def stack_on_map(lite_map,
width_stamp_arcminute,
pix_scale,
ra_range,
dec_range,
catalog=None,
n_random_points=None):
width_stamp_degrees = width_stamp_arcminute / 60.
Np = np.int(width_stamp_arcminute / pix_scale + 0.5)
pad = np.int(Np / 2 + 0.5)
print("Expected width in pixels = ", Np)
lmap = lite_map
stack = 0
N = 0
if catalog is not None:
looprange = range(0, len(catalog))
assert n_random_points is None
random = False
else:
assert n_random_points is not None
assert len(ra_range) == 2
assert len(dec_range) == 2
looprange = range(0, n_random_points)
random = True
for i in looprange:
if random:
ra = np.random.uniform(*ra_range)
dec = np.random.uniform(*dec_range)
else:
ra = catalog[i][1]
dec = catalog[i][2]
ix, iy = lmap.skyToPix(ra, dec)
if ix >= pad and ix < lmap.Nx - pad and iy >= pad and iy < lmap.Ny - pad:
print(i)
#print(ra,dec)
smap = lmap.selectSubMap(ra - width_stamp_degrees / 2.,
ra + width_stamp_degrees / 2.,
dec - width_stamp_degrees / 2.,
dec + width_stamp_degrees / 2.)
#print (smap.data.shape)
#cutout = zoom(smap.data.copy(),zoom=(float(Np)/smap.data.shape[0],float(Np)/smap.data.shape[1]))
cutout = resize(smap.data.copy(), output_shape=(Np, Np))
#print (cutout.shape)
stack = stack + cutout
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(smap)
N = N + 1.
else:
print("skip")
stack = stack / N
#print(stack.shape())
#print(smap.data.shape)
print(stack)
print(N)
io.quickPlot2d(stack, out_dir + "stackrandom.png")
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breal = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
cents, recons = breal.bin(stack)
pl = Plotter(labelX='Distance from Center (arcminutes)',
labelY='Temperature Fluctuation ($\mu K$)',
ftsize=10)
pl.add(cents, recons)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(out_dir + "randomprofiles.png")
return stack, cents, recons
fieldCoords = (raLeft, decLeft, raRight, decRight)
smap = lm.makeEmptyCEATemplateAdvanced(*fieldCoords,
pixScaleXarcmin=pixScale,
pixScaleYarcmin=pixScale)
smap.loadDataFromHealpixMap(hpPlanck, hpCoords="GALACTIC")
stamp = smap.data.copy() / tfact
stamp = zoom(stamp,
zoom=(float(Np) / stamp.shape[0], float(Np) / stamp.shape[1]))
#print ra, dec, stamp.shape
if i == 1:
lxMap, lyMap, modLMap, angMap, lx, ly = fmaps.getFTAttributesFromLiteMap(
smap)
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(smap)
polCombList = ["TT"]
Ny, Nx = stamp.shape
win = fmaps.initializeCosineWindowData(Ny, Nx, lenApod=40, pad=5)
#win = 1.
w2 = np.mean(win**2.)
fwhm = 5.0
tht_fwhm = np.deg2rad(fwhm / 60.)
l = np.arange(0., 10000.)
beamells = np.exp(-(tht_fwhm**2.) * (l**2.) / (8. * np.log(2.)))
beamTemplate = fmaps.makeTemplate(l, beamells, modLMap)
#print beamTemplate.shape
noiseT = 42.0
from enlib.resample import resample_fft, resample_bin
arcX = 20 * 60.
arcY = 10 * 60.
arc = 10. * 60.
px = 0.5
pxDn = 7.5
fineTemplate = lm.makeEmptyCEATemplate(arcX / 60.,
arcY / 60.,
pixScaleXarcmin=px,
pixScaleYarcmin=px)
lxMap, lyMap, modLMap, thetaMap, lx, ly = fmaps.getFTAttributesFromLiteMap(
fineTemplate)
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(fineTemplate)
# sigArc = 3.0
# sig = sigArc*np.pi/180./60.
# fineTemplate.data = np.exp(-modRMap**2./2./sig**2.)
import btip.inpaintStamp as inp
ell, Cl = np.loadtxt("../btip/data/cltt_lensed_Feb18.txt", unpack=True)
ell, Cl = inp.total_1d_power(ell,
Cl,
ellmax=modLMap.max(),
beamArcmin=1.4,
noiseMukArcmin=12.0,
TCMB=2.7255e6)
twoDPower = inp.spec1dTo2d(fineTemplate, ell, Cl) #,k=1) # !!!!!
fineTemplate.data = twoDPower
def stack_on_map(lite_map,
width_stamp_arcminute,
pix_scale,
ra_range,
dec_range,
catalog=None,
n_random_points=None):
width_stamp_degrees = width_stamp_arcminute / 60.
