Python mne.equalize_channels函数代码示例

def test_equalize_channels():
    """Test equalization of channels."""
    evoked1 = read_evokeds(fname, condition=0, proj=True)
    evoked2 = evoked1.copy()
    ch_names = evoked1.ch_names[2:]
    evoked1.drop_channels(evoked1.ch_names[:1])
    evoked2.drop_channels(evoked2.ch_names[1:2])
    my_comparison = [evoked1, evoked2]
    equalize_channels(my_comparison)
    for e in my_comparison:
        assert_equal(ch_names, e.ch_names)

def test_equalize_channels():
    """Test equalization of channels
    """
    epochs1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), proj=False)
    epochs2 = epochs1.copy()
    ch_names = epochs1.ch_names[2:]
    epochs1.drop_channels(epochs1.ch_names[:1])
    epochs2.drop_channels(epochs2.ch_names[1:2])
    my_comparison = [epochs1, epochs2]
    equalize_channels(my_comparison)
    for e in my_comparison:
        assert_equal(ch_names, e.ch_names)

def test_equalize_channels():
    """Test equalization of channels
    """
    raw1 = Raw(fif_fname, preload=True)

    raw2 = raw1.copy()
    ch_names = raw1.ch_names[2:]
    raw1.drop_channels(raw1.ch_names[:1])
    raw2.drop_channels(raw2.ch_names[1:2])
    my_comparison = [raw1, raw2]
    equalize_channels(my_comparison)
    for e in my_comparison:
        assert_equal(ch_names, e.ch_names)

def test_time_frequency():
    """Test time frequency transform (PSD and phase lock)
    """
    # Set parameters
    event_id = 1
    tmin = -0.2
    tmax = 0.5

    # Setup for reading the raw data
    raw = io.Raw(raw_fname)
    events = read_events(event_fname)

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

    # picks MEG gradiometers
    picks = pick_types(raw.info, meg='grad', eeg=False,
                            stim=False, include=include, exclude=exclude)

    picks = picks[:2]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0))
    data = epochs.get_data()
    times = epochs.times
    nave = len(data)

    freqs = np.arange(6, 20, 5)  # define frequencies of interest
    n_cycles = freqs / 4.

    # Test first with a single epoch
    power, itc = tfr_morlet(epochs[0], freqs=freqs, n_cycles=n_cycles,
                            use_fft=True, return_itc=True)

    power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
                            use_fft=True, return_itc=True)

    print(itc)  # test repr
    print(itc.ch_names) # test property
    itc = itc + power # test add
    itc = itc - power # test add
    itc -= power
    itc += power

    power.apply_baseline(baseline=(-0.1, 0), mode='logratio')

    assert_true('meg' in power)
    assert_true('grad' in power)
    assert_false('mag' in power)
    assert_false('eeg' in power)

    assert_equal(power.nave, nave)
    assert_equal(itc.nave, nave)
    assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
    assert_true(power.data.shape == itc.data.shape)
    assert_true(np.sum(itc.data >= 1) == 0)
    assert_true(np.sum(itc.data <= 0) == 0)

    power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
                            return_itc=True)

    assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
    assert_true(power.data.shape == itc.data.shape)
    assert_true(np.sum(itc.data >= 1) == 0)
    assert_true(np.sum(itc.data <= 0) == 0)

    Fs = raw.info['sfreq']  # sampling in Hz
    tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
    assert_true(tfr.shape == (len(picks), len(freqs), len(times)))

    single_power = single_trial_power(data, Fs, freqs, use_fft=False,
                                      n_cycles=2)

    assert_array_almost_equal(np.mean(single_power), power.data)

    power_pick = power.pick_channels(power.ch_names[:10:2])
    assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
    assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
    power_drop = power.drop_channels(power.ch_names[1:10:2])
    assert_equal(power_drop.ch_names, power_pick.ch_names)
    assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))

    mne.equalize_channels([power_pick, power_drop])
    assert_equal(power_pick.ch_names, power_drop.ch_names)
    assert_equal(power_pick.data.shape, power_drop.data.shape)

#.........这里部分代码省略.........
                                   itc2.data.shape[2]))
    assert_equal(itc2.ch_names[1:], gave.ch_names)
    assert_equal(gave.nave, 2)
    itc2.drop_channels(itc2.info["bads"])
    assert_array_almost_equal(gave.data, itc2.data)
    itc2.data = np.ones(itc2.data.shape)
    itc.data = np.zeros(itc.data.shape)
    itc2.nave = 2
    itc.nave = 1
    itc.drop_channels([itc.ch_names[0]])
    combined_itc = combine_tfr([itc2, itc])
    assert_array_almost_equal(combined_itc.data,
                              np.ones(combined_itc.data.shape) * 2 / 3)

