def test_io_inverse_operator():
    """Test IO of inverse_operator."""
    tempdir = _TempDir()
    inverse_operator = read_inverse_operator(fname_inv)
    x = repr(inverse_operator)
    assert (x)
    assert (isinstance(inverse_operator['noise_cov'], Covariance))
    # just do one example for .gz, as it should generalize
    _compare_io(inverse_operator, '.gz')

    # test warnings on bad filenames
    inv_badname = op.join(tempdir, 'test-bad-name.fif.gz')
    with pytest.warns(RuntimeWarning, match='-inv.fif'):
        write_inverse_operator(inv_badname, inverse_operator)
    with pytest.warns(RuntimeWarning, match='-inv.fif'):
        read_inverse_operator(inv_badname)

    # make sure we can write and read
    inv_fname = op.join(tempdir, 'test-inv.fif')
    args = (10, 1. / 9., 'dSPM')
    inv_prep = prepare_inverse_operator(inverse_operator, *args)
    write_inverse_operator(inv_fname, inv_prep)
    inv_read = read_inverse_operator(inv_fname)
    _compare(inverse_operator, inv_read)
    inv_read_prep = prepare_inverse_operator(inv_read, *args)
    _compare(inv_prep, inv_read_prep)
    inv_prep_prep = prepare_inverse_operator(inv_prep, *args)
    _compare(inv_prep, inv_prep_prep)

def test_make_inverse_operator_fixed():
    """Test MNE inverse computation (fixed orientation)
    """
    fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
    fwd_1 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=False)
    fwd_2 = read_forward_solution(fname_fwd, surf_ori=False, force_fixed=True)
    evoked = _get_evoked()
    noise_cov = read_cov(fname_cov)

    # can't make depth-weighted fixed inv without surf ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_1,
                  noise_cov, depth=0.8, loose=None, fixed=True)
    # can't make fixed inv with depth weighting without free ori fwd
    assert_raises(ValueError, make_inverse_operator, evoked.info, fwd_2,
                  noise_cov, depth=0.8, loose=None, fixed=True)

    # compare to C solution w/fixed
    inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov, depth=0.8,
                                   loose=None, fixed=True)
    _compare_io(inv_op)
    inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
    _compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

    # now compare to C solution
    # note that the forward solution must not be surface-oriented
    # to get equivalency (surf_ori=True changes the normals)
    inv_op = make_inverse_operator(evoked.info, fwd_2, noise_cov, depth=None,
                                   loose=None, fixed=True)
    inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
    _compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

    def test_make_inverse_operator_fixed(self):
        """Test MNE inverse computation w/ fixed orientation (& no depth
        weighting)
        """
        # can't make fixed inv without surf ori fwd
        assert_raises(ValueError, make_inverse_operator, evoked.info,
                      self.fwd_1, noise_cov, depth=0.8, loose=None, fixed=True)
        # can't make fixed inv with depth weighting without free ori fwd
        assert_raises(ValueError, make_inverse_operator, evoked.info,
                      self.fwd_2, noise_cov, depth=0.8, loose=None, fixed=True)
        inv_op = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
                                       depth=0.8, loose=None, fixed=True)
        _compare_io(inv_op)
        inverse_operator_fixed = read_inverse_operator(fname_inv_fixed)
        _compare_inverses_approx(inverse_operator_fixed, inv_op, evoked, 2)
        # Inverse has 306 channels - 4 proj = 302
        assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

        # Now without depth weighting, these should be equivalent
        inv_op = make_inverse_operator(evoked.info, self.fwd_2, noise_cov,
                                       depth=None, loose=None, fixed=True)
        inv_2 = make_inverse_operator(evoked.info, self.fwd_op, noise_cov,
                                      depth=None, loose=None, fixed=True)
        _compare_inverses_approx(inv_op, inv_2, evoked, 2)
        _compare_io(inv_op)
        # now compare to C solution
        inverse_operator_nodepth = read_inverse_operator(fname_inv_nodepth)
        _compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked, 2)
        # Inverse has 306 channels - 4 proj = 302
        assert_true(compute_rank_inverse(inverse_operator_fixed) == 302)

def test_io_inverse_operator():
    """Test IO of inverse_operator
    """
    tempdir = _TempDir()
    inverse_operator = read_inverse_operator(fname_inv)
    x = repr(inverse_operator)
    assert_true(x)
    assert_true(isinstance(inverse_operator['noise_cov'], Covariance))
    # just do one example for .gz, as it should generalize
    _compare_io(inverse_operator, '.gz')

