def get_synth_maps():
ulm_t1 = np.sqrt((np.random.rand(len(ellArr)) - 0.5 +
1j*(np.random.rand(len(ellArr)) - 0.5)) *
(lmax**2 - (ellArr-50)**2))
vlm_t1 = np.sqrt((np.random.rand(len(ellArr)) - 0.5 +
1j*(np.random.rand(len(ellArr)) - 0.5)) *
(lmax**2*1.5 - (ellArr-50)**2))
wlm_t1 = np.sqrt((np.random.rand(len(ellArr)) - 0.5 +
1j*(np.random.rand(len(ellArr)) - 0.5)) *
(lmax**2*1.7 - (ellArr-50)**2))
r_t1 = hp.alm2map(ulm_t1, NSIDE)
ulm_t1_imag = hp.map2alm(r_t1.imag)
ulm_t2 = ulm_t1 - 1j*ulm_t1_imag
r1map = hp.alm2map(ulm_t2, NSIDE)
r_map = hp.alm2map(hp.map2alm(r1map), NSIDE)
hlmp_t1 = (vlm_t1 + 1j*wlm_t1)/np.sqrt(2)
hlmm_t1 = (vlm_t1 - 1j*wlm_t1)/np.sqrt(2)
t1map, p1map = hp.alm2map_spin((hlmp_t1, hlmm_t1), NSIDE, 1, lmax)
hlmp_t1_imag, hlmm_t1_imag = hp.map2alm_spin((t1map.imag, p1map.imag), 1)
hlmp = hlmp_t1 - 1j*hlmp_t1_imag
hlmm = hlmm_t1 - 1j*hlmm_t1_imag
t1map, p1map = hp.alm2map_spin((hlmm, hlmp), NSIDE, 1, lmax)
h1map, h2map = hp.alm2map_spin(hp.map2alm_spin((t1map, p1map), 1), NSIDE, 1, lmax)
return r_map, h1map, h2map
def _get_wEBlm(window_scal, lmax=None, maps=False):
'''
Calculate the E/B-type alms from the vector and tensor windows.
Constructs the vector and tensor windows from the scalar wlms and
then masks the resulting maps. This masking results in non-zero B-mode
type wlms.
'''
mask = np.ones_like(window_scal)
mask[window_scal == 0] = 0
window_vect, window_tens = H_ext.window2vecttens(window_scal, mask=mask,
lmax=lmax)
wlm = H.map2alm(window_scal, lmax=lmax)
n = len(wlm)
wEBlm = np.empty([5, n], dtype=np.complex)
wEBlm[0, :] = -wlm
wlm_tmp = H.map2alm_spin(window_vect, 1, lmax=lmax)
wEBlm[1, :] = wlm_tmp[0]
wEBlm[-1, :] = wlm_tmp[1]
wlm_tmp = H.map2alm_spin(window_tens, 2, lmax=lmax)
wEBlm[2, :] = wlm_tmp[0]
wEBlm[-2, :] = wlm_tmp[1]
if maps:
window_vect = window_vect[0] + 1j*window_vect[1]
window_tens = window_tens[0] + 1j*window_tens[1]
return wEBlm, (window_vect, window_tens)
else:
return wEBlm
def get_ElmBlm(filename='test_elmblm.npy',
Qmap=None,
Umap=None,
mask=None,
lmax=100,
add_beam=None,
div_beam=None,
healpy_format=False,
recalc=False,
filtermap=False,
l0=None,
save=False):
"""computes and saves 2d (Elms, Blms) from given
Q and U maps, corrected for sqrt(fsky)
"""
if not recalc and os.path.exists(data_path + filename):
Elm2d, Blm2d = np.load(data_path + filename)
return Elm2d, Blm2d
fsky = mask.sum() / len(mask)
Elm, Blm = hp.map2alm_spin((Qmap * mask, Umap * mask), 2, lmax=lmax)
Elm /= np.sqrt(fsky)
Blm /= np.sqrt(fsky)
if add_beam is not None:
hp.sphtfunc.almxfl(Elm, add_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, add_beam, inplace=True)
if div_beam is not None:
hp.sphtfunc.almxfl(Elm, 1. / div_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, 1. / div_beam, inplace=True)
if not healpy_format:
ls, ms = hp.sphtfunc.Alm.getlm(lmax, np.arange(len(Elm)))
Elm = make2d_alm_square(Elm, lmax, ls, ms)
Blm = make2d_alm_square(Blm, lmax, ls, ms)
if save:
np.save(data_path + filename, [Elm, Blm])
return Elm, Blm
def test_map2alm_spin_spin_precomputed(self):
""" compare map2alm_spin outputs to some precomputed results for spin=1,2,3 """
for spin in [1,2,3]:
tglm, tclm = hp.map2alm_spin( [maps[spin].real, maps[spin].imag], spin, lmax_precomputed )
np.testing.assert_array_almost_equal( glms[spin], tglm, decimal=7)
np.testing.assert_array_almost_equal( clms[spin], tclm, decimal=7)
def get_spin1_alms(map_r, map_trans):
"""Get the vector spherical harmonic coefficients for spin1 harmonics.
Parameters:
-----------
map_r - np.ndarray(ndim=1, dtype=float)
map containing radial component of vector field
map_trans - list
len(map_trans) = 2
map_trans[0] - map of vector field corresponding to +1 component
map_trans[1] - map of vector field corresponding to -1 component
Returns:
--------
alm2r - spin0 spherical harmonic coefficients
alm2v - spin1 spherical harmonic coefficients for s=+1
alm2w - spin1 spherical harmonic coefficients for s=-1
"""
assert len(map_r) == len(map_trans[0]) == len(map_trans[1])
alm_r = hp.map2alm(map_r)
alm_pm = hp.map2alm_spin(map_trans, 1)
alm_v = -alm_pm[0]
alm_w = -1j * alm_pm[1]
return alm_r, alm_v, alm_w
def map2alm_spin(self,imap,lmax,spin_alm,spin_transform):
dmap = -irot2d(np.stack((imap,imap.conj())),spin=spin_alm).real
if self.hpix:
res = hp.map2alm_spin(dmap,lmax=lmax,spin=spin_transform)
return res
else:
return cs.map2alm(enmap.enmap(dmap,imap.wcs),spin=spin_transform,lmax=lmax)
def test_map2alm_spin_spin_precomputed(self):
""" compare map2alm_spin outputs to some precomputed results for spin=1,2,3 """
for spin in [1,2,3]:
tglm, tclm = hp.map2alm_spin( [maps[spin].real, maps[spin].imag], spin, lmax_precomputed )
np.testing.assert_array_almost_equal( glms[spin], tglm, decimal=7)
np.testing.assert_array_almost_equal( clms[spin], tclm, decimal=7)
def apply_ivf(self, det, tmap, pmap):
assert(self.lmax == 1000)
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
# qcinv filtering for temperature
dcf = self.lib_dir + "/dense_cache_det_" + det + ".pk"
# id preconditioners lmax nside im em tr cache
chain_descr = [ [ 2, ["split(dense("+dcf+"), 64, diag_cl)"], 256, 128, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 1, ["split(stage(2), 256, diag_cl)"], 512, 256, 3, 0.0, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()],
[ 0, ["split(stage(1), 512, diag_cl)"], 1000, 512, np.inf, 1.0e-6, qcinv.cd_solve.tr_cg, qcinv.cd_solve.cache_mem()] ]
ninv = ( hp.read_map( dmc.get_fname_iqumap(self.sim_lib.year, det, self.sim_lib.forered), hdu=1, field=3 ) /
dmc.sigma0[(self.sim_lib.year, self.sim_lib.forered, 'T')][det]**2 / 1e6 * mask_t )
n_inv_filt = qcinv.opfilt_tt.alm_filter_ninv( ninv, bl*pxw, marge_monopole=True, marge_dipole=True, marge_maps=[] )
chain = qcinv.multigrid.multigrid_chain( qcinv.opfilt_tt, chain_descr, self.cl, n_inv_filt )
tlm = np.zeros( qcinv.util_alm.lmax2nlm(self.lmax), dtype=np.complex )
chain.solve( tlm, tmap )
