def get_input_alms_v0p5(
cmb_set,
phi_set,
i,
path='/scratch/r/rbond/msyriac/data/sims/signal/v0.5_lenstest/'):
ps = powspec.read_camb_full_lens(path + "/lenspotentialCls.dat")
lmax = config_from_yaml(path + "/args.yml")['lmax']
ncomp = 3
cmb_seed = seedgen.get_cmb_seed(cmb_set, i)
phi_seed = seedgen.get_phi_seed(phi_set, i)
phi_alm, unlensed_cmb_alm, ainfo = plensing.rand_alm(ps_lensinput=ps,
lmax=lmax,
seed=cmb_seed,
phi_seed=phi_seed,
verbose=False,
ncomp=ncomp)
return plensing.phi_to_kappa(phi_alm), unlensed_cmb_alm
def get_input(input_name,set_idx,sim_idx,shape,wcs):
if input_name=='CMB':
cmb_type = 'LensedUnabberatedCMB'
signal_path = sints.dconfig['actsims']['signal_path']
cmb_file = os.path.join(signal_path, 'fullsky%s_alm_set%02d_%05d.fits' %(cmb_type, set_idx, sim_idx))
alms = hp.fitsfunc.read_alm(cmb_file, hdu = 1)
elif input_name=='tSZ':
ellmax = 8101
ells = np.arange(ellmax)
cyy = fgs.power_y(ells)[None,None]
cyy[0,0][ells<2] = 0
comptony_seed = seed_tracker.get_fg_seed(set_idx, sim_idx, 'comptony')
alms = curvedsky.rand_alm(cyy, seed = comptony_seed,lmax=ellmax)
elif input_name=='kappa':
signal_path = sints.dconfig['actsims']['signal_path']
k_file = os.path.join(signal_path, 'fullskyPhi_alm_%05d.fits' %(sim_idx))
palms = hp.fitsfunc.read_alm(k_file)
from pixell import lensing as plensing
alms = plensing.phi_to_kappa(palms)
omap = enmap.zeros((1,)+shape[-2:],wcs)
omap = curvedsky.alm2map(np.complex128(alms),omap,spin=0)[0]
return omap
def get_kappa_alm(i, path=config['signal_path']):
istr = str(i).zfill(5)
fname = path + "fullskyPhi_alm_%s.fits" % istr
return plensing.phi_to_kappa(hp.read_alm(fname))
npp = maps.interp(ells, np.nan_to_num(ps_noise[1, 1] / lbeam**2.))
# Filter
talm_y = qe.filter_alms(alm[0], lambda x: 1. /
(theory.lCl('TT', x) + ntt(x)), args.lmint,
args.lmaxt)
ealm_y = qe.filter_alms(alm[1], lambda x: 1. /
(theory.lCl('EE', x) + npp(x)), args.lminp,
args.lmaxp)
balm_y = qe.filter_alms(alm[2], lambda x: 1. /
(theory.lCl('BB', x) + npp(x)), args.lminp,
args.lmaxp)
# Inputs
ikalm = lensing.phi_to_kappa(
maps.change_alm_lmax(
hp.read_alm(sim_location + "fullskyPhi_alm_%s.fits" % (sindex)),
mlmax))
if args.all:
with bench.show("qe all"):
res = qe.qe_all(
shape,
wcs,
lambda x, y: theory.lCl(x, y),
mlmax,
talm_y,
ealm_y,
balm_y,
estimators=['TT', 'TE', 'EE', 'EB', 'TB', 'mv', 'mvpol'])
for key in res.keys():
ikalm = utils.change_alm_lmax(
utils.get_kappa_alm(sindex).astype(np.complex128), mlmax)
# Cross-correlate and plot
kalms = {}
icls = hp.alm2cl(ikalm, ikalm)
ells = np.arange(len(icls))
bin_edges = np.geomspace(2, mlmax, 15)
print(bin_edges)
binner = stats.bin1D(bin_edges)
