def makeHaloMassFunction(self,catalog):
assert catalog != None
#Infer halo mass function from Millenium Mh,z catalogue ; we use a power-law for this.
zeds,dz = numpy.linspace(0,1.8,10,retstep=True)#coarse redshift bin. these things change slowly.
self.HMF = {}
self.HMF['catalog'] = catalog
self.HMFzkeys,self.HMFdz = zeds-dz,dz
infer_from_data=True
if infer_from_data:
# Load in the catalog's list of halo masses and redshift.
# import cPickle
# F=open(self.HMFdata,'rb')
# inhalomass,inhaloZ = cPickle.load(F)
# F.close()
inhalomass,inhaloZ = pangloss.readPickle(catalog)
inhaloZ[inhaloZ<0]=0
for i in range(len(zeds)):
z=zeds[i]+dz/2.
Masses=inhalomass[inhaloZ>z-dz/2]
newinZ=inhaloZ[inhaloZ>z-dz/2]
Mhalos=Masses[newinZ<z+dz/2]
Massbins=numpy.linspace(10,20,101)
hist,bins=numpy.histogram(Mhalos,Massbins)
MOD = interpolate.splrep(Massbins[:-1],hist,s=0,k=1)
HMF = interpolate.splev(self.Mh_axis,MOD)
self.TCM = self.Mh_axis[HMF.argmax()+1:]
self.TCHM = HMF[HMF.argmax()+1:]
self.TCM=self.TCM[self.TCHM>0]
self.TCHM=self.TCHM[self.TCHM>0]
# Fit a powerlaw to the HMF
PLcoeff,ier = optimize.leastsq(self.getPL,[14.56,-1.])
self.HMF[i] = PLcoeff
# We've already fit a powerlaw to millenium: it's parameters as a function of z are
# included here.
# elif catalog='Millennium':
# for Z in zeds:
# z=Z+dz/2.
# if z>0 and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
return
def makeHaloMassFunction(self, catalog):
assert catalog != None
# Infer halo mass function from Millenium Mh,z catalogue ; we use a power-law for this.
zeds, dz = numpy.linspace(0, 1.8, 10, retstep=True) # coarse redshift bin. these things change slowly.
self.HMF = {}
self.HMF["catalog"] = catalog
self.HMFzkeys, self.HMFdz = zeds - dz, dz
infer_from_data = True
if infer_from_data:
# Load in the catalog's list of halo masses and redshift.
# import cPickle
# F=open(self.HMFdata,'rb')
# inhalomass,inhaloZ = cPickle.load(F)
# F.close()
inhalomass, inhaloZ = pangloss.readPickle(catalog)
inhaloZ[inhaloZ < 0] = 0
for i in range(len(zeds)):
z = zeds[i] + dz / 2.0
Masses = inhalomass[inhaloZ > z - dz / 2]
newinZ = inhaloZ[inhaloZ > z - dz / 2]
Mhalos = Masses[newinZ < z + dz / 2]
Massbins = numpy.linspace(10, 20, 101)
hist, bins = numpy.histogram(Mhalos, Massbins)
MOD = interpolate.splrep(Massbins[:-1], hist, s=0, k=1)
HMF = interpolate.splev(self.Mh_axis, MOD)
self.TCM = self.Mh_axis[HMF.argmax() + 1 :]
self.TCHM = HMF[HMF.argmax() + 1 :]
self.TCM = self.TCM[self.TCHM > 0]
self.TCHM = self.TCHM[self.TCHM > 0]
# Fit a powerlaw to the HMF
PLcoeff, ier = optimize.leastsq(self.getPL, [14.56, -1.0])
self.HMF[i] = PLcoeff
# We've already fit a powerlaw to millenium: it's parameters as a function of z are
# included here.
# elif catalog='Millennium':
# for Z in zeds:
# z=Z+dz/2.
# if z>0 and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
# if z> and z<:self.HMF[z]=
return
zperr = experiment.parameters['PhotozError']
# Stellar mass observations:
MserrP = experiment.parameters['PhotometricMstarError']
MserrS = experiment.parameters['SpectroscopicMstarError']
# Sampling Pr(kappah|D):
Ns = experiment.parameters['NRealisations']
# Reconstruct calibration lines of sight?
DoCal = experiment.parameters['ReconstructCalibrations']
# --------------------------------------------------------------------
# Load in stellar mass to halo relation, or make a new one:
try:
shmr = pangloss.readPickle('dummy') #SHMfile)
except IOError:
print "Reconstruct: generating the stellar mass to halo mass grid."
print "Reconstruct: this may take a moment..."
