def test_combiner_result_dtype():
"""Regression test: #391
The result should have the appropriate dtype not the dtype of the first
input."""
ccd = CCDData(np.ones((3, 3), dtype=np.uint16), unit='adu')
res = combine([ccd, ccd.multiply(2)])
# The default dtype of Combiner is float64
assert res.data.dtype == np.float64
ref = np.ones((3, 3)) * 1.5
np.testing.assert_array_almost_equal(res.data, ref)
res = combine([ccd, ccd.multiply(2), ccd.multiply(3)], dtype=int)
# The result dtype should be integer:
assert res.data.dtype == np.int_
ref = np.ones((3, 3)) * 2
np.testing.assert_array_almost_equal(res.data, ref)
def test_combine_average_ccddata():
fitsfile = get_pkg_data_filename('data/a8280271.fits')
ccd = CCDData.read(fitsfile, unit=u.adu)
ccd_list = [ccd] * 3
c = Combiner(ccd_list)
ccd_by_combiner = c.average_combine()
avgccd = combine(ccd_list, output_file=None, method='average', unit=u.adu)
# averaging same ccdData should give back same images
np.testing.assert_array_almost_equal(avgccd.data, ccd_by_combiner.data)
def test_sum_combine_uncertainty():
ccd_data = ccd_data_func()
ccd_list = [ccd_data, ccd_data, ccd_data]
c = Combiner(ccd_list)
ccd = c.sum_combine(uncertainty_func=np.sum)
uncert_ref = np.sum(c.data_arr, 0) * np.sqrt(3)
np.testing.assert_almost_equal(ccd.uncertainty.array, uncert_ref)
# Compare this also to the "combine" call
ccd2 = combine(ccd_list, method='sum', 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_clip_extrema_via_combine():
ccdlist = [CCDData(np.ones((3, 5)) * 90., unit="adu"),
CCDData(np.ones((3, 5)) * 20., unit="adu"),
CCDData(np.ones((3, 5)) * 10., unit="adu"),
CCDData(np.ones((3, 5)) * 40., unit="adu"),
CCDData(np.ones((3, 5)) * 25., unit="adu"),
CCDData(np.ones((3, 5)) * 35., unit="adu"),
]
ccdlist[0].data[0, 1] = 3.1
ccdlist[1].data[1, 2] = 100.1
ccdlist[1].data[2, 0] = 100.1
result = combine(ccdlist, clip_extrema=True, nlow=1, nhigh=1,)
expected = [[30.0, 22.5, 30.0, 30.0, 30.0],
[30.0, 30.0, 47.5, 30.0, 30.0],
[47.5, 30.0, 30.0, 30.0, 30.0]]
np.testing.assert_array_equal(result, expected)
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_combine_numpyndarray():
""" Test of numpy ndarray implementation: #493
Test the average combine using ``Combiner`` and ``combine`` with input
``img_list`` in the format of ``numpy.ndarray``.
"""
fitsfile = get_pkg_data_filename('data/a8280271.fits')
ccd = CCDData.read(fitsfile, unit=u.adu)
ccd_list = [ccd] * 3
c = Combiner(ccd_list)
ccd_by_combiner = c.average_combine()
fitsfilename_list = np.array([fitsfile] * 3)
avgccd = combine(fitsfilename_list, output_file=None,
method='average', unit=u.adu)
# averaging same fits images should give back same fits image
np.testing.assert_array_almost_equal(avgccd.data, ccd_by_combiner.data)
