id_high_planck = 0
for spec in ["TT", "EE", "TE"]:
for id_f, fpair in enumerate(freq_pairs):
f0, f1 = fpair
if (spec == "TT") & (f0 == "100") & (f1 == "143"): continue
if (spec == "TT") & (f0 == "100") & (f1 == "217"): continue
l_planck, _, _ = np.loadtxt("data/planck_data/spectrum_%s_%sx%s.dat"%(spec, f0, f1), unpack=True)
id_high_planck += len(l_planck)
std_planck = np.sqrt(variance_planck)[id_low_planck:id_high_planck]
lth, cl_th_and_fg = np.loadtxt("%s/best_fit_%sx%s_%s.dat" % (bestfit_dir, f0, f1, spec), unpack=True)
lb, cb = planck_utils.binning(lth, cl_th_and_fg, lmax, binning_file=binning_file)
ps_name = "%s_%sx%s" % (spec, f0, f1)
lmin_c, lmax_c = lrange[ps_name]
id=np.where((lb >= lmin_c) & (lb <= lmax_c))
lb, cb = lb[id], cb[id]
fac = lb * (lb + 1) / (2 * np.pi)
bin_start, bin_stop = bin_range[ps_name]
std_sim = np.sqrt(cov.diagonal())[bin_start:bin_stop]
mean_sim = mean_vec[bin_start:bin_stop]
plt.figure(figsize=(12, 8))
plt.errorbar(lb, cb * fac, color="grey", label = "input theory")
plt.errorbar(lb, mean_sim * fac, std_sim * fac, color="red", fmt = "." , label = "mean %s %s GHzx %s GHz"%(spec, f0, f1), alpha=0.4)
plt.xlabel(r"$\ell$", fontsize=20)
plt.ylabel(r"$D_\ell$", fontsize=20)
for spec in spectra:
if (spec=='TT' or spec=='EE' or spec=='BB') & (freq1==freq2):
if spec=='TT':
sigma_th=d['sigma_th_%s'%freq1]
if spec=='EE' or spec=='BB':
sigma_th=d['sigma_pol_th_%s'%freq1]
sigma_th = np.deg2rad(sigma_th)/60
nlth[spec]=lth*0+2*sigma_th**2
bl_hm1=bl[freq1,'hm1',spec]
bl_hm2=bl[freq1,'hm2',spec]
lb,nb_hm1= planck_utils.binning(l,ps_dict[freq1,freq2,spec,'hm1','hm1']*bl_hm1**2-ps_dict[freq1,freq2,spec,'hm1','hm2']*bl_hm1*bl_hm2,lmax,size=size)
lb,nb_hm2= planck_utils.binning(l,ps_dict[freq1,freq2,spec,'hm2','hm2']*bl_hm2**2-ps_dict[freq1,freq2,spec,'hm1','hm2']*bl_hm1*bl_hm2,lmax,size=size)
f=lb**2/(2*np.pi)
nl_interpol_1 = scipy.interpolate.interp1d(lb,nb_hm1, fill_value='extrapolate')
nl_interpol_2 = scipy.interpolate.interp1d(lb,nb_hm2, fill_value='extrapolate')
nl_interpol_mean= scipy.interpolate.interp1d(lb,(nb_hm1+nb_hm2)/2, fill_value='extrapolate')
nl_hm1[spec]=np.array([nl_interpol_1(i) for i in lth])
nl_hm2[spec]=np.array([nl_interpol_2(i) for i in lth])
nl_mean[spec]=np.array([nl_interpol_mean(i) for i in lth])
if spec=='TT':
plt.ylim(-0.002,0.002)
plt.plot(lth,nlth[spec],label='white noise %sx%s %s'%(freq1,freq2,spec))
for spec in ['TT', 'EE', 'TE', 'r']:
if spec != 'r':
if (fname != '100x143') & (fname != '100x217'):
cl_th_and_fg[spec,
fname] = (clth[spec] + fg[spec, fname][:lthmax]
) * 2 * np.pi / (lth * (lth + 1))
else:
cl_th_and_fg[spec,
fname] = clth[spec] * 2 * np.pi / (lth *
(lth + 1))
lb, cb_th_and_fg[spec, fname] = planck_utils.binning(
lth,
cl_th_and_fg[spec, fname],
lthmax,
binning_file=binning_file)
id = np.where((lb > lmin) & (lb < lmax))
cb_th_and_fg[spec, fname] = cb_th_and_fg[spec, fname][id]
else:
cl_th_and_fg[spec, fname] = cl_th_and_fg['TE', fname] / np.sqrt(
