def test_eximia_nxe():
    """Test reading Eximia NXE files"""
    fname = op.join(data_path(), 'eximia', 'test_eximia.nxe')
    raw = read_raw_eximia(fname, preload=True)
    assert_true('RawEximia' in repr(raw))
    _test_raw_reader(read_raw_eximia, fname=fname)
    fname_mat = op.join(data_path(), 'eximia', 'test_eximia.mat')
    mc = sio.loadmat(fname_mat)
    m_data = mc['data']
    m_header = mc['header']
    assert_equal(raw._data.shape, m_data.shape)
    assert_equal(m_header['Fs'][0, 0][0, 0], raw.info['sfreq'])
    m_names = [x[0][0] for x in m_header['label'][0, 0]]
    m_names = list(
        map(lambda x: x.replace('GATE', 'GateIn').replace('TRIG', 'Trig'),
            m_names))
    assert_equal(raw.ch_names, m_names)
    m_ch_types = [x[0][0] for x in m_header['chantype'][0, 0]]
    m_ch_types = list(
        map(lambda x: x.replace('unknown', 'stim').replace('trigger', 'stim'),
            m_ch_types))
    types_dict = {2: 'eeg', 3: 'stim', 202: 'eog'}
    ch_types = [types_dict[raw.info['chs'][x]['kind']]
                for x in range(len(raw.ch_names))]
    assert_equal(ch_types, m_ch_types)

    assert_array_equal(m_data, raw._data)

def test_io_egi_pns_mff_bug():
    """Test importing EGI MFF with PNS data (BUG)."""
    egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi_pns_bug.mff')
    with pytest.warns(RuntimeWarning, match='EGI PSG sample bug'):
        raw = read_raw_egi(egi_fname_mff, include=None, preload=True,
                           verbose='warning')
    egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat')
    mc = sio.loadmat(egi_fname_mat)
    pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True)
    pns_names = ['Resp. Temperature'[:15],
                 'Resp. Pressure',
                 'ECG',
                 'Body Position',
                 'Resp. Effort Chest'[:15],
                 'Resp. Effort Abdomen'[:15],
                 'EMG-Leg']
    mat_names = [
        'Resp_Temperature'[:15],
        'Resp_Pressure',
        'ECG',
        'Body_Position',
        'Resp_Effort_Chest'[:15],
        'Resp_Effort_Abdomen'[:15],
        'EMGLeg'

    ]
    for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names):
        print('Testing {}'.format(ch_name))
        mc_key = [x for x in mc.keys() if mat_name in x][0]
        cal = raw.info['chs'][ch_idx]['cal']
        mat_data = mc[mc_key] * cal
        mat_data[:, -1] = 0  # The MFF has one less sample, the last one
        raw_data = raw[ch_idx][0]
        assert_array_equal(mat_data, raw_data)

def test_io_egi_mff():
    """Test importing EGI MFF simple binary files."""
    egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi.mff')
    raw = read_raw_egi(egi_fname_mff, include=None)
    assert ('RawMff' in repr(raw))
    include = ['DIN1', 'DIN2', 'DIN3', 'DIN4', 'DIN5', 'DIN7']
    raw = _test_raw_reader(read_raw_egi, input_fname=egi_fname_mff,
                           include=include, channel_naming='EEG %03d')

    assert_equal('eeg' in raw, True)
    eeg_chan = [c for c in raw.ch_names if 'EEG' in c]
    assert_equal(len(eeg_chan), 129)
    picks = pick_types(raw.info, eeg=True)
    assert_equal(len(picks), 129)
    assert_equal('STI 014' in raw.ch_names, True)

    events = find_events(raw, stim_channel='STI 014')
    assert_equal(len(events), 8)
    assert_equal(np.unique(events[:, 1])[0], 0)
    assert (np.unique(events[:, 0])[0] != 0)
    assert (np.unique(events[:, 2])[0] != 0)

    pytest.raises(ValueError, read_raw_egi, egi_fname_mff, include=['Foo'],
                  preload=False)
    pytest.raises(ValueError, read_raw_egi, egi_fname_mff, exclude=['Bar'],
                  preload=False)
    for ii, k in enumerate(include, 1):
        assert (k in raw.event_id)
        assert (raw.event_id[k] == ii)

