def test_shear():
from orphics import lensing, io, cosmology, maps
deg = 20.
px = 2.0
tellmin = 30
tellmax = 3500
kellmin = 10
kellmax = 3000
bin_width = 20
beam_arcmin = 1.4
noise_uk_arcmin = 7.0
theory = cosmology.default_theory(lpad=30000)
shape, wcs = s.rect_geometry(width_deg=deg, px_res_arcmin=px)
flsims = lensing.FlatLensingSims(shape, wcs, theory, beam_arcmin,
noise_uk_arcmin)
kbeam = flsims.kbeam
modlmap = enmap.modlmap(shape, wcs)
fc = maps.FourierCalc(shape, wcs)
n2d = (noise_uk_arcmin * np.pi / 180. / 60.)**2. / flsims.kbeam**2.
tmask = s.mask_kspace(shape, wcs, lmin=tellmin, lmax=tellmax)
kmask = s.mask_kspace(shape, wcs, lmin=kellmin, lmax=kellmax)
bin_edges = np.arange(kellmin, kellmax, bin_width)
binner = s.bin2D(modlmap, bin_edges)
i = 0
unlensed, kappa, lensed, beamed, noise_map, observed = flsims.get_sim(
seed_cmb=(i, 1),
seed_kappa=(i, 2),
seed_noise=(i, 3),
lens_order=5,
return_intermediate=True)
_, kmap, _ = fc.power2d(observed)
pii2d, kinput, _ = fc.power2d(kappa)
feed_dict = {}
cltt = theory.lCl('TT', modlmap)
feed_dict['uC_T_T'] = theory.lCl('TT', modlmap)
feed_dict['tC_T_T'] = (cltt + n2d)
feed_dict['X'] = kmap / kbeam
feed_dict['Y'] = kmap / kbeam
ells = np.arange(0, 10000, 1)
ucltt = theory.lCl('TT', ells)
feed_dict['duC_T_T'] = s.interp(ells,
np.gradient(np.log(ucltt),
np.log(ells)))(modlmap)
sAl = s.A_l(shape, wcs, feed_dict, "shear", "TT", xmask=tmask, ymask=tmask)
sNl = s.N_l(shape,
wcs,
feed_dict,
"shear",
"TT",
xmask=tmask,
ymask=tmask,
Al=sAl)
sukappa = s.unnormalized_quadratic_estimator(shape,
wcs,
feed_dict,
"shear",
"TT",
xmask=tmask,
ymask=tmask)
snkappa = sAl * sukappa
pir2d3 = fc.f2power(snkappa, kinput)
cents, pir1d3 = binner.bin(pir2d3)
cents, pii1d = binner.bin(pii2d)
cents, prr1d = binner.bin(fc.f2power(snkappa, snkappa))
cents, Nlkk3 = binner.bin(sNl)
pl = io.Plotter(xyscale='loglog')
pl.add(ells, theory.gCl('kk', ells))
pl.add(cents, pii1d, color='k', lw=3)
pl.add(cents, pir1d3, label='shear')
pl.add(cents, prr1d)
pl.add(cents, Nlkk3, ls=":")
pl._ax.set_xlim(10, 3500)
pl.done("ncomp.png")
def test_pol():
from orphics import lensing, io, cosmology, maps
est = "hu_ok"
pols = ['TT', 'EE', 'TE', 'EB', 'TB']
# est = "hdv"
# pols = ['TT','EE','TE','ET','EB','TB']
deg = 5.
px = 2.0
tellmin = 30
tellmax = 3000
pellmin = 30
pellmax = 5000
kellmin = 10
kellmax = 5000
bin_width = 40
beam_arcmin = 1.5
noise_uk_arcmin = 10.0
theory = cosmology.default_theory(lpad=30000)
shape, wcs = s.rect_geometry(width_deg=deg, px_res_arcmin=px)
modlmap = enmap.modlmap(shape, wcs)
kbeam = s.gauss_beam(modlmap, beam_arcmin)
n2d = (noise_uk_arcmin * np.pi / 180. / 60.)**2. / kbeam**2.
tmask = s.mask_kspace(shape, wcs, lmin=tellmin, lmax=tellmax)
pmask = s.mask_kspace(shape, wcs, lmin=pellmin, lmax=pellmax)
kmask = s.mask_kspace(shape, wcs, lmin=kellmin, lmax=kellmax)
bin_edges = np.arange(kellmin, kellmax, bin_width)
binner = s.bin2D(modlmap, bin_edges)
feed_dict = {}
cltt = theory.lCl('TT', modlmap)
clee = theory.lCl('EE', modlmap)
clbb = theory.lCl('BB', modlmap)
clte = theory.lCl('TE', modlmap)
feed_dict['uC_T_T'] = cltt
feed_dict['tC_T_T'] = (cltt + n2d)
feed_dict['uC_E_E'] = clee
feed_dict['tC_E_E'] = (clee + n2d * 2.)
