def test_no_conversion():
    """ Test bti no-conversion option """
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        raw = read_raw_bti(pdf, config, hs, convert=False)
        raw_con = read_raw_bti(pdf, config, hs, convert=True)

        bti_info = _read_bti_header(pdf, config)
        dev_ctf_t = _correct_trans(bti_info["bti_transform"][0])
        assert_array_equal(dev_ctf_t, raw.info["dev_ctf_t"]["trans"])
        assert_array_equal(raw.info["dev_head_t"]["trans"], np.eye(4))
        assert_array_equal(raw.info["ctf_head_t"]["trans"], np.eye(4))
        dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
        assert_array_equal(t["trans"], np.eye(4))

        for ii, (old, new, con) in enumerate(zip(dig, raw.info["dig"], raw_con.info["dig"])):
            assert_equal(old["ident"], new["ident"])
            assert_array_equal(old["r"], new["r"])
            assert_true(not np.allclose(old["r"], con["r"]))

            if ii > 10:
                break

        ch_map = dict((ch["chan_label"], ch["coil_trans"]) for ch in bti_info["chs"])

        for ii, ch_label in enumerate(raw.bti_ch_labels):
            if not ch_label.startswith("A"):
                continue
            t1 = _correct_trans(ch_map[ch_label])
            t2 = raw.info["chs"][ii]["coil_trans"]
            t3 = raw_con.info["chs"][ii]["coil_trans"]
            assert_array_equal(t1, t2)
            assert_true(not np.allclose(t1, t3))

def test_info_no_rename_no_reorder_no_pdf():
    """Test private renaming, reordering and partial construction option."""
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        info, bti_info = _get_bti_info(
            pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
            rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
            ecg_ch='E31', eog_ch=('E63', 'E64'),
            rename_channels=False, sort_by_ch_name=False)
        info2, bti_info = _get_bti_info(
            pdf_fname=None, config_fname=config, head_shape_fname=hs,
            rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
            ecg_ch='E31', eog_ch=('E63', 'E64'),
            rename_channels=False, sort_by_ch_name=False)

        assert_equal(info['ch_names'],
                     [ch['ch_name'] for ch in info['chs']])
        assert_equal([n for n in info['ch_names'] if n.startswith('A')][:5],
                     ['A22', 'A2', 'A104', 'A241', 'A138'])
        assert_equal([n for n in info['ch_names'] if n.startswith('A')][-5:],
                     ['A133', 'A158', 'A44', 'A134', 'A216'])

        info = pick_info(info, pick_types(info, meg=True, stim=True,
                                          resp=True))
        info2 = pick_info(info2, pick_types(info2, meg=True, stim=True,
                                            resp=True))

        assert_true(info['sfreq'] is not None)
        assert_true(info['lowpass'] is not None)
        assert_true(info['highpass'] is not None)
        assert_true(info['meas_date'] is not None)

        assert_equal(info2['sfreq'], None)
        assert_equal(info2['lowpass'], None)
        assert_equal(info2['highpass'], None)
        assert_equal(info2['meas_date'], None)

        assert_equal(info['ch_names'], info2['ch_names'])
        assert_equal(info['ch_names'], info2['ch_names'])
        for key in ['dev_ctf_t', 'dev_head_t', 'ctf_head_t']:
            assert_array_equal(info[key]['trans'], info2[key]['trans'])

        assert_array_equal(
            np.array([ch['loc'] for ch in info['chs']]),
            np.array([ch['loc'] for ch in info2['chs']]))