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')
    with warnings.catch_warnings(record=True):  # maxshield
        raw = Raw(raw_fname, preload=False, proj=False,
                  allow_maxshield=True).crop(0., 1., False)
    raw_sss = maxwell.maxwell_filter(raw)

    # Test io on processed data
    tempdir = _TempDir()
    test_outname = op.join(tempdir, 'test_raw_sss.fif')
    raw_sss.save(test_outname)
    raw_sss_loaded = Raw(test_outname, preload=True, proj=False,
                         allow_maxshield=True)
    # Some numerical imprecision since save uses 'single' fmt
    assert_allclose(raw_sss_loaded._data[:, :], raw_sss._data[:, :],
                    rtol=1e-6, atol=1e-20)

def test_find_ch_connectivity():
    """Test computing the connectivity matrix."""
    data_path = testing.data_path()

    raw = read_raw_fif(raw_fname, preload=True)
    sizes = {'mag': 828, 'grad': 1700, 'eeg': 386}
    nchans = {'mag': 102, 'grad': 204, 'eeg': 60}
    for ch_type in ['mag', 'grad', 'eeg']:
        conn, ch_names = find_ch_connectivity(raw.info, ch_type)
        # Silly test for checking the number of neighbors.
        assert_equal(conn.getnnz(), sizes[ch_type])
        assert_equal(len(ch_names), nchans[ch_type])
    pytest.raises(ValueError, find_ch_connectivity, raw.info, None)

    # Test computing the conn matrix with gradiometers.
    conn, ch_names = _compute_ch_connectivity(raw.info, 'grad')
    assert_equal(conn.getnnz(), 2680)

    # Test ch_type=None.
    raw.pick_types(meg='mag')
    find_ch_connectivity(raw.info, None)

    bti_fname = op.join(data_path, 'BTi', 'erm_HFH', 'c,rfDC')
    bti_config_name = op.join(data_path, 'BTi', 'erm_HFH', 'config')
    raw = read_raw_bti(bti_fname, bti_config_name, None)
    _, ch_names = find_ch_connectivity(raw.info, 'mag')
    assert 'A1' in ch_names

    ctf_fname = op.join(data_path, 'CTF', 'testdata_ctf_short.ds')
    raw = read_raw_ctf(ctf_fname)
    _, ch_names = find_ch_connectivity(raw.info, 'mag')
    assert 'MLC11' in ch_names

    pytest.raises(ValueError, find_ch_connectivity, raw.info, 'eog')

def test_events_long():
    """Test events."""
    data_path = testing.data_path()
    raw_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw.fif'
    raw = read_raw_fif(raw_fname, preload=True)
    raw_tmin, raw_tmax = 0, 90

    tmin, tmax = -0.2, 0.5
    event_id = dict(aud_l=1, vis_l=3)

    # select gradiometers
    picks = pick_types(raw.info, meg='grad', eeg=False, eog=True,
                       stim=True, exclude=raw.info['bads'])

    # load data with usual Epochs for later verification
    raw = concatenate_raws([raw, raw.copy(), raw.copy(), raw.copy(),
                            raw.copy(), raw.copy()])
    assert 110 < raw.times[-1] < 130
    raw_cropped = raw.copy().crop(raw_tmin, raw_tmax)
    events_offline = find_events(raw_cropped)
    epochs_offline = Epochs(raw_cropped, events_offline, event_id=event_id,
                            tmin=tmin, tmax=tmax, picks=picks, decim=1,
                            reject=dict(grad=4000e-13, eog=150e-6),
                            baseline=None)
    epochs_offline.drop_bad()

