modlmap > args.fill_min, modlmap <
(args.fill_min +
args.radial_fit_annulus))].mean()
if args.fill_min is not None:
n2d_xflat_smoothed[:, :, modlmap < args.fill_min] = fill_val
n2d_xflat_smoothed[:, :, modlmap < 2] = 0
# lmax cut
if args.lmax is not None:
n2d_xflat_smoothed[:, :, modlmap > args.lmax] = 0
# remove off diagonals
if args.no_off:
n2d_xflat_smoothed = noise.null_off_diagonals(n2d_xflat_smoothed)
# output 1D spectrum
if args.debug:
print('plotting 1D spectrum of n2d_xflat_smoothed with filled values')
plots.plot_1dspec(n2d_xflat_smoothed,
modlmap,
pout + '_smoothed_filled',
lmin=500)
if args.do_only_filter_noise:
ngen.save_filter_noise(n2d_xflat_smoothed,
season=args.season,
patch=args.patch,
array=args.array,
coadd=coadd,
coadd_estimator=coadd,
flattened=True,
plot_fname=pout + "_n2d_flat")
del splits
radial_pairs = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (0, 3), (3, 0)]
if smooth:
n2d_flat_smoothed = noise.smooth_ps(
n2d_flat.copy(),
dfact=dfact,
radial_pairs=radial_pairs,
plot_fname=pout + "_n2d_flat_smoothed",
radial_fit_annulus=args.radial_fit_annulus)
else:
n2d_flat_smoothed = n2d_flat.copy()
if args.no_off: n2d_flat_smoothed = noise.null_off_diagonals(n2d_flat_smoothed)
del n2d_flat
covsqrt = noise.get_covsqrt(n2d_flat_smoothed, args.covsqrt_kind)
del n2d_flat_smoothed
ngen.save_covsqrt(covsqrt,
season=season,
patch=patch,
array=args.array,
coadd=coadd,
mask_patch=mask_patch)
if nsims > 0:
bin_edges = np.arange(40, 8000, 40)
p1ds = []
for i in range(nsims):