Python Raw.add_proj方法代码示例

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_rank_estimation():
    """Test raw rank estimation
    """
    iter_tests = itt.product([fif_fname, hp_fif_fname], ["norm", dict(mag=1e11, grad=1e9, eeg=1e5)])  # sss
    for fname, scalings in iter_tests:
        raw = Raw(fname)
        (_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info, meg_combined=True)
        n_meg = len(picks_meg)
        n_eeg = len(picks_eeg)

        raw = Raw(fname, preload=True)
        if "proc_history" not in raw.info:
            expected_rank = n_meg + n_eeg
        else:
            mf = raw.info["proc_history"][0]["max_info"]
            expected_rank = _get_sss_rank(mf) + n_eeg
        assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank)

        assert_array_equal(raw.estimate_rank(picks=picks_eeg, scalings=scalings), n_eeg)

        raw = Raw(fname, preload=False)
        if "sss" in fname:
            tstart, tstop = 0.0, 30.0
            raw.add_proj(compute_proj_raw(raw))
            raw.apply_proj()
        else:
            tstart, tstop = 10.0, 20.0

        raw.apply_proj()
        n_proj = len(raw.info["projs"])

        assert_array_equal(
            raw.estimate_rank(tstart=tstart, tstop=tstop, scalings=scalings),
            expected_rank - (1 if "sss" in fname else n_proj),
        )

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_proj():
    """Test SSP proj operations
    """
    tempdir = _TempDir()
    for proj in [True, False]:
        raw = Raw(fif_fname, preload=False, proj=proj)
        assert_true(all(p['active'] == proj 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:
            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=False)
        data, times = raw[:, 0:2]
        raw.apply_proj()
        data_proj_1 = np.dot(raw._projector, data)

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

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

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

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_set_channel_types():
    """Test set_channel_types
    """
    raw = Raw(raw_fname)
    # Error Tests
    # Test channel name exists in ch_names
    mapping = {'EEG 160': 'EEG060'}
    assert_raises(ValueError, raw.set_channel_types, mapping)
    # Test change to illegal channel type
    mapping = {'EOG 061': 'xxx'}
    assert_raises(ValueError, raw.set_channel_types, mapping)
    # Test changing type if in proj (avg eeg ref here)
    mapping = {'EEG 058': 'ecog', 'EEG 059': 'ecg', 'EEG 060': 'eog',
               'EOG 061': 'seeg', 'MEG 2441': 'eeg', 'MEG 2443': 'eeg'}
    assert_raises(RuntimeError, raw.set_channel_types, mapping)
    # Test type change
    raw2 = Raw(raw_fname, add_eeg_ref=False)
    raw2.info['bads'] = ['EEG 059', 'EEG 060', 'EOG 061']
    with warnings.catch_warnings(record=True):  # MEG channel change
        assert_raises(RuntimeError, raw2.set_channel_types, mapping)  # has prj
    raw2.add_proj([], remove_existing=True)
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw2.set_channel_types(mapping)
    assert_true(len(w) >= 1, msg=[str(ww.message) for ww in w])
    assert_true(all('The unit for channel' in str(ww.message) for ww in w))
    info = raw2.info
    assert_true(info['chs'][372]['ch_name'] == 'EEG 058')
    assert_true(info['chs'][372]['kind'] == FIFF.FIFFV_ECOG_CH)
    assert_true(info['chs'][372]['unit'] == FIFF.FIFF_UNIT_V)
    assert_true(info['chs'][372]['coil_type'] == FIFF.FIFFV_COIL_EEG)
    assert_true(info['chs'][373]['ch_name'] == 'EEG 059')
    assert_true(info['chs'][373]['kind'] == FIFF.FIFFV_ECG_CH)
    assert_true(info['chs'][373]['unit'] == FIFF.FIFF_UNIT_V)
    assert_true(info['chs'][373]['coil_type'] == FIFF.FIFFV_COIL_NONE)
    assert_true(info['chs'][374]['ch_name'] == 'EEG 060')
    assert_true(info['chs'][374]['kind'] == FIFF.FIFFV_EOG_CH)
    assert_true(info['chs'][374]['unit'] == FIFF.FIFF_UNIT_V)
    assert_true(info['chs'][374]['coil_type'] == FIFF.FIFFV_COIL_NONE)
    assert_true(info['chs'][375]['ch_name'] == 'EOG 061')
    assert_true(info['chs'][375]['kind'] == FIFF.FIFFV_SEEG_CH)
    assert_true(info['chs'][375]['unit'] == FIFF.FIFF_UNIT_V)
    assert_true(info['chs'][375]['coil_type'] == FIFF.FIFFV_COIL_EEG)
    for idx in pick_channels(raw.ch_names, ['MEG 2441', 'MEG 2443']):
        assert_true(info['chs'][idx]['kind'] == FIFF.FIFFV_EEG_CH)
        assert_true(info['chs'][idx]['unit'] == FIFF.FIFF_UNIT_V)
        assert_true(info['chs'][idx]['coil_type'] == FIFF.FIFFV_COIL_EEG)

