season,
patch,
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,
parser.add_argument("patch", type=str, help='Patch')
args = parser.parse_args()
bin_edges = np.arange(30, 10000, 100)
dm = datamodel.NoiseModel(args.season, args.array, args.patch)
pout = "%s%s_%s_%s_coadd_est_%s" % (datamodel.pout, args.season, args.array,
args.patch, True)
sout = "%s%s_%s_%s_coadd_est_%s" % (datamodel.paths['save'], args.season,
args.array, args.patch, True)
modlmap = dm.modlmap
n2d_data = dm.get_n2d_data(dm.get_map(), coadd_estimator=True)
corr = datamodel.corrcoef(n2d_data)
cents, c1ds_data = datamodel.get_p1ds(corr, modlmap, bin_edges)
dpi = 300
ncomps = c1ds_data.shape[0]
if ncomps == 3:
pols = ['150-I', '150-Q', '150-U']
elif ncomps == 6:
pols = ['90-I', '90-Q', '90-U', '150-I', '150-Q', '150-U']
pl = io.Plotter(xlabel="$\\ell$",
ylabel="$N_{XY}/\\sqrt{N_{XX}N_{YY}}$",
xyscale='linlin')
for i in range(c1ds_data.shape[0]):
for j in range(i + 1, c1ds_data.shape[0]):
polstring = "%s x %s" % (pols[i], pols[j])
pl.add(cents, c1ds_data[i, j], label=polstring)
