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_real_galaxy_saved():
"""Test accuracy of various calculations with real RealGalaxy vs. stored SHERA result"""
# read in real RealGalaxy from file
# rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
# This is an alternate way to give the directory -- as part of the catalog file name.
full_catalog_file = os.path.join(image_dir, catalog_file)
rgc = galsim.RealGalaxyCatalog(full_catalog_file)
rg = galsim.RealGalaxy(rgc, index=ind_real)
# read in expected result for some shear
shera_image = galsim.fits.read(shera_file)
shera_target_PSF_image = galsim.fits.read(shera_target_PSF_file)
shera_target_PSF_image.scale = shera_target_pixel_scale
# simulate the same galaxy with GalSim
tmp_gal = rg.withFlux(shera_target_flux).shear(g1=targ_applied_shear1,
g2=targ_applied_shear2)
tmp_psf = galsim.InterpolatedImage(shera_target_PSF_image)
tmp_gal = galsim.Convolve(tmp_gal, tmp_psf)
sim_image = tmp_gal.drawImage(scale=shera_target_pixel_scale,
method='no_pixel')
# there are centroid issues when comparing Shera vs. SBProfile outputs, so compare 2nd moments
# instead of images
sbp_res = sim_image.FindAdaptiveMom()
shera_res = shera_image.FindAdaptiveMom()
np.testing.assert_almost_equal(
sbp_res.observed_shape.e1,
shera_res.observed_shape.e1,
2,
err_msg="Error in comparison with SHERA result: e1")
np.testing.assert_almost_equal(
sbp_res.observed_shape.e2,
shera_res.observed_shape.e2,
2,
err_msg="Error in comparison with SHERA result: e2")
np.testing.assert_almost_equal(
sbp_res.moments_sigma,
shera_res.moments_sigma,
2,
err_msg="Error in comparison with SHERA result: sigma")
check_basic(rg, "RealGalaxy", approx_maxsb=True)
# Check picklability
do_pickle(
rgc, lambda x: [
x.getGal(ind_real),
x.getPSF(ind_real),
x.getNoiseProperties(ind_real)
])
do_pickle(
rgc,
lambda x: drawNoise(x.getNoise(ind_real, rng=galsim.BaseDeviate(123))))
do_pickle(
rg, lambda x: galsim.Convolve([x, galsim.Gaussian(sigma=1.7)]).
drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rgc)
do_pickle(rg)
def sample(self, idx, px, py, flux, filter_norm, filters, psfs):
obj = SimObj()
obj.index = idx
obj.x = px
obj.y = py
idx = np.random.randint(100)
morphology = galsim.RealGalaxy(self.real_galaxy_catalog,
index=idx,
flux=flux)
sed, redshift = self.generate_sed()
sed = sed.withFlux(1, filters[filter_norm])
sed = np.array([sed.calculateFlux(f) for _, f in filters.items()])
convolved_image = {
band: galsim.Convolve([morphology * sed[s], psfs[band]])
for s, band in enumerate(filters)
}
obj.index = idx
obj.redshift = redshift
obj.is_star = False
obj.catalog_index = idx
obj.component = "mix"
obj.sed = sed / np.sum(sed)
return [obj], [convolved_image]
def test_real_galaxy_saved():
"""Test accuracy of various calculations with real RealGalaxy vs. stored SHERA result"""
import time
t1 = time.time()
# read in real RealGalaxy from file
rgc = galsim.RealGalaxyCatalog(catalog_file, image_dir)
rg = galsim.RealGalaxy(rgc, index=ind_real)
# read in expected result for some shear
shera_image = galsim.fits.read(shera_file)
shera_target_PSF_image = galsim.fits.read(shera_target_PSF_file)
# simulate the same galaxy with GalSim
sim_image = galsim.simReal(rg,
shera_target_PSF_image,
shera_target_pixel_scale,
g1=targ_applied_shear1,
g2=targ_applied_shear2,
rand_rotate=False,
target_flux=shera_target_flux)
# there are centroid issues when comparing Shera vs. SBProfile outputs, so compare 2nd moments
# instead of images
sbp_res = sim_image.FindAdaptiveMom()
shera_res = shera_image.FindAdaptiveMom()
np.testing.assert_almost_equal(
sbp_res.observed_shape.e1,
shera_res.observed_shape.e1,
2,
err_msg="Error in comparison with SHERA result: e1")
np.testing.assert_almost_equal(
sbp_res.observed_shape.e2,
shera_res.observed_shape.e2,
2,
err_msg="Error in comparison with SHERA result: e2")
np.testing.assert_almost_equal(
sbp_res.moments_sigma,
shera_res.moments_sigma,
2,
err_msg="Error in comparison with SHERA result: sigma")
# Check picklability
do_pickle(
rgc, lambda x: [
x.getGal(ind_real),
x.getPSF(ind_real),
x.getNoiseProperties(ind_real)
])
do_pickle(
rgc,
lambda x: drawNoise(x.getNoise(ind_real, rng=galsim.BaseDeviate(123))))
do_pickle(
rg, lambda x: galsim.Convolve([x, galsim.Gaussian(sigma=1.7)]).
drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rgc)
do_pickle(rg)
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def test_real_galaxy_makeFromImage():
"""Test accuracy of various calculations with fake Gaussian RealGalaxy vs. ideal expectations"""
# read in faked Gaussian RealGalaxy from file
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
rg = galsim.RealGalaxy(rgc, index=1)
gal_image = rg.gal_image
psf = rg.original_psf
xi = rg.noise
rg_2 = galsim.RealGalaxy.makeFromImage(gal_image, psf, xi)
check_basic(rg_2, "RealGalaxy", approx_maxsb=True)
do_pickle(
rg_2, lambda x: [
x.gal_image, x.psf_image,
repr(x.noise), x.original_psf.flux, x.original_gal.flux, x.flux
])
do_pickle(rg_2, lambda x: x.drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rg_2)
# See if we get reasonably consistent results for rg and rg_2
psf = galsim.Kolmogorov(fwhm=0.6)
obj1 = galsim.Convolve(psf, rg)
obj2 = galsim.Convolve(psf, rg_2)
im1 = obj1.drawImage(scale=0.2, nx=12, ny=12)
im2 = obj2.drawImage(image=im1.copy())
atol = obj1.flux * 3e-5
np.testing.assert_allclose(im1.array, im2.array, rtol=0, atol=atol)
def _BuildRealGalaxy(config, key, base, ignore, gsparams):
"""@brief Build a RealGalaxy type GSObject from user input.
"""
if 'real_catalog' not in base:
raise ValueError(
"No real galaxy catalog available for building type = RealGalaxy")
real_cat = base['real_catalog']
# Special: if index is Sequence or Random, and max isn't set, set it to real_cat.nobjects-1
if 'id' not in config:
galsim.config.SetDefaultIndex(config, real_cat.nobjects)
kwargs, safe = galsim.config.GetAllParams(
config,
key,
base,
req=galsim.__dict__['RealGalaxy']._req_params,
opt=galsim.__dict__['RealGalaxy']._opt_params,
single=galsim.__dict__['RealGalaxy']._single_params,
ignore=ignore)
if gsparams: kwargs['gsparams'] = galsim.GSParams(**gsparams)
if 'rng' not in base:
raise ValueError("No base['rng'] available for %s.type = RealGalaxy" %
(key))
kwargs['rng'] = base['rng']
if 'index' in kwargs:
index = kwargs['index']
if index >= real_cat.nobjects:
raise IndexError(
"%s index has gone past the number of entries in the catalog" %
param_name)
return galsim.RealGalaxy(real_cat, **kwargs), safe
def test_cosmos_fluxnorm():
"""Check for flux normalization properties of COSMOSCatalog class."""
# Check that if we make a RealGalaxy catalog, and a COSMOSCatalog, and draw the real object, the
# fluxes should match very well. These correspond to 1s exposures.
test_ind = 54
rand_seed = 12345
cat = galsim.COSMOSCatalog(
file_name='real_galaxy_catalog_23.5_example.fits',
dir=datapath,
exclusion_level='none')
rgc = galsim.RealGalaxyCatalog(
file_name='real_galaxy_catalog_23.5_example.fits', dir=datapath)
final_psf = galsim.Airy(diam=1.2,
lam=800.) # PSF twice as big as HST in F814W.
gal1 = cat.makeGalaxy(test_ind,
gal_type='real',
rng=galsim.BaseDeviate(rand_seed))
gal2 = galsim.RealGalaxy(rgc,
index=test_ind,
rng=galsim.BaseDeviate(rand_seed))
gal1 = galsim.Convolve(gal1, final_psf)
gal2 = galsim.Convolve(gal2, final_psf)
im1 = gal1.drawImage(scale=0.05)
im2 = gal2.drawImage(scale=0.05)
# Then check that if we draw a parametric representation that is achromatic, that the flux
# matches reasonably well (won't be exact because model isn't perfect).
gal1_param = cat.makeGalaxy(test_ind,
gal_type='parametric',
chromatic=False)
gal1_param_final = galsim.Convolve(gal1_param, final_psf)
im1_param = gal1_param_final.drawImage(scale=0.05)
# Then check the same for a chromatic parametric representation that is drawn into the same
# band.
bp_file = os.path.join(galsim.meta_data.share_dir, 'bandpasses',
'ACS_wfc_F814W.dat')
bandpass = galsim.Bandpass(bp_file,
wave_type='nm').withZeropoint(25.94) #34.19)
gal1_chrom = cat.makeGalaxy(test_ind,
gal_type='parametric',
chromatic=True)
gal1_chrom = galsim.Convolve(gal1_chrom, final_psf)
im1_chrom = gal1_chrom.drawImage(bandpass, scale=0.05)
ref_val = [im1.array.sum(), im1.array.sum(), im1.array.sum()]
test_val = [im2.array.sum(), im1_param.array.sum(), im1_chrom.array.sum()]
np.testing.assert_allclose(
ref_val,
test_val,
rtol=0.1,
err_msg='Flux normalization problem in COSMOS galaxies')
# Finally, check that the original COSMOS info is stored properly after transformations, for
# both Sersic galaxies (like galaxy 0 in the catalog) and the one that is gal1_param above.
gal0_param = cat.makeGalaxy(0, gal_type='parametric', chromatic=False)
assert hasattr(gal0_param.shear(g1=0.05).original, 'index'), \
'Sersic galaxy does not retain index information after transformation'
assert hasattr(gal1_param.shear(g1=0.05).original, 'index'), \
'Bulge+disk galaxy does not retain index information after transformation'
def _BuildRealGalaxy(config, base, ignore, gsparams, logger, param_name='RealGalaxy'):
"""@brief Build a RealGalaxy from the real_catalog input item.
