def test_transform_data():
    """Test applying linear (time) transform to data."""
    # make up some data
    n_sensors, n_vertices, n_times = 10, 20, 4
    kernel = rng.randn(n_vertices, n_sensors)
    sens_data = rng.randn(n_sensors, n_times)

    vertices = np.arange(n_vertices)
    data = np.dot(kernel, sens_data)

    for idx, tmin_idx, tmax_idx in\
            zip([None, np.arange(n_vertices // 2, n_vertices)],
                [None, 1], [None, 3]):

        if idx is None:
            idx_use = slice(None, None)
        else:
            idx_use = idx

        data_f, _ = _my_trans(data[idx_use, tmin_idx:tmax_idx])

        for stc_data in (data, (kernel, sens_data)):
            stc = VolSourceEstimate(stc_data,
                                    vertices=vertices,
                                    tmin=0.,
                                    tstep=1.)
            stc_data_t = stc.transform_data(_my_trans,
                                            idx=idx,
                                            tmin_idx=tmin_idx,
                                            tmax_idx=tmax_idx)
            assert_allclose(data_f, stc_data_t)
    # bad sens_data
    sens_data = sens_data[..., np.newaxis]
    with pytest.raises(ValueError, match='sensor data must have 2'):
        VolSourceEstimate((kernel, sens_data), vertices)
def test_transform_data():
    """Test applying linear (time) transform to data"""
    # make up some data
    n_sensors, n_vertices, n_times = 10, 20, 4
    kernel = np.random.randn(n_vertices, n_sensors)
    sens_data = np.random.randn(n_sensors, n_times)

    vertices = np.arange(n_vertices)
    data = np.dot(kernel, sens_data)

    for idx, tmin_idx, tmax_idx in\
            zip([None, np.arange(n_vertices // 2, n_vertices)],
                [None, 1], [None, 3]):

        if idx is None:
            idx_use = slice(None, None)
        else:
            idx_use = idx

        data_f, _ = _my_trans(data[idx_use, tmin_idx:tmax_idx])

        for stc_data in (data, (kernel, sens_data)):
            stc = VolSourceEstimate(stc_data,
                                    vertices=vertices,
                                    tmin=0.,
                                    tstep=1.)
            stc_data_t = stc.transform_data(_my_trans,
                                            idx=idx,
                                            tmin_idx=tmin_idx,
                                            tmax_idx=tmax_idx)
            assert_allclose(data_f, stc_data_t)
def test_transform_data():
    """Test applying linear (time) transform to data"""
    # make up some data
    n_sensors, n_vertices, n_times = 10, 20, 4
    kernel = rng.randn(n_vertices, n_sensors)
    sens_data = rng.randn(n_sensors, n_times)

    vertices = np.arange(n_vertices)
    data = np.dot(kernel, sens_data)

    for idx, tmin_idx, tmax_idx in\
            zip([None, np.arange(n_vertices // 2, n_vertices)],
                [None, 1], [None, 3]):

        if idx is None:
            idx_use = slice(None, None)
        else:
            idx_use = idx

        data_f, _ = _my_trans(data[idx_use, tmin_idx:tmax_idx])

        for stc_data in (data, (kernel, sens_data)):
            stc = VolSourceEstimate(stc_data, vertices=vertices,
                                    tmin=0., tstep=1.)
            stc_data_t = stc.transform_data(_my_trans, idx=idx,
                                            tmin_idx=tmin_idx,
                                            tmax_idx=tmax_idx)
            assert_allclose(data_f, stc_data_t)