Python io.Raw类代码示例

def test_calculate_chpi_positions():
    """Test calculation of cHPI positions
    """
    trans, rot, t = head_pos_to_trans_rot_t(read_head_pos(pos_fname))
    with warnings.catch_warnings(record=True):
        raw = Raw(chpi_fif_fname, allow_maxshield=True, preload=True)
    t -= raw.first_samp / raw.info['sfreq']
    quats = _calculate_chpi_positions(raw, verbose='debug')
    trans_est, rot_est, t_est = head_pos_to_trans_rot_t(quats)
    _compare_positions((trans, rot, t), (trans_est, rot_est, t_est), 0.003)

    # degenerate conditions
    raw_no_chpi = Raw(test_fif_fname)
    assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
    raw_bad = raw.copy()
    for d in raw_bad.info['dig']:
        if d['kind'] == FIFF.FIFFV_POINT_HPI:
            d['coord_frame'] = 999
            break
    assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
    raw_bad = raw.copy()
    for d in raw_bad.info['dig']:
        if d['kind'] == FIFF.FIFFV_POINT_HPI:
            d['r'] = np.ones(3)
    raw_bad.crop(0, 1., copy=False)
    with warnings.catch_warnings(record=True):  # bad pos
        with catch_logging() as log_file:
            _calculate_chpi_positions(raw_bad, verbose=True)
    # ignore HPI info header and [done] footer
    for line in log_file.getvalue().strip().split('\n')[4:-1]:
        assert_true('0/5 good' in line)

def test_clean_info_bads():
    """Test cleaning info['bads'] when bad_channels are excluded """

    raw_file = op.join(op.dirname(__file__), "io", "tests", "data", "test_raw.fif")
    raw = Raw(raw_file)

    # select eeg channels
    picks_eeg = pick_types(raw.info, meg=False, eeg=True)

    # select 3 eeg channels as bads
    idx_eeg_bad_ch = picks_eeg[[1, 5, 14]]
    eeg_bad_ch = [raw.info["ch_names"][k] for k in idx_eeg_bad_ch]

    # select meg channels
    picks_meg = pick_types(raw.info, meg=True, eeg=False)

    # select randomly 3 meg channels as bads
    idx_meg_bad_ch = picks_meg[[0, 15, 34]]
    meg_bad_ch = [raw.info["ch_names"][k] for k in idx_meg_bad_ch]

    # simulate the bad channels
    raw.info["bads"] = eeg_bad_ch + meg_bad_ch

    # simulate the call to pick_info excluding the bad eeg channels
    info_eeg = pick_info(raw.info, picks_eeg)

    # simulate the call to pick_info excluding the bad meg channels
    info_meg = pick_info(raw.info, picks_meg)

    assert_equal(info_eeg["bads"], eeg_bad_ch)
    assert_equal(info_meg["bads"], meg_bad_ch)

def test_ica_rank_reduction():
    """Test recovery of full data when no source is rejected"""
    # Most basic recovery
    raw = Raw(raw_fname).crop(0.5, stop, False)
    raw.load_data()
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')[:10]
    n_components = 5
    max_pca_components = len(picks)
    for n_pca_components in [6, 10]:
        with warnings.catch_warnings(record=True):  # non-convergence
            warnings.simplefilter('always')
            ica = ICA(n_components=n_components,
                      max_pca_components=max_pca_components,
                      n_pca_components=n_pca_components,
                      method='fastica', max_iter=1).fit(raw, picks=picks)

        rank_before = raw.estimate_rank(picks=picks)
        assert_equal(rank_before, len(picks))
        raw_clean = ica.apply(raw, copy=True)
        rank_after = raw_clean.estimate_rank(picks=picks)
        # interaction between ICA rejection and PCA components difficult
        # to preduct. Rank_after often seems to be 1 higher then
        # n_pca_components
        assert_true(n_components < n_pca_components <= rank_after <=
                    rank_before)

def _get_data():
    # Read raw data
    raw = Raw(raw_fname)
    raw.info['bads'] = ['MEG 2443', 'EEG 053']  # 2 bads channels

    # Set picks
    picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
                                stim=False, exclude='bads')

    # Read several epochs
    event_id, tmin, tmax = 1, -0.2, 0.5
    events = mne.read_events(event_fname)[0:100]
    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                        picks=picks, baseline=(None, 0), preload=True,
                        reject=dict(grad=4000e-13, mag=4e-12))

