np.column_stack((dats[lmin_index:lmax_index, 0, 0], no_marginalized_ell)), header=header)
np.savetxt('data/{}/marginalized_ell_CMB_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(output_folder, lmin, lmax, N_det, fsky),
np.column_stack((dats[lmin_index:lmax_index, 0, 0], marginalized_ell)), header=header)
np.save('data/{}/full_fisher_mat_CMB_lmin={}_lmax={}_ndet={}_fsky={}.npy'.format(output_folder, lmin, lmax, N_det, fsky),
fisher_save)
np.savetxt('data/{}/ell_indeces_CMB_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(output_folder, lmin, lmax, N_det, fsky),
dats[lmin_index:lmax_index, 0, 0], header=header)
# utils.study_prior_tau_on_N_eff(fid, fisher, 'data/' + output_folder, header)
print 'finally how much constraint on parameters without prior?'
print ''
fisher_single = fisher.copy()
fisher_inv = np.linalg.inv(fisher_single)
utils.save_cov_matrix(
fisher_inv, 'data/{}/param_cov_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(output_folder, lmin, lmax, N_det, fsky))
np.savetxt('data/{}/invetered_sqrt_fisher_CMB_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(output_folder,
lmin, lmax, N_det, fsky), np.sqrt(fisher_inv), header=header)
np.savetxt('data/{}/fisher_mat_CMB_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(output_folder,
lmin, lmax, N_det, fsky), fisher_single, header=header)
print 'fisher=', fisher
utils.print_resume_stat(fisher_single, fid)
'data/{}/fisher_mat_joint_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(
output_folder, lmin, lmax, N_det, fsky),
fisher_single,
header=header)
print 'finally how much constraint on parameters without external prior?'
print ''
fisher_inv = np.linalg.inv(fisher_single)
utils.save_cov_matrix(
fisher_inv,
'data/{}/param_cov_lmin={}_lmax={}_ndet={}_fsky={}.txt'.format(
output_folder, lmin, lmax, N_det, fsky))
np.savetxt(
'data/{}/invetered_sqrt_fisher_joint_lmin={}_lmax={}_ndet={}_fsky={}.txt'
.format(output_folder, lmin, lmax, N_det, fsky),
np.sqrt(fisher_inv),
header=header)
print 'fisher=', fisher
no_lcdm_parameters = ['massless_neutrinos', 'w', 'omnuh2']
plot_now = ['omnuh2']
excluded_parameters = list(set(no_lcdm_parameters) - set(plot_now))
par_gaps, values, fid, fisher_single = utils.exclude_parameters_from_fisher(
excluded_parameters, par_gaps, values, fid, fisher_single)
utils.print_resume_stat(fisher_single, fid)
label['scalar_amp(1)'] = 'A_{s}'
label['scalar_spectral_index(1)'] = 'n_{s}'
label['omnuh2'] = r'\Omega_{\nu}h^{2}'
label['re_optical_depth'] = r'\tau'
label['ombh2'] = '\Omega_{b}h^{2}'
label['ombch2'] = '\Omega_{m}h^{2}'
label['omch2'] = '\Omega_{c}h^{2}'
label['helium_fraction'] = 'Y_{p}'
label['w'] = 'w'
label['wa'] = 'w_a'
label['scalar_nrun(1)'] = r'\alpha_{s}'
fisher_inv = np.linalg.inv(fisher_mat)
fisher_inplace = fisher_mat.copy()
utils.print_resume_stat(fisher_mat, fid)
# CYCLE ON PARAMETERS (KEYS HERE)
for key_y in ['omnuh2']:
print key_y
sigma_just_CMB_y = (np.sqrt(fisher_inv[fid.keys().index(key_y),
fid.keys().index(key_y)]))
fg = plt.figure(figsize=fig_dims)
ax1 = plt.subplot2grid((1, 1), (0, 0))
ax1.set_color_cycle(Set1_9.mpl_colors)
lines = ["-", "--", "-.", ":"]
linecycler = cycle(lines)
for i, key in enumerate(
['hubble', 'omch2', 'scalar_spectral_index(1)', 'scalar_amp(1)']):