def kappaToAlpha(self, kappaMap, test=False):
fKappa = fft_gen(kappaMap, axes=[-2, -1])
fAlpha = self.ftkernels * fKappa
pixScaleY, pixScaleX = kappaMap.pixshape()
Ny, Nx = kappaMap.shape
#retAlpha = (np.fft_gen.ifft_genshift(enmap.ifft_gen(fAlpha,normalize=False).real)+kappaMap*0.)*pixScaleY*pixScaleX/Nx/Ny
retAlpha = -(np.fft_gen.ifft_genshift(
ifft_gen(fAlpha, axes=[-2, -1], normalize=False).real[::-1]) +
kappaMap * 0.) * pixScaleY * pixScaleX / Nx / Ny
if test:
newKap = -np.nan_to_num(0.5 * enmap.div(retAlpha))
thetaMap = kappaMap.posmap()
thetaModMap = 60. * 180. * (np.sum(thetaMap**2, 0)**0.5) / np.pi
print(("newkappaint ", np.nanmean(newKap[thetaModMap < 10.])))
pl = Plotter()
pl.plot2d(kappaMap)
pl.done("output/oldKap.png")
pl = Plotter()
pl.plot2d(newKap)
pl.done("output/newKap.png")
ratio = np.nan_to_num(newKap / kappaMap)
print((thetaMap.shape))
print((ratio[thetaModMap < 5].mean()))
pl = Plotter()
pl.plot2d(ratio[200:-200, 200:-200])
pl.done("output/testratio.png")
return retAlpha
print(("Reconstructing" , i , " ..."))
qest.updateTEB_X(fot,foe,fob,alreadyFTed=True)
qest.updateTEB_Y(alreadyFTed=True)
for j, polComb in enumerate(polCombList):
kappa = qest.getKappa(polComb)
reconLm = lensedTLm.copy()
reconLm.data[:,:] = kappa[:,:].real
pl = Plotter()
pl.plot2d(reconLm.data)
pl.done("/gpfs01/astro/www/msyriac/plots/recon"+str(i)+".png")
print("crossing with input")
p2d = ft.powerFromLiteMap(kappaLm,reconLm,applySlepianTaper=False)
# p2d.powerMap = p2d.powerMap/w2
centers, means = stats.binInAnnuli(p2d.powerMap, p2d.modLMap, bin_edges)
listCrossPower[polComb].append( means )
p2d = ft.powerFromLiteMap(reconLm,applySlepianTaper=False)
# p2d.powerMap = p2d.powerMap/w4
centers, means = stats.binInAnnuli(p2d.powerMap, p2d.modLMap, bin_edges)
if not (saveMf):
try:
mf = np.loadtxt("data/meanfield.dat")
kappaStack -= mf
print("subtracted meanfield")
except:
pass
stepmin = kellmin
kappaStack = fmaps.stepFunctionFilterLiteMap(kappaStack,
modLMap,
kellmax,
ellMin=stepmin)
pl = Plotter()
pl.plot2d(kappaStack)
pl.done(outDir + "recon.png")
dt = pixScale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breal = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
cents, recons = breal.bin(kappaStack)
pl = Plotter()
pl.add(cents, recons)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(outDir + "profiles.png")
fsky = 0.4
N1 = hmf.N_of_z() * fsky
#hmf.sigN = np.loadtxt("temp.txt")
try:
hmf.sigN = np.loadtxt("tempSigN.txt")
N2 = hmf.N_of_z_SZ(SZProf) * fsky
except:
N2 = hmf.N_of_z_SZ(SZProf) * fsky
np.savetxt("tempSigN.txt", hmf.sigN)
pl = Plotter()
pl.plot2d(hmf.sigN)
pl.done(outDir + "signRefactor.png")
pl = Plotter(scaleY='log')
pl.add(zs, N1)
pl.add(zs, N2)
Ntot1 = np.trapz(N2, zs)
print(Ntot1)
sn, ntot = hmf.Mass_err(fsky, outmerr, SZProf)
print(ntot)
#q_arr = np.logspace(np.log10(6.),np.log10(500.),64)
qs = [6., 500., 64]
lensedMapY = fmaps.convolveBeam(lensedMapY, modLMap, beamTemplate)
if noiseT > 1.e-3:
lensedMapX = lensedMapX + gGenT.getMap(stepFilterEll=None)
if noiseT > 1.e-3:
lensedMapY = lensedMapY + gGenT.getMap(stepFilterEll=None)
# lensedMapYRot = np.rot90(lensedMapY.copy(),2)
lensedMapX = lensedMapX * win
lensedMapY = lensedMapY * win
# lensedMapYRot = lensedMapYRot*win
if i == 0:
pl = Plotter()
pl.plot2d(lensedMapX)
pl.done(outDir + "lensed.png")
fotX = np.nan_to_num(fft(lensedMapX, axes=[-2, -1]) / beamTemplate[:, :])
fotY = np.nan_to_num(fft(lensedMapY, axes=[-2, -1]) / beamTemplate[:, :])
# fotYRot = np.nan_to_num(fft(lensedMapYRot,axes=[-2,-1])/ beamTemplate[:,:])
if i % 1 == 0: print "Reconstructing", i, " ..."
