for id_ar2, ar2 in enumerate(arrays):
if id_ar1 > id_ar2: continue
l, bl_ar1 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv, ar1)])
l, bl_ar2 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv, ar2)])
lb, bb_ar1 = pspy_utils.naive_binning(l, bl_ar1, binning_file,
lmax)
lb, bb_ar2 = pspy_utils.naive_binning(l, bl_ar2, binning_file,
lmax)
nsplits = len(d["maps_%s_%s" % (sv, ar1)])
spec_name_noise = "%s_%s_%sx%s_%s_noise" % (type, sv, ar1, sv, ar2)
print(spec_name_noise)
lb, nbs = so_spectra.read_ps("%s/%s.dat" %
(spectra_dir, spec_name_noise),
spectra=spectra)
nl_dict = {}
for spec in spectra:
nbs_mean = nbs[spec] * bb_ar1 * bb_ar2
plt.figure(figsize=(12, 12))
if (spec == "TT" or spec == "EE" or spec == "BB") & (ar1
== ar2):
nl = scipy.interpolate.interp1d(lb,
nbs_mean,
fill_value="extrapolate")
nl_dict[spec] = np.array([nl(i) for i in lth])
id = np.where(lth <= np.min(lb))
"dr6_pa4_f150": "Planck_f143",
"dr6_pa4_f220": "Planck_f217",
"dr6_pa5_f090": "Planck_f100",
"dr6_pa5_f150": "Planck_f143",
"dr6_pa6_f090": "Planck_f100",
"dr6_pa6_f150": "Planck_f143"
}
spectra_and_covariances = {}
for act_map in reference_maps:
print("Calibration of %s ..." % act_map)
planck_map = reference_maps[act_map]
print("Using %s map ..." % planck_map)
lb, ps_actxact = so_spectra.read_ps(spec_dir + "Dl_%sx%s_cross.dat" %
(act_map, act_map),
spectra=spectra)
try:
cross_name = "%sx%s" % (act_map, planck_map)
lb, ps_actxplanck = so_spectra.read_ps(spec_dir + "Dl_%s_cross.dat" %
(cross_name),
spectra=spectra)
except:
cross_name = "%sx%s" % (planck_map, act_map)
lb, ps_actxplanck = so_spectra.read_ps(spec_dir + "Dl_%s_cross.dat" %
(cross_name),
spectra=spectra)
print("ACTxPlanck : %s" % cross_name)
cov = {}
actxact_name = "%sx%s" % (act_map, act_map)
freqs1=d['freq_%s'%exp1]
for id_f1,f1 in enumerate(freqs1):
for id_exp2,exp2 in enumerate(experiment):
freqs2=d['freq_%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
if (exp1!=exp2) & (kind=='noise'): continue
if (exp1!=exp2) & (kind=='auto'): continue
spec_name='%s_%s_%s_%sx%s_%s_%s'%(type,run_name,exp1,f1,exp2,f2,kind)
if hdf5:
lb,Db=so_spectra.read_ps_hdf5(spectra_hdf5,spec_name,spectra=spectra)
else:
lb,Db=so_spectra.read_ps(specDir+'/%s.dat'%spec_name,spectra=spectra)
Db_dict[kind,spec,exp1,f1,exp2,f2]=Db[spec]
#plt.figure(figsize=(8,7))
# if spec=='TT':
# plt.semilogy()
# plt.plot(lth,Dlth[spec])
# plt.errorbar(lb,Db[spec],fmt='.',color='red')
# plt.title(r'$D^{%s,%s_{%s}x%s_{%s}}_{%s,\ell}$'%(spec,exp1,f1,exp2,f2,kind),fontsize=20)
# plt.xlabel(r'$\ell$',fontsize=20)
# plt.savefig('%s/spectra_%s_%s_%s_%sx%s_%s_%s.png'%(plot_dir,run_name,spec,exp1,f1,exp2,f2,kind),bbox_inches='tight')
# plt.clf()
for spec in spectra:
for id_exp1, exp1 in enumerate(experiments):
freqs1 = d["freqs_%s" % exp1]
for id_f1, f1 in enumerate(freqs1):
for id_exp2, exp2 in enumerate(experiments):
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