# simple filtering for polarization.
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
ftl, fel, fbl = self.get_ftebl(det)
hp.almxfl( elm, fel / bl / pxw, inplace=True )
hp.almxfl( blm, fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def calc_alm(Imap, Qmap, Umap, mask=None,
lmax=200,
add_beam=None,div_beam=None,
add_beamP=None,div_beamP=None,
healpy_format=False):
"""computes alms, given
maps and a mask, corrected for sqrt(fsky), optionally corrected for beams
"""
fsky = mask.sum() / len(mask)
Tlm = hp.map2alm(Imap * mask, lmax=lmax) / np.sqrt(fsky)
Elm, Blm = hp.map2alm_spin( (Qmap*mask,Umap*mask), 2, lmax=lmax )
Elm /= np.sqrt(fsky)
Blm /= np.sqrt(fsky)
if (add_beam is not None) and (add_beamP is not None):
hp.sphtfunc.almxfl(Tlm, add_beam, inplace=True)
hp.sphtfunc.almxfl(Elm, add_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, add_beam, inplace=True)
if (div_beam is not None) and (div_beamP is not None):
hp.sphtfunc.almxfl(Tlm, 1./div_beam, inplace=True)
hp.sphtfunc.almxfl(Elm, 1./div_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, 1./div_beam, inplace=True)
if not healpy_format:
ls, ms = hp.sphtfunc.Alm.getlm(lmax, np.arange(len(Tlm)))
Tlm = make2d_alm_square(Tlm, lmax, ls, ms)
Elm = make2d_alm_square(Elm, lmax, ls, ms)
Blm = make2d_alm_square(Blm, lmax, ls, ms)
return Tlm,Elm,Blm
def get_ElmBlm(filename='test_elmblm.npy',
Qmap=None, Umap=None, mask=None,
lmax=100,add_beam=None,div_beam=None,
healpy_format=False,
recalc=False,
filtermap=False, l0=None,
save=False):
"""computes and saves 2d (Elms, Blms) from given
Q and U maps, corrected for sqrt(fsky)
"""
if not recalc and os.path.exists(data_path + filename):
Elm2d, Blm2d = np.load(data_path + filename)
return Elm2d, Blm2d
fsky = mask.sum() / len(mask)
Elm, Blm = hp.map2alm_spin( (Qmap*mask,Umap*mask), 2, lmax=lmax )
Elm /= np.sqrt(fsky)
Blm /= np.sqrt(fsky)
if add_beam is not None:
hp.sphtfunc.almxfl(Elm, add_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, add_beam, inplace=True)
if div_beam is not None:
hp.sphtfunc.almxfl(Elm, 1./div_beam, inplace=True)
hp.sphtfunc.almxfl(Blm, 1./div_beam, inplace=True)
if not healpy_format:
ls, ms = hp.sphtfunc.Alm.getlm(lmax, np.arange(len(Elm)))
Elm = make2d_alm_square(Elm, lmax, ls, ms)
Blm = make2d_alm_square(Blm, lmax, ls, ms)
if save:
np.save(data_path + filename, [Elm,Blm])
return Elm, Blm
def _cache_eblm(self, idx):
elm = self.unlcmbs.get_sim_elm(idx)
blm = None if 'b' not in self.fields else self.unlcmbs.get_sim_blm(idx)
dlm = self.get_sim_plm(idx)
assert 'o' not in self.fields, 'not implemented'
lmaxd = hp.Alm.getlmax(dlm.size)
hp.almxfl(dlm,
np.sqrt(
np.arange(lmaxd + 1, dtype=float) *
np.arange(1, lmaxd + 2)),
inplace=True)
Qlen, Ulen = self.lens_module.alm2lenmap_spin([elm, blm], [dlm, None],
self.nside_lens,
2,
nband=self.nbands,
facres=self.facres,
verbose=self.verbose)
elm, blm = hp.map2alm_spin([Qlen, Ulen], 2, lmax=self.lmax)
del Qlen, Ulen
hp.write_alm(os.path.join(self.lib_dir, 'sim_%04d_elm.fits' % idx),
elm)
del elm
hp.write_alm(os.path.join(self.lib_dir, 'sim_%04d_blm.fits' % idx),
blm)
def calc_IP2_equil(Imap, Qmap, Umap, lmax=100):
Tlm = hp.map2alm(Imap)
Elm, Blm = hp.map2alm_spin((Qmap, Umap), 2)
TEE = hp.alm2cl(Tlm, Elm**2)
TBB = hp.alm2cl(Tlm, Blm**2)
TEB = hp.alm2cl(Tlm, Elm * Blm)
ls = np.arange(len(TEE))
return ls, TEE, TBB, TEB
def test_forward_backward_spin(self):
""" compare map2alm_spin outputs to alm2map_spin inputs. tolerances are very loose. """
for spin in [1,2,3]:
tcl = np.ones(self.lmax+1); tcl[0:spin] = 0.
almg = hp.almxfl(self.almg, tcl, inplace=False)
almc = hp.almxfl(self.almc, tcl, inplace=False)
rmap, imap = hp.alm2map_spin( [almg, almc], self.nside, spin, self.lmax )
tglm, tclm = hp.map2alm_spin( [rmap, imap], spin, self.lmax )
np.testing.assert_allclose( almg, tglm, rtol=1.e-2, atol=1.e-2)
np.testing.assert_allclose( almc, tclm, rtol=1.e-2, atol=1.e-2)
def _apply_ivf_p(self, pmap, soltn=None):
assert len(pmap[0]) == hp.nside2npix(self.nside) and len(
pmap[0]) == len(pmap[1])
elm, blm = hp.map2alm_spin([m for m in pmap], 2, lmax=self.lmax_fl)
elm = hp.almxfl(
elm,
self.get_fel() * utils.cli(self.transf[:len(self.fel)]))
blm = hp.almxfl(
blm,
self.get_fbl() * utils.cli(self.transf[:len(self.fbl)]))
return elm, blm
def test_forward_backward_spin(self):
""" compare map2alm_spin outputs to alm2map_spin inputs. tolerances are very loose. """
for spin in [1,2,3]:
tcl = np.ones(self.lmax+1); tcl[0:spin] = 0.
