def test_crg_noise_pad():
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits', dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits', dir=image_dir)
# If we don't use noise_pad_size, then when we draw an image larger than the original postage
# stamp, it gets padded with (nearly) zeros. We can check this by measuring the variance around
# the edge of the image (so away from the galaxy light).
crg = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=0)
psf = galsim.Gaussian(fwhm=0.4)
obj = galsim.Convolve(crg, psf)
bandpass = f606w_cat.getBandpass()
img = obj.drawImage(bandpass, nx=24, ny=24, scale=0.2)
x = np.arange(24)
x, y = np.meshgrid(x, x)
edge = (x < 4) | (x > 19) | (y < 4) | (y > 19)
print(np.var(img.array[edge]))
edgevar = np.var(img.array[edge])
np.testing.assert_allclose(edgevar, 0.0, rtol=0, atol=1e-11)
# If we turn up noise_pad_size though, then the variance of the edge should match the variance
# computed via CRG
rng = galsim.BaseDeviate(577)
crg = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=0, rng=rng, noise_pad_size=4)
obj = galsim.Convolve(crg, psf)
img = obj.drawImage(bandpass, nx=24, ny=24, scale=0.2)
edgevar = np.var(img.array[edge])
print("expected variance: ", obj.noise.getVariance())
print("edge variance: ", edgevar)
# Not super accurate, but since we only have a handful of correlated pixels to use, that
# may be expected. More detailed tests of noise in test_crg_noise() show better accuracy.
np.testing.assert_allclose(obj.noise.getVariance(), edgevar, atol=0, rtol=0.3)
def test_area_norm():
"""Check that area_norm works as expected"""
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits', dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits', dir=image_dir)
psf = galsim.Gaussian(fwhm=0.6)
rng = galsim.BaseDeviate(5772)
crg1 = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], random=True, rng=rng.duplicate())
crg2 = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], random=True, rng=rng.duplicate(),
area_norm=galsim.real.HST_area)
assert crg1 != crg2
LSST_i = galsim.Bandpass(os.path.join(bppath, "LSST_r.dat"), 'nm')
obj1 = galsim.Convolve(crg1, psf)
obj2 = galsim.Convolve(crg2, psf)
im1 = obj1.drawImage(LSST_i, exptime=1, area=1)
im2 = obj2.drawImage(LSST_i, exptime=1, area=galsim.real.HST_area)
printval(im1, im2)
np.testing.assert_array_almost_equal(im1.array, im2.array)
np.testing.assert_almost_equal(
obj1.noise.getVariance(),
obj2.noise.getVariance() * galsim.real.HST_area**2)
# area_norm is equivalant to an overall scaling
crg3 = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], random=True, rng=rng.duplicate())
crg3 /= galsim.real.HST_area
obj3 = galsim.Convolve(crg3, psf)
im3 = obj3.drawImage(LSST_i, exptime=1, area=galsim.real.HST_area)
np.testing.assert_array_almost_equal(im3.array, im2.array)
np.testing.assert_almost_equal(obj3.noise.getVariance(), obj2.noise.getVariance())
rg1 = galsim.RealGalaxy(f606w_cat, index=1)
rg2 = galsim.RealGalaxy(f606w_cat, index=1, area_norm=galsim.real.HST_area)
assert rg1 != rg2
obj1 = galsim.Convolve(rg1, psf)
obj2 = galsim.Convolve(rg2, psf)
im1 = obj1.drawImage()
im2 = obj2.drawImage(exptime=1, area=galsim.real.HST_area)
printval(im1, im2)
np.testing.assert_array_almost_equal(im1.array, im2.array)
np.testing.assert_almost_equal(
obj1.noise.getVariance(),
obj2.noise.getVariance() * galsim.real.HST_area**2)
# area_norm is equivalant to an overall scaling
rg3 = galsim.RealGalaxy(f606w_cat, index=1)
rg3 /= galsim.real.HST_area
obj3 = galsim.Convolve(rg3, psf)
im3 = obj3.drawImage(exptime=1, area=galsim.real.HST_area)
np.testing.assert_array_almost_equal(im3.array, im2.array)
np.testing.assert_almost_equal(obj3.noise.getVariance(), obj2.noise.getVariance())
def test_ne():
""" Check that inequality works as expected."""