    # test warnings on bad filenames
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        inv_badname = op.join(tempdir, 'test-bad-name.fif.gz')
        write_inverse_operator(inv_badname, inverse_operator)
        read_inverse_operator(inv_badname)
    assert_naming(w, 'test_inverse.py', 2)

    # make sure we can write and read
    inv_fname = op.join(tempdir, 'test-inv.fif')
    args = (10, 1. / 9., 'dSPM')
    inv_prep = prepare_inverse_operator(inverse_operator, *args)
    write_inverse_operator(inv_fname, inv_prep)
    inv_read = read_inverse_operator(inv_fname)
    _compare(inverse_operator, inv_read)
    inv_read_prep = prepare_inverse_operator(inv_read, *args)
    _compare(inv_prep, inv_read_prep)
    inv_prep_prep = prepare_inverse_operator(inv_prep, *args)
    _compare(inv_prep, inv_prep_prep)

def test_io_inverse_operator():
    """Test IO of inverse_operator with GZip
    """
    inverse_operator = read_inverse_operator(fname_inv)
    # just do one example for .gz, as it should generalize
    _compare_io(inverse_operator, '.gz')

    # test warnings on bad filenames
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        inv_badname = op.join(tempdir, 'test-bad-name.fif.gz')
        write_inverse_operator(inv_badname, inverse_operator)
        read_inverse_operator(inv_badname)
    assert_true(len(w) == 2)

def test_make_inverse_operator_loose(evoked):
    """Test MNE inverse computation (precomputed and non-precomputed)."""
    # Test old version of inverse computation starting from forward operator
    noise_cov = read_cov(fname_cov)
    inverse_operator = read_inverse_operator(fname_inv)
    fwd_op = convert_forward_solution(read_forward_solution_meg(fname_fwd),
                                      surf_ori=True, copy=False)
    with catch_logging() as log:
        with pytest.deprecated_call():  # limit_depth_chs
            my_inv_op = make_inverse_operator(
                evoked.info, fwd_op, noise_cov, loose=0.2, depth=0.8,
                limit_depth_chs=False, verbose=True)
    log = log.getvalue()
    assert 'MEG: rank 302 computed' in log
    assert 'limit = 1/%d' % fwd_op['nsource'] in log
    _compare_io(my_inv_op)
    assert_equal(inverse_operator['units'], 'Am')
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked,
                             rtol=1e-2, atol=1e-5, depth_atol=1e-3)
    # Test MNE inverse computation starting from forward operator
    with catch_logging() as log:
        my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
                                          loose='auto', depth=0.8,
                                          fixed=False, verbose=True)
    log = log.getvalue()
    assert 'MEG: rank 302 computed from 305' in log
    _compare_io(my_inv_op)
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked,
                             rtol=1e-3, atol=1e-5)
    assert ('dev_head_t' in my_inv_op['info'])
    assert ('mri_head_t' in my_inv_op)

def test_apply_mne_inverse_raw():
    """Test MNE with precomputed inverse operator on Raw."""
    start = 3
    stop = 10
    raw = read_raw_fif(fname_raw)
    label_lh = read_label(fname_label % 'Aud-lh')
    _, times = raw[0, start:stop]
    inverse_operator = read_inverse_operator(fname_full)
    inverse_operator = prepare_inverse_operator(inverse_operator, nave=1,
                                                lambda2=lambda2, method="dSPM")
    for pick_ori in [None, "normal", "vector"]:
        stc = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
                                label=label_lh, start=start, stop=stop, nave=1,
                                pick_ori=pick_ori, buffer_size=None,
                                prepared=True)

        stc2 = apply_inverse_raw(raw, inverse_operator, lambda2, "dSPM",
                                 label=label_lh, start=start, stop=stop,
                                 nave=1, pick_ori=pick_ori,
                                 buffer_size=3, prepared=True)