    # Create an epochs object with one epoch and one channel of artificial data
    event_id, tmin, tmax = 1, 0.0, 1.0
    epochs_sin = mne.Epochs(raw, events[0:5], event_id, tmin, tmax, proj=True,
                            picks=[0], baseline=(None, 0), preload=True,
                            reject=dict(grad=4000e-13))
    freq = 10
    epochs_sin._data = np.sin(2 * np.pi * freq
                              * epochs_sin.times)[None, None, :]
    return epochs, epochs_sin

def test_cov_estimation_on_raw_segment():
    """Test estimation from raw on continuous recordings (typically empty room)
    """
    tempdir = _TempDir()
    raw = Raw(raw_fname, preload=False)
    cov = compute_raw_data_covariance(raw)
    cov_mne = read_cov(erm_cov_fname)
    assert_true(cov_mne.ch_names == cov.ch_names)
    assert_true(linalg.norm(cov.data - cov_mne.data, ord='fro')
                / linalg.norm(cov.data, ord='fro') < 1e-4)

    # test IO when computation done in Python
    cov.save(op.join(tempdir, 'test-cov.fif'))  # test saving
    cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
    assert_true(cov_read.ch_names == cov.ch_names)
    assert_true(cov_read.nfree == cov.nfree)
    assert_array_almost_equal(cov.data, cov_read.data)

    # test with a subset of channels
    picks = pick_channels(raw.ch_names, include=raw.ch_names[:5])
    cov = compute_raw_data_covariance(raw, picks=picks)
    assert_true(cov_mne.ch_names[:5] == cov.ch_names)
    assert_true(linalg.norm(cov.data - cov_mne.data[picks][:, picks],
                ord='fro') / linalg.norm(cov.data, ord='fro') < 1e-4)
    # make sure we get a warning with too short a segment
    raw_2 = raw.crop(0, 1)
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        cov = compute_raw_data_covariance(raw_2)
    assert_true(len(w) == 1)

def test_preload_modify():
    """Test preloading and modifying data
    """
    tempdir = _TempDir()
    for preload in [False, True, 'memmap.dat']:
        raw = Raw(fif_fname, preload=preload)

        nsamp = raw.last_samp - raw.first_samp + 1
        picks = pick_types(raw.info, meg='grad', exclude='bads')

        data = rng.randn(len(picks), nsamp // 2)

        try:
            raw[picks, :nsamp // 2] = data
        except RuntimeError as err:
            if not preload:
                continue
            else:
                raise err

        tmp_fname = op.join(tempdir, 'raw.fif')
        raw.save(tmp_fname, overwrite=True)

        raw_new = Raw(tmp_fname)
        data_new, _ = raw_new[picks, :nsamp / 2]

        assert_allclose(data, data_new)

def test_add_channels():
    """Test raw splitting / re-appending channel types
    """
    raw = Raw(test_fif_fname).crop(0, 1).load_data()
    raw_nopre = Raw(test_fif_fname, preload=False)
    raw_eeg_meg = raw.pick_types(meg=True, eeg=True, copy=True)
    raw_eeg = raw.pick_types(meg=False, eeg=True, copy=True)
    raw_meg = raw.pick_types(meg=True, eeg=False, copy=True)
    raw_stim = raw.pick_types(meg=False, eeg=False, stim=True, copy=True)
    raw_new = raw_meg.add_channels([raw_eeg, raw_stim], copy=True)
    assert_true(
        all(ch in raw_new.ch_names
            for ch in list(raw_stim.ch_names) + list(raw_meg.ch_names))
    )
    raw_new = raw_meg.add_channels([raw_eeg], copy=True)

    assert_true(ch in raw_new.ch_names for ch in raw.ch_names)
    assert_array_equal(raw_new[:, :][0], raw_eeg_meg[:, :][0])
    assert_array_equal(raw_new[:, :][1], raw[:, :][1])
    assert_true(all(ch not in raw_new.ch_names for ch in raw_stim.ch_names))

    # Now test errors
    raw_badsf = raw_eeg.copy()
    raw_badsf.info['sfreq'] = 3.1415927
    raw_eeg = raw_eeg.crop(.5)

    assert_raises(AssertionError, raw_meg.add_channels, [raw_nopre])
    assert_raises(RuntimeError, raw_meg.add_channels, [raw_badsf])
    assert_raises(AssertionError, raw_meg.add_channels, [raw_eeg])
    assert_raises(ValueError, raw_meg.add_channels, [raw_meg])
    assert_raises(AssertionError, raw_meg.add_channels, raw_badsf)

def test_chpi_subtraction():
    """Test subtraction of cHPI signals"""
    raw = Raw(chpi_fif_fname, allow_maxshield="yes", preload=True)
    with catch_logging() as log:
        filter_chpi(raw, include_line=False, verbose=True)
    assert_true("5 cHPI" in log.getvalue())
    # MaxFilter doesn't do quite as well as our algorithm with the last bit
    raw.crop(0, 16, copy=False)
    # remove cHPI status chans
    raw_c = Raw(sss_hpisubt_fname).crop(0, 16, copy=False).load_data()
    raw_c.pick_types(meg=True, eeg=True, eog=True, ecg=True, stim=True, misc=True)
    assert_meg_snr(raw, raw_c, 143, 624)