Np = np.int(width_stamp_arcminute / pix_scale + 0.5)
pad = np.int(Np / 2 + 0.5)
print("Expected width in pixels = ", Np)
lmap = lite_map
stack = 0
N = 0
if catalog is not None:
looprange = range(0, len(catalog))
assert n_random_points is None
random = False
else:
assert n_random_points is not None
assert len(ra_range) == 2
assert len(dec_range) == 2
looprange = range(0, n_random_points)
random = True
print(looprange)
for i in looprange:
banana = True
mass = catalog[i][10]
if random:
ra = np.random.uniform(*ra_range)
dec = np.random.uniform(*dec_range)
if random == False:
ra = catalog[i][1] #1 for ACT catalog 2 for SDSS
dec = catalog[i][2] #2 for ACT catalog 3 for SDSS
for j in range(0, 2130):
distance = np.sqrt((ra - RAps[j])**2 + (dec - DECps[j])**2)
crit = 0.25
if distance < crit:
banana = False
print('too close')
ix, iy = lmap.skyToPix(ra, dec)
if ix >= pad and ix < lmap.Nx - pad and iy >= pad and iy < lmap.Ny - pad and banana == True and mass > 8:
print(i)
#print(ra,dec)
smap = lmap.selectSubMap(ra - width_stamp_degrees / 2.,
ra + width_stamp_degrees / 2.,
dec - width_stamp_degrees / 2.,
dec + width_stamp_degrees / 2.)
#print (smap.data.shape)
#cutout = zoom(smap.data.copy(),zoom=(float(Np)/smap.data.shape[0],float(Np)/smap.data.shape[1])
cutout = resize(smap.data.copy(),
output_shape=(Np, Np)) - randomstack
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(smap)
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breali = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
a = breali.bin(cutout)[1]
profiles.append(a)
io.quickPlot2d(cutout, str(i) + "cutout.png")
#print (cutout.shape)
stack = stack + cutout
N = N + 1
else:
print("skip")
stack = stack / N #-randomstack
#print(stack.shape())
#print(smap.data.shape)
# print(stack)
print(N)
statistics = stats.getStats(profiles)
mean = statistics['mean']
error = statistics['errmean']
corrcoef = statistics['corr']
covmat = statistics['covmat']
print(mean / error)
np.save('statistics', statistics)
#np.save('newrandomstamp',stack)
# io.quickPlot2d(stack,out_dir+"newACTstack.png")
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breal = bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
cents, recons = breal.bin(stack)
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")
print(covmat)
io.quickPlot2d(covmat, 'covmat.png')
return (stack, cents, recons)
def NFWMatchedFilterSN(clusterCosmology,log10Moverh,c,z,ells,Nls,kellmax,overdensity=500.,critical=True,atClusterZ=True,arcStamp=100.,pxStamp=0.05,saveId=None,verbose=False,rayleighSigmaArcmin=None,returnKappa=False,winAtLens=None):
if rayleighSigmaArcmin is not None: assert rayleighSigmaArcmin>=pxStamp
M = 10.**log10Moverh
lmap = lm.makeEmptyCEATemplate(raSizeDeg=arcStamp/60., decSizeDeg=arcStamp/60.,pixScaleXarcmin=pxStamp,pixScaleYarcmin=pxStamp)
kellmin = 2.*np.pi/arcStamp*np.pi/60./180.
xMap,yMap,modRMap,xx,yy = fmaps.getRealAttributes(lmap)
lxMap,lyMap,modLMap,thetaMap,lx,ly = fmaps.getFTAttributesFromLiteMap(lmap)
cc = clusterCosmology
cmb = False
if winAtLens is None:
cmb = True
comS = cc.results.comoving_radial_distance(cc.cmbZ)*cc.h
comL = cc.results.comoving_radial_distance(z)*cc.h
winAtLens = (comS-comL)/comS
kappaReal, r500 = NFWkappa(cc,M,c,z,modRMap*180.*60./np.pi,winAtLens,overdensity=overdensity,critical=critical,atClusterZ=atClusterZ)
dAz = cc.results.angular_diameter_distance(z) * cc.h
th500 = r500/dAz
#fiveth500 = 10.*np.pi/180./60. #5.*th500
fiveth500 = 5.*th500
# print "5theta500 " , fiveth500*180.*60./np.pi , " arcminutes"
# print "maximum theta " , modRMap.max()*180.*60./np.pi, " arcminutes"
kInt = kappaReal.copy()
kInt[modRMap>fiveth500] = 0.
# print "mean kappa inside theta500 " , kInt[modRMap<fiveth500].mean()
# print "area of th500 disc " , np.pi*fiveth500**2.*(180.*60./np.pi)**2.
# print "estimated integral " , kInt[modRMap<fiveth500].mean()*np.pi*fiveth500**2.
k500 = simps(simps(kInt, yy), xx)
if verbose: print "integral of kappa inside disc ",k500
kappaReal[modRMap>fiveth500] = 0. #### !!!!!!!!! Might not be necessary!
