def test_correlated_alm():
lmax = 2000
ells = np.arange(0, lmax, 1)
def get_cls(ells, index, amplitude):
cls = amplitude * ells.astype(np.float32)**index
cls[ells < 2] = 0
return cls
Clf1f1 = get_cls(ells, -1, 1)
Clf2f2 = get_cls(ells, -1.3, 2)
Clf1f2 = get_cls(ells, -1.4, 0.5)
alm_f1 = hp.synalm(Clf1f1, lmax=lmax - 1)
alm_f2 = deltag.generate_correlated_alm(alm_f1, Clf1f1, Clf2f2, Clf1f2)
f1f1 = hp.alm2cl(alm_f1, alm_f1)
f2f2 = hp.alm2cl(alm_f2, alm_f2)
f1f2 = hp.alm2cl(alm_f1, alm_f2)
pl = io.Plotter(xyscale='linlog', scalefn=lambda x: x)
pl.add(ells, f1f1, color="C0", alpha=0.4)
pl.add(ells, f2f2, color="C1", alpha=0.4)
pl.add(ells, f1f2, color="C2", alpha=0.4)
pl.add(ells, Clf1f1, label="f1f1", color="C0", ls="--", lw=3)
pl.add(ells, Clf2f2, label="f2f2", color="C1", ls="--", lw=3)
pl.add(ells, Clf1f2, label="f1f2", color="C2", ls="--", lw=3)
pl.done()
def calculate(self):
"""
calculate various properties of the gaussian, fnl, gnl maps i.e. compute
* Ais
* As
* Cls
* dipoles using remove_dipole
"""
inpCls=self.inputCls[:self.lmax+1]
if (self.gausmap0!=None):
self.gausCls0=hp.alm2cl(self.gausalm0)[0:self.lmax+1]
self.gausCls1=hp.alm2cl(self.gausalm1)[0:self.lmax+1]
self.gausA0=get_A0(self.gausCls0[1:], inpCls[1:])
self.gausAi=Ais(self.gausmap1, self.lmax); self.gausA=AistoA(self.gausAi)
self.gausAi2=Ais(self.gausmap0, self.lmax); self.gausA2=AistoA(self.gausAi2)
self.gausmp=hp.remove_monopole(self.gausmap0, fitval=True)[1]
self.gausdipole=get_dipole(self.gausmap1)
if (self.fnlmaps0!=None):
self.fnlCls0=[]; self.fnlCls1=[]; self.fnlA0=[]; self.fnlAi=[]; self.fnlAi2=[]
self.fnlmp=[]; self.fnldipole=[]; self.fnlA=[]; self.fnlA2=[]
for i in range(self.Nfnls):
self.fnlCls0.append(hp.anafast(self.fnlmaps0[i], nspec=self.lmax))
self.fnlCls1.append(hp.anafast(self.fnlmaps1[i], nspec=self.lmax))
self.fnlA0.append(get_A0(self.fnlCls0[i][1:], inpCls[1:]))
self.fnlAi.append(Ais(self.fnlmaps1[i], self.lmax)); self.fnlA.append(AistoA(self.fnlAi[i]))
self.fnlAi2.append(Ais(self.fnlmaps0[i], self.lmax)); self.fnlA2.append(AistoA(self.fnlAi2[i]))
self.fnlmp.append(hp.remove_monopole(self.fnlmaps0[i], fitval=True)[1])
self.fnldipole.append(get_dipole(self.fnlmaps1[i]))
def get_spectra(tube, omap, nside, res, white, w2):
zeros = []
if nside is not None:
mlmax = nside * 2
alms = [
hp.map2alm(omap[i, 0, ...], lmax=mlmax, pol=True) / np.sqrt(w2)
for i in range(2)
]
else:
mlmax = 4096 * 2 / res
alms = [
cs.map2alm(omap[i, 0, ...], lmax=mlmax, spin=[0, 2]) / np.sqrt(w2)
for i in range(2)
]
for i in range(2):
for j in range(i, 2):
for p in range(3):
for q in range(p + 1, 3):
zeros.append(hp.alm2cl(alms[i][p], alms[j][q]))
for i in range(1, 3):
zeros.append(hp.alm2cl(alms[0][i], alms[1][i]))
c11 = [hp.alm2cl(alms[0][i], alms[0][i]) for i in range(3)]
c22 = [hp.alm2cl(alms[1][i], alms[1][i]) for i in range(3)]
cross = hp.alm2cl(alms[0][0], alms[1][0])
if white or tube[0] == "S":
zeros.append(cross)
c12 = []
else:
c12 = [cross]
ls = np.arange(c11[0].size)
return ls, c11, c22, c12, zeros
def check_EBlm2d(nu1=100,nu2=143, lmax=300,
maskfield=2, source_maskfield=0,
label_loc='lower right', xmax=None):
map_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nu1)
Q1,U1 =hp.read_map(data_path + map_name, field=(1,2))
map_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nu2)
Q2,U2 =hp.read_map(data_path + map_name, field=(1,2))
mask=hp.read_map(data_path + 'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
field=maskfield)
smask=hp.read_map(data_path + 'HFI_Mask_PointSrc_2048_R2.00.fits',
field=source_maskfield)
mask *= smask
hdulist = fits.open(data_path + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beam1 = hdulist[beam_index['{}P'.format(nu1)]].data.NOMINAL[0][:lmax+1]
beam2 = hdulist[beam_index['{}P'.format(nu2)]].data.NOMINAL[0][:lmax+1]
elm1,blm1 = get_ElmBlm(lmax=lmax, Qmap=Q1, Umap=U1, mask=mask,
healpy_format=False, recalc=True, div_beam=beam1)
elm_hp1,blm_hp1 = get_ElmBlm(lmax=lmax, Qmap=Q1, Umap=U1, mask=mask,
healpy_format=True, recalc=True, div_beam=beam1)
elm2,blm2 = get_ElmBlm(lmax=lmax, Qmap=Q2, Umap=U2, mask=mask,
healpy_format=False, recalc=True, div_beam=beam2)
elm_hp2,blm_hp2 = get_ElmBlm(lmax=lmax, Qmap=Q2, Umap=U2, mask=mask,
healpy_format=True, recalc=True, div_beam=beam2)
clee = cl_alm2d(alm1=elm1, alm2=elm2, lmax=lmax)
clbb = cl_alm2d(alm1=blm1,alm2=blm2, lmax=lmax)
l = np.arange(len(clee))
clee_hp = hp.alm2cl(elm_hp1,elm_hp2, lmax=lmax)
clbb_hp = hp.alm2cl(blm_hp1,blm_hp2, lmax=lmax)
l_hp = np.arange(len(clee_hp))
clplanck = np.loadtxt(data_path + 'bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl')
clee_planck = clplanck[:,3]
clbb_planck = clplanck[:,4]
l_planck = clplanck[:,0]
pl.figure()
pl.title('EE check')
pl.plot(l, clee*l*(l+1)/2./np.pi*1e12, label='2d')
pl.plot(l,clee_hp*l_hp*(l_hp+1)/2./np.pi*1e12, label='healpy')
pl.plot(l_planck, clee_planck, label='planck best fit')
pl.legend(loc=label_loc)
if xmax is None:
pl.xlim(xmax=lmax)
else:
pl.xlim(xmax=xmax)
pl.figure()
pl.title('BB check')
pl.plot(l, clbb*l*(l+1)/2./np.pi*1e12, label='2d')
pl.plot(l_hp,clbb_hp*l_hp*(l_hp+1)/2./np.pi*1e12, label='healpy')
pl.plot(l_planck, clbb_planck, label='planck best fit')
pl.legend(loc=label_loc)
if xmax is None:
pl.xlim(xmax=lmax)
else:
pl.xlim(xmax=xmax)
def cleaned_map():
weights = w_ell()[0]
for ell in np.arange(lmax + 1):
print('ell>>>', ell)
for m in np.arange(ell + 1):
for i in np.arange(map_num):
alm_comb[i, hp.Alm.getidx(lmax=lmax, l=ell, m=m)] = alm_comb[
i, hp.Alm.getidx(lmax=lmax, l=ell, m=m)] * weights[ell, i]
for j in np.arange(n_realization):
alm_comb_n[:, j, hp.Alm.getidx(
lmax=lmax, l=ell, m=m
)] = alm_comb_n[:, j,
hp.Alm.getidx(lmax=lmax, l=ell, m=m
)] * weights[ell]
cleaned_alm = np.sum(alm_comb, axis=0)
noise_bias = np.sum(alm_comb_n, axis=0)
Cl_noise = np.sum(hp.alm2cl(noise_bias, nspec=n_realization),
axis=0) / n_realization
np.save('Cl_noise_sim', Cl_noise)
print('shape_cleaned_alm', np.shape(cleaned_alm))
cleaned_Cl = hp.alm2cl(cleaned_alm)
Cl_debias = cleaned_Cl - Cl_noise
cleaned_map = hp.alm2map(cleaned_alm, nside=512)
ell = np.arange(len(cleaned_Cl))
cleaned_Dl = ell * (ell + 1) / 2 / np.pi * cleaned_Cl
cleaned_Dl_debias = ell * (ell + 1) / 2 / np.pi * Cl_debias
return cleaned_map, cleaned_Dl, cleaned_Dl_debias
def get_power(map_list, ivar_list, a, b, mask, N=20):
"""
Calculate the average coadded flattened power spectrum P_{ab} used to generate simulation for the splits.
Inputs:
map_list: list of source free splits
ivar_list: list of the inverse variance maps splits
a: 0,1,2 for I,Q,U respectively
b:0,1,2 for I,Q,U, respectively
N: window to smooth the power spectrum by in the rolling average.
mask: apodizing mask
Output:
1D power spectrum accounted for w2 from 0 to 10000
"""
pmap = enmap.pixsizemap(map_list[0].shape, map_list[0].wcs)
cl_ab = []
n = len(map_list)
#calculate the coadd maps
if a != b:
coadd_a = coadd_mapnew(map_list, ivar_list, a)
coadd_b = coadd_mapnew(map_list, ivar_list, b)
else:
coadd_a = coadd_mapnew(map_list, ivar_list, a)
for i in range(n):
print(i)
if a != b:
d_a = map_list[i][a] - coadd_a
noise_a = d_a * np.sqrt(ivar_eff(i, ivar_list) / pmap) * mask
alm_a = cs.map2alm(noise_a, lmax=10000)
d_b = map_list[i][b] - coadd_b
noise_b = d_b * np.sqrt(ivar_eff(i, ivar_list) / pmap) * mask
alm_b = cs.map2alm(noise_b, lmax=10000)
cls = hp.alm2cl(alm_a, alm_b)
cl_ab.append(cls)
else:
d_a = map_list[i][a] - coadd_a
noise_a = d_a * np.sqrt(ivar_eff(i, ivar_list) / pmap) * mask
print("generating alms")
alm_a = cs.map2alm(noise_a, lmax=10000)
cls = hp.alm2cl(alm_a)
cl_ab.append(cls)
cl_ab = np.array(cl_ab)
sqrt_ivar = np.sqrt(ivar_eff(0, ivar_list) / pmap)
mask_ivar = sqrt_ivar * 0 + 1
mask_ivar[sqrt_ivar <= 0] = 0
mask = mask * mask_ivar
mask[mask <= 0] = 0
w2 = np.sum((mask**2) * pmap) / np.pi / 4.
power = 1 / n / (n - 1) * np.sum(cl_ab, axis=0)
ls = np.arange(len(power))
power[~np.isfinite(power)] = 0
power = rolling_average(power, N)
bins = np.arange(len(power))
power = maps.interp(bins, power)(ls)
return power / w2
def run_alm2spec(path_name, overw):
#TODO to process spectrum, need to know the beamfunction? is it 5arcmin?
# beamf = {'12345-12345': hp.gauss_beam()}
# C_lS_unsc = trsf_s.apply_beamf(C_lS_unsc, cf, ['12345-12345'], speccomb, beamf)
# io.alert_cached(io.signal_sc_path_name)
cmb_tlm, cmb_elm, cmb_blm = cslib.load_alms('cmb', csu.sim_id)
C_lS_unsc = np.array([hp.alm2cl([cmb_tlm, cmb_elm, cmb_blm])[:,:csu.cf['pa']['lmax']+1]])
C_lS = trsf_s.apply_scale(C_lS_unsc, csu.cf['pa']["Spectrum_scale"])
io.save_data(C_lS, path_name)
C_lS = hp.alm2cl([cmb_tlm, cmb_elm, cmb_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 on_epoch_end(self, batch, logs={}):
self.epochs_waited += 1
if self.epochs_waited == self.patience:
X_val, y_val = self.validation_data.T[0].T, self.validation_data.T[
1].T
if self.is_3d:
X_val = np.expand_dims(X_val, axis=-1)
y_predict = self.model.predict(X_val)
trans = []
res = []
for i in range(np.squeeze(y_predict).T.shape[0]):
y_pred = np.squeeze(y_predict).T[i].T.flatten(
) # choose given freq band, then flatten out
y_pred = y_pred[self.rearr]
y_pred = hp.map2alm(y_pred)
y_pred = hp.alm2cl(y_pred)
# Get Cls for COSMO spectrum
y_v = np.squeeze(y_val)
y_v = y_v.T[i].T.flatten()
y_v = y_v[self.rearr]
y_v = hp.map2alm(y_v)
y_v = hp.alm2cl(y_v)
# compute transfer
trans.append(np.sqrt((y_pred / y_v))[1:])
# compute residual power spec
res.append(np.abs(((y_v - y_pred) / y_v))[1:])
res_avg = np.mean(np.array(res))
trans_avg = np.mean(np.array(trans))
self._data['val_avg_transfer'].append(trans_avg)
self._data['val_avg_res'].append(res_avg)
if self.record_spectra:
self._data['val_transfer'].append(np.array(trans))
self._data['val_res'].append(np.array(res))
print(" - val avg transfer: %0.4f" % (trans_avg))
print(" - val avg res: %0.4f" % (res_avg))
# reset number of epochs waited
self.epochs_waited = 0
else:
# increase num epochs waited
self.epochs_waited += 1
return
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 _harmonic_ilc_alm(components, instrument, alms, lbins=None, fsky=None):
cl_in = np.array([hp.alm2cl(alm) for alm in alms])
# Multipoles for the ILC bins
lmax = hp.Alm.getlmax(alms.shape[-1])
ell = hp.Alm.getlm(lmax)[0]
if lbins is not None:
ell = np.digitize(ell, lbins)
# NOTE: use lmax for indexing alms, ell.max() is the maximum bin index
# Make alms real
alms = np.asarray(alms, order='C')
alms = alms.view(np.float64)
alms[...,
np.arange(1, 2 * (lmax + 1), 2)] = hp.UNSEEN # Mask imaginary m = 0
ell = np.stack((ell, ell), axis=-1).reshape(-1)
if alms.ndim > 2: # TEB -> ILC indipendently on each Stokes
n_stokes = alms.shape[1]
assert n_stokes in [1, 3], "Alms must be either T only or T E B"
alms[:, 1:,
[0, 2, 2 * lmax + 2, 2 * lmax + 3]] = hp.UNSEEN # EB for ell < 2
ell = np.stack([ell] * n_stokes) # Replicate ell for every Stokes
ell += np.arange(n_stokes).reshape(-1, 1) * (ell.max() + 1
) # Add offset
res = ilc(components, instrument, alms, ell)
# Craft output
res.s[res.s == hp.UNSEEN] = 0.