"""
real_cat = galsim.config.GetInputObj('real_catalog', config, base, param_name)
# Special: if index is Sequence or Random, and max isn't set, set it to nobjects-1.
# But not if they specify 'id' which overrides that.
if 'id' not in config:
galsim.config.SetDefaultIndex(config, real_cat.getNObjects())
kwargs, safe = galsim.config.GetAllParams(config, base,
req = galsim.__dict__['RealGalaxy']._req_params,
opt = galsim.__dict__['RealGalaxy']._opt_params,
single = galsim.__dict__['RealGalaxy']._single_params,
ignore = ignore + ['num'])
if gsparams: kwargs['gsparams'] = galsim.GSParams(**gsparams)
if 'rng' not in base:
raise ValueError("No base['rng'] available for type = %s"%param_name)
kwargs['rng'] = base['rng']
if 'index' in kwargs:
index = kwargs['index']
if index >= real_cat.getNObjects():
raise IndexError(
"%s index has gone past the number of entries in the catalog"%index)
kwargs['real_galaxy_catalog'] = real_cat
if logger:
logger.debug('obj %d: %s kwargs = %s',base['obj_num'],param_name,kwargs)
gal = galsim.RealGalaxy(**kwargs)
return gal, safe
def _BuildRealGalaxy(config, key, base, ignore, gsparams, logger):
"""@brief Build a RealGalaxy from the real_catalog input item.
"""
if 'real_catalog' not in base:
raise ValueError(
"No real galaxy catalog available for building type = RealGalaxy")
if 'num' in config:
num, safe = ParseValue(config, 'num', base, int)
else:
num, safe = (0, True)
ignore.append('num')
if num < 0:
raise ValueError("Invalid num < 0 supplied for RealGalaxy: num = %d" %
num)
if num >= len(base['real_catalog']):
raise ValueError(
"Invalid num supplied for RealGalaxy (too large): num = %d" % num)
real_cat = base['real_catalog'][num]
# Special: if index is Sequence or Random, and max isn't set, set it to nobjects-1.
# But not if they specify 'id' which overrides that.
if 'id' not in config:
galsim.config.SetDefaultIndex(config, real_cat.getNObjects())
kwargs, safe1 = galsim.config.GetAllParams(
config,
key,
base,
req=galsim.__dict__['RealGalaxy']._req_params,
opt=galsim.__dict__['RealGalaxy']._opt_params,
single=galsim.__dict__['RealGalaxy']._single_params,
ignore=ignore)
safe = safe and safe1
if gsparams: kwargs['gsparams'] = galsim.GSParams(**gsparams)
if logger and galsim.RealGalaxy._takes_logger: kwargs['logger'] = logger
if 'rng' not in base:
raise ValueError("No base['rng'] available for %s.type = RealGalaxy" %
(key))
kwargs['rng'] = base['rng']
if 'index' in kwargs:
index = kwargs['index']
if index >= real_cat.getNObjects():
raise IndexError(
"%s index has gone past the number of entries in the catalog" %
index)
kwargs['real_galaxy_catalog'] = real_cat
if logger:
logger.debug('obj %d: RealGalaxy kwargs = %s', base['obj_num'],
str(kwargs))
gal = galsim.RealGalaxy(**kwargs)
return gal, safe
def _makeReal(self, indices, noise_pad_size, rng, gsparams):
return [
galsim.RealGalaxy(self.real_cat,
index=self.orig_index[i],
noise_pad_size=noise_pad_size,
rng=rng,
gsparams=gsparams) for i in indices
]
def test_cosmos_fluxnorm():
"""Check for flux normalization properties of COSMOSCatalog class."""
import time
t1 = time.time()
# Check that if we make a RealGalaxy catalog, and a COSMOSCatalog, and draw the real object, the
# fluxes should match very well. These correspond to 1s exposures.
test_ind = 54
rand_seed = 12345
cat = galsim.COSMOSCatalog(file_name='real_galaxy_catalog_example.fits',
dir=datapath,
exclude_fail=False,
exclude_bad=False)
rgc = galsim.RealGalaxyCatalog(
file_name='real_galaxy_catalog_example.fits', dir=datapath)
final_psf = galsim.Airy(diam=1.2,
lam=800.) # PSF twice as big as HST in F814W.
gal1 = cat.makeGalaxy(test_ind,
gal_type='real',
rng=galsim.BaseDeviate(rand_seed))
gal2 = galsim.RealGalaxy(rgc,
index=test_ind,
rng=galsim.BaseDeviate(rand_seed))
gal1 = galsim.Convolve(gal1, final_psf)
gal2 = galsim.Convolve(gal2, final_psf)
im1 = gal1.drawImage(scale=0.05)
im2 = gal2.drawImage(scale=0.05)
# Then check that if we draw a parametric representation that is achromatic, that the flux
# matches reasonably well (won't be exact because model isn't perfect).
gal1_param = cat.makeGalaxy(test_ind,
gal_type='parametric',
chromatic=False)
gal1_param = galsim.Convolve(gal1_param, final_psf)
im1_param = gal1_param.drawImage(scale=0.05)
# Then check the same for a chromatic parametric representation that is drawn into the same
# band.
bp_file = os.path.join(galsim.meta_data.share_dir, 'wfc_F814W.dat.gz')
bandpass = galsim.Bandpass(bp_file, wave_type='ang').thin().withZeropoint(
25.94) #34.19)
gal1_chrom = cat.makeGalaxy(test_ind,
gal_type='parametric',
chromatic=True)
gal1_chrom = galsim.Convolve(gal1_chrom, final_psf)
im1_chrom = gal1_chrom.drawImage(bandpass, scale=0.05)
ref_val = [im1.array.sum(), im1.array.sum(), im1.array.sum()]
test_val = [im2.array.sum(), im1_param.array.sum(), im1_chrom.array.sum()]
np.testing.assert_allclose(
ref_val,
test_val,
rtol=0.1,
err_msg='Flux normalization problem in COSMOS galaxies')
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def _makeSingleGalaxy(cosmos_catalog, index, gal_type, noise_pad_size=5, deep=False,
rng=None, sersic_prec=0.05, gsparams=None):
# A static function that mimics the functionality of COSMOSCatalog.makeGalaxy()
# for single index and chromatic=False.
# The only point of this class is to circumvent some pickling issues when using
# config objects with type : COSMOSGalaxy. It's a staticmethod, which means it
# cannot use any self attributes. Just methods. (Which also means we can use it
# through a proxy COSMOSCatalog object, which we need for the config layer.)
if not cosmos_catalog.canMakeReal():
if gal_type is None:
gal_type = 'parametric'
elif gal_type != 'parametric':
raise ValueError("Only 'parametric' galaxy type is allowed when use_real == False")
if gal_type not in ['real', 'parametric']:
raise ValueError("Invalid galaxy type %r"%gal_type)
if gal_type == 'real' and rng is None:
rng = galsim.BaseDeviate()
if gal_type == 'real':
real_params = cosmos_catalog.getRealParams(index)
gal = galsim.RealGalaxy(real_params, noise_pad_size=noise_pad_size, rng=rng,
gsparams=gsparams)
else:
record = cosmos_catalog.getParametricRecord(index)
gal = COSMOSCatalog._buildParametric(record, sersic_prec, gsparams, chromatic=False)
# If trying to use the 23.5 sample and "fake" a deep sample, rescale the size and flux as
# suggested in the GREAT3 handbook.
if deep:
if self.use_sample == '23.5':
# Rescale the flux to get a limiting mag of 25 in F814W when starting with a
# limiting mag of 23.5. Make the galaxies a factor of 0.6 smaller and appropriately
# fainter.
flux_factor = 10.**(-0.4*1.5)
size_factor = 0.6
gal = gal.dilate(size_factor) * flux_factor
else:
import warnings
warnings.warn(
'Ignoring `deep` argument, because the sample being used already '+
'corresponds to a flux limit of F814W<25.2')
# Store the orig_index as gal.index, since the above RealGalaxy initialization just sets it
# as 0. Plus, it isn't set at all if we make a parametric galaxy. And if we are doing the
# deep scaling, then it gets messed up by that. If we have done some transformations, and
# are also doing later transformation, it will take the `original` attribute that is already
# there. So having `index` doesn't help, and we also need `original.index`.
gal.index = cosmos_catalog.getOrigIndex(index)
if hasattr(gal, 'original'):
gal.original.index = cosmos_catalog.getOrigIndex(index)
return gal
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_flux():
""" Check that setting flux and flux_rescale work properly"""
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
psf = galsim.Gaussian(sigma=1.7)
rg1 = galsim.RealGalaxy(rgc, index=0)
rg2 = galsim.RealGalaxy(rgc, index=0, flux=1.7)
rg3 = galsim.RealGalaxy(rgc, index=0, flux=17)
rg4 = galsim.RealGalaxy(rgc, index=0, flux=-17)
rg5 = galsim.RealGalaxy(rgc, index=0, flux_rescale=1.7)
rg6 = galsim.RealGalaxy(rgc, index=0, flux_rescale=-17)
np.testing.assert_almost_equal(rg2.flux, 1.7)
np.testing.assert_almost_equal(rg3.flux, 17)
np.testing.assert_almost_equal(rg4.flux, -17)
np.testing.assert_almost_equal(rg5.flux, 1.7 * rg1.flux)
np.testing.assert_almost_equal(rg6.flux, -17 * rg1.flux)
check_basic(rg2, "RealGalaxy flux=1.7", approx_maxsb=True)
check_basic(rg3, "RealGalaxy flux=17", approx_maxsb=True)
check_basic(rg4, "RealGalaxy flux=-17", approx_maxsb=True)
check_basic(rg5, "RealGalaxy flux_rescale=1.7", approx_maxsb=True)
check_basic(rg6, "RealGalaxy flux_rescale=-17", approx_maxsb=True)
do_pickle(rg2)
do_pickle(rg3)
do_pickle(rg4)
do_pickle(rg5)
do_pickle(rg6)
def galSimRealGalaxy(flux,
real_galaxy_catalog,
index=None,
psfImage=None,
random=False,
returnObj=True,
plotFake=False,
drawMethod='auto',
addPoisson=False,
scale=1.0,
transform=None):
"""
Real galaxy
"""
if index is None:
random = True
realObj = galsim.RealGalaxy(real_galaxy_catalog,
index=index,
random=random)
index = realObj.index
# Pass the flux to the object
realObj = realObj.withFlux(flux)
#do the transformation from sky to pixel coordinates, if given
if transform is not None:
realObj = realObj.transform(*tuple(transform.ravel()))
# Convolve the Sersic model using the provided PSF image
if psfImage is not None:
# Convert the PSF Image Array into a GalSim Object
# Norm=True by default
psfObj = arrayToGSObj(psfImage, norm=True)
realFinal = galsim.Convolve([realObj, psfObj])
else:
realFinal = realFinal
# Make a PNG figure of the fake galaxy to check if everything is Ok
# TODO: For test, should be removed later
if plotFake:
plotFakeGalaxy(realFinal, galID=index, suffix='realga')
# Now, by default, the function will just return the GSObj
if returnObj:
return realFinal
else:
return galSimDrawImage(realFinal,
method=drawMethod,
scale=scale,
addPoisson=addPoisson)
def test_ne():
""" Check that inequality works as expected."""
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
gsp = galsim.GSParams(folding_threshold=1.1e-3)
gals = [
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, pad_factor=1.1),
galsim.RealGalaxy(rgc, index=0, noise_pad_size=5.0),
galsim.RealGalaxy(rgc, index=0, gsparams=gsp)
]
all_obj_diff(gals)
def _BuildRealGalaxy(config,
base,
ignore,
gsparams,
logger,
param_name='RealGalaxy'):
"""Build a RealGalaxy from the real_catalog input item.
