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")
marker="o",
alpha=0.5,
label="auto n0subbed")
io.save_cols(save_dir + "autokk.txt",
(lcents, rstats['mean'], rstats['errmean']))
pl.add(lcents, nlkk, ls="--", label="theory n0")
pl.add(lcents, nstats['mean'], ls="--", label="superdumb n0")
io.save_cols(save_dir + "sdn0.txt",
(lcents, nstats['mean'], nstats['errmean']))
pl.add(ellrange, clkk, color="k")
pl.legendOn(loc="lower left", labsize=9)
pl.done(out_dir + "cpower.png")
io.quickPlot2d(stats.cov2corr(astats['covmean']), out_dir + "corr.png")
io.quickPlot2d(stats.cov2corr(rstats['covmean']), out_dir + "rcorr.png")
pl = io.Plotter()
ldiff = (cstats['mean'] - istats['mean']) / istats['mean']
lerr = cstats['errmean'] / istats['mean']
io.save_cols(save_dir + "rxikk.txt",
(lcents, cstats['mean'], cstats['errmean']))
io.save_cols(save_dir + "ratkk.txt", (lcents, ldiff, lerr))
pl.addErr(lcents, ldiff, yerr=lerr, marker="o", ls="-")
pl._ax.axhline(y=0., ls="--", color="k")
pl._ax.set_ylim(-0.2, 0.1)
pl.done(out_dir + "powerdiff.png")
iltt2d = theory.lCl("TT", parray_dat.modlmap)
ccents, iltt = lbinner_dat.bin(iltt2d)
rxr = mpibox.stats['rxr']
sdn0 = mpibox.stats['superdumbs']
area = S0.area()*(180./np.pi)**2.
print(("area: ", area, " sq.deg."))
fsky = area/41250.
print(("fsky: ",fsky))
diagraw = np.sqrt(np.diagonal(rxr_raw['cov'])*cents*np.diff(lbin_edges)*fsky)
diagn0sub = np.sqrt(np.diagonal(rxr['cov'])*cents*np.diff(lbin_edges)*fsky)
idiag = np.sqrt(np.diagonal(ixr['cov'])*cents*np.diff(lbin_edges)*fsky)
cents,clkk1d = lbinner.bin(clkk2d)
idiag_est = clkk1d+(3.*(idiag**2.-clkk1d**2.)/clkk1d)
io.quickPlot2d(stats.cov2corr(rxr['covmean']),pout_dir+"rcorr.png")
io.quickPlot2d(stats.cov2corr(rxr_raw['covmean']),pout_dir+"corr.png")
# if paper and not(unlensed):
# np.save(pout_dir+"covmat_"+str(area)+"sqdeg.npy",rxr['cov'])
# np.save(pout_dir+"lbin_edges_"+str(area)+"sqdeg.npy",lbin_edges)
# import cPickle as pickle
# pickle.dump((cents,mpibox.stats['noisett']['mean']),open(pout_dir+"noise.pkl",'wb'))
ellrange = np.arange(2,kellmax,1)
clkk = theory.gCl('kk',ellrange)
pl = io.Plotter(scaleY='log',labelX="$L$",labelY="$C^{\\kappa\\kappa}_L$")#,scaleX='log')
pl.add(ellrange,clkk,color="k",lw=3)
io.save_cols(pout_dir+"_plot_clkk_theory.txt",(ellrange,clkk))
pl.addErr(cents-140,ixr['mean'],yerr=ixr['errmean'],ls="none",marker="o",label='Input x Reconstruction')
io.save_cols(pout_dir+"_plot_inputXrecon.txt",(cents,ixr['mean'],ixr['errmean']))
ls="-")
pl.addErr(cents,
sgn * kapparecon_stats['mean'],
yerr=kapparecon_stats['errmean'],
ls="--")
pl.done(out_dir + "kappa1d.png")
if not (random):
filename = "/profiles_simcmb_" + str(
simulated_cmb) + "_simkap_" + str(
simulated_kappa) + "_periodic_" + str(periodic) + ".txt"
np.savetxt(save_dir+filename,np.vstack((cents,kappa_stats['mean'],kappa_stats['errmean'],sgn*kapparecon_stats['mean'], \
kapparecon_stats['errmean'])).transpose(), \
header="# bin centers (arc) , input_kappa, input_kappa_err, recon_kappa, recon_kappa_err")
io.quickPlot2d(stats.cov2corr(kappa_stats['cov']),
out_dir + "kappa_corr.png")
reconstack = mpibox.stacks["recon_kappa2d"]
io.quickPlot2d(reconstack, out_dir + "reconstack.png")
if not (sigurd):
inpstack = mpibox.stacks["input_kappa2d"]
io.quickPlot2d(inpstack, out_dir + "inpstack.png")
inp = enmap.ndmap(
inpstack if abs(pixratio - 1.) < 1.e-3 else resample.resample_fft(
inpstack, shape_dat), wcs_dat)
pdiff = np.nan_to_num((inp - reconstack) * 100. / inp)
io.quickPlot2d(pdiff, out_dir + "pdiffstack.png", lim=20.)