if (exp1 != exp2) & (kind == "noise"): continue
if (exp1 != exp2) & (kind == "auto"): continue
spec_name = "%s_%s_%sx%s_%s_%s_%05d" % (type, exp1, f1, exp2, f2, kind, iii)
lb, Db = so_spectra.read_ps(specDir + "/%s.dat" % spec_name, spectra=spectra)
n_bins = len(lb)
vec = np.append(vec, Db[spec])
if (exp1 == exp2) & (f1 == f2):
if spec == "TT" or spec == "EE" or spec == "TE" :
vec_restricted = np.append(vec_restricted, Db[spec])
else:
if spec == "TT" or spec == "EE" or spec == "TE" or spec == "ET":
vec_restricted = np.append(vec_restricted, Db[spec])
vec_list += [vec]
vec_list_restricted += [vec_restricted]
for id_sv1, sv1 in enumerate(surveys):
arrays_1 = d["arrays_%s" % sv1]
for id_ar1, ar1 in enumerate(arrays_1):
for id_sv2, sv2 in enumerate(surveys):
arrays_2 = d["arrays_%s" % sv2]
for id_ar2, ar2 in enumerate(arrays_2):
if (id_sv1 == id_sv2) & (id_ar1 > id_ar2): continue
if (id_sv1 > id_sv2): continue
spec_name = "%s_%sx%s_%s" % (sv1, ar1, sv2, ar2)
spectra = [
"TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"
]
lb, Db = so_spectra.read_ps(spec_dir + "/%s_%s_cross.dat" %
(type, spec_name),
spectra=spectra)
# remove same array, same season ET
if (spec == "ET") & (ar1 == ar2) & (sv1 == sv2): continue
data_vec = np.append(data_vec, Db[spec])
# Lets combine the data (following the doc)
print("invert cov mat")
inv_cov_mat = np.linalg.inv(cov_mat)
proj_cov_mat = np.linalg.inv(np.dot(np.dot(P_mat, inv_cov_mat), P_mat.T))
proj_data_vec = np.dot(proj_cov_mat,
np.dot(P_mat, np.dot(inv_cov_mat, data_vec)))
print("is matrix positive definite:",
type = d["type"]
binning_file = d["binning_file"]
lth = np.arange(2, lmax + 2)
for sv in surveys:
arrays = d["arrays_%s" % sv]
for id_ar1, ar1 in enumerate(arrays):
for id_ar2, ar2 in enumerate(arrays):
if id_ar1 > id_ar2: continue
l, bl_ar1 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv, ar1)])
l, bl_ar2 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv, ar2)])
lb, nbs_ar1xar1 = so_spectra.read_ps(
"%s/%s_%s_%sx%s_%s_noise.dat" %
(spectra_dir, type, sv, ar1, sv, ar1),
spectra=spectra)
lb, nbs_ar1xar2 = so_spectra.read_ps(
"%s/%s_%s_%sx%s_%s_noise.dat" %
(spectra_dir, type, sv, ar1, sv, ar2),
spectra=spectra)
lb, nbs_ar2xar2 = so_spectra.read_ps(
"%s/%s_%s_%sx%s_%s_noise.dat" %
(spectra_dir, type, sv, ar2, sv, ar2),
spectra=spectra)
lb, bb_ar1 = pspy_utils.naive_binning(l, bl_ar1, binning_file,
lmax)
lb, bb_ar2 = pspy_utils.naive_binning(l, bl_ar2, binning_file,
lmax)
for id_sv2, sv2 in enumerate(surveys):
arrays_2 = d["arrays_%s" % sv2]
for id_ar2, ar2 in enumerate(arrays_2):
_, bl2 = pspy_utils.read_beam_file(d["beam_%s_%s" %
(sv2, ar2)])
bl2 = bl2[2:lmax + 2]
freq2 = d["nu_eff_%s_%s" % (sv2, ar2)]
if (id_sv1 == id_sv2) & (id_ar1 > id_ar2): continue
if (id_sv1 > id_sv2): continue
if (sv1 == sv2) & (ar1 == ar2):
spec_name_noise = "mean_%sx%s_%s_noise" % (ar1, ar2, sv1)
_, Nl = so_spectra.read_ps(ps_model_dir +