res.s = np.asarray(res.s, order='C').view(np.complex128)
cl_out = np.array([hp.alm2cl(alm) for alm in res.s])
res.cl_in = cl_in
res.cl_out = cl_out
if fsky:
res.cl_in /= fsky
res.cl_out /= fsky
res.fsky = fsky
lrange = np.arange(lmax + 1)
ldigitized = np.digitize(lrange, lbins)
with np.errstate(divide='ignore', invalid='ignore'):
res.l_ref = (np.bincount(ldigitized, lrange * 2 * lrange + 1) /
np.bincount(ldigitized, 2 * lrange + 1))
res.freq_cov *= 2 # sqrt(2) missing between complex-real alm conversion
if res.s.ndim > 2:
res.freq_cov = res.freq_cov.reshape(n_stokes, -1,
*res.freq_cov.shape[1:])
res.W = res.W.reshape(n_stokes, -1, *res.W.shape[1:])
return res
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 compare_power(input_file1,
input_file2,
nside=2048,
lmax=2000,
read_inputs=True,
object1=None,
object2=None,
display_plots=True):
''' simple power spectrum comparison '''
map_dens1 = create_healpix_map(input_file1,
nside,
read_inputs=read_inputs,
object1=object1)
map_dens2 = create_healpix_map(input_file2,
nside,
read_inputs=read_inputs,
object1=object2)
# scaling to remove monopole - this helps with power spectrum computation.
mask_octant = compute_mask(nside=nside, octant="upper")
map_dens1 = scale_map(map_dens1, mask_octant, nside=nside)
map_dens2 = scale_map(map_dens2, mask_octant, nside=nside)
print('comparing density maps')
hp.mollview(map_dens1)
hp.mollview(map_dens2)
hp.mollzoom(map_dens1 - map_dens2)
if display_plots:
plt.show()
alm_1 = hp.map2alm(map_dens1, lmax=lmax)
alm_2 = hp.map2alm(map_dens2, lmax=lmax)
cl_1 = hp.alm2cl(alm_1)
cl_2 = hp.alm2cl(alm_2)
print('computed power spectra')
l = np.arange(lmax + 1)
fsky = 1. / 8
plt.figure()
plt.plot(l, cl_1 * l * (l + 1) / (2. * np.pi) / fsky)
plt.plot(l, cl_2 * l * (l + 1) / (2. * np.pi) / fsky)
plt.savefig('cls_' + input_file1 + '_' + input_file2 + '.png', dpi=500)
if display_plots:
plt.show()
plt.figure()
plt.plot(l, np.abs(cl_1 - cl_2) / cl_1)
plt.savefig('cls_fractional_' + input_file1 + '_' + input_file2 + '.png',
dpi=500)
if display_plots:
plt.show()
return map_dens1, map_dens2, cl_1, cl_2
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 __call__(self, alm1, alm2):
assert alm1.lmaxt == alm2.lmaxt, (alm1.lmaxt, alm2.lmaxt)
assert alm1.lmaxe == alm2.lmaxe, (alm1.lmaxe, alm2.lmaxe)
assert alm1.lmaxb == alm2.lmaxb, (alm1.lmaxb, alm2.lmaxb)
ret = np.sum(
hp.alm2cl(alm1.tlm, alm2.tlm) *
(2. * np.arange(0, alm1.lmaxt + 1) + 1))
ret += np.sum(
hp.alm2cl(alm1.elm, alm2.elm) *
(2. * np.arange(0, alm1.lmaxe + 1) + 1))
ret += np.sum(
hp.alm2cl(alm1.blm, alm2.blm) *
(2. * np.arange(0, alm1.lmaxb + 1) + 1))
return ret
def CG_algo(Matrix, b, data_start, i_max, eps):
"""
Cf. algorithm B2 from Shewchuk 94 (page 50)
Matrix is the function to apply the matrix on a vector (eg A_matrix_func)
data_start is a data_class class, with real alms
"""
i = 0
x = data_start.alm.copy()
cl_th = data_start.cl_th
beam = data_start.beam
sigma = data_start.sigma
invvar = data_start.invvar
lmax = data_start.lmax
nside = data_start.nside
out = data_class([0, cl_th, beam, sigma, invvar, lmax, nside])
r = b - Matrix(data_start)
d = data_class([0, cl_th, beam, sigma, invvar, lmax, nside])
d.alm = r.copy()
delt_n = np.dot(r.T, r)
delt_0 = delt_n.copy()
iter_out_map = []
iter_out_cl = []
res = []
x_list = []
while i < i_max and delt_n > (eps ** 2 * delt_0):
q = Matrix(d)
alph = np.float(delt_n) / np.dot(d.alm.T, q)
x = x + alph * d.alm
if i % 10 == 0:
dat_temp = data_class([0, cl_th, beam, sigma, invvar, lmax, nside])
dat_temp.alm = x
r = b - Matrix(dat_temp)
else:
r = r - alph * q
delt_old = delt_n.copy()
delt_n = np.dot(r.T, r)
bet = delt_n / delt_old
d.alm = r + bet * d.alm
i += 1
out.alm = x
res.append(
hp.alm2cl(real2complex_alm(r - (b - Matrix(data_start))))
/ hp.alm2cl(real2complex_alm(b - Matrix(data_start)))
)
# x_list.append(real2complex_alm(x))
iter_out_map.append(return_map(out)[1])
iter_out_cl.append(return_map(out)[0])
return iter_out_map, iter_out_cl, res # x_list
def get_window_power_cl(self, corr={}, indxs={}):
win = {}
if not self.use_window:
win = {'cl': self.f_sky, 'M': self.coupling_M, 'xi': 1, 'xi_b': 1}
return win
m1m2 = np.absolute(self.m1_m2s[(corr[0], corr[1])]).flatten()
# print(m1m2[0],self.wig_3j)
wig_3j_1 = self.wig_3j[m1m2[0]]
wig_3j_2 = self.wig_3j[m1m2[1]]
z_bin1 = self.z_bins[corr[0]][indxs[0]]
z_bin2 = self.z_bins[corr[1]][indxs[1]]
alm1 = z_bin1['window_alm']
alm2 = z_bin2['window_alm']
win['cl'] = hp.alm2cl(alms1=alm1,
alms2=alm2,
lmax_out=self.window_lmax) #This is f_sky*cl.
win['M'] = self.coupling_matrix_large(
win['cl'], wig_3j_1, wig_3j_2) * (2 * self.l[:, None] + 1
) #FIXME: check ordering
#Note that this matrix leads to pseudo cl, which differs by factor of f_sky from true cl
if self.do_xi:
th, win['xi'] = self.HT.projected_correlation(l_cl=self.window_l,
m1_m2=(0, 0),
cl=win['cl'])
win['xi_b'] = self.binning.bin_1d(xi=win['xi'],
bin_utils=self.xi_bin_utils[(0,
0)])
del alm1
del alm2
return win
def calculate(self):
"""
calculate various properties of the gaussian, fnl, gnl maps i.e. compute
* Ais
* As
* Cls
* dipoles using remove_dipole
"""
inpCls=self.inputCls[:self.lmax+1]
if (self.gausmap!=None):
self.gausCls=hp.alm2cl(self.gausalm)[0:self.lmax+1]
self.gausA0=get_A0(self.gausCls[1:], inpCls[1:])
self.gausAi=Ais(self.gausmap, self.lmax); self.gausA=AistoA(self.gausAi)
self.gausmp=hp.remove_monopole(self.gausmap, fitval=True)[1]
self.gausdipole=get_dipole(self.gausmap)
if (self.gnlmaps!=None):
self.gnlCls=[]; self.gnlA0=[]; self.gnlAi=[]
self.gnlmp=[]; self.gnldipole=[]; self.gnlA=[]
for i in range(self.Ngnls):
self.gnlCls.append(hp.anafast(self.gnlmaps[i], nspec=self.lmax))
self.gnlA0.append(get_A0(self.gnlCls[i][1:], inpCls[1:]))
self.gnlAi.append(Ais(self.gnlmaps[i], self.lmax)); self.gnlA.append(AistoA(self.gnlAi[i]))
self.gnlmp.append(hp.remove_monopole(self.gnlmaps[i], fitval=True)[1])
self.gnldipole.append(get_dipole(self.gnlmaps[i]))
def VisualizeAlm(alm,figno=1,max_l=None,annot=''):
""" Visualize a healpy a_lm vector """
lmax = hp.Alm.getlmax(f_lm.size)
l,m = hp.Alm.getlm(lmax)
mag = np.zeros([lmax+1,lmax+1])
phs = np.zeros([lmax+1,lmax+1])
a_lm = np.zeros([lmax+1,lmax+1],dtype='complex128')
mag[m,l] = np.abs(alm)
phs[m,l] = np.angle(alm)
a_lm[m,l] = alm
cl = hp.alm2cl(alm)
# Decide the range of l to plot
if max_l != None:
max_l = (max_l if (max_l <= lmax) else lmax)
else:
max_l = lmax
print max_l
plt.figure(figno,figsize=(4,4))
plt.clf()
plt.subplot(211)
plt.imshow(mag[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.xlabel(r'$\ell$')
plt.ylabel(r'$m$')
plt.colorbar()
plt.title(annot+'Magnitude')
plt.subplot(212)
plt.imshow(phs[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.xlabel(r'$\ell$')
plt.ylabel(r'$m$')
plt.colorbar()
plt.title(annot+'Phase')
# plt.subplot(313)
#plt.semilogy(cl[0:max_l])
return {'mag':mag,'phs':phs,'cl':cl,'a_lm':a_lm}
def getsigmap(self, sigtype):
"""Generate a healpix CMB map with TT, EE, and BB"""
fn = self.inputmap.replace('rxxxx', 'r{:04d}'.format(self.rlz))
if 'EnoB' in self.sigtype:
fn = fn.replace('camb_planck2013', 'camb_planck2013_EnoB')
# Load
self.hmap = np.array(hp.read_map('input_maps/' + fn, field=(0, 1, 2)))
self.Nside = hp.npix2nside(self.hmap[0].size)
# Band limit. Technically we should not band limit and just simulate
# with beam rolloff, but the map should not have power beyond the
# nyquist limit of the beam postage stamp pixelizaiton, which for the
# case of our sidelobes is quite coarse (0.2 deg)
alm = hp.map2alm(self.hmap)
cl = hp.alm2cl(alm[0])
fl = np.zeros_like(cl)
l = np.arange(len(fl))
l0 = 500
l1 = 600
fl[0:l0] = 1.0
ll = np.arange(l1 - l0)
ll = ll * np.pi / np.max(ll)
fl[l0:l1] = 0.5 * (np.cos(ll) + 1)
alm2 = [hp.almxfl(k, fl) for k in alm]
self.hmap = np.array(hp.alm2map(alm2, self.Nside))
del alm
del alm2
if 'EnoB' in self.sigtype:
# no T->P for EnoB sims
self.hmap[0, :] = 0
def fisher_single(par,v) :
# v -> seen pixels
nb=len(par.bins)-1
npix=hp.nside2npix(par.nside)
npix_seen=len(par.ip_seen)
lmax=3*par.nside-1
larr=np.arange(lmax+1)
fisher=np.zeros([nb,nb])
pixsize=4*np.pi/hp.nside2npix(par.nside)
v_map=np.zeros(npix); v_map[par.ip_seen]=v
vcm1=invert_covar(par,v_map)
v_lm=hp.map2alm(v_map,iter=0)
vcm1_lm=hp.map2alm(vcm1,iter=0)
for iba in np.arange(nb) :
# print " Row %d"%iba
transfer=np.zeros(lmax+1); transfer[par.bins[iba]:par.bins[iba+1]]=1.
v_map2=hp.alm2map(hp.almxfl(v_lm,transfer),par.nside,verbose=False)/pixsize #Q_a * v
v_map2cm1=invert_covar(par,v_map2) #C^-1 * Q_a * v
va_lm=hp.map2alm(v_map2cm1,iter=0)
cl_vcm1_va=(2*larr+1)*hp.alm2cl(vcm1_lm,alms2=va_lm)
for ibb in np.arange(nb-iba)+iba :
fisher[iba,ibb]=np.sum(cl_vcm1_va[par.bins[ibb]:par.bins[ibb+1]])/pixsize**2
if iba!=ibb :
fisher[ibb,iba]=fisher[iba,ibb]
return fisher
def getSsim(ell, Cl, lmax=100, cutSky=False):
"""
Purpose:
create simulated S_{1/2} from input power spectrum
Note:
this calculates Jmn every time it is run so should not be used for ensembles
Procedure:
simulates full sky CMB, measures S_{1/2}
Inputs:
ell: the l values for the power spectrum
Cl: the power spectrum
lmax: the maximum ell value to use in calculation
Default: 100
cutSky: set to True to convert to real space, apply mask, etc.
Default: False
Note: true option not yet implemented
Returns:
simulated S_{1/2}
"""
# get Jmn matrix for harmonic space S_{1/2} calc.
myJmn = getJmn(lmax=lmax)[2:, 2:] # do not include monopole, dipole
#alm_prim,alm_late = hp.synalm((primCl,lateCl,crossCl),lmax=lmax,new=True)
almSim = hp.synalm(
Cl, lmax=lmax) # question: does this need to start at ell[0]=1?