bin = lambda x: binner.bin(ells, x)
print(ells.shape)
for est in ests:
pl = io.Plotter('CL')
pl2 = io.Plotter('rCL', xyscale='loglin')
kalms[est] = plensing.phi_to_kappa(
hp.almxfl(recon[est][0].astype(np.complex128),
Als[est][0])) # ignore curl
pl.add(ells, (ells * (ells + 1.) / 2.)**2. * Als[est][0], ls='--')
cls = hp.alm2cl(kalms[est], ikalm)
acls = hp.alm2cl(kalms[est], kalms[est])
pl.add(ells, acls, label='r x r')
pl.add(ells, cls, label='r x i')
pl.add(ells, icls, label='i x i')
pl2.add(*bin((cls - icls) / icls), marker='o')
pl2.hline(y=0)
pl2._ax.set_ylim(-0.1, 0.1)
pl2.done(f'simple_recon_diff_{est}.png')
pl._ax.set_ylim(1e-9, 1e-5)
pl.done(f'simple_recon_{est}.png')
kmap_func = {'rdn0': get_kmap_rdn0, 'mcn1': get_kmap_mcn1}
else:
# This is the filtered map loading function that RDN0 and MCN1 will use
kmap_func = {'rdn0': get_kmap, 'mcn1': get_kmap}
# Let's make a single map and cross-correlate with input
if rank == 0:
# Get kappa alm
ikalm = utils.change_alm_lmax(
utils.get_kappa_alm(1999).astype(np.complex128),
mlmax) # TODO: fix hardcoding
# New convention in falafel means maps are potential; we convert to convergence
r_nobh_1 = plensing.phi_to_kappa(q_nobh_1(Xdat, Xdat))
r_nobh_2 = plensing.phi_to_kappa(q_nobh_2(Xdat, Xdat))
uicls = cs.alm2cl(ikalm, ikalm)
uxcls_nobh_1 = cs.alm2cl(r_nobh_1[0], ikalm)
uxcls_nobh_2 = cs.alm2cl(r_nobh_2[0], ikalm)
uacls_nobh = cs.alm2cl(r_nobh_1, r_nobh_2)
np.save(f'{opath}uicls_{e1}_{e2}.npy', uicls)
np.save(f'{opath}uxcls_nobh_1_{e1}_{e2}.npy', uxcls_nobh_1)
np.save(f'{opath}uxcls_nobh_2_{e1}_{e2}.npy', uxcls_nobh_2)
np.save(f'{opath}uacls_nobh_{e1}_{e2}.npy', uacls_nobh)
if bh:
r_bh_1 = plensing.phi_to_kappa(q_bh_1(Xdat, Xdat))
r_bh_2 = plensing.phi_to_kappa(q_bh_2(Xdat, Xdat))
uxcls_bh_1 = cs.alm2cl(r_bh_1[0], ikalm)
uacls_bh = []
# Let's make a single map and cross-correlate with input
for task in my_tasks:
with bench.show("iKALM"):
# Get kappa alm
ikalm = utils.change_alm_lmax(
utils.get_kappa_alm(task).astype(np.complex128), mlmax)
with bench.show("Xdat"):
# Get data
Xdat = get_kmap((0, 0, task))
with bench.show("QE"):
# New convention in falafel means maps are potential; we convert to convergence
r_nobh_1 = plensing.phi_to_kappa(q_nobh_1(Xdat, Xdat))
r_nobh_2 = plensing.phi_to_kappa(q_nobh_2(
Xdat, Xdat)) if not (diag) else r_nobh_1.copy()
with bench.show("alm2cl and append"):
uicl = cs.alm2cl(ikalm, ikalm)
uxcl_nobh_1 = cs.alm2cl(r_nobh_1[0], ikalm)
uxcl_nobh_2 = cs.alm2cl(r_nobh_2[0], ikalm)
uacl_nobh = cs.alm2cl(r_nobh_1[0], r_nobh_2[0])
uicls.append(uicl)
uxcls_nobh_1.append(uxcl_nobh_1)
uxcls_nobh_2.append(uxcl_nobh_2)
uacls_nobh.append(uacl_nobh)
if bh: