def test_psdestimator():
    'Test methods of PSDEstimator\n    '
    raw = io.read_raw_fif(raw_fname, preload=False)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    psd = PSDEstimator((2 * np.pi), 0, np.inf)
    y = epochs.events[:, (- 1)]
    X = psd.fit_transform(epochs_data, y)
    assert_true((X.shape[0] == epochs_data.shape[0]))
    assert_array_equal(psd.fit(epochs_data, y).transform(epochs_data), X)
    assert_raises(ValueError, psd.fit, epochs, y)
    assert_raises(ValueError, psd.transform, epochs, y)
Test methods of PSDEstimator
def test_psdestimator():
    '\n    '
    raw = io.read_raw_fif(raw_fname, preload=False)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    psd = PSDEstimator((2 * np.pi), 0, np.inf)
    y = epochs.events[:, (- 1)]
    X = psd.fit_transform(epochs_data, y)
    assert_true((X.shape[0] == epochs_data.shape[0]))
    assert_array_equal(psd.fit(epochs_data, y).transform(epochs_data), X)
    assert_raises(ValueError, psd.fit, epochs, y)
    assert_raises(ValueError, psd.transform, epochs, y)