bgmin, bg, bgmax = np.percentile(bgchain, [16, 50, 84])
bymin, by, bymax = np.percentile(bychain, [16, 50, 84])
# Plot power spectra
figs_cl = lik.plot_data(sam.p0,
d,
save_figures=True,
save_data=True,
prefix=p.get_sampler_prefix(v['name']),
get_theory_1h=th1h,
get_theory_2h=th2h)
# Plot likelihood
figs_ch = lik.plot_chain(sam.chain,
save_figure=True,
prefix=p.get_sampler_prefix(v['name']))
print(" Best-fit parameters:")
pars = []
for i, nn, in enumerate(sam.parnames):
CHAIN = sam.chain[:, i]
vmin, vv, vmax = np.percentile(CHAIN, [16, 50, 84])
pars.append(vv)
errmin, errmax = vv - vmin, vmax - vv
print(" " + nn + " : %.3lE +/- (%.3lE %.3lE)" % (vv, errmax, errmin))
if nn == 'b_hydro':
bmeans.append(vv) # median
sbmeans[0].append(errmin) # min errorbar
sbmeans[1].append(errmax) # max errorbar
chain = sam.chain
pars.append(lik.chi2(sam.p0))
# bgchain = np.array([hm_bias(cosmo, 1./(1 + zarr), d.tracers[0][0],
# **(lik.build_kwargs(p0))) for p0 in sam.chain[::100]])
# bychain = np.array([hm_bias(cosmo, 1./(1 + zarr), d.tracers[1][1],
# **(lik.build_kwargs(p0))) for p0 in sam.chain[::100]])
# bgmin, bg, bgmax = np.percentile(bgchain, [16, 50, 84])
# bymin, by, bymax = np.percentile(bychain, [16, 50, 84])
# Plot power spectra
figs_cl = lik.plot_data(sam.p0, d, save_figures=True, save_data=True,
prefix=p.get_sampler_prefix(v['name']),
get_theory_1h=th1h, get_theory_2h=th2h)
# Plot likelihood
figs_ch = lik.plot_chain(sam.chain, taus=taus[:,s],
pars=None,
save_figure=True,
prefix=p.get_sampler_prefix(v['name']))
print(" Best-fit parameters:")
pars = []
for i, nn, in enumerate(sam.parnames):
CHAIN = sam.chain[:, i]
vmin, vv, vmax = np.percentile(CHAIN, [16, 50, 84])
pars.append(vv)
errmin, errmax = vv-vmin, vmax-vv
print(" " + nn + " : %.3lE +/- (%.3lE %.3lE)" % (vv, errmax, errmin))
if nn == 'b_hydro':
bmeans.append(vv) # median
sbmeans[0].append(errmin) # min errorbar
sbmeans[1].append(errmax) # max errorbar
chain = sam.chain
pars.append(lik.chi2(sam.p0))
hm_correction = None
for v in p.get('data_vectors'):
if v['name'] == bin_name:
d = DataManager(p, v, cosmo)
z, nz = np.loadtxt(d.tracers[0][0].dndz, unpack=True)
zmean = np.average(z, weights=nz)
sigz = np.sqrt(np.sum(nz * (z - zmean)**2) / np.sum(nz))
# Theory predictor wrapper
def th(pars):
return get_theory(p,
d,
cosmo,
return_separated=False,
hm_correction=hm_correction,
selection=sel,
**pars)
lik = Likelihood(p.get('params'),
d.data_vector,
d.covar,
th,
debug=p.get('mcmc')['debug'])
sam = Sampler(lik.lnprob, lik.p0, lik.p_free_names,
p.get_sampler_prefix(v['name']), p.get('mcmc'))
sam.get_chain()
figs_ch = lik.plot_chain(sam.chain,
save_figure=True,
prefix='notes/paper/')
plt.show()