        if pick_ori is None:
            assert_true(np.all(stc.data > 0))
            assert_true(np.all(stc2.data > 0))

        assert_true(stc.subject == 'sample')
        assert_true(stc2.subject == 'sample')
        assert_array_almost_equal(stc.times, times)
        assert_array_almost_equal(stc2.times, times)
        assert_array_almost_equal(stc.data, stc2.data)

def test_make_inverse_operator():
    """Test MNE inverse computation (precomputed and non-precomputed)."""
    # Test old version of inverse computation starting from forward operator
    evoked = _get_evoked()
    noise_cov = read_cov(fname_cov)
    inverse_operator = read_inverse_operator(fname_inv)
    fwd_op = convert_forward_solution(read_forward_solution_meg(fname_fwd),
                                      surf_ori=True, copy=False)
    with catch_logging() as log:
        my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
                                          loose=0.2, depth=0.8,
                                          limit_depth_chs=False, verbose=True)
    log = log.getvalue()
    assert 'rank 302 (3 small eigenvalues omitted)' in log
    _compare_io(my_inv_op)
    assert_equal(inverse_operator['units'], 'Am')
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked,
                             rtol=1e-2, atol=1e-5, depth_atol=1e-3)
    # Test MNE inverse computation starting from forward operator
    with catch_logging() as log:
        my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
                                          loose=0.2, depth=0.8, verbose=True)
    log = log.getvalue()
    assert 'rank 302 (3 small eigenvalues omitted)' in log
    _compare_io(my_inv_op)
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked,
                             rtol=1e-3, atol=1e-5)
    assert ('dev_head_t' in my_inv_op['info'])
    assert ('mri_head_t' in my_inv_op)

def test_tfr_with_inverse_operator():
    """Test time freq with MNE inverse computation"""

    tmin, tmax, event_id = -0.2, 0.5, 1

    # Setup for reading the raw data
    raw = io.Raw(fname_data)
    events = find_events(raw, stim_channel='STI 014')
    inverse_operator = read_inverse_operator(fname_inv)
    inv = prepare_inverse_operator(inverse_operator, nave=1,
                                   lambda2=1. / 9., method="dSPM")

    raw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more

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

    # Load condition 1
    event_id = 1
    events3 = events[:3]  # take 3 events to keep the computation time low
    epochs = Epochs(raw, events3, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
                    preload=True)

    # Compute a source estimate per frequency band
    bands = dict(alpha=[10, 10])
    label = read_label(fname_label)

    stcs = source_band_induced_power(epochs, inv, bands,
                                     n_cycles=2, use_fft=False, pca=True,
                                     label=label, prepared=True)

    stc = stcs['alpha']
    assert_true(len(stcs) == len(list(bands.keys())))
    assert_true(np.all(stc.data > 0))
    assert_array_almost_equal(stc.times, epochs.times)

    stcs_no_pca = source_band_induced_power(epochs, inv, bands,
                                            n_cycles=2, use_fft=False,
                                            pca=False, label=label,
                                            prepared=True)

    assert_array_almost_equal(stcs['alpha'].data, stcs_no_pca['alpha'].data)

    # Compute a source estimate per frequency band
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
                    preload=True)

    frequencies = np.arange(7, 30, 2)  # define frequencies of interest
    power, phase_lock = source_induced_power(epochs, inv,
                                             frequencies, label,
                                             baseline=(-0.1, 0),
                                             baseline_mode='percent',
                                             n_cycles=2, n_jobs=1,
                                             prepared=True)
    assert_true(np.all(phase_lock > 0))
    assert_true(np.all(phase_lock <= 1))
    assert_true(np.max(power) > 10)

