def test_ne():
"""Test base.py GSObjects for not-equals."""
# Define some universal gsps
gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)
# Pixel. Params include scale, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.Pixel(scale=1.0),
galsim.Pixel(scale=1.1),
galsim.Pixel(scale=1.0, flux=1.1),
galsim.Pixel(scale=1.0, gsparams=gsp)]
all_obj_diff(gals)
# Box. Params include width, height, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.Box(width=1.0, height=1.0),
galsim.Box(width=1.1, height=1.0),
galsim.Box(width=1.0, height=1.1),
galsim.Box(width=1.0, height=1.0, flux=1.1),
galsim.Box(width=1.0, height=1.0, gsparams=gsp)]
all_obj_diff(gals)
# TopHat. Params include radius, flux, gsparams.
# gsparams.
# The following should all test unequal:
gals = [galsim.TopHat(radius=1.0),
galsim.TopHat(radius=1.1),
galsim.TopHat(radius=1.0, flux=1.1),
galsim.TopHat(radius=1.0, gsparams=gsp)]
all_obj_diff(gals)
def test_box_shoot():
"""Test Box with photon shooting. Particularly the flux of the final image.
"""
rng = galsim.BaseDeviate(1234)
obj = galsim.Box(width=1.3, height=2.4, flux=1.e4)
im = galsim.Image(100,100, scale=1)
im.setCenter(0,0)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng)
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
obj = galsim.Pixel(scale=9.3, flux=1.e4)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng)
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
obj = galsim.TopHat(radius=4.7, flux=1.e4)
added_flux, photons = obj.drawPhot(im, poisson_flux=False, rng=rng)
print('obj.flux = ',obj.flux)
print('added_flux = ',added_flux)
print('photon fluxes = ',photons.flux.min(),'..',photons.flux.max())
print('image flux = ',im.array.sum())
assert np.isclose(added_flux, obj.flux)
assert np.isclose(im.array.sum(), obj.flux)
def test_flip():
"""Test several ways to flip a profile
"""
# The Shapelet profile has the advantage of being fast and not circularly symmetric, so
# it is a good test of the actual code for doing the flips (in SBTransform).
# But since the bug Rachel reported in #645 was actually in SBInterpolatedImage
# (one calculation implicitly assumed dx > 0), it seems worthwhile to run through all the
# classes to make sure we hit everything with negative steps for dx and dy.
prof_list = [
galsim.Shapelet(sigma=0.17, order=2,
bvec=[1.7, 0.01,0.03, 0.29, 0.33, -0.18]),
]
if __name__ == "__main__":
image_dir = './real_comparison_images'
catalog_file = 'test_catalog.fits'
