def test_bad_objects(metadata): from qa_common.filter_objects import good_measurement_indices data, conditions = metadata index = data.shape[1] / 2 conditions[3][index] = -50.0 per_object, per_image = good_measurement_indices(*conditions) expected = np.ones_like(per_object, dtype=bool) expected[index] = False assert np.all(per_image) & (per_object == expected).all()
def test_good_measurement_indices_all_good(metadata): from qa_common.filter_objects import good_measurement_indices data = metadata[0] per_object, per_image = good_measurement_indices(*metadata[1]) assert np.all(per_object) & np.all(per_image)