almg = hp.almxfl(self.almg, tcl, inplace=False)
almc = hp.almxfl(self.almc, tcl, inplace=False)
rmap, imap = hp.alm2map_spin( [almg, almc], self.nside, spin, self.lmax )
tglm, tclm = hp.map2alm_spin( [rmap, imap], spin, self.lmax )
np.testing.assert_allclose( almg, tglm, rtol=1.e-2, atol=1.e-2)
np.testing.assert_allclose( almc, tclm, rtol=1.e-2, atol=1.e-2)
def calc_prep(maps, s_cls, n_inv_filt):
qmap, umap = np.copy(maps[0]), np.copy(maps[1])
assert len(qmap) == len(umap)
lmax = len(n_inv_filt.b_transf) - 1
npix = len(qmap)
n_inv_filt.apply_map([qmap, umap])
elm, blm = map2alm_spin([qmap, umap], 2, lmax=lmax)
hp.almxfl(elm, n_inv_filt.b_transf * npix / (4. * np.pi), inplace=True)
hp.almxfl(blm, n_inv_filt.b_transf * npix / (4. * np.pi), inplace=True)
return eblm([elm, blm])
def calc_IP2_equil(Imap, Qmap, Umap,
lmax=100):
Tlm = hp.map2alm( Imap )
Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2 )
TEE = hp.alm2cl( Tlm, Elm**2 )
TBB = hp.alm2cl( Tlm, Blm**2 )
TEB = hp.alm2cl( Tlm, Elm*Blm )
ls = np.arange( len(TEE) )
return ls, TEE, TBB, TEB
def b_cov_T353_E143_B143(cl_file=pf.PLANCK_DATA_PATH+'bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl',lmax=100):
Imap = hp.read_map(pf.PLANCK_DATA_PATH + 'HFI_SkyMap_353_2048_R2.02_full.fits')
Tlm = hp.map2alm(Imap,lmax=lmax)
cltt = hp.alm2cl(Tlm,lmax=lmax)
mask = pf.get_planck_mask(psky=70)
Qmap, Umap = hp.read_map(pf.PLANCK_DATA_PATH + 'HFI_SkyMap_143_2048_R2.02_full.fits',field=(1,2))
Elm, Blm = hp.map2alm_spin( (Qmap*mask,Umap*mask), 2, lmax=lmax )
clee = hp.alm2cl(Elm,lmax=lmax)
clbb = hp.alm2cl(Blm,lmax=lmax)
cov = calc_b_cov_TEB(cltt, clee, clbb)
return cov
def apply_ivf(self, det, tmap, pmap):
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
bl = self.bl
pxw = hp.pixwin(self.nside)[0:self.lmax+1]
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
hp.almxfl( tlm, self.ftl / bl / pxw, inplace=True )
hp.almxfl( elm, self.fel / bl / pxw, inplace=True )
hp.almxfl( blm, self.fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def b_cov_TEB(lmax=100,frequency=353):
"""this one is map-based"""
Imap,Qmap, Umap = hp.read_map(pf.PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(frequency),field=(0,1,2))
mask = pf.get_planck_mask()
Tlm = hp.map2alm(Imap*mask,lmax=lmax)
cltt = hp.alm2cl(Tlm,lmax=lmax)
Elm, Blm = hp.map2alm_spin( (Qmap*mask,Umap*mask), 2, lmax=lmax )
clee = hp.alm2cl(Elm,lmax=lmax)
clbb = hp.alm2cl(Blm,lmax=lmax)
#hs = get_hs(lmax=100)
cov = calc_b_cov_TEB(cltt, clee, clbb)#/hs
return cov
def apodizedqu2pureeb(polxscal, polxvect, polxtens, lmax=None, mmax=None):
'''
Constructs the pure E/B alms from polarization maps apodized by the
scalar, vector, and tensor window
'''
alm = H.map2alm((-polxtens[0], -polxtens[1]),
lmax=lmax,
mmax=mmax,
pol=False)
if lmax is None:
lmax = H.Alm.getlmax(len(alm[0]))
ell, m_tmp = H.Alm.getlm(lmax)
idx = (ell >= 2)
apurelm = (np.zeros_like(alm[0]), np.zeros_like(alm[0]))
spinfact = 1.0 / np.sqrt(
(ell[idx] - 1.0) * ell[idx] * (ell[idx] + 1.0) * (ell[idx] + 2.0))
apurelm[0][idx] += spinfact * alm[0][idx]
apurelm[1][idx] += spinfact * alm[1][idx]
alm = H.map2alm_spin(polxvect, 1, lmax=lmax, mmax=mmax)
spinfact = 2.0 / np.sqrt((ell[idx] - 1.0) * (ell[idx] + 2.0))
apurelm[0][idx] += spinfact * alm[0][idx]
apurelm[1][idx] += spinfact * alm[1][idx]
alm = H.map2alm_spin(polxscal, 2, lmax=lmax, mmax=mmax)
spinfact = 1.0
apurelm[0][idx] += spinfact * alm[0][idx]
apurelm[1][idx] += spinfact * alm[1][idx]
return apurelm
def _get_sim_Tgclm(self, idx, k, swapped=False, xfilt1=None, xfilt2=None):
""" T only lensing potentials estimators """
f2map1 = self.f2map1 if not swapped else self.f2map2
f2map2 = self.f2map2 if not swapped else self.f2map1
xftl1 = xfilt1 if not swapped else xfilt2
xftl2 = xfilt2 if not swapped else xfilt1
tmap = f2map1.get_irestmap(idx, xfilt=xftl1) # healpy map
G, C = f2map2.get_gtmap(idx, k=k, xfilt=xftl2) # 2 healpy maps
G *= tmap
C *= tmap
del tmap
G, C = hp.map2alm_spin([G, C], 1, lmax=self.lmax_qlm['T'])
fl = - np.sqrt(np.arange(self.lmax_qlm['T'] + 1, dtype=float) * np.arange(1, self.lmax_qlm['T'] + 2))
hp.almxfl(G, fl, inplace=True)
hp.almxfl(C, fl, inplace=True)
return G, C
def apply_ivf(self, det, tmap, pmap):
mask_t = qcinv.util.load_map(self.mask_t)
mask_p = qcinv.util.load_map(self.mask_p)
tlm = hp.map2alm( tmap * mask_t, lmax=self.lmax, iter=0, regression=False )
elm, blm = hp.map2alm_spin( (pmap.real * mask_p, pmap.imag * mask_p), 2, lmax=self.lmax )