    # Degenerate cases
    raw_nohpi = Raw(test_fif_fname, preload=True)
    assert_raises(RuntimeError, filter_chpi, raw_nohpi)

    # When MaxFliter downsamples, like::
    #     $ maxfilter -nosss -ds 2 -f test_move_anon_raw.fif \
    #           -o test_move_anon_ds2_raw.fif
    # it can strip out some values of info, which we emulate here:
    raw = Raw(chpi_fif_fname, allow_maxshield="yes")
    with warnings.catch_warnings(record=True):  # uint cast suggestion
        raw = raw.crop(0, 1).load_data().resample(600.0, npad="auto")
    raw.info["buffer_size_sec"] = np.float64(2.0)
    raw.info["lowpass"] = 200.0
    del raw.info["maxshield"]
    del raw.info["hpi_results"][0]["moments"]
    del raw.info["hpi_subsystem"]["event_channel"]
    with catch_logging() as log:
        filter_chpi(raw, verbose=True)
    assert_true("2 cHPI" in log.getvalue())

def test_calculate_chpi_positions():
    """Test calculation of cHPI positions
    """
    trans, rot, t = get_chpi_positions(pos_fname)
    with warnings.catch_warnings(record=True):
        raw = Raw(raw_fif_fname, allow_maxshield=True, preload=True)
    t -= raw.first_samp / raw.info['sfreq']
    trans_est, rot_est, t_est = _calculate_chpi_positions(raw, verbose='debug')
    _compare_positions((trans, rot, t), (trans_est, rot_est, t_est))

    # degenerate conditions
    raw_no_chpi = Raw(test_fif_fname)
    assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
    raw_bad = raw.copy()
    for d in raw_bad.info['dig']:
        if d['kind'] == FIFF.FIFFV_POINT_HPI:
            d['coord_frame'] = 999
            break
    assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
    raw_bad = raw.copy()
    for d in raw_bad.info['dig']:
        if d['kind'] == FIFF.FIFFV_POINT_HPI:
            d['r'] = np.ones(3)
    raw_bad.crop(0, 1., copy=False)
    tempdir = _TempDir()
    log_file = op.join(tempdir, 'temp_log.txt')
    set_log_file(log_file, overwrite=True)
    try:
        _calculate_chpi_positions(raw_bad)
    finally:
        set_log_file()
    with open(log_file, 'r') as fid:
        for line in fid:
            assert_true('0/5 acceptable' in line)

def test_io_complex():
    """Test IO with complex data types
    """
    rng = np.random.RandomState(0)
    tempdir = _TempDir()
    dtypes = [np.complex64, np.complex128]

    raw = Raw(fif_fname, preload=True)
    picks = np.arange(5)
    start, stop = raw.time_as_index([0, 5])

    data_orig, _ = raw[picks, start:stop]

    for di, dtype in enumerate(dtypes):
        imag_rand = np.array(1j * rng.randn(data_orig.shape[0], data_orig.shape[1]), dtype)

        raw_cp = raw.copy()
        raw_cp._data = np.array(raw_cp._data, dtype)
        raw_cp._data[picks, start:stop] += imag_rand
        # this should throw an error because it's complex
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter("always")
            raw_cp.save(op.join(tempdir, "raw.fif"), picks, tmin=0, tmax=5, overwrite=True)
            # warning gets thrown on every instance b/c simplifilter('always')
            assert_equal(len(w), 1)

        raw2 = Raw(op.join(tempdir, "raw.fif"))
        raw2_data, _ = raw2[picks, :]
        n_samp = raw2_data.shape[1]
        assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])
        # with preloading
        raw2 = Raw(op.join(tempdir, "raw.fif"), preload=True)
        raw2_data, _ = raw2[picks, :]
        n_samp = raw2_data.shape[1]
        assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])

def test_bads_reconstruction():
    """Test Maxwell filter reconstruction of bad channels"""
    with warnings.catch_warnings(record=True):  # maxshield
        raw = Raw(raw_fname, allow_maxshield=True).crop(0., 1., False)
    raw.info['bads'] = bads
    raw_sss = maxwell_filter(raw)
    _assert_snr(raw_sss, Raw(sss_bad_recon_fname), 300.)

def manual_filter():
    fname='ec_rest_before_tsss_mc_rsl.fif'
    raw = Raw(fname, preload=False)
    raw.preload_data() #  data becomes numpy.float64
    _mmap_raw(raw)

    copy = False  # filter in-place
    zero_phase = True
    n_jobs = 1
    picks = pick_types(raw.info, exclude=[], meg=True)

    x = raw._data  # pointer?

