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)
