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)