bl = dmc.get_bl(self.sim_lib.year, det)[0:self.lmax+1]
pxw = hp.pixwin( self.sim_lib.nside )[0:self.lmax+1]
ftl, fel, fbl = self.get_ftebl(det)
hp.almxfl( tlm, ftl / bl / pxw, inplace=True )
hp.almxfl( elm, fel / bl / pxw, inplace=True )
hp.almxfl( blm, fbl / bl / pxw, inplace=True )
return tlm, elm, blm
def apply_alm(self, alm):
"""B^dagger N^{-1} B"""
lmax = alm.lmax
hp.almxfl(alm.elm, self.b_transf, inplace=True)
hp.almxfl(alm.blm, self.b_transf, inplace=True)
qmap, umap = alm2map_spin((alm.elm, alm.blm), self.nside, 2, lmax)
self.apply_map([qmap, umap]) # applies N^{-1}
npix = len(qmap)
telm, tblm = map2alm_spin([qmap, umap], 2, lmax=lmax)
alm.elm[:] = telm
alm.blm[:] = tblm
hp.almxfl(alm.elm, self.b_transf * (npix / (4. * np.pi)), inplace=True)
hp.almxfl(alm.blm, self.b_transf * (npix / (4. * np.pi)), inplace=True)
def lens_eblm(elm,
dlm,
blm=None,
nside=2048,
lmaxout=None,
verbose=True,
nband=16,
facres=0):
# FIXME : suppress the stupid intermediate step
lenmap = eblm2lensmap(nside,
elm,
dlm,
blm=blm,
verbose=verbose,
nband=nband,
facres=facres)
return hp.map2alm_spin([lenmap.real, lenmap.imag],
2,
lmax=(lmaxout or hp.Alm.getlmax(elm.size)))
def calc_prep(maps, s_cls, n_inv_filt):
tmap, qmap, umap = np.copy(maps[0]), np.copy(maps[1]), np.copy(maps[2])
assert(len(tmap) == len(qmap)); assert(len(tmap) == len(umap))
npix = len(tmap)
n_inv_filt.apply_map([tmap, qmap, umap])
lmax = len(n_inv_filt.b_transf) - 1
tlm = hp.map2alm(tmap, lmax=lmax, iter=0, regression=False)
tlm *= npix / (4.*np.pi)
elm, blm = hp.map2alm_spin( [qmap, umap], 2, lmax=lmax)
elm *= npix / (4.*np.pi); blm *= npix / (4.*np.pi)
hp.almxfl( tlm, n_inv_filt.b_transf, inplace=True )
hp.almxfl( elm, n_inv_filt.b_transf, inplace=True )
hp.almxfl( blm, n_inv_filt.b_transf, inplace=True )
return teblm([tlm, elm, blm])
def calc_prep(maps, s_cls, n_inv_filt):
tmap, qmap, umap = np.copy(maps[0]), np.copy(maps[1]), np.copy(maps[2])
assert (len(tmap) == len(qmap))
assert (len(tmap) == len(umap))
npix = len(tmap)
n_inv_filt.apply_map([tmap, qmap, umap])
lmax = len(n_inv_filt.b_transf) - 1
tlm = hp.map2alm(tmap, lmax=lmax, iter=0)
tlm *= npix / (4. * np.pi)
elm, blm = hp.map2alm_spin([qmap, umap], 2, lmax=lmax)
elm *= npix / (4. * np.pi)
blm *= npix / (4. * np.pi)
hp.almxfl(tlm, n_inv_filt.b_transf, inplace=True)
hp.almxfl(elm, n_inv_filt.b_transf, inplace=True)
hp.almxfl(blm, n_inv_filt.b_transf, inplace=True)
return teblm([tlm, elm, blm])
def _get_sim_Pgclm(self, idx, k, swapped=False, xfilt1=None, xfilt2=None):
"""
Pol. only lensing potentials estimators
"""
f2map1 = self.f2map1 if not swapped else self.f2map2
f2map2 = self.f2map2 if not swapped else self.f2map1
xftl1 = xfilt1 if not swapped else xfilt2
xftl2 = xfilt2 if not swapped else xfilt1
repmap, impmap = f2map1.get_irespmap(idx, xfilt=xftl1)
# complex spin 2 healpy maps
Gs, Cs = f2map2.get_gpmap(idx, 3, k=k, xfilt=xftl2) # 2 healpy maps
GC = (repmap - 1j * impmap) * (Gs + 1j * Cs) # (-2 , +3)
Gs, Cs = f2map2.get_gpmap(idx, 1, k=k, xfilt=xftl2)
GC -= (repmap + 1j * impmap) * (Gs - 1j * Cs) # (+2 , -1)
del repmap, impmap, Gs, Cs
G, C = hp.map2alm_spin([GC.real, GC.imag], 1, lmax=self.lmax_qlm['P'])
del GC
fl = - np.sqrt(np.arange(self.lmax_qlm['P'] + 1, dtype=float) * np.arange(1, self.lmax_qlm['P'] + 2))
hp.almxfl(G, fl, inplace=True)
hp.almxfl(C, fl, inplace=True)
return G, C
def analworker(i):
print "This is analysis worker starting for map", i + 1, "/", nmaps
QU_maps = hp.read_map(fits[i], field=(1, 2)) #(Q,U)
pixrecip = np.concatenate((np.ones(2),
np.reciprocal(
hp.pixwin(hp.get_nside(QU_maps[0]),
pol=True)[1][2:smoothing_lmax])
)) #P pixwin #Not defined for l < 2
pm_alms = hp.map2alm_spin(QU_maps, spin, lmax=smoothing_lmax - 1)
del QU_maps
hp.almxfl(pm_alms[0], pixrecip, inplace=True) #Correcting for pixwin
hp.almxfl(pm_alms[1], pixrecip, inplace=True) #Correcting for pixwin
#Reorder to S2LET alms
pm_alms[0] = ps.lm_hp2lm(pm_alms[0], smoothing_lmax)
pm_alms[1] = ps.lm_hp2lm(pm_alms[1], smoothing_lmax)
P_alms = -1. * pm_alms[0] - 1.j * pm_alms[1] #CHECK THIS IS CORRECT!
del pm_alms
wav_maps, scal_maps = ps.analysis_lm2wav_manualtiling(
P_alms, smoothing_lmax, ndir, spin, scal_tiles, wav_tiles.T.ravel(),
scal_bandlims, wav_bandlims)
del P_alms
np.save(scal_outfits[i], scal_maps)
del scal_maps
#Splitting up output wavelet maps
offset = 0
for j in xrange(jmax + 1):
for n in xrange(ndir):
bandlim = wav_bandlims[j]
nelem = bandlim * (2. * bandlim - 1.)