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
# Some of these are slow, so only do the Shapelet test as part of the normal unit tests.
prof_list += [
galsim.Airy(lam_over_diam=0.17, flux=1.7),
galsim.Airy(lam_over_diam=0.17, obscuration=0.2, flux=1.7),
# Box gets rendered with real-space convolution. The default accuracy isn't quite
# enough to get the flip to match at 6 decimal places.
galsim.Box(0.17, 0.23, flux=1.7,
gsparams=galsim.GSParams(realspace_relerr=1.e-6)),
# Without being convolved by anything with a reasonable k cutoff, this needs
# a very large fft.
galsim.DeVaucouleurs(half_light_radius=0.17, flux=1.7),
# I don't really understand why this needs a lower maxk_threshold to work, but
# without it, the k-space tests fail.
galsim.Exponential(scale_radius=0.17, flux=1.7,
gsparams=galsim.GSParams(maxk_threshold=1.e-4)),
galsim.Gaussian(sigma=0.17, flux=1.7),
galsim.Kolmogorov(fwhm=0.17, flux=1.7),
galsim.Moffat(beta=2.5, fwhm=0.17, flux=1.7),
galsim.Moffat(beta=2.5, fwhm=0.17, flux=1.7, trunc=0.82),
galsim.OpticalPSF(lam_over_diam=0.17, obscuration=0.2, nstruts=6,
coma1=0.2, coma2=0.5, defocus=-0.1, flux=1.7),
# Like with Box, we need to increase the real-space convolution accuracy.
# This time lowering both relerr and abserr.
galsim.Pixel(0.23, flux=1.7,
gsparams=galsim.GSParams(realspace_relerr=1.e-6,
realspace_abserr=1.e-8)),
# Note: RealGalaxy should not be rendered directly because of the deconvolution.
# Here we convolve it by a Gaussian that is slightly larger than the original PSF.
galsim.Convolve([ galsim.RealGalaxy(rgc, index=0, flux=1.7), # "Real" RealGalaxy
galsim.Gaussian(sigma=0.08) ]),
galsim.Convolve([ galsim.RealGalaxy(rgc, index=1, flux=1.7), # "Fake" RealGalaxy
galsim.Gaussian(sigma=0.08) ]), # (cf. test_real.py)
galsim.Spergel(nu=-0.19, half_light_radius=0.17, flux=1.7),
galsim.Spergel(nu=0., half_light_radius=0.17, flux=1.7),
galsim.Spergel(nu=0.8, half_light_radius=0.17, flux=1.7),
galsim.Sersic(n=2.3, half_light_radius=0.17, flux=1.7),
galsim.Sersic(n=2.3, half_light_radius=0.17, flux=1.7, trunc=0.82),
# The shifts here caught a bug in how SBTransform handled the recentering.
# Two of the shifts (0.125 and 0.375) lead back to 0.0 happening on an integer
# index, which now works correctly.
galsim.Sum([ galsim.Gaussian(sigma=0.17, flux=1.7).shift(-0.2,0.125),
galsim.Exponential(scale_radius=0.23, flux=3.1).shift(0.375,0.23)]),
galsim.TopHat(0.23, flux=1.7),
# Box and Pixel use real-space convolution. Convolve with a Gaussian to get fft.
galsim.Convolve([ galsim.Box(0.17, 0.23, flux=1.7).shift(-0.2,0.1),
galsim.Gaussian(sigma=0.09) ]),
galsim.Convolve([ galsim.TopHat(0.17, flux=1.7).shift(-0.275,0.125),
galsim.Gaussian(sigma=0.09) ]),
# Test something really crazy with several layers worth of transformations
galsim.Convolve([
galsim.Sum([
galsim.Gaussian(sigma=0.17, flux=1.7).shear(g1=0.1,g2=0.2).shift(2,3),
galsim.Kolmogorov(fwhm=0.33, flux=3.9).transform(0.31,0.19,-0.23,0.33) * 88.,
galsim.Box(0.11, 0.44, flux=4).rotate(33 * galsim.degrees) / 1.9
]).shift(-0.3,1),
galsim.AutoConvolve(galsim.TopHat(0.5).shear(g1=0.3,g2=0)).rotate(3*galsim.degrees),
(galsim.AutoCorrelate(galsim.Box(0.2, 0.3)) * 11).shift(3,2).shift(2,-3) * 0.31
]).shift(0,0).transform(0,-1,-1,0).shift(-1,1)
]
s = galsim.Shear(g1=0.11, g2=-0.21)
s1 = galsim.Shear(g1=0.11, g2=0.21) # Appropriate for the flips around x and y axes
s2 = galsim.Shear(g1=-0.11, g2=-0.21) # Appropriate for the flip around x=y
# Also use shears with just a g1 to get dx != dy, but dxy, dyx = 0.
q = galsim.Shear(g1=0.11, g2=0.)
q1 = galsim.Shear(g1=0.11, g2=0.) # Appropriate for the flips around x and y axes
q2 = galsim.Shear(g1=-0.11, g2=0.) # Appropriate for the flip around x=y
decimal=6 # Oddly, these aren't as precise as I would have expected.
# Even when we only go to this many digits of accuracy, the Exponential needed
# a lower than default value for maxk_threshold.
im = galsim.ImageD(16,16, scale=0.05)
for prof in prof_list:
print('prof = ',prof)
# Not all profiles are expected to have a max_sb value close to the maximum pixel value,
# so mark the ones where we don't want to require this to be true.
close_maxsb = True
name = str(prof)
if ('DeVauc' in name or 'Sersic' in name or 'Spergel' in name or
'Optical' in name or 'shift' in name):