    # Test meaningful error when setting channel type with unknown unit
    raw.info['chs'][0]['unit'] = 0.
    ch_types = {raw.ch_names[0]: 'misc'}
    assert_raises(ValueError, raw.set_channel_types, ch_types)

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_proj():
    """Test SSP proj operations
    """
    tempdir = _TempDir()
    for proj in [True, False]:
        raw = Raw(fif_fname, preload=False, proj=proj)
        assert_true(all(p['active'] == proj 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:
            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_equal(len(raw.info['projs']), n_proj - 1)
            raw.add_proj(projs, remove_existing=False)
            # Test that already existing projections are not added.
            assert_equal(len(raw.info['projs']), n_proj)
            raw.add_proj(projs[:-1], remove_existing=True)
            assert_equal(len(raw.info['projs']), n_proj - 1)

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

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

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

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

    tempdir = _TempDir()
    out_fname = op.join(tempdir, 'test_raw.fif')
    raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002, copy=False)
    raw.pick_types(meg=False, eeg=True)
    raw.info['projs'] = [raw.info['projs'][-1]]
    raw._data.fill(0)
    raw._data[-1] = 1.
    raw.save(out_fname)
    raw = read_raw_fif(out_fname, proj=True, preload=False)
    assert_allclose(raw[:, :][0][:1], raw[0, :][0])

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]

#.........这里部分代码省略.........

    # going in reverse order so the last fname is the first file (need later)
    raws = [None] * len(tmins)
    for ri in range(len(tmins) - 1, -1, -1):
        fname = op.join(tempdir, 'test_raw_split-%d_raw.fif' % ri)
        raw.save(fname, tmin=tmins[ri], tmax=tmaxs[ri])
        raws[ri] = Raw(fname)
    events = [find_events(r, stim_channel='STI 014') for r in raws]
    last_samps = [r.last_samp for r in raws]
    first_samps = [r.first_samp for r in raws]

    # test concatenation of split file
    assert_raises(ValueError, concatenate_raws, raws, True, events[1:])
    all_raw_1, events1 = concatenate_raws(raws, preload=False,
                                          events_list=events)
    assert_equal(raw.first_samp, all_raw_1.first_samp)
    assert_equal(raw.last_samp, all_raw_1.last_samp)
    assert_allclose(raw[:, :][0], all_raw_1[:, :][0])
    raws[0] = Raw(fname)
    all_raw_2 = concatenate_raws(raws, preload=True)
    assert_allclose(raw[:, :][0], all_raw_2[:, :][0])

    # test proper event treatment for split files
    events2 = concatenate_events(events, first_samps, last_samps)
    events3 = find_events(all_raw_2, stim_channel='STI 014')
    assert_array_equal(events1, events2)
    assert_array_equal(events1, events3)

    # test various methods of combining files
    raw = Raw(fif_fname, preload=True)
    n_times = raw.n_times
    # make sure that all our data match
    times = list(range(0, 2 * n_times, 999))
    # add potentially problematic points
    times.extend([n_times - 1, n_times, 2 * n_times - 1])

    raw_combo0 = Raw([fif_fname, fif_fname], preload=True)
    _compare_combo(raw, raw_combo0, times, n_times)
    raw_combo = Raw([fif_fname, fif_fname], preload=False)
    _compare_combo(raw, raw_combo, times, n_times)
    raw_combo = Raw([fif_fname, fif_fname], preload='memmap8.dat')
    _compare_combo(raw, raw_combo, times, n_times)
    assert_raises(ValueError, Raw, [fif_fname, ctf_fname])
    assert_raises(ValueError, Raw, [fif_fname, fif_bad_marked_fname])
    assert_equal(raw[:, :][0].shape[1] * 2, raw_combo0[:, :][0].shape[1])
    assert_equal(raw_combo0[:, :][0].shape[1], raw_combo0.n_times)