wav_outfits = wav_outfits_root[i] + '_j' + str(j) + '_n' + str(
n + 1) + '.npy'
np.save(wav_outfits, wav_maps[offset:offset + nelem])
offset += nelem
del wav_maps
return 0
def apply_alm(self, alm):
# applies Y^T N^{-1} Y
lmax = alm.lmax
hp.almxfl(alm.tlm, self.b_transf, inplace=True)
hp.almxfl(alm.elm, self.b_transf, inplace=True)
hp.almxfl(alm.blm, self.b_transf, inplace=True)
tmap = hp.alm2map(alm.tlm, self.nside)
qmap, umap = hp.alm2map_spin( (alm.elm, alm.blm), self.nside, 2 )
self.apply_map( [tmap, qmap, umap] )
alm.tlm[:] = hp.map2alm(tmap, lmax=lmax, iter=0, regression=False)
alm.tlm[:] *= (self.npix / (4.*np.pi))
telm, tblm = hp.map2alm_spin( [qmap, umap], 2, lmax=lmax )
alm.elm[:] = telm; alm.blm[:] = tblm
hp.almxfl(alm.tlm, self.b_transf, inplace=True)
hp.almxfl(alm.elm, self.b_transf, inplace=True)
hp.almxfl(alm.blm, self.b_transf, inplace=True)
def apply_alm(self, alm):
# applies Y^T N^{-1} Y
lmax = alm.lmax
hp.almxfl(alm.tlm, self.b_transf, inplace=True)
hp.almxfl(alm.elm, self.b_transf, inplace=True)
hp.almxfl(alm.blm, self.b_transf, inplace=True)
tmap = hp.alm2map(alm.tlm, self.nside)
qmap, umap = hp.alm2map_spin((alm.elm, alm.blm), self.nside, 2)
self.apply_map([tmap, qmap, umap])
alm.tlm[:] = hp.map2alm(tmap, lmax=lmax, iter=0)
alm.tlm[:] *= (self.npix / (4. * np.pi))
telm, tblm = hp.map2alm_spin([qmap, umap], 2, lmax=lmax)
alm.elm[:] = telm
alm.blm[:] = tblm
hp.almxfl(alm.tlm, self.b_transf, inplace=True)
hp.almxfl(alm.elm, self.b_transf, inplace=True)
hp.almxfl(alm.blm, self.b_transf, inplace=True)
def check_cl_sims(nmaps=1,lmax=1000,nside=2048,
read_file=False,
filename='testsky.fits',frequency=100,
beam=None, beamP=None, smear=True,
nonoise=False,
cl_file='bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl'):
if read_file and os.path.exists(data_path + filename):
Tmap, Qmap, Umap = hp.read_map(data_path + filename, field=(0,1,2))
else:
if nonoise:
Tmap, Qmap, Umap = simulate_cmb(nside=nside, lmax=lmax,save=False,
smear=smear, beam=beam, beamP=beamP,
cl_file=cl_file)
else:
Tmap, Qmap, Umap = observe_cmb_sky(save=False, nside=nside, npix=None, lmax=3000,
frequency=frequency, beam=beam, beamP=beamP,
cl_file=cl_file)
Tlm = hp.map2alm(Tmap, lmax=lmax)
Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2, lmax=lmax )
if smear:
if (beam is None) or (beamP is None) :
hdulist = fits.open(data_path + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beam = hdulist[beam_index['{}'.format(frequency)]].data.NOMINAL[0][:lmax+1]
beamP = hdulist[beam_index['{}P'.format(frequency)]].data.NOMINAL[0][:lmax+1]
hp.sphtfunc.almxfl(Tlm, 1./beam, inplace=True)
hp.sphtfunc.almxfl(Elm, 1./beamP, inplace=True)
hp.sphtfunc.almxfl(Blm, 1./beamP, inplace=True)
ls = np.arange(lmax+1)
factor = ls * ( ls + 1. ) / (2.*np.pi)
cltt = hp.alm2cl(Tlm) * factor
clee = hp.alm2cl(Elm) * factor
clbb = hp.alm2cl(Blm) * factor
clte = hp.alm2cl(Tlm, Elm) * factor
cl = get_theory_cls()
ls_theory = cl[0][:lmax+1]
factor_theory = ls_theory * ( ls_theory + 1. ) / (2.*np.pi)
cltt_theory = cl[1][:lmax+1] * factor_theory
clte_theory = cl[2][:lmax+1] * factor_theory
clee_theory = cl[3][:lmax+1] * factor_theory
clbb_theory = cl[4][:lmax+1] * factor_theory
plt.figure()
plt.plot(ls, cltt, label='sims')
plt.plot(ls_theory, cltt_theory,label='theory TT')
plt.legend()
plt.figure()
plt.plot(ls, clte, label='sims')
plt.plot(ls_theory, clte_theory,label='theory TE')
plt.legend()
plt.figure()
plt.plot(ls, clee, label='sims')
plt.plot(ls_theory, clee_theory,label='theory EE')
plt.legend()
plt.figure()
plt.plot(ls, clbb, label='sims')
plt.plot(ls_theory, clbb_theory,label='theory BB')
plt.legend()
def get_alms(
maps=None,
mask=None,
maplabel='353',
showI=False,
pol=True,
intensity=True,
rewrite=False,
writemap=False,
savealms=True,
masktype='PowerSpectra', #'GalPlane2',
lmax=100):
"""Each written map file must contain I,Q,U, and each alms file
must contain Tlm, Elm, and Blm.
"""
newname = 'alms_lmax{}_mask_{}__'.format(lmax,
masktype) + maplabel + '.npy'
if not os.path.exists(data_path + newname) or rewrite:
print 'alms file {} does not exist; calculating alms...'.format(
newname)
if mask is None:
if masktype == 'PowerSpectra':
maskname = 'HFI_PowerSpect_Mask_2048_R1.10.fits'
maskfield = 0
elif masktype == 'GalPlane60':
maskname = 'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
maskfield = 2
elif masktype == 'no':
maskname = 'HFI_PowerSpect_Mask_2048_R1.10.fits'
maskfield = 0
mask = hp.read_map(data_path + maskname, field=maskfield)
if masktype == 'no':
mask = mask * 0. + 1.
masknside = hp.get_nside(mask)
if maps is None:
Imap, Qmap, Umap = hp.read_map(
data_path +
'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(maplabel),
hdu=1,
field=(0, 1, 2))
mapnside = hp.get_nside(Imap)
else:
if intensity and pol:
Imap = maps[0]
Qmap = maps[1]
Umap = maps[2]
mapnside = hp.get_nside(Imap)
elif intensity and not pol:
Imap = maps[0]
mapnside = hp.get_nside(Imap)
elif pol and not intensity:
Qmap = maps[0]
Umap = maps[1]
mapnside = hp.get_nside(Qmap)
if masknside != mapnside:
print 'adjusting mask to match map resolution...'