    # just check reading data | corner case
    raw1 = read_raw_bti(
        pdf_fname=pdf, config_fname=config, head_shape_fname=None,
        sort_by_ch_name=False, preload=True)
    # just check reading data | corner case
    raw2 = read_raw_bti(
        pdf_fname=pdf, config_fname=config, head_shape_fname=None,
        rename_channels=False,
        sort_by_ch_name=True, preload=True)

    sort_idx = [raw1.bti_ch_labels.index(ch) for ch in raw2.bti_ch_labels]
    raw1._data = raw1._data[sort_idx]
    assert_array_equal(raw1._data, raw2._data)
    assert_array_equal(raw2.bti_ch_labels, raw2.ch_names)

def test_bytes_io():
    """ Test bti bytes-io API """
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)

        with open(pdf, 'rb') as fid:
            pdf = six.BytesIO(fid.read())
        with open(config, 'rb') as fid:
            config = six.BytesIO(fid.read())
        with open(hs, 'rb') as fid:
            hs = six.BytesIO(fid.read())
        raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
        repr(raw2)
        assert_array_equal(raw[:][0], raw2[:][0])

def test_bytes_io():
    """ Test bti bytes-io API """
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        raw = read_raw_bti(pdf, config, hs, convert=True)

        with open(pdf, "rb") as fid:
            pdf = six.BytesIO(fid.read())
        with open(config, "rb") as fid:
            config = six.BytesIO(fid.read())
        with open(hs, "rb") as fid:
            hs = six.BytesIO(fid.read())
        raw2 = read_raw_bti(pdf, config, hs, convert=True)
        repr(raw2)
        assert_array_equal(raw._data, raw2._data)

def test_plot_trans():
    """Test plotting of -trans.fif files and MEG sensor layouts
    """
    evoked = read_evokeds(evoked_fname)[0]
    with warnings.catch_warnings(record=True):  # 4D weight tables
        bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
                           preload=False).info
    infos = dict(
        Neuromag=evoked.info,
        CTF=read_raw_ctf(ctf_fname).info,
        BTi=bti,
        KIT=read_raw_kit(sqd_fname).info,
    )
    for system, info in infos.items():
        ref_meg = False if system == 'KIT' else True
        plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
                   subjects_dir=subjects_dir, ref_meg=ref_meg)
    # KIT ref sensor coil def not defined
    assert_raises(RuntimeError, plot_trans, infos['KIT'], None,
                  meg_sensors=True, ref_meg=True)
    info = infos['Neuromag']
    assert_raises(ValueError, plot_trans, info, trans_fname,
                  subject='sample', subjects_dir=subjects_dir,
                  ch_type='bad-chtype')
    assert_raises(TypeError, plot_trans, 'foo', trans_fname,
                  subject='sample', subjects_dir=subjects_dir)
    # no-head version
    plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
    # EEG only with strange options
    with warnings.catch_warnings(record=True) as w:
        plot_trans(evoked.copy().pick_types(meg=False, eeg=True).info,
                   trans=trans_fname, meg_sensors=True)
    assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])

def test_bytes_io():
    """ Test bti bytes-io API """
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        with warnings.catch_warnings(record=True):  # weight tables
            raw = read_raw_bti(pdf, config, hs, convert=True, preload=False)

        with open(pdf, "rb") as fid:
            pdf = six.BytesIO(fid.read())
        with open(config, "rb") as fid:
            config = six.BytesIO(fid.read())
        with open(hs, "rb") as fid:
            hs = six.BytesIO(fid.read())
        with warnings.catch_warnings(record=True):  # weight tables
            raw2 = read_raw_bti(pdf, config, hs, convert=True, preload=False)
        repr(raw2)
        assert_array_equal(raw[:][0], raw2[:][0])

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_raw():
    """ Test bti conversion to Raw object """

    for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
                                         exported_fnames):
        # rx = 2 if 'linux' in pdf else 0
        assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
        assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
        if op.exists(tmp_raw_fname):
            os.remove(tmp_raw_fname)
        with Raw(exported, preload=True) as ex:
            with read_raw_bti(pdf, config, hs) as ra:
                assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
                assert_array_almost_equal(ex.info['dev_head_t']['trans'],
                                          ra.info['dev_head_t']['trans'], 7)
                dig1, dig2 = [np.array([d['r'] for d in r_.info['dig']])
                              for r_ in (ra, ex)]
                assert_array_equal(dig1, dig2)

                coil1, coil2 = [np.concatenate([d['coil_trans'].flatten()
                                for d in r_.info['chs'][:NCH]])
                                for r_ in (ra, ex)]
                assert_array_almost_equal(coil1, coil2, 7)

