multistep_path = d["multistep_path"]
noise_dir = "noise_model"
mcm_dir = "mcms"
plot_dir = "plots/mc_spectra/"
mc_dir = "montecarlo"
bestfit_dir = "best_fits"
pspy_utils.create_directory(plot_dir)
spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"]
spin_pairs = ["spin0xspin0", "spin0xspin2", "spin2xspin0", "spin2xspin2"]
nsims = iStop - iStart
lth, Dlth = pspy_utils.ps_lensed_theory_to_dict(clfile,
output_type=type,
lmax=lmax,
start_at_zero=False)
theory = {}
bin_theory = {}
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 include_sys == True:
mc_dir = 'monteCarlo_syst'
plot_name = 'robustness'
else:
mc_dir = 'monteCarlo'
plot_name = 'bias'
freq_pairs = []
for c1, freq1 in enumerate(freqs):
for c2, freq2 in enumerate(freqs):
if c1 > c2: continue
freq_pairs += [[freq1, freq2]]
lth, psth = pspy_utils.ps_lensed_theory_to_dict(d['theoryfile'],
output_type='Cl',
lmax=lthmax,
lstart=2)
plt.figure(figsize=(18, 10))
color_array = ['red', 'blue', 'green', 'gray', 'purple', 'orange']
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)]
import SO_noise_utils
d = so_dict.so_dict()
d.read_from_file(sys.argv[1])
scan_list = d["scan_list"]
lmax = d["lmax"]
niter = d["niter"]
spectra = d["spectra"]
split_list = d["split_list"]
runs = d["runs"]
spin_pairs = d["spin_pairs"]
binning_file = d["binning_file_name"]
clfile = d["clfile"]
lth, ps_theory = pspy_utils.ps_lensed_theory_to_dict(clfile, "Dl", lmax=lmax)
spectra_dir = "spectra"
plot_dir = "plot/covariance"
cov_dir = "covariance"
mcm_dir = "mcms"
window_dir = "windows"
pspy_utils.create_directory(plot_dir)
pspy_utils.create_directory(cov_dir)
fsky = {}
for scan in scan_list:
for run in runs:
print(scan, run)
cov_dict = pickle.load(pkl_file)
pkl_file.close()
result_ps[test] = ps_dict
result_cov[test] = cov_dict
test_names += [test]
if compute_T_only == True:
spectra = ["TT"]
else:
spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"]
test_names.remove("exact")
# Make plots
lth, clth = pspy_utils.ps_lensed_theory_to_dict(clfile, type, lmax=lmax)
cross = "sim_%03d_split0xsim_%03d_split1" % (id_sim, id_sim)
spectra_plot_utils.plot_spectra_comparison(lb, result_ps, result_cov, cross,
["TT", "TE", "EE"], test_names,
plot_dir, lth, clth)
spectra_plot_utils.plot_spectra_comparison(lb, result_ps, result_cov, cross,
["TB", "EB", "BB"], test_names,
plot_dir, lth, clth)
spectra_plot_utils.delta_Cl_over_sigma(lb, result_ps, result_cov, cross,
spectra, test_names, plot_dir)
spectra_plot_utils.cov_plot(lb, result_cov, cross, spectra, test_names,
plot_dir)