px = 0.5
dell = 10
bin_edges = np.arange(kmin, kmax, dell) + dell
theory = loadTheorySpectraFromCAMB(cambRoot,
unlensedEqualsLensed=False,
useTotal=False,
lpad=9000)
lmap = lm.makeEmptyCEATemplate(raSizeDeg=deg,
decSizeDeg=deg,
pixScaleXarcmin=px,
pixScaleYarcmin=px)
myNls = NlGenerator(lmap, theory, bin_edges, gradCut=gradCut)
ellkk = np.arange(2, 9000, 1)
Clkk = theory.gCl("kk", ellkk)
pl = Plotter(scaleY='log', scaleX='log')
sns = []
snsM = []
overdensity = 500.
critical = True
atClusterZ = True
kellmax = 8000
outDir = "/gpfs01/astro/www/msyriac/plots/"
for beam in beamrange:
beamX = beam
beamY = beam
print beam
doCurl=False,
TOnly=True,
halo=True,
gradCut=10000,
verbose=True)
# CHECK THAT NORM MATCHES HU/OK
data2d = qest.AL['TT']
modLMap = qest.N.modLMap
bin_edges = np.arange(2, kellmax, 10)
centers, Nlbinned = binInAnnuli(data2d, modLMap, bin_edges)
huFile = '/astro/u/msyriac/repos/cmb-lensing-projections/data/NoiseCurvesKK/hu_tt.csv'
huell, hunl = np.loadtxt(huFile, unpack=True, delimiter=',')
pl = Plotter(scaleY='log', scaleX='log')
pl.add(ellkk, 4. * Clkk / 2. / np.pi)
pl.add(centers, 4. * Nlbinned / 2. / np.pi) #,ls="none",marker="o")
pl.add(huell, hunl, ls='--') #,ls="none",marker="o")
pl.done("testbin.png")
passMap = bigMap[:, :] * window[:, :]
passMap = passMap - passMap.mean()
if k == 0:
pl = Plotter()
pl.plot2d(passMap)
pl.done("bigmap.png")
print "Reconstructing", i, " ..."
qest.updateTEB_X(passMap.astype(float) / 2.7255e6)
def main(argv):
# Read some parameters from the ini file
config = ConfigParser.SafeConfigParser()
config.read("input/general.ini")
fileRoot = config.get('sims', 'root_location') + config.get(
'sims', 'sim_root')
savePath = config.get('sims', 'root_location') + "output/"
istart = config.getint('sims', 'istart')
iend = config.getint('sims', 'iend')
widthArcmin = config.getfloat('cutouts', 'widthArcmin')
pixScaleArcmin = config.getfloat('sims', 'pixScaleArcmin')
npixx = int(widthArcmin / pixScaleArcmin / 2.)
npixy = int(widthArcmin / pixScaleArcmin / 2.)
stackTot = 0. # stack on lensed cmb + sz
stackSZ = 0. # stack on sz
stackDx = 0. # stack on deflection_x
stackDy = 0. # stack on deflection_y
stackKappa = 0. # stack on kappa = div.Deflection / 2
stackAlt = 0. # stack on kappa = div.Deflection / 2 calculated with FFTs
stackEst = 0. # stack on kappa reconstruction
totnum = 0 # total number of stamps read
skip = 0 # number skipped because stamp falls outside edges
for i in range(istart, iend + 1):
froot = fileRoot + str(i).zfill(3)
# read catalog of clusters and make a selection on mass
catFile = froot + ".txt"
xcens, ycens, m200s = np.loadtxt(catFile,
unpack=True,
usecols=[1, 2, 6])
selection = m200s > 1.e14
# open cmb map
imgFile = froot + "_tSZ_MOD.fits"
print "Loading fits file ", i, "with ", len(
xcens[selection]), " clusters ..."
hd = pyfits.open(imgFile)
# open kappa reconstruction and apply a dumb correction to the pixel scale
lmap = lm.liteMapFromFits(savePath + "kappa" + str(i).zfill(3) +
".fits")
px = np.abs(lmap.x1-lmap.x0)/lmap.Nx*np.pi/180.\
*np.cos(np.pi/180.*0.5*(lmap.y0+lmap.y1))*180./np.pi*60.
lmap.pixScaleX = px / 180. * np.pi / 60.
print "pixel scale arcminutes X , ", lmap.pixScaleX * 180. * 60. / np.pi
print "pixel scale arcminutes X , ", lmap.pixScaleY * 180. * 60. / np.pi
tot = hd[0].data
sz = hd[6].data + hd[7].data
unl = hd[3].data
dx = hd[8].data * np.pi / 180. / 60.
dy = hd[9].data * np.pi / 180. / 60.
