equal(len(w), 1) assert_true('is untested' in str(w[0].message)) # Neither match is particularly good because our algorithm actually differs assert_meg_snr(raw_sss_mv, read_crop(sss_movecomp_reg_in_st4s_fname, lims), 0.6, 1.3) tSSS_fname = op.join(sss_path, 'test_move_anon_st4s_raw_sss.fif') assert_meg_snr(raw_sss_mv, read_crop(tSSS_fname, lims), 0.6, 1.0, chpi_med_tol=None) assert_meg_snr(read_crop(sss_movecomp_reg_in_st4s_fname), read_crop(tSSS_fname), 0.8, 1.0, chpi_med_tol=None) # # Movement compensation, regularization, tSSS at the beginning # raw_sss_mc = maxwell_filter(raw_nohpi, head_pos=head_pos, st_duration=4., origin=mf_head_origin) assert_meg_snr(raw_sss_mc, read_crop(tSSS_fname, lims), 0.6, 1.0, chpi_med_tol=None) assert_meg_snr(raw_sss_mc, raw_sss_mv, 0.6, 1.4) # some degenerate cases raw_erm = read_crop(erm_fname) assert_raises(ValueError, maxwell_filter, raw_erm, coord_frame='meg', head_pos=head_pos) # can't do ERM file assert_raises(ValueError, maxwell_filter, raw, head_pos=head_pos[:, :9]) # bad shape assert_raises(TypeError, maxwell_filter, raw, head_pos='foo') # bad type assert_raises(ValueError, maxwell_filter, raw, head_pos=head_pos[::-1]) head_pos_bad = head_pos.copy() head_pos_bad[0, 0] = raw.first_samp / raw.info['sfreq'] - 1e-2 assert_raises(ValueError, maxwell_filter, raw, head_pos=head_pos_bad) head_pos_bad = head_pos.copy() head_pos_bad[0, 4] = 1. # off by more than 1 m with warnings.catch_warnings(record=True) as w: maxwell_filter(raw, head_pos=head_pos_bad, bad_condition='ignore') assert_true(any('greater than 1 m' in str(ww.message) for ww in w)) # make sure numerical error doesn't screw it up, though head_pos_bad = head_pos.copy() head_pos_bad[0, 0] = raw.first_samp / raw.info['sfreq'] - 5e-4 raw_sss_tweak = maxwell_filter(raw, head_pos=head_pos_bad, origin=mf_head_origin) assert_meg_snr(raw_sss_tweak, raw_sss, 2., 10., chpi_med_tol=11) @slow_test def test_other_systems(): """Test Maxwell filtering on KIT, BTI, and CTF files.""" # KIT kit_dir = op.join(io_dir, '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') raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path) with warnings.catch_warnings(record=True): # head fit assert_raises(RuntimeError, maxwell_filter, raw_kit) raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True) _assert_n_free(raw_sss, 65, 65) raw_sss_auto = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True, mag_scale='auto') assert_allclose(raw_sss._data, raw_sss_auto._data) # XXX this KIT origin fit is terrible! Eventually we should get a # corrected HSP file with proper coverage with warnings.catch_warnings(record=True): with catch_logging() as log_file: assert_raises(RuntimeError, maxwell_filter, raw_kit, ignore_ref=True, regularize=None) # bad condition raw_sss = maxwell_filter(raw_kit, origin='auto', ignore_ref=True, bad_condition='warning', verbose='warning') log_file = log_file.getvalue() assert_true('badly conditioned' in log_file) assert_true('more than 20 mm from' in log_file) # fits can differ slightly based on scipy version, so be lenient here _assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg # Let's set the origin with warnings.catch_warnings(record=True): with catch_logging() as log_file: raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True, bad_condition='warning', regularize=None, verbose='warning') log_file = log_file.getvalue() assert_true('badly conditioned' in log_file) _assert_n_free(raw_sss, 80) # Now with reg with warnings.catch_warnings(record=True): with catch_logging() as log_file: raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True, verbose=True) log_file = log_file.getvalue() assert_true('badly conditioned' not in log_file) _assert_n_free(raw_sss, 65) # BTi bti_dir = op.join(io_dir, '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') with