def test_proj_raw_duration(duration, sfreq):
    """Test equivalence of `duration` options."""
    n_ch, n_dim = 30, 3
    rng = np.random.RandomState(0)
    signals = rng.randn(n_dim, 10000)
    mixing = rng.randn(n_ch, n_dim) + [0, 1, 2]
    data = np.dot(mixing, signals)
    raw = RawArray(data, create_info(n_ch, sfreq, 'eeg'))
    raw.set_eeg_reference(projection=True)
    n_eff = int(round(raw.info['sfreq'] * duration))
    # crop to an even "duration" number of epochs
    stop = ((len(raw.times) // n_eff) * n_eff - 1) / raw.info['sfreq']
    raw.crop(0, stop)
    proj_def = compute_proj_raw(raw, n_eeg=n_dim)
    proj_dur = compute_proj_raw(raw, duration=duration, n_eeg=n_dim)
    proj_none = compute_proj_raw(raw, duration=None, n_eeg=n_dim)
    assert len(proj_dur) == len(proj_none) == len(proj_def) == n_dim
    # proj_def is not in here because it does not necessarily evenly divide
    # the signal length:
    for pu, pn in zip(proj_dur, proj_none):
        assert_allclose(pu['data']['data'], pn['data']['data'])
    # but we can test it here since it should still be a small subspace angle:
    for proj in (proj_dur, proj_none, proj_def):
        computed = np.concatenate([p['data']['data'] for p in proj], 0)
        angle = np.rad2deg(linalg.subspace_angles(computed.T, mixing)[0])
        assert angle < 1e-5

def test_apply_function_verbose():
    """Test apply function verbosity
    """
    n_chan = 2
    n_times = 3
    ch_names = [str(ii) for ii in range(n_chan)]
    raw = RawArray(np.zeros((n_chan, n_times)),
                   create_info(ch_names, 1., 'mag'))
    # test return types in both code paths (parallel / 1 job)
    assert_raises(TypeError, raw.apply_function, bad_1,
                  None, None, 1)
    assert_raises(ValueError, raw.apply_function, bad_2,
                  None, None, 1)
    assert_raises(TypeError, raw.apply_function, bad_1,
                  None, None, 2)
    assert_raises(ValueError, raw.apply_function, bad_2,
                  None, None, 2)

    # check our arguments
    tempdir = _TempDir()
    test_name = op.join(tempdir, 'test.log')
    set_log_file(test_name)
    try:
        raw.apply_function(printer, None, None, 1, verbose=False)
        with open(test_name) as fid:
            assert_equal(len(fid.readlines()), 0)
        raw.apply_function(printer, None, None, 1, verbose=True)
        with open(test_name) as fid:
            assert_equal(len(fid.readlines()), n_chan)
    finally:
        set_log_file(None)

def write_mnefiff(data, filename):
    """Export data to MNE using FIFF format.

    Parameters
    ----------
    data : instance of ChanTime
        data with only one trial
    filename : path to file
        file to export to (include '.mat')

    Notes
    -----
    It cannot store data larger than 2 GB.
    The data is assumed to have only EEG electrodes.
    It overwrites a file if it exists.
    """
    from mne import create_info, set_log_level
    from mne.io import RawArray

    set_log_level(WARNING)

    TRIAL = 0
    info = create_info(list(data.axis['chan'][TRIAL]), data.s_freq, ['eeg', ] *
                       data.number_of('chan')[TRIAL])

    UNITS = 1e-6  # mne wants data in uV
    fiff = RawArray(data.data[0] * UNITS, info)

    if data.attr['chan']:
        fiff.set_channel_positions(data.attr['chan'].return_xyz(),
                                   data.attr['chan'].return_label())

    fiff.save(filename, overwrite=True)

def _raw_annot(meas_date, orig_time):
    info = create_info(ch_names=10, sfreq=10.)
    raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10)
    raw.info['meas_date'] = meas_date
    annot = Annotations([.5], [.2], ['dummy'], orig_time)
    raw.set_annotations(annotations=annot)
    return raw

def test_annotation_property_deprecation_warning():
    """Test that assigning annotations warns and nowhere else."""
    with pytest.warns(None) as w:
        raw = RawArray(np.random.rand(1, 1), create_info(1, 1))
    assert len(w) is 0
    with pytest.warns(DeprecationWarning, match='by assignment is deprecated'):
        raw.annotations = None

def test_filter_picks():
    """Test filtering default channel picks"""
    ch_types = ['mag', 'grad', 'eeg', 'seeg', 'misc', 'stim']
    info = create_info(ch_names=ch_types, ch_types=ch_types, sfreq=256)
    raw = RawArray(data=np.zeros((len(ch_types), 1000)), info=info)