np.save(save_dir + "/reconstack", reconstack)
if not (cluster):
pl = io.Plotter()
pl.add(bin_cents,hist)
pl.done(out_dir+"histogram_first_bin.png")
pl = io.Plotter(scaleX='log',scaleY='log')
pl.addErr(cents,kappa_stats['mean'],yerr=kappa_stats['errmean'],ls="-")
pl.addErr(cents,kapparecon_stats['mean'],yerr=kapparecon_stats['errmean'],ls="--")
pl._ax.set_xlim(0.1,10.)
pl._ax.set_ylim(0.001,0.63)
pl.done(out_dir+"kappa1d.png")
io.quickPlot2d(stats.cov2corr(kappa_stats['cov']),out_dir+"kappa_corr.png")
reconstack = mpibox.stacks["recon_kappa2d"]
io.quickPlot2d(reconstack,out_dir+"reconstack.png")
reconstack = mpibox.stacks["drecon_kappa2d"]
io.quickPlot2d(reconstack,out_dir+"dreconstack.png")
ureconstack = mpibox.stacks["urecon_kappa2d"]
io.quickPlot2d(ureconstack,out_dir+"ureconstack.png")
io.quickPlot2d(ureconstack-reconstack,out_dir+"diff_ud_reconstack.png")
inpstack = mpibox.stacks["input_kappa2d"]
io.quickPlot2d(inpstack,out_dir+"inpstack.png")
inp = enmap.ndmap(inpstack if abs(pixratio-1.)<1.e-3 else resample.resample_fft(inpstack,shape_dat),wcs_dat)
pdiff = np.nan_to_num((inp-reconstack)*100./inp)
io.quickPlot2d(pdiff,out_dir+"pdiffstack.png",lim=20.)
pl.add(lcents,diag,label=str(i))
i+=1
pl.legendOn()
pl.done("test.png")
cov3 = "/gpfs01/astro/www/msyriac/plots/experiment_0.3arc_0.5uk_2000_2.91260385523sqdeg_covmat_dl300.npy"
lbin3 = "/gpfs01/astro/www/msyriac/plots/experiment_0.3arc_0.5uk_2000_2.91260385523sqdeg_lbin_edges_dl300.npy"
fsky3 = 2.91260385523/41250.
cov4 = "/gpfs01/astro/www/msyriac/plots/experiment_0.3arc_0.5uk_2000_2.91260385523sqdeg_autocovmat_dl300.npy"
lbin4 = "/gpfs01/astro/www/msyriac/plots/experiment_0.3arc_0.5uk_2000_2.91260385523sqdeg_lbin_edges_dl300.npy"
fsky4 = 2.91260385523/41250.
import orphics.tools.stats as stats
i = 0
for cov,lbin,fsky in zip([cov3,cov4],[lbin3,lbin4],[fsky3,fsky4]):
covmat = np.load(cov)
lbin_edges = np.load(lbin)
lcents = (lbin_edges[1:]+lbin_edges[:-1])/2.
lmin = lcents[0]
lmax = lcents[-1]
corr = stats.cov2corr(covmat)
io.quickPlot2d(np.rot90(corr),"corr"+str(i)+".pdf",extent=[lmin,lmax,lmin,lmax],ticksize=10,labsize=10,ftsize=10,lim=[0.,1.0])
i+=1