"""
real_cat = galsim.config.GetInputObj('real_catalog', config, base,
param_name)
# Special: if index is Sequence or Random, and max isn't set, set it to nobjects-1.
# But not if they specify 'id' or have 'random=True', which overrides that.
if 'id' not in config:
if 'random' not in config:
galsim.config.SetDefaultIndex(config, real_cat.getNObjects())
else:
if not config['random']:
galsim.config.SetDefaultIndex(config, real_cat.getNObjects())
# Need to do this to avoid being caught by the GetAllParams() call, which will flag
# it if it has 'index' and 'random' set (but 'random' is False, so really it's OK).
del config['random']
kwargs, safe = galsim.config.GetAllParams(
config,
base,
req=galsim.__dict__['RealGalaxy']._req_params,
opt=galsim.__dict__['RealGalaxy']._opt_params,
single=galsim.__dict__['RealGalaxy']._single_params,
ignore=ignore + ['num'])
if gsparams: kwargs['gsparams'] = galsim.GSParams(**gsparams)
kwargs['rng'] = galsim.config.GetRNG(config, base, logger, param_name)
if 'index' in kwargs:
index = kwargs['index']
if index >= real_cat.getNObjects() or index < 0:
raise galsim.GalSimConfigError(
"index=%s has gone past the number of entries in the RealGalaxyCatalog"
% index)
kwargs['real_galaxy_catalog'] = real_cat
logger.debug('obj %d: %s kwargs = %s', base.get('obj_num', 0), param_name,
kwargs)
gal = galsim.RealGalaxy(**kwargs)
return gal, safe
def _makeSingleGalaxy(cosmos_catalog,
index,
gal_type,
noise_pad_size=5,
deep=False,
rng=None,
gsparams=None):
# A static function that mimics the functionality of COSMOSCatalog.makeGalaxy()
# for single index and chromatic=False.
# The only point of this class is to circumvent some pickling issues when using
# config objects with type : COSMOSGalaxy. It's a staticmethod, which means it
# cannot use any self attributes. Just methods. (Which also means we can use it
# through a proxy COSMOSCatalog object, which we need for the config layer.)
if not cosmos_catalog.canMakeReal():
if gal_type is None:
gal_type = 'parametric'
elif gal_type != 'parametric':
raise ValueError(
"Only 'parametric' galaxy type is allowed when use_real == False"
)
if gal_type not in ['real', 'parametric']:
raise ValueError("Invalid galaxy type %r" % gal_type)
if gal_type == 'real' and rng is None:
rng = galsim.BaseDeviate()
if gal_type == 'real':
real_params = cosmos_catalog.getRealParams(index)
gal = galsim.RealGalaxy(real_params,
noise_pad_size=noise_pad_size,
rng=rng,
gsparams=gsparams)
else:
record = cosmos_catalog.getParametricRecord(index)
gal = COSMOSCatalog._buildParametric(record, gsparams=gsparams)
# If deep, rescale the size and flux
if deep:
# Rescale the flux to get a limiting mag of 25 in F814W. Current limiting mag is 23.5,
# so it's a magnitude difference of 1.5. Make the galaxies a factor of 0.6 smaller and
# appropriately fainter.
flux_factor = 10.**(-0.4 * 1.5)
size_factor = 0.6
gal = gal.dilate(size_factor) * flux_factor
return gal
def test_ne():
import time
t1 = time.time()
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
gsp = galsim.GSParams(folding_threshold=1.1e-3)
gals = [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, pad_factor=1.1),
galsim.RealGalaxy(rgc, index=0, noise_pad_size=5.0),
galsim.RealGalaxy(rgc, index=0, gsparams=gsp)]
all_obj_diff(gals)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def test_real_galaxy_saved():
"""Test accuracy of various calculations with real RealGalaxy vs. stored SHERA result"""
import time
t1 = time.time()
# read in real RealGalaxy from file
rgc = galsim.RealGalaxyCatalog(catalog_file, image_dir)
rg = galsim.RealGalaxy(rgc, index=ind_real)
# read in expected result for some shear
shera_image = galsim.fits.read(shera_file)
shera_target_PSF_image = galsim.fits.read(shera_target_PSF_file)
# simulate the same galaxy with GalSim
sim_image = galsim.simReal(rg,
shera_target_PSF_image,
shera_target_pixel_scale,
g1=targ_applied_shear1,
g2=targ_applied_shear2,
rand_rotate=False,
target_flux=shera_target_flux)
# there are centroid issues when comparing Shera vs. SBProfile outputs, so compare 2nd moments
# instead of images
sbp_res = sim_image.FindAdaptiveMom()
shera_res = shera_image.FindAdaptiveMom()
np.testing.assert_almost_equal(
sbp_res.observed_shape.e1,
shera_res.observed_shape.e1,
2,
err_msg="Error in comparison with SHERA result: e1")
np.testing.assert_almost_equal(
sbp_res.observed_shape.e2,
shera_res.observed_shape.e2,
2,
err_msg="Error in comparison with SHERA result: e2")
np.testing.assert_almost_equal(
sbp_res.moments_sigma,
shera_res.moments_sigma,
2,
err_msg="Error in comparison with SHERA result: sigma")
t2 = time.time()
print 'time for %s = %.2f' % (funcname(), t2 - t1)
def main(argv):
"""
Make a fits image cube using real COSMOS galaxies from a catalog describing the training
sample.
- The number of images in the cube matches the number of rows in the catalog.
- Each image size is computed automatically by GalSim based on the Nyquist size.
- Both galaxies and stars.
- PSF is a double Gaussian, the same for each galaxy.
- Galaxies are randomly rotated to remove the imprint of any lensing shears in the COSMOS
data.
- The same shear is applied to each galaxy.
- Noise is Poisson using a nominal sky value of 1.e6 ADU/arcsec^2,
the noise in the original COSMOS data.
"""
logging.basicConfig(format="%(message)s", level=logging.INFO, stream=sys.stdout)
logger = logging.getLogger("demo6")
# Define some parameters we'll use below.
cat_file_name = 'real_galaxy_catalog_23.5_example.fits'
dir = 'data'
# Make output directory if not already present.
if not os.path.isdir('output'):
os.mkdir('output')
cube_file_name = os.path.join('output','cube_real.fits')
psf_file_name = os.path.join('output','psf_real.fits')
random_seed = 1512413
sky_level = 1.e6 # ADU / arcsec^2
pixel_scale = 0.16 # arcsec
gal_flux = 1.e5 # arbitrary choice, makes nice (not too) noisy images
gal_g1 = -0.027 #
gal_g2 = 0.031 #
gal_mu = 1.082 # mu = ( (1-kappa)^2 - g1^2 - g2^2 )^-1
psf_inner_fwhm = 0.6 # arcsec
psf_outer_fwhm = 2.3 # arcsec
psf_inner_fraction = 0.8 # fraction of total PSF flux in the inner Gaussian
psf_outer_fraction = 0.2 # fraction of total PSF flux in the inner Gaussian
ngal = 100
logger.info('Starting demo script 6 using:')
logger.info(' - real galaxies from catalog %r',cat_file_name)
logger.info(' - double Gaussian PSF')
logger.info(' - pixel scale = %.2f',pixel_scale)
logger.info(' - Applied gravitational shear = (%.3f,%.3f)',gal_g1,gal_g2)
logger.info(' - Poisson noise (sky level = %.1e).', sky_level)
# Read in galaxy catalog
# Note: dir is the directory both for the catalog itself and also the directory prefix
# for the image files listed in the catalog.
# If the images are in a different directory, you may also specify image_dir, which gives
# the relative path from dir to wherever the images are located.
real_galaxy_catalog = galsim.RealGalaxyCatalog(cat_file_name, dir=dir)
logger.info('Read in %d real galaxies from catalog', real_galaxy_catalog.nobjects)
# Make the double Gaussian PSF
psf1 = galsim.Gaussian(fwhm = psf_inner_fwhm, flux = psf_inner_fraction)
psf2 = galsim.Gaussian(fwhm = psf_outer_fwhm, flux = psf_outer_fraction)
psf = psf1+psf2
# Draw the PSF with no noise.
psf_image = psf.drawImage(scale = pixel_scale)
# write to file
psf_image.write(psf_file_name)
logger.info('Created PSF and wrote to file %r',psf_file_name)
# Build the images
all_images = []
for k in range(ngal):
logger.debug('Start work on image %d',k)
t1 = time.time()
# Initialize the random number generator we will be using.
rng = galsim.UniformDeviate(random_seed+k+1)
gal = galsim.RealGalaxy(real_galaxy_catalog, index = k)
logger.debug(' Read in training sample galaxy and PSF from file')
t2 = time.time()
# Set the flux
gal = gal.withFlux(gal_flux)
# Rotate by a random angle
theta = 2.*math.pi * rng() * galsim.radians
gal = gal.rotate(theta)
# Apply the desired shear
gal = gal.shear(g1=gal_g1, g2=gal_g2)
# Also apply a magnification mu = ( (1-kappa)^2 - |gamma|^2 )^-1
# This conserves surface brightness, so it scales both the area and flux.
gal = gal.magnify(gal_mu)
# Make the combined profile
final = galsim.Convolve([psf, gal])
# Offset by up to 1/2 pixel in each direction
# We had previously (in demo4 and demo5) used shift(dx,dy) as a way to shift the center of
# the image. Since that is applied to the galaxy, the units are arcsec (since the galaxy
# profile itself doesn't know about the pixel scale). Here, the offset applies to the
# drawn image, which does know about the pixel scale, so the units of offset are pixels,
# not arcsec. Here, we apply an offset of up to half a pixel in each direction.
dx = rng() - 0.5
dy = rng() - 0.5
# Draw the profile
if k == 0:
# Note that the offset argument may be a galsim.PositionD object or a tuple (dx,dy).
im = final.drawImage(scale=pixel_scale, offset=(dx,dy))
xsize, ysize = im.array.shape
else:
im = galsim.ImageF(xsize,ysize)
final.drawImage(im, scale=pixel_scale, offset=(dx,dy))
logger.debug(' Drew image')
t3 = time.time()
# Add a constant background level
background = sky_level * pixel_scale**2
im += background
# Add Poisson noise. This time, we don't give a sky_level, since we have already
# added it to the image, so we don't want any more added. The sky_level parameter
# really defines how much _extra_ sky should be added above what is already in the image.
im.addNoise(galsim.PoissonNoise(rng))
logger.debug(' Added Poisson noise')
t4 = time.time()
# Store that into the list of all images
all_images += [im]
t5 = time.time()
logger.debug(' Times: %f, %f, %f, %f',t2-t1, t3-t2, t4-t3, t5-t4)
logger.info('Image %d: size = %d x %d, total time = %f sec', k, xsize, ysize, t5-t1)
logger.info('Done making images of galaxies')