warnings.catch_warnings(record=True): # weght table raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False) picks = pick_types(raw_bti.info, meg='mag', exclude=()) power = np.sqrt(np.sum(raw_bti[picks][0] ** 2)) raw_sss = maxwell_filter(raw_bti) _assert_n_free(raw_sss, 70) _assert_shielding(raw_sss, power, 0.5) raw_sss_auto = maxwell_filter(raw_bti, mag_scale='auto', verbose=True) _assert_shielding(raw_sss_auto, power, 0.7) # CTF raw_ctf = read_crop(fname_ctf_raw) assert_equal(raw_ctf.compensation_grade, 3) assert_raises(RuntimeError, maxwell_filter, raw_ctf) # compensated raw_ctf.apply_gradient_compensation(0) assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04)) _assert_n_free(raw_sss, 68) _assert_shielding(raw_sss, raw_ctf, 1.8) raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True) _assert_n_free(raw_sss, 70) _assert_shielding(raw_sss, raw_ctf, 12) raw_sss_auto = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True, mag_scale='auto') assert_allclose(raw_sss._data, raw_sss_auto._data) def test_spherical_harmonics(): """Test spherical harmonic functions.""" from scipy.special import sph_harm az, pol = np.meshgrid(np.linspace(0, 2 * np.pi, 30), np.linspace(0, np.pi, 20)) # As of Oct 16, 2015, Anancoda has a bug in scipy due to old compilers (?): # https://github.com/ContinuumIO/anaconda-issues/issues/479 if (PY3 and LooseVersion(scipy.__version__) >= LooseVersion('0.15') and 'Continuum Analytics' in sys.version): raise SkipTest('scipy sph_harm bad in Py3k on Anaconda') # Test our basic spherical harmonics for degree in range(1, int_order): for order in range(0, degree + 1): sph = _sph_harm(order, degree, az, pol) sph_scipy = sph_harm(order, degree, az, pol) assert_allclose(sph, sph_scipy, atol=1e-7) def test_spherical_conversions(): """Test spherical harmonic conversions.""" # Test our real<->complex conversion functions az, pol = np.meshgrid(np.linspace(0, 2 * np.pi, 30), np.linspace(0, np.pi, 20)) for degree in range(1, int_order): for order in range(0, degree + 1): sph = _sph_harm(order, degree, az, pol) # ensure that we satisfy the conjugation property assert_allclose(_sh_negate(sph, order), _sph_harm(-order, degree, az, pol)) # ensure our conversion functions work sph_real_pos = _sh_complex_to_real(sph, order) sph_real_neg = _sh_complex_to_real(sph, -order) sph_2 = _sh_real_to_complex([sph_real_pos, sph_real_neg], order) assert_allclose(sph, sph_2, atol=1e-7) @testing.requires_testing_data def test_multipolar_bases(): """Test multipolar moment basis calculation using sensor information.""" from scipy.io import loadmat # Test our basis calculations info = read_info(raw_fname) coils = _prep_meg_channels(info, accurate=True, elekta_defs=True, do_es=True)[0] # Check against a known benchmark sss_data = loadmat(bases_fname) exp = dict(int_order=int_order, ext_order=ext_order) for origin in ((0, 0, 0.04), (0, 0.02, 0.02)): o_str = ''.join('%d' % (1000 * n) for n in origin) exp.update(origin=origin) S_tot = _sss_basis_basic(exp, coils, method='alternative') # Test our real<->complex conversion functions S_tot_complex = _bases_real_to_complex(S_tot, int_order, ext_order) S_tot_round = _bases_complex_to_real(S_tot_complex, int_order, ext_order) assert_allclose(S_tot, S_tot_round, atol=1e-7) S_tot_mat = np.concatenate([sss_data['Sin' + o_str], sss_data['Sout' + o_str]], axis=1) S_tot_mat_real = _bases_complex_to_real(S_tot_mat, int_order, ext_order) S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real, int_order, ext_order) assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7) assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8) assert_allclose(S_tot, S_tot_mat_real, rtol=1e-4, atol=1e-8) # Now normalize our columns S_tot /= np.sqrt(np.sum(S_tot * S_tot, axis=0))[np.newaxis] S_tot_complex /= np.sqrt(np.sum( (S_tot_complex * S_tot_complex.conj()).real, axis=0))[np.newaxis] # Check against a known benchmark S_tot_mat = np.concatenate([sss_data['SNin' + o_str], sss_data['SNout' + o_str]], axis=1) # Check this roundtrip S_tot_mat_real = _bases_complex_to_real(S_tot_mat, int_order, ext_order) S_tot_mat_round = _bases_real_to_complex(S_tot_mat_real, int_order, ext_order) assert_allclose(S_tot_mat, S_tot_mat_round, atol=1e-7) assert_allclose(S_tot_complex, S_tot_mat, rtol=1e-4, atol=1e-8) # Now test our optimized version S_tot = _sss_basis_basic(exp, coils) S_tot_fast = _trans_sss_basis( exp, all_coils=_prep_mf_coils(info), trans=info['dev_head_t']) # there are some sign differences for columns (order/degrees) # in here, likely due to Condon-Shortley. Here we use a # Magnetometer channel to figure out the flips because the # gradiometer channels have effectively zero values for first three # external components (i.e., S_tot[grad_picks, 80:83]) flips = (np.sign(S_tot_fast[2]) != np.sign(S_tot[2])) flips = 1 - 2 * flips assert_allclose(S_tot, S_tot_fast * flips, atol=1e-16) @testing.requires_testing_data def test_basic(): """Test Maxwell filter basic version.""" # Load testing data (raw, SSS std origin, SSS non-standard origin) raw = read_crop(raw_fname, (0., 1.)) raw_err = read_crop(raw_fname).apply_proj() raw_erm = read_crop(erm_fname) assert_raises(RuntimeError, maxwell_filter, raw_err) assert_raises(TypeError, maxwell_filter, 1.) # not a raw assert_raises(ValueError, maxwell_filter, raw, int_order=20) # too many n_int_bases = int_order ** 2 + 2 * int_order n_ext_bases = ext_order ** 2 + 2 * ext_order nbases = n_int_bases + n_ext_bases # Check number of bases computed correctly assert_equal(_get_n_moments([int_order, ext_order]).sum(), nbases) # Test SSS computation at the standard head origin assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_equal(len(raw_sss.info['projs']), 1) # avg EEG assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference') assert_meg_snr(raw_sss, read_crop(sss_std_fname), 200., 1000.) py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal'] assert_equal(len(py_cal), 0) py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc'] assert_equal(len(py_ctc), 0) py_st = raw_sss.info['proc_history'][0]['max_info']['max_st'] assert_equal(len(py_st), 0) assert_raises(RuntimeError, maxwell_filter, raw_sss) # Test SSS computation at non-standard head origin raw_sss = maxwell_filter(raw, origin=[0., 0.02, 0.02], regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, read_crop(sss_nonstd_fname), 250., 700.) # Test SSS computation at device origin sss_erm_std = read_crop(sss_erm_std_fname) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', origin=mf_meg_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, sss_erm_std, 100., 900.) for key in ('job', 'frame'): vals = [x.info['proc_history'][0]['max_info']['sss_info'][key] for x in [raw_sss, sss_erm_std]] assert_equal(vals[0], vals[1]) # Check against SSS functions from proc_history sss_info = raw_sss.info['proc_history'][0]['max_info'] assert_equal(_get_n_moments(int_order), proc_history._get_sss_rank(sss_info)) # Degenerate cases assert_raises(ValueError, maxwell_filter, raw, coord_frame='foo') assert_raises(ValueError, maxwell_filter, raw, origin='foo') assert_raises(ValueError, maxwell_filter, raw, origin=[0] * 4) assert_raises(ValueError, maxwell_filter, raw, mag_scale='foo') @testing.requires_testing_data def test_maxwell_filter_additional(): """Test processing of Maxwell filtered data.""" # TODO: Future tests integrate with mne/io/tests/test_proc_history # Load testing data (raw, SSS std origin, SSS non-standard origin) data_path = op.join(testing.data_path(download=False)) file_name = 'test_move_anon' raw_fname = op.join(data_path, 'SSS', file_name + '_raw.fif') # Use 