pl = Plotter()
pl.plot2d(dx)
pl.done("dx.png")
pl = Plotter()
pl.plot2d(dy)
pl.done("dy.png")
kappa = 0.5 * (np.gradient(dx, axis=0) + np.gradient(dy, axis=1))
pl = Plotter()
pl.plot2d(kappa)
pl.done("rkappa.png")
diffmap = tot - sz - unl
#alt kappa
import orphics.analysis.flatMaps as fmaps
lxMap, lyMap, modLMap, thetaMap, lx, ly = fmaps.getFTAttributesFromLiteMap(
lmap)
win = fmaps.initializeCosineWindow(lmap, lenApod=100, pad=10)
Ny = lmap.Ny
kgradx = lx * fft2(dy * win) * 1j
kgrady = ly.reshape((Ny, 1)) * fft2(dx * win) * 1j
altkappa = 0.5 * ifft2(kgradx + kgrady).real
saveMap = lmap.copy()
saveMap.data = altkappa
saveMap.writeFits(savePath + "inputKappa" + str(i).zfill(3) + ".fits",
overWrite=True)
pl = Plotter()
pl.plot2d(altkappa)
pl.done("fkappa.png")
if i == 0:
pl = Plotter()
pl.plot2d(diffmap * win)
pl.done("diff.png")
#sys.exit()
bigY, bigX = tot.shape
print "Map area ", bigX * bigY * pixScaleArcmin * pixScaleArcmin / 60. / 60., " sq. deg."
doRandom = False
if doRandom:
xlbound = xcens[selection].min()
xrbound = xcens[selection].max()
ylbound = ycens[selection].min()
yrbound = ycens[selection].max()
for xcen, ycen, m200 in zip(xcens[selection], ycens[selection],
m200s[selection]):
totnum += 1
if doRandom:
xcen = np.random.randint(xlbound, xrbound)
ycen = np.random.randint(ylbound, yrbound)
else:
xcen = int(xcen)
ycen = int(ycen)
if ycen - npixy < 0 or xcen - npixx < 0 or ycen + npixy > (
bigY - 1) or xcen + npixx > (bigX - 1):
skip += 1
continue
cutOutTot = tot[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutSZ = sz[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutDx = dx[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutDy = dy[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutKappa = kappa[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutAlt = altkappa[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
cutOutEst = lmap.data[(ycen - npixy):(ycen + npixy),
(xcen - npixx):(xcen + npixx)]
stackTot += cutOutTot
stackSZ += cutOutSZ
stackDx += cutOutDx
stackDy += cutOutDy
stackKappa += cutOutKappa
stackAlt += cutOutAlt
stackEst += cutOutEst
print "Percentage near bounds = ", skip * 100. / totnum, " %"
if doRandom:
rs = "R"
else:
rs = ""
N = totnum - skip
stackD = np.sqrt(stackDx**2. + stackDy**2.)
pl = Plotter()
pl.plot2d(stackD / N)
pl.done("plots/stackD" + rs + ".png")
pl = Plotter()
pl.plot2d(stackKappa / N)
pl.done("plots/stackK" + rs + ".png")
pl = Plotter()
pl.plot2d(stackAlt / N)
pl.done("plots/stackA" + rs + ".png")
pl = Plotter()
pl.plot2d(stackTot / N)
pl.done("plots/stackTot" + rs + ".png")
pl = Plotter()
pl.plot2d(stackSZ / N)
pl.done("plots/stackSZ" + rs + ".png")
pl = Plotter()
pl.plot2d(stackEst / N)
pl.done("plots/stackE" + rs + ".png")
tellminX,
tellmaxX,
pellminX,
pellmaxX,
beamY=beamY,
noiseTY=noiseTY,
noisePY=noisePY,
tellminY=tellminY,
tellmaxY=tellmaxY,
pellminY=pellminY,
pellmaxY=pellmaxY)
ls, Nls = myNls.getNl(polComb=polComb, halo=halo)
ellkk = np.arange(2, 9000, 1)
Clkk = theory.gCl("kk", ellkk)
pl = Plotter(scaleY='log', scaleX='log')
pl.add(ellkk, 4. * Clkk / 2. / np.pi)
pl.add(ls, 4. * Nls / 2. / np.pi, label="act,act")
beamX = 7.0
noiseTX = 30.