# Now write the image to disk.
# We write the images to a fits data cube.
galsim.fits.writeCube(all_images, cube_file_name)
logger.info('Wrote image to fits data cube %r',cube_file_name)
def test_real_galaxy_ideal():
"""Test accuracy of various calculations with fake Gaussian RealGalaxy vs. ideal expectations"""
ind_fake = 1 # index of mock galaxy (Gaussian) in catalog
fake_gal_fwhm = 0.7 # arcsec
fake_gal_shear1 = 0.29 # shear representing intrinsic shape component 1
fake_gal_shear2 = -0.21 # shear representing intrinsic shape component 2
# note non-round, to detect possible issues with x<->y or others that might not show up using
# circular galaxy
fake_gal_flux = 1000.0
fake_gal_orig_PSF_fwhm = 0.1 # arcsec
fake_gal_orig_PSF_shear1 = 0.0
fake_gal_orig_PSF_shear2 = -0.07
# read in faked Gaussian RealGalaxy from file
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
assert len(rgc) == rgc.getNObjects() == rgc.nobjects == len(rgc.cat)
rg = galsim.RealGalaxy(rgc, index=ind_fake)
# as a side note, make sure it behaves okay given a legit RNG and a bad RNG
# or when trying to specify the galaxy too many ways
rg_1 = galsim.RealGalaxy(rgc, index=ind_fake, rng=galsim.BaseDeviate(1234))
rg_2 = galsim.RealGalaxy(rgc, random=True)
assert_raises(TypeError, galsim.RealGalaxy, rgc, index=ind_fake, rng='foo')
assert_raises(TypeError, galsim.RealGalaxy, rgc)
assert_raises(TypeError,
galsim.RealGalaxy,
rgc,
index=ind_fake,
flux=12,
flux_rescale=2)
assert_raises(ValueError, galsim.RealGalaxy, rgc, index=ind_fake, id=0)
assert_raises(ValueError,
galsim.RealGalaxy,
rgc,
index=ind_fake,
random=True)
assert_raises(ValueError, galsim.RealGalaxy, rgc, id=0, random=True)
# Different RNGs give different random galaxies.
rg_3 = galsim.RealGalaxy(rgc, random=True, rng=galsim.BaseDeviate(12345))
rg_4 = galsim.RealGalaxy(rgc, random=True, rng=galsim.BaseDeviate(67890))
assert rg_3.index != rg_4.index, 'Different seeds did not give different random objects!'
gsp = galsim.GSParams(xvalue_accuracy=1.e-8, kvalue_accuracy=1.e-8)
rg_5 = galsim.RealGalaxy(rgc,
random=True,
rng=galsim.BaseDeviate(67890),
gsparams=gsp)
assert rg_5 != rg_4
assert rg_5 == rg_4.withGSParams(gsp)
check_basic(rg, "RealGalaxy", approx_maxsb=True)
check_basic(rg_1, "RealGalaxy", approx_maxsb=True)
check_basic(rg_2, "RealGalaxy", approx_maxsb=True)
do_pickle(
rgc, lambda x: [
x.getGalImage(ind_fake),
x.getPSFImage(ind_fake),
x.getNoiseProperties(ind_fake)
])
do_pickle(
rgc,
lambda x: drawNoise(x.getNoise(ind_fake, rng=galsim.BaseDeviate(123))))
do_pickle(rgc)
do_pickle(
rg, lambda x: [
x.gal_image, x.psf_image,
repr(x.noise), x.original_psf.flux, x.original_gal.flux, x.flux
])
do_pickle(rg, lambda x: x.drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rg_1, lambda x: x.drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rg)
do_pickle(rg_1)
## for the generation of the ideal right answer, we need to add the intrinsic shape of the
## galaxy and the lensing shear using the rule for addition of distortions which is ugly, but oh
## well:
targ_pixel_scale = [0.18, 0.25] # arcsec
targ_PSF_fwhm = [0.7, 1.0] # arcsec
targ_PSF_shear1 = [-0.03, 0.0]
targ_PSF_shear2 = [0.05, -0.08]
targ_applied_shear1 = 0.06
targ_applied_shear2 = -0.04
fwhm_to_sigma = 1.0 / (2.0 * np.sqrt(2.0 * np.log(2.0)))
(d1, d2) = galsim.utilities.g1g2_to_e1e2(fake_gal_shear1, fake_gal_shear2)
(d1app, d2app) = galsim.utilities.g1g2_to_e1e2(targ_applied_shear1,
targ_applied_shear2)
denom = 1.0 + d1 * d1app + d2 * d2app
dapp_sq = d1app**2 + d2app**2
d1tot = (d1 + d1app + d2app / dapp_sq * (1.0 - np.sqrt(1.0 - dapp_sq)) *
(d2 * d1app - d1 * d2app)) / denom
d2tot = (d2 + d2app + d1app / dapp_sq * (1.0 - np.sqrt(1.0 - dapp_sq)) *
(d1 * d2app - d2 * d1app)) / denom
# convolve with a range of Gaussians, with and without shear (note, for this test all the
# original and target ePSFs are Gaussian - there's no separate pixel response so that everything
# can be calculated analytically)
for tps in targ_pixel_scale:
for tpf in targ_PSF_fwhm:
for tps1 in targ_PSF_shear1:
for tps2 in targ_PSF_shear2:
print('tps,tpf,tps1,tps2 = ', tps, tpf, tps1, tps2)
# make target PSF
targ_PSF = galsim.Gaussian(fwhm=tpf).shear(g1=tps1,
g2=tps2)
# simulate image
tmp_gal = rg.withFlux(fake_gal_flux).shear(
g1=targ_applied_shear1, g2=targ_applied_shear2)
final_tmp_gal = galsim.Convolve(targ_PSF, tmp_gal)
sim_image = final_tmp_gal.drawImage(scale=tps,
method='no_pixel')
# galaxy sigma, in units of pixels on the final image
sigma_ideal = (fake_gal_fwhm / tps) * fwhm_to_sigma
# compute analytically the expected galaxy moments:
mxx_gal, myy_gal, mxy_gal = ellip_to_moments(
d1tot, d2tot, sigma_ideal)
# compute analytically the expected PSF moments:
targ_PSF_e1, targ_PSF_e2 = galsim.utilities.g1g2_to_e1e2(
tps1, tps2)
targ_PSF_sigma = (tpf / tps) * fwhm_to_sigma
mxx_PSF, myy_PSF, mxy_PSF = ellip_to_moments(
targ_PSF_e1, targ_PSF_e2, targ_PSF_sigma)
# get expected e1, e2, sigma for the PSF-convolved image
tot_e1, tot_e2, tot_sigma = moments_to_ellip(
mxx_gal + mxx_PSF, myy_gal + myy_PSF,
mxy_gal + mxy_PSF)
# compare with images that are expected
expected_gaussian = galsim.Gaussian(flux=fake_gal_flux,
sigma=tps * tot_sigma)
expected_gaussian = expected_gaussian.shear(e1=tot_e1,
e2=tot_e2)
expected_image = galsim.ImageD(sim_image.array.shape[0],
sim_image.array.shape[1])
expected_gaussian.drawImage(expected_image,
scale=tps,
method='no_pixel')
printval(expected_image, sim_image)
np.testing.assert_array_almost_equal(
sim_image.array,
expected_image.array,
decimal=3,
err_msg=
"Error in comparison of ideal Gaussian RealGalaxy calculations"
)
def main(argv):
"""
Make images using variable PSF and shear:
- The main image is 10 x 10 postage stamps.
- Each postage stamp is 48 x 48 pixels.
- The second HDU has the corresponding PSF image.
- Applied shear is from a power spectrum P(k) ~ k^1.8.
- Galaxies are real galaxies oriented in a ring test of 20 each.
- The PSF is Gaussian with FWHM, ellipticity and position angle functions of (x,y)
- Noise is Poisson using a nominal sky value of 1.e6.
"""
logging.basicConfig(format="%(message)s",
level=logging.INFO,
stream=sys.stdout)
logger = logging.getLogger("demo10")
# Define some parameters we'll use below.
# Normally these would be read in from some parameter file.
n_tiles = 10 # number of tiles in each direction.
stamp_size = 48 # pixels
pixel_scale = 0.44 # arcsec / pixel
sky_level = 1.e6 # ADU / arcsec^2
# The random seed is used for both the power spectrum realization and the random properties
# of the galaxies.
random_seed = 3339201
# Make output directory if not already present.
if not os.path.isdir('output'):
os.mkdir('output')
file_name = os.path.join('output', 'power_spectrum.fits')
# These will be created for each object below. The values we'll use will be functions
# of (x,y) relative to the center of the image. (r = sqrt(x^2+y^2))
# psf_fwhm = 0.9 + 0.5 * (r/100)^2 -- arcsec
# psf_e = 0.4 * (r/100)^1.5 -- large value at the edge, so visible by eye.
# psf_beta = atan2(y/x) + pi/2 -- tangential pattern
gal_dilation = 3 # Make the galaxies a bit larger than their original size.
gal_signal_to_noise = 100 # Pretty high.
psf_signal_to_noise = 1000 # Even higher.
logger.info('Starting demo script 10')
# Read in galaxy catalog
cat_file_name = 'real_galaxy_catalog_23.5_example.fits'
dir = 'data'
real_galaxy_catalog = galsim.RealGalaxyCatalog(cat_file_name, dir=dir)
logger.info('Read in %d real galaxies from catalog',
real_galaxy_catalog.nobjects)
# List of IDs to use. We select 5 particularly irregular galaxies for this demo.
# Then we'll choose randomly from this list.
id_list = [106416, 106731, 108402, 116045, 116448]
# Make the 5 galaxies we're going to use here rather than remake them each time.
# This means the Fourier transforms of the real galaxy images don't need to be recalculated
# each time, so it's a bit more efficient.
gal_list = [
galsim.RealGalaxy(real_galaxy_catalog, id=id) for id in id_list
]
# Grab the index numbers before we transform them and lose the index attribute.
cosmos_index = [gal.index for gal in gal_list]
# Make the galaxies a bit larger than their original observed size.
gal_list = [gal.dilate(gal_dilation) for gal in gal_list]
# Setup the PowerSpectrum object we'll be using:
ps = galsim.PowerSpectrum(lambda k: k**1.8)
# The argument here is "e_power_function" which defines the E-mode power to use.
# There is also a b_power_function if you want to include any B-mode power:
# ps = galsim.PowerSpectrum(e_power_function, b_power_function)
# You may even omit the e_power_function argument and have a pure B-mode power spectrum.
# ps = galsim.PowerSpectrum(b_power_function = b_power_function)
# All the random number generator classes derive from BaseDeviate.
# When we construct another kind of deviate class from any other
# kind of deviate class, the two share the same underlying random number
# generator. Sometimes it can be clearer to just construct a BaseDeviate
# explicitly and then construct anything else you need from that.
# Note: A BaseDeviate cannot be used to generate any values. It can
# only be used in the constructor for other kinds of deviates.
# The seeds for the objects are random_seed+1..random_seed+nobj.
# The seeds for things at the image or file level use random_seed itself.
nobj = n_tiles * n_tiles
rng = galsim.BaseDeviate(random_seed)
# Have the PowerSpectrum object build a grid of shear values for us to use.
grid_g1, grid_g2 = ps.buildGrid(grid_spacing=stamp_size * pixel_scale,
ngrid=n_tiles,
rng=rng)
# Setup the images:
gal_image = galsim.ImageF(stamp_size * n_tiles, stamp_size * n_tiles)
psf_image = galsim.ImageF(stamp_size * n_tiles, stamp_size * n_tiles)
