def test_compute_proj_epochs():
    """Test SSP computation on epochs"""
    event_id, tmin, tmax = 1, -0.2, 0.3

    raw = Raw(raw_fname, preload=True)
    events = read_events(event_fname)
    bad_ch = 'MEG 2443'
    picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
                       exclude=[])
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=None, proj=False)

    evoked = epochs.average()
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
    write_proj(op.join(tempdir, 'proj.fif.gz'), projs)
    for p_fname in [proj_fname, proj_gz_fname,
                    op.join(tempdir, 'proj.fif.gz')]:
        projs2 = read_proj(p_fname)

        assert_true(len(projs) == len(projs2))

        for p1, p2 in zip(projs, projs2):
            assert_true(p1['desc'] == p2['desc'])
            assert_true(p1['data']['col_names'] == p2['data']['col_names'])
            assert_true(p1['active'] == p2['active'])
            # compare with sign invariance
            p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
            p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
            if bad_ch in p1['data']['col_names']:
                bad = p1['data']['col_names'].index('MEG 2443')
                mask = np.ones(p1_data.size, dtype=np.bool)
                mask[bad] = False
                p1_data = p1_data[:, mask]
                p2_data = p2_data[:, mask]
            corr = np.corrcoef(p1_data, p2_data)[0, 1]
            assert_array_almost_equal(corr, 1.0, 5)

    # test that you can compute the projection matrix
    projs = activate_proj(projs)
    proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])

    assert_true(nproj == 2)
    assert_true(U.shape[1] == 2)

    # test that you can save them
    epochs.info['projs'] += projs
    evoked = epochs.average()
    evoked.save(op.join(tempdir, 'foo.fif'))

    projs = read_proj(proj_fname)

    projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
    assert_true(len(projs_evoked) == 2)
    # XXX : test something

    # test parallelization
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2)
    projs = activate_proj(projs)
    proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
    assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)

def test_compute_proj_raw():
    """Test SSP computation on raw"""
    # Test that the raw projectors work
    raw_time = 2.5  # Do shorter amount for speed
    raw = Raw(raw_fname, preload=True).crop(0, raw_time, False)
    for ii in (0.25, 0.5, 1, 2):
        with warnings.catch_warnings(record=True) as w:
            projs = compute_proj_raw(raw, duration=ii - 0.1, stop=raw_time,
                                     n_grad=1, n_mag=1, n_eeg=0)
            assert_true(len(w) == 1)

        # test that you can compute the projection matrix
        projs = activate_proj(projs)
        proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])

        assert_true(nproj == 2)
        assert_true(U.shape[1] == 2)

        # test that you can save them
        raw.info['projs'] += projs
        raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True)

    # Test that purely continuous (no duration) raw projection works
    with warnings.catch_warnings(record=True) as w:
        projs = compute_proj_raw(raw, duration=None, stop=raw_time,
                                 n_grad=1, n_mag=1, n_eeg=0)
        assert_true(len(w) == 1)

    # test that you can compute the projection matrix
    projs = activate_proj(projs)
    proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])

    assert_true(nproj == 2)
    assert_true(U.shape[1] == 2)

    # test that you can save them
    raw.info['projs'] += projs
    raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif'))

    # test resampled-data projector, upsampling instead of downsampling
    # here to save an extra filtering (raw would have to be LP'ed to be equiv)
    raw_resamp = cp.deepcopy(raw)
    raw_resamp.resample(raw.info['sfreq'] * 2, n_jobs=2)
    with warnings.catch_warnings(record=True) as w:
        projs = compute_proj_raw(raw_resamp, duration=None, stop=raw_time,
                                 n_grad=1, n_mag=1, n_eeg=0)
    projs = activate_proj(projs)
    proj_new, _, _ = make_projector(projs, raw.ch_names, bads=[])
    assert_array_almost_equal(proj_new, proj, 4)

    # test with bads
    raw.load_bad_channels(bads_fname)  # adds 2 bad mag channels
    with warnings.catch_warnings(record=True) as w:
        projs = compute_proj_raw(raw, n_grad=0, n_mag=0, n_eeg=1)