    # create the mock-client object
    rt_client = MockRtClient(raw)
    rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks, decim=1,
                         reject=dict(grad=4000e-13, eog=150e-6), baseline=None,
                         isi_max=1.)

    rt_epochs.start()
    rt_client.send_data(rt_epochs, picks, tmin=raw_tmin, tmax=raw_tmax,
                        buffer_size=1000)

    expected_events = epochs_offline.events.copy()
    expected_events[:, 0] = expected_events[:, 0] - raw_cropped.first_samp
    assert np.all(expected_events[:, 0] <=
                  (raw_tmax - tmax) * raw.info['sfreq'])
    assert_array_equal(rt_epochs.events, expected_events)
    assert len(rt_epochs) == len(epochs_offline)

    data_picks = pick_types(epochs_offline.info, meg='grad', eeg=False,
                            eog=True,
                            stim=False, exclude=raw.info['bads'])

    for ev_num, ev in enumerate(rt_epochs.iter_evoked()):
        if ev_num == 0:
            X_rt = ev.data[None, data_picks, :]
            y_rt = int(ev.comment)  # comment attribute contains the event_id
        else:
            X_rt = np.concatenate((X_rt, ev.data[None, data_picks, :]), axis=0)
            y_rt = np.append(y_rt, int(ev.comment))

    X_offline = epochs_offline.get_data()[:, data_picks, :]
    y_offline = epochs_offline.events[:, 2]
    assert_array_equal(X_rt, X_offline)
    assert_array_equal(y_rt, y_offline)

def test_interpolation_ctf_comp():
    """Test interpolation with compensated CTF data."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
    raw = io.read_raw_ctf(raw_fname, preload=True)
    raw.info['bads'] = [raw.ch_names[5], raw.ch_names[-5]]
    raw.interpolate_bads(mode='fast')
    assert raw.info['bads'] == []

def test_io_egi_crop_no_preload():
    """Test crop non-preloaded EGI MFF data (BUG)."""
    egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi.mff')
    raw = read_raw_egi(egi_fname_mff, preload=False)
    raw.crop(17.5, 20.5)
    raw.load_data()
    raw_preload = read_raw_egi(egi_fname_mff, preload=True)
    raw_preload.crop(17.5, 20.5)
    raw_preload.load_data()
    assert_allclose(raw._data, raw_preload._data)

def test_inverse_ctf_comp():
    """Test interpolation with compensated CTF data."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
    raw = mne.io.read_raw_ctf(raw_fname)
    raw.apply_gradient_compensation(1)
    sphere = make_sphere_model()
    cov = make_ad_hoc_cov(raw.info)
    src = mne.setup_volume_source_space(
        pos=dict(rr=[[0., 0., 0.01]], nn=[[0., 1., 0.]]))
    fwd = make_forward_solution(raw.info, None, src, sphere, eeg=False)
    inv = make_inverse_operator(raw.info, fwd, cov, loose=1.)
    apply_inverse_raw(raw, inv, 1. / 9.)

def test_io_egi_pns_mff():
    """Test importing EGI MFF with PNS data."""
    egi_fname_mff = op.join(data_path(), 'EGI', 'test_egi_pns.mff')
    raw = read_raw_egi(egi_fname_mff, include=None, preload=True,
                       verbose='error')
    assert ('RawMff' in repr(raw))
    pns_chans = pick_types(raw.info, ecg=True, bio=True, emg=True)
    assert_equal(len(pns_chans), 7)
    names = [raw.ch_names[x] for x in pns_chans]
    pns_names = ['Resp. Temperature'[:15],
                 'Resp. Pressure',
                 'ECG',
                 'Body Position',
                 'Resp. Effort Chest'[:15],
                 'Resp. Effort Abdomen'[:15],
                 'EMG-Leg']
    _test_raw_reader(read_raw_egi, input_fname=egi_fname_mff,
                     channel_naming='EEG %03d', verbose='error')
    assert_equal(names, pns_names)
    mat_names = [
        'Resp_Temperature'[:15],
        'Resp_Pressure',
        'ECG',
        'Body_Position',
        'Resp_Effort_Chest'[:15],
        'Resp_Effort_Abdomen'[:15],
        'EMGLeg'

