def test_3d_combiner_with_scaling():
ccd_data = ccd_data_func()
# The factors below are not particularly important; just avoid anything
# whose average is 1.
ccd_data = CCDData(np.ones((5, 5, 5)), unit=u.adu)
ccd_data_lower = CCDData(3 * np.ones((5, 5, 5)), unit=u.adu)
ccd_data_higher = CCDData(0.9 * np.ones((5, 5, 5)), unit=u.adu)
combiner = Combiner([ccd_data, ccd_data_higher, ccd_data_lower])
# scale each array to the mean of the first image
scale_by_mean = lambda x: ccd_data.data.mean() / np.ma.average(x)
combiner.scaling = scale_by_mean
avg_ccd = combiner.average_combine()
# Does the mean of the scaled arrays match the value to which it was
# scaled?
np.testing.assert_almost_equal(avg_ccd.data.mean(),
ccd_data.data.mean())
assert avg_ccd.shape == ccd_data.shape
median_ccd = combiner.median_combine()
# Does median also scale to the correct value?
np.testing.assert_almost_equal(np.median(median_ccd.data),
np.median(ccd_data.data))
# Set the scaling manually...
combiner.scaling = [scale_by_mean(combiner.data_arr[i]) for i in range(3)]
avg_ccd = combiner.average_combine()
np.testing.assert_almost_equal(avg_ccd.data.mean(),
ccd_data.data.mean())
assert avg_ccd.shape == ccd_data.shape
def test_combine_limitedmem_scale_fitsimages():
fitsfile = get_pkg_data_filename('data/a8280271.fits')
ccd = CCDData.read(fitsfile, unit=u.adu)
ccd_list = [ccd] * 5
c = Combiner(ccd_list)
# scale each array to the mean of the first image
scale_by_mean = lambda x: ccd.data.mean() / np.ma.average(x)
c.scaling = scale_by_mean
ccd_by_combiner = c.average_combine()
fitsfilename_list = [fitsfile] * 5
avgccd = combine(fitsfilename_list, output_file=None, method='average',
mem_limit=1e6, scale=scale_by_mean, unit=u.adu)
np.testing.assert_array_almost_equal(
avgccd.data, ccd_by_combiner.data, decimal=4)
def test_combiner_scaling_fails():
ccd_data = ccd_data_func()
combiner = Combiner([ccd_data, ccd_data.copy()])
# Should fail unless scaling is set to a function or list-like
with pytest.raises(TypeError):
combiner.scaling = 5