    Fs = 1000.
    Fp = 20.
    Fstop = 21.
    filter_length='10s'

    print('x.shape before prepping:', x.shape)
    h_fft, n_h, n_edge, orig_shape = _do_prep(Fs, Fp, Fstop, x, copy, picks,
                                              filter_length, zero_phase)
    print('x.shape after prepping:', x.shape)

    print('Before filtering:')
    print(x[0][:10])
    for p in picks:
        mangle_x(x[p])
        # _1d_overlap_filter(x[p], h_fft, n_h, n_edge, zero_phase,
        #                           dict(use_cuda=False))
    #x.shape = orig_shape
    print('After filtering:')
    print(x[0][:10])

    print('Data type:', raw._data[0][:5])
    print(type(raw._data))

def test_compute_proj_eog():
    """Test computation of EOG SSP projectors"""
    raw = Raw(raw_fname).crop(0, 10, False)
    raw.preload_data()
    for average in [False, True]:
        n_projs_init = len(raw.info['projs'])
        projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
                                         bads=['MEG 2443'], average=average,
                                         avg_ref=True, no_proj=False,
                                         l_freq=None, h_freq=None,
                                         reject=None, tmax=dur_use)
        assert_true(len(projs) == (7 + n_projs_init))
        assert_true(np.abs(events.shape[0] -
                    np.sum(np.less(eog_times, dur_use))) <= 1)
        # XXX: better tests

        # This will throw a warning b/c simplefilter('always')
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
                                             average=average, bads=[],
                                             avg_ref=True, no_proj=False,
                                             l_freq=None, h_freq=None,
                                             tmax=dur_use)
            assert_equal(len(w), 1)
        assert_equal(projs, None)

def test_compute_proj_ecg():
    """Test computation of ECG SSP projectors"""
    raw = Raw(raw_fname).crop(0, 10, False)
    raw.preload_data()
    for average in [False, True]:
        # For speed, let's not filter here (must also not reject then)
        projs, events = compute_proj_ecg(raw, n_mag=2, n_grad=2, n_eeg=2,
                                         ch_name='MEG 1531', bads=['MEG 2443'],
                                         average=average, avg_ref=True,
                                         no_proj=True, l_freq=None,
                                         h_freq=None, reject=None,
                                         tmax=dur_use, qrs_threshold=0.5)
        assert_true(len(projs) == 7)
        # heart rate at least 0.5 Hz, but less than 3 Hz
        assert_true(events.shape[0] > 0.5 * dur_use and
                    events.shape[0] < 3 * dur_use)
        # XXX: better tests

        # without setting a bad channel, this should throw a warning
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            projs, events = compute_proj_ecg(raw, n_mag=2, n_grad=2, n_eeg=2,
                                             ch_name='MEG 1531', bads=[],
                                             average=average, avg_ref=True,
                                             no_proj=True, l_freq=None,
                                             h_freq=None, tmax=dur_use)
            assert_equal(len(w), 1)
        assert_equal(projs, None)

def test_cov_estimation_on_raw():
    """Test estimation from raw (typically empty room)"""
    tempdir = _TempDir()
    raw = Raw(raw_fname, preload=False)
    cov_mne = read_cov(erm_cov_fname)

    cov = compute_raw_covariance(raw, tstep=None)
    assert_equal(cov.ch_names, cov_mne.ch_names)
    assert_equal(cov.nfree, cov_mne.nfree)
    assert_snr(cov.data, cov_mne.data, 1e4)

    cov = compute_raw_covariance(raw)  # tstep=0.2 (default)
    assert_equal(cov.nfree, cov_mne.nfree - 119)  # cutoff some samples
    assert_snr(cov.data, cov_mne.data, 1e2)

    # test IO when computation done in Python
    cov.save(op.join(tempdir, 'test-cov.fif'))  # test saving
    cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
    assert_true(cov_read.ch_names == cov.ch_names)
    assert_true(cov_read.nfree == cov.nfree)
    assert_array_almost_equal(cov.data, cov_read.data)

    # test with a subset of channels
    picks = pick_channels(raw.ch_names, include=raw.ch_names[:5])
    cov = compute_raw_covariance(raw, picks=picks, tstep=None)
    assert_true(cov_mne.ch_names[:5] == cov.ch_names)
    assert_snr(cov.data, cov_mne.data[picks][:, picks], 1e4)
    cov = compute_raw_covariance(raw, picks=picks)
    assert_snr(cov.data, cov_mne.data[picks][:, picks], 90)  # cutoff samps
    # make sure we get a warning with too short a segment
    raw_2 = raw.crop(0, 1)
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        cov = compute_raw_covariance(raw_2)
    assert_true(any('Too few samples' in str(ww.message) for ww in w))