# Update the image WCS to use the image center as the origin of the WCS.
# The class that acts like a PixelScale except for this offset is called OffsetWCS.
im_center = gal_image.true_center
wcs = galsim.OffsetWCS(scale=pixel_scale, origin=im_center)
gal_image.wcs = wcs
psf_image.wcs = wcs
# We will place the tiles in a random order. To do this, we make two lists for the
# ix and iy values. Then we apply a random permutation to the lists (in tandem).
ix_list = []
iy_list = []
for ix in range(n_tiles):
for iy in range(n_tiles):
ix_list.append(ix)
iy_list.append(iy)
# This next function will use the given random number generator, rng, and use it to
# randomly permute any number of lists. All lists will have the same random permutation
# applied.
galsim.random.permute(rng, ix_list, iy_list)
# Initialize the OutputCatalog for the truth values
names = [
'gal_num', 'x_image', 'y_image', 'psf_e1', 'psf_e2', 'psf_fwhm',
'cosmos_id', 'cosmos_index', 'theta', 'g1', 'g2', 'shift_x', 'shift_y'
]
types = [
int, float, float, float, float, float, str, int, float, float, float,
float, float
]
truth_catalog = galsim.OutputCatalog(names, types)
# Build each postage stamp:
for k in range(nobj):
# The usual random number generator using a different seed for each galaxy.
rng = galsim.BaseDeviate(random_seed + k + 1)
# Determine the bounds for this stamp and its center position.
ix = ix_list[k]
iy = iy_list[k]
b = galsim.BoundsI(ix * stamp_size + 1, (ix + 1) * stamp_size,
iy * stamp_size + 1, (iy + 1) * stamp_size)
sub_gal_image = gal_image[b]
sub_psf_image = psf_image[b]
pos = wcs.toWorld(b.true_center)
# The image comes out as about 211 arcsec across, so we define our variable
# parameters in terms of (r/100 arcsec), so roughly the scale size of the image.
rsq = (pos.x**2 + pos.y**2)
r = math.sqrt(rsq)
psf_fwhm = 0.9 + 0.5 * rsq / 100**2 # arcsec
psf_e = 0.4 * (r / 100.)**1.5
psf_beta = (math.atan2(pos.y, pos.x) + math.pi / 2) * galsim.radians
# Define the PSF profile
psf = galsim.Gaussian(fwhm=psf_fwhm)
psf_shape = galsim.Shear(e=psf_e, beta=psf_beta)
psf = psf.shear(psf_shape)
# Define the galaxy profile:
# For this demo, we are doing a ring test where the same galaxy profile is drawn at many
# orientations stepped uniformly in angle, making a ring in e1-e2 space.
# We're drawing each profile at 20 different orientations and then skipping to the
# next galaxy in the list. So theta steps by 1/20 * 360 degrees:
theta_deg = (k % 20) * 360. / 20
theta = theta_deg * galsim.degrees
# The index needs to increment every 20 objects so we use k/20 using integer math.
index = k // 20
gal = gal_list[index]
# This makes a new copy so we're not changing the object in the gal_list.
gal = gal.rotate(theta)
# Apply the shear from the power spectrum. We should either turn the gridded shears
# grid_g1[iy, ix] and grid_g2[iy, ix] into gridded reduced shears using a utility called
# galsim.lensing.theoryToObserved, or use ps.getShear() which by default gets the reduced
# shear. ps.getShear() is also more flexible because it can get the shear at positions that
# are not on the original grid, as long as they are contained within the bounds of the full
# grid. So in this example we'll use ps.getShear().
alt_g1, alt_g2 = ps.getShear(pos)
gal = gal.shear(g1=alt_g1, g2=alt_g2)
# Apply half-pixel shift in a random direction.
shift_r = pixel_scale * 0.5
ud = galsim.UniformDeviate(rng)
t = ud() * 2. * math.pi
dx = shift_r * math.cos(t)
dy = shift_r * math.sin(t)
gal = gal.shift(dx, dy)
# Make the final image, convolving with the psf
final = galsim.Convolve([psf, gal])
# Draw the image
final.drawImage(sub_gal_image)
# For the PSF image, we don't match the galaxy shift. Rather, we use the offset
# parameter to drawImage to apply a random offset of up to 0.5 pixels in each direction.
# Note the difference in units between shift and offset. The shift is applied to the
# surface brightness profile, so it is in sky coordinates (as all dimension are for
# GSObjects), which are arcsec here. The offset though is applied to the image itself,
# so it is in pixels. Hence, we don't multiply by pixel_scale.
psf_dx = ud() - 0.5
psf_dy = ud() - 0.5
psf_offset = galsim.PositionD(psf_dx, psf_dy)
# Draw the PSF image:
# We use real space integration over the pixels to avoid some of the
# artifacts that can show up with Fourier convolution.
# The level of the artifacts is quite low, but when drawing with
# so little noise, they are apparent with ds9's zscale viewing.
psf.drawImage(sub_psf_image, method='real_space', offset=psf_offset)
# Build the noise model: Poisson noise with a given sky level.
sky_level_pixel = sky_level * pixel_scale**2
noise = galsim.PoissonNoise(rng, sky_level=sky_level_pixel)
# Add noise to the PSF image, using the normal noise model, but scaling the
# PSF flux high enough to reach the desired signal-to-noise.
# See demo5.py for more info about how this works.
sub_psf_image.addNoiseSNR(noise, psf_signal_to_noise)
# And also to the galaxy image using its signal-to-noise.
sub_gal_image.addNoiseSNR(noise, gal_signal_to_noise)
# Add the truth values to the truth catalog
row = [
k, b.true_center.x, b.true_center.y, psf_shape.e1,
psf_shape.e2, psf_fwhm, id_list[index], cosmos_index[index],
(theta_deg % 360.), alt_g1, alt_g2, dx, dy
]
truth_catalog.addRow(row)
logger.info('Galaxy (%d,%d): position relative to center = %s', ix, iy,
str(pos))
logger.info('Done making images of postage stamps')
# In this case, we'll attach the truth catalog as an additional HDU in the same file as
# the image data.
truth_hdu = truth_catalog.writeFitsHdu()
# Now write the images to disk.
images = [gal_image, psf_image, truth_hdu]
# Any items in the "images" list that is already an hdu is just used directly.
# The actual images are converted to FITS hdus that contain the image data.
galsim.fits.writeMulti(images, file_name)
logger.info('Wrote image to %r', file_name)
def test_real_galaxy_ideal():
"""Test accuracy of various calculations with fake Gaussian RealGalaxy vs. ideal expectations"""
import time
t1 = time.time()
# read in faked Gaussian RealGalaxy from file
rgc = galsim.RealGalaxyCatalog(catalog_file, dir=image_dir)
rg = galsim.RealGalaxy(rgc, index=ind_fake)
# as a side note, make sure it behaves okay given a legit RNG and a bad RNG
# or when trying to specify the galaxy too many ways
rg_1 = galsim.RealGalaxy(rgc, index = ind_fake, rng = galsim.BaseDeviate(1234))
rg_2 = galsim.RealGalaxy(rgc, random=True)
try:
np.testing.assert_raises(TypeError, galsim.RealGalaxy, rgc, index=ind_fake, rng='foo')
np.testing.assert_raises(AttributeError, galsim.RealGalaxy, rgc, index=ind_fake, id=0)
np.testing.assert_raises(AttributeError, galsim.RealGalaxy, rgc, index=ind_fake, random=True)
np.testing.assert_raises(AttributeError, galsim.RealGalaxy, rgc, id=0, random=True)
np.testing.assert_raises(AttributeError, galsim.RealGalaxy, rgc)
except ImportError:
print 'The assert_raises tests require nose'
do_pickle(rgc, lambda x: [ x.getGal(ind_fake), x.getPSF(ind_fake),
x.getNoiseProperties(ind_fake) ])
do_pickle(rgc, lambda x: drawNoise(x.getNoise(ind_fake,rng=galsim.BaseDeviate(123))))
do_pickle(rgc)
do_pickle(rg, lambda x: [ x.gal_image, x.psf_image, repr(x.noise),
x.original_psf.flux, x.original_gal.flux, x.flux ])
do_pickle(rg, lambda x: x.drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rg_1, lambda x: x.drawImage(nx=20, ny=20, scale=0.7))
do_pickle(rg)
do_pickle(rg_1)
## for the generation of the ideal right answer, we need to add the intrinsic shape of the
## galaxy and the lensing shear using the rule for addition of distortions which is ugly, but oh
## well:
(d1, d2) = galsim.utilities.g1g2_to_e1e2(fake_gal_shear1, fake_gal_shear2)
(d1app, d2app) = galsim.utilities.g1g2_to_e1e2(targ_applied_shear1, targ_applied_shear2)
denom = 1.0 + d1*d1app + d2*d2app
dapp_sq = d1app**2 + d2app**2
d1tot = (d1 + d1app + d2app/dapp_sq*(1.0 - np.sqrt(1.0-dapp_sq))*(d2*d1app - d1*d2app))/denom
d2tot = (d2 + d2app + d1app/dapp_sq*(1.0 - np.sqrt(1.0-dapp_sq))*(d1*d2app - d2*d1app))/denom
# convolve with a range of Gaussians, with and without shear (note, for this test all the
# original and target ePSFs are Gaussian - there's no separate pixel response so that everything
# can be calculated analytically)
for tps in targ_pixel_scale:
for tpf in targ_PSF_fwhm:
for tps1 in targ_PSF_shear1:
for tps2 in targ_PSF_shear2:
print 'tps,tpf,tps1,tps2 = ',tps,tpf,tps1,tps2
# make target PSF
targ_PSF = galsim.Gaussian(fwhm = tpf).shear(g1=tps1, g2=tps2)
# simulate image
sim_image = galsim.simReal(
rg, targ_PSF, tps,
g1 = targ_applied_shear1, g2 = targ_applied_shear2,
rand_rotate = False, target_flux = fake_gal_flux)
# galaxy sigma, in units of pixels on the final image
sigma_ideal = (fake_gal_fwhm/tps)*fwhm_to_sigma
# compute analytically the expected galaxy moments:
mxx_gal, myy_gal, mxy_gal = ellip_to_moments(d1tot, d2tot, sigma_ideal)
# compute analytically the expected PSF moments:
targ_PSF_e1, targ_PSF_e2 = galsim.utilities.g1g2_to_e1e2(tps1, tps2)
targ_PSF_sigma = (tpf/tps)*fwhm_to_sigma
mxx_PSF, myy_PSF, mxy_PSF = ellip_to_moments(
targ_PSF_e1, targ_PSF_e2, targ_PSF_sigma)
# get expected e1, e2, sigma for the PSF-convolved image
tot_e1, tot_e2, tot_sigma = moments_to_ellip(
mxx_gal+mxx_PSF, myy_gal+myy_PSF, mxy_gal+mxy_PSF)
# compare with images that are expected
expected_gaussian = galsim.Gaussian(
flux = fake_gal_flux, sigma = tps*tot_sigma)
expected_gaussian = expected_gaussian.shear(e1 = tot_e1, e2 = tot_e2)
expected_image = galsim.ImageD(
sim_image.array.shape[0], sim_image.array.shape[1])
expected_gaussian.drawImage(expected_image, scale=tps, method='no_pixel')
printval(expected_image,sim_image)
np.testing.assert_array_almost_equal(
sim_image.array, expected_image.array, decimal = 3,
err_msg = "Error in comparison of ideal Gaussian RealGalaxy calculations")
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def test_cosmos_random():
"""Check the random object functionality of the COSMOS catalog."""