    # -- Deal with meg mag grad exception
    ch_types = ('misc', 'stim', 'meg', 'eeg', 'seeg')

    # -- Filter data channels
    for ch_type in ('mag', 'grad', 'eeg', 'seeg'):
        picks = dict((ch, ch == ch_type) for ch in ch_types)
        picks['meg'] = ch_type if ch_type in ('mag', 'grad') else False
        raw_ = raw.pick_types(copy=True, **picks)
        # Avoid RuntimeWarning due to Attenuation
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always')
            raw_.filter(10, 30)
            assert_true(len(w) == 1)

    # -- Error if no data channel
    for ch_type in ('misc', 'stim'):
        picks = dict((ch, ch == ch_type) for ch in ch_types)
        raw_ = raw.pick_types(copy=True, **picks)
        assert_raises(RuntimeError, raw_.filter, 10, 30)

def test_chunk_duration():
    """Test chunk_duration."""
    # create dummy raw
    raw = RawArray(data=np.empty([10, 10], dtype=np.float64),
                   info=create_info(ch_names=10, sfreq=1.),
                   first_samp=0)
    raw.info['meas_date'] = 0
    raw.set_annotations(Annotations(description='foo', onset=[0],
                                    duration=[10], orig_time=None))

    # expected_events = [[0, 0, 1], [0, 0, 1], [1, 0, 1], [1, 0, 1], ..
    #                    [9, 0, 1], [9, 0, 1]]
    expected_events = np.atleast_2d(np.repeat(range(10), repeats=2)).T
    expected_events = np.insert(expected_events, 1, 0, axis=1)
    expected_events = np.insert(expected_events, 2, 1, axis=1)

    events, events_id = events_from_annotations(raw, chunk_duration=.5,
                                                use_rounding=False)
    assert_array_equal(events, expected_events)

    # test chunk durations that do not fit equally in annotation duration
    expected_events = np.zeros((3, 3))
    expected_events[:, -1] = 1
    expected_events[:, 0] = np.arange(0, 9, step=3)
    events, events_id = events_from_annotations(raw, chunk_duration=3.)
    assert_array_equal(events, expected_events)

def test_resample_raw():
    """Test resampling using RawArray."""
    x = np.zeros((1, 1001))
    sfreq = 2048.
    raw = RawArray(x, create_info(1, sfreq, 'eeg'))
    raw.resample(128, npad=10)
    data = raw.get_data()
    assert data.shape == (1, 63)

 def raw_factory(meas_date):
     raw = RawArray(data=np.empty((10, 10)),
                    info=create_info(ch_names=10, sfreq=10., ),
                    first_samp=10)
     raw.info['meas_date'] = meas_date
     raw.set_annotations(annotations=Annotations(onset=[.5],
                                                 duration=[.2],
                                                 description='dummy',
                                                 orig_time=None))
     return raw

def test_time_as_index_ref(offset, origin):
    """Test indexing of raw times."""
    meas_date = 1
    info = create_info(ch_names=10, sfreq=10.)
    raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10)
    raw.info['meas_date'] = meas_date

    relative_times = raw.times
    inds = raw.time_as_index(relative_times + offset,
                             use_rounding=True,
                             origin=origin)
    assert_array_equal(inds, np.arange(raw.n_times))

def test_mark_flat(first_samp):
    """Test marking flat segments."""
    # Test if ECG analysis will work on data that is not preloaded
    n_ch, n_times = 11, 1000
    data = np.random.RandomState(0).randn(n_ch, n_times)
    assert not (np.diff(data, axis=-1) == 0).any()  # nothing flat at first
    info = create_info(n_ch, 1000., 'eeg')
    info['meas_date'] = (1, 2)
    # test first_samp != for gh-6295
    raw = RawArray(data, info, first_samp=first_samp)
    raw.info['bads'] = [raw.ch_names[-1]]

