alms_beamed[0] += fglms[2 * fcount]
alms_beamed[1] += fglms[2 * fcount + 1]
alms_beamed = maps_to_params_utils.multiply_alms(
alms_beamed, bl, ncomp)
fcount += 1
for k in range(nsplits):
noisy_alms = alms_beamed.copy()
noisy_alms[0] += nlms["T", k][fid]
noisy_alms[1] += nlms["E", k][fid]
noisy_alms[2] += nlms["B", k][fid]
split = sph_tools.alm2map(noisy_alms, template)
# Now that we have generated a split of data of experiment exp
# and frequency freq, we take its harmonic transform
split = maps_to_params_utils.remove_mean(
split, window_tuple, ncomp)
master_alms[exp, freq,
k] = sph_tools.get_alms(split, window_tuple, niter,
lmax)
# We now form auto and cross power spectra from the alms
ps_dict = {}
for id_exp1, exp1 in enumerate(experiments):
freqs1 = d["freqs_%s" % exp1]
nsplits1 = d["nsplits_%s" % exp1]
l, bl_pol = np.loadtxt(d["beam_%s_%s_pol" % (freq, hm)],
unpack=True)
hm_alms[0] = hp.sphtfunc.almxfl(hm_alms[0], bl_T)
hm_alms[1] = hp.sphtfunc.almxfl(hm_alms[1], bl_pol)
hm_alms[2] = hp.sphtfunc.almxfl(hm_alms[2], bl_pol)
if use_ffp10 == False:
hm_alms[0] += nlms["T", k][f_id]
hm_alms[1] += nlms["E", k][f_id]
hm_alms[2] += nlms["B", k][f_id]
for i in range(3):
hm_alms[i] = hp.sphtfunc.almxfl(hm_alms[i], pixwin)
pl_map = sph_tools.alm2map(hm_alms, template)
if use_ffp10 == True:
noise_map = so_map.read_map(
"%s/%s/ffp10_noise_%s_%s_map_mc_%05d.fits" %
(d["ffp10_dir"], freq, freq, hm, iii))
noise_map.data *= 10**6
pl_map.data += noise_map.data
window_t = so_map.read_map("%s/window_T_%s_%s-%s.fits" %
(windows_dir, exp, freq, hm))
window_pol = so_map.read_map("%s/window_P_%s_%s-%s.fits" %
(windows_dir, exp, freq, hm))
window_tuple = (window_t, window_pol)
del window_t, window_pol
cov_map = so_map.read_map("%s" % map, fields_healpix=4)
del win_T, win_pol
# we add fg alms to cmb alms in temperature
alms_beamed = alms.copy()
alms_beamed[0] += fglms[id_freq[d["nu_eff_%s_%s" % (sv, ar)]]]
# we convolve signal + foreground with the beam of the array
l, bl = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv, ar)])
alms_beamed = curvedsky.almxfl(alms_beamed, bl)
if scenario == "noE": alms_beamed[1] *= 0
if scenario == "noB": alms_beamed[2] *= 0
# generate our signal only sim
split = sph_tools.alm2map(alms_beamed, template[sv])
# compute the alms of the sim
master_alms[sv, ar, "nofilter"] = sph_tools.get_alms(
split, window_tuple, niter, lmax, dtype=sim_alm_dtype)
# apply the k-space filter
binary = so_map.read_map("%s/binary_%s_%s.fits" %
(window_dir, sv, ar))
norm, split = data_analysis_utils.get_filtered_map(
split,
binary,
filter[sv],
noisy_alms[1] += nlms['E', k][freq_id]
noisy_alms[2] += nlms['B', k][freq_id]
if include_sys == True:
l, Tl_T = np.loadtxt(d['TF_%s_%s_T' % (freq, hm)], unpack=True)
l, Tl_pol = np.loadtxt(d['TF_%s_%s_pol' % (freq, hm)],
unpack=True)
noisy_alms[0] = hp.sphtfunc.almxfl(noisy_alms[0], Tl_T)
noisy_alms[1] = hp.sphtfunc.almxfl(noisy_alms[1], Tl_pol)
noisy_alms[2] = hp.sphtfunc.almxfl(noisy_alms[2], Tl_pol)
for i in range(3):
noisy_alms[i] = hp.sphtfunc.almxfl(noisy_alms[i], pixwin)
pl_map = sph_tools.alm2map(noisy_alms, template)
window_T = so_map.read_map('%s/window_T_%s_%s-%s.fits' %
(auxMapDir, experiment, freq, hm))
window_pol = so_map.read_map('%s/window_P_%s_%s-%s.fits' %
(auxMapDir, experiment, freq, hm))
window_tuple = (window_T, window_pol)
del window_T, window_pol
cov_map = so_map.read_map('%s' % map, fields_healpix=4)
badpix = (cov_map.data == hp.pixelfunc.UNSEEN)
for i in range(3):
pl_map.data[i][badpix] = 0.0
if remove_mono_dipo_T:
pl_map.data[0] = planck_utils.subtract_mono_di(
pl_map.data[0], window_tuple[0].data, pl_map.nside)