    # with all data preloaded, result should be preloaded
    raw_combo = Raw(fif_fname, preload=True)
    raw_combo.append(Raw(fif_fname, preload=True))
    assert_true(raw_combo.preload is True)
    assert_equal(raw_combo.n_times, raw_combo._data.shape[1])
    _compare_combo(raw, raw_combo, times, n_times)

    # with any data not preloaded, don't set result as preloaded
    raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
                                  Raw(fif_fname, preload=False)])
    assert_true(raw_combo.preload is False)
    assert_array_equal(find_events(raw_combo, stim_channel='STI 014'),
                       find_events(raw_combo0, stim_channel='STI 014'))
    _compare_combo(raw, raw_combo, times, n_times)

    # user should be able to force data to be preloaded upon concat
    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=True)],
                                 preload=True)
    assert_true(raw_combo.preload is True)
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=True)],
                                 preload='memmap3.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=True),
                                  Raw(fif_fname, preload=True)],
                                 preload='memmap4.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    raw_combo = concatenate_raws([Raw(fif_fname, preload=False),
                                  Raw(fif_fname, preload=False)],
                                 preload='memmap5.dat')
    _compare_combo(raw, raw_combo, times, n_times)

    # verify that combining raws with different projectors throws an exception
    raw.add_proj([], remove_existing=True)
    assert_raises(ValueError, raw.append, Raw(fif_fname, preload=True))

    # now test event treatment for concatenated raw files
    events = [find_events(raw, stim_channel='STI 014'),
              find_events(raw, stim_channel='STI 014')]
    last_samps = [raw.last_samp, raw.last_samp]
    first_samps = [raw.first_samp, raw.first_samp]
    events = concatenate_events(events, first_samps, last_samps)
    events2 = find_events(raw_combo0, stim_channel='STI 014')
    assert_array_equal(events, events2)

    # check out the len method
    assert_equal(len(raw), raw.n_times)
    assert_equal(len(raw), raw.last_samp - raw.first_samp + 1)

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_rank():
    """Test cov rank estimation"""
    raw_sample = Raw(raw_fname)

    raw_sss = Raw(hp_fif_fname)
    raw_sss.add_proj(compute_proj_raw(raw_sss))

    cov_sample = compute_raw_data_covariance(raw_sample)
    cov_sample_proj = compute_raw_data_covariance(
        raw_sample.copy().apply_proj())

    cov_sss = compute_raw_data_covariance(raw_sss)
    cov_sss_proj = compute_raw_data_covariance(
        raw_sss.copy().apply_proj())

    picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
    picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)

    info_sample = pick_info(raw_sample.info, picks_all_sample)
    picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
                                             eeg=True))]
    picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
    picks_stack_sample += [('all',
                            pick_types(info_sample, meg=True, eeg=True))]

    info_sss = pick_info(raw_sss.info, picks_all_sss)
    picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
    picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
    picks_stack_somato += [('all',
                            pick_types(info_sss, meg=True, eeg=True))]

    iter_tests = list(itt.product(
        [(cov_sample, picks_stack_sample, info_sample),
         (cov_sample_proj, picks_stack_sample, info_sample),
         (cov_sss, picks_stack_somato, info_sss),
         (cov_sss_proj, picks_stack_somato, info_sss)],  # sss
        [dict(mag=1e15, grad=1e13, eeg=1e6)]
    ))

    for (cov, picks_list, this_info), scalings in iter_tests:
        for ch_type, picks in picks_list:

            this_very_info = pick_info(this_info, picks)

            # compute subset of projs
            this_projs = [c['active'] and
                          len(set(c['data']['col_names'])
                              .intersection(set(this_very_info['ch_names']))) >
                          0 for c in cov['projs']]
            n_projs = sum(this_projs)

            # count channel types
            ch_types = [channel_type(this_very_info, idx)
                        for idx in range(len(picks))]
            n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
                                    ['eeg', 'mag', 'grad']]
            n_meg = n_mag + n_grad
            if ch_type in ('all', 'eeg'):
                n_projs_eeg = 1
            else:
                n_projs_eeg = 0