ClSim = hp.alm2cl(almSim)
return np.dot(ClSim[2:], np.dot(myJmn, ClSim[2:]))
def _get_cls_fg(self):
print('======= COMPUTATION OF CL_FGS =======')
if self.n_stokes == 3:
d_spectra = self.d_fgs
else: # Only P is provided, add T for map2alm
d_spectra = np.zeros((self.n_freqs, 3, self.d_fgs.shape[2]),
dtype=self.d_fgs.dtype)
d_spectra[:, 1:] = self.d_fgs
# Compute cross-spectra
almBs = [
hp.map2alm(freq_map, lmax=self.lmax, iter=10)[2]
for freq_map in d_spectra
]
Cl_fgs = np.zeros((self.n_freqs, self.n_freqs, self.lmax + 1),
dtype=self.d_fgs.dtype)
for f1 in range(self.n_freqs):
for f2 in range(self.n_freqs):
if f1 > f2:
Cl_fgs[f1, f2] = Cl_fgs[f2, f1]
else:
Cl_fgs[f1, f2] = hp.alm2cl(almBs[f1],
almBs[f2],
lmax=self.lmax)
Cl_fgs = Cl_fgs[..., self.lmin:] / self.fsky
return Cl_fgs
def VisualizeAlm(alm,figno=1,max_l=None):
""" Visualize a healpy a_lm vector """
lmax = hp.Alm.getlmax(f_lm.size)
l,m = hp.Alm.getlm(lmax)
mag = np.zeros([lmax+1,lmax+1])
phs = np.zeros([lmax+1,lmax+1])
mag[m,l] = np.abs(alm)
phs[m,l] = np.angle(alm)
cl = hp.alm2cl(alm)
# Decide the range of l to plot
if max_l != None:
max_l = (max_l if (max_l <= lmax) else lmax)
else:
max_l = lmax
print max_l
plt.figure(figno)
plt.clf()
plt.subplot(211)
plt.imshow(mag[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.colorbar()
plt.subplot(212)
plt.imshow(phs[0:max_l,0:max_l],interpolation='nearest',origin='lower')
plt.colorbar()
# plt.subplot(313)
#plt.semilogy(cl[0:max_l])
return {'mag':mag,'phs':phs,'cl':cl}
def VisualizeAlm(alm, figno=1, max_l=None):
""" Visualize a healpy a_lm vector """
lmax = hp.Alm.getlmax(f_lm.size)
l, m = hp.Alm.getlm(lmax)
mag = np.zeros([lmax + 1, lmax + 1])
phs = np.zeros([lmax + 1, lmax + 1])
mag[m, l] = np.abs(alm)
phs[m, l] = np.angle(alm)
cl = hp.alm2cl(alm)
# Decide the range of l to plot
if max_l != None:
max_l = (max_l if (max_l <= lmax) else lmax)
else:
max_l = lmax
print max_l
plt.figure(figno)
plt.clf()
plt.subplot(211)
plt.imshow(mag[0:max_l, 0:max_l], interpolation='nearest', origin='lower')
plt.colorbar()
plt.subplot(212)
plt.imshow(phs[0:max_l, 0:max_l], interpolation='nearest', origin='lower')
plt.colorbar()
# plt.subplot(313)
#plt.semilogy(cl[0:max_l])
return {'mag': mag, 'phs': phs, 'cl': cl}
def map2power(iqu, mpibox):
from orphics.tools.stats import bin2D, bin1D
bin_edges = np.arange(200, 4000, 40)
print("Map 2 alm...")
alm = curvedsky.map2alm(iqu.astype("float64"), lmax=5000)
del iqu
cls = hp.alm2cl(alm)
del alm
fineells = np.arange(0, cls.shape[1], 1)
print("Binning...")
lbinner = bin1D(bin_edges)
def b(cls):
ells, cl1d = lbinner.binned(fineells, fineells * cls)
ells, norm = lbinner.binned(fineells, fineells)
cl1d /= norm
return ells, cl1d
ells, cltt = b(cls[0, :])
ells, clee = b(cls[1, :])
ells, clbb = b(cls[2, :])
ells, clte = b(cls[3, :])
ells, cleb = b(cls[4, :])
ells, cltb = b(cls[5, :])
mpibox.add_to_stats("TT", cltt)
mpibox.add_to_stats("EE", clee)
mpibox.add_to_stats("BB", clbb)
mpibox.add_to_stats("TE", clte)
mpibox.add_to_stats("EB", cleb)
mpibox.add_to_stats("TB", cltb)
return ells
def check_Tlm2d(nu=100,
lmax=300,
experiment='planck',
maskfield=2,
source_maskfield=0,
label_loc='lower right',
xmax=None):
if experiment == 'planck':
Imap_name = PLANCK_DATA_PATH + 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(
nu)
Imap = hp.read_map(Imap_name)
mask = hp.read_map(PLANCK_DATA_PATH +
'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
field=maskfield)
smask = hp.read_map(PLANCK_DATA_PATH + 'HFI_Mask_PointSrc_2048_R2.00.fits',
field=source_maskfield)
mask *= smask
hdulist = fits.open(PLANCK_DATA_PATH + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beam = hdulist[BEAM_INDEX['{}'.format(nu)]].data.NOMINAL[0][:lmax + 1]
tlm = get_Tlm(lmax=lmax,
Imap=Imap,
mask=mask,
healpy_format=False,
recalc=True,
div_beam=beam)
tlm_hp = get_Tlm(lmax=lmax,
Imap=Imap,
mask=mask,
healpy_format=True,
recalc=True,
div_beam=beam)
cl = cl_alm2d(alm1=tlm, lmax=lmax)
l = np.arange(len(cl))
cl_hp = hp.alm2cl(tlm_hp, lmax=lmax)
l_hp = np.arange(len(cl_hp))
clplanck = np.loadtxt(
data_path +
'bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl')
cl_planck = clplanck[:, 1]
l_planck = clplanck[:, 0]
pl.figure()
pl.title('TT check')
pl.plot(l, cl * l * (l + 1) / 2. / np.pi * 1e12, label='2d')
pl.plot(l_hp,
cl_hp * l_hp * (l_hp + 1) / 2. / np.pi * 1e12,
label='healpy')
pl.plot(l_planck, cl_planck, label='planck best fit')
pl.legend(loc=label_loc)
if xmax is None:
pl.xlim(xmax=lmax)
else:
pl.xlim(xmax=xmax)
def psd_unseen_helper(x, Nside):
"""Compute the Power Spectral Density for heaply maps (incomplete data)."""
if len(x.shape) == 2 and x.shape[1] > 1:
return np.stack([psd_unseen(x[ind, ]) for ind in range(len(x))])
y = np.zeros(shape=[hp.nside2npix(Nside)])
y[:] = hp.UNSEEN
y[:len(x)] = x
hatx = hp.map2alm(hp.reorder(y, n2r=True))
return hp.alm2cl(hatx)
def return_map(map_class):
"""
We solve for C^{-1/2}x, here is to recover x
"""
Shalf = np.sqrt(map_class.cl_th[: map_class.lmax + 1])
alm_out = hp.almxfl(real2complex_alm(map_class.alm), Shalf)
cl_out = hp.alm2cl(alm_out)
map_out = hp.alm2map(alm_out, map_class.nside)
return cl_out, map_out
def get_spectra(emap1, emap2=None, lmax=5000):
atol = np.min(np.array(emap1.pixshape()/eutils.arcmin))
alm1 = curvedsky.map2alm(emap1, lmax=lmax, atol=atol).astype(np.complex128)
alm2 = alm1 if emap2 is None else curvedsky.map2alm(emap2, lmax=lmax, atol=atol).astype(np.complex128)
cl = hp.alm2cl(alm1, alm2, lmax=lmax)
l = np.arange(len(cl))
return (l, cl)
def make_sim(self,seed):
with bench.show("Lensing operation...") if self.rank==0 else ignore():
full,kappa = lensing.rand_map(self.fshape, self.fwcs, self.ps, lmax=self.lmax,
maplmax=self.lmax, seed=seed, verbose=True if self.rank==0 else False, dtype=self.dtype,output="lk")
alms = curvedsky.map2alm(full,lmax=self.lmax)
ps_data = hp.alm2cl(alms.astype(np.complex128))
del alms
self.mpibox.add_to_stats("fullsky_ps",ps_data)
south = full.submap(self.pos_south)
equator = full.submap(self.pos_eq)
ksouth = kappa.submap(self.pos_south)
kequator = kappa.submap(self.pos_eq)
del full
del kappa
if self.count==0:
self.shape['s'], self.wcs['s'] = south.shape, south.wcs
self.shape['e'], self.wcs['e'] = equator.shape, equator.wcs
for m in ['s','e']:
self.taper[m],self.w2[m] = fmaps.get_taper(self.shape[m],taper_percent = 18.0,pad_percent = 4.0,weight=None)
self.w4[m] = np.mean(self.taper[m]**4.)
self.w3[m] = np.mean(self.taper[m]**3.)
self.rotator = fmaps.MapRotatorEquator(self.shape['s'],self.wcs['s'],self.wdeg,self.hdeg,width_multiplier=0.6,
height_multiplier=1.2,downsample=True,verbose=True if self.rank==0 else False,
pix_target_override_arcmin=self.pix_intermediate)
self.taper['r'] = self.rotator.rotate(self.taper['s'])
self.w2['r'] = np.mean(self.taper['r']**2.)
self.w4['r'] = np.mean(self.taper['r']**4.)
self.w3['r'] = np.mean(self.taper['r']**3.)
self.shape['r'], self.wcs['r'] = self.rotator.shape_final, self.rotator.wcs_final
self.fc = {}
self.binner = {}
self.modlmap = {}
for m in ['s','e','r']:
self.fc[m] = enmap.FourierCalc(self.shape[m],self.wcs[m])
self.modlmap[m] = enmap.modlmap(self.shape[m],self.wcs[m])
self.binner[m] = bin2D(self.modlmap[m],self.bin_edges)
self.cents = self.binner['s'].centers
self._init_qests()
self.count += 1
south *= self.taper['s']
equator *= self.taper['e']
ksouth *= self.taper['s']
kequator *= self.taper['e']
return south,equator,ksouth,kequator
def calculate(self):
"""
calculate various properties of the gaussian, fnl, gnl maps i.e. compute
* Ais
* As
* Cls
* dipoles using remove_dipole
"""
inpCls = self.inputCls[:self.lmax + 1]
if (self.gausmap0 != None):
self.gausCls0 = hp.alm2cl(self.gausalm0)[0:self.lmax + 1]
self.gausCls1 = hp.alm2cl(self.gausalm1)[0:self.lmax + 1]
self.gausA0 = get_A0(self.gausCls0[1:], inpCls[1:])
self.gausAi = Ais(self.gausmap1, self.lmax)
self.gausA = AistoA(self.gausAi)
self.gausAi2 = Ais(self.gausmap0, self.lmax)
self.gausA2 = AistoA(self.gausAi2)
self.gausmp = hp.remove_monopole(self.gausmap0, fitval=True)[1]
self.gausdipole = get_dipole(self.gausmap1)
if (self.fnlmaps1 != None):
self.fnlCls0 = []
self.fnlCls1 = []
self.fnlA0 = []
self.fnlAi = []
self.fnlAi2 = []
self.fnlmp = []
self.fnldipole = []
self.fnlA = []
self.fnlA2 = []
for i in range(self.Nfnls):
#self.fnlCls0.append(hp.anafast(self.fnlmaps0[i], nspec=self.lmax))
self.fnlCls1.append(
hp.anafast(self.fnlmaps1[i], nspec=self.lmax))
#self.fnlA0.append(get_A0(self.fnlCls0[i][1:], inpCls[1:]))
self.fnlAi.append(Ais(self.fnlmaps1[i], self.lmax))
self.fnlA.append(AistoA(self.fnlAi[i]))
#self.fnlAi2.append(Ais(self.fnlmaps0[i], self.lmax)); self.fnlA2.append(AistoA(self.fnlAi2[i]))
#self.fnlmp.append(hp.remove_monopole(self.fnlmaps0[i], fitval=True)[1])
self.fnldipole.append(get_dipole(self.fnlmaps1[i]))
def get_sim_cl(self, key, idx):
assert len(key) == 2, key
f1, f2 = key
i = self.fields.index(f1)
j = self.fields.index(f2)
if i > j: return self.get_sim_cl(f2 + f1, idx)
fname = self.lib_dir + '/cl%s_%04d.dat' % (key, idx)
if not os.path.exists(fname):
alm1 = self.get_sim_alm(idx, f1)
alm2 = alm1 if f2 == f1 else self.get_sim_alm(idx, f2)
np.savetxt(fname, hp.alm2cl(alm1, alms2=alm2))
return np.loadtxt(fname)
def test_field_get_alms():
nside = 32
npix = hp.nside2npix(nside)
mp = np.random.randn(3, npix)
msk = np.ones(npix)
# Spin 0
f = nmt.NmtField(msk, [mp[0]], n_iter=0)
alm = f.get_alms()[0]
cl_tt_nmt = hp.alm2cl(alm)
# Spin 2
f = nmt.NmtField(msk, mp[1:], n_iter=0)
alm = f.get_alms()
cl_ee_nmt = hp.alm2cl(alm[0])
cl_bb_nmt = hp.alm2cl(alm[1])
cl_tt, cl_ee, cl_bb, cl_te, cl_eb, cl_tb = hp.anafast(mp, iter=0, pol=True)
assert (np.all(np.fabs(cl_tt_nmt / cl_tt - 1) < 1E-10))
assert (np.all(np.fabs(cl_ee_nmt[2:] / cl_ee[2:] - 1) < 1E-10))
assert (np.all(np.fabs(cl_bb_nmt[2:] / cl_bb[2:] - 1) < 1E-10))
def check_simulation(a, b, map_list, sim_list, ivar_list, mask):
"""
Check whether simulated power spectrum P_{ab} is consistent with data. Returns list of (split_sim-coadd,split_data-coadd)
weighted by the mask*effective_ivar.