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits',
dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits',
dir=image_dir)
crg1 = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=0)
crg2 = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=1)
covspec1 = crg1.covspec
covspec2 = crg2.covspec
gsp = galsim.GSParams(folding_threshold=1.1e-3)
objs = [
galsim.RealGalaxy(rgc, index=0),
galsim.RealGalaxy(rgc, index=1),
galsim.RealGalaxy(rgc, index=0, x_interpolant='Linear'),
galsim.RealGalaxy(rgc, index=0, k_interpolant='Linear'),
galsim.RealGalaxy(rgc, index=0, flux=1.1),
galsim.RealGalaxy(rgc, index=0, flux_rescale=1.2),
galsim.RealGalaxy(rgc, index=0, area_norm=2),
galsim.RealGalaxy(rgc, index=0, pad_factor=1.1),
galsim.RealGalaxy(rgc, index=0, noise_pad_size=5.0),
galsim.RealGalaxy(rgc, index=0, gsparams=gsp), crg1, crg2,
galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat],
index=0,
k_interpolant='Linear'), covspec1, covspec2
]
all_obj_diff(objs)
for obj in objs[:-5]:
do_pickle(obj)
# CovarianceSpectrum and ChromaticRealGalaxy are both reprable, but their reprs are rather
# large, so the eval(repr) checks take a long time.
# Therefore, run them from command line, but not from pytest.
if __name__ == '__main__':
do_pickle(crg1)
do_pickle(covspec1)
else:
do_pickle(crg1, irreprable=True)
do_pickle(covspec1, irreprable=True)
def test_pickle_crg():
"""Just do some pickling tests of ChromaticRealGalaxy."""
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits', dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits', dir=image_dir)
crg = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=0)
do_pickle(crg)
do_pickle(crg, lambda x: x.drawImage(f606w_cat.getBandpass()))
# Check that missing band raises ValueError
orig_band = f606w_cat.band
f606w_cat.band = 'eggs'
with assert_raises(ValueError):
f606w_cat.getBandpass()
f606w_cat.band = orig_band
f606w_cat.getBandpass()
def test_crg_noise_draw_transform_commutativity():
"""Test commutativity of ChromaticRealGalaxy correlated noise under operations of drawImage and
applying transformations.
"""
LSST_i = galsim.Bandpass(os.path.join(bppath, "LSST_r.dat"), 'nm')
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits',
dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits',
dir=image_dir)
psf = galsim.Gaussian(fwhm=0.6)
crg = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat],
id=14886,
maxk=psf.maxk)
factor = 1.5
g1 = g2 = 0.1
mu = 1.2
theta = 45 * galsim.degrees
jac = [1.1, 0.1, -0.1, 1.2]
orig = galsim.Convolve(crg, psf)
orig.drawImage(LSST_i)
draw_transform_img = galsim.ImageD(16, 16, scale=0.2)
transform_draw_img = draw_transform_img.copy()
multiplied = orig * factor
multiplied.drawImage(LSST_i) # needed to populate noise property
(orig.noise * factor**2).drawImage(image=draw_transform_img)
multiplied.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
divided = orig / factor
divided.drawImage(LSST_i)
(orig.noise / factor**2).drawImage(image=draw_transform_img)
divided.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
expanded = orig.expand(factor)
expanded.drawImage(LSST_i)
orig.noise.expand(factor).drawImage(image=draw_transform_img)
expanded.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
dilated = orig.dilate(factor)
dilated.drawImage(LSST_i)
orig.noise.dilate(factor).drawImage(image=draw_transform_img)
dilated.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
magnified = orig.magnify(mu)
magnified.drawImage(LSST_i)
orig.noise.magnify(mu).drawImage(image=draw_transform_img)
magnified.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
lensed = orig.lens(g1, g2, mu)
lensed.drawImage(LSST_i)
orig.noise.lens(g1, g2, mu).drawImage(image=draw_transform_img)
lensed.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
rotated = orig.rotate(theta)
rotated.drawImage(LSST_i)
orig.noise.rotate(theta).drawImage(image=draw_transform_img)
rotated.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
sheared = orig.shear(g1=g1, g2=g2)
sheared.drawImage(LSST_i)
orig.noise.shear(g1=g1, g2=g2).drawImage(image=draw_transform_img)
sheared.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
transformed = orig.transform(*jac)
transformed.drawImage(LSST_i)
orig.noise.transform(*jac).drawImage(image=draw_transform_img)
transformed.noise.drawImage(image=transform_draw_img)
np.testing.assert_array_almost_equal(draw_transform_img.array,
transform_draw_img.array)
def test_crg_roundtrip():
"""Test that drawing a ChromaticRealGalaxy using the HST collecting area and filter gives back
the original image.
