Python Raw.save方法代码示例

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_compensation_raw():
    raw1 = Raw(ctf_comp_fname, compensation=None)
    assert_true(raw1.comp is None)
    data1, times1 = raw1[:, :]
    raw2 = Raw(ctf_comp_fname, compensation=3)
    data2, times2 = raw2[:, :]
    assert_true(raw2.comp is None)  # unchanged (data come with grade 3)
    assert_array_equal(times1, times2)
    assert_array_equal(data1, data2)
    raw3 = Raw(ctf_comp_fname, compensation=1)
    data3, times3 = raw3[:, :]
    assert_true(raw3.comp is not None)
    assert_array_equal(times1, times3)
    # make sure it's different with a different compensation:
    assert_true(np.mean(np.abs(data1 - data3)) > 1e-12)
    assert_raises(ValueError, Raw, ctf_comp_fname, compensation=33)

    # Try IO with compensation
    temp_file = op.join(tempdir, "raw.fif")

    raw1.save(temp_file, overwrite=True)
    raw4 = Raw(temp_file)
    data4, times4 = raw4[:, :]
    assert_array_equal(times1, times4)
    assert_array_equal(data1, data4)

    # Now save the file that has modified compensation
    # and make sure we can the same data as input ie. compensation
    # is undone
    raw3.save(temp_file, overwrite=True)
    raw5 = Raw(temp_file)
    data5, times5 = raw5[:, :]
    assert_array_equal(times1, times5)
    assert_allclose(data1, data5, rtol=1e-12, atol=1e-22)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_preload_modify():
    """ Test preloading and modifying data
    """
    for preload in [False, True, "memmap.dat"]:
        raw = Raw(fif_fname, preload=preload)

        nsamp = raw.last_samp - raw.first_samp + 1
        picks = pick_types(raw.info, meg="grad", exclude="bads")

        data = np.random.randn(len(picks), nsamp / 2)

        try:
            raw[picks, : nsamp / 2] = data
        except RuntimeError as err:
            if not preload:
                continue
            else:
                raise err

        tmp_fname = op.join(tempdir, "raw.fif")
        raw.save(tmp_fname, overwrite=True)

        raw_new = Raw(tmp_fname)
        data_new, _ = raw_new[picks, : nsamp / 2]

        assert_allclose(data, data_new)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_preload_modify():
    """ Test preloading and modifying data
    """
    for preload in [False, True, 'memmap.dat']:
        raw = Raw(fif_fname, preload=preload)

        nsamp = raw.last_samp - raw.first_samp + 1
        picks = pick_types(raw.info, meg='grad', exclude='bads')

        data = np.random.randn(len(picks), nsamp / 2)

        try:
            raw[picks, :nsamp / 2] = data
        except RuntimeError as err:
            if not preload:
                continue
            else:
                raise err

        tmp_fname = op.join(tempdir, 'raw.fif')
        raw.save(tmp_fname)

        raw_new = Raw(tmp_fname)
        data_new, _ = raw_new[picks, :nsamp / 2]

        assert_array_almost_equal(data, data_new)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_subject_info():
    """Test reading subject information
    """
    raw = Raw(fif_fname)
    raw.crop(0, 1)
    assert_true(raw.info['subject_info'] is None)
    # fake some subject data
    keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
            'hand']
    vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
    subject_info = dict()
    for key, val in zip(keys, vals):
        subject_info[key] = val
    raw.info['subject_info'] = subject_info
    out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
    raw.save(out_fname, overwrite=True)
    raw_read = Raw(out_fname)
    for key in keys:
        assert_equal(subject_info[key], raw_read.info['subject_info'][key])
    raw_read.anonymize()
    assert_true(raw_read.info.get('subject_info') is None)
    out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif')
    raw_read.save(out_fname_anon, overwrite=True)
    raw_read = Raw(out_fname_anon)
    assert_true(raw_read.info.get('subject_info') is None)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
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(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(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(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(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)))

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_proj():
    """Test SSP proj operations
    """
    for proj_active in [True, False]:
        raw = Raw(fif_fname, preload=False, proj_active=proj_active)
        assert_true(all(p['active'] == proj_active for p in raw.info['projs']))

        data, times = raw[0:2, :]
        data1, times1 = raw[0:2]
        assert_array_equal(data, data1)
        assert_array_equal(times, times1)

