# 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)
# --------------------------------------------------------------------
# Read in lightcones from pickles:
calcones = []
for i in range(Nc):
calcones.append(pangloss.readPickle(calpickles[i]))
obscone = pangloss.readPickle(obspickle)
# 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)
# --------------------------------------------------------------------
# Read in lightcones from pickles:
calcones = []
for i in range(Nc):
calcones.append(pangloss.readPickle(calpickles[i]))
obscone = pangloss.readPickle(obspickle)
D1s = self.Da_ps[i]
D2 = self.Da_l
D12 = self.Da_pl[i]
self.beta[i] = (D12*self.Da_s)/(D2*D1s)
return
# ---------------------------------------------------------------------------
def snap(self,z):
snapped_p = numpy.digitize(z,self.redshifts-(self.dz)/2.0)-1
snapped_p[snapped_p < 0] = 0 # catalogs have some blue-shifted objects!
snapped_z = self.redshifts[snapped_p]
return snapped_z,snapped_p
# ---------------------------------------------------------------------------
def __str__(self):
return '1-D Grid of %i planes seperated in redshift by dz= %f' % (self.nplanes,self.dz)
# ============================================================================
if __name__ == '__main__':
nplanes=100
g=Grid(0.6,1.4,nplanes=nplanes)
testfile = "/data/tcollett/Pangloss/grid%i.grid"%nplanes
pangloss.writePickle(g,testfile)
# ============================================================================
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)
else:
print "Calibrate: Unrecognised comparatorType "+comparatorType
print "Calibrate: If you want to use a comparatorType other than median "
print "Calibrate: or mean, you'll need to code it up!"
print "Calibrate: (This should be easy, but you can ask [email protected] for help)."
exit()
jd.append(callist[i])
pangloss.writePickle(callist,jointdistfile)
# Also store the joint dist as a pangloss pdf:
pangloss.writePickle(jd,jointdistasPDFfile)
# Plot:
plotfile = jointdistasPDFfile.split('.')[0]+'.png'
jd.plot("Kappah_median","kappa_ext",weight=None,output=plotfile,title="The joint distribution of $\kappa_{\mathrm{ext}}$ and calibrator \n\n (more correlated means a better calibrator!)")
print "Calibrate: calibration joint PDF saved in:"
print "Calibrate: "+jointdistfile
print "Calibrate: and "+jointdistasPDFfile
print "Calibrate: you can view this PDF in "+plotfile
# --------------------------------------------------------------------
# Mode 2: calibrate a real line of sight's Pr(kappah|D) using the
lc.plots('kappa',
output=CALIB_DIR +
"/example_snapshot_kappa_uncalib_z=1.4.png")
lc.plots('mu',
output=CALIB_DIR +
"/example_snapshot_mu_uncalibz=1.4.png")
del lc
pk = numpy.array(pk)
pmu = numpy.array(pmu)
# --------------------------------------------------------------------
# Write PDFs to pickles
pangloss.writePickle(pk, CALIB_DIR + "/Pofk_z=" + str(zs) + ".pickle")
pangloss.writePickle(pmu, CALIB_DIR + "/PofMu_z=" + str(zs) + ".pickle")
del pk
del pmu
print "Magnifier: saved PofMu to " + CALIB_DIR + "/Pofk_z=" + str(
zs) + ".pickle"
pmu = pangloss.readPickle(CALIB_DIR + "/PofMu_z=" + str(zs) + ".pickle")
pk = pangloss.readPickle(CALIB_DIR + "/Pofk_z=" + str(zs) + ".pickle")
# --------------------------------------------------------------------
# Plot contributions to total kappa and mass at redshifts
if plot_contributions:
print "Drill: Pickling lightcones from this patch of sky..."
for k in range(Ncones):
if k % 200 == 0 and k !=0:
print ("Drill: ...on cone %i out of %i..." % (k,Ncones))
lc = pangloss.Lightcone(table,'simulated',[x[k],y[k]],Rc)
if kappamaps is not None:
lc.kappa_hilbert = MSconvergence.at(x[k],y[k],coordinate_system='physical')
# Coming soon...
# lc.gamma1_hilbert = MSgamma1.at(x[k],y[k],coordinate_system='physical')
# lc.gamma2_hilbert = MSgamma2.at(x[k],y[k],coordinate_system='physical')
calpickle = experiment.getLightconePickleName('simulated',pointing=count)
pangloss.writePickle(lc,calpickle)
count += 1
# Save memory!
del table
del lc
del catalog
print "Drill: ...done."