# if cmb: print z,fiveth500*180.*60./np.pi
Ukappa = kappaReal/k500
# pl = Plotter()
# pl.plot2d(Ukappa)
# pl.done("output/kappa.png")
ellmax = kellmax
ellmin = kellmin
Uft = fftfast.fft(Ukappa,axes=[-2,-1])
if rayleighSigmaArcmin is not None:
Prayleigh = rayleigh(modRMap*180.*60./np.pi,rayleighSigmaArcmin)
outDir = "/gpfs01/astro/www/msyriac/plots/"
# io.quickPlot2d(Prayleigh,outDir+"rayleigh.png")
rayK = fftfast.fft(ifftshift(Prayleigh),axes=[-2,-1])
rayK /= rayK[modLMap<1.e-3]
Uft = Uft.copy()*rayK
Upower = np.real(Uft*Uft.conjugate())
# pl = Plotter()
# pl.plot2d(fftshift(Upower))
# pl.done("output/upower.png")
Nls[Nls<0.]=0.
s = splrep(ells,Nls,k=3)
Nl2d = splev(modLMap,s)
Nl2d[modLMap<ellmin]=np.inf
Nl2d[modLMap>ellmax] = np.inf
area = lmap.Nx*lmap.Ny*lmap.pixScaleX*lmap.pixScaleY
Upower = Upower *area / (lmap.Nx*lmap.Ny)**2
filter = np.nan_to_num(Upower/Nl2d)
#filter = np.nan_to_num(1./Nl2d)
filter[modLMap>ellmax] = 0.
filter[modLMap<ellmin] = 0.
# pl = Plotter()
# pl.plot2d(fftshift(filter))
# pl.done("output/filter.png")
# if (cmb): print Upower.sum()
# if not(cmb) and z>2.5:
# bin_edges = np.arange(500,ellmax,100)
# binner = bin2D(modLMap, bin_edges)
# centers, nl2dells = binner.bin(Nl2d)
# centers, upowerells = binner.bin(np.nan_to_num(Upower))
# centers, filterells = binner.bin(filter)
# from orphics.tools.io import Plotter
# pl = Plotter(scaleY='log')
# pl.add(centers,upowerells,label="upower")
# pl.add(centers,nl2dells,label="noise")
# pl.add(centers,filterells,label="filter")
# pl.add(ells,Nls,ls="--")
# pl.legendOn(loc='upper right')
# #pl._ax.set_ylim(0,1e-8)
# pl.done("output/filterells.png")
# sys.exit()
varinv = filter.sum()
std = np.sqrt(1./varinv)
sn = k500/std
if verbose: print sn
if saveId is not None:
np.savetxt("data/"+saveId+"_m"+str(log10Moverh)+"_z"+str(z)+".txt",np.array([log10Moverh,z,1./sn]))
if returnKappa:
return sn,fftfast.ifft(Uft,axes=[-2,-1],normalize=True).real*k500
return sn, k500, std
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
def stack_on_map(lite_map,
width_stamp_arcminute,
pix_scale,
ra_range,
dec_range,
catalog=None,
n_random_points=None):
from skimage.transform import resize
import orphics.tools.stats as stats
width_stamp_degrees = width_stamp_arcminute / 60.
Np = np.int(width_stamp_arcminute / pix_scale + 0.5)
pad = np.int(Np / 2 + 0.5)
print("Expected width in pixels = ", Np)
lmap = lite_map
stack = 0
N = 0
if catalog is not None:
looprange = goodobjects
print(looprange)
assert n_random_points is None
random = False
else:
assert n_random_points is not None
assert len(ra_range) == 2
assert len(dec_range) == 2
looprange = range(0, n_random_points)
random = True
for i in looprange:
if random:
ra = np.random.uniform(*ra_range)
dec = np.random.uniform(*dec_range)
if random == False:
ra = catalog[i][1] #1 for ACT catalog 2 for SDSS and redmapper
dec = catalog[i][2] #2 for ACT catalog 3 for SDSS and redmapper
ix, iy = lmap.skyToPix(ra, dec)
if ix >= pad and ix < lmap.Nx - pad and iy >= pad and iy < lmap.Ny - pad:
print(i)
smap = lmap.selectSubMap(ra - width_stamp_degrees / 2.,
ra + width_stamp_degrees / 2.,
dec - width_stamp_degrees / 2.,
dec + width_stamp_degrees / 2.)
#cutout = zoom(smap.data.copy(),zoom=(float(Np)/smap.data.shape[0],float(Np)/smap.data.shape[1]))
cutout = resize(smap.data.copy(), output_shape=(Np, Np))
cutouts.append(cutout - randomstack)
stack = stack + cutout
xMap, yMap, modRMap, xx, yy = fmaps.getRealAttributes(smap)
N = N + 1.
ixs.append(ix)
iys.append(iy)
modRMaps.append(modRMap)
else:
print("skip")
stack = stack / N - randomstack
print(N)
if catalog is not None:
io.quickPlot2d(stack, out_dir + "stack.png")
else:
np.save('randomstamp', stack)
dt = pix_scale
arcmax = 20.
thetaRange = np.arange(0., arcmax, dt)
breal = stats.bin2D(modRMap * 180. * 60. / np.pi, thetaRange)
cents, recons = breal.bin(stack)
pl = Plotter(labelX='Distance from Center (arcminutes)',
labelY='Temperature Fluctuation ($\mu K$)',
ftsize=10)
pl.add(cents, recons)
pl._ax.axhline(y=0., ls="--", alpha=0.5)
pl.done(out_dir + "profiles.png")
return stack, cents, recons