    #
    # First make a channel flat the whole time
    #
    raw_0 = raw.copy()
    raw_0._data[0] = 0.
    for kwargs, bads, want_times in [
            # Anything < 1 will mark spatially
            (dict(bad_percent=100.), [], 0),
            (dict(bad_percent=99.9), [raw.ch_names[0]], n_times),
            (dict(), [raw.ch_names[0]], n_times)]:  # default (1)
        raw_time = mark_flat(raw_0.copy(), verbose='debug', **kwargs)
        want_bads = raw.info['bads'] + bads
        assert raw_time.info['bads'] == want_bads
        n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1]
        assert n_good_times == want_times

    #
    # Now make a channel flat for 20% of the time points
    #
    raw_0 = raw.copy()
    n_good_times = int(round(0.8 * n_times))
    raw_0._data[0, n_good_times:] = 0.
    threshold = 100 * (n_times - n_good_times) / n_times
    for kwargs, bads, want_times in [
            # Should change behavior at bad_percent=20
            (dict(bad_percent=100), [], n_good_times),
            (dict(bad_percent=threshold), [], n_good_times),
            (dict(bad_percent=threshold - 1e-5), [raw.ch_names[0]], n_times),
            (dict(), [raw.ch_names[0]], n_times)]:
        raw_time = mark_flat(raw_0.copy(), verbose='debug', **kwargs)
        want_bads = raw.info['bads'] + bads
        assert raw_time.info['bads'] == want_bads
        n_good_times = raw_time.get_data(reject_by_annotation='omit').shape[1]
        assert n_good_times == want_times

    with pytest.raises(TypeError, match='must be an instance of BaseRaw'):
        mark_flat(0.)
    with pytest.raises(ValueError, match='not convert string to float'):
        mark_flat(raw, 'x')

def test_picks_by_channels():
    """Test creating pick_lists."""
    rng = np.random.RandomState(909)

    test_data = rng.random_sample((4, 2000))
    ch_names = ['MEG %03d' % i for i in [1, 2, 3, 4]]
    ch_types = ['grad', 'mag', 'mag', 'eeg']
    sfreq = 250.0
    info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
    _assert_channel_types(info)
    raw = RawArray(test_data, info)

    pick_list = _picks_by_type(raw.info)
    assert_equal(len(pick_list), 3)
    assert_equal(pick_list[0][0], 'mag')
    pick_list2 = _picks_by_type(raw.info, meg_combined=False)
    assert_equal(len(pick_list), len(pick_list2))
    assert_equal(pick_list2[0][0], 'mag')

    pick_list2 = _picks_by_type(raw.info, meg_combined=True)
    assert_equal(len(pick_list), len(pick_list2) + 1)
    assert_equal(pick_list2[0][0], 'meg')

    test_data = rng.random_sample((4, 2000))
    ch_names = ['MEG %03d' % i for i in [1, 2, 3, 4]]
    ch_types = ['mag', 'mag', 'mag', 'mag']
    sfreq = 250.0
    info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
    raw = RawArray(test_data, info)
    # This acts as a set, not an order
    assert_array_equal(pick_channels(info['ch_names'], ['MEG 002', 'MEG 001']),
                       [0, 1])

    # Make sure checks for list input work.
    pytest.raises(ValueError, pick_channels, ch_names, 'MEG 001')
    pytest.raises(ValueError, pick_channels, ch_names, ['MEG 001'], 'hi')

    pick_list = _picks_by_type(raw.info)
    assert_equal(len(pick_list), 1)
    assert_equal(pick_list[0][0], 'mag')
    pick_list2 = _picks_by_type(raw.info, meg_combined=True)
    assert_equal(len(pick_list), len(pick_list2))
    assert_equal(pick_list2[0][0], 'mag')