"""
shape = ivar_list[0].shape
wcs = ivar_list[0].wcs
pmap = enmap.pixsizemap(shape, wcs)
sim_coadd = []
data_coadd = []
for i in range(len(sim_list)):
dsim = sim_list[i] - coadd_map(sim_list, ivar_list)
dsim = dsim * mask * ivar_eff(i, ivar_list) / pmap
testalm = cs.map2alm(dsim, lmax=10000)
testalm = testalm.astype(np.complex128)
testcl = hp.alm2cl(testalm)
sim_coadd.append(testcl)
if a == b:
for i in range(len(map_list)):
dataco = map_list[i][a] - coadd_mapnew(map_list, ivar_list, a)
dataco = dataco * mask * ivar_eff(i, ivar_list) / pmap
testalm = cs.map2alm(dataco, lmax=10000)
testalm = testalm.astype(np.complex128)
testcl = hp.alm2cl(testalm)
data_coadd.append(testcl)
else:
for i in range(len(map_list)):
data_a = map_list[i][a] - coadd_mapnew(map_list, ivar_list, a)
data_a = data_a * mask * ivar_eff(i, ivar_list) / pmap
data_b = map_list[i][b] - coadd_mapnew(map_list, ivar_list, b)
data_b = data_b * mask * ivar_eff(i, ivar_list) / pmap
testalm_a = cs.map2alm(data_a, lmax=10000)
testalm_a = testalm_a.astype(np.complex128)
testalm_b = cs.map2alm(data_b, lmax=10000)
testalm_b = testalm_b.astype(np.complex128)
testcl = hp.alm2cl(testalm_a, testalm_b)
data_coadd.append(testcl)
sim_coadd = np.array(sim_coadd)
data_coadd = np.array(data_coadd)
return (sim_coadd, data_coadd)
def test_alm2cl(self):
nside = 32
lmax = 64
lmax_out = 100
seed = 12345
np.random.seed(seed)
# Input power spectrum and alm
alm_syn = hp.synalm(self.cla, lmax=lmax)
cl_out = hp.alm2cl(alm_syn, lmax_out=lmax_out - 1)
np.testing.assert_array_almost_equal(cl_out, self.cla[:lmax_out], decimal=4)
def check_TE2d(nu=100, lmax=300,
maskfield=2, source_maskfield=0,
label_loc='lower right', xmax=None):
map_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nu)
I,Q,U =hp.read_map(data_path + map_name, field=(0,1,2))
mask=hp.read_map(data_path + 'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
field=maskfield)
smask=hp.read_map(data_path + 'HFI_Mask_PointSrc_2048_R2.00.fits',
field=source_maskfield)
mask *= smask
hdulist = fits.open(data_path + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beamP = hdulist[beam_index['{}P'.format(nu)]].data.NOMINAL[0][:lmax+1]
beam = hdulist[beam_index['{}'.format(nu)]].data.NOMINAL[0][:lmax+1]
#tlm = get_Tlm(lmax=lmax, Imap=I, mask=mask,
# healpy_format=False, recalc=True, div_beam=beam)
#elm,blm = get_ElmBlm(lmax=lmax, Qmap=Q, Umap=U, mask=mask,
# healpy_format=False, recalc=True, div_beam=beamP)
tlm_hp = get_Tlm(lmax=lmax, Imap=I, mask=mask,
healpy_format=True, recalc=True, div_beam=beam)
elm_hp,blm_hp = get_ElmBlm(lmax=lmax, Qmap=Q, Umap=U, mask=mask,
healpy_format=True, recalc=True, div_beam=beamP)
#cltt = cl_alm2d(tlm, lmax)
#clee = cl_alm2d(elm, lmax)
#clbb = cl_alm2d(blm, lmax)
#l = np.arange(len(clee))
clte_hp = hp.alm2cl(tlm_hp, elm_hp, lmax=lmax)
#clee_hp = hp.alm2cl(elm_hp, lmax=lmax)
#clbb_hp = hp.alm2cl(blm_hp, lmax=lmax)
l_hp = np.arange(len(clte_hp))
clplanck = np.loadtxt(data_path + 'bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl')
clte_planck = clplanck[:,2]
#clee_planck = clplanck[:,3]
#clbb_planck = clplanck[:,4]
l_planck = clplanck[:,0]
pl.figure()
pl.title('TE check')
#pl.plot(l, clee*l*(l+1)/2./np.pi*1e12, label='2d')
pl.plot(l_hp,clte_hp*l_hp*(l_hp+1)/2./np.pi*1e12, label='healpy')
pl.plot(l_planck, clte_planck, label='planck best fit')
pl.legend(loc=label_loc)
if xmax is None:
pl.xlim(xmax=lmax)
else:
pl.xlim(xmax=xmax)
def test_no_stokes(self):
NFREQ = 3
NSIDE = 2
np.random.seed(0)
alms = [
hp.map2alm(np.random.normal(size=(12 * NSIDE**2)))
for i in range(NFREQ)
]
res = _empirical_harmonic_covariance(alms)
ref = np.empty_like(res)
for f1 in range(NFREQ):
for f2 in range(NFREQ):
ref[f1, f2] = hp.alm2cl(alms[f1], alms[f2])
aac(ref, res)
def test_utils_contract_almxblm_cl(self):
# Check if contraction matches hp.alm2cl.
alm = np.ones(10, dtype=np.complex128)
alm += 1j * np.ones(10, dtype=np.complex128)
alm[:4] = 1
lmax = 3
ells = np.asarray([0, 1, 2, 3])
cl = hp.alm2cl(alm)
ans_exp = np.sum(cl * (2 * ells + 1))
ans = utils.contract_almxblm(alm, np.conj(alm))
self.assertEqual(ans, ans_exp)
def correlation2pt(map1, map2=None, npoints=100):
if map2 is None:
map2 = map1
alm1 = hp.map2alm(map1)
alm2 = hp.map2alm(map2)
clcross = hp.alm2cl(alm1, alm2)
thetas = np.linspace(0., np.pi, npoints)
corr = np.zeros(npoints)
for i,theta in enumerate(thetas):
for l,cl in enumerate(clcross):
lfactor = (2*l + 1.)/(4.*np.pi) * scipy.special.lpmv(0, l, np.cos(theta))
corr[i] += lfactor * cl
return thetas, corr
def wiener_filter_for_alm(alm, lmax=None, fwhm=0.0, f_sky=1.0, sky_prior=None):
if lmax is None:
lmax = hp.Alm.getlmax(len(alm), None)
if sky_prior is None:
spectra_th = np.load("/global/homes/b/banerji/simulation/spectra/r_001/lensedtot_cls.npy")[0,:lmax+1]
else:
spectra_th = estimate_cl(sky_prior, lmax, fwhm=fwhm, pol=False)
Bl = hp.gauss_beam(fwhm=fwhm, lmax=lmax, pol=False)
spectra_ob = hp.alm2cl(alm, lmax_out=lmax)
spectra_ob /= f_sky*Bl**2
filter_response = spectra_ob/spectra_th
filter_response[:2] = 1.0
filter_response = np.sqrt(filter_response)
return filter_response
def check_Tlm2d(nu=100, lmax=300,
maskfield=2, source_maskfield=0,
label_loc='lower right', xmax=None):
Imap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nu)
Imap =hp.read_map(data_path + Imap_name)
mask=hp.read_map(data_path + 'HFI_Mask_GalPlane-apo0_2048_R2.00.fits',
field=maskfield)
smask=hp.read_map(data_path + 'HFI_Mask_PointSrc_2048_R2.00.fits',
field=source_maskfield)
mask *= smask
hdulist = fits.open(data_path + 'HFI_RIMO_Beams-100pc_R2.00.fits')
beam = hdulist[beam_index['{}'.format(nu)]].data.NOMINAL[0][:lmax+1]
tlm = get_Tlm(lmax=lmax, Imap=Imap, mask=mask,
healpy_format=False, recalc=True, div_beam=beam)
tlm_hp = get_Tlm(lmax=lmax, Imap=Imap, mask=mask,
healpy_format=True, recalc=True, div_beam=beam)
cl = cl_alm2d(alm1=tlm, lmax=lmax)
l = np.arange(len(cl))
cl_hp = hp.alm2cl(tlm_hp, lmax=lmax)
l_hp = np.arange(len(cl_hp))
clplanck = np.loadtxt(data_path + 'bf_base_cmbonly_plikHMv18_TT_lowTEB_lmax4000.minimum.theory_cl')
cl_planck = clplanck[:,1]
l_planck = clplanck[:,0]
pl.figure()
pl.title('TT check')
pl.plot(l, cl*l*(l+1)/2./np.pi*1e12, label='2d')
pl.plot(l_hp,cl_hp*l_hp*(l_hp+1)/2./np.pi*1e12, label='healpy')
pl.plot(l_planck, cl_planck, label='planck best fit')
pl.legend(loc=label_loc)
if xmax is None:
pl.xlim(xmax=lmax)
else:
pl.xlim(xmax=xmax)
def plot_tests():
spec_mf_b = hp.alm2cl(hp.almxfl(mf_lm_new,bl))
spec_mf = hp.alm2cl((mf_lm_new))
spec = hp.alm2cl(dlm)
diff = hp.alm2cl(dlm - (hp.almxfl(mf_lm_new,bl)))
fluc_l = hp.alm2cl(fluc)
nnn = hp.alm2cl(dlm-hp.almxfl(mf_lm_new,bl))
plt.figure()
plt.plot(spec_mf,label='mean field ($\hat{s}$)')
plt.plot(spec_mf_b,label='beamed mean field ($A\hat{s}$)')
plt.plot(spec,label='data (d)')
plt.plot(spec - spec_mf_b,label='($d_\ell - (A\hat{s})_\ell$)')
plt.plot(diff,label='($d - (A\hat{s})$)$_\ell$')
plt.plot(fluc_l,"-.",label='($\hat{f}$)$_\ell$')
plt.plot(nl,"-.",label="noise")
plt.plot(cl,"-.",label="trial spectrum")
plt.yscale("log")
plt.legend(loc = 'best')
def calibrate_fsky(mode, nsim=1,
smear=True,apo=2,
psky=70, f=353,
nside=2048,lmax=1000,
visual_check=False,
beam_file=pf.PLANCK_DATA_PATH+'HFI_RIMO_Beams-100pc_R2.00.fits',
mask_sources=True, put_mask=True):
"""No noise treatment here"""
fsky_correction = []
TTm = np.zeros(lmax + 1)
EEm = np.zeros(lmax + 1)
BBm = np.zeros(lmax + 1)
if put_mask:
print 'reading mask...'
mask = pf.get_planck_mask(psky=psky,
mask_sources=mask_sources,
apodization=apo)
else:
mask = None
print 'reading beams...'
hdulist = pf.fits.open(beam_file)
beam = hdulist[pf.BEAM_INDEX['{}'.format(f)]].data.NOMINAL[0][:lmax+1]
beamP = hdulist[pf.BEAM_INDEX['{}P'.format(f)]].data.NOMINAL[0][:lmax+1]
beam = beam[:lmax+1]
beamP = beamP[:lmax+1]
if mode == 'fg':
ls, cls_theory = get_theory_fg(f=f, lmax=lmax, psky=psky, apo=apo)
if mode == 'cmb':
ls, cls_theor = pf.get_theory_cmb(lmax=lmax, mode='cl')
factor = ls*(1+ls)
for i in np.arange(nsim):
print 'sim #{}...'.format(i+1)
I, Q, U = pf.simulate_cmb_map(nside=nside, lmax=lmax,
frequency=f,smear=smear,
cls_theory=cls_theory,
beam=beam, beamP=beamP,
save=False,
beam_file=None)
print 'Cl #{}...'.format(i+1)
Tlm, Elm, Blm = pf.calc_alm(I, Q, U, mask=mask,
lmax=lmax,
add_beam=None,add_beamP=None,
div_beam=beam,div_beamP=beamP,
healpy_format=True)
TT = hp.alm2cl(Tlm)
EE = hp.alm2cl(Elm)
BB = hp.alm2cl(Blm)
#ls, TT, EE, BB, TE, TB, EB = measure_dlcl(mask=mask, Imap=I, Qmap=Q, Umap=U,
# beam=beam, beamP=beamP,
# mode='cl',frequency=f,
# lmax=lmax,lmin=0,
# put_mask=put_mask,
# psky=psky,
# mask_sources=mask_sources,
# apodization=apo)
TTm += TT/nsim; EEm += EE/nsim; BBm += BB/nsim
if visual_check:
hp.mollview(I)
hp.mollview(Q)
hp.mollview(U)
fsky_correction.append(cls_theory[0] / TTm)
fsky_correction.append(cls_theory[1] / EEm)
fsky_correction.append(cls_theory[2] / BBm)
fsky_correction = np.array(fsky_correction)
fsky_correction[np.isnan(fsky_correction)] = 0.
fsky_correction[fsky_correction==np.inf] = 0.
return ls, fsky_correction, TTm, EEm, BBm, cls_theory
lmax = 3*nside-1 l,m = hp.Alm.getlm(lmax) n_lm = len(l) ell = np.arange(0,lmax+1) below_horizon = np.where(theta > np.pi/2.)[0] nu = np.linspace(100,200,num=203)*u.MHz # Define primary beam #A = np.exp(-np.power(theta,2)/(2.*0.1)) #A = np.exp(-np.power(theta,2)/(2.*0.1)) A = np.ones_like(theta) A[below_horizon] = 0. a_lm = hp.map2alm(A,lmax=lmax) Cl_A = hp.alm2cl(a_lm,lmax=lmax) Cl_G = hp.gauss_beam(np.radians(90.),lmax) A_nu = np.outer(A,np.ones_like(nu)) #apod = np.ones_like(theta) #apod = np.exp(-np.power(theta,2)/(2.*0.5)) #disc = hp.query_disc(nside,[0,0,1],np.radians(80.)) #apod[disc] = 0. #apod = 1-np.exp(-np.power(theta,2)/(2.*0.5)) #apod = #apod = np.power(np.sin(phi),2) # Define fringe bmag = 30. bvec = np.array([0,1,0])*bmag b = np.outer(bvec,np.ones(npix))*u.m
bdots = np.sum(b*s,axis=0)
fringe = np.exp(-2.*np.pi*1j*np.outer(bdots.value,nu.to(u.Hz).value)/c.c.value)
if True:
fringe[below_horizon] = 0.
beam[below_horizon] = 0.