mask = hp.pixelfunc.ud_grade(mask, nside_out=mapnside)
if showI:
hp.mollview(Imap * mask)
alms = []
if intensity:
Imap = Imap * mask
Tlm = hp.map2alm(Imap, lmax=lmax)
alms.append(Tlm)
if pol:
Qmap *= mask
Umap *= mask
Elm, Blm = hp.map2alm_spin((Qmap, Umap), 2, lmax=lmax)
alms.append(Elm)
alms.append(Blm)
#this will only work if get_intensity and get_pol
if writemap and intensity and pol:
hp.fitsfunc.write_map(data_path + newname, [Imap, Qmap, Umap])
if savealms and intensity and pol:
np.save(data_path + newname, alms)
return alms
else:
alms = np.load(data_path + newname, 'r')
if intensity and pol:
return alms[0], alms[1], alms[2]
else:
if intensity:
return alms[0]
if pol:
return alms[1], alms[2]
def calc_TEB(Imap_name='HFI_SkyMap_353_2048_R2.02_full.fits',
Pmap_name='HFI_SkyMap_353_2048_R2.02_full.fits',
nus=None, fwhm=0.063, nside=16, lmax=100,
lmaps_only=False, filename=None):
"""Master function for computing the bispectrum TEB
"""
# read from file if it's there
if filename is None:
filename = 'bispectrum_lmax{}'.format(lmax)
if nus is not None:
filename += '_{}-{}-{}GHz.npy'.format(nus[0],nus[1],nus[1])
else:
filename += '_{}'.format(Imap_name[-5])
if Imap_name != Pmap_name:
filename += '_{}.npy'.format(Pmap_name[-5])
else:
filename += '.npy'
print 'looking for {} ...'.format(filename)
if os.path.exists(filename) and not lmaps_only:
bispectrum = np.load(filename, 'r')
return bispectrum
# compute it, if the file doesn't exist
if nus is not None:
Imap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[0])
Pmap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[1])
title = '$I_{%i} P^2_{%i}$ (equilateral)' % (nus[0],nus[1])
Imap = prepare_map( Imap_name, field=0,
nside_out=nside, fwhm=fwhm )
Tlm = hp.map2alm( Imap, lmax=lmax )
Qmap, Umap = prepare_map( Pmap_name, field=(1,2),
nside_out=nside, fwhm=fwhm )
Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2, lmax=lmax )
if lmax is None:
lmax = hp.sphtfunc.Alm.getlmax(len(Tlm))
ls, ms = hp.sphtfunc.Alm.getlm(lmax,np.arange(len(Tlm)))
lmin = ls.min()
mapsize = len(Imap)
pixelsize = hp.pixelfunc.nside2pixarea(nside)
Ylm = calc_Ylm(Imap, ls, ms)
#return Ylm, Tlm, ls, ms
print 'calculating Tl,El,Bl ...'
Tl = sum_over_m(Tlm, Ylm, ls,
lmax=lmax, lmin=lmin, mapsize=mapsize)
El = sum_over_m(Elm, Ylm, ls,
lmax=lmax, lmin=lmin, mapsize=mapsize)
Bl = sum_over_m(Blm, Ylm, ls,
lmax=lmax, lmin=lmin, mapsize=mapsize)
if lmaps_only:
return Tl,El,Bl
hs = get_hs(lmin=lmin, lmax=lmax)
print 'calculating bispectrum ...'
bispectrum = calc_bispectrum(Tl, El, Bl, hs,
pixelsize,
lmax=lmax, lmin=lmin,
mapsize=mapsize)
clean_bispectrum_of_naninf(bispectrum, hs, inplace=True)
np.save(filename, bispectrum)
return bispectrum
def get_alms(maps=None,
mask=None,
maplabel='353',
showI=False,
pol=True,
intensity=True,
rewrite=False,
writemap=False,
savealms=True,
masktype='PowerSpectra',#'GalPlane2',
lmax=100):
"""Each written map file must contain I,Q,U, and each alms file
must contain Tlm, Elm, and Blm.
"""
newname = 'alms_lmax{}_mask_{}__'.format(lmax, masktype) + maplabel + '.npy'
if not os.path.exists(data_path + newname) or rewrite:
print 'alms file {} does not exist; calculating alms...'.format(newname)
if mask is None:
if masktype == 'PowerSpectra':
maskname = 'HFI_PowerSpect_Mask_2048_R1.10.fits'
maskfield = 0
elif masktype == 'GalPlane60':
maskname = 'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
maskfield = 2
elif masktype == 'no':
maskname = 'HFI_PowerSpect_Mask_2048_R1.10.fits'
maskfield = 0
mask = hp.read_map(data_path + maskname, field=maskfield)
if masktype == 'no':
mask = mask*0. + 1.
masknside = hp.get_nside(mask)
if maps is None:
Imap,Qmap,Umap = hp.read_map( data_path + 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(maplabel),hdu=1, field=(0,1,2) )
mapnside = hp.get_nside(Imap)
else:
if intensity and pol:
Imap = maps[0]
Qmap = maps[1]
Umap = maps[2]
mapnside = hp.get_nside(Imap)
elif intensity and not pol:
Imap = maps[0]
mapnside = hp.get_nside(Imap)
elif pol and not intensity:
Qmap = maps[0]
Umap = maps[1]
mapnside = hp.get_nside(Qmap)
if masknside != mapnside:
print 'adjusting mask to match map resolution...'
mask = hp.pixelfunc.ud_grade(mask, nside_out=mapnside)
if showI:
hp.mollview(Imap*mask)
alms = []
if intensity:
Imap = Imap*mask
Tlm = hp.map2alm(Imap, lmax=lmax)
alms.append(Tlm)
if pol:
Qmap *= mask
Umap *= mask
Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2, lmax=lmax )
alms.append(Elm)
alms.append(Blm)
#this will only work if get_intensity and get_pol
if writemap and intensity and pol:
hp.fitsfunc.write_map( data_path + newname, [Imap, Qmap, Umap])
if savealms and intensity and pol:
np.save(data_path + newname, alms)
return alms
else:
alms = np.load(data_path + newname, 'r')
if intensity and pol:
return alms[0], alms[1], alms[2]
else:
if intensity:
return alms[0]
if pol:
return alms[1], alms[2]
lat_HG = np.linspace(-59.8 + 90, 59.8 + 90, 300) * np.pi / 180
lon_HG = np.linspace(0.2, 359.8, 900) * np.pi / 180
dtheta_obs, dphi_obs = lat_HG[1] - lat_HG[0], lon_HG[1] - lon_HG[0]
LON, LAT = np.meshgrid(lon_HG, lat_HG)
print(f"[{args.year}] [{args.daynum}] Making healPy maps")
vlat_map, vlat_mask, th_map, ph_map = make_map(LAT[mask], LON[mask],
vlat[mask], NSIDE)
vlon_map, vlon_mask, th_map, ph_map = make_map(LAT[mask], LON[mask],
vlon[mask], NSIDE)
map2trans = get_spin1_maps((vlat_map, vlon_map), vlat_mask)
