def test_time_frequency():
    """Test time frequency transform (PSD and phase lock)
    """
    # Set parameters
    event_id = 1
    tmin = -0.2
    tmax = 0.498  # Allows exhaustive decimation testing

    # Setup for reading the raw data
    raw = io.read_raw_fif(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)

    epochs_nopicks = Epochs(raw, events, event_id, tmin, tmax,
                            baseline=(None, 0))

    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)
    # Now compute evoked
    evoked = epochs.average()
    power_evoked = tfr_morlet(evoked, freqs, n_cycles, use_fft=True,
                              return_itc=False)
    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:
    # 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(decim / float(len(times))))
    freqs = range(50, 55)
    decim = 2
    _, n_chan, n_time = data.shape
    tfr = cwt_morlet(data[0, :, :], sfreq=epochs.info['sfreq'],
                     freqs=freqs, decim=decim)
    assert_equal(tfr.shape, (n_chan, len(freqs), n_time // 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)
dataset/ETHPy150Open mne-tools/mne-python/mne/time_frequency/tests/test_tfr.py/test_time_frequency/swapped operands of `/`