    ]
    egi_fname_mat = op.join(data_path(), 'EGI', 'test_egi_pns.mat')
    mc = sio.loadmat(egi_fname_mat)
    for ch_name, ch_idx, mat_name in zip(pns_names, pns_chans, mat_names):
        print('Testing {}'.format(ch_name))
        mc_key = [x for x in mc.keys() if mat_name in x][0]
        cal = raw.info['chs'][ch_idx]['cal']
        mat_data = mc[mc_key] * cal
        raw_data = raw[ch_idx][0]
        assert_array_equal(mat_data, raw_data)

def test_eeglab_event_from_annot():
    """Test all forms of obtaining annotations."""
    base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
    raw_fname_mat = op.join(base_dir, 'test_raw.set')
    raw_fname = raw_fname_mat
    montage = op.join(base_dir, 'test_chans.locs')
    event_id = {'rt': 1, 'square': 2}
    raw1 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                           preload=False)

    annotations = read_annotations(raw_fname)
    assert len(raw1.annotations) == 154
    raw1.set_annotations(annotations)
    events_b, _ = events_from_annotations(raw1, event_id=event_id)
    assert len(events_b) == 154

def test_plot_ctf():
    """Test plotting of CTF evoked."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'testdata_ctf.ds')

    raw = mne.io.read_raw_ctf(raw_fname, preload=True)
    events = np.array([[200, 0, 1]])
    event_id = 1
    tmin, tmax = -0.1, 0.5  # start and end of an epoch in sec.
    picks = mne.pick_types(raw.info, meg=True, stim=True, eog=True,
                           ref_meg=True, exclude='bads')[::20]
    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                        picks=picks, preload=True, decim=10, verbose='error')
    evoked = epochs.average()
    evoked.plot_joint(times=[0.1])
    mne.viz.plot_compare_evokeds([evoked, evoked])

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')

    with warnings.catch_warnings(record=True):  # maxshield
        # Use 2.0 seconds of data to get stable cov. estimate
        raw = Raw(raw_fname, preload=False, proj=False,
                  allow_maxshield=True).crop(0., 2., False)

    # Get MEG channels, compute Maxwell filtered data
    raw.load_data()
    raw.pick_types(meg=True, eeg=False)
    int_order, ext_order = 8, 3
    raw_sss = maxwell.maxwell_filter(raw, int_order=int_order,
                                     ext_order=ext_order)

    # Test io on processed data
    tempdir = _TempDir()
    test_outname = op.join(tempdir, 'test_raw_sss.fif')
    raw_sss.save(test_outname)
    raw_sss_loaded = Raw(test_outname, preload=True, proj=False,
                         allow_maxshield=True)

    # Some numerical imprecision since save uses 'single' fmt
    assert_allclose(raw_sss_loaded._data[:, :], raw_sss._data[:, :],
                    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, maxwell.get_num_moments(int_order, 0))