print(f"[{args.year}] [{args.daynum}] SHT of healPy maps")
alm_pm = hp.map2alm_spin(map2trans, 1)
alm_v = -alm_pm[0] * sqrt(2)
alm_w = -1j * alm_pm[1] * sqrt(2)
ellmax = hp.sphtfunc.Alm.getlmax(len(alm_v))
ellArr, emmArr = hp.sphtfunc.Alm.getlm(ellmax)
if args.chris:
lctdir = "LCT_chris"
else:
lctdir = "LCT"
if args.testrun:
np.savez_compressed(f"/scratch/g.samarth/HMIDATA/" +
f"{lctdir}/alm_test.npz",
vlm=alm_v,
wlm=alm_w)
def check_cl_sims(
nmaps=1,
lmax=1000,
nside=2048,
read_file=False,
filename='testsky.fits',
frequency=100,
beam=None,
beamP=None,
smear=True,
nonoise=False,
cl_file='bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl'
):
if read_file and os.path.exists(data_path + filename):
Tmap, Qmap, Umap = hp.read_map(data_path + filename, field=(0, 1, 2))
else:
if nonoise:
Tmap, Qmap, Umap = simulate_cmb(nside=nside,
lmax=lmax,
save=False,
smear=smear,
beam=beam,
beamP=beamP,
cl_file=cl_file)
else:
Tmap, Qmap, Umap = observe_cmb_sky(save=False,
nside=nside,
npix=None,
lmax=3000,
frequency=frequency,
beam=beam,
beamP=beamP,
cl_file=cl_file)
Tlm = hp.map2alm(Tmap, lmax=lmax)
Elm, Blm = hp.map2alm_spin((Qmap, Umap), 2, lmax=lmax)
if smear:
if (beam is None) or (beamP is None):
hdulist = fits.open(data_path + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beam = hdulist[beam_index['{}'.format(
frequency)]].data.NOMINAL[0][:lmax + 1]
beamP = hdulist[beam_index['{}P'.format(
frequency)]].data.NOMINAL[0][:lmax + 1]
hp.sphtfunc.almxfl(Tlm, 1. / beam, inplace=True)
hp.sphtfunc.almxfl(Elm, 1. / beamP, inplace=True)
hp.sphtfunc.almxfl(Blm, 1. / beamP, inplace=True)
ls = np.arange(lmax + 1)
factor = ls * (ls + 1.) / (2. * np.pi)
cltt = hp.alm2cl(Tlm) * factor
clee = hp.alm2cl(Elm) * factor
clbb = hp.alm2cl(Blm) * factor
clte = hp.alm2cl(Tlm, Elm) * factor
cl = get_theory_cmb()
ls_theory = cl[0][:lmax + 1]
factor_theory = ls_theory * (ls_theory + 1.) / (2. * np.pi)
cltt_theory = cl[1][:lmax + 1] * factor_theory
clee_theory = cl[2][:lmax + 1] * factor_theory
clbb_theory = cl[3][:lmax + 1] * factor_theory
clte_theory = cl[4][:lmax + 1] * factor_theory
plt.figure()
plt.plot(ls, cltt, label='sims')
plt.plot(ls_theory, cltt_theory, label='theory TT')
plt.legend()
plt.figure()
plt.plot(ls, clte, label='sims')
plt.plot(ls_theory, clte_theory, label='theory TE')
plt.legend()
plt.figure()
plt.plot(ls, clee, label='sims')
plt.plot(ls_theory, clee_theory, label='theory EE')
plt.legend()
plt.figure()
plt.plot(ls, clbb, label='sims')
plt.plot(ls_theory, clbb_theory, label='theory BB')
plt.legend()
def measure_dlcl(mode='dl',frequency=353,
mask=None,
lmax=600,lmin=40,
put_mask=True,
psky=70,
mask_sources=True,
apodization=2,
beam=None,beamP=None,
Imap=None,Qmap=None,Umap=None,
Imap2=None,Qmap2=None,Umap2=None,
fsky_correction=True):
"""
If mode=='dl', returns D quantity = Cl *ls*(ls+1)/2./np.pi*1e12 [units: uK_CMB^2]
"""
if put_mask:
if mask is None:
print 'reading masks...'
mask = pf.get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apodization)
else:
mask = map.copy()*0. + 1.
if (beam is None) or (beamP is None):
print 'reading beams...'
beam_file = pf.PLANCK_DATA_PATH+'HFI_RIMO_Beams-100pc_R2.00.fits'
hdulist = pf.fits.open(beam_file)
beam = hdulist[pf.BEAM_INDEX['{}'.format(frequency)]].data.NOMINAL[0][:lmax+1]
beamP = hdulist[pf.BEAM_INDEX['{}P'.format(frequency)]].data.NOMINAL[0][:lmax+1]
beam = beam[lmin:lmax+1]
beamP = beamP[lmin:lmax+1]
fsky = mask.sum() / len(mask)
ls = np.arange(lmin, lmax+1)
if mode == 'dl':
factor = ls * (ls+1) / (2.*np.pi) * 1e12 / fsky
if mode == 'cl':
factor = 1. / fsky
if fsky_correction:
fcfilename = pf.FGS_RESULTS_PATH + 'fskycorr_fg_psky{}_apo{}_lmax1000_TT_EE_BB.npy'.format(psky,apodization)
if os.path.exists(fcfilename):
fcorr = np.load(fcfilename)
fcorr_TT = fcorr[0][lmin:lmax+1]
fcorr_EE = fcorr[1][lmin:lmax+1]
fcorr_BB = fcorr[2][lmin:lmax+1]
else:
fcorr = None
else:
fcorr = None
if (Imap is None) or (Qmap is None) or (Umap is None):
print 'reading maps...'
mapname1 = pf.PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_halfmission-1.fits'.format(frequency)
mapname2 = pf.PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_halfmission-2.fits'.format(frequency)
Imap = hp.read_map(mapname1,field=0)
Imap2 = hp.read_map(mapname2,field=0)
Qmap, Umap = hp.read_map(mapname1,field=(1,2))
Qmap2, Umap2 = hp.read_map(mapname2,field=(1,2))
Tlm1 = hp.map2alm(Imap*mask, lmax=lmax)
Elm1, Blm1 = hp.map2alm_spin( (Qmap*mask, Umap*mask), 2, lmax=lmax )
if (Imap2 is None) or (Qmap2 is None) or (Umap2 is None):
Tlm2 = Tlm1
Elm2 = Elm1
Blm2 = Blm1
else:
Tlm2 = hp.map2alm(Imap2*mask, lmax=lmax)
Elm2, Blm2 = hp.map2alm_spin( (Qmap2*mask,Umap2*mask), 2, lmax=lmax )
TT = hp.alm2cl(Tlm1, Tlm2)
EE = hp.alm2cl(Elm1, Elm2)
BB = hp.alm2cl(Blm1, Blm2)
EE = EE[lmin:] * factor / beamP**2
TT = TT[lmin:] * factor / beam**2
BB = BB[lmin:] * factor / beamP**2
TE = hp.alm2cl(Tlm1, Elm2)
EB = hp.alm2cl(Blm1, Elm2)
TB = hp.alm2cl(Blm1, Tlm2)
TE = TE[lmin:] * factor / beam / beamP
TB = TB[lmin:] * factor / beam / beamP
EB = EB[lmin:] * factor / beamP**2
if fcorr is not None:
TT *= fcorr_TT
EE *= fcorr_EE
BB *= fcorr_BB
return ls, TT, EE, BB, TE, TB, EB
def map2alm_spin(maps, spin, lmax=None, mmax=None):
assert spin >= 0, spin
if spin > 0:
return hp.map2alm_spin(maps, spin, lmax=lmax, mmax=mmax)
else:
return -hp.map2alm(maps[0], lmax=lmax, mmax=mmax, iter=0), 0.
