def createDoubleVoigtCatalogs(workdir,
zs=[0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
massrange=[3e14, 7e14, 1.2e15, 1.7e15, 2.5e15]):
bpz = ldac.openObjectFile('%s/bpz.cat' % workdir, 'STDTAB')
sizes = np.ones(len(bpz))
snratios = np.ones(len(bpz))
fields = [
ldac.openObjectFile(x) for x in glob.glob('%s/field_*.cat' % workdir)
]
cs.createCutoutSuite(zs=zs,
massrange=massrange,
goodbpz=bpz,
sizes=sizes,
snratios=snratios,
outputdir=workdir,
simcats=fields,
shape_distro=cs.voigtDistro2,
shape_distro_kw_sets=100 * [{
'alpha': 0.2,
'sigma2': 0.23,
'gamma2': 0.025,
'sigma1': 0.18,
'gamma1': 0.016
}])
def createPrecutSimFiles(cluster,
filter,
image,
mass,
redshift,
bpzfile,
reconfile,
outdir,
nfields=50):
if not os.path.exists(outdir):
os.mkdir(outdir)
clusterdir = '/u/ki/dapple/subaru/%s/LENSING_%s_%s_aper/%s' % (
cluster, filter, filter, image)
lensing = ldac.openObjectFile('%s/cut_lensing.cat' % clusterdir)
bpz = ldac.openObjectFile(bpzfile)
recon = ldac.openObjectFile(reconfile, 'COS30PHOTZ')
bpz = bpz.matchById(recon, selfid='ID')
recon = recon.matchById(bpz, otherid='ID')
assert (len(bpz) == len(recon))
bpz = bpz.filter(
np.logical_and(recon['zp_best'] > redshift + 0.1,
recon['zp_best'] < 1.25))
maxradii = np.max(
np.sqrt((lensing['Xpos'] - 5000)**2 + (lensing['Ypos'] - 5000)**2))
fields = []
for i in range(nfields):
field = cs.bootstrapField(bpz,
np.ones(len(bpz)),
np.ones(len(bpz)),
galdensity=None,
maxradii=maxradii,
id='ID',
ngals=10000)
field.saveas('%s/%s.%s.%s.master_%d.cat' %
(outdir, cluster, filter, image, i),
clobber=True)
fields.append(field)
cs.createCutoutSuite([redshift], [mass],
bpz,
None,
None,
outdir,
simcats=fields,
shape_distro_kw_sets=nfields * [{
'sigma': 0.25
}])
def createSimFiles(cluster,
filter,
image,
mass,
redshift,
bpzfile,
outdir,
nfields=25):
if not os.path.exists(outdir):
os.mkdir(outdir)
clusterdir = '/u/ki/dapple/subaru/%s/LENSING_%s_%s_aper/%s' % (
cluster, filter, filter, image)
lensing = ldac.openObjectFile('%s/cut_lensing.cat' % clusterdir)
bpz = ldac.openObjectFile(bpzfile, 'COS30PHOTZ')
ngals = len(lensing)
maxradii = np.max(
np.sqrt((lensing['Xpos'] - 5000)**2 + (lensing['Ypos'] - 5000)**2))
fields = []
for i in range(nfields):
field = cs.bootstrapField(bpz,
np.ones(len(bpz)),
np.ones(len(bpz)),
galdensity=galdensity,
maxradii=maxradii,
id='SeqNr',
ngals=ngals)
field.saveas('%s/%s.%s.%s.master_%d.cat' %
(outdir, cluster, filter, image, i),
clobber=True)
fields.append(field)
cs.createCutoutSuite([redshift], [mass],
bpz,
None,
None,
outdir,
simcats=fields,
shape_distro_kw_sets=nfields * [{
'sigma': 0.25
}])
def createCatalogs(workdir,
zs=[0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
massrange=[3e14, 7e14, 1.2e15, 1.7e15, 2.5e15]):
bpz = ldac.openObjectFile('%s/bpz.cat' % workdir, 'STDTAB')
if bpz is None:
bpz = ldac.openObjectFile('%s/bpz.cat' % workdir, 'COS30PHOTZ')
sizes = np.ones(len(bpz))
snratios = np.ones(len(bpz))
fields = [
ldac.openObjectFile(x) for x in glob.glob('%s/field_*.cat' % workdir)
]
cs.createCutoutSuite(zs=zs,
massrange=massrange,
goodbpz=bpz,
sizes=sizes,
snratios=snratios,
outputdir=workdir,
simcats=fields,
idcol='SeqNr')
def makeNonGaussSims(zs, rsrange, outputdir):
nsamples = len(fields)
shapedistro_samples = sdr.selectPosteriorSamples(shapedistro_datavec, nsamples)
binned_distro = BinnedShapeSamples(voigt.voigtSamples,
voigtdistro.bin_selectors,
'sigma gamma size'.split())
cs.createCutoutSuite(zs,
rsrange,
bpz,
None,
None,
pdzrange,
pdzs,
outputdir,
simcats = fields,
sourcecat = None,
shape_distro =
shape_distro_kw_sets = 30*[{'sigma' : 0.25}]):