for name1, alm1, c1 in zip(nameList, almList, np.arange(count)):
for name2, alm2, c2 in zip(nameList, almList, np.arange(count)):
if c1 > c2: continue
l, ps = so_spectra.get_spectra(alm1, alm2, spectra=spectra)
spec_name = '%sx%s' % (name1, name2)
lb, Db_dict[spec_name] = so_spectra.bin_spectra(l,
ps,
binning_file,
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
spec_name_list += [spec_name]
so_spectra.write_ps('%s/spectra_%s.dat' % (spectraDir, spec_name),
lb,
Db_dict[spec_name],
type=type,
spectra=spectra)
if c1 == c2:
print('auto %dx%d' % (c1, c2))
for s1 in spectra:
Db_dict_auto[s1] += [Db_dict[spec_name][s1]]
else:
print('cross %dx%d' % (c1, c2))
for s1 in spectra:
Db_dict_cross[s1] += [Db_dict[spec_name][s1]]
for s1 in spectra:
Db_dict_auto[s1] = np.mean(Db_dict_auto[s1], axis=0)
Db_dict_cross[s1] = np.mean(Db_dict_cross[s1], axis=0)
else:
nl_hm1[spec]=np.zeros(len(lth))
nl_hm2[spec]=np.zeros(len(lth))
nl_mean[spec]=np.zeros(len(lth))
np.savetxt('%s/noise_T_hm1_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_hm1['TT']]))
np.savetxt('%s/noise_P_hm1_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_hm1['EE']]))
np.savetxt('%s/noise_T_hm2_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_hm2['TT']]))
np.savetxt('%s/noise_P_hm2_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_hm2['EE']]))
np.savetxt('%s/noise_T_mean_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_mean['TT']/2]))
np.savetxt('%s/noise_P_mean_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nl_mean['EE']/2]))
np.savetxt('%s/noise_T_th_mean_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nlth['TT']/2]))
np.savetxt('%s/noise_P_th_mean_%s_%sx%s_%s.dat'%(ps_model_dir,experiment,freq1,experiment,freq2), np.transpose([lth,nlth['EE']/2]))
spec_name_noise_hm1='hm1_%s_%sx%s_%s_noise'%(experiment,freq1,experiment,freq2)
spec_name_noise_hm2='hm2_%s_%sx%s_%s_noise'%(experiment,freq1,experiment,freq2)
spec_name_noise_mean='mean_%s_%sx%s_%s_noise'%(experiment,freq1,experiment,freq2)
so_spectra.write_ps(ps_model_dir+'/%s.dat'%spec_name_noise_hm1,lth,nl_hm1,type,spectra=spectra)
so_spectra.write_ps(ps_model_dir+'/%s.dat'%spec_name_noise_hm2,lth,nl_hm2,type,spectra=spectra)
so_spectra.write_ps(ps_model_dir+'/%s.dat'%spec_name_noise_mean,lth,nl_mean,type,spectra=spectra)
spec_name = "%s_%s_%s_%dx%s_%s_%d_%05d" % (
type, exp1, f1, s1, exp2, f2, s2, iii)
lb, ps = so_spectra.bin_spectra(l,
ps_master,
binning_file,
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
if write_all_spectra:
so_spectra.write_ps(specDir +
"/%s.dat" % spec_name,
lb,
ps,
type,
spectra=spectra)
for count, spec in enumerate(spectra):
if (s1 == s2) & (exp1 == exp2):
if count == 0:
print(
"auto %s_%s split%d X %s_%s split%d"
% (exp1, f1, s1, exp2, f2, s2))
ps_dict[spec, "auto"] += [ps[spec]]
else:
if count == 0:
print(
"cross %s_%s split%d X %s_%s split%d"
% (exp1, f1, s1, exp2, f2, s2))
else:
Dl_interpolate[spec] = np.zeros(len(l))
if (spec == 'TT' or spec == 'EE'
or spec == 'BB') & (f1 == f2) & (exp1 == exp2):
Nl = scipy.interpolate.interp1d(
lb, Nb[spec], fill_value='extrapolate')
Nl_interpolate[spec] = np.array([Nl(i) for i in l])
Nl_interpolate[spec][:30] = 0
Nl_interpolate[spec] *= bl1 * bl2
else:
Nl_interpolate[spec] = np.zeros(len(l))
spec_name_combined = '%s_%s_%sx%s_%s_cross' % (type, exp1, f1,
exp2, f2)
so_spectra.write_ps(ps_model_dir +