qest.updateTEB_X(fotX, alreadyFTed=True)
qest.updateTEB_Y(fotY, alreadyFTed=True)
kappa = qest.getKappa(polCombList[0]).real / w2
# qest.updateTEB_Y(-fotYRot,alreadyFTed=True)
# kappaRot = qest.getKappa(polCombList[0]).real/w2
kappaStack += kappa
inputKappaStack = 0.
szStack = 0.
N = numClusters
for i in range(N):
print i
kappa = enmap.read_map(saveName + "_kappa_" + str(i) + "_" +
str(snap) + ".hdf")
inputKappaMap = enmap.read_map(saveName + "_inpkappa_" + str(i) + "_" +
str(snap) + ".hdf")
szMap = enmap.read_map(saveName + "_sz_" + str(i) + "_" + str(snap) +
".hdf")
kappaStack += kappa
inputKappaStack += inputKappaMap
szStack += szMap
pl = Plotter()
pl.plot2d(kappaStack / N)
pl.done(outDir + "recon_" + str(snap) + ".png")
pl = Plotter()
pl.plot2d(inputKappaStack / N)
pl.done(outDir + "truestack_" + str(snap) + ".png")
pl = Plotter()
pl.plot2d(szStack / N)
pl.done(outDir + "szstack_" + str(snap) + ".png")
print(oml)
# cbias = np.zeros((mrange.size,zcents.size))
# for i,z in enumerate(zcents):
# cbias[:,i] = hbias(10**mrange,zcents,cc.h,cc.om,oml)
Mg, zg = np.meshgrid(10**mrange, zcents)
cbias = hbias(Mg, zg, cc.h, cc.om, oml).T
print(cbias)
print((hb.shape))
print((cbias.shape))
#sys.exit()
pl = Plotter()
pl.plot2d(hb)
pl.done(outDir + "hb.png")
pl = Plotter()
pl.plot2d(cbias)
pl.done(outDir + "cbias.png")
ls = "-"
lab = ""
pl = Plotter(labelX="$z$", labelY="b", ftsize=14)
pl.add(zcents,
hb[np.where(np.isclose(mrange, 14.0)), :].ravel(),
ls=ls,
label=lab + " 10^14 Msol/h")
pl.add(zcents,
hb[np.where(np.isclose(mrange, 14.3)), :].ravel(),
def fitAuto(self,keyData,keyTheory,amplitudeRange=np.arange(0.1,2.0,0.01),constRange=np.arange(0.1,2.0,0.01),debug=False,store=False):
# evaluate likelihood on a 2d grid and fit to a gaussian
# store fit as new theory curve
width = amplitudeRange[1]-amplitudeRange[0]
height = constRange[1]-constRange[0]
Likelihood = lambda x,y: np.exp(-0.5*self.chisqAuto(keyData,keyTheory,amp=x,const=y))
#Likelihood = lambda x,y: -0.5*self.chisqAuto(keyData,keyTheory,amp=x,const=y)
Likes = np.array([[Likelihood(x,y) for x in amplitudeRange] for y in constRange])
ampLike = np.sum(Likes,axis=0)
constLike = np.sum(Likes,axis=1)
ampLike = ampLike / (ampLike.sum()*width) #normalize
constLike = constLike / (constLike.sum()*height) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,ampLike,p0=[amplitudeRange.mean(),0.1*amplitudeRange.mean()])[0]
constBest,constErr = cfit(norm.pdf,constRange,constLike,p0=[constRange.mean(),0.1*constRange.mean()])[0]
if debug:
