# Get arrays from array
splits = dm.get_splits(season=season,
patch=patch,
arrays=dm.array_freqs[args.array],
srcfree=True)
ivars = dm.get_splits_ivar(season=season,
patch=patch,
arrays=dm.array_freqs[args.array])
noise.plot(pout + "_splits", splits)
noise.plot(pout + "_ivars", ivars)
modlmap = splits.modlmap()
n2d_flat = noise.get_n2d_data(splits,
ivars,
mask,
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)
splits = enmap.enmap([dm.get_splits(q) for q in qid])
nsplits = dm.ainfo(args.qid, 'nsplits')
# Get inverse variance map
ivars = enmap.enmap([dm.get_ivars(q) for q in qid])
modlmap = splits.modlmap()
if calc_covsqrt:
flatstring = "un" if args.do_only_filter_noise else ""
n2d_xflat = noise.get_n2d_data(
splits,
ivars,
mask,
coadd_estimator=coadd,
flattened=not (args.do_only_filter_noise),
plot_fname=pout + "_n2d_%sflat" % flatstring if args.debug else None,
dtype=dm.dtype)
# output 1D spectrum
if args.debug:
print('plotting 1D spectrum of n2d_xflat')
plots.plot_1dspec(n2d_xflat, modlmap, pout, lmin=500)
ncomps = n2d_xflat.shape[0]
if ncomps == 1:
npol = 1
else:
npol = 3
if args.verbose:
mask = sints.get_act_mr3_crosslinked_mask(region)
dm = sints.models['act_mr3'](region=mask, calibrated=True)
splits = dm.get_splits(season=season,
patch=patch,
arrays=dm.array_freqs[array],
srcfree=True)
ivars = dm.get_splits_ivar(season=season,
patch=patch,
arrays=dm.array_freqs[array])
npower = noise.get_n2d_data(splits,
ivars,
mask,
coadd_estimator=True,
flattened=False,
plot_fname=None,
dtype=dm.dtype)
ndown, nfitted, nparams = covtools.noise_average(npower[0, 0],
dfact=(16, 16),
lmin=300,
lmax=8000,
wnoise_annulus=500,
bin_annulus=20,
lknee_guess=3000,
alpha_guess=-4,
method="fft",
radial_fit=True)