2.0 seconds of data to get stable cov. estimate raw = read_crop(raw_fname, (0., 2.)) # Get MEG channels, compute Maxwell filtered data raw.load_data() raw.pick_types(meg=True, eeg=False) int_order = 8 raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None, bad_condition='ignore') # Test io on processed data tempdir = _TempDir() test_outname = op.join(tempdir, 'test_raw_sss.fif') raw_sss.save(test_outname) raw_sss_loaded = read_crop(test_outname).load_data() # Some numerical imprecision since save uses 'single' fmt assert_allclose(raw_sss_loaded[:][0], raw_sss[:][0], rtol=1e-6, atol=1e-20) # Test rank of covariance matrices for raw and SSS processed data cov_raw = compute_raw_covariance(raw) cov_sss = compute_raw_covariance(raw_sss) scalings = None cov_raw_rank = _estimate_rank_meeg_cov(cov_raw['data'], raw.info, scalings) cov_sss_rank = _estimate_rank_meeg_cov(cov_sss['data'], raw_sss.info, scalings) assert_equal(cov_raw_rank, raw.info['nchan']) assert_equal(cov_sss_rank, _get_n_moments(int_order)) @slow_test @testing.requires_testing_data def test_bads_reconstruction(): """Test Maxwell filter reconstruction of bad channels.""" raw = read_crop(raw_fname, (0., 1.)) raw.info['bads'] = bads raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, read_crop(sss_bad_recon_fname), 300.) @requires_svd_convergence @testing.requires_testing_data def test_spatiotemporal_maxwell(): """Test Maxwell filter (tSSS) spatiotemporal processing.""" # Load raw testing data raw = read_crop(raw_fname) # Test that window is less than length of data assert_raises(ValueError, maxwell_filter, raw, st_duration=1000.) # Check both 4 and 10 seconds because Elekta handles them differently # This is to ensure that std/non-std tSSS windows are correctly handled st_durations = [4., 10.] tols = [325., 200.] for st_duration, tol in zip(st_durations, tols): # Load tSSS data depending on st_duration and get data tSSS_fname = op.join(sss_path, 'test_move_anon_st%0ds_raw_sss.fif' % st_duration) tsss_bench = read_crop(tSSS_fname) # Because Elekta's tSSS sometimes(!) lumps the tail window of data # onto the previous buffer if it's shorter than st_duration, we have to # crop the data here to compensate for Elekta's tSSS behavior. if st_duration == 10.: tsss_bench.crop(0, st_duration, copy=False) # Test sss computation at the standard head origin. Same cropping issue # as mentioned above. if st_duration == 10.: raw_tsss = maxwell_filter(raw.crop(0, st_duration), origin=mf_head_origin, st_duration=st_duration, regularize=None, bad_condition='ignore') else: raw_tsss = maxwell_filter(raw, st_duration=st_duration, origin=mf_head_origin, regularize=None, bad_condition='ignore', verbose=True) raw_tsss_2 = maxwell_filter(raw, st_duration=st_duration, origin=mf_head_origin, regularize=None, bad_condition='ignore', st_fixed=False, verbose=True) assert_meg_snr(raw_tsss, raw_tsss_2, 100., 1000.) assert_equal(raw_tsss.estimate_rank(), 140) assert_equal(raw_tsss_2.estimate_rank(), 140) assert_meg_snr(raw_tsss, tsss_bench, tol) py_st = raw_tsss.info['proc_history'][0]['max_info']['max_st'] assert_true(len(py_st) > 0) assert_equal(py_st['buflen'], st_duration) assert_equal(py_st['subspcorr'], 0.98) # Degenerate cases assert_raises(ValueError, maxwell_filter, raw, st_duration=10., st_correlation=0.) @requires_svd_convergence @testing.requires_testing_data def test_spatiotemporal_only(): """Test tSSS-only processing.""" # Load raw testing data raw = read_crop(raw_fname, (0, 2)).load_data() picks = pick_types(raw.info, meg='mag', exclude=()) power = np.sqrt(np.sum(raw[picks][0] ** 2)) # basics raw_tsss = maxwell_filter(raw, st_duration=1., st_only=True) assert_equal(raw_tsss.estimate_rank(), 366) _assert_shielding(raw_tsss, power, 10) # temporal proj will actually reduce spatial DOF with small windows! raw_tsss = maxwell_filter(raw, st_duration=0.1, st_only=True) assert_true(raw_tsss.estimate_rank() < 350) _assert_shielding(raw_tsss, power, 40) # with movement head_pos = read_head_pos(pos_fname) raw_tsss = maxwell_filter(raw, st_duration=1., st_only=True, head_pos=head_pos) assert_equal(raw_tsss.estimate_rank(), 366) _assert_shielding(raw_tsss, power, 12) with warnings.catch_warnings(record=True): # st_fixed False raw_tsss = maxwell_filter(raw, st_duration=1., st_only=True, head_pos=head_pos, st_fixed=False) assert_equal(raw_tsss.estimate_rank(), 366) _assert_shielding(raw_tsss, power, 12) # should do nothing raw_tsss = maxwell_filter(raw, st_duration=1., st_correlation=1., st_only=True) assert_allclose(raw[:][0], raw_tsss[:][0]) # degenerate assert_raises(ValueError, maxwell_filter, raw, st_only=True) # no ST # two-step process equivalent to single-step process raw_tsss = maxwell_filter(raw, st_duration=1., st_only=True) raw_tsss = maxwell_filter(raw_tsss) raw_tsss_2 = maxwell_filter(raw, st_duration=1.) assert_meg_snr(raw_tsss, raw_tsss_2, 1e5) # now also with head movement, and a bad MEG channel assert_equal(len(raw.info['bads']), 0) raw.info['bads'] = ['EEG001', 'MEG2623'] raw_tsss = maxwell_filter(raw, st_duration=1., st_only=True, head_pos=head_pos) assert_equal(raw.info['bads'], ['EEG001', 'MEG2623']) assert_equal(raw_tsss.info['bads'], ['EEG001', 'MEG2623']) # don't reset raw_tsss = maxwell_filter(raw_tsss, head_pos=head_pos) assert_equal(raw_tsss.info['bads'], ['EEG001']) # do reset MEG bads raw_tsss_2 = maxwell_filter(raw, st_duration=1., head_pos=head_pos) assert_equal(raw_tsss_2.info['bads'], ['EEG001']) assert_meg_snr(raw_tsss, raw_tsss_2, 1e5) @testing.requires_testing_data def test_fine_calibration(): """Test Maxwell filter fine calibration.""" # Load testing data (raw, SSS std origin, SSS non-standard origin) raw = read_crop(raw_fname, (0., 1.)) sss_fine_cal = read_crop(sss_fine_cal_fname) # Test 1D SSS fine calibration raw_sss = maxwell_filter(raw, calibration=fine_cal_fname, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, sss_fine_cal, 82, 611) py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal'] assert_true(py_cal is not None) assert_true(len(py_cal) > 0) mf_cal = sss_fine_cal.info['proc_history'][0]['max_info']['sss_cal'] # we identify these differently mf_cal['cal_chans'][mf_cal['cal_chans'][:, 1] == 3022, 1] = 3024 assert_allclose(py_cal['cal_chans'], mf_cal['cal_chans']) assert_allclose(py_cal['cal_corrs'], mf_cal['cal_corrs'], rtol=1e-3, atol=1e-3) # Test 3D SSS fine calibration (no equivalent func in MaxFilter yet!) # very low SNR as proc differs, eventually we should add a better test raw_sss_3D = maxwell_filter(raw, calibration=fine_cal_fname_3d, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss_3D, sss_fine_cal, 1.0, 6.) raw_ctf = read_crop(fname_ctf_raw).apply_gradient_compensation(0) assert_raises(RuntimeError, maxwell_filter, raw_ctf, origin=(0., 0., 0.04), calibration=fine_cal_fname) @slow_test @testing.requires_testing_data def test_regularization(): """Test Maxwell filter regularization.""" # Load testing data (raw, SSS std origin, SSS non-standard origin) min_tols = (100., 2.6, 1.0) med_tols = (1000., 21.4, 3.7) origins = ((0., 0., 0.04), (0.,) * 3, (0., 0.02, 0.02)) coord_frames = ('head', 'meg', 'head') raw_fnames = (raw_fname, erm_fname, sample_fname) sss_fnames = (sss_reg_in_fname, sss_erm_reg_in_fname, sss_samp_reg_in_fname) comp_tols = [0, 1, 4] for ii, rf in enumerate(raw_fnames): raw = read_crop(rf, (0., 1.)) sss_reg_in = read_crop(sss_fnames[ii]) # Test "in" regularization raw_sss = maxwell_filter(raw, coord_frame=coord_frames[ii], origin=origins[ii]) assert_meg_snr(raw_sss, sss_reg_in, min_tols[ii], med_tols[ii], msg=rf) # check components match _check_reg_match(raw_sss, sss_reg_in, comp_tols[ii]) def _check_reg_match(sss_py, sss_mf, comp_tol): """Helper to check regularization.""" info_py = sss_py.info['proc_history'][0]['max_info']['sss_info'] assert_true(info_py is not None) assert_true(len(info_py) > 0) info_mf = sss_mf.info['proc_history'][0]['max_info']['sss_info'] n_in = None for inf in (info_py, info_mf): if n_in is None: n_in = _get_n_moments(inf['in_order']) else: assert_equal(n_in, _get_n_moments(inf['in_order'])) assert_equal(inf['components'][:n_in].sum(), inf['nfree']) assert_allclose(info_py['nfree'], info_mf['nfree'], atol=comp_tol, err_msg=sss_py._filenames[0]) @testing.requires_testing_data def test_cross_talk(): """Test Maxwell filter cross-talk cancellation.""" raw = read_crop(raw_fname, (0., 1.)) raw.info['bads'] = bads sss_ctc = read_crop(sss_ctc_fname) raw_sss = maxwell_filter(raw, cross_talk=ctc_fname, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, sss_ctc, 275.) py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc'] assert_true(len(py_ctc) > 0) assert_raises(ValueError, maxwell_filter, raw, cross_talk=raw) assert_raises(ValueError, maxwell_filter, raw, cross_talk=raw_fname) mf_ctc = sss_ctc.info['proc_history'][0]['max_info']['sss_ctc'] del mf_ctc['block_id'] # we don't write this assert_equal(object_diff(py_ctc, mf_ctc), '') raw_ctf = read_crop(fname_ctf_raw).apply_gradient_compensation(0) assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04)) _assert_n_free(raw_sss, 68) raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True) _assert_n_free(raw_sss, 70) raw_missing = raw.copy().crop(0, 0.1).load_data().pick_channels( [raw.ch_names[pi] for pi in pick_types(raw.info, meg=True, exclude=())[3:]]) with warnings.catch_warnings(record=True) as w: maxwell_filter(raw_missing, cross_talk=ctc_fname) assert_equal(len(w), 1) assert_true('Not all cross-talk channels in raw' in str(w[0].message)) # MEG channels not in cross-talk assert_raises(RuntimeError, maxwell_filter, raw_ctf, origin=(0., 0., 0.04), cross_talk=ctc_fname) @testing.requires_testing_data def test_head_translation(): """Test Maxwell filter head translation.""" raw = read_crop(raw_fname, (0., 1.)) # First try with an unchanged destination raw_sss = maxwell_filter(raw, destination=raw_fname, origin=mf_head_origin, regularize=None, bad_condition='ignore') assert_meg_snr(raw_sss, read_crop(sss_std_fname, (0., 1.)), 200.) # Now with default with warnings.catch_warnings(record=True): with catch_logging() as log: raw_sss = maxwell_filter(raw, destination=mf_head_origin, origin=mf_head_origin, regularize=None, bad_condition='ignore', verbose='warning') assert_true('over 25 mm' in log.getvalue()) assert_meg_snr(raw_sss, read_crop(sss_trans_default_fname), 125.) destination = np.eye(4) destination[2, 3] = 0.04 assert_allclose(raw_sss.info['dev_head_t']['trans'], destination) # Now to sample's head pos with warnings.catch_warnings(record=True): with catch_logging() as log: raw_sss = maxwell_filter(raw, destination=sample_fname, origin=mf_head_origin, regularize=None, bad_condition='ignore', verbose='warning') assert_true('= 25.6 mm' in log.getvalue()) assert_meg_snr(raw_sss, read_crop(sss_trans_sample_fname), 350.) assert_allclose(raw_sss.info['dev_head_t']['trans'], read_info(sample_fname)['dev_head_t']['trans']) # Degenerate cases assert_raises(RuntimeError, maxwell_filter, raw, destination=mf_head_origin, coord_frame='meg') assert_raises(ValueError, maxwell_filter, raw, destination=[0.] * 4) # TODO: Eventually add simulation tests mirroring Taulu's original paper # that calculates the localization error: # http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1495874 def _assert_shielding(raw_sss, erm_power, shielding_factor, meg='mag'): """Helper to assert a minimum shielding factor using empty-room power.""" picks = pick_types(raw_sss.info, meg=meg, ref_meg=False) if isinstance(erm_power, _BaseRaw): picks_erm = pick_types(raw_sss.info, meg=meg, ref_meg=False) assert_allclose(picks, picks_erm) erm_power = np.sqrt((erm_power[picks_erm][0] ** 2).sum()) sss_power = raw_sss[picks][0].ravel() sss_power = np.sqrt(np.sum(sss_power * sss_power)) factor = erm_power / sss_power assert_true(factor >= shielding_factor, 'Shielding factor %0.3f < %0.3f' % (factor, shielding_factor)) @buggy_mkl_svd @slow_test @requires_svd_convergence @testing.requires_testing_data def test_shielding_factor(): """Test Maxwell filter shielding factor using empty room.""" raw_erm = read_crop(erm_fname).load_data() picks = pick_types(raw_erm.info, meg='mag') erm_power = raw_erm[picks][0] erm_power = np.sqrt(np.sum(erm_power * erm_power)) erm_power_grad = raw_erm[pick_types(raw_erm.info, meg='grad')][0] erm_power_grad = np.sqrt(np.sum(erm_power * erm_power)) # Vanilla SSS (second value would be for meg=True instead of meg='mag') _assert_shielding(read_crop(sss_erm_std_fname), erm_power, 10) # 1.5) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None) _assert_shielding(raw_sss, erm_power, 12) # 1.5) _assert_shielding(raw_sss, erm_power_grad, 0.45, 'grad') # 1.5) # Using different mag_scale values raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, mag_scale='auto') _assert_shielding(raw_sss, erm_power, 12) _assert_shielding(raw_sss, erm_power_grad, 0.48, 'grad') raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, mag_scale=1.) # not a good choice _assert_shielding(raw_sss, erm_power, 7.3) _assert_shielding(raw_sss, erm_power_grad, 0.2, 'grad') raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, mag_scale=1000., bad_condition='ignore') _assert_shielding(raw_sss, erm_power, 4.0) _assert_shielding(raw_sss, erm_power_grad, 0.1, 'grad') # Fine cal _assert_shielding(read_crop(sss_erm_fine_cal_fname), erm_power, 12) # 2.0) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, origin=mf_meg_origin, calibration=fine_cal_fname) _assert_shielding(raw_sss, erm_power, 12) # 2.0) # Crosstalk _assert_shielding(read_crop(sss_erm_ctc_fname), erm_power, 12) # 2.1) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, origin=mf_meg_origin, cross_talk=ctc_fname) _assert_shielding(raw_sss, erm_power, 12) # 2.1) # Fine cal + Crosstalk raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, calibration=fine_cal_fname, origin=mf_meg_origin, cross_talk=ctc_fname) _assert_shielding(raw_sss, erm_power, 13) # 2.2) # tSSS _assert_shielding(read_crop(sss_erm_st_fname), erm_power, 37) # 5.8) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, origin=mf_meg_origin, st_duration=1.) _assert_shielding(raw_sss, erm_power, 37) # 5.8) # Crosstalk + tSSS raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, cross_talk=ctc_fname, origin=mf_meg_origin, st_duration=1.) _assert_shielding(raw_sss, erm_power, 38) # 5.91) # Fine cal + tSSS raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, calibration=fine_cal_fname, origin=mf_meg_origin, st_duration=1.) _assert_shielding(raw_sss, erm_power, 38) # 5.98) # Fine cal + Crosstalk + tSSS _assert_shielding(read_crop(sss_erm_st1FineCalCrossTalk_fname), erm_power, 39) # 6.07) raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None, calibration=fine_cal_fname, origin=mf_meg_origin, cross_talk=ctc_fname, st_duration=1.) _assert_shielding(raw_sss, erm_power, 39) # 6.05) # Fine cal + Crosstalk + tSSS + Reg-in _assert_shielding(read_crop(sss_erm_st1FineCalCrossTalkRegIn_fname), erm_power, 57) # 6.97) raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname, cross_talk=ctc_fname, st_duration=1., origin=mf_meg_origin, coord_frame='meg', regularize='in') _assert_shielding(raw_sss, erm_power, 53) # 6.64) raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname, cross_talk=ctc_fname, st_duration=1., coord_frame='meg', regularize='in') _assert_shielding(raw_sss, erm_power, 