    # test that bad channels can be excluded
    proj, nproj, U = make_projector(projs, raw.ch_names,
                                    bads=raw.ch_names)
    assert_array_almost_equal(proj, np.eye(len(raw.ch_names)))

def test_compute_proj_parallel():
    """Test computation of ExG projectors using parallelization"""
    raw_0 = Raw(raw_fname).crop(0, 10, copy=False)
    raw_0.load_data()
    raw = raw_0.copy()
    projs, _ = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
                                bads=['MEG 2443'], average=False,
                                avg_ref=True, no_proj=False, n_jobs=1,
                                l_freq=None, h_freq=None, reject=None,
                                tmax=dur_use)
    raw_2 = raw_0.copy()
    projs_2, _ = compute_proj_eog(raw_2, n_mag=2, n_grad=2, n_eeg=2,
                                  bads=['MEG 2443'], average=False,
                                  avg_ref=True, no_proj=False, n_jobs=2,
                                  l_freq=None, h_freq=None, reject=None,
                                  tmax=dur_use)
    projs = activate_proj(projs)
    projs_2 = activate_proj(projs_2)
    projs, _, _ = make_projector(projs, raw_2.info['ch_names'],
                                 bads=['MEG 2443'])
    projs_2, _, _ = make_projector(projs_2, raw_2.info['ch_names'],
                                   bads=['MEG 2443'])
    assert_array_almost_equal(projs, projs_2, 10)

def test_inverse_residual():
    """Test MNE inverse application."""
    # use fname_inv as it will be faster than fname_full (fewer verts and chs)
    evoked = _get_evoked().pick_types()
    inv = read_inverse_operator(fname_inv_fixed_depth)
    fwd = read_forward_solution(fname_fwd)
    fwd = convert_forward_solution(fwd, force_fixed=True, surf_ori=True)
    fwd = pick_channels_forward(fwd, evoked.ch_names)
    matcher = re.compile(r'.* ([0-9]?[0-9]?[0-9]?\.[0-9])% variance.*')
    for method in ('MNE', 'dSPM', 'sLORETA'):
        with catch_logging() as log:
            stc, residual = apply_inverse(
                evoked, inv, method=method, return_residual=True, verbose=True)
        log = log.getvalue()
        match = matcher.match(log.replace('\n', ' '))
        assert match is not None
        match = float(match.group(1))
        assert 45 < match < 50
        if method == 'MNE':  # must be first!
            recon = apply_forward(fwd, stc, evoked.info)
            proj_op = make_projector(evoked.info['projs'], evoked.ch_names)[0]
            recon.data[:] = np.dot(proj_op, recon.data)
            residual_fwd = evoked.copy()
            residual_fwd.data -= recon.data
        corr = np.corrcoef(residual_fwd.data.ravel(),
                           residual.data.ravel())[0, 1]
        assert corr > 0.999
    with catch_logging() as log:
        _, residual = apply_inverse(
            evoked, inv, 0., 'MNE', return_residual=True, verbose=True)
    log = log.getvalue()
    match = matcher.match(log.replace('\n', ' '))
    assert match is not None
    match = float(match.group(1))
    assert match == 100.
    assert_array_less(np.abs(residual.data), 1e-15)

    # Degenerate: we don't have the right representation for eLORETA for this
    with pytest.raises(ValueError, match='eLORETA does not .* support .*'):
        apply_inverse(evoked, inv, method="eLORETA", return_residual=True)

def test_compute_proj_epochs():
    """Test SSP computation on epochs."""
    tempdir = _TempDir()
    event_id, tmin, tmax = 1, -0.2, 0.3

    raw = read_raw_fif(raw_fname, preload=True)
    events = read_events(event_fname)
    bad_ch = "MEG 2443"
    picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False, exclude=[])
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=None, proj=False)

    evoked = epochs.average()
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
    write_proj(op.join(tempdir, "test-proj.fif.gz"), projs)
    for p_fname in [proj_fname, proj_gz_fname, op.join(tempdir, "test-proj.fif.gz")]:
        projs2 = read_proj(p_fname)

        assert_true(len(projs) == len(projs2))