        # test adding / deleting proj
        if proj_active:
            assert_raises(ValueError, raw.add_proj, [],
                          {'remove_existing': True})
            assert_raises(ValueError, raw.del_proj, 0)
        else:
            projs = deepcopy(raw.info['projs'])
            n_proj = len(raw.info['projs'])
            raw.del_proj(0)
            assert_true(len(raw.info['projs']) == n_proj - 1)
            raw.add_proj(projs, remove_existing=False)
            assert_true(len(raw.info['projs']) == 2 * n_proj - 1)
            raw.add_proj(projs, remove_existing=True)
            assert_true(len(raw.info['projs']) == n_proj)

    # test apply_proj() with and without preload
    for preload in [True, False]:
        raw = Raw(fif_fname, preload=preload, proj_active=False)
        data, times = raw[:, 0:2]
        raw.apply_projector()
        data_proj_1 = np.dot(raw._projector, data)

        # load the file again without proj
        raw = Raw(fif_fname, preload=preload, proj_active=False)

        # write the file with proj. activated, make sure proj has been applied
        raw.save(op.join(tempdir, 'raw.fif'), proj_active=True)
        raw2 = Raw(op.join(tempdir, 'raw.fif'), proj_active=False)
        data_proj_2, _ = raw2[:, 0:2]
        assert_array_almost_equal(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # read orig file with proj. active
        raw2 = Raw(fif_fname, preload=preload, proj_active=True)
        data_proj_2, _ = raw2[:, 0:2]
        assert_array_almost_equal(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # test that apply_projector works
        raw.apply_projector()
        data_proj_2, _ = raw[:, 0:2]
        assert_array_almost_equal(data_proj_1, data_proj_2)
        assert_array_almost_equal(data_proj_2,
                                  np.dot(raw._projector, data_proj_2))

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_save():
    """ Test saving raw"""
    raw = Raw(fif_fname, preload=True)
    assert_raises(ValueError, raw.save, fif_fname)
    new_fname = op.join(op.abspath(op.curdir), 'break.fif')
    raw.save(op.join(tempdir, new_fname))
    new_raw = Raw(op.join(tempdir, new_fname))
    assert_raises(ValueError, new_raw.save, new_fname)
    new_raw.close()
    os.remove(new_fname)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_io_raw():
    """Test IO for raw data (Neuromag + CTF)
    """
    for fname in [fif_fname, ctf_fname]:
        raw = Raw(fname)

        nchan = raw.info['nchan']
        ch_names = raw.info['ch_names']
        meg_channels_idx = [k for k in range(nchan)
                                            if ch_names[k][0] == 'M']
        n_channels = 100
        meg_channels_idx = meg_channels_idx[:n_channels]
        start, stop = raw.time_to_index(0, 5)
        data, times = raw[meg_channels_idx, start:(stop + 1)]
        meg_ch_names = [ch_names[k] for k in meg_channels_idx]

        # Set up pick list: MEG + STI 014 - bad channels
        include = ['STI 014']
        include += meg_ch_names
        picks = pick_types(raw.info, meg=True, eeg=False,
                                stim=True, misc=True, include=include,
                                exclude=raw.info['bads'])
        print "Number of picked channels : %d" % len(picks)

        # Writing with drop_small_buffer True
        raw.save('raw.fif', picks, tmin=0, tmax=4, buffer_size_sec=3,
                 drop_small_buffer=True)
        raw2 = Raw('raw.fif')

        sel = pick_channels(raw2.ch_names, meg_ch_names)
        data2, times2 = raw2[sel, :]
        assert_true(times2.max() <= 3)

        # Writing
        raw.save('raw.fif', picks, tmin=0, tmax=5)

        if fname == fif_fname:
            assert_true(len(raw.info['dig']) == 146)

        raw2 = Raw('raw.fif')

        sel = pick_channels(raw2.ch_names, meg_ch_names)
        data2, times2 = raw2[sel, :]

        assert_array_almost_equal(data, data2)
        assert_array_almost_equal(times, times2)
        assert_array_almost_equal(raw.info['dev_head_t']['trans'],
                                  raw2.info['dev_head_t']['trans'])
        assert_array_almost_equal(raw.info['sfreq'], raw2.info['sfreq'])

        if fname == fif_fname:
            assert_array_almost_equal(raw.info['dig'][0]['r'],
                                      raw2.info['dig'][0]['r'])

        fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif')