def calc_TEB(Imap_name='HFI_SkyMap_353_2048_R2.02_full.fits',
Pmap_name='HFI_SkyMap_353_2048_R2.02_full.fits',
nus=None,
fwhm=0.063,
nside=16,
lmax=100,
lmaps_only=False,
filename=None):
"""Master function for computing the bispectrum TEB
"""
# read from file if it's there
if filename is None:
filename = 'bispectrum_lmax{}'.format(lmax)
if nus is not None:
filename += '_{}-{}-{}GHz.npy'.format(nus[0], nus[1], nus[1])
else:
filename += '_{}'.format(Imap_name[-5])
if Imap_name != Pmap_name:
filename += '_{}.npy'.format(Pmap_name[-5])
else:
filename += '.npy'
print 'looking for {} ...'.format(filename)
if os.path.exists(filename) and not lmaps_only:
bispectrum = np.load(filename, 'r')
return bispectrum
# compute it, if the file doesn't exist
if nus is not None:
Imap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[0])
Pmap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[1])
title = '$I_{%i} P^2_{%i}$ (equilateral)' % (nus[0], nus[1])
Imap = prepare_map(Imap_name, field=0, nside_out=nside, fwhm=fwhm)
Tlm = hp.map2alm(Imap, lmax=lmax)
Qmap, Umap = prepare_map(Pmap_name,
field=(1, 2),
nside_out=nside,
fwhm=fwhm)
Elm, Blm = hp.map2alm_spin((Qmap, Umap), 2, lmax=lmax)
if lmax is None:
lmax = hp.sphtfunc.Alm.getlmax(len(Tlm))
ls, ms = hp.sphtfunc.Alm.getlm(lmax, np.arange(len(Tlm)))
lmin = ls.min()
mapsize = len(Imap)
pixelsize = hp.pixelfunc.nside2pixarea(nside)
Ylm = calc_Ylm(Imap, ls, ms)
#return Ylm, Tlm, ls, ms
print 'calculating Tl,El,Bl ...'
Tl = sum_over_m(Tlm, Ylm, ls, lmax=lmax, lmin=lmin, mapsize=mapsize)
El = sum_over_m(Elm, Ylm, ls, lmax=lmax, lmin=lmin, mapsize=mapsize)
Bl = sum_over_m(Blm, Ylm, ls, lmax=lmax, lmin=lmin, mapsize=mapsize)
if lmaps_only:
return Tl, El, Bl
hs = get_hs(lmin=lmin, lmax=lmax)
print 'calculating bispectrum ...'
bispectrum = calc_bispectrum(Tl,
El,
Bl,
hs,
pixelsize,
lmax=lmax,
lmin=lmin,
mapsize=mapsize)
clean_bispectrum_of_naninf(bispectrum, hs, inplace=True)
np.save(filename, bispectrum)
return bispectrum
def measure_dlcl(mode='dl',
frequency=353,
mask=None,
lmax=600,
lmin=40,
put_mask=True,
psky=70,
mask_sources=True,
apodization=2,
beam=None,
beamP=None,
Imap=None,
Qmap=None,
Umap=None,
Imap2=None,
Qmap2=None,
Umap2=None,
fsky_correction=True):
"""
If mode=='dl', returns D quantity = Cl *ls*(ls+1)/2./np.pi*1e12 [units: uK_CMB^2]
"""
if put_mask:
if mask is None:
print 'reading masks...'
mask = pf.get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apodization)
else:
mask = map.copy() * 0. + 1.
if (beam is None) or (beamP is None):
print 'reading beams...'
beam_file = pf.PLANCK_DATA_PATH + 'HFI_RIMO_Beams-100pc_R2.00.fits'
hdulist = pf.fits.open(beam_file)
beam = hdulist[pf.BEAM_INDEX['{}'.format(
frequency)]].data.NOMINAL[0][:lmax + 1]
beamP = hdulist[pf.BEAM_INDEX['{}P'.format(
frequency)]].data.NOMINAL[0][:lmax + 1]
beam = beam[lmin:lmax + 1]
beamP = beamP[lmin:lmax + 1]
fsky = mask.sum() / len(mask)
ls = np.arange(lmin, lmax + 1)
if mode == 'dl':
factor = ls * (ls + 1) / (2. * np.pi) * 1e12 / fsky
if mode == 'cl':
factor = 1. / fsky
if fsky_correction:
fcfilename = pf.FGS_RESULTS_PATH + 'fskycorr_fg_psky{}_apo{}_lmax1000_TT_EE_BB.npy'.format(
psky, apodization)
if os.path.exists(fcfilename):
fcorr = np.load(fcfilename)
fcorr_TT = fcorr[0][lmin:lmax + 1]
fcorr_EE = fcorr[1][lmin:lmax + 1]
fcorr_BB = fcorr[2][lmin:lmax + 1]
else:
fcorr = None
else:
fcorr = None
if (Imap is None) or (Qmap is None) or (Umap is None):
print 'reading maps...'
mapname1 = pf.PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_halfmission-1.fits'.format(
frequency)
mapname2 = pf.PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_halfmission-2.fits'.format(
frequency)
Imap = hp.read_map(mapname1, field=0)
Imap2 = hp.read_map(mapname2, field=0)
Qmap, Umap = hp.read_map(mapname1, field=(1, 2))
Qmap2, Umap2 = hp.read_map(mapname2, field=(1, 2))
Tlm1 = hp.map2alm(Imap * mask, lmax=lmax)
Elm1, Blm1 = hp.map2alm_spin((Qmap * mask, Umap * mask), 2, lmax=lmax)
if (Imap2 is None) or (Qmap2 is None) or (Umap2 is None):
Tlm2 = Tlm1
Elm2 = Elm1
Blm2 = Blm1
else:
Tlm2 = hp.map2alm(Imap2 * mask, lmax=lmax)
Elm2, Blm2 = hp.map2alm_spin((Qmap2 * mask, Umap2 * mask),
2,
lmax=lmax)
TT = hp.alm2cl(Tlm1, Tlm2)
EE = hp.alm2cl(Elm1, Elm2)
BB = hp.alm2cl(Blm1, Blm2)
EE = EE[lmin:] * factor / beamP**2
TT = TT[lmin:] * factor / beam**2
BB = BB[lmin:] * factor / beamP**2
TE = hp.alm2cl(Tlm1, Elm2)
EB = hp.alm2cl(Blm1, Elm2)
TB = hp.alm2cl(Blm1, Tlm2)
TE = TE[lmin:] * factor / beam / beamP
TB = TB[lmin:] * factor / beam / beamP
EB = EB[lmin:] * factor / beamP**2
if fcorr is not None:
TT *= fcorr_TT
EE *= fcorr_EE
BB *= fcorr_BB
return ls, TT, EE, BB, TE, TB, EB
def g2eb(g1, g2):
nside = gnside(g1)
(ae, ab) = hp.map2alm_spin((g1, g2), 2)
ke = hp.alm2map(ae, nside, pol=False)
kb = hp.alm2map(ab, nside, pol=False)
return ke, kb