'/%s.dat' % spec_name_combined,
l,
Dl_interpolate,
type,
spectra=spectra)
spec_name_noise = '%s_%s_%sx%s_%s_noise' % (type, exp1, f1,
exp2, f2)
so_spectra.write_ps(ps_model_dir + '/%s.dat' % spec_name_noise,
l,
Nl_interpolate,
type,
spectra=spectra)
prefix="%s/%s_%sx%s_%s" %
(mcm_dir, sv1, ar1, sv2, ar2),
spin_pairs=spin_pairs)
# we compute the power spectra of the sim (with and without the k-space filter applied)
for filt in ["filter", "nofilter"]:
l, ps_master = so_spectra.get_spectra_pixell(
master_alms[sv1, ar1, filt],
master_alms[sv2, ar2, filt],
spectra=spectra)
lb, ps = so_spectra.bin_spectra(l,
ps_master,
binning_file,
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
so_spectra.write_ps(
tf_dir + "/%s_%s_%s_%05d.dat" %
(spec_name, filt, scenario, iii),
lb,
ps,
type,
spectra=spectra)
print("sim number %05d done in %.02f s" % (iii, time.time() - t0))
freqs2 = d["freqs_%s" % exp2]
for id_f2, f2 in enumerate(freqs2):
if (id_exp1 == id_exp2) & (id_f1 > id_f2): continue
if (id_exp1 > id_exp2): continue
spec_name = "%s_%sx%s_%s" % (exp1, f1, exp2, f2)
for iii in isims:
spec_name_cross = "spectra/%s_%s_cross_%05d.dat" % (
type, spec_name, iii)
l, ps = so_spectra.read_ps(spec_name_cross,
spectra=spectra)
so_spectra.write_ps("%s/%s_%s_%05d.dat" %
(like_dir, type, spec_name, iii),
l,
ps,
type,
spectra=spectra)
prefix = "%s/%s" % (mcm_dir, spec_name)
mbb_inv, Bbl = so_mcm.read_coupling(prefix=prefix,
spin_pairs=spin_pairs)
Bbl_TT = Bbl["spin0xspin0"]
Bbl_TE = Bbl["spin0xspin2"]
Bbl_EE = Bbl["spin2xspin2"][:Bbl_TE.shape[0], :Bbl_TE.shape[1]]
np.savetxt("%s/Bbl_%s_TT.dat" % (like_dir, spec_name), Bbl_TT)
np.savetxt("%s/Bbl_%s_TE.dat" % (like_dir, spec_name), Bbl_TE)
np.savetxt("%s/Bbl_%s_EE.dat" % (like_dir, spec_name), Bbl_EE)
mcm_inv, mbb_inv, Bbl = so_mcm.read_coupling(
prefix=prefix, spin_pairs=spin_pairs, unbin=True)
l, ps = so_spectra.get_spectra(alms[hm1, freq1],
alms[hm2, freq2],
spectra=spectra)
spec_name = '%s_%sx%s_%s-%sx%s' % (experiment, freq1,
experiment, freq2, hm1, hm2)
l, cl, lb, Db = planck_utils.process_planck_spectra(
l,
ps,
binning_file,
lmax,
type=type,
mcm_inv=mcm_inv,
spectra=spectra)
spec_name_list += [spec_name]
so_spectra.write_ps('%s/spectra_%s.dat' %
(spectraDir, spec_name),
lb,
Db,
type=type,
spectra=spectra)
so_spectra.write_ps('%s/spectra_unbin_%s.dat' %
(spectraDir, spec_name),
l,
cl,
type=type,
spectra=spectra)
mcm_inv, mbb_inv, Bbl = so_mcm.read_coupling(
prefix=prefix, spin_pairs=spin_pairs, unbin=True)
l, ps = so_spectra.get_spectra(alms[hm1, freq1],
alms[hm2, freq2],
spectra=spectra)
spec_name = "%s_%sx%s_%s-%sx%s" % (exp, freq1, exp, freq2,
hm1, hm2)
l, cl, lb, Db = planck_utils.process_planck_spectra(
l,
ps,
binning_file,
lmax,
mcm_inv=mcm_inv,
spectra=spectra)
spec_name_list += [spec_name]
so_spectra.write_ps("%s/sim_spectra_%s_%04d.dat" %
(sims_dir, spec_name, iii),
lb,
Db,
type=type,
spectra=spectra)
so_spectra.write_ps("%s/sim_spectra_unbin_%s_%04d.dat" %
(sims_dir, spec_name, iii),
l,
cl,
type=type,
spectra=spectra)
print("sim %04d take %.02f seconds to compute" % (iii, time.time() - t0))
if (spec == "TT" or spec == "EE" or spec == "BB") & (f0 == f1):
bl_hm1 = bl[f0, "hm1", spec]
bl_hm2 = bl[f0, "hm2", spec]
nth_hm1 = ps_dict_auto1[spec] * bl_hm1**2 - ps_dict_cross[
spec] * bl_hm1 * bl_hm2
nth_hm2 = ps_dict_auto2[spec] * bl_hm2**2 - ps_dict_cross[
spec] * bl_hm1 * bl_hm2
