def fit(self,keyData,keyTheory,amplitudeRange=np.arange(0.1,2.0,0.01),debug=False,numbins=-1):
# evaluate likelihood on a 1d grid and fit to a gaussian
# store fit as new theory curve
width = amplitudeRange[1]-amplitudeRange[0]
Likelihood = lambda x: np.exp(-0.5*self.chisq(keyData,keyTheory,amp=x,numbins=numbins))
Likes = np.array([Likelihood(x) for x in amplitudeRange])
Likes = Likes / (Likes.sum()*width) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,Likes,p0=[1.0,0.5])[0]
if debug:
fitVals = np.array([norm.pdf(x,ampBest,ampErr) for x in amplitudeRange])
pl = Plotter()
pl.add(amplitudeRange,Likes,label="likes")
pl.add(amplitudeRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/debug_coreFit.png")
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]['isFit'] = True
return fitKey
def makeTemplate(l, Fl, mod, Nx, Ny, debug=False):
"""
Given 1d function Fl of l, creates the 2d version
of Fl on 2d k-space defined by mod
"""
FlSpline = splrep(l, Fl, k=3)
ll = np.ravel(mod)
kk = (splev(ll, FlSpline))
template = np.reshape(kk, [Ny, Nx])
if debug:
print(kk)
myFig = Plotter("$l$", "$F_l$", scaleX="log", scaleY="log")
#myFig.add(l,Fl)
myFig.add(ll, kk)
myFig.done(fileName="output/interp.png")
plotme([mod], saveFile="output/mod.png", axoff=True, clbar=False)
plotme([template], saveFile="output/temp.png", axoff=True, clbar=False)
plotme([np.log(template)],
saveFile="output/logtemp.png",
axoff=True,
clbar=False)
sys.exit()
return template
def stat_analysis(cutouts, binsize, arcmax, cents, modRMaps):
profiles = []
for i in range(0, len(cutouts)):
thetaRange = np.arange(0., arcmax, binsize)
breali = bin2D(modRMaps[i] * 180. * 60. / np.pi, thetaRange)
a = breali.bin(cutouts[i])[1]
profiles.append(a)
statistics = stats.getStats(profiles)
mean = statistics['mean']
error = statistics['errmean']
covmat = statistics['cov']
corrcoef = stats.cov2corr(covmat)
io.quickPlot2d(corrcoef, 'corrcoef.png')
pl = Plotter(labelX='Distance from Center (arcminutes)',
labelY='Temperature Fluctuation ($\mu K$)',
ftsize=10)
pl.add(cents, mean)
pl.addErr(cents, mean, yerr=error)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(out_dir + "error.png")
lensedMapX = fmaps.convolveBeam(lensedMapX, modLMap, beamTemplate)
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
lmax = 3000
cosmoDict = dictFromSection(Config, cosmologyName)
constDict = dictFromSection(Config, 'constants')
clusterDict = dictFromSection(Config, clusterParams)
cc = ClusterCosmology(cosmoDict, constDict, lmax, pickling=True)
HMF = Halo_MF(cc)
cambOutFile = lambda i: "/home/msyriac/software/CAMB_wa/testWaFid_matterpower_" + str(
i) + ".dat"
zrange = np.arange(0., 3.05, 0.05)
kh, z, pk, s8 = HMF.pk(zrange)
#pl = Plotter(scaleY='log',scaleX='log')
pl = Plotter()
Nzs = pk.shape[0]
for i, z in enumerate(zrange[::-1]):
kh_camb, P_camb = np.loadtxt(cambOutFile(i), unpack=True)
if i == 0:
kmin = max(kh_camb[0], kh[0])
kmax = min(kh_camb[-1], kh[-1])
keval = np.logspace(np.log10(kmin), np.log10(kmax), 20)
pcambfunc = interp1d(kh_camb, P_camb)
pfunc = interp1d(kh, pk[Nzs - i - 1, :])
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")
kmin = 40
deg = 10.
px = 0.5
dell = 10
cambRoot = "data/ell28k_highacc"
theory = loadTheorySpectraFromCAMB(cambRoot,unlensedEqualsLensed=False,useTotal=False,lpad=9000)
lmap = lm.makeEmptyCEATemplate(raSizeDeg=deg, decSizeDeg=deg,pixScaleXarcmin=px,pixScaleYarcmin=px)
frange = np.arange(2.,3100.,1.)
frangeC = np.arange(100.,5000.,20.)