# For most of this test, we'll use the selectRandomIndex() routine, which does not try to
# construct the GSObjects. This makes the test go fast. However, we will at the end have a
# test to ensure that calling makeGalaxy() while requesting a random object has the same
# behavior as using selectRandomIndex() in limited cases.
# Initialize the catalog. The first will have weights, while the second will not (since they
# are in the RealGalaxyCatalog).
cat = galsim.COSMOSCatalog(
file_name='real_galaxy_catalog_23.5_example.fits', dir=datapath)
cat_param = galsim.COSMOSCatalog(
file_name='real_galaxy_catalog_23.5_example.fits',
dir=datapath,
use_real=False)
assert cat_param.real_cat is None
assert cat.real_cat is not None
# Check for exception handling if bad inputs given for the random functionality.
assert_raises(ValueError, cat.selectRandomIndex, 0)
assert_raises(ValueError, cat.selectRandomIndex, 10.7)
assert_raises(TypeError, cat.selectRandomIndex, 10, rng=3)
# Check that random objects give the right <weight> without/with weighting.
wt = cat.real_cat.weight[cat.orig_index]
wt /= np.max(wt)
avg_weight_val = np.sum(wt) / len(wt)
wavg_weight_val = np.sum(wt**2) / np.sum(wt)
with assert_raises(AssertionError):
np.testing.assert_almost_equal(avg_weight_val, wavg_weight_val, 3)
# Make sure we use enough objects that the mean weights converge properly.
randind_wt = cat.selectRandomIndex(30000, rng=galsim.BaseDeviate(1234))
wtrand = cat.real_cat.weight[cat.orig_index[randind_wt]] / \
np.max(cat.real_cat.weight[cat.orig_index])
# The average value of wtrand should be wavgw_weight_val in this case, since we used the weights
# to probabilistically select galaxies.
np.testing.assert_almost_equal(
np.mean(wtrand),
wavg_weight_val,
3,
err_msg='Average weight for random sample is wrong')
# The example catalog doesn't have weights, so it does unweighted selection, which emits a
# warning. We know about this and want to ignore it here.
with assert_warns(galsim.GalSimWarning):
randind = cat_param.selectRandomIndex(30000,
rng=galsim.BaseDeviate(1234))
wtrand = cat.real_cat.weight[cat.orig_index[randind]] / \
np.max(cat.real_cat.weight[cat.orig_index])
# The average value of wtrand should be avg_weight_val, since we did not do a weighted
# selection.
np.testing.assert_almost_equal(
np.mean(wtrand),
avg_weight_val,
3,
err_msg='Average weight for random sample is wrong')
# Check for consistency of randoms with same random seed. Do this both for the weighted and the
# unweighted calculation.
# Check for inconsistency of randoms with different random seed, or same seed but without/with
# weighting.
rng1 = galsim.BaseDeviate(1234)
rng2 = galsim.BaseDeviate(1234)
ind1 = cat.selectRandomIndex(10, rng=rng1)
ind2 = cat.selectRandomIndex(10, rng=rng2)
np.testing.assert_array_equal(
ind1,
ind2,
err_msg='Different random indices selected with same seed!')
with assert_warns(galsim.GalSimWarning):
ind1p = cat_param.selectRandomIndex(10, rng=rng1)
with assert_warns(galsim.GalSimWarning):
ind2p = cat_param.selectRandomIndex(10, rng=rng2)
np.testing.assert_array_equal(
ind1p,
ind2p,
err_msg='Different random indices selected with same seed!')
rng3 = galsim.BaseDeviate(5678)
ind3 = cat.selectRandomIndex(10, rng=rng3)
with assert_warns(galsim.GalSimWarning):
ind3p = cat_param.selectRandomIndex(10) # initialize RNG based on time
assert_raises(AssertionError, np.testing.assert_array_equal, ind1, ind1p)
assert_raises(AssertionError, np.testing.assert_array_equal, ind1, ind3)
assert_raises(AssertionError, np.testing.assert_array_equal, ind1p, ind3p)
# Finally, make sure that directly calling selectRandomIndex() gives the same random ones as
# makeGalaxy(). We'll do one real object because they are slower, and multiple parametric (just
# to make sure that the multi-object selection works consistently).
use_seed = 567
obj = cat.makeGalaxy(rng=galsim.BaseDeviate(use_seed))
ind = cat.selectRandomIndex(1, rng=galsim.BaseDeviate(use_seed))
obj_2 = cat.makeGalaxy(ind)
# Note: for real galaxies we cannot require that obj==obj_2, just that obj.index==obj_2.index.
# That's because we want to make sure the same galaxy is being randomly selected, but we cannot
# require that noise padding be the same, given the inconsistency in how the BaseDeviates are
# used in the above cases.
assert obj.index == obj_2.index, 'makeGalaxy selects random objects inconsistently'
n_random = 3
with assert_warns(galsim.GalSimWarning):
# Warns because we aren't using weights
objs = cat_param.makeGalaxy(rng=galsim.BaseDeviate(use_seed),
n_random=n_random)
with assert_warns(galsim.GalSimWarning):
inds = cat_param.selectRandomIndex(n_random,
rng=galsim.BaseDeviate(use_seed))
objs_2 = cat_param.makeGalaxy(inds)
for i in range(n_random):
# With parametric objects there is no noise padding, so we can require completely identical
# GSObjects (not just equal indices).
assert objs[i] == objs_2[
i], 'makeGalaxy selects random objects inconsistently'
# Finally, check for consistency with random object selected from RealGalaxyCatalog. For this
# case, we need to make another COSMOSCatalog that does not flag the bad objects.
use_seed = 31415
cat = galsim.COSMOSCatalog(
file_name='real_galaxy_catalog_23.5_example.fits',
dir=datapath,
exclusion_level='none')
rgc = galsim.RealGalaxyCatalog(
file_name='real_galaxy_catalog_23.5_example.fits', dir=datapath)
ind_cc = cat.selectRandomIndex(1, rng=galsim.BaseDeviate(use_seed))
foo = galsim.RealGalaxy(rgc, random=True, rng=galsim.BaseDeviate(use_seed))
ind_rgc = foo.index
assert ind_cc==ind_rgc,\
'Different weighted random index selected from COSMOSCatalog and RealGalaxyCatalog'
# Also check for the unweighted case. Just remove that info from the catalogs and redo the
# test.
cat.real_cat = None
del rgc.weight
with assert_warns(galsim.GalSimWarning):
ind_cc = cat.selectRandomIndex(1, rng=galsim.BaseDeviate(use_seed))
foo = galsim.RealGalaxy(rgc, random=True, rng=galsim.BaseDeviate(use_seed))
ind_rgc = foo.index
assert ind_cc==ind_rgc,\
'Different unweighted random index selected from COSMOSCatalog and RealGalaxyCatalog'
# Check that setting _n_rng_calls properly tracks the RNG calls for n_random=1 and >1.
test_seed = 123456
ud = galsim.UniformDeviate(test_seed)
with assert_warns(galsim.GalSimWarning):
obj, n_rng_calls = cat.selectRandomIndex(1, rng=ud, _n_rng_calls=True)
ud2 = galsim.UniformDeviate(test_seed)
ud2.discard(n_rng_calls)
assert ud() == ud2(
), '_n_rng_calls kwarg did not give proper tracking of RNG calls'
ud = galsim.UniformDeviate(test_seed)
with assert_warns(galsim.GalSimWarning):
obj, n_rng_calls = cat.selectRandomIndex(17, rng=ud, _n_rng_calls=True)
ud2 = galsim.UniformDeviate(test_seed)
ud2.discard(n_rng_calls)
assert ud() == ud2(
), '_n_rng_calls kwarg did not give proper tracking of RNG calls'
# Invalid to both privide index and ask for random selection
with assert_raises(galsim.GalSimIncompatibleValuesError):
cat_param.makeGalaxy(index=(11, 13, 17), n_random=3)
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 main(argv):
"""
Make a fits image cube using real COSMOS galaxies from a catalog describing the training
sample.
- The number of images in the cube matches the number of rows in the catalog.
- Each image size is computed automatically by GalSim based on the Nyquist size.
- Both galaxies and stars.
- PSF is a double Gaussian, the same for each galaxy.
- Galaxies are randomly rotated to remove the imprint of any lensing shears in the COSMOS
data.
- The same shear is applied to each galaxy.
- Noise is Poisson using a nominal sky value of 1.e6 ADU/arcsec^2,
the noise in the original COSMOS data.
"""
logging.basicConfig(format="%(message)s",
level=logging.INFO,
stream=sys.stdout)
logger = logging.getLogger("demo6")
# Define some parameters we'll use below.
cat_file_name = 'real_galaxy_catalog_example.fits'
# This script is designed to be run from the examples directory so dir is a relative path.
# But the '../examples/' part lets bin/demo6 also be run from the bin directory.
dir = '../examples/data'
# Make output directory if not already present.
if not os.path.isdir('output'):
os.mkdir('output')
cube_file_name = os.path.join('output', 'cube_real.fits')
psf_file_name = os.path.join('output', 'psf_real.fits')
random_seed = 1512413
sky_level = 1.e6 # ADU / arcsec^2
pixel_scale = 0.15 # arcsec
gal_flux = 1.e5 # arbitrary choice, makes nice (not too) noisy images
gal_g1 = -0.027 #
gal_g2 = 0.031 #
gal_mu = 1.082 # mu = ( (1-kappa)^2 - g1^2 - g2^2 )^-1
psf_inner_fwhm = 0.6 # arcsec
psf_outer_fwhm = 2.3 # arcsec
psf_inner_fraction = 0.8 # fraction of total PSF flux in the inner Gaussian
psf_outer_fraction = 0.2 # fraction of total PSF flux in the inner Gaussian
ngal = 100
logger.info('Starting demo script 6 using:')
logger.info(' - real galaxies from catalog %r', cat_file_name)
logger.info(' - double Gaussian PSF')
logger.info(' - pixel scale = %.2f', pixel_scale)
logger.info(' - Applied gravitational shear = (%.3f,%.3f)', gal_g1,
gal_g2)
logger.info(' - Poisson noise (sky level = %.1e).', sky_level)
# Read in galaxy catalog
# Note: dir is the directory both for the catalog itself and also the directory prefix
# for the image files listed in the catalog.
# If the images are in a different directory, you may also specify image_dir, which gives
# the relative path from dir to wherever the images are located.
real_galaxy_catalog = galsim.RealGalaxyCatalog(cat_file_name, dir=dir)