            # check sss
            if 'proc_history' in this_very_info:
                mf = this_very_info['proc_history'][0]['max_info']
                n_free = _get_sss_rank(mf)
                if 'mag' not in ch_types and 'grad' not in ch_types:
                    n_free = 0
                # - n_projs XXX clarify
                expected_rank = n_free + n_eeg
                if n_projs > 0 and ch_type in ('all', 'eeg'):
                    expected_rank -= n_projs_eeg
            else:
                expected_rank = n_meg + n_eeg - n_projs

            C = cov['data'][np.ix_(picks, picks)]
            est_rank = _estimate_rank_meeg_cov(C, this_very_info,
                                               scalings=scalings)

            assert_equal(expected_rank, est_rank)

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import add_proj [as 别名]
def test_rank():
    """Test cov rank estimation"""
    # Test that our rank estimation works properly on a simple case
    evoked = read_evokeds(ave_fname, condition=0, baseline=(None, 0),
                          proj=False)
    cov = read_cov(cov_fname)
    ch_names = [ch for ch in evoked.info['ch_names'] if '053' not in ch and
                ch.startswith('EEG')]
    cov = prepare_noise_cov(cov, evoked.info, ch_names, None)
    assert_equal(cov['eig'][0], 0.)  # avg projector should set this to zero
    assert_true((cov['eig'][1:] > 0).all())  # all else should be > 0

    # Now do some more comprehensive tests
    raw_sample = Raw(raw_fname)

    raw_sss = Raw(hp_fif_fname)
    raw_sss.add_proj(compute_proj_raw(raw_sss))

    cov_sample = compute_raw_covariance(raw_sample)
    cov_sample_proj = compute_raw_covariance(
        raw_sample.copy().apply_proj())

    cov_sss = compute_raw_covariance(raw_sss)
    cov_sss_proj = compute_raw_covariance(
        raw_sss.copy().apply_proj())

    picks_all_sample = pick_types(raw_sample.info, meg=True, eeg=True)
    picks_all_sss = pick_types(raw_sss.info, meg=True, eeg=True)

    info_sample = pick_info(raw_sample.info, picks_all_sample)
    picks_stack_sample = [('eeg', pick_types(info_sample, meg=False,
                                             eeg=True))]
    picks_stack_sample += [('meg', pick_types(info_sample, meg=True))]
    picks_stack_sample += [('all',
                            pick_types(info_sample, meg=True, eeg=True))]

    info_sss = pick_info(raw_sss.info, picks_all_sss)
    picks_stack_somato = [('eeg', pick_types(info_sss, meg=False, eeg=True))]
    picks_stack_somato += [('meg', pick_types(info_sss, meg=True))]
    picks_stack_somato += [('all',
                            pick_types(info_sss, meg=True, eeg=True))]

    iter_tests = list(itt.product(
        [(cov_sample, picks_stack_sample, info_sample),
         (cov_sample_proj, picks_stack_sample, info_sample),
         (cov_sss, picks_stack_somato, info_sss),
         (cov_sss_proj, picks_stack_somato, info_sss)],  # sss
        [dict(mag=1e15, grad=1e13, eeg=1e6)]
    ))

    for (cov, picks_list, this_info), scalings in iter_tests:
        for ch_type, picks in picks_list:

            this_very_info = pick_info(this_info, picks)

            # compute subset of projs
            this_projs = [c['active'] and
                          len(set(c['data']['col_names'])
                              .intersection(set(this_very_info['ch_names']))) >
                          0 for c in cov['projs']]
            n_projs = sum(this_projs)

            # count channel types
            ch_types = [channel_type(this_very_info, idx)
                        for idx in range(len(picks))]
            n_eeg, n_mag, n_grad = [ch_types.count(k) for k in
                                    ['eeg', 'mag', 'grad']]
            n_meg = n_mag + n_grad
            if ch_type in ('all', 'eeg'):
                n_projs_eeg = 1
            else:
                n_projs_eeg = 0

            # check sss
            if 'proc_history' in this_very_info:
                mf = this_very_info['proc_history'][0]['max_info']
                n_free = _get_sss_rank(mf)
                if 'mag' not in ch_types and 'grad' not in ch_types:
                    n_free = 0
                # - n_projs XXX clarify
                expected_rank = n_free + n_eeg
                if n_projs > 0 and ch_type in ('all', 'eeg'):
                    expected_rank -= n_projs_eeg
            else:
                expected_rank = n_meg + n_eeg - n_projs

            C = cov['data'][np.ix_(picks, picks)]
            est_rank = _estimate_rank_meeg_cov(C, this_very_info,
                                               scalings=scalings)

            assert_equal(expected_rank, est_rank)