## V_G(nu) = s_00(u) T(nu)
# Average compensates for any nside changes, normalized to beam solid angle (?)
omega_nu = 4.*np.pi*beam_nu.sum(axis=0)/npix
T_nu = np.average(beam_nu*fringe,axis=0)
window = np.hanning(len(T_nu))
dtrans_T_nu = np.fft.fftshift(np.fft.fft(window*T_nu))
Ptau_T_nu = np.abs(dtrans_T_nu)
f_lm = hp.map2alm(fringe[:,100])
C_f = hp.alm2cl(f_lm)
a_lm = hp.map2alm(beam_nu[:,100])
C_a = hp.alm2cl(a_lm)
C_fa = hp.alm2cl(f_lm*a_lm)
figno = 5
title = 'Gah'#'Sin^16(phi)'
filename = ''
plt.figure(figno)
plt.clf()
plt.subplot(231)
plt.plot(nu,T_nu.real,'b')
plt.plot(nu,T_nu.real-T_nu.mean(),'b--')
plt.plot(nu,window*T_nu.real,'g')
else:no_cache = []
for i,file in enumerate(args):
print file
name = file.split('/')[-1]
try:
if not name in no_cache and not ('all' in no_cache):
s_cl = n.loadtxt(name+'.cl')
print "loading",name+'.cl'
except(IOError):
no_cache.append(name)
if name in no_cache or 'all' in no_cache:
s = hp.read_map(file)
print "computing alms"
s_alm = hp.map2alm(s)
s_cl = hp.alm2cl(s_alm)
n.savetxt(name+'.cl',s_cl)
cls[file] = s_cl * 0.002**(opts.conv[i])
figure(88)
for cl in cls:
s_cl = cls[cl]
loglog(s_cl*n.linspace(0,len(s_cl),len(s_cl))**2,label=cl)
legend(loc='lower right')
xlabel('$\ell$')
ylabel('$\ell^2 C_\ell$ $[K^2]$')
figure(89)
for cl in cls:
s_cl = cls[cl]
def main(i_sim=0):
'''
MPI Setup
'''
o_comm = MPI.COMM_WORLD
i_rank = o_comm.Get_rank() # current core number -- e.g., i in arange(i_size)
i_size = o_comm.Get_size() # number of cores assigned to run this program
o_status = MPI.Status()
i_work_tag = 0
i_die_tag = 1
'''
Loading and calculating power spectrum components
'''
# Get run parameters
s_fn_params = 'data/params.pkl'
(i_lmax, i_nside, s_fn_map, s_map_name, s_fn_mask, s_fn_mll, s_fn_beam,
s_fn_alphabeta, s_fn_cltt) = get_params(s_fn_params)
s_fn_cltt = ('sims/na_cltt_sim_%i.npy' % i_sim)
if (i_rank == 0):
f_t1 = time.time()
print ""
print "Run parameters:"
print "(Using %i cores)" % i_size
print "lmax: %i, nside: %i, map name: %s" % (i_lmax, i_nside, s_map_name)
print "beam: %s, alpha_beta: %s, cltt: %s" % (s_fn_beam, s_fn_alphabeta, s_fn_cltt)
print ""
print "Loading ell, r, dr, alpha, beta, cltt, and beam..."
na_l, na_r, na_dr, na_alpha, na_beta = np.loadtxt(s_fn_alphabeta,
usecols=(0,1,2,3,4), unpack=True, skiprows=3)
na_l = np.unique(na_l)
na_r = np.unique(na_r)[::-1]
na_l = na_l[:i_lmax]
i_num_ell = len(na_l)
i_num_r = len(na_r)
na_alpha = na_alpha.reshape(i_num_ell, i_num_r)
na_beta = na_beta.reshape(i_num_ell, i_num_r)
na_dr = na_dr.reshape(i_num_ell, i_num_r)
na_dr = na_dr[0]
if (i_rank == 0):
print "(sizes from file load)"
print "i_num_r: %i, i_num_ell: %i" % (i_num_r, i_num_ell)
if (len(sys.argv) > 2):
i_lmax_run = int(sys.argv[2])
else:
i_lmax_run = i_lmax
if (len(sys.argv) > 3):
i_num_r_run = int(sys.argv[3])
else:
i_num_r_run = i_num_r
i_lmax_run = min(i_lmax_run, len(na_l))
i_num_r_run = min(i_num_r, i_num_r_run)
i_r_steps = i_num_r / i_num_r_run
na_mask = hp.read_map(s_fn_mask)
s_fn_mll = 'output/na_mll_%i_lmax.npy' % i_lmax_run
na_mll = np.load(s_fn_mll)
na_mll_inv = np.linalg.inv(na_mll)
if (i_rank == 0):
print "(sizes for run)"
print "i_num_r_run: %i, i_lmax_run: %i" % (i_num_r_run, i_lmax_run)
na_l = na_l[:i_lmax_run]
na_r = na_r[::i_r_steps]
na_dr = na_dr[::i_r_steps]
na_alpha = na_alpha[:i_lmax_run, ::i_r_steps]
na_beta = na_beta[:i_lmax_run, ::i_r_steps]
na_cltt = np.load(s_fn_cltt)
na_cltt = na_cltt[:i_lmax_run]
na_bl = np.load(s_fn_beam)
na_bl = na_bl[:i_lmax_run]
# f_t2 = time.time()
if (i_rank == 0):
print ""
print "Calculating full kurtosis power spectra..."
na_alm = hp.synalm(na_cltt, lmax=i_lmax_run, verbose=False)
# f_t3 = time.time()
na_work = np.zeros(2, dtype='i')
na_result = np.zeros((2,i_lmax_run), dtype='d')
li_dims = [i_num_r_run, i_num_r_run]
# master loop
if (i_rank == 0):
na_kl22_data = np.zeros(i_lmax_run)
na_kl31_data = np.zeros(i_lmax_run)
# send initial jobs
for i_rank_out in range(1, i_size):
na_work = np.array(cart_index(i_rank_out-1, li_dims), dtype='i')
o_comm.Send([na_work, MPI.INT], dest=i_rank_out, tag=i_work_tag)
na_work = np.array(cart_index(i_size-1, li_dims), dtype='i')
i_r1_start = na_work[0]
i_r2_start = na_work[1]
for i_r1 in range(i_r1_start, i_num_r_run):
if (i_r1 % (i_num_r / 10) == 0):
print "Finished %i%% of jobs... (%.2f s)" % (i_r1 * 100 / i_num_r_run,
time.time() - f_t1)
for i_r2 in range(i_r2_start, i_num_r_run):
na_work = np.array([i_r1, i_r2], dtype='i')
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
#print "received results from core %i" % o_status.Get_source()
o_comm.Send([na_work,MPI.INT], dest=o_status.Get_source(),
tag=i_work_tag)
na_kl22_data += na_result[0]
na_kl31_data += na_result[1]
for i_rank_out in range(1, i_size):
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
na_kl22_data += na_result[0]
na_kl31_data += na_result[1]
o_comm.Send([np.array([9999], dtype='i'), MPI.INT],
dest=o_status.Get_source(), tag=i_die_tag)
#slave loop:
else:
while(1):
o_comm.Recv([na_work, MPI.INT], source=0, status=o_status,
tag=MPI.ANY_TAG)
if (o_status.Get_tag() == i_die_tag):
break
i_r1 = na_work[0]
i_r2 = na_work[1]
#print "doing work for r = %i on core %i" % (i_r, i_rank)
na_Almr1 = hp.almxfl(na_alm, na_alpha[:,i_r1] / na_cltt * na_bl)
na_Blmr1 = hp.almxfl(na_alm, na_beta[:,i_r1] / na_cltt * na_bl)
na_Almr2 = hp.almxfl(na_alm, na_alpha[:,i_r2] / na_cltt * na_bl)
na_Blmr2 = hp.almxfl(na_alm, na_beta[:,i_r2] / na_cltt * na_bl)
# f_t4 = time.time() #all da maps
na_Ar1n = hp.alm2map(na_Almr1, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Br1n = hp.alm2map(na_Blmr1, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Ar2n = hp.alm2map(na_Almr2, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Br2n = hp.alm2map(na_Blmr2, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Ar1n = na_Ar1n * na_mask
na_Br1n = na_Br1n * na_mask
na_Ar2n = na_Ar2n * na_mask
na_Br2n = na_Br2n * na_mask
# f_t5 = time.time()
#print "starting map2alm for r = %i on core %i" % (i_r, i_rank)
na_ABlmr1 = hp.map2alm(na_Ar1n*na_Br1n, lmax=i_lmax_run)
if i_r1 == i_r2:
na_B2lmr1 = hp.map2alm(na_Br1n*na_Br1n, lmax=i_lmax_run)
na_AB2lmr1 = hp.map2alm(na_Ar1n*na_Br1n*na_Br1n, lmax=i_lmax_run)
na_ABAlmr1 = hp.map2alm(na_Ar1n*na_Br1n*na_Ar1n, lmax=i_lmax_run)
na_ABlmr2 = hp.map2alm(na_Ar2n*na_Br2n, lmax=i_lmax_run)
na_B2lmr2 = hp.map2alm(na_Br2n*na_Br2n, lmax=i_lmax_run)
#print "finished map2alm for r = %i on core %i" % (i_r, i_rank)
# f_t6 = time.time()
na_Jl_ABA_B = hp.alm2cl(na_ABAlmr1, na_Blmr2, lmax=i_lmax_run)
na_Jl_AB_AB = hp.alm2cl(na_ABlmr1, na_ABlmr2, lmax=i_lmax_run)
na_Jl_ABA_B = na_Jl_ABA_B[1:]
na_Jl_AB_AB = na_Jl_AB_AB[1:]
if i_r1 == i_r2:
na_Ll_AB2_B = hp.alm2cl(na_AB2lmr1, na_Blmr1, lmax=i_lmax_run)
na_Ll_AB_B2 = hp.alm2cl(na_ABlmr1, na_B2lmr1, lmax=i_lmax_run)
na_Ll_AB2_B = na_Ll_AB2_B[1:]
na_Ll_AB_B2 = na_Ll_AB_B2[1:]
#f_t7 = time.time()
na_result = np.zeros((2,i_lmax_run), dtype='d')
if i_r1 == i_r2:
na_result[0] += ((5./3.)**2. * na_Jl_AB_AB
* na_r[i_r1]**2. * na_dr[i_r1] * na_r[i_r2]**2. * na_dr[i_r2]
+ 2. * na_Ll_AB_B2 * na_r[i_r1]**2. * na_dr[i_r1]) #kl22
na_result[1] += ((5./3.)**2. * na_Jl_ABA_B
* na_r[i_r1]**2. * na_dr[i_r1] * na_r[i_r2]**2. * na_dr[i_r2]
+ 2. * na_Ll_AB2_B * na_r[i_r1]**2. * na_dr[i_r1]) #kl31
else:
na_result[0] += ((5./3.)**2. * na_Jl_AB_AB
* na_r[i_r1]**2. * na_dr[i_r1] * na_r[i_r2]**2. * na_dr[i_r2]) #kl22
na_result[1] += ((5./3.)**2. * na_Jl_ABA_B
* na_r[i_r1]**2. * na_dr[i_r1] * na_r[i_r2]**2. * na_dr[i_r2]) #kl31
#print "finished work for r = %i on core %i" % (i_r, i_rank)
o_comm.Send([na_result,MPI.DOUBLE], dest=0, tag=1)
# print "Load time: %.2f s" % (f_t2 - f_t1)
# print "synalm time: %.2f s" % (f_t3 - f_t2)
# print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
f_t8 = time.time()
if (i_rank == 0):
s_fn_kl22_data_no_mll = 'output/na_kl22_data_g_sim_%i_%i_rsteps_%i_lmax_no_mll.dat' % (i_sim, i_num_r_run, i_lmax_run)
s_fn_kl31_data_no_mll = 'output/na_kl31_data_g_sim_%i_%i_rsteps_%i_lmax_no_mll.dat' % (i_sim, i_num_r_run, i_lmax_run)
print ""
print "Saving power spectrum to %s (not mll corrected)" % s_fn_kl22_data_no_mll
print "Saving power spectrum to %s (not mll corrected)" % s_fn_kl31_data_no_mll
np.savetxt(s_fn_kl22_data_no_mll, na_kl22_data)
np.savetxt(s_fn_kl31_data_no_mll, na_kl31_data)
s_fn_kl22_data = 'output/na_kl22_data_g_sim_%i_%i_rsteps_%i_lmax.dat' % (i_sim, i_num_r_run, i_lmax_run)
s_fn_kl31_data = 'output/na_kl31_data_g_sim_%i_%i_rsteps_%i_lmax.dat' % (i_sim, i_num_r_run, i_lmax_run)
print ""
print "Saving power spectrum to %s" % s_fn_kl22_data
print "Saving power spectrum to %s" % s_fn_kl31_data
na_kl22_data = np.dot(na_mll_inv, na_kl22_data)
na_kl31_data = np.dot(na_mll_inv, na_kl31_data)
np.savetxt(s_fn_kl22_data, na_kl22_data)
np.savetxt(s_fn_kl31_data, na_kl31_data)
# print "Finished in %.2f s" % (f_t8 - f_t1)
# # print "Load time: %.2f s" % (f_t2 - f_t1)
# # print "synalm time: %.2f s" % (f_t3 - f_t2)
# # print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# # print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# # print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# # print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
return
def main():
'''
MPI Setup
'''
o_comm = MPI.COMM_WORLD
i_rank = o_comm.Get_rank() # current core number -- e.g., i in arange(i_size)
i_size = o_comm.Get_size() # number of cores assigned to run this program
o_status = MPI.Status()
i_work_tag = 0
i_die_tag = 1
'''
Loading and calculating power spectrum components
'''
# Get run parameters
s_fn_params = 'data/params.pkl'
(i_lmax, i_nside, s_fn_map, s_map_name, s_fn_mask, s_fn_mll, s_fn_beam,
s_fn_alphabeta, s_fn_cltt) = get_params(s_fn_params)
#s_fn_cltt = 'sims/cl_fnl_0.dat'
if (i_rank == 0):
s_fn_cl21_data = 'output/cl21_data.dat'
s_fn_cl21_data_no_mll = 'output/cl21_data_no_mll.dat'
#s_fn_cl21_data = 'output/cl21_ps_smica.dat'
#s_fn_cl21_data_no_mll = 'output/cl21_ps_smica_no_mll.dat'
f_t1 = time.time()
print ""
print "Run parameters:"
print "(Using %i cores)" % i_size
print "lmax: %i, nside: %i, map name: %s" % (i_lmax, i_nside, s_map_name)
print "beam: %s, alpha_beta: %s, cltt: %s" % (s_fn_beam, s_fn_alphabeta, s_fn_cltt)
print ""
print "Loading ell, r, dr, alpha, beta, cltt, and beam..."