# Preloading the header information usually speeds up subsequent access.
# Basically, it tells pyfits to read all the headers in once and save them, rather
# than re-open the galaxy catalog fits file each time you want to access a new galaxy.
# If you are doing more than a few galaxies, then it seems to be worthwhile.
real_galaxy_catalog.preload()
logger.info('Read in %d real galaxies from catalog',
real_galaxy_catalog.nobjects)
# Make the ePSF
# first make the double Gaussian PSF
psf1 = galsim.Gaussian(fwhm=psf_inner_fwhm, flux=psf_inner_fraction)
psf2 = galsim.Gaussian(fwhm=psf_outer_fwhm, flux=psf_outer_fraction)
psf = psf1 + psf2
# make the pixel response
pix = galsim.Pixel(pixel_scale)
# convolve PSF and pixel response function to get the effective PSF (ePSF)
epsf = galsim.Convolve([psf, pix])
# Draw this one with no noise.
epsf_image = epsf.draw(dx=pixel_scale)
# write to file
epsf_image.write(psf_file_name)
logger.info('Created ePSF and wrote to file %r', psf_file_name)
# Build the images
all_images = []
for k in range(ngal):
logger.debug('Start work on image %d', k)
t1 = time.time()
# Initialize the random number generator we will be using.
rng = galsim.UniformDeviate(random_seed + k)
gal = galsim.RealGalaxy(real_galaxy_catalog, index=k)
logger.debug(' Read in training sample galaxy and PSF from file')
t2 = time.time()
# Set the flux
gal.setFlux(gal_flux)
# Rotate by a random angle
theta = 2. * math.pi * rng() * galsim.radians
gal.applyRotation(theta)
# Apply the desired shear
gal.applyShear(g1=gal_g1, g2=gal_g2)
# Also apply a magnification mu = ( (1-kappa)^2 - |gamma|^2 )^-1
# This conserves surface brightness, so it scales both the area and flux.
gal.applyMagnification(gal_mu)
# Make the combined profile
final = galsim.Convolve([psf, pix, gal])
# Draw the profile
if k == 0:
im = final.draw(dx=pixel_scale)
xsize, ysize = im.array.shape
else:
im = galsim.ImageF(xsize, ysize)
final.draw(im, dx=pixel_scale)
logger.debug(' Drew image')
t3 = time.time()
# Add a constant background level
background = sky_level * pixel_scale**2
im += background
# Add Poisson noise. This time, we don't give a sky_level, since we have already
# added it to the image, so we don't want any more added. The sky_level parameter
# really defines how much _extra_ sky should be added above what is already in the image.
im.addNoise(galsim.PoissonNoise(rng))
logger.debug(' Added Poisson noise')
t4 = time.time()
# Store that into the list of all images
all_images += [im]
t5 = time.time()
logger.debug(' Times: %f, %f, %f, %f', t2 - t1, t3 - t2, t4 - t3,
t5 - t4)
logger.info('Image %d: size = %d x %d, total time = %f sec', k, xsize,
ysize, t5 - t1)
logger.info('Done making images of galaxies')
# Now write the image to disk.
# We write the images to a fits data cube.
galsim.fits.writeCube(all_images, cube_file_name)
logger.info('Wrote image to fits data cube %r', cube_file_name)
def main(argv):
"""
Make images using variable PSF and shear:
- The main image is 10 x 10 postage stamps.
- Each postage stamp is 48 x 48 pixels.
- The second HDU has the corresponding PSF image.
- Applied shear is from a power spectrum P(k) ~ k^1.8.
- Galaxies are real galaxies oriented in a ring test of 20 each.
- The PSF is Gaussian with FWHM, ellipticity and position angle functions of (x,y)
- Noise is Poisson using a nominal sky value of 1.e6.
"""
logging.basicConfig(format="%(message)s",
level=logging.INFO,
stream=sys.stdout)
logger = logging.getLogger("demo10")
# Define some parameters we'll use below.
# Normally these would be read in from some parameter file.
n_tiles = 10 # number of tiles in each direction.
stamp_size = 48 # pixels
pixel_scale = 0.44 # arcsec / pixel
sky_level = 1.e6 # ADU / arcsec^2
# The random seed is used for both the power spectrum realization and the random properties
# of the galaxies.
random_seed = 3339201
# Make output directory if not already present.
if not os.path.isdir('output'):
os.mkdir('output')
file_name = os.path.join('output', 'power_spectrum.fits')
# These will be created for each object below. The values we'll use will be functions
# of (x,y) relative to the center of the image. (r = sqrt(x^2+y^2))
# psf_fwhm = 0.9 + 0.5 * (r/100)^2 -- arcsec
# psf_e = 0.4 * (r/100)^1.5 -- large value at the edge, so visible by eye.
# psf_beta = atan2(y/x) + pi/2 -- tangential pattern
gal_dilation = 3 # Make the galaxies a bit larger than their original size.
gal_signal_to_noise = 100 # Pretty high.
psf_signal_to_noise = 1000 # Even higher.
logger.info('Starting demo script 10')
# Read in galaxy catalog
cat_file_name = 'real_galaxy_catalog_example.fits'
dir = 'data'
real_galaxy_catalog = galsim.RealGalaxyCatalog(cat_file_name, dir=dir)
logger.info('Read in %d real galaxies from catalog',
real_galaxy_catalog.nobjects)
# List of IDs to use. We select 5 particularly irregular galaxies for this demo.
# Then we'll choose randomly from this list.
id_list = [106416, 106731, 108402, 116045, 116448]
# Make the 5 galaxies we're going to use here rather than remake them each time.
# This means the Fourier transforms of the real galaxy images don't need to be recalculated
# each time, so it's a bit more efficient.
gal_list = [
galsim.RealGalaxy(real_galaxy_catalog, id=id) for id in id_list
]
# Make the galaxies a bit larger than their original observed size.
gal_list = [gal.dilate(gal_dilation) for gal in gal_list]
# Setup the PowerSpectrum object we'll be using:
ps = galsim.PowerSpectrum(lambda k: k**1.8)
# The argument here is "e_power_function" which defines the E-mode power to use.
# There is also a b_power_function if you want to include any B-mode power:
# ps = galsim.PowerSpectrum(e_power_function, b_power_function)
# You may even omit the e_power_function argument and have a pure B-mode power spectrum.
# ps = galsim.PowerSpectrum(b_power_function = b_power_function)
# All the random number generator classes derive from BaseDeviate.
# When we construct another kind of deviate class from any other
# kind of deviate class, the two share the same underlying random number
# generator. Sometimes it can be clearer to just construct a BaseDeviate
# explicitly and then construct anything else you need from that.
# Note: A BaseDeviate cannot be used to generate any values. It can
# only be used in the constructor for other kinds of deviates.
# The seeds for the objects are random_seed..random_seed+nobj-1 (which comes later),
# so use the next one.
nobj = n_tiles * n_tiles
rng = galsim.BaseDeviate(random_seed + nobj)
# Setup the images:
gal_image = galsim.ImageF(stamp_size * n_tiles, stamp_size * n_tiles)
psf_image = galsim.ImageF(stamp_size * n_tiles, stamp_size * n_tiles)
# Update the image WCS to use the image center as the origin of the WCS.
# The class that acts like a PixelScale except for this offset is called OffsetWCS.
im_center = gal_image.bounds.trueCenter()
wcs = galsim.OffsetWCS(scale=pixel_scale, origin=im_center)
gal_image.wcs = wcs
psf_image.wcs = wcs
# We will place the tiles in a random order. To do this, we make two lists for the
# ix and iy values. Then we apply a random permutation to the lists (in tandem).
ix_list = []
iy_list = []
for ix in range(n_tiles):
for iy in range(n_tiles):
ix_list.append(ix)
iy_list.append(iy)
# This next function will use the given random number generator, rng, and use it to
# randomly permute any number of lists. All lists will have the same random permutation
# applied.
galsim.random.permute(rng, ix_list, iy_list)
# Now have the PowerSpectrum object build a grid of shear values for us to use.
# Also, because of some technical details about how the config stuff handles the random
# number generator here, we need to duplicate the rng object if we want to have the
# two output files match. This means that technically, the same sequence of random numbers
# will be used in building the grid as will be used by the other uses of rng (permuting the
# postage stamps and adding noise). But since they are used in such completely different
# ways, it is hard to imagine how this could lead to any kind of bias in the images.
grid_g1, grid_g2 = ps.buildGrid(grid_spacing=stamp_size * pixel_scale,
ngrid=n_tiles,
rng=rng.duplicate())
# Build each postage stamp:
for k in range(nobj):
# The usual random number generator using a different seed for each galaxy.
rng = galsim.BaseDeviate(random_seed + k)
# Determine the bounds for this stamp and its center position.
ix = ix_list[k]
iy = iy_list[k]
b = galsim.BoundsI(ix * stamp_size + 1, (ix + 1) * stamp_size,
iy * stamp_size + 1, (iy + 1) * stamp_size)
sub_gal_image = gal_image[b]
sub_psf_image = psf_image[b]
pos = wcs.toWorld(b.trueCenter())
# The image comes out as about 211 arcsec across, so we define our variable
# parameters in terms of (r/100 arcsec), so roughly the scale size of the image.
r = math.sqrt(pos.x**2 + pos.y**2) / 100
psf_fwhm = 0.9 + 0.5 * r**2 # arcsec
psf_e = 0.4 * r**1.5
psf_beta = (math.atan2(pos.y, pos.x) + math.pi / 2) * galsim.radians
# Define the PSF profile
psf = galsim.Gaussian(fwhm=psf_fwhm)
psf = psf.shear(e=psf_e, beta=psf_beta)
# Define the galaxy profile:
# For this demo, we are doing a ring test where the same galaxy profile is drawn at many
# orientations stepped uniformly in angle, making a ring in e1-e2 space.
# We're drawing each profile at 20 different orientations and then skipping to the
# next galaxy in the list. So theta steps by 1/20 * 360 degrees:
theta = k / 20. * 360. * galsim.degrees
# The index needs to increment every 20 objects so we use k/20 using integer math.
index = k / 20
gal = gal_list[index]
# This makes a new copy so we're not changing the object in the gal_list.
gal = gal.rotate(theta)
# Apply the shear from the power spectrum. We should either turn the gridded shears
# grid_g1[iy, ix] and grid_g2[iy, ix] into gridded reduced shears using a utility called
# galsim.lensing.theoryToObserved, or use ps.getShear() which by default gets the reduced
# shear. ps.getShear() is also more flexible because it can get the shear at positions that
# are not on the original grid, as long as they are contained within the bounds of the full
# grid. So in this example we'll use ps.getShear().
alt_g1, alt_g2 = ps.getShear(pos)
gal = gal.shear(g1=alt_g1, g2=alt_g2)
# Apply half-pixel shift in a random direction.
shift_r = pixel_scale * 0.5
ud = galsim.UniformDeviate(rng)
theta = ud() * 2. * math.pi
dx = shift_r * math.cos(theta)
dy = shift_r * math.sin(theta)
gal = gal.shift(dx, dy)
# Make the final image, convolving with the psf
final = galsim.Convolve([psf, gal])
# Draw the image
final.drawImage(sub_gal_image)
# Now add noise to get our desired S/N
# See demo5.py for more info about how this works.
sky_level_pixel = sky_level * pixel_scale**2
noise = galsim.PoissonNoise(rng, sky_level=sky_level_pixel)
sub_gal_image.addNoiseSNR(noise, gal_signal_to_noise)
# For the PSF image, we also shift the PSF by the same amount.
psf = psf.shift(dx, dy)