    # pick_types type check
    pytest.raises(ValueError, raw.pick_types, eeg='string')

    # duplicate check
    names = ['MEG 002', 'MEG 002']
    assert len(pick_channels(raw.info['ch_names'], names)) == 1
    assert len(raw.copy().pick_channels(names)[0][0]) == 1

def test_annotations():
    """Test annotation class."""
    raw = read_raw_fif(fif_fname)
    onset = np.array(range(10))
    duration = np.ones(10)
    description = np.repeat('test', 10)
    dt = datetime.utcnow()
    meas_date = raw.info['meas_date']
    # Test time shifts.
    for orig_time in [None, dt, meas_date[0], meas_date]:
        annot = Annotations(onset, duration, description, orig_time)

    assert_raises(ValueError, Annotations, onset, duration, description[:9])
    assert_raises(ValueError, Annotations, [onset, 1], duration, description)
    assert_raises(ValueError, Annotations, onset, [duration, 1], description)

    # Test combining annotations with concatenate_raws
    raw2 = raw.copy()
    orig_time = (meas_date[0] + meas_date[1] * 0.000001 +
                 raw2.first_samp / raw2.info['sfreq'])
    annot = Annotations(onset, duration, description, orig_time)
    raw2.annotations = annot
    assert_array_equal(raw2.annotations.onset, onset)
    concatenate_raws([raw, raw2])
    assert_array_almost_equal(onset + 20., raw.annotations.onset, decimal=2)
    assert_array_equal(annot.duration, raw.annotations.duration)
    assert_array_equal(raw.annotations.description, np.repeat('test', 10))

    # Test combining with RawArray and orig_times
    data = np.random.randn(2, 1000) * 10e-12
    sfreq = 100.
    info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2,
                       sfreq=sfreq)
    info['meas_date'] = 0
    raws = []
    for i, fs in enumerate([1000, 100, 12]):
        raw = RawArray(data.copy(), info, first_samp=fs)
        ants = Annotations([1., 2.], [.5, .5], 'x', fs / sfreq)
        raw.annotations = ants
        raws.append(raw)
    raw = concatenate_raws(raws)
    assert_array_equal(raw.annotations.onset, [1., 2., 11., 12., 21., 22.])
    raw.annotations.delete(2)
    assert_array_equal(raw.annotations.onset, [1., 2., 12., 21., 22.])
    raw.annotations.append(5, 1.5, 'y')
    assert_array_equal(raw.annotations.onset, [1., 2., 12., 21., 22., 5])
    assert_array_equal(raw.annotations.duration, [.5, .5, .5, .5, .5, 1.5])
    assert_array_equal(raw.annotations.description, ['x', 'x', 'x', 'x', 'x',
                                                     'y'])

def test_date_none(tmpdir):
    """Test that DATE_NONE is used properly."""
    # Regression test for gh-5908
    n_chans = 139
    n_samps = 20
    data = np.random.random_sample((n_chans, n_samps))
    ch_names = ['E{}'.format(x) for x in range(n_chans)]
    ch_types = ['eeg'] * n_chans
    info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=2048)
    assert info['meas_date'] is None
    raw = RawArray(data=data, info=info)
    fname = op.join(str(tmpdir), 'test-raw.fif')
    raw.save(fname)
    raw_read = read_raw_fif(fname, preload=True)
    assert raw_read.info['meas_date'] is None

def test_crop():
    """Test cropping raw files
    """
    # split a concatenated file to test a difficult case
    raw = concatenate_raws([Raw(f) for f in [fif_fname, fif_fname]])
    split_size = 10.  # in seconds
    sfreq = raw.info['sfreq']
    nsamp = (raw.last_samp - raw.first_samp + 1)

    # do an annoying case (off-by-one splitting)
    tmins = np.r_[1., np.round(np.arange(0., nsamp - 1, split_size * sfreq))]
    tmins = np.sort(tmins)
    tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
    tmaxs /= sfreq
    tmins /= sfreq
    raws = [None] * len(tmins)
    for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
        raws[ri] = raw.copy().crop(tmin, tmax, copy=False)
    all_raw_2 = concatenate_raws(raws, preload=False)
    assert_equal(raw.first_samp, all_raw_2.first_samp)
    assert_equal(raw.last_samp, all_raw_2.last_samp)
    assert_array_equal(raw[:, :][0], all_raw_2[:, :][0])

    tmins = np.round(np.arange(0., nsamp - 1, split_size * sfreq))
    tmaxs = np.concatenate((tmins[1:] - 1, [nsamp - 1]))
    tmaxs /= sfreq
    tmins /= sfreq