    # more tests
    power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
                            return_itc=True)

    assert (power.data.shape == (len(picks), len(freqs), len(times)))
    assert (power.data.shape == itc.data.shape)
    assert (np.sum(itc.data >= 1) == 0)
    assert (np.sum(itc.data <= 0) == 0)

    tfr = tfr_morlet(epochs[0], freqs, use_fft=True, n_cycles=2, average=False,
                     return_itc=False).data[0]
    assert (tfr.shape == (len(picks), len(freqs), len(times)))
    tfr2 = tfr_morlet(epochs[0], freqs, use_fft=True, n_cycles=2,
                      decim=slice(0, 2), average=False,
                      return_itc=False).data[0]
    assert (tfr2.shape == (len(picks), len(freqs), 2))

    single_power = tfr_morlet(epochs, freqs, 2, average=False,
                              return_itc=False).data
    single_power2 = tfr_morlet(epochs, freqs, 2, decim=slice(0, 2),
                               average=False, return_itc=False).data
    single_power3 = tfr_morlet(epochs, freqs, 2, decim=slice(1, 3),
                               average=False, return_itc=False).data
    single_power4 = tfr_morlet(epochs, freqs, 2, decim=slice(2, 4),
                               average=False, return_itc=False).data

    assert_array_almost_equal(np.mean(single_power, axis=0), power.data)
    assert_array_almost_equal(np.mean(single_power2, axis=0),
                              power.data[:, :, :2])
    assert_array_almost_equal(np.mean(single_power3, axis=0),
                              power.data[:, :, 1:3])
    assert_array_almost_equal(np.mean(single_power4, axis=0),
                              power.data[:, :, 2:4])

    power_pick = power.pick_channels(power.ch_names[:10:2])
    assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
    assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
    power_drop = power.drop_channels(power.ch_names[1:10:2])
    assert_equal(power_drop.ch_names, power_pick.ch_names)
    assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))

    mne.equalize_channels([power_pick, power_drop])
    assert_equal(power_pick.ch_names, power_drop.ch_names)
    assert_equal(power_pick.data.shape, power_drop.data.shape)

    # Test decimation:
    # 2: multiple of len(times) even
    # 3: multiple odd
    # 8: not multiple, even
    # 9: not multiple, odd
    for decim in [2, 3, 8, 9]:
        for use_fft in [True, False]:
            power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2,
                                    use_fft=use_fft, return_itc=True,
                                    decim=decim)
            assert_equal(power.data.shape[2],
                         np.ceil(float(len(times)) / decim))
    freqs = list(range(50, 55))
    decim = 2
    _, n_chan, n_time = data.shape
    tfr = tfr_morlet(epochs[0], freqs, 2., decim=decim, average=False,
                     return_itc=False).data[0]
    assert_equal(tfr.shape, (n_chan, len(freqs), n_time // decim))

    # Test cwt modes
    Ws = morlet(512, [10, 20], n_cycles=2)
    pytest.raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
    for use_fft in [True, False]:
        for mode in ['same', 'valid', 'full']:
            cwt(data[0], Ws, use_fft=use_fft, mode=mode)

    # Test decim parameter checks
    pytest.raises(TypeError, tfr_morlet, epochs, freqs=freqs,
                  n_cycles=n_cycles, use_fft=True, return_itc=True,
                  decim='decim')

    # When convolving in time, wavelets must not be longer than the data
    pytest.raises(ValueError, cwt, data[0, :, :Ws[0].size - 1], Ws,
                  use_fft=False)
    with pytest.warns(UserWarning, match='one of the wavelets is longer'):
        cwt(data[0, :, :Ws[0].size - 1], Ws, use_fft=True)

    # Check for off-by-one errors when using wavelets with an even number of
    # samples
    psd = cwt(data[0], [Ws[0][:-1]], use_fft=False, mode='full')
    assert_equal(psd.shape, (2, 1, 420))

#.........这里部分代码省略.........
    assert_raises(ValueError, tfr_morlet, evoked, freqs, 1., return_itc=True)
    power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
                            use_fft=True, return_itc=True)
    # Test picks argument
    power_picks, itc_picks = tfr_morlet(epochs_nopicks, freqs=freqs,
                                        n_cycles=n_cycles, use_fft=True,
                                        return_itc=True, picks=picks)
    # the actual data arrays here are equivalent, too...
    assert_array_almost_equal(power.data, power_picks.data)
    assert_array_almost_equal(itc.data, itc_picks.data)
    assert_array_almost_equal(power.data, power_evoked.data)

    print(itc)  # test repr
    print(itc.ch_names)  # test property
    itc += power  # test add
    itc -= power  # test add

    power.apply_baseline(baseline=(-0.1, 0), mode='logratio')

    assert_true('meg' in power)
    assert_true('grad' in power)
    assert_false('mag' in power)
    assert_false('eeg' in power)

    assert_equal(power.nave, nave)
    assert_equal(itc.nave, nave)
    assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
    assert_true(power.data.shape == itc.data.shape)
    assert_true(np.sum(itc.data >= 1) == 0)
    assert_true(np.sum(itc.data <= 0) == 0)

