array,
coadd=coadd,
mask_patch=mask_patch)
n2d_sim = noise.get_n2d_data(sims,
ivars2,
emask,
coadd_estimator=coadd,
flattened=False,
plot_fname=pout + "_n2d_sim" if
(args.debug and i == 0) else None,
dtype=dm.dtype)
#n2d_sim = noise.get_n2d_data(sims,ivars2,emask,coadd_estimator=coadd,flattened=True,plot_fname=pout+"_n2d_sim" if (args.debug and i==0) else None,dtype = dm.dtype)
del sims
cents, op1ds_sim = noise.get_p1ds(n2d_sim, modlmap, bin_edges)
p1ds.append(op1ds_sim.copy().reshape(-1))
p1dstats = stats.get_stats(np.array(p1ds))
#del covsqrt
# For verification
#splits = dm.get_splits(season=args.season,patch=args.patch,arrays=dm.array_freqs[args.array],srcfree=True)
#splits = dm.get_splits(args.qid)
splits = enmap.enmap([dm.get_splits(q) for q in qid])
#splits = np.expand_dims(splits,axis=0)
if args.extract_mask is not None:
splits = enmap.extract(splits, eshape, ewcs)
n2d_data = noise.get_n2d_data(splits,
ivars2,
emask,
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] = get_stats(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] = get_stats(listAllReconPower[polComb])
fp = interp1d(centers,statsRecon[polComb]['mean'],fill_value='extrapolate')
pl.add(ellkk,(fp(ellkk))-Clkk,color=col,lw=2)
Nlkk2d = qest.N.Nlkk[polComb]
ncents, npow = stats.bin_in_annuli(Nlkk2d, p2d.modLMap, bin_edges)
pl.add(ncents,npow,color=col,lw=2,ls="--")
avgInputPower = totAllInputPower/N
pl.add(centers,avgInputPower,color='cyan',lw=3) # ,label = "input x input"
ymap2, cmap2, dmap2, mask2, ras2, decs2, wt2)
if rank == 0:
print(i2)
hplot(ystack2, "fig_all_cmass_ystack_%s_%s" % (cversion, 'boss'))
hplot(cstack2, "fig_all_cmass_cstack_%s_%s" % (cversion, 'boss'))
hplot(dstack2, "fig_all_cmass_dstack_%s_%s" % (cversion, 'boss'))
ystack = (ystack1 + ystack2) / (i1 + i2)
cstack = (cstack1 + cstack2) / (i1 + i2)
dstack = (dstack1 + dstack2) / (i1 + i2)
hplot(ystack, "fig_all_cmass_ystack_%s_%s" % (cversion, 'both'))
hplot(cstack, "fig_all_cmass_cstack_%s_%s" % (cversion, 'both'))
hplot(dstack, "fig_all_cmass_dstack_%s_%s" % (cversion, 'both'))
sy1 = stats.get_stats(y1ds1)
sc1 = stats.get_stats(c1ds1)
sd1 = stats.get_stats(d1ds1)
sy2 = stats.get_stats(y1ds2)
sc2 = stats.get_stats(c1ds2)
sd2 = stats.get_stats(d1ds2)
y1 = sy1['mean']
ey1 = sy1['errmean']
c1 = sc1['mean']
ec1 = sc1['errmean']
d1 = sd1['mean']
ed1 = sd1['errmean']
uicls = putils.allgatherv(uicls, comm)
uxcls_nobh_1 = putils.allgatherv(uxcls_nobh_1, comm)
uxcls_nobh_2 = putils.allgatherv(uxcls_nobh_2, comm)
uacls_nobh = putils.allgatherv(uacls_nobh, comm)
if bh:
uxcls_bh_1 = putils.allgatherv(uxcls_bh_1, comm)
uxcls_bh_2 = putils.allgatherv(uxcls_bh_2, comm)
uacls_bh = putils.allgatherv(uacls_bh, comm)
if rank == 0:
with bench.show("Labels"):
labs = solenspipe.get_labels()
with bench.show("Stats"):
suicls = stats.get_stats(uicls)
suxcls_nobh_1 = stats.get_stats(uxcls_nobh_1)
suxcls_nobh_2 = stats.get_stats(uxcls_nobh_2)
suacls_nobh = stats.get_stats(uacls_nobh)
if bh:
suxcls_bh_1 = stats.get_stats(uxcls_bh_1)
suxcls_bh_2 = stats.get_stats(uxcls_bh_2)
suacls_bh = stats.get_stats(uacls_bh)
with bench.show("Save"):
np.save(f'{solenspipe.opath}mean_uicls_{e1}_{e2}.npy', uicls)
np.save(f'{solenspipe.opath}mean_uxcls_nobh_1_{e1}_{e2}.npy',
uxcls_nobh_1)
np.save(f'{solenspipe.opath}mean_uxcls_nobh_2_{e1}_{e2}.npy',
uxcls_nobh_2)
np.save(f'{solenspipe.opath}mean_uacls_nobh_{e1}_{e2}.npy', uacls_nobh)