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_combiner_median():
ccd_data = ccd_data_func()
ccd_list = [ccd_data, ccd_data, ccd_data]
c = Combiner(ccd_list)
ccd = c.median_combine()
assert isinstance(ccd, CCDData)
assert ccd.shape == (100, 100)
assert ccd.unit == u.adu
assert ccd.meta['NCOMBINE'] == len(ccd_list)
def test_combiner_minmax():
ccd_list = [CCDData(np.zeros((10, 10)), unit=u.adu),
CCDData(np.zeros((10, 10)) - 1000, unit=u.adu),
CCDData(np.zeros((10, 10)) + 1000, unit=u.adu)]
c = Combiner(ccd_list)
c.minmax_clipping(min_clip=-500, max_clip=500)
ccd = c.median_combine()
assert ccd.data.mean() == 0
def test_combiner_mask_median():
data = np.zeros((10, 10))
data[5, 5] = 1
mask = (data == 0)
ccd = CCDData(data, unit=u.adu, mask=mask)
ccd_list = [ccd, ccd, ccd]
c = Combiner(ccd_list)
ccd = c.median_combine()
assert ccd.data[0, 0] == 0
assert ccd.data[5, 5] == 1
assert ccd.mask[0, 0]
assert not ccd.mask[5, 5]
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_median_combine_uncertainty():
ccd_data = ccd_data_func()
ccd_list = [ccd_data, ccd_data, ccd_data]
c = Combiner(ccd_list)
ccd = c.median_combine(uncertainty_func=np.sum)
uncert_ref = np.sum(c.data_arr, 0) / np.sqrt(3)
np.testing.assert_array_equal(ccd.uncertainty.array, uncert_ref)
# Compare this also to the "combine" call
ccd2 = combine(ccd_list, method='median',
combine_uncertainty_function=np.sum)
np.testing.assert_array_equal(ccd.data, ccd2.data)
np.testing.assert_array_equal(
ccd.uncertainty.array, ccd2.uncertainty.array)
def test_combiner_uncertainty_median_mask():
mad_to_sigma = 1.482602218505602
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.median_combine()
# Just the standard deviation of ccd data.
ref_uncertainty = np.ones((10, 10)) * mad_to_sigma * mad([1, 2, 3])
# Correction because we combined two images.
ref_uncertainty /= np.sqrt(3) # 0.855980789955
ref_uncertainty[5, 5] = mad_to_sigma * \
mad([2, 3]) / np.sqrt(2) # 0.524179041254
np.testing.assert_array_almost_equal(ccd.uncertainty.array,
ref_uncertainty)