def test_add_noise():
    """Test noise addition."""
    rng = np.random.RandomState(0)
    data_path = testing.data_path()
    raw = read_raw_fif(data_path + '/MEG/sample/sample_audvis_trunc_raw.fif')
    raw.del_proj()
    picks = pick_types(raw.info, eeg=True, exclude=())
    cov = compute_raw_covariance(raw, picks=picks)
    with pytest.raises(RuntimeError, match='to be loaded'):
        add_noise(raw, cov)
    raw.crop(0, 1).load_data()
    with pytest.raises(TypeError, match='Raw, Epochs, or Evoked'):
        add_noise(0., cov)
    with pytest.raises(TypeError, match='Covariance'):
        add_noise(raw, 0.)
    # test a no-op (data preserved)
    orig_data = raw[:][0]
    zero_cov = cov.copy()
    zero_cov['data'].fill(0)
    add_noise(raw, zero_cov)
    new_data = raw[:][0]
    assert_allclose(orig_data, new_data, atol=1e-30)
    # set to zero to make comparisons easier
    raw._data[:] = 0.
    epochs = EpochsArray(np.zeros((1, len(raw.ch_names), 100)),
                         raw.info.copy())
    epochs.info['bads'] = []
    evoked = epochs.average(picks=np.arange(len(raw.ch_names)))
    for inst in (raw, epochs, evoked):
        with catch_logging() as log:
            add_noise(inst, cov, random_state=rng, verbose=True)
        log = log.getvalue()
        want = ('to {0}/{1} channels ({0}'
                .format(len(cov['names']), len(raw.ch_names)))
        assert want in log
        if inst is evoked:
            inst = EpochsArray(inst.data[np.newaxis], inst.info)
        if inst is raw:
            cov_new = compute_raw_covariance(inst, picks=picks,
                                             verbose='error')  # samples
        else:
            cov_new = compute_covariance(inst, verbose='error')  # avg ref
        assert cov['names'] == cov_new['names']
        r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
        assert r > 0.99

def test_lcmv_ctf_comp():
    """Test interpolation with compensated CTF data."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'somMDYO-18av.ds')
    raw = mne.io.read_raw_ctf(raw_fname, preload=True)

    events = mne.make_fixed_length_events(raw, duration=0.2)[:2]
    epochs = mne.Epochs(raw, events, tmin=0., tmax=0.2)
    evoked = epochs.average()

    with pytest.warns(RuntimeWarning,
                      match='Too few samples .* estimate may be unreliable'):
        data_cov = mne.compute_covariance(epochs)
    fwd = mne.make_forward_solution(evoked.info, None,
                                    mne.setup_volume_source_space(pos=15.0),
                                    mne.make_sphere_model())
    filters = mne.beamformer.make_lcmv(evoked.info, fwd, data_cov)
    assert 'weights' in filters

def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state):
    """Generate data."""

    data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
    raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')

    raw = read_raw_fif(raw_fname, preload=True, add_eeg_ref=False)

    if ch_type == 'eeg':
        picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
    else:
        picks = pick_types(raw.info, meg=ch_type,
                           eeg=False, exclude='bads')

    # select a small number of channels for the test
    number_of_channels_to_use = 5
    idx_perm = random_permutation(picks.shape[0], random_state)
    picks = picks[idx_perm[:number_of_channels_to_use]]

    with warnings.catch_warnings(record=True):  # deprecated params
        raw.filter(1, 45, picks=picks)
    # Eventually we will need to add these, but for now having none of
    # them is a nice deprecation sanity check.
    #           filter_length='10s',
    #           l_trans_bandwidth=0.5, h_trans_bandwidth=0.5,
    #           phase='zero-double', fir_window='hann')  # use the old way
    X = raw[picks, :][0][:, ::20]

    # Subtract the mean
    mean_X = X.mean(axis=1)
    X -= mean_X[:, None]

    # pre_whitening: z-score
    X /= np.std(X)

    T = X.shape[1]
    cov_X = np.dot(X, X.T) / T

    # Let's whiten the data
    U, D, _ = svd(cov_X)
    W = np.dot(U, U.T / np.sqrt(D)[:, None])
    Y = np.dot(W, X)

    return Y

def test_eeglab_event_from_annot(recwarn):
    """Test all forms of obtaining annotations."""
    base_dir = op.join(testing.data_path(download=False), 'EEGLAB')
    raw_fname_mat = op.join(base_dir, 'test_raw.set')
    raw_fname = raw_fname_mat
    montage = op.join(base_dir, 'test_chans.locs')
    event_id = {'rt': 1, 'square': 2}
    raw1 = read_raw_eeglab(input_fname=raw_fname, montage=montage,
                           event_id=event_id, preload=False)

