def test_b_sign(self):
"""
We generate a random IQU map with geometry such that cdelt[0]<0
We transform this to TEB with map2harm and map2alm followed by
scalar harm2map and alm2map and use these as reference T,E,B maps.
We flip the original map along the RA direction.
We transform this to TEB with map2harm and map2alm followed by
scalar harm2map and alm2map and use these as comparison T,E,B maps.
We compare these maps.
"""
ells,cltt,clee,clbb,clte = np.loadtxt(DATA_PREFIX+"cosmo2017_10K_acc3_lensedCls.dat",unpack=True)
ps_cmb = np.zeros((3,3,ells.size))
ps_cmb[0,0] = cltt
ps_cmb[1,1] = clee
ps_cmb[2,2] = clbb
ps_cmb[1,0] = clte
ps_cmb[0,1] = clte
np.random.seed(100)
# Curved-sky is fine
lmax = 1000
alm = curvedsky.rand_alm_healpy(ps_cmb,lmax=lmax)
shape,iwcs = enmap.fullsky_geometry(res=np.deg2rad(10./60.))
wcs = enmap.empty(shape,iwcs)[...,::-1].wcs
shape = (3,) + shape
imap = curvedsky.alm2map(alm,enmap.empty(shape,wcs))
oalm = curvedsky.map2alm(imap.copy(),lmax=lmax)
rmap = curvedsky.alm2map(oalm,enmap.empty(shape,wcs),spin=0)
imap2 = imap.copy()[...,::-1]
oalm = curvedsky.map2alm(imap2.copy(),lmax=lmax)
rmap2 = curvedsky.alm2map(oalm,enmap.empty(shape,wcs),spin=0)
assert np.all(np.isclose(rmap[0],rmap2[0]))
assert np.all(np.isclose(rmap[1],rmap2[1]))
assert np.all(np.isclose(rmap[2],rmap2[2]))
# Flat-sky
px = 2.0
N = 300
shape,iwcs = enmap.geometry(pos=(0,0),res=np.deg2rad(px/60.),shape=(300,300))
shape = (3,) + shape
a = enmap.zeros(shape,iwcs)
a = a[...,::-1]
wcs = a.wcs
seed = 100
imap = enmap.rand_map(shape,wcs,ps_cmb,seed=seed)
kmap = enmap.map2harm(imap.copy())
rmap = enmap.harm2map(kmap,spin=0) # reference map
imap = imap[...,::-1]
kmap = enmap.map2harm(imap.copy())
rmap2 = enmap.harm2map(kmap,spin=0)[...,::-1] # comparison map
assert np.all(np.isclose(rmap[0],rmap2[0]))
assert np.all(np.isclose(rmap[1],rmap2[1],atol=1e0))
assert np.all(np.isclose(rmap[2],rmap2[2],atol=1e0))
def build_iN_constcorr(data,
maps,
smooth="angular",
brel=2,
lsigma=500,
lmin=100,
ivars=None,
constcov=False):
if ivars is None: ivars = data.ivars
#enmap.write_map("constcorr_imap1.fits", maps)
if not constcov: maps = maps * ivars**0.5
#enmap.write_map("constcorr_imap2.fits", maps)
fhmap = enmap.map2harm(maps, spin=0,
normalize="phys") / maps.pixsize()**0.5
del maps
N = (fhmap[:, None] * np.conj(fhmap[None, :])).real
N /= np.maximum(data.fapod, 1e-8)
del fhmap
# Smooth in piwwin-space, since things are expected to be more isotopic there
N = N * data.wy[:, None]**2 * data.wx[None, :]**2
if smooth == "angular":
N = smooth_ps_angular(N, brel=brel)
elif smooth == "gauss":
N = smooth_ps_gauss(N, lsigma=lsigma)
elif smooth == "mixed":
N = smooth_ps_mixed(N, brel=brel, lsigma=lsigma)
else:
raise ValueError("Unrecognized smoothing '%s'" % str(smooth))
#enmap.write_map("N.fits", N)
iN = analysis.safe_pow(N, -1)
#enmap.write_map("iN.fits", iN)
iN = iN * data.wy[:, None]**2 * data.wx[None, :]**2
iN[:, :, data.l < lmin] = 0
return iN
# wmap = enmap.ifft(enmap.fft(wmap)*matched_filter).real**2
# lim = max(np.median(wmap)*tol1**2, np.max(wmap)*tol2**2)
# mask |= wmap > lim
# return mask
comm = mpi.COMM_WORLD
beam1d = get_beam(args.beam)
ifiles = sorted(sum([glob.glob(ifile) for ifile in args.ifiles], []))
for ind in range(comm.rank, len(ifiles), comm.size):
ifile = ifiles[ind]
if args.verbose: print(ifile)
ofile = args.odir + "/" + ifile
imap = enmap.read_map(ifile)
if args.mask is not None: mask = imap == args.mask
if args.apodize:
imap = imap.apod(args.apodize)
# We will apply a semi-matched-filter to T
l = np.maximum(1, imap.modlmap())
beam2d = enmap.samewcs(np.interp(l, np.arange(len(beam1d)), beam1d), imap)
matched_filter = (1 + (l / args.lknee)**args.alpha)**-1 * beam2d
fmap = enmap.map2harm(imap, iau=True)
fmap[0] *= matched_filter
omap = enmap.ifft(fmap).real
if args.mask is not None:
omap[mask] = 0
del mask
utils.mkdir(os.path.dirname(ofile))
enmap.write_map(ofile, omap)
del omap
# Fourier magnitude map
modlmap = enmap.modlmap(shape, wcs)
# Unlensed CMB theory
theory = cosmology.default_theory()
cltt2d = theory.uCl('TT', modlmap)
clte2d = theory.uCl('TE', modlmap)
clee2d = theory.uCl('EE', modlmap)
power = np.zeros((3, 3, shape[0], shape[1]))
power[0, 0] = cltt2d
power[1, 1] = clee2d
power[1, 2] = clte2d
power[2, 1] = clte2d
# Unlensed CMB generator
mgen = maps.MapGen((3, ) + shape, wcs, power)
for j, task in enumerate(my_tasks):
print(f'Rank {rank} performing task {task} as index {j}')
cmb = mgen.get_map(
seed=cutils.get_seed('lensed', task, False)
) # unlensed map ; you can save it if you want, before lensing it
cmb = lens_map(cmb) # do the lensing
dcmb = cmb.resample(
(3, dNpix, dNpix)
) # downsample the lensed CMB map (if saving unlensed, do the same to it)
# For some reason, I was saving the Fourier transforms... probably since it is easier to apply a beam later
kmap = enmap.map2harm(dcmb, iau=False)
enmap.write_map(f'{savedir}lensed_kmap_real_{task:06d}.fits', kmap.real)
enmap.write_map(f'{savedir}lensed_kmap_imag_{task:06d}.fits', kmap.imag)