                loc1, loc2 = [np.concatenate([d['loc'].flatten()
                              for d in r_.info['chs'][:NCH]])
                              for r_ in (ra, ex)]
                assert_array_equal(loc1, loc2)

                assert_array_equal(ra._data[:NCH], ex._data[:NCH])
                assert_array_equal(ra.cals[:NCH], ex.cals[:NCH])
                ra.save(tmp_raw_fname)
            with Raw(tmp_raw_fname) as r:
                print(r)
        os.remove(tmp_raw_fname)

def test_no_conversion():
    """ Test bti no-conversion option """

    get_info = partial(
        _get_bti_info,
        rotation_x=0.0, translation=(0.0, 0.02, 0.11), convert=False,
        ecg_ch='E31', eog_ch=('E63', 'E64'),
        rename_channels=False, sort_by_ch_name=False)

    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        with warnings.catch_warnings(record=True):  # weight tables
            raw_info, _ = get_info(pdf, config, hs, convert=False)
        with warnings.catch_warnings(record=True):  # weight tables
            raw_info_con = read_raw_bti(
                pdf_fname=pdf, config_fname=config, head_shape_fname=hs,
                convert=True, preload=False).info

        pick_info(raw_info_con,
                  pick_types(raw_info_con, meg=True, ref_meg=True),
                  copy=False)
        pick_info(raw_info,
                  pick_types(raw_info, meg=True, ref_meg=True), copy=False)
        bti_info = _read_bti_header(pdf, config)
        dev_ctf_t = _correct_trans(bti_info['bti_transform'][0])
        assert_array_equal(dev_ctf_t, raw_info['dev_ctf_t']['trans'])
        assert_array_equal(raw_info['dev_head_t']['trans'], np.eye(4))
        assert_array_equal(raw_info['ctf_head_t']['trans'], np.eye(4))
        dig, t = _process_bti_headshape(hs, convert=False, use_hpi=False)
        assert_array_equal(t['trans'], np.eye(4))

        for ii, (old, new, con) in enumerate(zip(
                dig, raw_info['dig'], raw_info_con['dig'])):
            assert_equal(old['ident'], new['ident'])
            assert_array_equal(old['r'], new['r'])
            assert_true(not np.allclose(old['r'], con['r']))

            if ii > 10:
                break

        ch_map = dict((ch['chan_label'],
                       ch['loc']) for ch in bti_info['chs'])

        for ii, ch_label in enumerate(raw_info['ch_names']):
            if not ch_label.startswith('A'):
                continue
            t1 = ch_map[ch_label]  # correction already performed in bti_info
            t2 = raw_info['chs'][ii]['loc']
            t3 = raw_info_con['chs'][ii]['loc']
            assert_allclose(t1, t2, atol=1e-15)
            assert_true(not np.allclose(t1, t3))
            idx_a = raw_info_con['ch_names'].index('MEG 001')
            idx_b = raw_info['ch_names'].index('A22')
            assert_equal(
                raw_info_con['chs'][idx_a]['coord_frame'],
                FIFF.FIFFV_COORD_DEVICE)
            assert_equal(
                raw_info['chs'][idx_b]['coord_frame'],
                FIFF.FIFFV_MNE_COORD_4D_HEAD)

def test_pick_refs():
    """Test picking of reference sensors
    """
    infos = list()
    # 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)
    infos.append(raw_kit.info)
    # 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):  # weight tables
        raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
    infos.append(raw_bti.info)
    # CTF
    fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
    raw_ctf = read_raw_fif(fname_ctf_raw, add_eeg_ref=False)
    raw_ctf.apply_gradient_compensation(2)
    infos.append(raw_ctf.info)
    for info in infos:
        info['bads'] = []
        assert_raises(ValueError, pick_types, info, meg='foo')
        assert_raises(ValueError, pick_types, info, ref_meg='foo')
        picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
        picks_meg = pick_types(info, meg=True, ref_meg=False)
        picks_ref = pick_types(info, meg=False, ref_meg=True)
        assert_array_equal(picks_meg_ref,
                           np.sort(np.concatenate([picks_meg, picks_ref])))
        picks_grad = pick_types(info, meg='grad', ref_meg=False)
        picks_ref_grad = pick_types(info, meg=False, ref_meg='grad')
        picks_meg_ref_grad = pick_types(info, meg='grad', ref_meg='grad')
        assert_array_equal(picks_meg_ref_grad,
                           np.sort(np.concatenate([picks_grad,
                                                   picks_ref_grad])))
        picks_mag = pick_types(info, meg='mag', ref_meg=False)
        picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
        picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
        assert_array_equal(picks_meg_ref_mag,
                           np.sort(np.concatenate([picks_mag,
                                                   picks_ref_mag])))
        assert_array_equal(picks_meg,
                           np.sort(np.concatenate([picks_mag, picks_grad])))
        assert_array_equal(picks_ref,
                           np.sort(np.concatenate([picks_ref_mag,
                                                   picks_ref_grad])))
        assert_array_equal(picks_meg_ref, np.sort(np.concatenate(
            [picks_grad, picks_mag, picks_ref_grad, picks_ref_mag])))
        for pick in (picks_meg_ref, picks_meg, picks_ref,
                     picks_grad, picks_ref_grad, picks_meg_ref_grad,
                     picks_mag, picks_ref_mag, picks_meg_ref_mag):
            if len(pick) > 0:
                pick_info(info, pick)

def run():
    """Run command."""
    from mne.commands.utils import get_optparser

    parser = get_optparser(__file__)

    parser.add_option('-p', '--pdf', dest='pdf_fname',
                      help='Input data file name', metavar='FILE')
    parser.add_option('-c', '--config', dest='config_fname',
                      help='Input config file name', metavar='FILE',
                      default='config')
    parser.add_option('--head_shape', dest='head_shape_fname',
                      help='Headshape file name', metavar='FILE',
                      default='hs_file')
    parser.add_option('-o', '--out_fname', dest='out_fname',
                      help='Name of the resulting fiff file',
                      default='as_data_fname')
    parser.add_option('-r', '--rotation_x', dest='rotation_x', type='float',
                      help='Compensatory rotation about Neuromag x axis, deg',
                      default=2.0)
    parser.add_option('-T', '--translation', dest='translation', type='str',
                      help='Default translation, meter',
                      default=(0.00, 0.02, 0.11))
    parser.add_option('--ecg_ch', dest='ecg_ch', type='str',
                      help='4D ECG channel name',
                      default='E31')
    parser.add_option('--eog_ch', dest='eog_ch', type='str',
                      help='4D EOG channel names',
                      default='E63,E64')

    options, args = parser.parse_args()

    pdf_fname = options.pdf_fname
    if pdf_fname is None:
        parser.print_help()
        sys.exit(1)

    config_fname = options.config_fname
    head_shape_fname = options.head_shape_fname
    out_fname = options.out_fname
    rotation_x = options.rotation_x
    translation = options.translation
    ecg_ch = options.ecg_ch
    eog_ch = options.ecg_ch.split(',')

    if out_fname == 'as_data_fname':
        out_fname = pdf_fname + '_raw.fif'

    raw = read_raw_bti(pdf_fname=pdf_fname, config_fname=config_fname,
                       head_shape_fname=head_shape_fname,
                       rotation_x=rotation_x, translation=translation,
                       ecg_ch=ecg_ch, eog_ch=eog_ch)

    raw.save(out_fname)
    raw.close()
    if is_main:
        sys.exit(0)

