a_gal=0.8,
a_cibp=1,
a_cibc=1,
a_radps=rads[i] * rads[j],
a_ksz=1,
a_tsz=0)
pl.add(ells, t1d, color=f"C{c}", lw=1, ls='--')
tfgres[i, j] = t1d.copy()[:lmax]
tfgres[j, i] = t1d.copy()[:lmax]
c += 1
pl._ax.set_xlim(2, 5800)
#pl._ax.set_xlim(2,300) # !!!
pl.done("debugfits.png")
corr = cov2corr(cfgres)
tcorr = cov2corr(tfgres)
corr[~np.isfinite(corr)] = 0
tcorr[~np.isfinite(tcorr)] = 0
assert np.all(tcorr <= 1)
hmat = correlated_hybrid_matrix(cfgres, theory_corr=tcorr)
hmat[~np.isfinite(hmat)] = 0
hcorr = cov2corr(hmat)
hcorr[~np.isfinite(hcorr)] = 0
print(hcorr.max())
assert np.all(hcorr <= (1 + 1e-5))
sample_ells = [500, 1000]
for sell in sample_ells:
cmat = cfgres
s.add_to_stats("cbb", cl22_coupled[3] / w2)
# Naive PS calculation
p2d, _, _ = fc.power2d(imaps, imaps)
mtt = binit(p2d[0, 0])
mte = binit(p2d[0, 1])
mee = binit(p2d[1, 1])
s.add_to_stats("mtt", mtt)
s.add_to_stats("mee", mee)
s.add_to_stats("mte", mte)
s.get_stats()
# PLOT STUFF
io.plot_img(stats.cov2corr(s.stats["uke"]['cov']),
io.dout_dir + "cov.png",
flip=False)
io.plot_img(stats.cov2corr(s.stats["cke"]['cov']),
io.dout_dir + "ccov.png",
flip=False)
def gstats(key):
return s.stats[key]['mean'], s.stats[key]['errmean']
pl = io.Plotter(yscale='log', xlabel="$L$", ylabel="$C_L$")
pl.add(ellrange, clkk, color='r', label='Input KK')
pl.add(ellrange, clkg, color='g', label='Input KG')
pl.add(ellrange, clgg, color='b', label='Input GG')
marker="o",
alpha=0.5,
label="auto n0subbed")
pl.add(lcents, nlkk, ls="--", label="theory n0")
pl.add(lcents, nstats['mean'], ls="--", label="superdumb n0")
pl.add(lcents,
nstats['mean'] + clkk1d,
ls="--",
label="superdumb n0 + clkk")
pl.add(ellrange, clkk, color="k")
pl.legendOn(loc="lower left", labsize=9)
pl._ax.set_xlim(30, 1e5)
pl._ax.set_ylim(1e-12, 1e-5)
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")
np.save(out_dir + str(area) + "sqdeg_covmat_dl300.npy", rstats['cov'])
np.save(out_dir + str(area) + "sqdeg_autocovmat_dl300.npy", astats['cov'])
np.save(out_dir + str(area) + "sqdeg_lbin_edges_dl300.npy", lbin_edges)
import pickle as pickle
pickle.dump((lcents, mpibox.stats['noisett']['mean']),
open(out_dir + "noise_mpismall.pkl", 'wb'))
pl = io.Plotter()
ldiff = (cstats['mean'] - istats['mean']) * 100. / istats['mean']
lerr = cstats['errmean'] * 100. / istats['mean']
pl.addErr(lcents, ldiff, yerr=lerr, marker="o", ls="-")
pl._ax.axhline(y=0., ls="--", color="k")
pl.done(out_dir + "powerdiff.png")
c = c + 1
pl._ax.scatter(c, bcov[i, j], color=f"C{seed}", marker="o")
vals[seed].append(bcov[i, j])
gvals[seed].append(gcov[i, j])
if i == j:
print(qid1, gcov[i, j])
if qid1 == 'p03': gcov[i, j] = bcov[i, j] = 0
if gcov[i, j] == 0.:
gcov[i, j] = 700000
if bcov[i, j] == 0.: bcov[i, j] = 700000
print(np.diagonal(bcov))
print(bcov)
np.savetxt(f"bcov_{seed}.txt", bcov, delimiter=',')
np.savetxt(f"gcov_{seed}.txt", gcov, delimiter=',')
gcorr = stats.cov2corr(gcov)
bcorr = stats.cov2corr(bcov)
print(gcorr.min(), gcorr.max())
print(bcorr.min(), bcorr.max())
io.plot_img(gcorr, f"det_gcov_{seed}.png", flip=False, lim=[0.5, 1])
io.plot_img(bcorr, f"det_bcov_{seed}.png", flip=False, lim=[0.5, 1])
print(seed)
gi = np.linalg.inv(gcov)
bi = np.linalg.inv(bcov)
bi2 = utils.eigpow(bcov, -1)
print(np.linalg.eigh(gcov)[0])
print(np.linalg.eigh(bcov)[0])
pl.done("detscatter.png")
pl = io.Plotter(xyscale='linlin', xlabel='a', ylabel='r')
aperrs = np.array(aperrs)
apmeans = np.array(apmeans)
bfit, bcov, chisquare, pte = stats.fit_linear_model(
x,
apmeans,
ycov=np.diag(aperrs**2.),
funcs=[lambda x: yflux(x, 1.), lambda x: dflux(x, 1.)])
Yfit, Dfit = bfit
eYfit, eDfit = np.sqrt(np.diagonal(bcov))
#print(dT,Y,D)
print(Yfit, Dfit)
print(Yfit / eYfit, Dfit / eDfit)
print(chisquare, pte)
print(bcov)
bcor = stats.cov2corr(bcov)
print(bcor)
# print(freqs)
# print(Yfit[0])
# print(Dfit[0])
# print(dTfit[0])
# print(Sflux(freqs,Yfit[0],Dfit[0],dTfit[0]))
# print(np.abs(Sflux(freqs,Yfit[0],Dfit[0],dTfit[0])))
pl = io.Plotter(xlabel='$\\nu$ (GHz)',
ylabel='Flux ($K-\mathrm{arcmin}^2$)',
yscale='log')
pl.add(freqs, np.abs(yflux(freqs, Yfit[0])), label='Y')
pl.add(freqs, dflux(freqs, Dfit[0]), label="D")
pl.add(freqs, np.abs(Sflux(freqs, Yfit[0], Dfit[0])), label='Fit')
pl.add_err(x, np.abs(apmeans), yerr=aperrs, marker="o", ls="none")