na_mask = hp.read_map(s_fn_mask)
#s_fn_mll = 'output/na_mll_%i_lmax.npy' % i_lmax
s_fn_mll = 'output/na_mll_1499_lmax.npy'
na_mll = np.load(s_fn_mll)
na_mll_inv = np.linalg.inv(na_mll)
na_l, na_r, na_dr, na_alpha, na_beta = np.loadtxt(s_fn_alphabeta,
usecols=(0,1,2,3,4), unpack=True, skiprows=3)
na_l = np.unique(na_l)
na_r = np.unique(na_r)[::-1]
i_num_r = len(na_r)
try:
na_cltt = np.load(s_fn_cltt)
except:
na_cltt = np.loadtxt(s_fn_cltt)
na_bl = np.load(s_fn_beam)
na_alpha = na_alpha.reshape(len(na_l), i_num_r)
na_beta = na_beta.reshape(len(na_l), i_num_r)
na_dr = na_dr.reshape(len(na_l), i_num_r)
na_dr = na_dr[0]
i_num_ell = min(len(na_l), len(na_cltt), len(na_bl), i_lmax)
na_l = na_l[:i_num_ell]
na_cltt = na_cltt[:i_num_ell]
na_bl = na_bl[:i_num_ell]
na_alpha = na_alpha[:i_num_ell,:]
na_beta = na_beta[:i_num_ell,:]
if (i_rank == 0):
print "i_num_r: %i, i_num_ell: %i" % (i_num_r, i_num_ell)
# f_t2 = time.time()
if (i_rank == 0):
print ""
print "Calculating full skewness power spectrum..."
s_fn_alm = 'output/na_alm_data.fits'
#s_fn_alm = 'data/ps_sim/alm_ps_smica_ell_2000.fits'
na_alm = hp.read_alm(s_fn_alm)
na_alm = na_alm[:hp.Alm.getsize(i_num_ell)]
# f_t3 = time.time()
na_cl21_data = np.zeros(i_num_ell)
na_work = np.zeros(1, dtype='i')
na_result = np.zeros(i_num_ell, dtype='d')
# master loop
if (i_rank == 0):
# send initial jobs
for i_rank_out in range(1,i_size):
na_work = np.array([i_rank_out-1], dtype='i')
o_comm.Send([na_work, MPI.INT], dest=i_rank_out, tag=i_work_tag)
for i_r in range(i_size-1,i_num_r):
if (i_r % (i_num_r / 10) == 0):
print "Finished %i%% of jobs... (%.2f s)" % (i_r * 100 / i_num_r,
time.time() - f_t1)
na_work = np.array([i_r], dtype='i')
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
#print "received results from core %i" % o_status.Get_source()
o_comm.Send([na_work,MPI.INT], dest=o_status.Get_source(),
tag=i_work_tag)
na_cl21_data += na_result
for i_rank_out in range(1,i_size):
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
na_cl21_data += na_result
print "cl21_data = %.6f, na_result = %.6f" % (np.average(na_cl21_data), np.average(na_result))
o_comm.Send([np.array([9999], dtype='i'), MPI.INT],
dest=o_status.Get_source(), tag=i_die_tag)
#slave loop:
else:
while(1):
o_comm.Recv([na_work, MPI.INT], source=0, status=o_status,
tag=MPI.ANY_TAG)
if (o_status.Get_tag() == i_die_tag):
break
i_r = na_work[0]
#print "doing work for r = %i on core %i" % (i_r, i_rank)
na_Alm = hp.almxfl(na_alm, na_alpha[:,i_r] / na_cltt * na_bl)
na_Blm = hp.almxfl(na_alm, na_beta[:,i_r] / na_cltt * na_bl)
# print ("doing work for r=%i, alpha=(%.2f), beta=(%.2f)" %
# (int(na_r[i_r]), na_alpha[0,i_r], na_beta[0,i_r]))
# f_t4 = time.time()
na_An = hp.alm2map(na_Alm, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Bn = hp.alm2map(na_Blm, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
# *REMBER TO MULTIPLY BY THE MASK!* -- already doing this in cltt.py...
na_An = na_An * na_mask
na_Bn = na_Bn * na_mask
# f_t5 = time.time()
#print "starting map2alm for r = %i on core %i" % (i_r, i_rank)
na_B2lm = hp.map2alm(na_Bn*na_Bn, lmax=i_num_ell)
na_ABlm = hp.map2alm(na_An*na_Bn, lmax=i_num_ell)
#print "finished map2alm for r = %i on core %i" % (i_r, i_rank)
# f_t6 = time.time()
na_clAB2 = hp.alm2cl(na_Alm, na_B2lm, lmax=i_num_ell)
na_clABB = hp.alm2cl(na_ABlm, na_Blm, lmax=i_num_ell)
#na_clAB2 = na_clAB2[:-1] # just doing this to make things fit...
#na_clABB = na_clABB[:-1] # just doing this to make things fit...
na_clAB2 = na_clAB2[1:]
na_clABB = na_clABB[1:]
#f_t7 = time.time()
na_result = np.zeros(i_num_ell, dtype='d')
na_result += (na_clAB2 + 2 * na_clABB) * na_r[i_r]**2. * na_dr[i_r]
print ("finished work for r=%i, avg(alpha)=%.2f, avg(beta)=%.2f, avg(result)=%.4g" %
(int(na_r[i_r]), np.average(na_alpha[:,i_r]),
np.average(na_beta[:,i_r]), np.average(na_result)))
#print "finished work for r = %i on core %i" % (i_r, i_rank)
o_comm.Send([na_result,MPI.DOUBLE], dest=0, tag=1)
# print "Load time: %.2f s" % (f_t2 - f_t1)
# print "synalm time: %.2f s" % (f_t3 - f_t2)
# print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
f_t8 = time.time()
if (i_rank == 0):
print ""
print ("Saving power spectrum to %s (not mll corrected)"
% s_fn_cl21_data_no_mll)
np.savetxt(s_fn_cl21_data_no_mll, na_cl21_data)
print ""
print "Saving power spectrum to %s (mll corrected)" % s_fn_cl21_data
na_cl21_data = np.dot(na_mll_inv, na_cl21_data)
np.savetxt(s_fn_cl21_data, na_cl21_data)
# print "Finished in %.2f s" % (f_t8 - f_t1)
# # print "Load time: %.2f s" % (f_t2 - f_t1)
# # print "synalm time: %.2f s" % (f_t3 - f_t2)
# # print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# # print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# # print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# # print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
return
def main(run_type='data', nsim=0, fnl=0):
'''
MPI Setup
'''
o_comm = MPI.COMM_WORLD
i_rank = o_comm.Get_rank() # current core number -- e.g., i in arange(i_size)
i_size = o_comm.Get_size() # number of cores assigned to run this program
o_status = MPI.Status()
i_work_tag = 0
i_die_tag = 1
'''
Loading and calculating power spectrum components
'''
if (run_type == 'fnl'):
nl = 1024
else:
nl = 1499
if (run_type == 'data'):
fn_map = h._fn_map
elif (run_type == 'sim'):
fn_map = 'output/map_sim_%i.fits' % nsim
elif (run_type == 'fnl'):
#print "fnl value: %i" % fnl
fn_map = 'data/fnl_sims/map_fnl_%i_sim_%i.fits' % (int(fnl), nsim)
# (1) read map (either map_data or map_sim), mask, mll, and create mll_inv;
map_in = hp.read_map(fn_map)
mask = hp.read_map(h._fn_mask)
if (run_type == 'fnl'):
mask = 1.
fn_mll = 'output/na_mll_%i_lmax.npy' % nl
mll = np.load(fn_mll)
if (run_type == 'fnl'):
mll = np.identity(nl)
mll_inv = np.linalg.inv(mll)
nside = hp.get_nside(map_in)
if (i_rank == 0):
if (run_type == 'data'):
fn_cl21 = 'output/cl21_data.dat'
fn_cl21_no_mll = 'output/cl21_data_no_mll.dat'
elif (run_type == 'sim'):
fn_cl21 = 'output/cl21_sim_%i.dat' % nsim
fn_cl21_no_mll = 'output/cl21_no_mll_%i.dat' % nsim
elif (run_type == 'fnl'):
fn_cl21 = 'output/cl21_fnl_%i_sim_%i.dat' % (int(fnl), nsim)
fn_cl21_no_mll = 'output/cl21_fnl_%i_sim_%i_no_mll.dat' % (int(fnl), nsim)
f_t1 = time.time()
print ""
print "Run parameters:"
print "(Using %i cores)" % i_size
print "nl: %i, nside: %i, map: %s" % (nl, nside, fn_map)
print "beam: %s, alpha_beta: %s, cltt: %s" % (h._fn_beam, h._fn_alphabeta, h._fn_cltt)
print ""
print "Loading ell, r, dr, alpha, beta, cltt, and beam..."
# (2) normalize, remove mono-/dipole, and mask map to create map_masked;
map_in /= (1e6 * 2.7)
map_in = hp.remove_dipole(map_in)
map_masked = map_in * mask
# (3) create alm_masked (map2alm on map_masked), cltt_masked (anafast on
# map_masked), and cltt_corrected (dot cltt_masked with mll_inv)
if (run_type == 'data' or run_type == 'sim'):
alm_masked = hp.map2alm(map_masked)
elif (run_type == 'fnl'):
fn_almg = ('data/fnl_sims/alm_l_%04d_v3.fits' % (nsim,))
almg = hp.read_alm(fn_almg)
fn_almng = ('data/fnl_sims/alm_nl_%04d_v3.fits' % (nsim,))
almng = hp.read_alm(fn_almng)
alm = almg + fnl * almng
alm_masked = alm
cltt_masked = hp.anafast(map_masked)
cltt_masked = cltt_masked[:nl]
cltt_corrected = np.dot(mll_inv, cltt_masked)
# stuff with alpha, beta, r, dr, and beam
l, r, dr, alpha, beta = np.loadtxt(h._fn_alphabeta,
usecols=(0,1,2,3,4), unpack=True, skiprows=3)
l = np.unique(l)
r = np.unique(r)[::-1]
nr = len(r)
if (run_type == 'data' or run_type == 'sim'):
cltt_denom = np.load('output/cltt_theory.npy') #replace with 'output/na_cltt.npy'
cltt_denom = cltt_denom[:nl]
elif (run_type == 'fnl'):
cltt_denom = np.loadtxt('joe/cl_wmap5_bao_sn.dat', usecols=(1,), unpack=True)
alpha = alpha.reshape(len(l), nr)
beta = beta.reshape(len(l), nr)
dr = dr.reshape(len(l), nr)
dr = dr[0]
if (run_type != 'fnl'):
beam = np.load(h._fn_beam)
else:
#beam = np.ones(len(cltt_denom))
beam = np.load(h._fn_beam)
noise = np.zeros(len(cltt_denom))
nlm = hp.synalm(noise, lmax=nl)
####### TEMPORARY -- change beam #######
#beam = np.ones(len(cltt_denom))
#noise = np.zeros(len(cltt_denom))
#nlm = hp.synalm(noise, lmax=nl)
########################################
l = l[:nl]
beam = beam[:nl]
alpha = alpha[:nl,:]
beta = beta[:nl,:]
if (i_rank == 0):
print "nr: %i, nl: %i" % (nr, nl)
f_t2 = time.time()
if (i_rank == 0):
print ""
print "Time to create alms from maps: %.2f s" % (f_t2 - f_t1)
print "Calculating full skewness power spectrum..."
cl21 = np.zeros(nl)
work = np.zeros(1, dtype='i')
result = np.zeros(nl, dtype='d')
# master loop
if (i_rank == 0):
# send initial jobs
for i_rank_out in range(1,i_size):
work = np.array([i_rank_out-1], dtype='i')
o_comm.Send([work, MPI.INT], dest=i_rank_out, tag=i_work_tag)
for i_r in range(i_size-1,nr):
if (i_r % (nr / 10) == 0):
print "Finished %i%% of jobs... (%.2f s)" % (i_r * 100 / nr,
time.time() - f_t2)
work = np.array([i_r], dtype='i')
o_comm.Recv([result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
#print "received results from core %i" % o_status.Get_source()
o_comm.Send([work,MPI.INT], dest=o_status.Get_source(),
tag=i_work_tag)
cl21 += result
for i_rank_out in range(1,i_size):
o_comm.Recv([result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
cl21 += result
print "cl21 = %.6f, result = %.6f" % (np.average(cl21), np.average(result))
o_comm.Send([np.array([9999], dtype='i'), MPI.INT],
dest=o_status.Get_source(), tag=i_die_tag)
#slave loop:
else:
while(1):
o_comm.Recv([work, MPI.INT], source=0, status=o_status,
tag=MPI.ANY_TAG)
if (o_status.Get_tag() == i_die_tag):
break
i_r = work[0]
#print ("i_r: %i" % i_r)
#print ("alm_masked: ", alm_masked)
#print ("cltt_denom: ", cltt_denom)
#print ("beam: ", beam)
#print ("mask: ", mask)
#print ("fn_map: ", fn_map)