        for p1, p2 in zip(projs, projs2):
            assert_true(p1["desc"] == p2["desc"])
            assert_true(p1["data"]["col_names"] == p2["data"]["col_names"])
            assert_true(p1["active"] == p2["active"])
            # compare with sign invariance
            p1_data = p1["data"]["data"] * np.sign(p1["data"]["data"][0, 0])
            p2_data = p2["data"]["data"] * np.sign(p2["data"]["data"][0, 0])
            if bad_ch in p1["data"]["col_names"]:
                bad = p1["data"]["col_names"].index("MEG 2443")
                mask = np.ones(p1_data.size, dtype=np.bool)
                mask[bad] = False
                p1_data = p1_data[:, mask]
                p2_data = p2_data[:, mask]
            corr = np.corrcoef(p1_data, p2_data)[0, 1]
            assert_array_almost_equal(corr, 1.0, 5)
            if p2["explained_var"]:
                assert_array_almost_equal(p1["explained_var"], p2["explained_var"])

    # test that you can compute the projection matrix
    projs = activate_proj(projs)
    proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])

    assert_true(nproj == 2)
    assert_true(U.shape[1] == 2)

    # test that you can save them
    epochs.info["projs"] += projs
    evoked = epochs.average()
    evoked.save(op.join(tempdir, "foo-ave.fif"))

    projs = read_proj(proj_fname)

    projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
    assert_true(len(projs_evoked) == 2)
    # XXX : test something

    # test parallelization
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2, desc_prefix="foobar")
    assert_true(all("foobar" in x["desc"] for x in projs))
    projs = activate_proj(projs)
    proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
    assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)

    # test warnings on bad filenames
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        proj_badname = op.join(tempdir, "test-bad-name.fif.gz")
        write_proj(proj_badname, projs)
        read_proj(proj_badname)
    assert_naming(w, "test_proj.py", 2)

def test_compute_proj_epochs():
    """Test SSP computation on epochs."""
    tempdir = _TempDir()
    event_id, tmin, tmax = 1, -0.2, 0.3

    raw = read_raw_fif(raw_fname, preload=True)
    events = read_events(event_fname)
    bad_ch = 'MEG 2443'
    picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
                       exclude=[])
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=None, proj=False)

    evoked = epochs.average()
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
    write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs)
    for p_fname in [proj_fname, proj_gz_fname,
                    op.join(tempdir, 'test-proj.fif.gz')]:
        projs2 = read_proj(p_fname)

        assert len(projs) == len(projs2)

        for p1, p2 in zip(projs, projs2):
            assert p1['desc'] == p2['desc']
            assert p1['data']['col_names'] == p2['data']['col_names']
            assert p1['active'] == p2['active']
            # compare with sign invariance
            p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
            p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
            if bad_ch in p1['data']['col_names']:
                bad = p1['data']['col_names'].index('MEG 2443')
                mask = np.ones(p1_data.size, dtype=np.bool)
                mask[bad] = False
                p1_data = p1_data[:, mask]
                p2_data = p2_data[:, mask]
            corr = np.corrcoef(p1_data, p2_data)[0, 1]
            assert_array_almost_equal(corr, 1.0, 5)
            if p2['explained_var']:
                assert_array_almost_equal(p1['explained_var'],
                                          p2['explained_var'])

    # test that you can compute the projection matrix
    projs = activate_proj(projs)
    proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])

    assert nproj == 2
    assert U.shape[1] == 2

    # test that you can save them
    epochs.info['projs'] += projs
    evoked = epochs.average()
    evoked.save(op.join(tempdir, 'foo-ave.fif'))

    projs = read_proj(proj_fname)

    projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
    assert len(projs_evoked) == 2
    # XXX : test something

    # test parallelization
    projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1,
                                desc_prefix='foobar')
    assert all('foobar' in x['desc'] for x in projs)
    projs = activate_proj(projs)
    proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
    assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)

    # test warnings on bad filenames
    proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
    with pytest.warns(RuntimeWarning, match='-proj.fif'):
        write_proj(proj_badname, projs)
    with pytest.warns(RuntimeWarning, match='-proj.fif'):
        read_proj(proj_badname)