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")
def get_stats(self,verbose=True):
"""
Collect from all MPI cores and calculate statistics for
1d measurements.
"""
import orphics.tools.stats as stats
import numpy as np
if self.rank in self.loopover:
for k,label in enumerate(self.vectors.keys()):
self.comm.send(np.array(self.vectors[label]).shape[0], dest=self.root, tag=self.tag_start*2000+k)
for k,label in enumerate(self.vectors.keys()):
send_dat = np.array(self.vectors[label]).astype(np.float64)
self.comm.Send(send_dat, dest=self.root, tag=self.tag_start+k)
else:
self.stats = {}
self.numobj = {}
for k,label in enumerate(self.vectors.keys()):
self.numobj[label] = []
self.numobj[label].append(np.array(self.vectors[label]).shape[0])
for core in self.loopover: #range(1,self.numcores):
if verbose: print("Waiting for core ", core , " / ", self.numcores)
data = self.comm.recv(source=core, tag=self.tag_start*2000+k)
self.numobj[label].append(data)
for k,label in enumerate(self.vectors.keys()):
self.vectors[label] = np.array(self.vectors[label])
for core in self.loopover: #range(1,self.numcores):
if verbose: print("Waiting for core ", core , " / ", self.numcores)
for k,label in enumerate(self.vectors.keys()):
expected_shape = (self.numobj[label][core],self.vectors[label].shape[1])
data_vessel = np.empty(expected_shape, dtype=np.float64)
self.comm.Recv(data_vessel, source=core, tag=self.tag_start+k)
self.vectors[label] = np.append(self.vectors[label],data_vessel,axis=0)
for k,label in enumerate(self.vectors.keys()):
self.stats[label] = stats.getStats(self.vectors[label])
def plot_stats(cents, cont, ells, ells_pp, theory):
if len(cont['tt']) < 3: return
print("Calculating stats...")
st = {}
for spec in ['tt', 'ee', 'te', 'bb', 'pp']:
st[spec] = stats.getStats(cont[spec])
print("Plotting...")
pl = io.Plotter(scaleY='log', scaleX='log')
for spec in ['tt', 'ee', 'bb']:
pl.add(ells, theory[spec] * ells**2., lw=2)
pl.addErr(cents,
st[spec]['mean'] * cents**2.,
yerr=st[spec]['errmean'] * cents**2.,
ls="none",
marker="o")
pl.done(os.environ['WORK'] + "/web/plots/clsauto.png")
pl = io.Plotter(scaleX='log')
for spec in ['te']:
pl.add(ells, theory[spec] * ells**2., lw=2)
pl.addErr(cents,
st[spec]['mean'] * cents**2.,
yerr=st[spec]['errmean'] * cents**2.,
ls="none",
marker="o")
pl.done(os.environ['WORK'] + "/web/plots/clste.png")
pl = io.Plotter(scaleY='log')
for spec in ['pp']:
pl.add(ells_pp, theory[spec], lw=2)
pl.addErr(cents,
st[spec]['mean'],
yerr=st[spec]['errmean'],
ls="none",
marker="o")
pl._ax.set_xlim(2, 3000)
pl.done(os.environ['WORK'] + "/web/plots/clspp.png")
comm.Recv(rcvInputPowerMat, source=job, tag=i)
listAllCrossPower[polComb] = np.vstack(
(listAllCrossPower[polComb], rcvInputPowerMat))
print "Waiting for ", job, " ", polComb, " auto"
comm.Recv(rcvInputPowerMat, source=job, tag=i + 80)
listAllReconPower[polComb] = np.vstack(
(listAllReconPower[polComb], rcvInputPowerMat))
statsCross = {}
statsRecon = {}
pl = Plotter(scaleY='log')