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_io_raw():
    """Test IO for raw data (Neuromag + CTF + gz)
    """
    fnames_in = [fif_fname, fif_gz_fname, ctf_fname]
    fnames_out = ["raw.fif", "raw.fif.gz", "raw.fif"]
    for fname_in, fname_out in zip(fnames_in, fnames_out):
        raw = Raw(fname_in)

        nchan = raw.info["nchan"]
        ch_names = raw.info["ch_names"]
        meg_channels_idx = [k for k in range(nchan) if ch_names[k][0] == "M"]
        n_channels = 100
        meg_channels_idx = meg_channels_idx[:n_channels]
        start, stop = raw.time_as_index([0, 5])
        data, times = raw[meg_channels_idx, start : (stop + 1)]
        meg_ch_names = [ch_names[k] for k in meg_channels_idx]

        # Set up pick list: MEG + STI 014 - bad channels
        include = ["STI 014"]
        include += meg_ch_names
        picks = pick_types(
            raw.info, meg=True, eeg=False, stim=True, misc=True, include=include, exclude=raw.info["bads"]
        )
        print "Number of picked channels : %d" % len(picks)

        # Writing with drop_small_buffer True
        raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3, drop_small_buffer=True)
        raw2 = Raw(fname_out, preload=True)

        sel = pick_channels(raw2.ch_names, meg_ch_names)
        data2, times2 = raw2[sel, :]
        assert_true(times2.max() <= 3)

        # Writing
        raw.save(fname_out, picks, tmin=0, tmax=5)

        if fname_in == fif_fname or fname_in == fif_fname + ".gz":
            assert_true(len(raw.info["dig"]) == 146)

        raw2 = Raw(fname_out)

        sel = pick_channels(raw2.ch_names, meg_ch_names)
        data2, times2 = raw2[sel, :]

        assert_array_almost_equal(data, data2)
        assert_array_almost_equal(times, times2)
        assert_array_almost_equal(raw.info["dev_head_t"]["trans"], raw2.info["dev_head_t"]["trans"])
        assert_array_almost_equal(raw.info["sfreq"], raw2.info["sfreq"])

        if fname_in == fif_fname or fname_in == fif_fname + ".gz":
            assert_array_almost_equal(raw.info["dig"][0]["r"], raw2.info["dig"][0]["r"])

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_save():
    """ Test saving raw"""
    raw = Raw(fif_fname, preload=False)
    # can't write over file being read
    assert_raises(ValueError, raw.save, fif_fname)
    raw = Raw(fif_fname, preload=True)
    # can't overwrite file without overwrite=True
    assert_raises(IOError, raw.save, fif_fname)

    # test abspath support
    new_fname = op.join(op.abspath(op.curdir), 'break.fif')
    raw.save(op.join(tempdir, new_fname), overwrite=True)
    new_raw = Raw(op.join(tempdir, new_fname), preload=False)
    assert_raises(ValueError, new_raw.save, new_fname)
    # make sure we can overwrite the file we loaded when preload=True
    new_raw = Raw(op.join(tempdir, new_fname), preload=True)
    new_raw.save(op.join(tempdir, new_fname), overwrite=True)
    os.remove(new_fname)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_compensation():
    """Test compensation
    """
    raw = Raw(ctf_comp_fname, compensation=None)
    comp1 = make_compensator(raw.info, 3, 1, exclude_comp_chs=False)
    assert_true(comp1.shape == (340, 340))
    comp2 = make_compensator(raw.info, 3, 1, exclude_comp_chs=True)
    assert_true(comp2.shape == (311, 340))

    # make sure that changing the comp doesn't modify the original data
    raw2 = Raw(ctf_comp_fname, compensation=2)
    assert_true(get_current_comp(raw2.info) == 2)
    fname = op.join(tempdir, 'ctf-raw.fif')
    raw2.save(fname)
    raw2 = Raw(fname, compensation=None)
    data, _ = raw[:, :]
    data2, _ = raw2[:, :]
    assert_allclose(data, data2, rtol=1e-9, atol=1e-20)
    for ch1, ch2 in zip(raw.info['chs'], raw2.info['chs']):
        assert_true(ch1['coil_type'] == ch2['coil_type'])

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_output_formats():
    """Test saving and loading raw data using multiple formats
    """
    formats = ['short', 'int', 'single', 'double']
    tols = [1e-4, 1e-7, 1e-7, 1e-15]

