def test_combiner_dtype():
ccd_data = ccd_data_func()
ccd_list = [ccd_data, ccd_data, ccd_data]
c = Combiner(ccd_list, dtype=np.float32)
assert c.data_arr.dtype == np.float32
avg = c.average_combine()
# dtype of average should match input dtype
assert avg.dtype == c.dtype
med = c.median_combine()
# dtype of median should match dtype of input
assert med.dtype == c.dtype
result_sum = c.sum_combine()
# dtype of sum should match dtype of input
assert result_sum.dtype == c.dtype
def test_combiner_uncertainty_sum_mask():
mask = np.zeros((10, 10), dtype=np.bool_)
mask[5, 5] = True
ccd_with_mask = CCDData(np.ones((10, 10)), unit=u.adu, mask=mask)
ccd_list = [ccd_with_mask,
CCDData(np.ones((10, 10)) * 2, unit=u.adu),
CCDData(np.ones((10, 10)) * 3, unit=u.adu)]
c = Combiner(ccd_list)
ccd = c.sum_combine()
# Just the standard deviation of ccd data.
ref_uncertainty = np.ones((10, 10)) * np.std([1, 2, 3])
ref_uncertainty *= np.sqrt(3)
ref_uncertainty[5, 5] = np.std([2, 3]) * np.sqrt(2)
np.testing.assert_array_almost_equal(ccd.uncertainty.array,
ref_uncertainty)