def test_apply_inverse_sphere():
    """Test applying an inverse with a sphere model (rank-deficient)."""
    evoked = _get_evoked()
    evoked.pick_channels(evoked.ch_names[:306:8])
    evoked.info['projs'] = []
    cov = make_ad_hoc_cov(evoked.info)
    sphere = make_sphere_model('auto', 'auto', evoked.info)
    fwd = read_forward_solution(fname_fwd)
    vertices = [fwd['src'][0]['vertno'][::5],
                fwd['src'][1]['vertno'][::5]]
    stc = SourceEstimate(np.zeros((sum(len(v) for v in vertices), 1)),
                         vertices, 0., 1.)
    fwd = restrict_forward_to_stc(fwd, stc)
    fwd = make_forward_solution(evoked.info, fwd['mri_head_t'], fwd['src'],
                                sphere, mindist=5.)
    evoked = EvokedArray(fwd['sol']['data'].copy(), evoked.info)
    assert fwd['sol']['nrow'] == 39
    assert fwd['nsource'] == 101
    assert fwd['sol']['ncol'] == 303
    tempdir = _TempDir()
    temp_fname = op.join(tempdir, 'temp-inv.fif')
    inv = make_inverse_operator(evoked.info, fwd, cov, loose=1.)
    # This forces everything to be float32
    write_inverse_operator(temp_fname, inv)
    inv = read_inverse_operator(temp_fname)
    stc = apply_inverse(evoked, inv, method='eLORETA',
                        method_params=dict(eps=1e-2))
    # assert zero localization bias
    assert_array_equal(np.argmax(stc.data, axis=0),
                       np.repeat(np.arange(101), 3))

def test_make_inverse_operator_fixed():
    """Test MNE inverse computation (fixed orientation)."""
    fwd = read_forward_solution_meg(fname_fwd)
    evoked = _get_evoked()
    noise_cov = read_cov(fname_cov)

    # can't make fixed inv with depth weighting without free ori fwd
    fwd_fixed = convert_forward_solution(fwd, force_fixed=True,
                                         use_cps=True)
    pytest.raises(ValueError, make_inverse_operator, evoked.info, fwd_fixed,
                  noise_cov, depth=0.8, fixed=True)

    # now compare to C solution
    # note that the forward solution must not be surface-oriented
    # to get equivalency (surf_ori=True changes the normals)
    with catch_logging() as log:
        inv_op = make_inverse_operator(  # test depth=0. alias for depth=None
            evoked.info, fwd, noise_cov, depth=0., fixed=True,
            use_cps=False, verbose=True)
    log = log.getvalue()
    assert 'rank 302 (3 small eigenvalues omitted)' in log
    assert 'EEG channels: 0' in repr(inv_op)
    assert 'MEG channels: 305' in repr(inv_op)
    del fwd_fixed
    inverse_operator_nodepth = read_inverse_operator(fname_inv_fixed_nodepth)
    # XXX We should have this but we don't (MNE-C doesn't restrict info):
    # assert 'EEG channels: 0' in repr(inverse_operator_nodepth)
    assert 'MEG channels: 305' in repr(inverse_operator_nodepth)
    _compare_inverses_approx(inverse_operator_nodepth, inv_op, evoked,
                             rtol=1e-5, atol=1e-4)
    # Inverse has 306 channels - 6 proj = 302
    assert (compute_rank_inverse(inverse_operator_nodepth) == 302)
    # Now with depth
    fwd_surf = convert_forward_solution(fwd, surf_ori=True)  # not fixed
    for kwargs, use_fwd in zip([dict(fixed=True), dict(loose=0.)],
                               [fwd, fwd_surf]):  # Should be equiv.
        inv_op_depth = make_inverse_operator(
            evoked.info, use_fwd, noise_cov, depth=0.8, use_cps=True,
            **kwargs)
        inverse_operator_depth = read_inverse_operator(fname_inv_fixed_depth)
        # Normals should be the adjusted ones
        assert_allclose(inverse_operator_depth['source_nn'],
                        fwd_surf['source_nn'][2::3], atol=1e-5)
        _compare_inverses_approx(inverse_operator_depth, inv_op_depth, evoked,
                                 rtol=1e-3, atol=1e-4)

def test_source_psd_epochs():
    """Test multi-taper source PSD computation in label from epochs"""

    raw = fiff.Raw(fname_data)
    inverse_operator = read_inverse_operator(fname_inv)
    label = read_label(fname_label)

    event_id, tmin, tmax = 1, -0.2, 0.5
    lambda2, method = 1. / 9., 'dSPM'
    bandwidth = 8.
    fmin, fmax = 0, 100

    picks = fiff.pick_types(raw.info, meg=True, eeg=False, stim=True,
                            ecg=True, eog=True, include=['STI 014'],
                            exclude='bads')
    reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)

    events = find_events(raw)
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=reject)

    # only look at one epoch
    epochs.drop_bad_epochs()
    one_epochs = epochs[:1]

    # return list
    stc_psd = compute_source_psd_epochs(one_epochs, inverse_operator,
                                        lambda2=lambda2, method=method,
                                        pick_normal=True, label=label,
                                        bandwidth=bandwidth,
                                        fmin=fmin, fmax=fmax)[0]

    # return generator
    stcs = compute_source_psd_epochs(one_epochs, inverse_operator,
                                     lambda2=lambda2, method=method,
                                     pick_normal=True, label=label,
                                     bandwidth=bandwidth,
                                     fmin=fmin, fmax=fmax,
                                     return_generator=True)

    for stc in stcs:
        stc_psd_gen = stc

    assert_array_almost_equal(stc_psd.data, stc_psd_gen.data)

    # compare with direct computation
    stc = apply_inverse_epochs(one_epochs, inverse_operator,
                               lambda2=lambda2, method=method,
                               pick_normal=True, label=label)[0]

    sfreq = epochs.info['sfreq']
    psd, freqs = multitaper_psd(stc.data, sfreq=sfreq, bandwidth=bandwidth,
                                fmin=fmin, fmax=fmax)

    assert_array_almost_equal(psd, stc_psd.data)
    assert_array_almost_equal(freqs, stc_psd.times)

def test_io_inverse_operator():
    """Test IO of inverse_operator
    """
    for inv in [inverse_operator, inverse_operator_vol]:
        inv_init = copy.deepcopy(inv)
        write_inverse_operator('test-inv.fif', inv)
        this_inv = read_inverse_operator('test-inv.fif')

        _compare(inv, inv_init)
        _compare(inv, this_inv)

def test_io_inverse_operator():
    """Test IO of inverse_operator
    """
    for inv in [inverse_operator, inverse_operator_vol]:
        inv_init = copy.deepcopy(inv)
        for out_file in ['test-inv.fif', 'test-inv.fif.gz']:
            write_inverse_operator(out_file, inv)
            this_inv = read_inverse_operator(out_file)

            _compare(inv, inv_init)
            _compare(inv, this_inv)

def test_inverse_operator_volume():
    """Test MNE inverse computation on volume source space"""
    evoked = fiff.Evoked(fname_data, setno=0, baseline=(None, 0))
    inverse_operator_vol = read_inverse_operator(fname_vol_inv)
    stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
    stc.save('tmp-vl.stc')
    stc2 = read_source_estimate('tmp-vl.stc')
    assert_true(np.all(stc.data > 0))
    assert_true(np.all(stc.data < 35))
    assert_array_almost_equal(stc.data, stc2.data)
    assert_array_almost_equal(stc.times, stc2.times)

def test_apply_inverse_operator():
    """Test MNE inverse application
    """
    inverse_operator = read_inverse_operator(fname_full)
    evoked = _get_evoked()

    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator) == 302)

    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator) == 302)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 10e-9)
    assert_true(stc.data.mean() > 1e-11)

    # test if using prepared and not prepared inverse operator give the same
    # result
    inv_op = prepare_inverse_operator(inverse_operator, nave=evoked.nave,
                                      lambda2=lambda2, method="MNE")
    stc2 = apply_inverse(evoked, inv_op, lambda2, "MNE")
    assert_array_almost_equal(stc.data, stc2.data)
    assert_array_almost_equal(stc.times, stc2.times)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 10.0)
    assert_true(stc.data.mean() > 0.1)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 35)
    assert_true(stc.data.mean() > 0.1)

    # test without using a label (so delayed computation is used)
    label = read_label(fname_label % 'Aud-lh')
    stc = apply_inverse(evoked, inv_op, lambda2, "MNE")
    stc_label = apply_inverse(evoked, inv_op, lambda2, "MNE",
                              label=label)
    assert_equal(stc_label.subject, 'sample')
    label_stc = stc.in_label(label)
    assert_true(label_stc.subject == 'sample')
    assert_array_almost_equal(stc_label.data, label_stc.data)

