def test_xsvs():
    images = []
    for i in range(5):
        int_array = np.tril((i + 2) * np.ones(10))
        int_array[int_array == 0] = (i + 1)
        images.append(int_array)

    images_sets = [np.asarray(images), ]
    roi_data = np.array(([4, 2, 2, 2], [0, 5, 4, 4]), dtype=np.int64)
    label_array = roi.rectangles(roi_data, shape=images[0].shape)

    prob_k_all, std = xsvs.xsvs(images_sets, label_array, timebin_num=2,
                                number_of_img=5, max_cts=6)

    assert_array_almost_equal(prob_k_all[0, 0],
                              np.array([0., 0., 0.2, 0.2, 0.4]))
    assert_array_almost_equal(prob_k_all[0, 1],
                              np.array([0., 0.2, 0.2, 0.2, 0.4]))

    imgs = []
    for i in range(6):
        int_array = np.tril((i + 2) * np.ones(10))
        int_array[int_array == 0] = (i / 1)
        imgs.append(int_array)

    # testing for bad images
    bad_list = [5]
    # convert each bad image to np.nan array
    images1 = mask.bad_to_nan_gen(imgs, bad_list)

    new_prob_k, new_std = xsvs.xsvs((images1, ), label_array,
                                    timebin_num=2, number_of_img=5,
                                    max_cts=6)

    assert_array_almost_equal(new_prob_k[0, 0],
                              np.array([0., 0., 0.2, 0.2, 0.4]))
    assert_array_almost_equal(new_prob_k[0, 1],
                              np.array([0., 0.2, 0.2, 0.2, 0.4]))
dataset/ETHPy150Open scikit-beam/scikit-beam/skbeam/core/tests/test_speckle.py/test_xsvs/operator `+` -> `/`