    # let's fake a raw file with different formats
    raw = Raw(fif_fname, preload=True)
    raw.crop(0, 1, copy=False)

    temp_file = op.join(tempdir, 'raw.fif')
    for ii, (format, tol) in enumerate(zip(formats, tols)):
        # Let's test the overwriting error throwing while we're at it
        if ii > 0:
            assert_raises(IOError, raw.save, temp_file, format=format)
        raw.save(temp_file, format=format, overwrite=True)
        raw2 = Raw(temp_file)
        raw2_data = raw2[:, :][0]
        assert_allclose(raw2_data, raw._data, rtol=tol, atol=1e-25)
        assert_true(raw2.orig_format == format)

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_load_bad_channels():
    """Test reading/writing of bad channels
    """
    # Load correctly marked file (manually done in mne_process_raw)
    raw_marked = Raw(fif_bad_marked_fname)
    correct_bads = raw_marked.info['bads']
    raw = Raw(fif_fname)
    # Make sure it starts clean
    assert_array_equal(raw.info['bads'], [])

    # Test normal case
    raw.load_bad_channels(bad_file_works)
    # Write it out, read it in, and check
    raw.save(op.join(tempdir, 'foo_raw.fif'))
    raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
    assert_equal(correct_bads, raw_new.info['bads'])
    # Reset it
    raw.info['bads'] = []

    # Test bad case
    assert_raises(ValueError, raw.load_bad_channels, bad_file_wrong)

    # Test forcing the bad case
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw.load_bad_channels(bad_file_wrong, force=True)
        n_found = sum(['1 bad channel' in str(ww.message) for ww in w])
        assert_equal(n_found, 1)  # there could be other irrelevant errors
        # write it out, read it in, and check
        raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
        raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
        assert_equal(correct_bads, raw_new.info['bads'])

    # Check that bad channels are cleared
    raw.load_bad_channels(None)
    raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
    raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
    assert_equal([], raw_new.info['bads'])

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_load_bad_channels():
    """Test reading/writing of bad channels
    """

    # Load correctly marked file (manually done in mne_process_raw)
    raw_marked = Raw(fif_bad_marked_fname)
    correct_bads = raw_marked.info['bads']
    raw = Raw(fif_fname)
    # Make sure it starts clean
    assert_array_equal(raw.info['bads'], [])

    # Test normal case
    raw.load_bad_channels(bad_file_works)
    # Write it out, read it in, and check
    raw.save(op.join(tempdir, 'foo_raw.fif'))
    raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
    assert_equal(correct_bads, raw_new.info['bads'])
    # Reset it
    raw.info['bads'] = []

    # Test bad case
    assert_raises(ValueError, raw.load_bad_channels, bad_file_wrong)

    # Test forcing the bad case
    with warnings.catch_warnings(record=True) as w:
        raw.load_bad_channels(bad_file_wrong, force=True)
        assert_equal(len(w), 1)
        # write it out, read it in, and check
        raw.save(op.join(tempdir, 'foo_raw.fif'))
        raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
        assert_equal(correct_bads, raw_new.info['bads'])

    # Check that bad channels are cleared
    raw.load_bad_channels(None)
    raw.save(op.join(tempdir, 'foo_raw.fif'))
    raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
    assert_equal([], raw_new.info['bads'])

# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import save [as 别名]
def test_compute_proj():
    """Test SSP computation"""
    event_id, tmin, tmax = 1, -0.2, 0.3

    raw = Raw(raw_fname, preload=True)
    events = read_events(event_fname)
    exclude = []
    bad_ch = 'MEG 2443'
    picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
                            exclude=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('proj.fif.gz', projs)
    for p_fname in [proj_fname, proj_gz_fname, '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('foo.fif')

    projs = read_proj(proj_fname)

    projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
    # 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_array_equal(proj, proj_par)

    # Test that the raw projectors work
    for ii in (1, 2, 4, 8, 12, 24):
        raw = Raw(raw_fname)
        projs = compute_proj_raw(raw, duration=ii-0.1, n_grad=1, n_mag=1,
                                 n_eeg=0)

        # 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
        raw.info['projs'] += projs
        raw.save('foo_%d_raw.fif' % ii)

    # Test that purely continuous (no duration) raw projection works
    raw = Raw(raw_fname)
    projs = compute_proj_raw(raw, duration=None, n_grad=1, n_mag=1, n_eeg=0)

    # 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
    raw.info['projs'] += projs
    raw.save('foo_rawproj_continuous_raw.fif')