    # Test we get errors when using custom ref or no average proj is present
    evoked.info['custom_ref_applied'] = True
    assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")
    evoked.info['custom_ref_applied'] = False
    evoked.info['projs'] = []  # remove EEG proj
    assert_raises(ValueError, apply_inverse, evoked, inv_op, lambda2, "MNE")

 def test_make_inverse_operator_diag(self):
     """Test MNE inverse computation with diagonal noise cov
     """
     inv_op = make_inverse_operator(evoked.info, self.fwd_op,
                                    noise_cov.as_diag(), loose=0.2,
                                    depth=0.8)
     _compare_io(inv_op)
     inverse_operator_diag = read_inverse_operator(fname_inv_diag)
     # This one's only good to zero decimal places, roundoff error (?)
     _compare_inverses_approx(inverse_operator_diag, inv_op, evoked, 0)
     # Inverse has 306 channels - 4 proj = 302
     assert_true(compute_rank_inverse(inverse_operator_diag) == 302)

def test_inverse_operator_volume():
    """Test MNE inverse computation on volume source space
    """
    inverse_operator_vol = read_inverse_operator(fname_vol_inv)
    _compare_io(inverse_operator_vol)
    stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
    stc.save(op.join(tempdir, 'tmp-vl.stc'))
    stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
    assert_true(np.all(stc.data > 0))
    assert_true(np.all(stc.data < 35))
    assert_array_almost_equal(stc.data, stc2.data)
    assert_array_almost_equal(stc.times, stc2.times)

def test_apply_inverse_operator():
    """Test MNE inverse computation (precomputed and non-precomputed)
    """
    inverse_operator = read_inverse_operator(fname_inv)
    evoked = _get_evoked()
    noise_cov = read_cov(fname_cov)

    # Test old version of inverse computation starting from forward operator
    fwd_op = read_forward_solution(fname_fwd, surf_ori=True)
    my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
                                      loose=0.2, depth=0.8,
                                      limit_depth_chs=False)
    _compare_io(my_inv_op)
    assert_true(inverse_operator['units'] == 'Am')
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2,
                             check_depth=False)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator) == 302)

    # Test MNE inverse computation starting from forward operator
    my_inv_op = make_inverse_operator(evoked.info, fwd_op, noise_cov,
                                      loose=0.2, depth=0.8)
    _compare_io(my_inv_op)
    _compare_inverses_approx(my_inv_op, inverse_operator, evoked, 2)
    # Inverse has 306 channels - 4 proj = 302
    assert_true(compute_rank_inverse(inverse_operator) == 302)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "MNE")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 10e-10)
    assert_true(stc.data.mean() > 1e-11)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "sLORETA")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 10.0)
    assert_true(stc.data.mean() > 0.1)

    stc = apply_inverse(evoked, inverse_operator, lambda2, "dSPM")
    assert_true(stc.subject == 'sample')
    assert_true(stc.data.min() > 0)
    assert_true(stc.data.max() < 35)
    assert_true(stc.data.mean() > 0.1)

    my_stc = apply_inverse(evoked, my_inv_op, lambda2, "dSPM")

    assert_true('dev_head_t' in my_inv_op['info'])
    assert_true('mri_head_t' in my_inv_op)

    assert_true(my_stc.subject == 'sample')
    assert_equal(stc.times, my_stc.times)
    assert_array_almost_equal(stc.data, my_stc.data, 2)

def _compare_io(inv_op, out_file_ext='.fif'):
    if out_file_ext == '.fif':
        out_file = op.join(tempdir, 'test-inv.fif')
    elif out_file_ext == '.gz':
        out_file = op.join(tempdir, 'test-inv.fif.gz')
    else:
        raise ValueError('IO test could not complete')
    # Test io operations
    inv_init = copy.deepcopy(inv_op)
    write_inverse_operator(out_file, inv_op)
    read_inv_op = read_inverse_operator(out_file)
    _compare(inv_init, read_inv_op)
    _compare(inv_init, inv_op)