ps = Dlth[spec] + flth[:lmax]
if kind == "cross":
ps_th[spec] = ps
elif kind == "noise":
ps_th[spec] = nlth[spec]
elif kind == "auto":
ns = len(d["maps_%s_%s" % (sv1, ar1)])
ps_th[spec] = ps + nlth[spec] * ns
theory[sv1, ar1, sv2, ar2, kind] = ps_th
bin_theory[sv1, ar1, sv2, ar2,
kind] = so_mcm.apply_Bbl(Bbl,
ps_th,
spectra=spectra)
os.system("cp %s/multistep2.js %s/multistep2.js" % (multistep_path, plot_dir))
filename = "%s/SO_spectra.html" % plot_dir
g = open(filename, mode='w')
g.write('<html>\n')
g.write('<head>\n')
g.write('<title> SO spectra </title>\n')
g.write('<script src="multistep2.js"></script>\n')
g.write('<script> add_step("sub", ["c","v"]) </script> \n')
g.write('<script> add_step("all", ["j","k"]) </script> \n')
g.write('<script> add_step("type", ["a","z"]) </script> \n')
g.write('</head> \n')
g.write('<body> \n')
g.write('<div class=sub> \n')
continue
spec_name = "%s_%s_%sx%s_%s" % (type, sv1, ar1, sv2, ar2)
lb, Db = so_spectra.read_ps(
spec_dir + "/%s_cross_00000.dat" % spec_name, spectra=spectra
)
if chi2_with_respect_to_theory:
spin_pairs = ["spin0xspin0", "spin0xspin2", "spin2xspin0", "spin2xspin2"]
clfile = "%s/lcdm.dat" % bestfit_dir
lth, Dlth = pspy_utils.ps_lensed_theory_to_dict(
clfile, output_type=type, lmax=lmax, start_at_zero=False
)
prefix = "%s/%s_%sx%s_%s" % (mcm_dir, sv1, ar1, sv2, ar2)
mbb_inv, Bbl = so_mcm.read_coupling(prefix=prefix, spin_pairs=spin_pairs)
Db_th = so_mcm.apply_Bbl(Bbl, Dlth, spectra=spectra)
n_bins = len(lb)
cov = cov_EB[
spec_id * n_bins : (spec_id + 1) * n_bins,
spec_id * n_bins : (spec_id + 1) * n_bins,
]
# Let's only use the diagonal element (the rest is too noisy given the number of sim)
cov = np.diag(np.diagonal(cov))
inv_cov = np.linalg.inv(cov)
# This bit is doing a mcmc fit for alpha
def compute_loglike_data(alpha):
chi2 = compute_chi2_data(Db, inv_cov, alpha)
return -0.5 * chi2
flth_all = 0
for foreground in fg_components[spec.lower()]:
l, flth = np.loadtxt("%s/%s_%s_%sx%s.dat" % (fg_dir,spec.lower(), foreground, f1, f2)
,unpack=True)
flth_all += flth[:lmax]
ps = Dlth[spec] + flth_all
if kind == "cross":
ps_th[spec] = ps
elif kind == "noise":
ps_th[spec] = nlth[spec] * lth**2 / (2 * np.pi)
elif kind == "auto":
ps_th[spec] = ps + nlth[spec] * lth**2 / (2 * np.pi) * ns[exp1]
theory[exp1, f1, exp2, f2, kind] = ps_th
bin_theory[exp1, f1, exp2, f2, kind] = so_mcm.apply_Bbl(Bbl, ps_th, spectra=spectra)
os.system("cp %s/multistep2.js %s/multistep2.js" % (multistep_path, plot_dir))
filename = "%s/SO_spectra.html" % plot_dir
g = open(filename, mode='w')
g.write('<html>\n')
g.write('<head>\n')
g.write('<title> SO spectra </title>\n')
g.write('<script src="multistep2.js"></script>\n')
g.write('<script> add_step("sub", ["c","v"]) </script> \n')
g.write('<script> add_step("all", ["j","k"]) </script> \n')
g.write('<script> add_step("type", ["a","z"]) </script> \n')
g.write('</head> \n')
g.write('<body> \n')
g.write('<div class=sub> \n')
# we will compare simulation power spectrum to theory
# we need to add foreground in TT
lth, Dlth = pspy_utils.ps_lensed_theory_to_dict(clfile,
output_type=type,
lmax=lmax,
start_at_zero=False)
n1, n2 = spec.split("x")
nu_eff_1 = d["nu_eff_%s" % (n1)]
nu_eff_2 = d["nu_eff_%s" % (n2)]
_, flth = np.loadtxt("%s/fg_%sx%s_TT.dat" %
(bestfit_dir, nu_eff_1, nu_eff_2),
unpack=True)
Dlth["TT"] = Dlth["TT"] + flth[:lmax]
bin_theory = so_mcm.apply_Bbl(Bbl, Dlth, spectra=spectra)
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)