close_maxsb = False
# Make sure we hit all 4 fill functions.
# image_x uses fillXValue with izero, jzero
# image_x1 uses fillXValue with izero, jzero, and unequal dx,dy
# image_x2 uses fillXValue with dxy, dyx
# image_k uses fillKValue with izero, jzero
# image_k1 uses fillKValue with izero, jzero, and unequal dx,dy
# image_k2 uses fillKValue with dxy, dyx
image_x = prof.drawImage(image=im.copy(), method='no_pixel')
image_x1 = prof.shear(q).drawImage(image=im.copy(), method='no_pixel')
image_x2 = prof.shear(s).drawImage(image=im.copy(), method='no_pixel')
image_k = prof.drawImage(image=im.copy())
image_k1 = prof.shear(q).drawImage(image=im.copy())
image_k2 = prof.shear(s).drawImage(image=im.copy())
if close_maxsb:
np.testing.assert_allclose(
image_x.array.max(), prof.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image_x1.array.max(), prof.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image_x2.array.max(), prof.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around y axis (i.e. x -> -x)
flip1 = prof.transform(-1, 0, 0, 1)
image2_x = flip1.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, image2_x.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x test")
image2_x1 = flip1.shear(q1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, image2_x1.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x1 test")
image2_x2 = flip1.shear(s1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, image2_x2.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed x2 test")
image2_k = flip1.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, image2_k.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k test")
image2_k1 = flip1.shear(q1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, image2_k1.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k1 test")
image2_k2 = flip1.shear(s1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, image2_k2.array[:,::-1], decimal=decimal,
err_msg="Flipping image around y-axis failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip1.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip1.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip1.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around x axis (i.e. y -> -y)
flip2 = prof.transform(1, 0, 0, -1)
image2_x = flip2.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, image2_x.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x test")
image2_x1 = flip2.shear(q1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, image2_x1.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x1 test")
image2_x2 = flip2.shear(s1).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, image2_x2.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed x2 test")
image2_k = flip2.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, image2_k.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k test")
image2_k1 = flip2.shear(q1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, image2_k1.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k1 test")
image2_k2 = flip2.shear(s1).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, image2_k2.array[::-1,:], decimal=decimal,
err_msg="Flipping image around x-axis failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip2.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip2.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip2.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
# Flip around x=y (i.e. y -> x, x -> y)
flip3 = prof.transform(0, 1, 1, 0)
image2_x = flip3.drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x.array, np.transpose(image2_x.array), decimal=decimal,
err_msg="Flipping image around x=y failed x test")
image2_x1 = flip3.shear(q2).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x1.array, np.transpose(image2_x1.array), decimal=decimal,
err_msg="Flipping image around x=y failed x1 test")
image2_x2 = flip3.shear(s2).drawImage(image=im.copy(), method='no_pixel')
np.testing.assert_array_almost_equal(
image_x2.array, np.transpose(image2_x2.array), decimal=decimal,
err_msg="Flipping image around x=y failed x2 test")
image2_k = flip3.drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k.array, np.transpose(image2_k.array), decimal=decimal,
err_msg="Flipping image around x=y failed k test")
image2_k1 = flip3.shear(q2).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k1.array, np.transpose(image2_k1.array), decimal=decimal,
err_msg="Flipping image around x=y failed k1 test")
image2_k2 = flip3.shear(s2).drawImage(image=im.copy())
np.testing.assert_array_almost_equal(
image_k2.array, np.transpose(image2_k2.array), decimal=decimal,
err_msg="Flipping image around x=y failed k2 test")
if close_maxsb:
np.testing.assert_allclose(
image2_x.array.max(), flip3.max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x1.array.max(), flip3.shear(q).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
np.testing.assert_allclose(
image2_x2.array.max(), flip3.shear(s).max_sb*im.scale**2, rtol=0.2,
err_msg="max_sb did not match maximum pixel value")
do_pickle(prof, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip1, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip2, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(flip3, lambda x: x.drawImage(image=im.copy(), method='no_pixel'))
do_pickle(prof)
do_pickle(flip1)
do_pickle(flip2)
do_pickle(flip3)
def test_tophat():
"""Test the generation of a specific tophat profile against a known result.