print ("Drill: All %i calibration lightcones made." % (count))
# --------------------------------------------------------------------
# Now, make any observed lightcones required:
D1s = self.Da_ps[i]
D2 = self.Da_l
D12 = self.Da_pl[i]
self.beta[i] = (D12*self.Da_s)/(D2*D1s)
return
# ---------------------------------------------------------------------------
def snap(self,z):
snapped_p = numpy.digitize(z,self.redshifts-(self.dz)/2.0)-1
snapped_p[snapped_p < 0] = 0 # catalogs have some blue-shifted objects!
snapped_z = self.redshifts[snapped_p]
return snapped_z,snapped_p
# ---------------------------------------------------------------------------
def __str__(self):
return '1-D Grid of %i planes seperated in redshift by dz= %f' % (self.nplanes,self.dz)
# ============================================================================
if __name__ == '__main__':
nplanes=100
g=Grid(0.6,1.4,nplanes=nplanes)
testfile = "/data/tcollett/Pangloss/grid%i.grid"%nplanes
pangloss.writePickle(g,testfile)
# ============================================================================
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)
else:
print "Calibrate: Unrecognised comparatorType " + comparatorType
print "Calibrate: If you want to use a comparatorType other than median "
print "Calibrate: or mean, you'll need to code it up!"
print "Calibrate: (This should be easy, but you can ask [email protected] for help)."
exit()
jd.append(callist[i])
pangloss.writePickle(callist, jointdistfile)
# Also store the joint dist as a pangloss pdf:
pangloss.writePickle(jd, jointdistasPDFfile)
# Plot:
plotfile = jointdistasPDFfile.split('.')[0] + '.png'
jd.plot(
"Kappah_median",
"kappa_ext",
weight=None,
output=plotfile,
title=
"The joint distribution of $\kappa_{\mathrm{ext}}$ and calibrator \n\n (more correlated means a better calibrator!)"
)
(k, Ncones))
lc = pangloss.Lightcone(table, 'simulated', [x[k], y[k]],
Rc)
if kappamaps is not None:
lc.kappa_hilbert = MSconvergence.at(
x[k], y[k], coordinate_system='physical')
# Coming soon...
# lc.gamma1_hilbert = MSgamma1.at(x[k],y[k],coordinate_system='physical')
# lc.gamma2_hilbert = MSgamma2.at(x[k],y[k],coordinate_system='physical')
calpickle = experiment.getLightconePickleName(
'simulated', pointing=count)
pangloss.writePickle(lc, calpickle)
count += 1
# Save memory!
del table
del lc
del catalog
print "Drill: ...done."
print("Drill: All %i calibration lightcones made." % (count))
# --------------------------------------------------------------------
# Now, make any observed lightcones required:
if obscat != 'none':
Mstell_cont[j:,] = lc.findContributions("stellarmass")
# Make a nice visualisation of one of the lightcones
if j == 0:
lc.plots("kappa", output=CALIB_DIR + "/example_snapshot_kappa_uncalib_z=1.4.png")
lc.plots("mu", output=CALIB_DIR + "/example_snapshot_mu_uncalibz=1.4.png")
del lc
pk = numpy.array(pk)
pmu = numpy.array(pmu)
# --------------------------------------------------------------------
# Write PDFs to pickles
pangloss.writePickle(pk, CALIB_DIR + "/Pofk_z=" + str(zs) + ".pickle")
pangloss.writePickle(pmu, CALIB_DIR + "/PofMu_z=" + str(zs) + ".pickle")
del pk
del pmu
print "Magnifier: saved PofMu to " + CALIB_DIR + "/Pofk_z=" + str(zs) + ".pickle"
pmu = pangloss.readPickle(CALIB_DIR + "/PofMu_z=" + str(zs) + ".pickle")
pk = pangloss.readPickle(CALIB_DIR + "/Pofk_z=" + str(zs) + ".pickle")
# --------------------------------------------------------------------
# Plot contributions to total kappa and mass at redshifts
if plot_contributions:
mean_kappa_cont = numpy.mean(kappa_cont, axis=0)
