def get_overall_best_fit(self, pars, **specargs):
"""Returns the overall best-fit, the chi-square and the N.d.o.f."""
if type(pars) == str: pars = [pars]
for s, v in enumerate(self.p.get("data_vectors")):
d = DataManager(self.p, v, self.cosmo, all_data=False)
self.d = d
lik = Likelihood(self.p.get('params'),
d.data_vector,
d.covar,
self._th,
template=d.templates)
sam = Sampler(lik.lnprob, lik.p0, lik.p_free_names,
self.p.get_sampler_prefix(v['name']),
self.p.get('mcmc'))
sam.get_chain()
sam.update_p0(sam.chain[np.argmax(sam.probs)])
kwargs = lik.build_kwargs(sam.p0)
w = kwargs["width"]
zz, NN = self._get_dndz(d.tracers[0][0].dndz, w)
zmean = np.average(zz, weights=NN)
kwargs["z"] = zmean
kwargs["chi2"] = lik.chi2(sam.p0)
all_pars = self.p.p.get("params")
dof = np.sum(
[param["vary"] for param in all_pars if "vary" in param])
kwargs["dof"] = len(lik.dv) - dof
kwargs["PTE"] = 1 - chi2.cdf(kwargs["chi2"], kwargs["dof"])
if s == 0:
keys = ["z", "chi2", "dof", "PTE"] + pars
OV_BF = {k: kwargs[k] for k in keys}
else:
for k in keys:
OV_BF[k] = np.vstack((OV_BF[k], kwargs[k]))
return OV_BF
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))
pars.append(len(d.data_vector))
np.save(p.get_outdir() + "best_fit_params_" + run_name + "_"
+v["name"]+".npy", np.array(pars))
print(" chi^2 = %lf" % (lik.chi2(sam.p0)))
print(" n_data = %d" % (len(d.data_vector)))
# print(" b_g = %.3lE +/- (%.3lE %.3lE) " % (bg, bg-bgmin, bgmax-bg))
# print(" b_y = %.3lE +/- (%.3lE %.3lE) " % (by, by-bymin, bymax-by))
fig, ax = plt.subplots()
ax.errorbar(zmeans, 1-np.array(bmeans), yerr=np.flip(sbmeans, 0), fmt='ro')
ax.set_xlabel('$z$', fontsize=15)
ax.set_ylabel('$1-b$', fontsize=15)
fig.savefig(p.get_sampler_prefix('b_hydro')+'all.pdf', bbox_inches='tight')
th,
template=d.templates,
debug=p.get('mcmc')['debug'])
# Set up sampler
p0 = p.get_map_p0(lik.p_free_names) # p0 for particular map
sam = Sampler(lik.lnprob, p0, lik.p_free_names,
p.get_sampler_prefix(v['name']), p.get('mcmc'))
# print(dict(zip(lik.p_free_names, p0)))
# print("chisq:", lik.chi2(p0))
# exit(1)
# Compute best fit and covariance
if not sam.read_properties():
print(" Computing best-fit and covariance")
sam.get_best_fit(update_p0=True)
cov0 = sam.get_covariance(update_cov=True)
sam.save_properties()
print(" Best-fit parameters:")
for n, val, s in zip(sam.parnames, sam.p0, np.sqrt(np.diag(sam.covar))):
print(" " + n + " : %.3lE +- %.3lE" % (val, s))
print(" chi^2 = %lf" % (lik.chi2(sam.p0)))
print(" n_data = %d" % (len(d.data_vector)))
if sam.nsteps > 0:
print(" Sampling:")
sam.sample(carry_on=p.get('mcmc')['continue_mcmc'],
verbosity=1,
use_mpi=args.no_mpi)
hm_correction=hm_correction,
selection=sel,
**pars)
# Set up likelihood
lik = Likelihood(p.get('params'),
d.data_vector,
d.covar,
th,
template=d.templates,
debug=p.get('mcmc')['debug'])
p0 = extract_map_p0(p, v, lik.p_free_names) # p0 for particular map
# Benchmarks
print(dict(zip(lik.p_free_names, p0)))
print("chisq:", lik.chi2(p0))
exit(1)
# Set up sampler
sam = Sampler(lik.lnprob, lik.p0, lik.p_free_names,
p.get_sampler_prefix(v['name']), p.get('mcmc'))
# Benchmarks
# print(dict(zip(lik.p_free_names, lik.p0)))
# print("chisq:", lik.chi2(lik.p0))
# Compute best fit and covariance around it
if not sam.read_properties():
print(" Computing best-fit and covariance")
sam.get_best_fit(update_p0=True)
cov0 = sam.get_covariance(update_cov=True)
sam.save_properties()
indd = unequal_enumerate(lsd)
tvd = eval_and_unwrap(params, thd.th, indd)
tv1hd = eval_and_unwrap(params, thd.th1h, indd)
tv2hd = eval_and_unwrap(params, thd.th2h, indd)
indg = unequal_enumerate(lsg)
tvg = eval_and_unwrap(params, thg.th, indg)
tv1hg = eval_and_unwrap(params, thg.th1h, indg)
tv2hg = eval_and_unwrap(params, thg.th2h, indg)
# Reshape data vector
dv = unwrap(likg.dv, indg)
# Compute error bars and reshape
ev = unwrap(np.sqrt(np.diag(likg.cv)), indg)
# Compute chi^2
chi2 = likd.chi2(sam.p0)
dof = len(likd.dv)
for i in range(2):
# set-up subplot
gs = GridSpecFromSubplotSpec(2,
1,
subplot_spec=gs_main[s, i],
height_ratios=[3, 1],
hspace=0)
ax1 = f.add_subplot(gs[0])
ax2 = f.add_subplot(gs[1])
# Create mask
lmin = v["twopoints"][i]["lmin"]
for nam, val, sig in zip(sam.parnames, sam.p0,
np.sqrt(np.diag(sam.covar))):
param_jk_values[nam] = val
print(" " + nam + " : %.3lE +- %.3lE" % (val, sig))
if nam == p.get("mcmc")["save_par"]: par.append(val)
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))
zarr = np.linspace(zmean - sigz, zmean + sigz, 10)
bg = np.mean(
hm_bias(cosmo, 1. / (1 + zarr), d.tracers[0][0].profile,
**(lik.build_kwargs(sam.p0))))
by = np.mean(
hm_bias(cosmo, 1. / (1 + zarr), d.tracers[1][1].profile,
**(lik.build_kwargs(sam.p0))))
chi2 = lik.chi2(sam.p0)
print(" b_g : %.3lE" % bg)
print(" b_y : %.3lE" % by)
print(" chi^2 = %lf" % chi2)
print(" n_data = %d" % (len(d.data_vector)))
param_jk_values['b_g'] = bg
param_jk_values['b_y'] = by
param_jk_values['chi2'] = chi2
np.savez(p.get_sampler_prefix(v['name']) + "jk%d_vals" % jk_region,
M1=np.array(param_jk_values['M1']),
Mmin=np.array(param_jk_values['Mmin']),
b_hydro=np.array(param_jk_values['b_hydro']),
r_corr=np.array(param_jk_values['r_corr']),
width=np.array(param_jk_values['width']),
b_g=np.array(param_jk_values['b_g']),