    # grand average
    itc2 = itc.copy()
    itc2.info['bads'] = [itc2.ch_names[0]]  # test channel drop
    gave = grand_average([itc2, itc])
    assert_equal(gave.data.shape, (itc2.data.shape[0] - 1,
                                   itc2.data.shape[1],
                                   itc2.data.shape[2]))
    assert_equal(itc2.ch_names[1:], gave.ch_names)
    assert_equal(gave.nave, 2)
    itc2.drop_channels(itc2.info["bads"])
    assert_array_almost_equal(gave.data, itc2.data)
    itc2.data = np.ones(itc2.data.shape)
    itc.data = np.zeros(itc.data.shape)
    itc2.nave = 2
    itc.nave = 1
    itc.drop_channels([itc.ch_names[0]])
    combined_itc = combine_tfr([itc2, itc])
    assert_array_almost_equal(combined_itc.data,
                              np.ones(combined_itc.data.shape) * 2 / 3)

    # more tests
    power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2, use_fft=False,
                            return_itc=True)

    assert_true(power.data.shape == (len(picks), len(freqs), len(times)))
    assert_true(power.data.shape == itc.data.shape)
    assert_true(np.sum(itc.data >= 1) == 0)
    assert_true(np.sum(itc.data <= 0) == 0)

    Fs = raw.info['sfreq']  # sampling in Hz
    tfr = cwt_morlet(data[0], Fs, freqs, use_fft=True, n_cycles=2)
    assert_true(tfr.shape == (len(picks), len(freqs), len(times)))

    single_power = single_trial_power(data, Fs, freqs, use_fft=False,
                                      n_cycles=2)

    assert_array_almost_equal(np.mean(single_power), power.data)

    power_pick = power.pick_channels(power.ch_names[:10:2])
    assert_equal(len(power_pick.ch_names), len(power.ch_names[:10:2]))
    assert_equal(power_pick.data.shape[0], len(power.ch_names[:10:2]))
    power_drop = power.drop_channels(power.ch_names[1:10:2])
    assert_equal(power_drop.ch_names, power_pick.ch_names)
    assert_equal(power_pick.data.shape[0], len(power_drop.ch_names))

    mne.equalize_channels([power_pick, power_drop])
    assert_equal(power_pick.ch_names, power_drop.ch_names)
    assert_equal(power_pick.data.shape, power_drop.data.shape)

    # Test decimation
    for decim in [2, 3]:
        for use_fft in [True, False]:
            power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=2,
                                    use_fft=use_fft, return_itc=True,
                                    decim=decim)
            assert_equal(power.data.shape[2],
                         np.ceil(float(len(times)) / decim))

    # Test cwt modes
    Ws = morlet(512, [10, 20], n_cycles=2)
    assert_raises(ValueError, cwt, data[0, :, :], Ws, mode='foo')
    for use_fft in [True, False]:
        for mode in ['same', 'valid', 'full']:
            # XXX JRK: full wavelet decomposition needs to be implemented
            if (not use_fft) and mode == 'full':
                assert_raises(ValueError, cwt, data[0, :, :], Ws,
                              use_fft=use_fft, mode=mode)
                continue
            cwt(data[0, :, :], Ws, use_fft=use_fft, mode=mode)

matplotlib.use('Agg')

subject = sys.argv[1]

# Load epochs from both conditions
epochs_classic = mne.read_epochs(epochs_folder + "%s_classic_ar-epo.fif" % (
    subject))
epochs_plan = mne.read_epochs(epochs_folder + "%s_plan_ar-epo.fif" % (subject))

# Fix the events for the plan epochs so they can be concatenated
epochs_plan.event_id["press"] = 2
epochs_plan.event_id["plan"] = epochs_plan.event_id.pop("press")
epochs_plan.events[:, 2] = 2

# Equalise channels and epochs, and concatenate epochs
mne.equalize_channels([epochs_classic, epochs_plan])
mne.epochs.equalize_epoch_counts([epochs_classic, epochs_plan])

# Dirty hack # TODO: Check this from the Maxfilter side
# epochs_classic.info['dev_head_t'] = epochs_plan.info['dev_head_t']

epochs = mne.concatenate_epochs([epochs_classic, epochs_plan])

# Crop and downsmample to make it faster
epochs.crop(tmin=-3.5, tmax=0)
epochs.resample(250)

# Setup the y vector and GAT
y = np.concatenate(
    (np.zeros(len(epochs["press"])), np.ones(len(epochs["plan"]))))
gat = GeneralizationAcrossTime(