    # going in revere order so the last fname is the first file (need it later)
    raws = [None] * len(tmins)
    for ri, (tmin, tmax) in enumerate(zip(tmins, tmaxs)):
        raws[ri] = raw.copy().crop(tmin, tmax, copy=False)
    # test concatenation of split file
    all_raw_1 = concatenate_raws(raws, preload=False)

    all_raw_2 = raw.copy().crop(0, None, copy=False)
    for ar in [all_raw_1, all_raw_2]:
        assert_equal(raw.first_samp, ar.first_samp)
        assert_equal(raw.last_samp, ar.last_samp)
        assert_array_equal(raw[:, :][0], ar[:, :][0])

    # test shape consistency of cropped raw
    data = np.zeros((1, 1002001))
    info = create_info(1, 1000)
    raw = RawArray(data, info)
    for tmin in range(0, 1001, 100):
        raw1 = raw.copy().crop(tmin=tmin, tmax=tmin + 2, copy=False)
        assert_equal(raw1[:][0].shape, (1, 2001))

def test_raw_reject():
    """Test raw data getter with annotation reject."""
    sfreq = 100.
    info = create_info(['a', 'b', 'c', 'd', 'e'], sfreq, ch_types='eeg')
    raw = RawArray(np.ones((5, 15000)), info)
    with pytest.warns(RuntimeWarning, match='outside the data range'):
        raw.set_annotations(Annotations([2, 100, 105, 148],
                                        [2, 8, 5, 8], 'BAD'))
    data, times = raw.get_data([0, 1, 3, 4], 100, 11200,  # 1-112 sec
                               'omit', return_times=True)
    bad_times = np.concatenate([np.arange(200, 400),
                                np.arange(10000, 10800),
                                np.arange(10500, 11000)])
    expected_times = np.setdiff1d(np.arange(100, 11200), bad_times) / sfreq
    assert_allclose(times, expected_times)

    # with orig_time and complete overlap
    raw = read_raw_fif(fif_fname)
    raw.set_annotations(Annotations(onset=[1, 4, 5] + raw._first_time,
                                    duration=[1, 3, 1],
                                    description='BAD',
                                    orig_time=raw.info['meas_date']))
    t_stop = 18.
    assert raw.times[-1] > t_stop
    n_stop = int(round(t_stop * raw.info['sfreq']))
    n_drop = int(round(4 * raw.info['sfreq']))
    assert len(raw.times) >= n_stop
    data, times = raw.get_data(range(10), 0, n_stop, 'omit', True)
    assert data.shape == (10, n_stop - n_drop)
    assert times[-1] == raw.times[n_stop - 1]
    assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])

    data, times = raw.get_data(range(10), 0, n_stop, 'NaN', True)
    assert_array_equal(data.shape, (10, n_stop))
    assert times[-1] == raw.times[n_stop - 1]
    t_1, t_2 = raw.time_as_index([1, 2], use_rounding=True)
    assert np.isnan(data[:, t_1:t_2]).all()  # 1s -2s
    assert not np.isnan(data[:, :t_1].any())
    assert not np.isnan(data[:, t_2:].any())
    assert_array_equal(data[:, -100:], raw[:10, n_stop - 100:n_stop][0])
    assert_array_equal(raw.get_data(), raw[:][0])

    # Test _sync_onset
    times = [10, -88, 190]
    onsets = _sync_onset(raw, times)
    assert_array_almost_equal(onsets, times - raw.first_samp /
                              raw.info['sfreq'])
    assert_array_almost_equal(times, _sync_onset(raw, onsets, True))

def test_raw_reject():
    """Test raw data getter with annotation reject."""
    info = create_info(['a', 'b', 'c', 'd', 'e'], 100, ch_types='eeg')
    raw = RawArray(np.ones((5, 15000)), info)
    with warnings.catch_warnings(record=True):  # one outside range
        raw.annotations = Annotations([2, 100, 105, 148], [2, 8, 5, 8], 'BAD')
    data = raw.get_data([0, 1, 3, 4], 100, 11200, 'omit')
    assert_array_equal(data.shape, (4, 9900))