lb, nb_hm1 = planck_utils.binning(l, nth_hm1, lmax, size=8)
lb, nb_hm2 = planck_utils.binning(l, nth_hm2, lmax, size=8)
nb_mean = (nb_hm1 + nb_hm2) / 4
nl_interpol_mean = scipy.interpolate.interp1d(
lb, nb_mean, fill_value="extrapolate")
nl_mean = np.array([nl_interpol_mean(i) for i in lth])
else:
nl_mean = np.zeros(len(lth))
nl_sim_mean[spec] += [nl_mean]
for spec in spectra:
nl_sim_mean[spec] = np.mean(nl_sim_mean[spec], axis=0)
so_spectra.write_ps(ps_model_dir + "/%s_%s_noise.dat" %
(spec_name_noise_mean, name),
lth,
nl_sim_mean,
type,
spectra=spectra)
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
if writeAll:
if hdf5:
so_spectra.write_ps_hdf5(spectra_hdf5,
spec_name,
lb,
Db,
spectra=spectra)
else:
so_spectra.write_ps(specDir +
'/%s.dat' % spec_name,
lb,
Db,
type,
spectra=spectra)
for spec in spectra:
if (s1 == s2) & (exp1 == exp2):
if spec == 'TT':
print(
'auto %s_%s split%d X %s_%s split%d' %
(exp1, f1, s1, exp2, f2, s2))
Db_dict[exp1, f1, exp2, f2, spec,
'auto'] += [Db[spec]]
else:
if spec == 'TT':
print(
'cross %s_%s split%d X %s_%s split%d' %
prefix=prefix, spin_pairs=spin_pairs, unbin=True)
l, ps = so_spectra.get_spectra(alms[hm1, freq1],
alms[hm2, freq2],
spectra=spectra)
spec_name = '%s_%sx%s_%s-%sx%s' % (
experiment, freq1, experiment, freq2, hm1, hm2)
l, cl, lb, Db = planck_utils.process_planck_spectra(
l,
ps,
binning_file,
lmax,
type=type,
mcm_inv=mcm_inv,
spectra=spectra)
spec_name_list += [spec_name]
so_spectra.write_ps('%s/sim_spectra_%s_%04d.dat' %
(simSpectraDir, spec_name, iii),
lb,
Db,
type=type,
spectra=spectra)
so_spectra.write_ps('%s/sim_spectra_unbin_%s_%04d.dat' %
(simSpectraDir, spec_name, iii),
l,
cl,
type=type,
spectra=spectra)
print('sim %04d take %.02f seconds to compute' % (iii, time.time() - t0))
plt.plot(lth,
nl_dict[spec],
label="interpolate",
color="lightblue")
else:
nl_dict[spec] = np.zeros(len(lth))
plt.plot(lb,
nbs_mean,
".",
label="%s %sx%s" % (sv, ar1, ar2),
color="red")
plt.legend(fontsize=20)
plt.savefig("%s/noise_interpolate_%sx%s_%s_%s.png" %
(plot_dir, ar1, ar2, sv, spec),
bbox_inches="tight")
plt.clf()
plt.close()
spec_name_noise_mean = "mean_%sx%s_%s_noise" % (ar1, ar2, sv)
so_spectra.write_ps(ps_model_dir +
"/%s.dat" % spec_name_noise_mean,
lth,
nl_dict,
type,
spectra=spectra)
(mcm_dir, scan, run))
alm = {}
for split in split_list:
alm[split] = sph_tools.get_alms(sim[split], window, niter, lmax)
for c0, s0 in enumerate(split_list):
for c1, s1 in enumerate(split_list):
if c1 > c0: continue
spec_name = "%s_%sx%s_%s" % (scan, s0, s1, run)
l, ps = so_spectra.get_spectra(alm[s0],
alm[s1],
spectra=spectra)
lb, Db_dict[spec_name] = so_spectra.bin_spectra(
l,
ps,
binning_file,
lmax,
type="Dl",
mbb_inv=mbb_inv,
spectra=spectra)
so_spectra.write_ps("%s/spectra_%s.dat" %
(spectra_dir, spec_name),
lb,
Db_dict[spec_name],
type="Dl",
spectra=spectra)
l, ps_filt = so_spectra.get_spectra(alm0_filt,
alm1_filt,
spectra=spectra)
lb, Db_dict = so_spectra.bin_spectra(l,
ps,
binning_file,
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
lb, Db_dict_filt = so_spectra.bin_spectra(l,
ps_filt,
binning_file,
lmax,
type=type,
mbb_inv=mbb_inv,
spectra=spectra)
so_spectra.write_ps('%s/spectra_%03d.dat' % (spectraDir, iii),
lb,
Db_dict,
type=type,
spectra=spectra)
so_spectra.write_ps('%s/spectra_filt_%03d.dat' % (spectraDir, iii),
lb,
Db_dict_filt,
type=type,
spectra=spectra)
print(time.time() - t0)