"""
f606w_cat = galsim.RealGalaxyCatalog('AEGIS_F606w_catalog.fits',
dir=image_dir)
f814w_cat = galsim.RealGalaxyCatalog('AEGIS_F814w_catalog.fits',
dir=image_dir)
indices = [0] if __name__ != "__main__" else list(range(len(f606w_cat)))
for index in indices:
orig_f606w = f606w_cat.getGalImage(index)
orig_f814w = f814w_cat.getGalImage(index)
crg = galsim.ChromaticRealGalaxy([f606w_cat, f814w_cat], index=index)
# Note that getPSF() return value already includes convolution by the pixel
f606w_obj = galsim.Convolve(crg, f606w_cat.getPSF(index))
f814w_obj = galsim.Convolve(crg, f814w_cat.getPSF(index))
f606w = f606w_cat.getBandpass()
f814w = f814w_cat.getBandpass()
im_f606w = f606w_obj.drawImage(f606w,
image=orig_f606w.copy(),
method='no_pixel')
im_f814w = f814w_obj.drawImage(f814w,
image=orig_f814w.copy(),
method='no_pixel')
printval(im_f606w, orig_f606w)
printval(im_f814w, orig_f814w)
orig_f606w_mom = galsim.hsm.FindAdaptiveMom(orig_f606w)
orig_f814w_mom = galsim.hsm.FindAdaptiveMom(orig_f814w)
im_f606w_mom = galsim.hsm.FindAdaptiveMom(im_f606w)
im_f814w_mom = galsim.hsm.FindAdaptiveMom(im_f814w)
# Images are only pixel-by-pixel consistent to 5% or so. However, if you actually plot the
# residuals (which you can do by flipping False->True in printval in galsim_test_helpers),
# they appear as ringing over the whole image. Probably it's unrealistic to expect this
# test to work perfectly since we're effectively deconvolving and then reconvolving by the
# same PSF, not a fatter PSF.
np.testing.assert_allclose(orig_f606w.array,
im_f606w.array,
rtol=0.,
atol=5e-2 * orig_f606w.array.max())
np.testing.assert_allclose(orig_f814w.array,
im_f814w.array,
rtol=0.,
atol=5e-2 * orig_f814w.array.max())
# Happily, the pixel-by-pixel residuals don't appear to affect the moments much:
np.testing.assert_allclose(orig_f606w_mom.moments_amp,
im_f606w_mom.moments_amp,
rtol=1e-3,
atol=0)
np.testing.assert_allclose(orig_f606w_mom.moments_centroid.x,
im_f606w_mom.moments_centroid.x,
rtol=0.,
atol=1e-2)
np.testing.assert_allclose(orig_f606w_mom.moments_centroid.y,
im_f606w_mom.moments_centroid.y,
rtol=0.,
atol=1e-2)
np.testing.assert_allclose(orig_f606w_mom.moments_sigma,
im_f606w_mom.moments_sigma,
rtol=1e-3,
atol=0)
np.testing.assert_allclose(orig_f606w_mom.observed_shape.g1,
im_f606w_mom.observed_shape.g1,
rtol=0,
atol=2e-4)
np.testing.assert_allclose(orig_f606w_mom.observed_shape.g2,
im_f606w_mom.observed_shape.g2,
rtol=0,
atol=2e-4)
np.testing.assert_allclose(orig_f814w_mom.moments_amp,
im_f814w_mom.moments_amp,
rtol=1e-3,
atol=0)
np.testing.assert_allclose(orig_f814w_mom.moments_centroid.x,
im_f814w_mom.moments_centroid.x,
rtol=0.,
atol=1e-2)
np.testing.assert_allclose(orig_f814w_mom.moments_centroid.y,
im_f814w_mom.moments_centroid.y,
rtol=0.,
atol=1e-2)
np.testing.assert_allclose(orig_f814w_mom.moments_sigma,
im_f814w_mom.moments_sigma,
rtol=1e-3,
atol=0)
np.testing.assert_allclose(orig_f814w_mom.observed_shape.g1,
im_f814w_mom.observed_shape.g1,
rtol=0,
atol=1e-4)
np.testing.assert_allclose(orig_f814w_mom.observed_shape.g2,
im_f814w_mom.observed_shape.g2,
rtol=0,
atol=1e-4)
# Check some errors
cats = [f606w_cat, f814w_cat]
assert_raises(TypeError,
galsim.ChromaticRealGalaxy,
real_galaxy_catalogs=cats)
assert_raises(TypeError, galsim.ChromaticRealGalaxy, cats, index=3, id=4)
assert_raises(TypeError,
galsim.ChromaticRealGalaxy,
cats,
index=3,
random=True)
assert_raises(TypeError,
galsim.ChromaticRealGalaxy,
cats,
id=4,
random=True)
assert_raises(TypeError,
galsim.ChromaticRealGalaxy,
cats,
random=True,
rng='foo')