Clkk = theory.gCl("kk",frange)
pl = Plotter(scaleY='log',labelX="$L$",labelY="$C_L$")
for polComb in ['EB']:
#for delens,lines,alpha in zip([False,True],['-','--'],[1.0,0.5]):
for delens,lines,alpha in zip([False],['-'],[1.0]):
sns = []
for noiseFile in ['fiducial']+glob.glob("data/colin*"):
#for noiseFile in ['fiducial']+glob.glob("data/colin???.txt"):
myNls = NlGenerator(lmap,theory,gradCut=gradCut)
kmax = getMax(polComb,tellmaxY,pellmaxY)
bin_edges = np.arange(kmin,kmax,dell)+dell
priorList['omch2'] = 0.002
priorList['ombh2'] = 0.00023
priorList['ns'] = 0.006
priorList['As'] = 5.e-12
priorList['alpha_ym'] = 0.179
priorList['b_ym'] = 0.08
priorList['beta_ym'] = 0.1
priorList['gamma_ym'] = 0.1
priorList['Ysig'] = 0.127
priorList['gammaYsig'] = 1.0
priorList['betaYsig'] = 1.0
import os
if 'mnu' in fishName:
pl = Plotter(labelY="$\sigma(" + paramLatexList[paramList.index("mnu")] +
")$ (meV)",
labelX="Iteration",
ftsize=20)
elif 'w0' in fishName:
pl = Plotter(labelY="$\\frac{\sigma(" +
paramLatexList[paramList.index("w0")] + ")}{" +
paramLatexList[paramList.index("w0")] + "}\%$",
labelX="Iteration",
ftsize=20)
#for doBAO in [False,True]:
for fishSection in ["fisher-" + fishName, "fisher-" + fishName + "-DESI"]:
priorNameList = []
priorValueList = []
iterations = 0
experimentName = "SO-v2-6m"
beams = listFromConfig(Config,experimentName,'beams')
noises = listFromConfig(Config,experimentName,'noises')
freqs = listFromConfig(Config,experimentName,'freqs')
lmax = int(Config.getfloat(experimentName,'lmax'))
lknee = listFromConfig(Config,experimentName,'lknee')[0]
alpha = listFromConfig(Config,experimentName,'alpha')[0]
fsky = Config.getfloat(experimentName,'fsky')
SZProfExample = SZ_Cluster_Model(clusterCosmology=cc,clusterDict=clusterDict,rms_noises = noises,fwhms=beams,freqs=freqs,lmax=lmax,lknee=lknee,alpha=alpha,tsz_cib=True)
#print SZProfExample.nl / SZProfExample.nl_new
pl = Plotter()
pl.add(SZProfExample.evalells,old_div(SZProfExample.nl_old, SZProfExample.nl))
pl.done("tests/new_nl_test.png")
#ILC = ILC_simple(clusterCosmology=cc, rms_noises = noises,fwhms=beams,freqs=freqs,lmax=lmax,lknee=lknee,alpha=alpha)
#ILC2 = ILC_simple(clusterCosmology=cc, rms_noises = noises[3:],fwhms=beams[3:],freqs=freqs[3:],lmax=lmax,lknee=lknee,alpha=alpha)
#ILC3 = ILC_simple(clusterCosmology=cc, rms_noises = noises[3:6],fwhms=beams[3:6],freqs=freqs[3:6],lmax=lmax,lknee=lknee,alpha=alpha)
ILC = ILC_simple(clusterCosmology=cc, rms_noises = noises,fwhms=beams,freqs=freqs,lmax=lmax,lknee=lknee,alpha=alpha)
lsedges = np.arange(300,8001,100)
el_ilc, cls_ilc, err_ilc, s2n = ILC.Forecast_Cellcmb(lsedges,fsky)
el_ilc_c, cls_ilc_c, err_ilc_c, s2n_c = ILC.Forecast_Cellcmb(lsedges,fsky,constraint='tsz')
print((s2n,s2n_c))
ILC = ILC_simple(clusterCosmology=cc, rms_noises = noises,fwhms=beams,freqs=freqs,lmax=7000,lknee=lknee,alpha=alpha)
# Read config
iniFile = "input/params.ini"
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
px = 180 * 60 / 60000.