feed_dict['uC_B_B'] = clbb
feed_dict['tC_B_B'] = (clbb + n2d * 2.)
feed_dict['uC_T_E'] = clte
feed_dict['tC_T_E'] = clte
ells = np.arange(0, 10000, 1)
pl = io.Plotter(xyscale='loglog')
pl.add(ells, theory.gCl('kk', ells))
imask = {'T': tmask, 'E': pmask, 'B': pmask}
for pol in pols:
print(pol)
X, Y = pol
cents, Nl = binner.bin(
s.N_l(shape,
wcs,
feed_dict,
est,
pol,
xmask=imask[X],
ymask=imask[Y]))
pl.add(cents, Nl, label=pol)
pl._ax.set_xlim(10, kellmax)
pl.done("nls.png")
def test_lens_recon():
from orphics import lensing, io, cosmology, maps
from enlib import bench
deg = 10.
px = 2.0
tellmin = 100
tellmax = 3000
kellmin = 40
kellmax = 3000
grad_cut = None
bin_width = 80
beam_arcmin = 0.01
noise_uk_arcmin = 0.01
theory = cosmology.default_theory(lpad=30000)
shape, wcs = s.rect_geometry(width_deg=deg, px_res_arcmin=px)
flsims = lensing.FlatLensingSims(shape, wcs, theory, beam_arcmin,
noise_uk_arcmin)
kbeam = flsims.kbeam
modlmap = enmap.modlmap(shape, wcs)
fc = maps.FourierCalc(shape, wcs)
n2d = (noise_uk_arcmin * np.pi / 180. / 60.)**2. / flsims.kbeam**2.
tmask = s.mask_kspace(shape, wcs, lmin=tellmin, lmax=tellmax)
kmask = s.mask_kspace(shape, wcs, lmin=kellmin, lmax=kellmax)
with bench.show("orphics init"):
qest = lensing.qest(shape,
wcs,
theory,
noise2d=n2d,
kmask=tmask,
kmask_K=kmask,
pol=False,
grad_cut=grad_cut,
unlensed_equals_lensed=True,
bigell=30000)
bin_edges = np.arange(kellmin, kellmax, bin_width)
binner = s.bin2D(modlmap, bin_edges)
i = 0
unlensed, kappa, lensed, beamed, noise_map, observed = flsims.get_sim(
seed_cmb=(i, 1),
seed_kappa=(i, 2),
seed_noise=(i, 3),
lens_order=5,
return_intermediate=True)
kmap = enmap.fft(observed, normalize="phys")
# _,kmap,_ = fc.power2d(observed)
with bench.show("orphics"):
kkappa = qest.kappa_from_map("TT",
kmap / kbeam,
alreadyFTed=True,
returnFt=True)
pir2d, kinput = fc.f1power(kappa, kkappa)
pii2d = fc.f2power(kinput, kinput)
prr2d = fc.f2power(kkappa, kkappa)
cents, pir1d = binner.bin(pir2d)
cents, pii1d = binner.bin(pii2d)
cents, prr1d = binner.bin(prr2d)
feed_dict = {}
cltt = theory.lCl('TT', modlmap)
feed_dict['uC_T_T'] = theory.lCl('TT', modlmap)
feed_dict['tC_T_T'] = cltt + n2d
feed_dict['X'] = kmap / kbeam
feed_dict['Y'] = kmap / kbeam
with bench.show("symlens init"):
Al = s.A_l(shape,
wcs,
feed_dict,
"hdv",
"TT",
xmask=tmask,
ymask=tmask)
Nl = s.N_l_from_A_l_optimal(shape, wcs, Al)
with bench.show("symlens"):
ukappa = s.unnormalized_quadratic_estimator(shape,
wcs,
feed_dict,
"hdv",
"TT",
xmask=tmask,
ymask=tmask)
nkappa = Al * ukappa
pir2d2 = fc.f2power(nkappa, kinput)
cents, pir1d2 = binner.bin(pir2d2)
cents, Nlkk = binner.bin(qest.N.Nlkk['TT'])
cents, Nlkk2 = binner.bin(Nl)
pl = io.Plotter(xyscale='linlog')
pl.add(cents, pii1d, color='k', lw=3)
pl.add(cents, pir1d, label='orphics')
pl.add(cents, pir1d2, label='hdv symlens')
pl.add(cents, Nlkk, ls="--", label='orphics')
pl.add(cents, Nlkk2, ls="-.", label='symlens')
pl.done("ncomp.png")