# create Alm, Blm (almxfl with alm_masked and beta / cltt_denom
# * beam, etc.)
Alm = np.zeros(alm_masked.shape[0],complex)
Blm = np.zeros(alm_masked.shape[0],complex)
clAB2 = np.zeros(nl+1)
clABB = np.zeros(nl+1)
#Alm = hp.almxfl(alm_masked, alpha[:,i_r] / cltt_denom * beam)
#Blm = hp.almxfl(alm_masked, beta[:,i_r] / cltt_denom * beam)
#for li in xrange(2,nl):
# I = hp.Alm.getidx(nl,li,np.arange(min(nl,li)+1))
# Alm[I]=alpha[li-2][i_r]*(alm_masked[I]*beam[li]+nlm[I])/(cltt_denom[li]*beam[li]**2+noise[li])
# Blm[I]=beta[li-2][i_r]*(alm_masked[I]*beam[li]+nlm[I])/(cltt_denom[li]*beam[li]**2+noise[li])
if (run_type == 'fnl'):
for li in xrange(2,nl):
I = hp.Alm.getidx(nl,li,np.arange(min(nl,li)+1))
Alm[I]=alpha[li-2][i_r]*(alm_masked[I]*beam[li]+nlm[I])/(cltt_denom[li]*beam[li]**2+noise[li])
Blm[I]=beta[li-2][i_r]*(alm_masked[I]*beam[li]+nlm[I])/(cltt_denom[li]*beam[li]**2+noise[li])
else:
for li in xrange(2,nl):
I = hp.Alm.getidx(nl,li,np.arange(min(nl,li)+1))
Alm[I]=alpha[li-2][i_r]*(alm_masked[I])/cltt_denom[li]*beam[li]
Blm[I]=beta[li-2][i_r]*(alm_masked[I])/cltt_denom[li]*beam[li]
############################# DEBUG ################################
if i_r == 0:
cltt_Alm = hp.alm2cl(Alm)
cltt_Blm = hp.alm2cl(Blm)
np.savetxt('debug2/cltt_%s_Alm.dat' % run_type, cltt_Alm)
np.savetxt('debug2/cltt_%s_Blm.dat' % run_type, cltt_Blm)
####################################################################
#An = hp.alm2map(Alm, nside=nside, fwhm=0.00145444104333,
# verbose=False)
#Bn = hp.alm2map(Blm, nside=nside, fwhm=0.00145444104333,
# verbose=False)
An = hp.alm2map(Alm, nside=nside)
Bn = hp.alm2map(Blm, nside=nside)
############################# DEBUG ################################
if i_r == 0:
cltt_An = hp.anafast(An)
cltt_Bn = hp.anafast(Bn)
np.savetxt('debug2/cltt_%s_An.dat' % run_type, cltt_An)
np.savetxt('debug2/cltt_%s_Bn.dat' % run_type, cltt_Bn)
####################################################################
An = An * mask
Bn = Bn * mask
############################# DEBUG ################################
#if i_r == 0:
# print "saving alpha, beta for %i" % i_r
# np.savetxt('debug2/alpha_ir_%i' % i_r, alpha[:,i_r])
# np.savetxt('debug2/beta_ir_%i' % i_r, beta[:,i_r])
# print "(An * Bn)[:10] == An[:10] * Bn[:10]:", (An * Bn)[:10] == An[:10] * Bn[:10]
####################################################################
B2lm = hp.map2alm(Bn*Bn, lmax=nl)
ABlm = hp.map2alm(An*Bn, lmax=nl)
############################# DEBUG ################################
if i_r == 0:
cltt_B2lm = hp.alm2cl(B2lm)
cltt_ABlm = hp.alm2cl(ABlm)
np.savetxt('debug2/cltt_%s_B2lm.dat' % run_type, cltt_B2lm)
np.savetxt('debug2/cltt_%s_ABlm.dat' % run_type, cltt_ABlm)
####################################################################
#clAB2 = hp.alm2cl(Alm, B2lm, lmax=nl)
#clABB = hp.alm2cl(ABlm, Blm, lmax=nl)
for li in xrange(2,nl+1):
I = hp.Alm.getidx(nl,li,np.arange(min(nl,li)+1))
clAB2[li] = (Alm[I[0]]*B2lm[I[0]].conj()
+2.*sum(Alm[I[1:]]*B2lm[I[1:]].conj()))/(2.0*li+1.0)
clABB[li] = (Blm[I[0]]*ABlm[I[0]].conj()
+2.*sum(Blm[I[1:]]*ABlm[I[1:]].conj()))/(2.0*li+1.0)
############################# DEBUG ################################
if i_r == 0:
np.savetxt('debug2/clAB2_%s.dat' % run_type, clAB2)
np.savetxt('debug2/clABB_%s.dat' % run_type, clABB)
####################################################################
clAB2 = clAB2[1:]
clABB = clABB[1:]
result = np.zeros(nl, dtype='d')
result += (clAB2 + 2 * clABB) * r[i_r]**2. * dr[i_r]
############################# DEBUG ################################
np.savetxt('debug2/cl21_%s.dat' % run_type, result)
####################################################################
print ("finished work for r=%i, dr=%.2f, avg(alpha)=%.2f, avg(beta)=%.2f, avg(result)=%.4g" %
(int(r[i_r]), dr[i_r], np.average(alpha[:,i_r]),
np.average(beta[:,i_r]), np.average(result)))
############################# DEBUG ################################
#if i_r == 0:
# print "finished debug -- goodbye!"
# exit()
####################################################################
o_comm.Send([result,MPI.DOUBLE], dest=0, tag=1)
f_t8 = time.time()
if (i_rank == 0):
print ""
print ("Saving power spectrum to %s (not mll corrected)"
% fn_cl21_no_mll)
np.savetxt(fn_cl21_no_mll, cl21)
print ""
print "Saving power spectrum to %s (mll corrected)" % fn_cl21
cl21 = np.dot(mll_inv, cl21)
np.savetxt(fn_cl21, cl21)
return
paper_rich = pb.bradley_paper_polarized_beams(bradley_paper_file) pbxx = paper_rich['xx'] pbxx = hpt.rotate_healpix_map(pbxx,[0,-120]) #hp.mollview(pbxx) #hp.mollview(hpt.rotate_healpix_map(pbxx,[0,-120])) #hp.graticule() #plt.show() npix = pbxx.size nside = hp.npix2nside(npix) lmax = 3*nside-1 alm_pbxx = hp.map2alm(pbxx,lmax=lmax) cl_pbxx = hp.alm2cl(alm_pbxx) l,m= hp.Alm.getlm(lmax) rot = np.exp(-1j*np.radians(45.)*m) rotmap = hp.alm2map(alm_pbxx*rot,nside) #hp.mollview(pbxx) #hp.mollview(rotmap) #plt.show() xydip = pb.xy_ideal_dipole() # F = f_theta(theta,phi) theta_hat + f_phi phi_hat # theta_hat = ()xhat + ()yhat + ()zhat Baltaz = np.array([[np.zeros(npix)],[xydip['xt']],[xydip['xp']]]) hprot = [0,90]
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
# for i in xrange(CAB2l.shape[1]):
# for j in xrange(CAB2l.shape[0]):
# clab2[i] += dR*R[j][i]*R[j][i]*CAB2l[j][i]
# clabb[i] += dR*R[j][i]*R[j][i]*CABBl[j][i]
cloc = (clab2[r] + 2 * clabb[r]) * (R[r]) ** 2.0 * dR[r]
############## DEBUG ##############
clAB2_jon = clAB2_jon[1:]
clABB_jon = clABB_jon[1:]
result_jon = zeros(nl, dtype="d")
result_jon += (clAB2_jon + 2 * clABB_jon) * (R[r]) ** 2.0 * dR[r]
###################################
cltt_Alm_jon = hp.alm2cl(Alm_jon)
cltt_Blm_jon = hp.alm2cl(Blm_jon)
cltt_An = hp.anafast(An)
cltt_Bn = hp.anafast(Bn)
cltt_B2lm_jon = hp.alm2cl(B2lm_jon)
cltt_ABlm_jon = hp.alm2cl(ABlm_jon)
savetxt("../debug2/cltt_joe_Alm.dat", cltt_Alm_jon)
savetxt("../debug2/cltt_joe_Blm.dat", cltt_Blm_jon)
savetxt("../debug2/cltt_joe_An.dat", cltt_An)
savetxt("../debug2/cltt_joe_Bn.dat", cltt_Bn)
savetxt("../debug2/cltt_joe_B2lm.dat", cltt_B2lm_jon)
savetxt("../debug2/cltt_joe_ABlm.dat", cltt_ABlm_jon)
savetxt("../debug2/clAB2_joe.dat", clAB2_jon)
savetxt("../debug2/clABB_joe.dat", clABB_jon)
print ("r: %i" % R[r])
prt("Writing gradient")
healpy.write_map("%s/grad%03d_%d.fits" % (args.odir, sim, args.order), [phi_dtheta, phi_dphi])
del aphi
prt("Computing lensed positions")
opos, rot = offset_pos(ipos, phi_dtheta, phi_dphi, pol=args.pol, geodesic=args.geodesic)
del phi, phi_dtheta, phi_dphi
prt("Starting taylor expansion")
# Interpolate maps one at a time
maps = []
for comp in cmb:
for m in taylor_interpol_iter(comp, opos, args.order, verbose=args.verbose, lmax=args.lmax):
pass
maps.append(m)
del opos, cmb
prt("Rotating")
rm = apply_rotation(maps, rot)
if "l" in args.output:
prt("Writing lensed map")
healpy.write_map("%s/lcmb%03d_%d.fits" % (args.odir, sim, args.order), rm)
if "s" in args.output:
prt("Computing power spectrum")
alm = healpy.map2alm(rm, use_weights=True, iter=1)
spec = np.array(healpy.alm2cl(alm))
if spec.ndim == 1: spec = np.reshape(spec, [1,spec.size])
n = spec.shape[1]
l = np.arange(spec.shape[1])
spec[:,1:] *= l[1:n]*(l[1:n]+1)/(2*np.pi)
prt("Writing spectrum")
np.savetxt("%s/spec%03d_%d.txt" % (args.odir, sim, args.order), spec.T, fmt=" %15.7e")
def main(run_type="data", nsim=0, fnl=0):
"""
MPI Setup
"""
o_comm = MPI.COMM_WORLD
i_rank = o_comm.Get_rank() # current core number -- e.g., i in arange(i_size)
i_size = o_comm.Get_size() # number of cores assigned to run this program
o_status = MPI.Status()
i_work_tag = 0
i_die_tag = 1
"""
Loading and calculating power spectrum components
"""
if run_type == "fnl":
nl = 1024
else:
nl = 1499
if run_type == "data":
fn_map = h._fn_map
elif run_type == "sim":
fn_map = "output/map_sim_%i.fits" % nsim
elif run_type == "fnl":
# print "fnl value: %i" % fnl
fn_map = "data/fnl_sims/map_fnl_%i_sim_%i.fits" % (int(fnl), nsim)
# (1) read map (either map_data or map_sim), mask, mll, and create mll_inv;
map_in = hp.read_map(fn_map)
mask = hp.read_map(h._fn_mask)
if run_type == "fnl":
mask = 1.0
fn_mll = "output/na_mll_%i_lmax.npy" % nl
mll = np.load(fn_mll)
if run_type == "fnl":
mll = np.identity(nl)
mll_inv = np.linalg.inv(mll)
nside = hp.get_nside(map_in)
if i_rank == 0:
if run_type == "data":
fn_cl21 = "output/cl21_data.dat"
fn_cl21_no_mll = "output/cl21_data_no_mll.dat"
elif run_type == "sim":
fn_cl21 = "output/cl21_sim_%i.dat" % nsim
fn_cl21_no_mll = "output/cl21_no_mll_%i.dat" % nsim
elif run_type == "fnl":
fn_cl21 = "output/cl21_fnl_%i_sim_%i.dat" % (int(fnl), nsim)
fn_cl21_no_mll = "output/cl21_fnl_%i_sim_%i_no_mll.dat" % (int(fnl), nsim)
f_t1 = time.time()
print ""
print "Run parameters:"
print "(Using %i cores)" % i_size
print "nl: %i, nside: %i, map: %s" % (nl, nside, fn_map)
print "beam: %s, alpha_beta: %s, cltt: %s" % (h._fn_beam, h._fn_alphabeta, h._fn_cltt)
print ""
print "Loading ell, r, dr, alpha, beta, cltt, and beam..."
# (2) normalize, remove mono-/dipole, and mask map to create map_masked;
map_in /= 1e6 * 2.7
map_in = hp.remove_dipole(map_in)
map_masked = map_in * mask
# (3) create alm_masked (map2alm on map_masked), cltt_masked (anafast on
# map_masked), and cltt_corrected (dot cltt_masked with mll_inv)
alm_masked = hp.map2alm(map_masked)
alm_masked = alm_masked[: hp.Alm.getsize(nl)]
cltt_masked = hp.anafast(map_masked)
cltt_masked = cltt_masked[:nl]
cltt_corrected = np.dot(mll_inv, cltt_masked)
# stuff with alpha, beta, r, dr, and beam
l, r, dr, alpha, beta = np.loadtxt(h._fn_alphabeta, usecols=(0, 1, 2, 3, 4), unpack=True, skiprows=3)
l = np.unique(l)
r = np.unique(r)[::-1]
nr = len(r)
cltt_denom = np.load("output/cltt_theory.npy") # replace with 'output/na_cltt.npy'
cltt_denom = cltt_denom[:nl]
alpha = alpha.reshape(len(l), nr)
beta = beta.reshape(len(l), nr)
dr = dr.reshape(len(l), nr)
dr = dr[0]
if run_type != "fnl":
beam = np.load(h._fn_beam)
else:
beam = np.ones(len(cltt_denom))
l = l[:nl]
beam = beam[:nl]
alpha = alpha[:nl, :]
beta = beta[:nl, :]
if i_rank == 0:
print "nr: %i, nl: %i" % (nr, nl)
f_t2 = time.time()
if i_rank == 0:
print ""
print "Time to create alms from maps: %.2f s" % (f_t2 - f_t1)
print "Calculating full skewness power spectrum..."
cl21 = np.zeros(nl)
work = np.zeros(1, dtype="i")
result = np.zeros(nl, dtype="d")
# master loop
if i_rank == 0:
# send initial jobs
for i_rank_out in range(1, i_size):
work = np.array([i_rank_out - 1], dtype="i")
o_comm.Send([work, MPI.INT], dest=i_rank_out, tag=i_work_tag)
for i_r in range(i_size - 1, nr):
if i_r % (nr / 10) == 0:
print "Finished %i%% of jobs... (%.2f s)" % (i_r * 100 / nr, time.time() - f_t2)
work = np.array([i_r], dtype="i")
o_comm.Recv([result, MPI.DOUBLE], source=MPI.ANY_SOURCE, status=o_status, tag=MPI.ANY_TAG)
# print "received results from core %i" % o_status.Get_source()
o_comm.Send([work, MPI.INT], dest=o_status.Get_source(), tag=i_work_tag)
cl21 += result
for i_rank_out in range(1, i_size):
o_comm.Recv([result, MPI.DOUBLE], source=MPI.ANY_SOURCE, status=o_status, tag=MPI.ANY_TAG)
cl21 += result
print "cl21 = %.6f, result = %.6f" % (np.average(cl21), np.average(result))
o_comm.Send([np.array([9999], dtype="i"), MPI.INT], dest=o_status.Get_source(), tag=i_die_tag)
# slave loop:
else:
while 1:
o_comm.Recv([work, MPI.INT], source=0, status=o_status, tag=MPI.ANY_TAG)
if o_status.Get_tag() == i_die_tag:
break
i_r = work[0]