    events_a = find_events(raw1)
    events_b = read_events_eeglab(raw_fname, event_id=event_id)
    annotations = read_annotations_eeglab(raw_fname)
    assert raw1.annotations is None
    raw1.set_annotations(annotations)
    events_c, _ = events_from_annotations(raw1, event_id=event_id)

    assert_array_equal(events_a, events_b)
    assert_array_equal(events_a, events_c)

def test_min_distance_fit_dipole():
    """Test dipole min_dist to inner_skull"""
    data_path = testing.data_path()
    raw_fname = data_path + '/MEG/sample/sample_audvis_trunc_raw.fif'

    subjects_dir = op.join(data_path, 'subjects')
    fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
    fname_trans = op.join(data_path, 'MEG', 'sample',
                          'sample_audvis_trunc-trans.fif')
    fname_bem = op.join(subjects_dir, 'sample', 'bem',
                        'sample-1280-1280-1280-bem-sol.fif')

    subject = 'sample'

    raw = Raw(raw_fname, preload=True)

    # select eeg data
    picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
    info = pick_info(raw.info, picks)

    # Let's use cov = Identity
    cov = read_cov(fname_cov)
    cov['data'] = np.eye(cov['data'].shape[0])

    # Simulated scal map
    simulated_scalp_map = np.zeros(picks.shape[0])
    simulated_scalp_map[27:34] = 1

    simulated_scalp_map = simulated_scalp_map[:, None]

    evoked = EvokedArray(simulated_scalp_map, info, tmin=0)

    min_dist = 5.  # distance in mm

    dip, residual = fit_dipole(evoked, cov, fname_bem, fname_trans,
                               min_dist=min_dist)

    dist = _compute_depth(dip, fname_bem, fname_trans, subject, subjects_dir)

    assert_true(min_dist < (dist[0] * 1000.) < (min_dist + 1.))

    assert_raises(ValueError, fit_dipole, evoked, cov, fname_bem, fname_trans,
                  -1.)

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))

def test_plot_ctf():
    """Test plotting of CTF evoked."""
    ctf_dir = op.join(testing.data_path(download=False), 'CTF')
    raw_fname = op.join(ctf_dir, 'testdata_ctf.ds')

    raw = mne.io.read_raw_ctf(raw_fname, preload=True)
    events = np.array([[200, 0, 1]])
    event_id = 1
    tmin, tmax = -0.1, 0.5  # start and end of an epoch in sec.
    picks = mne.pick_types(raw.info, meg=True, stim=True, eog=True,
                           ref_meg=True, exclude='bads')[::20]
    epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
                        picks=picks, preload=True, decim=10, verbose='error')
    evoked = epochs.average()
    evoked.plot_joint(times=[0.1])
    mne.viz.plot_compare_evokeds([evoked, evoked])

    # make sure axes position is "almost" unchanged
    # when axes were passed to plot_joint by the user
    times = [0.1, 0.2, 0.3]
    fig = plt.figure()

    # create custom axes for topomaps, colorbar and the timeseries
    gs = gridspec.GridSpec(3, 7, hspace=0.5, top=0.8)
    topo_axes = [fig.add_subplot(gs[0, idx * 2:(idx + 1) * 2])
                 for idx in range(len(times))]
    topo_axes.append(fig.add_subplot(gs[0, -1]))
    ts_axis = fig.add_subplot(gs[1:, 1:-1])

    def get_axes_midpoints(axes):
        midpoints = list()
        for ax in axes[:-1]:
            pos = ax.get_position()
            midpoints.append([pos.x0 + (pos.width * 0.5),
                              pos.y0 + (pos.height * 0.5)])
        return np.array(midpoints)

    midpoints_before = get_axes_midpoints(topo_axes)
    evoked.plot_joint(times=times, ts_args={'axes': ts_axis},
                      topomap_args={'axes': topo_axes}, title=None)
    midpoints_after = get_axes_midpoints(topo_axes)
    assert (np.linalg.norm(midpoints_before - midpoints_after) < 0.1).all()