noisePX = np.sqrt(2.) * noiseTX
tellminX = 2
tellmaxX = 3000
myNls.updateNoise(beamX,
noiseTX,
noisePX,
tellminX,
tellmaxX,
pellminX,
def getDLnMCMB(ells,Nls,clusterCosmology,log10Moverh,z,concentration,arcStamp,pxStamp,arc_upto,bin_width,expectedSN,Nclusters=1000,numSims=30,saveId=None,numPoints=1000,nsigma=8.,overdensity=500.,critical=True,atClusterZ=True):
import flipper.liteMap as lm
if saveId is not None: from orphics.tools.output import Plotter
M = 10.**log10Moverh
cc = clusterCosmology
stepfilter_ellmax = max(ells)
lmap = lm.makeEmptyCEATemplate(raSizeDeg=arcStamp/60., decSizeDeg=arcStamp/60.,pixScaleXarcmin=pxStamp,pixScaleYarcmin=pxStamp)
xMap,yMap,modRMap,xx,xy = fmaps.getRealAttributes(lmap)
lxMap,lyMap,modLMap,thetaMap,lx,ly = fmaps.getFTAttributesFromLiteMap(lmap)
kappaMap,retR500 = NFWkappa(cc,M,concentration,z,modRMap*180.*60./np.pi,winAtLens,overdensity,critical,atClusterZ)
finetheta = np.arange(0.01,arc_upto,0.01)
finekappa,retR500 = NFWkappa(cc,M,concentration,z,finetheta,winAtLens,overdensity,critical,atClusterZ)
kappaMap = fmaps.stepFunctionFilterLiteMap(kappaMap,modLMap,stepfilter_ellmax)
generator = fmaps.GRFGen(lmap,ells,Nls)
bin_edges = np.arange(0.,arc_upto,bin_width)
binner = bin2D(modRMap*180.*60./np.pi, bin_edges)
centers, thprof = binner.bin(kappaMap)
if saveId is not None:
pl = Plotter()
pl.plot2d(kappaMap)
pl.done("output/"+saveId+"kappa.png")
expectedSNGauss = expectedSN*np.sqrt(numSims)
sigma = 1./expectedSNGauss
amplitudeRange = np.linspace(1.-nsigma*sigma,1.+nsigma*sigma,numPoints)
lnLikes = 0.
bigStamp = 0.
for i in range(numSims):
profiles,totstamp = getProfiles(generator,stepfilter_ellmax,kappaMap,binner,Nclusters)
bigStamp += totstamp
stats = getStats(profiles)
if i==0 and (saveId is not None):
pl = Plotter()
pl.add(centers,thprof,lw=2,color='black')
pl.add(finetheta,finekappa,lw=2,color='black',ls="--")
pl.addErr(centers,stats['mean'],yerr=stats['errmean'],lw=2)
pl._ax.set_ylim(-0.01,0.3)
pl.done("output/"+saveId+"profile.png")
pl = Plotter()
pl.plot2d(totstamp)
pl.done("output/"+saveId+"totstamp.png")
Likes = getAmplitudeLikelihood(stats['mean'],stats['covmean'],amplitudeRange,thprof)
lnLikes += np.log(Likes)
width = amplitudeRange[1]-amplitudeRange[0]
Likes = np.exp(lnLikes)
Likes = Likes / (Likes.sum()*width) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,Likes,p0=[1.0,0.5])[0]
sn = ampBest/ampErr/np.sqrt(numSims)
snAll = ampBest/ampErr
if snAll<5.: print "WARNING: ", saveId, " run with mass ", M , " and redshift ", z , " has overall S/N<5. \
Consider re-running with a greater numSims, otherwise estimate of per Ncluster S/N will be noisy."
if saveId is not None:
Fit = np.array([np.exp(-0.5*(x-ampBest)**2./ampErr**2.) for x in amplitudeRange])
Fit = Fit / (Fit.sum()*width) #normalize
pl = Plotter()
pl.add(amplitudeRange,Likes,label="like")
pl.add(amplitudeRange,Fit,label="fit")
pl.legendOn(loc = 'lower left')
pl.done("output/"+saveId+"like.png")
pl = Plotter()
pl.plot2d(bigStamp/numSims)
pl.done("output/"+saveId+"bigstamp.png")
np.savetxt("data/"+saveId+"_m"+str(log10Moverh)+"_z"+str(z)+".txt",np.array([log10Moverh,z,1./sn]))
return 1./sn
import ConfigParser
config = ConfigParser.SafeConfigParser()
config.read("input/general.ini")
savePath = config.get('sims', 'root_location') + "output/"
N = 38
for i in range(1, N + 1):
print i
lmap = lm.liteMapFromFits(savePath + "kappa" + str(i).zfill(3) + ".fits")
if i == 1:
mf = lmap.data * 0.
mf += lmap.data
mf = mf / (N - 1)
lmap = lm.liteMapFromFits(savePath + "kappa000.fits")
from orphics.tools.output import Plotter
pl = Plotter()
pl.plot2d(mf)
pl.done("mf.png")
pl = Plotter()
pl.plot2d(lmap.data - mf)
pl.done("mfsub.png")