pl = Plotter()
pl.plot2d(Likes)
pl.done("output/like2d.png")
pl = Plotter()
fitVals = np.array([norm.pdf(x,ampBest,ampErr) for x in amplitudeRange])
pl.add(amplitudeRange,ampLike,label="amplikes")
pl.add(amplitudeRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/amplike1d.png")
pl = Plotter()
fitVals = np.array([norm.pdf(x,constBest,constErr) for x in constRange])
pl.add(constRange,constLike,label="constlikes")
pl.add(constRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/constlike1d.png")
#sys.exit()
if not(store):
return constBest,constErr
else:
self.datas[keyData]['binned'] -= constBest
self.datas[keyData]['unbinned'] -= constBest
fitKey = keyData+"_fitTo_"+keyTheory
self.datas[fitKey] = {}
self.datas[fitKey]['covmat'] = None
self.datas[fitKey]['binned'] = self.datas[keyTheory]['binned']*ampBest
self.datas[fitKey]['unbinned'] = self.datas[keyTheory]['unbinned']*ampBest
self.datas[fitKey]['label'] = keyData+" fit to "+keyTheory+" with amp "+'{0:.2f}'.format(ampBest)+"+-"+'{0:.2f}'.format(ampErr)
self.datas[fitKey]['amp']=(ampBest,ampErr)
self.datas[fitKey]['const']=(constBest,constErr)
self.datas[fitKey]['isFit'] = True
return fitKey
# sngrid = 1./jointgrid
# lab = "joint"
# pl.add(outzgrid,sngrid[np.where(np.isclose(outmgrid,14.0)),:].ravel(),ls=ls,label=lab+" 10^14 Msol/h")
# pl.add(outzgrid,sngrid[np.where(np.isclose(outmgrid,14.3)),:].ravel(),ls=ls,label=lab+" 10^14.3 Msol/h")
# pl.add(outzgrid,sngrid[np.where(np.isclose(outmgrid,14.5)),:].ravel(),ls=ls,label=lab+" 10^14.5 Msol/h")
# pl.add(outzgrid,sngrid[np.where(np.isclose(outmgrid,14.7)),:].ravel(),ls=ls,label=lab+" 10^14.7 Msol/h")
pl.legendOn(loc='upper right', labsize=8)
pl.done(outDir + "FigSN.pdf")
from orphics.tools.io import Plotter
pgrid = np.rot90(old_div(1., jointgrid))
pl = Plotter(labelX="$\\mathrm{log}_{10}(M)$", labelY="$z$", ftsize=14)
pl.plot2d(pgrid,
extent=[mmin, mmax, zmin, zmax],
levels=[1.0, 3.0, 5.0],
labsize=14)
pl.done(outDir + "joint.png")
# tmgrid = np.arange(mmin,mmax,0.5)
# tzgrid = np.arange(zmin,zmax,0.5)
# coarsegrid = interpolateGrid(jointgrid,outmgrid,outzgrid,tmgrid,tzgrid,regular=False,kind="cubic",bounds_error=False,fill_value=np.inf)
# from orphics.tools.output import Plotter
# pgrid = np.rot90(1./coarsegrid)
# pl = Plotter(labelX="$\\mathrm{log}_{10}(M)$",labelY="$z$",ftsize=14)
# pl.plot2d(pgrid,extent=[mmin,mmax,zmin,zmax],levels=[1.0,3.0,5.0],labsize=14)
# pl.done(outDir+"coarse.png")
# pickle.dump((tmgrid,tzgrid,coarsegrid),open("data/testGrid.pkl",'wb'))
