if key == key_y:
continue
# DO NOT TEST PRIOR ON ONE PARMS IN ITSELF; TRIVIAL
# ERROR ON Y IN THE CMB S4
# RELATIVE ERROR ON X IN THE CMB S4 we need relative cause this is how prior are used
sigma_just_CMB_x = (np.sqrt(fisher_inv[fid.keys().index(key),
fid.keys().index(key)]) /
np.abs(fid[key]))
# the abs value abbove is taken to deal with the negative fiducial value of w
prior_value = np.linspace(sigma_just_CMB_x / 10.,
sigma_just_CMB_x * 4.5, 900)
# print sigma_just_CMB_x
# sigma_just_CMB_y_percent =sigma_just_CMB_y_percent /fid[key_y]
normalize_x = prior_value / sigma_just_CMB_x # prior respect to actual error
# compute the new sigma on y given the prior
new_sigma = utils.return_simgax_y_prior(fid, fisher_mat, key_y, key,
prior_value)
normalize_y = new_sigma / sigma_just_CMB_y # make the new sigma y relative.
# plot
line_plot = ax1.plot(normalize_x,
new_sigma * 94. * 1000.,
label=r'$\sigma({0})={1:.1f}\%$'.format(
str(label[key]),
np.abs(sigma_just_CMB_x * 100.)),
linestyle=next(linecycler))
new_sigma_all = utils.return_simgax_all_prior(fid, fisher_mat, key_y)
plt.plot(normalize_x,
new_sigma_all * 94. * 1000.,
label=r'All',
for i, key in enumerate(['re_optical_depth']):
if key == key_y:
continue
# DO NOT TEST PRIOR ON ONE PARMS IN ITSELF; TRIVIAL
# ERROR ON Y IN THE CMB S4
# RELATIVE ERROR ON X IN THE CMB S4 we need relative cause this is how prior are used
sigma_just_CMB_x = (np.sqrt(fisher_inv[fid.keys().index(key), fid.keys().index(key)]) / np.abs(fid[key]))
# the abs value abbove is taken to deal with the negative fiducial value of w
prior_value = np.linspace(0.001, 0.08, 900)
# print sigma_just_CMB_x
# sigma_just_CMB_y_percent =sigma_just_CMB_y_percent /fid[key_y]
# normalize_x = prior_value / sigma_just_CMB_x # prior respect to actual error
# compute the new sigma on y given the prior
new_sigma = utils.return_simgax_y_prior(fid, fisher_mat, key_y, key, prior_value)
# normalize_y = new_sigma / sigma_just_CMB_y # make the new sigma y relative.
# plot
# *93.14 * 1000. to go to M_nu
# line_plot = ax1.plot(prior_value*100., new_sigma * 94. * 1000., label=r'$\sigma_{{\rm{{pipeline}}}}({0})={1:.1f}\%$'.format(
# str(label[key]), np.abs(sigma_just_CMB_x * 100.)), linestyle=next(linecycler))
line_plot = ax1.plot(prior_value*100., new_sigma * 94. * 1000., label=r'$\sigma_{{\rm{{pipeline}}}}({0})/{0}$'.format(
str(label[key]), np.abs(sigma_just_CMB_x * 100.)), linestyle=next(linecycler))
ax1.minorticks_on()
# low_lim = np.min(0.8 * np.amin(new_sigma),0.8 * 23.24 / 100. * fid['omnuh2'])
# print 'lowlim', low_lim
print ax1.get_ylim()
ax1.set_ylim((18, 32))
# ax1.set_ylim(0,1)
print ax1.get_ylim()
