def recon_from_config(Config,
recon_section,
parray,
theory,
lmax,
grad_cut=None,
pol=False,
u_equals_l=True):
min_ell = fmaps.minimum_ell(parray.shape, parray.wcs)
lb = ellbounds_from_config(Config, recon_section, min_ell)
tellmin = lb['tellminY']
tellmax = lb['tellmaxY']
pellmin = lb['pellminY']
pellmax = lb['pellmaxY']
kellmin = lb['kellmin']
kellmax = lb['kellmax']
lxmap_dat, lymap_dat, modlmap_dat, angmap_dat, lx_dat, ly_dat = fmaps.get_ft_attributes_enmap(
parray.shape, parray.wcs)
template_dat = fmaps.simple_flipper_template_from_enmap(
parray.shape, parray.wcs)
nT = parray.nT
nP = parray.nP
kbeam_dat = parray.lbeam
fMaskCMB_T = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=tellmin,
lmax=tellmax)
fMaskCMB_P = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=pellmin,
lmax=pellmax)
fMask = fmaps.fourierMask(lx_dat,
ly_dat,
modlmap_dat,
lmin=kellmin,
lmax=kellmax)
with oio.nostdout():
from alhazen.quadraticEstimator import Estimator
qest = Estimator(template_dat,
theory,
theorySpectraForNorm=None,
noiseX2dTEB=[nT, nP, nP],
noiseY2dTEB=[nT, nP, nP],
fmaskX2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskY2dTEB=[fMaskCMB_T, fMaskCMB_P, fMaskCMB_P],
fmaskKappa=fMask,
kBeamX=kbeam_dat,
kBeamY=kbeam_dat,
doCurl=False,
TOnly=not (pol),
halo=True,
uEqualsL=u_equals_l,
gradCut=grad_cut,
bigell=lmax)
return tellmin, tellmax, pellmin, pellmax, kellmin, kellmax, qest
Config.write(open(fout_dir + "config.ini", 'w'))
lens_order = Config.get(sim_section, 'lens_order')
pol = args.do_pol
if pol: raise NotImplementedError
shape_sim, wcs_sim, shape_dat, wcs_dat = aio.enmaps_from_config(
Config, sim_section, analysis_section, pol=pol)
if rank == 0:
print(("Dat shape : ", shape_dat))
print(("Sim shape : ", shape_sim))
analysis_resolution = np.min(
enmap.extent(shape_dat, wcs_dat) / shape_dat[-2:]) * 60. * 180. / np.pi
pixratio = analysis_resolution / Config.getfloat(sim_section, "pixel_arcmin")
min_ell = fmaps.minimum_ell(shape_dat, wcs_dat)
parray_dat = aio.patch_array_from_config(Config,
expf_name,
shape_dat,
wcs_dat,
dimensionless=False)
parray_sim = aio.patch_array_from_config(Config,
expf_name,
shape_sim,
wcs_sim,
dimensionless=False)
# Efficiently distribute sims over MPI cores
num_each, each_tasks = mpi_distribute(Nsims, numcores)
# Initialize a container for stats and stacks