def test_raw():
    """ Test bti conversion to Raw object """
    for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
                                         exported_fnames):
        # rx = 2 if 'linux' in pdf else 0
        assert_raises(ValueError, read_raw_bti, pdf, 'eggs', preload=False)
        assert_raises(ValueError, read_raw_bti, pdf, config, 'spam',
                      preload=False)
        if op.exists(tmp_raw_fname):
            os.remove(tmp_raw_fname)
        ex = Raw(exported, preload=True)
        with warnings.catch_warnings(record=True):  # weight tables
            ra = read_raw_bti(pdf, config, hs, preload=False)
        assert_true('RawBTi' in repr(ra))
        assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
        assert_array_almost_equal(ex.info['dev_head_t']['trans'],
                                  ra.info['dev_head_t']['trans'], 7)
        with warnings.catch_warnings(record=True):  # headshape
            assert_dig_allclose(ex.info, ra.info)
        coil1, coil2 = [np.concatenate([d['loc'].flatten()
                        for d in r_.info['chs'][:NCH]])
                        for r_ in (ra, ex)]
        assert_array_almost_equal(coil1, coil2, 7)

        loc1, loc2 = [np.concatenate([d['loc'].flatten()
                      for d in r_.info['chs'][:NCH]])
                      for r_ in (ra, ex)]
        assert_allclose(loc1, loc2)

        assert_allclose(ra[:NCH][0], ex[:NCH][0])
        assert_array_equal([c['range'] for c in ra.info['chs'][:NCH]],
                           [c['range'] for c in ex.info['chs'][:NCH]])
        assert_array_equal([c['cal'] for c in ra.info['chs'][:NCH]],
                           [c['cal'] for c in ex.info['chs'][:NCH]])
        assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])

        # check our transforms
        for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
            if ex.info[key] is None:
                pass
            else:
                assert_true(ra.info[key] is not None)
                for ent in ('to', 'from', 'trans'):
                    assert_allclose(ex.info[key][ent],
                                    ra.info[key][ent])

        ra.save(tmp_raw_fname)
        re = Raw(tmp_raw_fname)
        print(re)
        for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
            assert_true(isinstance(re.info[key], dict))
            this_t = re.info[key]['trans']
            assert_equal(this_t.shape, (4, 4))
            # cehck that matrix by is not identity
            assert_true(not np.allclose(this_t, np.eye(4)))
        os.remove(tmp_raw_fname)

def test_crop():
    """ Test crop raw """
    raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
    y, t = raw[:]
    t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
    mask = (t0 <= t) * (t <= t1)
    raw_ = raw.crop(t0, t1)
    y_, _ = raw_[:]
    assert_true(y_.shape[1] == mask.sum())
    assert_true(y_.shape[0] == y.shape[0])

def test_raw():
    """ Test bti conversion to Raw object """
    for pdf, config, hs, exported in zip(pdf_fnames, config_fnames, hs_fnames,
                                         exported_fnames):
        # rx = 2 if 'linux' in pdf else 0
        assert_raises(ValueError, read_raw_bti, pdf, 'eggs')
        assert_raises(ValueError, read_raw_bti, pdf, config, 'spam')
        if op.exists(tmp_raw_fname):
            os.remove(tmp_raw_fname)
        ex = Raw(exported, preload=True)
        ra = read_raw_bti(pdf, config, hs)
        assert_true('RawBTi' in repr(ra))
        assert_equal(ex.ch_names[:NCH], ra.ch_names[:NCH])
        assert_array_almost_equal(ex.info['dev_head_t']['trans'],
                                  ra.info['dev_head_t']['trans'], 7)
        dig1, dig2 = [np.array([d['r'] for d in r_.info['dig']])
                      for r_ in (ra, ex)]
        assert_array_almost_equal(dig1, dig2, 18)
        coil1, coil2 = [np.concatenate([d['loc'].flatten()
                        for d in r_.info['chs'][:NCH]])
                        for r_ in (ra, ex)]
        assert_array_almost_equal(coil1, coil2, 7)

        loc1, loc2 = [np.concatenate([d['loc'].flatten()
                      for d in r_.info['chs'][:NCH]])
                      for r_ in (ra, ex)]
        assert_allclose(loc1, loc2)

        assert_array_equal(ra._data[:NCH], ex._data[:NCH])
        assert_array_equal(ra._cals[:NCH], ex._cals[:NCH])

        # check our transforms
        for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
            if ex.info[key] is None:
                pass
            else:
                assert_true(ra.info[key] is not None)
                for ent in ('to', 'from', 'trans'):
                    assert_allclose(ex.info[key][ent],
                                    ra.info[key][ent])