shmr = pangloss.SHMR(method=SHMrelation)
shmr.makeHaloMassFunction(HMFfile)
shmr.makeCDFs()
pangloss.writePickle(shmr, SHMfile)
print "Reconstruct: SHMR saved to " + SHMfile
# --------------------------------------------------------------------
# Make redshift grid:
grid = pangloss.Grid(zd, zs, nplanes=100)
# --------------------------------------------------------------------
zperr = experiment.parameters['PhotozError']
# Stellar mass observations:
MserrP = experiment.parameters['PhotometricMstarError']
MserrS = experiment.parameters['SpectroscopicMstarError']
# Sampling Pr(kappah|D):
Ns = experiment.parameters['NRealisations']
# Reconstruct calibration lines of sight?
DoCal = experiment.parameters['ReconstructCalibrations']
# --------------------------------------------------------------------
# Load in stellar mass to halo relation, or make a new one:
try:
shmr = pangloss.readPickle('dummy')#SHMfile)
except IOError:
print "Reconstruct: generating the stellar mass to halo mass grid."
print "Reconstruct: this may take a moment..."
shmr = pangloss.SHMR(method=SHMrelation)
shmr.makeHaloMassFunction(HMFfile)
shmr.makeCDFs()
pangloss.writePickle(shmr,SHMfile)
print "Reconstruct: SHMR saved to "+SHMfile
# --------------------------------------------------------------------
# Make redshift grid:
grid = pangloss.Grid(zd,zs,nplanes=100)
# --------------------------------------------------------------------
# Reconstruct calibration lines of sight?
DoCal = experiment.parameters['ReconstructCalibrations']
# --------------------------------------------------------------------
# Make redshift grid:
grid = pangloss.Grid(zd, zs, nplanes=100)
# --------------------------------------------------------------------
# Read in lightcones from pickles:
calcones = []
for i in xrange(Nc):
calcones.append(pangloss.readPickle(calpickles[i]))
if i == 0: print calpickles[i]
if DoCal == "False": #must be string type
calcones = []
calpickles = []
allcones = calcones
allconefiles = calpickles
# ==============================================================
# Find the overdensity of lightcones cut at m<22 in F125W
# ==============================================================
print "Magnifier: finding the distribution of lightcones with density..."
lc_dens = []
pfile = x.split('.')[0].split("_lightcone")[0]+"_"+EXP_NAME+"_KappaHilbert_Kappah_"+comparatorType+".pickle"
calresultpickles.append(pfile)
else:
print "Calibrate: Unrecognised comparator "+Comparator
print "Calibrate: If you want to use a comparator other than kappa_h, "
print "Calibrate: you'll need to code it up!"
print "Calibrate: (This should be easy, but you can ask [email protected] for help)."
exit()
# Now calculate comparators:
callist=numpy.empty((Nc,2))
jd=pangloss.PDF(["kappa_ext",comparator+'_'+comparatorType])
for i in range(Nc):
C = calresultpickles[i]
pdf = pangloss.readPickle(C)
if comparator=="Kappah":
if comparatorType=="median":
# Recall that we created a special file for this
# choice of comparator and comparator type, in
# Reconstruct. You could also use the
# comparatortype=="mean" code, swapping mean for median.
callist[i,0]=pdf[0]
callist[i,1]=pdf[1][0]
elif comparatorType=="mean":
callist[i,0] = pdf.truth[0]
callist[i,1] = numpy.mean(pdf.samples)
)[0] + "_" + EXP_NAME + "_KappaHilbert_Kappah_" + comparatorType + ".pickle"
calresultpickles.append(pfile)
else:
print "Calibrate: Unrecognised comparator " + Comparator
print "Calibrate: If you want to use a comparator other than kappa_h, "
print "Calibrate: you'll need to code it up!"
print "Calibrate: (This should be easy, but you can ask [email protected] for help)."
exit()
# Now calculate comparators:
callist = numpy.empty((Nc, 2))
jd = pangloss.PDF(["kappa_ext", comparator + '_' + comparatorType])
for i in range(Nc):
C = calresultpickles[i]
pdf = pangloss.readPickle(C)
if comparator == "Kappah":
if comparatorType == "median":
# Recall that we created a special file for this
# choice of comparator and comparator type, in
# Reconstruct. You could also use the
# comparatortype=="mean" code, swapping mean for median.
callist[i, 0] = pdf[0]
callist[i, 1] = pdf[1][0]
elif comparatorType == "mean":
callist[i, 0] = pdf.truth[0]
callist[i, 1] = numpy.mean(pdf.samples)
# Reconstruct calibration lines of sight?
DoCal = experiment.parameters["ReconstructCalibrations"]
# --------------------------------------------------------------------
# Make redshift grid:
grid = pangloss.Grid(zd, zs, nplanes=100)
# --------------------------------------------------------------------
# Read in lightcones from pickles:
calcones = []
for i in xrange(Nc):
calcones.append(pangloss.readPickle(calpickles[i]))
if i == 0:
print calpickles[i]
if DoCal == "False": # must be string type
calcones = []
calpickles = []
allcones = calcones
allconefiles = calpickles
# ==============================================================
# Find the overdensity of lightcones cut at m<22 in F125W
# ==============================================================
print "Magnifier: finding the distribution of lightcones with density..."