# create Alm, Blm (almxfl with alm_masked and beta / cltt_denom
# * beam, etc.)
Alm = hp.almxfl(alm_masked, alpha[:, i_r] / cltt_denom * beam)
Blm = hp.almxfl(alm_masked, beta[:, i_r] / cltt_denom * beam)
############################# DEBUG ################################
# cltt_Alm = hp.alm2cl(Alm)
# cltt_Blm = hp.alm2cl(Blm)
# np.savetxt('debug2/cltt_%s_Alm.dat' % run_type, cltt_Alm)
# np.savetxt('debug2/cltt_%s_Blm.dat' % run_type, cltt_Blm)
####################################################################
An = hp.alm2map(Alm, nside=nside, fwhm=0.00145444104333, verbose=False)
Bn = hp.alm2map(Blm, nside=nside, fwhm=0.00145444104333, verbose=False)
############################# DEBUG ################################
# cltt_An = hp.anafast(An)
# cltt_Bn = hp.anafast(Bn)
# np.savetxt('debug2/cltt_%s_An.dat' % run_type, cltt_An)
# np.savetxt('debug2/cltt_%s_Bn.dat' % run_type, cltt_Bn)
####################################################################
An = An * mask
Bn = Bn * mask
############################# DEBUG ################################
if i_r == 100:
print "saving alpha, beta for %i" % i_r
np.savetxt("debug2/alpha_ir_%i" % i_r, alpha[:, i_r])
np.savetxt("debug2/beta_ir_%i" % i_r, beta[:, i_r])
print "(An * Bn)[:10] == An[:10] * Bn[:10]:", (An * Bn)[:10] == An[:10] * Bn[:10]
####################################################################
B2lm = hp.map2alm(Bn * Bn, lmax=nl)
ABlm = hp.map2alm(An * Bn, lmax=nl)
############################# DEBUG ################################
# cltt_B2lm = hp.alm2cl(B2lm)
# cltt_ABlm = hp.alm2cl(ABlm)
# np.savetxt('debug2/cltt_%s_B2lm.dat' % run_type, cltt_B2lm)
# np.savetxt('debug2/cltt_%s_ABlm.dat' % run_type, cltt_ABlm)
####################################################################
clAB2 = hp.alm2cl(Alm, B2lm, lmax=nl)
clABB = hp.alm2cl(ABlm, Blm, lmax=nl)
############################# DEBUG ################################
# np.savetxt('debug2/clAB2_%s.dat' % run_type, clAB2)
# np.savetxt('debug2/clABB_%s.dat' % run_type, clABB)
####################################################################
clAB2 = clAB2[1:]
clABB = clABB[1:]
result = np.zeros(nl, dtype="d")
result += (clAB2 + 2 * clABB) * r[i_r] ** 2.0 * dr[i_r]
############################# DEBUG ################################
# np.savetxt('debug2/cl21_%s.dat' % run_type, result)
####################################################################
print (
"finished work for r=%i, dr=%.2f, avg(alpha)=%.2f, avg(beta)=%.2f, avg(result)=%.4g"
% (int(r[i_r]), dr[i_r], np.average(alpha[:, i_r]), np.average(beta[:, i_r]), np.average(result))
)
############################# DEBUG ################################
# print "finished debug -- goodbye!"
# exit()
####################################################################
o_comm.Send([result, MPI.DOUBLE], dest=0, tag=1)
f_t8 = time.time()
if i_rank == 0:
print ""
print ("Saving power spectrum to %s (not mll corrected)" % fn_cl21_no_mll)
np.savetxt(fn_cl21_no_mll, cl21)
print ""
print "Saving power spectrum to %s (mll corrected)" % fn_cl21
cl21 = np.dot(mll_inv, cl21)
np.savetxt(fn_cl21, cl21)
return
def main(i_sim=1):
'''
MPI Setup
'''
o_comm = MPI.COMM_WORLD
i_rank = o_comm.Get_rank() # current core number -- e.g., i in arange(i_size)
i_size = o_comm.Get_size() # number of cores assigned to run this program
o_status = MPI.Status()
i_work_tag = 0
i_die_tag = 1
'''
Loading and calculating power spectrum components
'''
# Get run parameters
s_fn_params = 'data/params.pkl'
(i_lmax, i_nside, s_fn_map, s_map_name, s_fn_mask, s_fn_mll, s_fn_beam,
s_fn_alphabeta, s_fn_cltt) = get_params(s_fn_params)
#s_fn_cltt = ('data/fnl_sims/cl_fnl_0_sim_%04d.fits' % (i_sim,))
f_fnl = 1.0
if (i_rank == 0):
s_fn_cl21_data = ('data/cl21_fnl_sims/cl21_fnl_%i_sim_%04d.dat'
% (int(f_fnl), i_sim))
s_fn_cl21_data_no_mll = ('data/cl21_fnl_sims/cl21_fnl_%i_sim_%04d_no_mll.dat'
% (int(f_fnl), i_sim))
f_t1 = time.time()
print ""
print "Run parameters:"
print "(Using %i cores)" % i_size
print "lmax: %i, nside: %i, map name: %s" % (i_lmax, i_nside, s_map_name)
print "beam: %s, alpha_beta: %s, cltt: %s" % (s_fn_beam, s_fn_alphabeta, s_fn_cltt)
print ""
print "Loading ell, r, dr, alpha, beta, cltt, and beam..."
i_lmax = 1024
na_mask = hp.read_map(s_fn_mask)
s_fn_mll = 'output/na_mll_%i_lmax.npy' % i_lmax
na_mll = np.load(s_fn_mll)
na_mll_inv = np.linalg.inv(na_mll)
na_l, na_r, na_dr, na_alpha, na_beta = np.loadtxt(s_fn_alphabeta,
usecols=(0,1,2,3,4), unpack=True, skiprows=3)
na_l = np.unique(na_l)
na_r = np.unique(na_r)[::-1]
na_l = na_l[:i_lmax]
i_num_ell = len(na_l)
i_num_r = len(na_r)
if (i_rank == 0):
print "i_num_r: %i, i_num_ell: %i" % (i_num_r, i_num_ell)
# assuming number of r's is set correctly and number of ells isn't necessarily
# related to lmax, i_num_ell, etc.
i_num_ell_alpha_beta_r = len(na_alpha)/i_num_r
na_alpha = na_alpha.reshape(i_num_ell_alpha_beta_r, i_num_r)
na_beta = na_beta.reshape(i_num_ell_alpha_beta_r, i_num_r)
na_dr = na_dr.reshape(i_num_ell_alpha_beta_r, i_num_r)
na_dr = na_dr[0]
na_alpha = na_alpha[:i_num_ell,:]
na_beta = na_beta[:i_num_ell,:]
na_dr = na_dr[:i_num_ell]
try:
na_cltt = np.load(s_fn_cltt)
except:
na_cltt = np.loadtxt(s_fn_cltt)
na_cltt = na_cltt[:i_num_ell]
na_bl = np.load(s_fn_beam)
na_bl = na_bl[:i_num_ell]
# f_t2 = time.time()
if (i_rank == 0):
print ""
print "Calculating full skewness power spectrum..."
#na_alm = hp.synalm(na_cltt, lmax=i_num_ell, verbose=False)
s_fn_almg = ('data/fnl_sims/alm_l_%04d_v3.fits' % (i_sim,))
na_almg = hp.read_alm(s_fn_almg)
na_almg = na_almg[:hp.Alm.getsize(i_num_ell)]
s_fn_almng = ('data/fnl_sims/alm_nl_%04d_v3.fits' % (i_sim,))
na_almng = hp.read_alm(s_fn_almng)
na_almng = na_almng[:hp.Alm.getsize(i_num_ell)]
na_alm = na_almg + f_fnl * na_almng
# f_t3 = time.time()
na_cl21_data = np.zeros(i_num_ell)
na_work = np.zeros(1, dtype='i')
na_result = np.zeros(i_num_ell, dtype='d')
# if (i_rank == 0):
# print ("na_alm: %s, na_alpha: %s, na_beta: %s, na_cltt: %s, na_bl: %s" %
# (str(np.shape(na_alm)), str(np.shape(na_alpha)),
# str(np.shape(na_beta)), str(np.shape(na_cltt)),
# str(np.shape(na_bl)) ))
# else:
# print "core: %i" % i_rank
# print ("na_alm: %s, na_alpha: %s, na_beta: %s, na_cltt: %s, na_bl: %s" %
# (str(np.shape(na_alm)), str(np.shape(na_alpha)),
# str(np.shape(na_beta)), str(np.shape(na_cltt)),
# str(np.shape(na_bl)) ))
# master loop
if (i_rank == 0):
# send initial jobs
# print ("na_alm: %s, na_alpha: %s, na_beta: %s, na_cltt: %s, na_bl: %s" %
# (str(np.shape(na_alm)), str(np.shape(na_alpha)),
# str(np.shape(na_beta)), str(np.shape(na_cltt)),
# str(np.shape(na_bl)) ))
for i_rank_out in range(1,i_size):
na_work = np.array([i_rank_out-1], dtype='i')
o_comm.Send([na_work, MPI.INT], dest=i_rank_out, tag=i_work_tag)
for i_r in range(i_size-1,i_num_r):
if (i_r % (i_num_r / 10) == 0):
print "Finished %i%% of jobs... (%.2f s)" % (i_r * 100 / i_num_r,
time.time() - f_t1)
na_work = np.array([i_r], dtype='i')
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
#print "received results from core %i" % o_status.Get_source()
o_comm.Send([na_work,MPI.INT], dest=o_status.Get_source(),
tag=i_work_tag)
na_cl21_data += na_result
for i_rank_out in range(1,i_size):
o_comm.Recv([na_result, MPI.DOUBLE], source=MPI.ANY_SOURCE,
status=o_status, tag=MPI.ANY_TAG)
na_cl21_data += na_result
print "cl21 so far..."
print na_cl21_data[:10], na_cl21_data[10:]
o_comm.Send([np.array([9999], dtype='i'), MPI.INT],
dest=o_status.Get_source(), tag=i_die_tag)
#slave loop:
else:
while(1):
o_comm.Recv([na_work, MPI.INT], source=0, status=o_status,
tag=MPI.ANY_TAG)
if (o_status.Get_tag() == i_die_tag):
break
i_r = na_work[0]
#print "doing work for r = %i on core %i" % (i_r, i_rank)
# print ("na_alm: %s, na_alpha: %s, na_beta: %s, na_cltt: %s, na_bl: %s" %
# (str(np.shape(na_alm)), str(np.shape(na_alpha)),
# str(np.shape(na_beta)), str(np.shape(na_cltt)),
# str(np.shape(na_bl)) ))
na_Alm = hp.almxfl(na_alm, np.nan_to_num(na_alpha[:,i_r] / na_cltt) * na_bl)
na_Blm = hp.almxfl(na_alm, np.nan_to_num(na_beta[:,i_r] / na_cltt) * na_bl)
# f_t4 = time.time()
na_An = hp.alm2map(na_Alm, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
na_Bn = hp.alm2map(na_Blm, nside=i_nside, fwhm=0.00145444104333,
verbose=False)
# *REMBER TO MULTIPLY BY THE MASK!*
na_An = na_An * na_mask
na_Bn = na_Bn * na_mask
# f_t5 = time.time()
#print "starting map2alm for r = %i on core %i" % (i_r, i_rank)
na_B2lm = hp.map2alm(na_Bn*na_Bn, lmax=i_num_ell)
na_ABlm = hp.map2alm(na_An*na_Bn, lmax=i_num_ell)
#print "finished map2alm for r = %i on core %i" % (i_r, i_rank)
# f_t6 = time.time()
na_clAB2 = hp.alm2cl(na_Alm, na_B2lm, lmax=i_num_ell)
na_clABB = hp.alm2cl(na_ABlm, na_Blm, lmax=i_num_ell)
na_clAB2 = na_clAB2[1:]
na_clABB = na_clABB[1:]
#f_t7 = time.time()
# print ("na_clAB2: %s, na_clABB: %s, na_r: %s, na_dr: %s" %
# (str(np.shape(na_clAB2)), str(np.shape(na_clABB)),
# str(np.shape(na_r)), str(np.shape(na_dr)) ))
na_result = np.zeros(i_num_ell, dtype='d')
na_result += (na_clAB2 + 2 * na_clABB) * na_r[i_r]**2. * na_dr[i_r]
#print "finished work for r = %i on core %i" % (i_r, i_rank)
o_comm.Send([na_result,MPI.DOUBLE], dest=0, tag=1)
# print "Load time: %.2f s" % (f_t2 - f_t1)
# print "synalm time: %.2f s" % (f_t3 - f_t2)
# print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
f_t8 = time.time()
if (i_rank == 0):
print ""
print ("Saving power spectrum to %s (not mll corrected)"
% s_fn_cl21_data_no_mll)
np.savetxt(s_fn_cl21_data_no_mll, na_cl21_data)
print ""
print "Saving power spectrum to %s (mll corrected)" % s_fn_cl21_data
na_cl21_data = np.dot(na_mll_inv, na_cl21_data)
np.savetxt(s_fn_cl21_data, na_cl21_data)
# print "Finished in %.2f s" % (f_t8 - f_t1)
# # print "Load time: %.2f s" % (f_t2 - f_t1)
# # print "synalm time: %.2f s" % (f_t3 - f_t2)
# # print "almxfl time: %.2f s" % ((f_t4 - f_t3) / 2.)
# # print "alm2map time: %.2f s" % ((f_t5 - f_t4) / 2.)
# # print "map2alm time: %.2f s" % ((f_t6 - f_t5) / 2.)
# # print "alm2cl time: %.2f s" % ((f_t7 - f_t6) / 2.)
return
bl = np.load('output/na_bl.npy')
ir = 10
alm_data = alm_data[:hp.Alm.getsize(nl)]
alm_sim = alm_sim[:hp.Alm.getsize(nl)]
cltt_corrected = cltt_corrected[:nl]
bl = bl[:nl]
#data
Alm_data = hp.almxfl(alm_data, alpha[:,ir] / cltt_corrected * bl)
Blm_data = hp.almxfl(alm_data, beta[:,ir] / cltt_corrected * bl)
# make cls and save them away
cltt_data_Alm = hp.alm2cl(Alm_data)
cltt_data_Blm = hp.alm2cl(Blm_data)
np.savetxt('debug/cltt_data_Alm.dat', cltt_data_Alm)
np.savetxt('debug/cltt_data_Blm.dat', cltt_data_Blm)
An_data = hp.alm2map(Alm_data, nside=2048, fwhm=0.00145444104333, verbose=False)
Bn_data = hp.alm2map(Blm_data, nside=2048, fwhm=0.00145444104333, verbose=False)
# make cls and save them away
cltt_data_An = hp.anafast(An_data)
cltt_data_Bn = hp.anafast(Bn_data)
np.savetxt('debug/cltt_data_An.dat', cltt_data_An)
np.savetxt('debug/cltt_data_Bn.dat', cltt_data_Bn)
B2lm_data = hp.map2alm(Bn_data*Bn_data, lmax=nl)
ABlm_data = hp.map2alm(An_data*Bn_data, lmax=nl)