"""
savedImg = galsim.fits.read(os.path.join(imgdir, "tophat_101.fits"))
myImg = galsim.ImageF(savedImg.bounds, scale=0.2)
myImg.setCenter(0,0)
test_flux = 1.8
# There are numerical issues with using radius = 1, since many points are right on the edge
# of the circle. e.g. (+-1,0), (0,+-1), (+-0.6,+-0.8), (+-0.8,+-0.6). And in practice, some
# of these end up getting drawn and not others, which means it's not a good choice for a unit
# test since it wouldn't be any less correct for a different subset of these points to be
# drawn. Using r = 1.01 solves this problem and makes the result symmetric.
tophat = galsim.TopHat(radius=1.01, flux=1)
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat disagrees with expected result")
np.testing.assert_array_equal(
tophat.radius, 1.01,
err_msg="TopHat radius returned wrong value")
# Check with default_params
tophat = galsim.TopHat(radius=1.01, flux=1, gsparams=default_params)
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat with default_params disagrees with expected result")
tophat = galsim.TopHat(radius=1.01, flux=1, gsparams=galsim.GSParams())
tophat.drawImage(myImg, method="sb", use_true_center=False)
np.testing.assert_array_almost_equal(
myImg.array, savedImg.array, 5,
err_msg="Using GSObject TopHat with GSParams() disagrees with expected result")
# Use non-unity values.
tophat = galsim.TopHat(flux=1.7, radius=2.3)
# Test photon shooting.
do_shoot(tophat,myImg,"TopHat")
# Test shoot and kvalue
scale = 0.2939
im = galsim.ImageF(16,16, scale=scale)
# The choices of radius here are fairly specific. If the edge of the circle comes too close
# to the center of one of the pixels, then the test will fail, since the Fourier draw method
# will blur the edge a bit and give some flux to that pixel.
for radius in [ 1.2, 0.93, 2.11 ]:
tophat = galsim.TopHat(radius=radius, flux=test_flux)
check_basic(tophat, "TopHat with radius = %f"%radius)
do_shoot(tophat,im,"TopHat with radius = %f"%radius)
do_kvalue(tophat,im,"TopHat with radius = %f"%radius)
# This is also a profile that may be convolved using real space convolution, so test that.
conv = galsim.Convolve(tophat, galsim.Pixel(scale=scale), real_space=True)
check_basic(conv, "TopHat convolved with pixel in real space",
approx_maxsb=True, scale=0.2)
do_kvalue(conv,im, "TopHat convolved with pixel in real space")
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(tophat.centroid, cen)
np.testing.assert_almost_equal(tophat.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(tophat.flux, test_flux)
np.testing.assert_almost_equal(tophat.xValue(cen), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(radius-0.001, 0.), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(0., radius-0.001), tophat.max_sb)
np.testing.assert_almost_equal(tophat.xValue(radius+0.001, 0.), 0.)
np.testing.assert_almost_equal(tophat.xValue(0., radius+0.001), 0.)
# Check picklability
do_pickle(tophat, lambda x: x.drawImage(method='no_pixel'))
do_pickle(tophat)
do_pickle(galsim.TopHat(1))
# Check sheared tophat the same way
tophat = galsim.TopHat(radius=1.2, flux=test_flux)
# Again, the test is very sensitive to the choice of shear here. Most values fail because
# some pixel center gets too close to the resulting ellipse for the fourier draw to match
# the real-space draw at the required accuracy.
tophat = tophat.shear(galsim.Shear(g1=0.15, g2=-0.33))
check_basic(tophat, "Sheared TopHat")
do_shoot(tophat,im, "Sheared TopHat")
do_kvalue(tophat,im, "Sheared TopHat")
cen = galsim.PositionD(0, 0)
np.testing.assert_equal(tophat.centroid, cen)
np.testing.assert_almost_equal(tophat.kValue(cen), (1+0j) * test_flux)
np.testing.assert_almost_equal(tophat.flux, test_flux)
np.testing.assert_almost_equal(tophat.xValue(cen), tophat.max_sb)
# Check picklability
do_pickle(tophat, lambda x: x.drawImage(method='no_pixel'))
do_pickle(tophat)
# Check real-space convolution of the sheared tophat.
conv = galsim.Convolve(tophat, galsim.Pixel(scale=scale), real_space=True)
check_basic(conv, "Sheared TopHat convolved with pixel in real space",
approx_maxsb=True, scale=0.2)
do_kvalue(conv,im, "Sheared TopHat convolved with pixel in real space")