gradCut = 60000.
expName = "DM-18arcsec"
lensName = "lensing"
ls, Nls, ellbb, dlbb, efficiency = lensNoise(Config,
expName,
lensName,
px=px,
gradCut=gradCut,
bigell=gradCut,
plot=True)
print efficiency
print ls
print Nls
from orphics.tools.io import Plotter
pl = Plotter(scaleY='log')
pl.add(ls, Nls)
pl._ax.set_ylim(1e-12, 1e-6)
#pl._ax.set_xlim(2,4000)
pl.done("nls.png")
Mexp_edges, z_edges, lndM = pickle.load(open(calFile,"rb"))
HMF = Halo_MF(cc,Mexp_edges,z_edges)
HMF.sigN = siggrid.copy()
#MM = 10**np.linspace(13.,14.,5)
#print SZProfExample.quickVar(MM,zz,tmaxN=tmaxN,numts=numts)
#sys.exit()
#print z_edges
#print HMF.N_of_z()
Nzs = HMF.N_of_z_SZ(fsky,SZProfExample)*np.diff(z_edges)
zcents = old_div((z_edges[1:]+z_edges[:-1]),2.)
pl = Plotter()
pl.add(zcents,Nzs)
pl.done("nz.png")
print((HMF.Mass_err(fsky,lndM*24.0,SZProfExample)))
#print "quickvar " , np.sqrt(SZProfExample.quickVar(MM,zz,tmaxN=tmaxN,numts=numts))
#print "filtvar " , np.sqrt(SZProfExample.filter_variance(MM,zz))
#print "y_m",SZProfExample.Y_M(MM,zz)
SZProf = SZ_Cluster_Model(cc,clusterDict,rms_noises = noise,fwhms=beam,freqs=freq,lknee=lknee,alpha=alpha)
Ndn = HMF.N_of_mqz_SZ(lndM*massMultiplier,qbin_edges,SZProf)
dNdp = old_div((getNmzq(Nup,mexp_edges,z_edges,qbin_edges)-getNmzq(Ndn,mexp_edges,z_edges,qbin_edges)),val)
Nz = dNdp.copy().sum(axis=-1).sum(axis=0)
Nm = dNdp.copy().sum(axis=-1).sum(axis=-1)
Nq = dNdp.copy().sum(axis=0).sum(axis=0)
yNzs[key].append((val,Nz,Nm,Nq))
pl = Plotter(labelX="$z$",labelY="$dN$")
xstep = 0.01
for i,val in enumerate(vals):
assert yNzs[key][i][0]==val
pl.add(getCents(z_edges)+((i-old_div(len(vals),2))*xstep),yNzs[key][i][1],label=key+" "+str(val))
pl.legendOn(labsize=10,loc='upper right')
pl.done(outDir+key+"_Nz_step.png")
pl = Plotter(labelX="$M$",labelY="$dN$")
xstep = 0.01
for i,val in enumerate(vals):
assert yNzs[key][i][0]==val
pl.add(getCents(mexp_edges)+((i-old_div(len(vals),2))*xstep),yNzs[key][i][2],label=key+" "+str(val))
pl.legendOn(labsize=10,loc='upper right')
pl.done(outDir+key+"_Nm_step.png")
pl = Plotter(labelX="$q$",labelY="$dN$",scaleX='log')
xstep = 0.1
TCMB = Config.getfloat('general','TCMB')
gradCut = Config.getint('general','gradCut')
halo = Config.getboolean('general','halo')
fsky = Config.getfloat('general','sqDeg')/41250.
kmin = 40
deg = 10.
px = 0.5
dell = 10
kellrange = np.arange(80.,2100.,10.)