pl.add(ellkk, Clkk, color='black', lw=2)
for polComb, col in zip(polCombList, colorList):
statsCross[polComb] = getStats(listAllCrossPower[polComb])
pl.addErr(centers,
statsCross[polComb]['mean'],
yerr=statsCross[polComb]['errmean'],
ls="none",
marker="o",
markersize=8,
label="recon x input " + polComb,
color=col,
mew=2,
elinewidth=2)
statsRecon[polComb] = getStats(listAllReconPower[polComb])
fp = interp1d(centers,
statsRecon[polComb]['mean'],
fill_value='extrapolate')
cpower = fmaps.get_simple_power_enmap(enmap1=kappa_recon,
enmap2=downk)
cents_pwr, cclkk = dbinner_dat.bin(cpower)
apowers[polcomb].append(aclkk)
cpowers[polcomb].append(cclkk)
kappa_stack[polcomb] += kappa_recon
if cluster:
profstats = {}
for polcomb in pol_list:
kappa_stack[polcomb] /= Nsims
k = kappa_stack[polcomb]
io.quickPlot2d(k, out_dir + "kappa_recon_" + polcomb + ".png")
profstats[polcomb] = stats.getStats(profiles[polcomb])
pl = io.Plotter(scaleX='log')
cents, inp_profile = binner_sim.bin(kappa_map)
pl.add(cents, inp_profile, ls="--")
inp_kappa = fmaps.filter_map(kappa_map,
kappa_map.copy() * 0. + 1.,
modlmap_sim,
lowPass=kellmax,
highPass=kellmin)
inp_kappa -= inp_kappa.mean()
cents, inp_profile = binner_sim.bin(inp_kappa)
pl.add(cents, inp_profile, ls="-")
down_input = enmap.downgrade(kappa_map,
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)
pl.add(cents_pwr, en0subbed, alpha=0.4, ls="--")
except:
print("skipping")
continue
clkkn0pers.append((sn0subbed - en0subbed) * 100. / en0subbed)
clkkpers.append((saclkk - eaclkk) * 100. / eaclkk)
sdppers.append((ssdp - esdp) * 100. / esdp)
clttpers.append((sdcltt - edcltt) * 100. / edcltt)
pl._ax.set_xlim(0, 3000)
pl._ax.set_ylim(1.e-9, 1.e-6)
pl.done("clkk.png")
clkkn0stats = stats.getStats(clkkn0pers)
clkkstats = stats.getStats(clkkpers)
sdpstats = stats.getStats(sdppers)
clttstats = stats.getStats(clttpers)
pl = io.Plotter(labelX="$L$", labelY="% diff")
pl.addErr(cents_pwr,
clkkn0stats['mean'],
yerr=clkkn0stats['errmean'],
label="sdn0subbed clkk",
ls="-")
pl.addErr(cents_pwr,
clkkstats['mean'],
yerr=clkkstats['errmean'],
label="raw clkk",
alpha=0.2,
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
super_dumbs.append(sdp)
n0sub = kpower - sd
cents_pwr, n0subbed = dbinner_dat.bin(n0sub)
n0subs.append(n0subbed)
clkk = parray_dat.clkk
astats = {}
cstats = {}
for polcomb in pol_list:
kappa_stack[polcomb] /= Nsims
k = kappa_stack[polcomb]
io.quickPlot2d(k, out_dir + "kappa_recon_" + polcomb + ".png")
astats[polcomb] = stats.getStats(apowers[polcomb])
cstats[polcomb] = stats.getStats(cpowers[polcomb])
n0stats = stats.getStats(n0subs)
pl = io.Plotter(scaleY='log')
pl.add(fine_ells, clkk, alpha=0.2)
vals = []
for j, polcomb in enumerate(pol_list):
vals.append((cstats[polcomb]['mean'] + cstats[polcomb]['errmean']).ravel())
vals.append((cstats[polcomb]['mean'] - cstats[polcomb]['errmean']).ravel())
pl.addErr(cents_pwr,
cstats[polcomb]['mean'],
yerr=cstats[polcomb]['errmean'],
label=polcomb,
ls="none",