"/%s.dat" % spec_name_noise,
spectra=spectra)
for spec in ["TT", "TE", "ET", "EE"]:
name = "%sx%s_%s" % (freq1, freq2, spec)
_, ps_th = np.loadtxt("%s/best_fit_%s.dat" %
(bestfit_dir, name),
unpack=True)
ps_all["%s&%s" % (sv1, ar1),
"%s&%s" % (sv2, ar2),
spec] = bl1 * bl2 * ps_th[:lmax]
if (sv1 == sv2) & (ar1 == ar2):
ns[sv1] = len(d["maps_%s_%s" % (sv1, ar1)])
for spec in spectra:
for id_sv1, sv1 in enumerate(surveys):
arrays_1 = d["arrays_%s" % sv1]
for id_ar1, ar1 in enumerate(arrays_1):
for id_sv2, sv2 in enumerate(surveys):
arrays_2 = d["arrays_%s" % sv2]
for id_ar2, ar2 in enumerate(arrays_2):
if (id_sv1 == id_sv2) & (id_ar1 > id_ar2) : continue
if (id_sv1 > id_sv2) : continue
if (sv1 != sv2) & (kind == "noise"): continue
if (sv1 != sv2) & (kind == "auto"): continue
spec_name = "%s_%s_%sx%s_%s_%s_%05d" % (type, sv1, ar1, sv2, ar2, kind, iii)
lb, Db = so_spectra.read_ps(spec_dir + "/%s.dat" % spec_name, spectra=spectra)
n_bins = len(lb)
vec = np.append(vec, Db[spec])
if (sv1 == sv2) & (ar1 == ar2):
if spec == "TT" or spec == "EE" or spec == "TE" :
vec_restricted = np.append(vec_restricted, Db[spec])
else:
if spec == "TT" or spec == "EE" or spec == "TE" or spec == "ET":
vec_restricted = np.append(vec_restricted, Db[spec])
vec_list += [vec]
vec_list_restricted += [vec_restricted]
mean, std = {}, {}
for spectrum in ["TT", "EE", "BB"]:
nofilt_list = []
filt_list = []
tf_list = []
for iii in range(iStart, iStop):
spec_name_no_filter = "%s_%s_nofilter_standard_%05d" % (type, spec,
iii)
spec_name_filter = "%s_%s_filter_standard_%05d" % (type, spec, iii)
lb, ps_nofilt = so_spectra.read_ps(spec_dir +
"/%s.dat" % spec_name_no_filter,
spectra=spectra)
lb, ps_filt = so_spectra.read_ps(spec_dir +
"/%s.dat" % spec_name_filter,
spectra=spectra)
nofilt_list += [ps_nofilt[spectrum]]
filt_list += [ps_filt[spectrum]]
tf_list += [ps_filt[spectrum] / ps_nofilt[spectrum]]
mean[spectrum, "nofilt"] = np.mean(nofilt_list, axis=0)
mean[spectrum, "filt"] = np.mean(filt_list, axis=0)
mean[spectrum, "tf"] = np.mean(tf_list, axis=0)
std[spectrum, "nofilt"] = np.std(nofilt_list, axis=0)
std[spectrum, "filt"] = np.std(filt_list, axis=0)
bin_start = 0
bin_stop = 0
for spec in ["TT", "EE", "TE"]:
for fpair in freq_pairs:
f0, f1 = fpair
if (spec == "TT") & (f0 == "100") & (f1 == "143"): continue
if (spec == "TT") & (f0 == "100") & (f1 == "217"): continue
spec_name = "Planck_%sxPlanck_%s-hm1xhm2" % (f0, f1)
lb, ps_dict = so_spectra.read_ps("%s/sim_spectra_%s_%04d.dat" %
(sim_spectra_dir, spec_name, iii),
spectra=spectra)
if f0 != f1:
spec_name2 = "Planck_%sxPlanck_%s-hm2xhm1" % (f0, f1)
lb, ps_dict2 = so_spectra.read_ps(
"%s/sim_spectra_%s_%04d.dat" %
(sim_spectra_dir, spec_name2, iii),
spectra=spectra)
ps_dict[spec] = (ps_dict[spec] + ps_dict2[spec]) / 2
if spec == "TE":
ps_dict["ET"] = (ps_dict["ET"] + ps_dict2["ET"]) / 2
if spec == "TE":
ps_dict["TE"] = (ps_dict["TE"] + ps_dict["ET"]) / 2