import os.path as op import itertools as itt from numpy.testing import assert_array_equal import numpy as np import pytest from mne import (read_evokeds, read_cov, compute_raw_covariance, pick_types, pick_info) from mne.cov import prepare_noise_cov from mne.datasets import testing from mne.io import read_raw_fif from mne.io.pick import _picks_by_type, _get_channel_types from mne.io.proj import _has_eeg_average_ref_proj from mne.proj import compute_proj_raw from mne.rank import (estimate_rank, compute_rank, _get_rank_sss, _compute_rank_int, _estimate_rank_raw) base_dir = op.join(op.dirname(__file__), '..', 'io', 'tests', 'data') cov_fname = op.join(base_dir, 'test-cov.fif') raw_fname = op.join(base_dir, 'test_raw.fif') ave_fname = op.join(base_dir, 'test-ave.fif') ctf_fname = op.join(base_dir, 'test_ctf_raw.fif') hp_fif_fname = op.join(base_dir, 'test_chpi_raw_sss.fif') testing_path = testing.data_path(download=False) data_dir = op.join(testing_path, 'MEG', 'sample') mf_fif_fname = op.join(testing_path, 'SSS', 'test_move_anon_raw_sss.fif') def test_estimate_rank(): """Test rank estimation.""" data = np.eye(10) assert_array_equal(estimate_rank(data, return_singular=True)[1], np.ones(10)) data[0, 0] = 0 assert estimate_rank(data) == 9 pytest.raises(ValueError, estimate_rank, data, 'foo') @pytest.mark.slowtest @pytest.mark.parametrize( 'fname, ref_meg', ((raw_fname, False), (hp_fif_fname, False), (ctf_fname, False), (ctf_fname, True))) @pytest.mark.parametrize( 'scalings', ('norm', dict(mag=1e11, grad=1e9, eeg=1e5))) @pytest.mark.parametrize('tol_kind, tol', [ ('absolute', 1e-4), ('relative', 1e-6), ]) def test_raw_rank_estimation(fname, ref_meg, scalings, tol_kind, tol): """Test raw rank estimation.""" if ref_meg and scalings != 'norm': # Adjust for CTF data (scale factors are quite different) if tol_kind == 'relative': scalings = dict(mag=1.) else: scalings = dict(mag=1e31) raw = read_raw_fif(fname) raw.crop(0, min(4., raw.times[-1])).load_data() out = _picks_by_type(raw.info, ref_meg=ref_meg, meg_combined=True) has_eeg = 'eeg' in raw if has_eeg: (_, picks_meg), (_, picks_eeg) = out else: (_, picks_meg), = out picks_eeg = [] n_meg = len(picks_meg) n_eeg = len(picks_eeg) if len(raw.info['proc_history']) == 0: expected_rank = n_meg + n_eeg else: expected_rank = _get_rank_sss(raw.info) + n_eeg got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg, tol=tol, tol_kind=tol_kind) assert got_rank == expected_rank if 'sss' in fname: raw.add_proj(compute_proj_raw(raw)) raw.apply_proj() n_proj = len(raw.info['projs']) want_rank = expected_rank - (0 if 'sss' in fname else n_proj) got_rank = _estimate_rank_raw(raw, scalings=scalings, with_ref_meg=ref_meg, tol=tol, tol_kind=tol_kind) assert got_rank == want_rank @pytest.mark.slowtest @pytest.mark.parametrize('meg', ('separate', 'combined')) @pytest.mark.parametrize('rank_method, proj', [('info', True), ('info', False), (None, True), (None, False)]) def test_cov_rank_estimation(rank_method, proj, meg): """Test cov rank estimation.""" # Test that our rank estimation works properly on a simple case evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0), proj=False) cov = read_cov(cov_fname) ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and ch.startswith('EEG')] cov = prepare_noise_cov(cov, evoked.info, ch_names, None) assert cov['eig'][0] <= 1e-25 # avg projector should set this to zero assert (cov['eig'][1:] > 1e-16).all() # all else should be > 0 # Now do some more comprehensive tests raw_sample = read_raw_fif(raw_fname) assert not _has_eeg_average_ref_proj(raw_sample.info) raw_sss = read_raw_fif(hp_fif_fname) assert not _has_eeg_average_ref_proj(raw_sss.info) raw_sss.add_proj(compute_proj_raw(raw_sss, meg=meg)) cov_sample = compute_raw_covariance(raw_sample) cov_sample_proj = compute_raw_covariance(raw_sample.copy().apply_proj()) cov_sss = compute_raw_covariance(raw_sss) cov_sss_proj = compute_raw_covariance(raw_sss.copy().apply_proj()) picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True) picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True) info_sample = pick_info(raw_sample.info, picks_all_sample) picks_stack_sample = [('eeg', pick_types(info_sample, meg=False, eeg=True))] picks_stack_sample += [('meg', pick_types(info_sample, meg=True))] picks_stack_sample += [('all', pick_types(info_sample, meg=True, eeg=True))] info_sss = pick_info(raw_sss.info, picks_all_sss) picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))] picks_stack_somato += [('meg', pick_types(info_sss, meg=True))] picks_stack_somato += [('all', pick_types(info_sss, meg=True, eeg=True))] iter_tests = list(itt.product( [(cov_sample, picks_stack_sample, info_sample), (cov_sample_proj, picks_stack_sample, info_sample), (cov_sss, picks_stack_somato, info_sss), (cov_sss_proj, picks_stack_somato, info_sss)], # sss [dict(mag=1e15, grad=1e13, eeg=1e6)], )) for (cov, picks_list, iter_info), scalings in iter_tests: rank = compute_rank(cov, rank_method, scalings, iter_info, proj=proj) rank['all'] = sum(rank.values()) for