        # Make sure concatenation works
        raw_concat = concatenate_raws([ra.copy(), ra])
        assert_equal(raw_concat.n_times, 2 * ra.n_times)

        ra.save(tmp_raw_fname)
        re = Raw(tmp_raw_fname)
        print(re)
        for key in ('dev_head_t', 'dev_ctf_t', 'ctf_head_t'):
            assert_true(isinstance(re.info[key], dict))
            this_t = re.info[key]['trans']
            assert_equal(this_t.shape, (4, 4))
            # cehck that matrix by is not identity
            assert_true(not np.allclose(this_t, np.eye(4)))
        os.remove(tmp_raw_fname)

def test_crop_append():
    """ Test crop and append raw """
    raw = read_raw_bti(pdf_fnames[0], config_fnames[0], hs_fnames[0])
    raw.preload_data()  # currently does nothing
    y, t = raw[:]
    t0, t1 = 0.25 * t[-1], 0.75 * t[-1]
    mask = (t0 <= t) * (t <= t1)
    raw_ = raw.crop(t0, t1)
    y_, _ = raw_[:]
    assert_true(y_.shape[1] == mask.sum())
    assert_true(y_.shape[0] == y.shape[0])

    raw2 = raw.copy()
    assert_raises(RuntimeError, raw.append, raw2, preload=False)
    raw.append(raw2)
    assert_allclose(np.tile(raw2[:, :][0], (1, 2)), raw[:, :][0])

def test_plot_trans():
    """Test plotting of -trans.fif files and MEG sensor layouts."""
    from mayavi import mlab
    evoked = read_evokeds(evoked_fname)[0]
    with warnings.catch_warnings(record=True):  # 4D weight tables
        bti = read_raw_bti(pdf_fname, config_fname, hs_fname, convert=True,
                           preload=False).info
    infos = dict(
        Neuromag=evoked.info,
        CTF=read_raw_ctf(ctf_fname).info,
        BTi=bti,
        KIT=read_raw_kit(sqd_fname).info,
    )
    for system, info in infos.items():
        ref_meg = False if system == 'KIT' else True
        plot_trans(info, trans_fname, subject='sample', meg_sensors=True,
                   subjects_dir=subjects_dir, ref_meg=ref_meg)
        mlab.close(all=True)
    # KIT ref sensor coil def is defined
    plot_trans(infos['KIT'], None, meg_sensors=True, ref_meg=True)
    mlab.close(all=True)
    info = infos['Neuromag']
    assert_raises(ValueError, plot_trans, info, trans_fname,
                  subject='sample', subjects_dir=subjects_dir,
                  ch_type='bad-chtype')
    assert_raises(TypeError, plot_trans, 'foo', trans_fname,
                  subject='sample', subjects_dir=subjects_dir)
    # no-head version
    plot_trans(info, None, meg_sensors=True, dig=True, coord_frame='head')
    mlab.close(all=True)
    # all coord frames
    for coord_frame in ('meg', 'head', 'mri'):
        plot_trans(info, meg_sensors=True, dig=True, coord_frame=coord_frame,
                   trans=trans_fname, subject='sample',
                   subjects_dir=subjects_dir)
        mlab.close(all=True)
    # EEG only with strange options
    evoked_eeg_ecog = evoked.copy().pick_types(meg=False, eeg=True)
    evoked_eeg_ecog.info['projs'] = []  # "remove" avg proj
    evoked_eeg_ecog.set_channel_types({'EEG 001': 'ecog'})
    with warnings.catch_warnings(record=True) as w:
        plot_trans(evoked_eeg_ecog.info, subject='sample', trans=trans_fname,
                   source='outer_skin', meg_sensors=True, skull=True,
                   eeg_sensors=['original', 'projected'], ecog_sensors=True,
                   brain='white', head=True, subjects_dir=subjects_dir)
    mlab.close(all=True)
    assert_true(['Cannot plot MEG' in str(ww.message) for ww in w])

def test_transforms():
    """ Test transformations """
    bti_trans = (0.0, 0.02, 0.11)
    bti_dev_t = Transform("ctf_meg", "meg", _get_bti_dev_t(0.0, bti_trans))
    for pdf, config, hs in zip(pdf_fnames, config_fnames, hs_fnames):
        raw = read_raw_bti(pdf, config, hs)
        dev_ctf_t = raw.info["dev_ctf_t"]
        dev_head_t_old = raw.info["dev_head_t"]
        ctf_head_t = raw.info["ctf_head_t"]