    # with orig_time and complete overlap
    raw = read_raw_fif(fif_fname)
    raw.annotations = Annotations([44, 47, 48], [1, 3, 1], 'BAD',
                                  raw.info['meas_date'])
    data, times = raw.get_data(range(10), 0, 6000, 'omit', True)
    assert_array_equal(data.shape, (10, 4799))
    assert_equal(times[-1], raw.times[5999])
    assert_array_equal(data[:, -100:], raw[:10, 5900:6000][0])

    data, times = raw.get_data(range(10), 0, 6000, 'NaN', True)
    assert_array_equal(data.shape, (10, 6000))
    assert_equal(times[-1], raw.times[5999])
    assert_true(np.isnan(data[:, 313:613]).all())  # 1s -2s
    assert_true(not np.isnan(data[:, 614].any()))
    assert_array_equal(data[:, -100:], raw[:10, 5900:6000][0])
    assert_array_equal(raw.get_data(), raw[:][0])

    # Test _sync_onset
    times = [10, -88, 190]
    onsets = _sync_onset(raw, times)
    assert_array_almost_equal(onsets, times - raw.first_samp /
                              raw.info['sfreq'])
    assert_array_almost_equal(times, _sync_onset(raw, onsets, True))

def _get_data():
    """Helper to get some starting data"""
    # raw with ECG channel
    raw = Raw(raw_fname).crop(0.0, 5.0).load_data()
    data_picks = pick_types(raw.info, meg=True, eeg=True)
    other_picks = pick_types(raw.info, meg=False, stim=True, eog=True)
    picks = np.sort(np.concatenate((data_picks[::16], other_picks)))
    raw = raw.pick_channels([raw.ch_names[p] for p in picks])
    ecg = RawArray(np.zeros((1, len(raw.times))), create_info(["ECG 063"], raw.info["sfreq"], "ecg"))
    for key in ("dev_head_t", "buffer_size_sec", "highpass", "lowpass", "filename", "dig"):
        ecg.info[key] = raw.info[key]
    raw.add_channels([ecg])

    src = read_source_spaces(src_fname)
    trans = read_trans(trans_fname)
    sphere = make_sphere_model("auto", "auto", raw.info)
    stc = _make_stc(raw, src)
    return raw, src, stc, trans, sphere

def test_apply_function_verbose():
    """Test apply function verbosity."""
    n_chan = 2
    n_times = 3
    ch_names = [str(ii) for ii in range(n_chan)]
    raw = RawArray(np.zeros((n_chan, n_times)),
                   create_info(ch_names, 1., 'mag'))
    # test return types in both code paths (parallel / 1 job)
    pytest.raises(TypeError, raw.apply_function, bad_1)
    pytest.raises(ValueError, raw.apply_function, bad_2)
    pytest.raises(TypeError, raw.apply_function, bad_1, n_jobs=2)
    pytest.raises(ValueError, raw.apply_function, bad_2, n_jobs=2)

    # check our arguments
    with catch_logging() as sio:
        out = raw.apply_function(printer, verbose=False)
        assert len(sio.getvalue()) == 0
        assert out is raw
        raw.apply_function(printer, verbose=True)
        assert sio.getvalue().count('\n') == n_chan

def _get_data():
    """Helper to get some starting data."""
    # raw with ECG channel
    raw = read_raw_fif(raw_fname).crop(0., 5.0).load_data()
    data_picks = pick_types(raw.info, meg=True, eeg=True)
    other_picks = pick_types(raw.info, meg=False, stim=True, eog=True)
    picks = np.sort(np.concatenate((data_picks[::16], other_picks)))
    raw = raw.pick_channels([raw.ch_names[p] for p in picks])
    raw.info.normalize_proj()
    ecg = RawArray(np.zeros((1, len(raw.times))),
                   create_info(['ECG 063'], raw.info['sfreq'], 'ecg'))
    for key in ('dev_head_t', 'buffer_size_sec', 'highpass', 'lowpass', 'dig'):
        ecg.info[key] = raw.info[key]
    raw.add_channels([ecg])

    src = read_source_spaces(src_fname)
    trans = read_trans(trans_fname)
    sphere = make_sphere_model('auto', 'auto', raw.info)
    stc = _make_stc(raw, src)
    return raw, src, stc, trans, sphere