ch_type, picks in picks_list: this_info = pick_info(iter_info, picks) # compute subset of projs, active and inactive n_projs_applied = sum(proj['active'] and len(set(proj['data']['col_names']) & set(this_info['ch_names'])) > 0 for proj in cov['projs']) n_projs_info = sum(len(set(proj['data']['col_names']) & set(this_info['ch_names'])) > 0 for proj in this_info['projs']) # count channel types ch_types = _get_channel_types(this_info) n_eeg, n_mag, n_grad = [ch_types.count(k) for k in ['eeg', 'mag', 'grad']] n_meg = n_mag + n_grad has_sss = (n_meg > 0 and len(this_info['proc_history']) > 0) if has_sss: n_meg = _get_rank_sss(this_info) expected_rank = n_meg + n_eeg if rank_method is None: if meg == 'combined' or not has_sss: if proj: expected_rank -= n_projs_info else: expected_rank -= n_projs_applied else: # XXX for now it just uses the total count assert rank_method == 'info' if proj: expected_rank -= n_projs_info assert rank[ch_type] == expected_rank @pytest.mark.slowtest # ~3 sec apiece on Azure means overall it's slow @testing.requires_testing_data @pytest.mark.parametrize('fname, rank_orig', ((hp_fif_fname, 120), (mf_fif_fname, 67))) @pytest.mark.parametrize('n_proj, meg', ((0, 'combined'), (10, 'combined'), (10, 'separate'))) @pytest.mark.parametrize('tol_kind, tol', [ ('absolute', 'float32'), ('relative', 'float32'), ('relative', 1e-5), ]) def test_maxfilter_get_rank(n_proj, fname, rank_orig, meg, tol_kind, tol): """Test maxfilter rank lookup.""" raw = read_raw_fif(fname).crop(0, 5).load_data().pick_types(meg=True) assert raw.info['projs'] == [] mf = raw.info['proc_history'][0]['max_info'] assert mf['sss_info']['nfree'] == rank_orig assert compute_rank(raw, 'info')['meg'] == rank_orig assert compute_rank(raw.copy().pick('grad'), 'info')['grad'] == rank_orig assert compute_rank(raw.copy().pick('mag'), 'info')['mag'] == rank_orig mult = 1 + (meg == 'separate') rank = rank_orig - mult * n_proj if n_proj > 0: # Let's do some projection raw.add_proj(compute_proj_raw(raw, n_mag=n_proj, n_grad=n_proj, meg=meg, verbose=True)) raw.apply_proj() data_orig = raw[:][0] # degenerate cases with pytest.raises(ValueError, match='tol must be'): _estimate_rank_raw(raw, tol='foo') with pytest.raises(TypeError, match='must be a string or a'): _estimate_rank_raw(raw, tol=None) allowed_rank = [rank_orig if meg == 'separate' else rank] if fname == mf_fif_fname: # Here we permit a -1 because for mf_fif_fname we miss by 1, which is # probably acceptable. If we use the entire duration instead of 5 sec # this problem goes away, but the test is much slower. allowed_rank.append(allowed_rank[0] - 1) # multiple ways of hopefully getting the same thing # default tol=1e-4, scalings='norm' rank_new = _estimate_rank_raw(raw, tol_kind=tol_kind) assert rank_new in allowed_rank rank_new = _estimate_rank_raw( raw, tol=tol, tol_kind=tol_kind) if fname == mf_fif_fname and tol_kind == 'relative' and tol != 'auto': pass # does not play nicely with row norms of _estimate_rank_raw else: assert rank_new in allowed_rank rank_new = _estimate_rank_raw( raw, scalings=dict(), tol=tol, tol_kind=tol_kind) assert rank_new in allowed_rank scalings = dict(grad=1e13, mag=1e15) rank_new = _compute_rank_int( raw, None, scalings=scalings, tol=tol, tol_kind=tol_kind, verbose='debug') assert rank_new in allowed_rank # XXX default scalings mis-estimate sometimes :( if fname == hp_fif_fname: allowed_rank.append(allowed_rank[0] - 2) rank_new = _compute_rank_int( raw, None, tol=tol, tol_kind=tol_kind, verbose='debug') assert rank_new in allowed_rank del allowed_rank rank_new = _compute_rank_int(raw, 'info') assert rank_new == rank assert_array_equal(raw[:][0], data_orig) def test_explicit_bads_pick(): """Test when bads channels are explicitly passed + default picks=None.""" raw = read_raw_fif(raw_fname).crop(0, 5).load_data() raw.pick_types(eeg=True, meg=True, ref_meg=True) # Covariance # Default picks=None raw.info['bads'] = list() noise_cov_1 = compute_raw_covariance(raw, picks=None) rank = compute_rank(noise_cov_1, info=raw.info) assert rank == dict(meg=303, eeg=60) assert raw.info['bads'] == [] raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] noise_cov = compute_raw_covariance(raw, picks=None) rank = compute_rank(noise_cov, info=raw.info) assert rank == dict(meg=302, eeg=57) assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] # Explicit picks picks = pick_types(raw.info, meg=True, eeg=True, exclude=[]) noise_cov_2 = compute_raw_covariance(raw, picks=picks) rank = compute_rank(noise_cov_2, info=raw.info) assert rank == dict(meg=303, eeg=60) assert raw.info['bads'] == ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] assert_array_equal(noise_cov_1['data'], noise_cov_2['data']) assert noise_cov_1['names'] == noise_cov_2['names'] # Raw raw.info['bads'] = list() rank = compute_rank(raw) assert rank == dict(meg=303, eeg=60) raw.info['bads'] = ['EEG 002', 'EEG 012', 'EEG 015', 'MEG 0122'] rank = compute_rank(raw) assert rank == dict(meg=302, eeg=57)