        # 1) get BTI->Neuromag
        bti_dev_t = Transform("ctf_meg", "meg", _get_bti_dev_t(0.0, bti_trans))

        # 2) get Neuromag->BTI head
        t = combine_transforms(invert_transform(bti_dev_t), dev_ctf_t, "meg", "ctf_head")
        # 3) get Neuromag->head
        dev_head_t_new = combine_transforms(t, ctf_head_t, "meg", "head")

        assert_array_equal(dev_head_t_new["trans"], dev_head_t_old["trans"])

def test_transforms():
    """Test transformations."""
    bti_trans = (0.0, 0.02, 0.11)
    bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))
    for pdf, config, hs, in zip(pdf_fnames, config_fnames, hs_fnames):
        raw = read_raw_bti(pdf, config, hs, preload=False)
        dev_ctf_t = raw.info['dev_ctf_t']
        dev_head_t_old = raw.info['dev_head_t']
        ctf_head_t = raw.info['ctf_head_t']

        # 1) get BTI->Neuromag
        bti_dev_t = Transform('ctf_meg', 'meg', _get_bti_dev_t(0.0, bti_trans))

        # 2) get Neuromag->BTI head
        t = combine_transforms(invert_transform(bti_dev_t), dev_ctf_t,
                               'meg', 'ctf_head')
        # 3) get Neuromag->head
        dev_head_t_new = combine_transforms(t, ctf_head_t, 'meg', 'head')

        assert_array_equal(dev_head_t_new['trans'], dev_head_t_old['trans'])

def test_pick_refs():
    """Test picking of reference sensors."""
    infos = list()
    # 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)
    infos.append(raw_kit.info)
    # 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')
    raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
    infos.append(raw_bti.info)
    # CTF
    fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
    raw_ctf = read_raw_fif(fname_ctf_raw)
    raw_ctf.apply_gradient_compensation(2)
    for info in infos:
        info['bads'] = []
        pytest.raises(ValueError, pick_types, info, meg='foo')
        pytest.raises(ValueError, pick_types, info, ref_meg='foo')
        picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
        picks_meg = pick_types(info, meg=True, ref_meg=False)
        picks_ref = pick_types(info, meg=False, ref_meg=True)
        assert_array_equal(picks_meg_ref,
                           np.sort(np.concatenate([picks_meg, picks_ref])))
        picks_grad = pick_types(info, meg='grad', ref_meg=False)
        picks_ref_grad = pick_types(info, meg=False, ref_meg='grad')
        picks_meg_ref_grad = pick_types(info, meg='grad', ref_meg='grad')
        assert_array_equal(picks_meg_ref_grad,
                           np.sort(np.concatenate([picks_grad,
                                                   picks_ref_grad])))
        picks_mag = pick_types(info, meg='mag', ref_meg=False)
        picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
        picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
        assert_array_equal(picks_meg_ref_mag,
                           np.sort(np.concatenate([picks_mag,
                                                   picks_ref_mag])))
        assert_array_equal(picks_meg,
                           np.sort(np.concatenate([picks_mag, picks_grad])))
        assert_array_equal(picks_ref,
                           np.sort(np.concatenate([picks_ref_mag,
                                                   picks_ref_grad])))
        assert_array_equal(picks_meg_ref, np.sort(np.concatenate(
            [picks_grad, picks_mag, picks_ref_grad, picks_ref_mag])))

        for pick in (picks_meg_ref, picks_meg, picks_ref,
                     picks_grad, picks_ref_grad, picks_meg_ref_grad,
                     picks_mag, picks_ref_mag, picks_meg_ref_mag):
            if len(pick) > 0:
                pick_info(info, pick)