cl_th_and_fg['TT', fname] * cl_th_and_fg['EE', fname])
cb_th_and_fg[spec, fname] = cb_th_and_fg['TE', fname] / np.sqrt(
cb_th_and_fg['TT', fname] * cb_th_and_fg['EE', fname])
l, mean[spec, fname], std[spec, fname] = np.loadtxt(
'%s/spectra_%s_%s_hm1xhm2.dat' % (mc_dir, spec, fname),
unpack=True)
d = so_dict.so_dict()
d.read_from_file(sys.argv[1])
binning_file = d["binning_file"]
freqs = d["freqs"]
lmax = d["lmax"]
clfile = d["theoryfile"]
lth, Clth = pspy_utils.ps_lensed_theory_to_dict(clfile,
output_type="Cl",
lmax=lmax,
start_at_zero=False)
Cb_th = {}
lb, Cb_th["EE"] = planck_utils.binning(lth, Clth["EE"], lmax, binning_file)
lb, Cb_th["BB"] = planck_utils.binning(lth, Clth["BB"], lmax, binning_file)
if d["use_ffp10"]:
mc_dir = "montecarlo_ffp10_larger_bin"
else:
mc_dir = "montecarlo"
chain_dir = "chains"
pspy_utils.create_directory(chain_dir)
lmin, lmax = 100, 1500
id = np.where((lb >= lmin) & (lb <= lmax))
lb, Cb_th["EE"], Cb_th["BB"] = lb[id], Cb_th["EE"][id], Cb_th["BB"][id]
nbins = len(lb)
if spec == "EE" or spec == "BB":
sigma_th = d["sigma_pol_th_%s" % freq1]
sigma_th = np.deg2rad(sigma_th) / 60
nlth[spec] = lth * 0 + 2 * sigma_th**2
bl_hm1 = bl[freq1, "hm1", spec]
bl_hm2 = bl[freq1, "hm2", spec]
nth_hm1 = ps_dict["hm1", "hm1", spec] * bl_hm1**2 - ps_dict[
"hm1", "hm2", spec] * bl_hm1 * bl_hm2
nth_hm2 = ps_dict["hm2", "hm2", spec] * bl_hm2**2 - ps_dict[
"hm1", "hm2", spec] * bl_hm1 * bl_hm2
lb, nb_hm1 = planck_utils.binning(l, nth_hm1, lmax, size=size)
lb, nb_hm2 = planck_utils.binning(l, nth_hm2, lmax, size=size)
nl_interpol_1 = scipy.interpolate.interp1d(
lb, nb_hm1, fill_value="extrapolate")
nl_interpol_2 = scipy.interpolate.interp1d(
lb, nb_hm2, fill_value="extrapolate")
# Factor 4 since the noise on the 'mean' if 1/2 of the mean noise
nl_interpol_mean = scipy.interpolate.interp1d(
lb, (nb_hm1 + nb_hm2) / 4, fill_value="extrapolate")
nl_hm1[spec] = np.array([nl_interpol_1(i) for i in lth])
nl_hm2[spec] = np.array([nl_interpol_2(i) for i in lth])
nl_mean[spec] = np.array([nl_interpol_mean(i) for i in lth])
for fpair, color in zip(freq_pairs, color_array):
f0, f1 = fpair
fname = '%sx%s' % (f0, f1)
cl = {}
error = {}
mc_error = {}
model = {}
lmin, lmax = d['lrange_%sx%s' % (f0, f1)]
for spec in ['TT', 'EE', 'TE']:
model[spec, fname] = psth[spec]
lb, model[spec,
fname] = planck_utils.binning(lth,
model[spec, fname],
lthmax,
binning_file=binning_file)
id = np.where((lb > lmin) & (lb < lmax))
model[spec, fname] = model[spec, fname][id]
model['r', fname] = model['TE', fname] / np.sqrt(
model['TT', fname] * model['EE', fname])
l, r, std_r = np.loadtxt('%s/spectra_r_%s_hm1xhm2.dat' % (mc_dir, fname),
unpack=True)
cov_TTTT = np.loadtxt('%s/diagonal_select_cov_mat_TT_%s_%s_TT_%s_%s.dat' %
(mc_dir, fname, 'hm1xhm2', fname, 'hm1xhm2'))
cov_EEEE = np.loadtxt('%s/diagonal_select_cov_mat_EE_%s_%s_EE_%s_%s.dat' %
(mc_dir, fname, 'hm1xhm2', fname, 'hm1xhm2'))
cov_TETE = np.loadtxt('%s/diagonal_select_cov_mat_TE_%s_%s_TE_%s_%s.dat' %