# Draw the PSF image:
# We use real space integration over the pixels to avoid some of the
# artifacts that can show up with Fourier convolution.
# The level of the artifacts is quite low, but when drawing with
# so little noise, they are apparent with ds9's zscale viewing.
psf.drawImage(sub_psf_image, method='real_space')
# Again, add noise, but at higher S/N this time.
sub_psf_image.addNoiseSNR(noise, psf_signal_to_noise)
logger.info('Galaxy (%d,%d): position relative to center = %s', ix, iy,
str(pos))
logger.info('Done making images of postage stamps')
# Now write the images to disk.
images = [gal_image, psf_image]
galsim.fits.writeMulti(images, file_name)
logger.info('Wrote image to %r', file_name)
def main(args):
# Draw the original galaxies and measure their shapes
rgc = galsim.RealGalaxyCatalog(catalog_filename, dir=catalog_dir)
g1_list = []
g2_list = []
sigma_list = []
for i in range(args.first_index, args.first_index + args.nitems):
test_image = galsim.ImageD(imsize, imsize)
real_galaxy = galsim.RealGalaxy(rgc, index=i)
real_galaxy.original_image.draw(test_image)
shape = CatchAdaptiveMomErrors(test_image)
if shape == -10:
g1_list.append(-10)
g2_list.append(-10)
sigma_list.append(-10)
else:
g1_list.append(shape.observed_shape.g1)
g2_list.append(shape.observed_shape.g2)
sigma_list.append(shape.moments_sigma)
g1_list = numpy.array(g1_list)
g2_list = numpy.array(g2_list)
# Define the config dictionaries we will use for all the following tests
config_and_file_list = get_config(nitems=args.nitems,
first_index=args.first_index,
file_root=args.file_root)
i = 1 # For printing status statements
# Now, run through the various things we need to test in loops.
for base_config, output_file in config_and_file_list:
f = open(output_file, 'w')
for padding in padding_list: # Amount of padding
for interpolant in use_interpolants: # Possible interpolants
print 'Angle test ',
for angle in angle_list: # Possible rotation angles
print i,
i += 1
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
angle=angle,
x_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
angle=angle,
k_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print ''
print 'Shear/magnification test ',
for (
g1, g2, mag
) in shear_and_magnification_list: # Shear and magnification
print i, "(", g1, g2, ")",
i += 1
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
shear=(g1, g2),
magnification=mag,
x_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
shear=(g1, g2),
magnification=mag,
k_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print ''
for shift in shift_list:
print i, "(", shift, ")",
i += 1
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
shift=shift,
x_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print_results(f,
g1_list,
g2_list,
sigma_list,
test_realgalaxy(base_config,
shift=shift,
k_interpolant=interpolant,
padding=padding,
seed=rseed +
args.first_index),
first_index=args.first_index)
print ''
f.close()
def test_whiten():
"""Test the options in config to whiten images
"""
real_gal_dir = os.path.join('..','examples','data')
real_gal_cat = 'real_galaxy_catalog_23.5_example.fits'
config = {
'image' : {
'type' : 'Single',
'random_seed' : 1234,
'pixel_scale' : 0.05,
},
'stamp' : {
'type' : 'Basic',
'size' : 32,
},
'gal' : {
'type' : 'RealGalaxy',
'index' : 79,
'flux' : 100,
},
'psf' : { # This is really slow if we don't convolve by a PSF.
'type' : 'Gaussian',
'sigma' : 0.05
},
'input' : {
'real_catalog' : {
'dir' : real_gal_dir ,
'file_name' : real_gal_cat
}
}
}
# First build by hand (no whitening yet)
rng = galsim.BaseDeviate(1234 + 1)
rgc = galsim.RealGalaxyCatalog(os.path.join(real_gal_dir, real_gal_cat))
gal = galsim.RealGalaxy(rgc, index=79, flux=100, rng=rng)
psf = galsim.Gaussian(sigma=0.05)
final = galsim.Convolve(gal,psf)
im1a = final.drawImage(nx=32, ny=32, scale=0.05)
# Compare to what config builds
galsim.config.ProcessInput(config)
im1b, cv1b = galsim.config.BuildStamp(config, do_noise=False)
np.testing.assert_equal(cv1b, 0.)
np.testing.assert_equal(im1b.array, im1a.array)
# Now add whitening, but no noise yet.
cv1a = final.noise.whitenImage(im1a)
print('From whiten, current_var = ',cv1a)
galsim.config.RemoveCurrent(config)
config['image']['noise'] = { 'whiten' : True, }
im1c, cv1c = galsim.config.BuildStamp(config, do_noise=False)
print('From BuildStamp, current_var = ',cv1c)
np.testing.assert_equal(cv1c, cv1a)
np.testing.assert_equal(im1c.array, im1a.array)
rng1 = rng.duplicate() # Save current state of rng
# 1. Gaussian noise
#####
config['image']['noise'] = {
'type' : 'Gaussian',
'variance' : 50,
'whiten' : True,
}
galsim.config.RemoveCurrent(config)
im2a = im1a.copy()
im2a.addNoise(galsim.GaussianNoise(sigma=math.sqrt(50-cv1a), rng=rng))
im2b, cv2b = galsim.config.BuildStamp(config)
np.testing.assert_almost_equal(cv2b, 50)
np.testing.assert_almost_equal(im2b.array, im2a.array, decimal=5)
# If whitening already added too much noise, raise an exception
config['image']['noise']['variance'] = 1.e-5
try:
np.testing.assert_raises(RuntimeError, galsim.config.BuildStamp,config)
except ImportError:
pass
# 2. Poisson noise
#####
config['image']['noise'] = {
'type' : 'Poisson',
'sky_level_pixel' : 50,
'whiten' : True,
}
galsim.config.RemoveCurrent(config)
im3a = im1a.copy()
sky = 50 - cv1a
rng.reset(rng1.duplicate())
im3a.addNoise(galsim.PoissonNoise(sky_level=sky, rng=rng))
im3b, cv3b = galsim.config.BuildStamp(config)
np.testing.assert_almost_equal(cv3b, 50, decimal=5)
np.testing.assert_almost_equal(im3b.array, im3a.array, decimal=5)
# It's more complicated if the sky is quoted per arcsec and the wcs is not uniform.
config2 = galsim.config.CopyConfig(config)
galsim.config.RemoveCurrent(config2)
config2['image']['sky_level'] = 100
config2['image']['wcs'] = {
'type' : 'UVFunction',
'ufunc' : '0.05*x + 0.001*x**2',
'vfunc' : '0.05*y + 0.001*y**2',
}
del config2['image']['pixel_scale']
del config2['wcs']
config2['image']['noise']['symmetrize'] = 4 # Also switch to symmetrize, just to mix it up.
del config2['image']['noise']['whiten']
rng.reset(1234+1) # Start fresh, since redoing the whitening/symmetrizing
wcs = galsim.UVFunction(ufunc='0.05*x + 0.001*x**2', vfunc='0.05*y + 0.001*y**2')
im3c = galsim.Image(32,32, wcs=wcs)
im3c = final.drawImage(im3c)
cv3c = final.noise.symmetrizeImage(im3c,4)
sky = galsim.Image(im3c.bounds, wcs=wcs)
wcs.makeSkyImage(sky, 100)
mean_sky = np.mean(sky.array)
im3c += sky
extra_sky = 50 - cv3c
im3c.addNoise(galsim.PoissonNoise(sky_level=extra_sky, rng=rng))
im3d, cv3d = galsim.config.BuildStamp(config2)
np.testing.assert_almost_equal(cv3d, 50 + mean_sky, decimal=4)
np.testing.assert_almost_equal(im3d.array, im3c.array, decimal=5)
config['image']['noise']['sky_level_pixel'] = 1.e-5
try:
np.testing.assert_raises(RuntimeError, galsim.config.BuildStamp,config)
except ImportError:
pass
# 3. CCDNoise
#####
config['image']['noise'] = {
'type' : 'CCD',
'sky_level_pixel' : 25,
'read_noise' : 5,
'gain' : 1,
'whiten' : True,
}
galsim.config.RemoveCurrent(config)
im4a = im1a.copy()
rn = math.sqrt(25-cv1a)
rng.reset(rng1.duplicate())
im4a.addNoise(galsim.CCDNoise(sky_level=25, read_noise=rn, gain=1, rng=rng))
im4b, cv4b = galsim.config.BuildStamp(config)
np.testing.assert_almost_equal(cv4b, 50, decimal=5)
np.testing.assert_almost_equal(im4b.array, im4a.array, decimal=5)
# Repeat with gain != 1
config['image']['noise']['gain'] = 3.7
galsim.config.RemoveCurrent(config)
im5a = im1a.copy()
rn = math.sqrt(25-cv1a * 3.7**2)
rng.reset(rng1.duplicate())
im5a.addNoise(galsim.CCDNoise(sky_level=25, read_noise=rn, gain=3.7, rng=rng))
im5b, cv5b = galsim.config.BuildStamp(config)
np.testing.assert_almost_equal(cv5b, 50, decimal=5)
np.testing.assert_almost_equal(im5b.array, im5a.array, decimal=5)
# And again with a non-trivial sky image
galsim.config.RemoveCurrent(config2)
config2['image']['noise'] = config['image']['noise']
config2['image']['noise']['symmetrize'] = 4
del config2['image']['noise']['whiten']
rng.reset(1234+1)
im5c = galsim.Image(32,32, wcs=wcs)
im5c = final.drawImage(im5c)
cv5c = final.noise.symmetrizeImage(im5c, 4)
sky = galsim.Image(im5c.bounds, wcs=wcs)
wcs.makeSkyImage(sky, 100)
mean_sky = np.mean(sky.array)
im5c += sky
rn = math.sqrt(25-cv5c * 3.7**2)
im5c.addNoise(galsim.CCDNoise(sky_level=25, read_noise=rn, gain=3.7, rng=rng))
im5d, cv5d = galsim.config.BuildStamp(config2)
np.testing.assert_almost_equal(cv5d, 50 + mean_sky, decimal=4)
np.testing.assert_almost_equal(im5d.array, im5c.array, decimal=5)
config['image']['noise']['sky_level_pixel'] = 1.e-5
config['image']['noise']['read_noise'] = 0
try:
np.testing.assert_raises(RuntimeError, galsim.config.BuildStamp,config)
except ImportError:
pass
# 4. COSMOSNoise
#####
file_name = os.path.join(galsim.meta_data.share_dir,'acs_I_unrot_sci_20_cf.fits')
config['image']['noise'] = {
'type' : 'COSMOS',
'file_name' : file_name,
'variance' : 50,
'whiten' : True,
}
galsim.config.RemoveCurrent(config)
im6a = im1a.copy()
rng.reset(rng1.duplicate())
noise = galsim.getCOSMOSNoise(file_name=file_name, variance=50, rng=rng)
noise -= galsim.UncorrelatedNoise(cv1a, rng=rng, wcs=noise.wcs)
im6a.addNoise(noise)
im6b, cv6b = galsim.config.BuildStamp(config)
np.testing.assert_almost_equal(cv6b, 50, decimal=5)
np.testing.assert_almost_equal(im6b.array, im6a.array, decimal=5)
config['image']['noise']['variance'] = 1.e-5
del config['_current_cn_tag']
try:
np.testing.assert_raises(RuntimeError, galsim.config.BuildStamp,config)
except ImportError:
pass