Nlmvinv = 0.
pl = Plotter(scaleY='log')
for polComb in ['TT','TE','EE','EB']:
kmax = getMax(polComb,tellmaxY,pellmaxY)
bin_edges = np.arange(kmin,kmax,dell)+dell
lmap = lm.makeEmptyCEATemplate(raSizeDeg=deg, decSizeDeg=deg,pixScaleXarcmin=px,pixScaleYarcmin=px)
myNls = NlGenerator(lmap,theory,bin_edges,gradCut=gradCut)
myNls.updateBins(bin_edges)
nTX,nPX,nTY,nPY = myNls.updateNoise(beamX,noiseTX,noisePX,tellminX,tellmaxX, \
pellminX,pellmaxX,beamY=beamY,noiseTY=noiseTY, \
noisePY=noisePY,tellminY=tellminY,tellmaxY=tellmaxY, \
pellminY=pellminY,pellmaxY=pellmaxY,lkneesX=(lkneeTX,lkneePX),alphasX=(alphaTX,alphaPX), \
lkneesY=(lkneeTY,lkneePY),alphasY=(alphaTY,alphaPY),lxcutTX=lxcutTX, \
lxcutTY=lxcutTY,lycutTX=lycutTX,lycutTY=lycutTY, \
lxcutPX=lxcutPX,lxcutPY=lxcutPY,lycutPX=lycutPX,lycutPY=lycutPY, \
fgFileX=fgFileX,beamFileX=beamFileX,fgFileY=fgFileY,beamFileY=beamFileY )
oml = 1. - cc.om - cc.ob - cc.omnuh2 / cc.h / cc.h
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,
#beamRange = np.arange(1.5,9.5,0.5)
#beamRange = np.arange(9.5,30.5,2.5)
#beamRange = np.arange(1.5,5.0,0.5)
beamX = 10.0
noiseRange = np.arange(3.0, 30.0, 4.0)
swap = False
#for polComb in ['TT','EB']:
for polComb in ['EB']:
for delens in [False, True]:
if polComb == 'TT' and delens: continue
pl = Plotter(scaleY='log', labelX="$L$", labelY="$C_L$")
sns = []
#for beamX in beamRange:
for noiseTX in noiseRange:
noisePX = np.sqrt(2.) * noiseTX
myNls = NlGenerator(lmap, theory, gradCut=gradCut)
kmax = getMax(polComb, tellmaxY, pellmaxY)
bin_edges = np.arange(kmin, kmax, dell) + dell
myNls.updateBins(bin_edges)
if swap:
tempB = beamY
beamY = beamX
alpha=alpha)
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)
myNls = NlGenerator(lmap, theory, bin_edges, gradCut=gradCut)
from scipy.interpolate import interp1d
from orphics.tools.io import Plotter
ellkk = np.arange(2, 9000, 1)
Clkk = theory.gCl("kk", ellkk)
clfunc = interp1d(ellkk, Clkk, bounds_error=False, fill_value="extrapolate")
kellmax = 8000
cc = ClusterCosmology(cosmoDict, constDict, kellmax, pickling=True)
theory = cc.theory
pl = Plotter(labelX="Beam (arcmin)",
labelY="$\\sigma(M)/M$ for $N=1000$",
ftsize=16)
for miscenter in [False, True]:
for lensName, linestyle in zip(["CMB_all", "CMB_pol"], ["-", "--"]):
for doFg in [False, True]:
if lensName == "CMB_pol" and not (doFg): continue
if lensName == "CMB_all" and not (doFg) and miscenter: continue
sns = []
for beamNow in beamList:
pols = Config.get(lensName, 'polList').split(',')
if doFg:
fgFuncY = fgFunc