    # test CTF expected failures directly
    info = raw_ctf.info
    info['bads'] = []
    picks_meg_ref = pick_types(info, meg=True, ref_meg=True)
    picks_meg = pick_types(info, meg=True, ref_meg=False)
    picks_ref = pick_types(info, meg=False, ref_meg=True)
    picks_mag = pick_types(info, meg='mag', ref_meg=False)
    picks_ref_mag = pick_types(info, meg=False, ref_meg='mag')
    picks_meg_ref_mag = pick_types(info, meg='mag', ref_meg='mag')
    for pick in (picks_meg_ref, picks_ref, picks_ref_mag, picks_meg_ref_mag):
        if len(pick) > 0:
            pick_info(info, pick)

    for pick in (picks_meg, picks_mag):
        if len(pick) > 0:
            with catch_logging() as log:
                pick_info(info, pick, verbose=True)
            assert ('Removing {} compensators'.format(len(info['comps']))
                    in log.getvalue())

def test_make_forward_solution_kit():
    """Test making fwd using KIT, BTI, and CTF (compensated) files
    """
    fname_bem = op.join(subjects_dir, 'sample', 'bem',
                        'sample-5120-bem-sol.fif')
    kit_dir = op.join(op.dirname(__file__), '..', '..', 'io', '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')
    mri_path = op.join(kit_dir, 'trans-sample.fif')
    fname_kit_raw = op.join(kit_dir, 'test_bin_raw.fif')

    bti_dir = op.join(op.dirname(__file__), '..', '..', 'io', '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')
    fname_bti_raw = op.join(bti_dir, 'exported4D_linux_raw.fif')

    fname_ctf_raw = op.join(op.dirname(__file__), '..', '..', 'io', 'tests',
                            'data', 'test_ctf_comp_raw.fif')

    # first set up a testing source space
    fname_src = op.join(temp_dir, 'oct2-src.fif')
    src = setup_source_space('sample', fname_src, 'oct2',
                             subjects_dir=subjects_dir)

    # first use mne-C: convert file, make forward solution
    fwd = do_forward_solution('sample', fname_kit_raw, src=fname_src,
                              mindist=0.0, bem=fname_bem, mri=mri_path,
                              eeg=False, meg=True, subjects_dir=subjects_dir)
    assert_true(isinstance(fwd, Forward))

    # now let's use python with the same raw file
    fwd_py = make_forward_solution(fname_kit_raw, mindist=0.0,
                                   src=src, eeg=False, meg=True,
                                   bem=fname_bem, mri=mri_path)
    _compare_forwards(fwd, fwd_py, 157, 108)
    assert_true(isinstance(fwd_py, Forward))

    # now let's use mne-python all the way
    raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
    # without ignore_ref=True, this should throw an error:
    assert_raises(NotImplementedError, make_forward_solution, raw_py.info,
                  mindist=0.0, src=src, eeg=False, meg=True,
                  bem=fname_bem, mri=mri_path)
    fwd_py = make_forward_solution(raw_py.info, mindist=0.0,
                                   src=src, eeg=False, meg=True,
                                   bem=fname_bem, mri=mri_path,
                                   ignore_ref=True)
    _compare_forwards(fwd, fwd_py, 157, 108,
                      meg_rtol=1e-3, meg_atol=1e-7)

    # BTI python end-to-end versus C
    fwd = do_forward_solution('sample', fname_bti_raw, src=fname_src,
                              mindist=0.0, bem=fname_bem, mri=mri_path,
                              eeg=False, meg=True, subjects_dir=subjects_dir)
    raw_py = read_raw_bti(bti_pdf, bti_config, bti_hs)
    fwd_py = make_forward_solution(raw_py.info, mindist=0.0,
                                   src=src, eeg=False, meg=True,
                                   bem=fname_bem, mri=mri_path)
    _compare_forwards(fwd, fwd_py, 248, 108)

    # now let's test CTF w/compensation
    fwd_py = make_forward_solution(fname_ctf_raw, mindist=0.0,
                                   src=src, eeg=False, meg=True,