58) # 7.0) _assert_shielding(raw_sss, erm_power_grad, 1.6, 'grad') raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname, cross_talk=ctc_fname, st_duration=1., coord_frame='meg', regularize='in', mag_scale='auto') _assert_shielding(raw_sss, erm_power, 51) _assert_shielding(raw_sss, erm_power_grad, 1.5, 'grad') raw_sss = maxwell_filter(raw_erm, calibration=fine_cal_fname_3d, cross_talk=ctc_fname, st_duration=1., coord_frame='meg', regularize='in') # Our 3D cal has worse defaults for this ERM than the 1D file _assert_shielding(raw_sss, erm_power, 54) # Show it by rewriting the 3D as 1D and testing it temp_dir = _TempDir() temp_fname = op.join(temp_dir, 'test_cal.dat') with open(fine_cal_fname_3d, 'r') as fid: with open(temp_fname, 'w') as fid_out: for line in fid: fid_out.write(' '.join(line.strip().split(' ')[:14]) + '\n') raw_sss = maxwell_filter(raw_erm, calibration=temp_fname, cross_talk=ctc_fname, st_duration=1., coord_frame='meg', regularize='in') # Our 3D cal has worse defaults for this ERM than the 1D file _assert_shielding(raw_sss, erm_power, 44) @slow_test @requires_svd_convergence @testing.requires_testing_data def test_all(): """Test maxwell filter using all options.""" raw_fnames = (raw_fname, raw_fname, erm_fname, sample_fname) sss_fnames = (sss_st1FineCalCrossTalkRegIn_fname, sss_st1FineCalCrossTalkRegInTransSample_fname, sss_erm_st1FineCalCrossTalkRegIn_fname, sss_samp_fname) fine_cals = (fine_cal_fname, fine_cal_fname, fine_cal_fname, fine_cal_mgh_fname) coord_frames = ('head', 'head', 'meg', 'head') ctcs = (ctc_fname, ctc_fname, ctc_fname, ctc_mgh_fname) mins = (3.5, 3.5, 1.2, 0.9) meds = (10.8, 10.4, 3.2, 6.) st_durs = (1., 1., 1., None) destinations = (None, sample_fname, None, None) origins = (mf_head_origin, mf_head_origin, mf_meg_origin, mf_head_origin) for ii, rf in enumerate(raw_fnames): raw = read_crop(rf, (0., 1.)) with warnings.catch_warnings(record=True): # head fit off-center sss_py = maxwell_filter( raw, calibration=fine_cals[ii], cross_talk=ctcs[ii], st_duration=st_durs[ii], coord_frame=coord_frames[ii], destination=destinations[ii], origin=origins[ii]) sss_mf = read_crop(sss_fnames[ii]) assert_meg_snr(sss_py, sss_mf, mins[ii], meds[ii], msg=rf) @slow_test @requires_svd_convergence @testing.requires_testing_data def test_triux(): """Test TRIUX system support.""" raw = read_crop(tri_fname, (0, 0.999)) raw.fix_mag_coil_types() # standard sss_py = maxwell_filter(raw, coord_frame='meg', regularize=None) assert_meg_snr(sss_py, read_crop(tri_sss_fname), 37, 700) # cross-talk sss_py = maxwell_filter(raw, coord_frame='meg', regularize=None, cross_talk=tri_ctc_fname) assert_meg_snr(sss_py, read_crop(tri_sss_ctc_fname), 35, 700) # fine cal sss_py = maxwell_filter(raw, coord_frame='meg', regularize=None, calibration=tri_cal_fname) assert_meg_snr(sss_py, read_crop(tri_sss_cal_fname), 31, 360) # ctc+cal sss_py = maxwell_filter(raw, coord_frame='meg', regularize=None, calibration=tri_cal_fname, cross_talk=tri_ctc_fname) assert_meg_snr(sss_py, read_crop(tri_sss_ctc_cal_fname), 31, 350) # regularization sss_py = maxwell_filter(raw, coord_frame='meg', regularize='in') sss_mf = read_crop(tri_sss_reg_fname) assert_meg_snr(sss_py, sss_mf, 0.6, 9) _check_reg_match(sss_py, sss_mf, 1) # all three sss_py = maxwell_filter(raw, coord_frame='meg', regularize='in', calibration=tri_cal_fname, cross_talk=tri_ctc_fname) sss_mf = read_crop(tri_sss_ctc_cal_reg_in_fname) assert_meg_snr(sss_py, sss_mf, 0.6, 9) _check_reg_match(sss_py, sss_mf, 1) # tSSS raw = read_crop(tri_fname).fix_mag_coil_types() sss_py = maxwell_filter(raw, coord_frame='meg', regularize=None, st_duration=4., verbose=True) assert_meg_snr(sss_py, read_crop(tri_sss_st4_fname), 700., 1600) run_tests_if_main()