print corner_labels
N_parameters = len(corner_labels)
base_dir = "../../fit_mass_functions/output/%s/" % name
base_save = base_dir + "%s_" % name
N_cosmos = 39
#MCMC configuration
nwalkers, nsteps = 16, 5000
nburn = 2000
for i in range(1): #N_cosmos):
if not rotated:
fullchain = np.loadtxt(base_dir + "chains/Box%03d_chain.txt" % (i))
else:
fullchain = np.loadtxt(base_dir +
"rotated_chains/rotated_Box%03d_chain.txt" %
(i))
chain = fullchain[nwalkers * nburn:]
#Now with chainconsumer
fig = ChainConsumer().add_chain(chain, parameters=corner_labels).plot()
plt.subplots_adjust(bottom=0.15, left=0.15)
if not rotated:
fig.savefig("fig_corner.pdf")
else:
fig.savefig("fig_Rcorner.pdf")
plt.show()
logL = np.loadtxt(path + "Powerspectrum_THANN_prob.out",
usecols=(0),
unpack=True) #=== loglikelyhood =====#
#df = pd.DataFrame({'$\zeta$':n_ion,'$Rmfp$':R_mfp,'$Mhalo_{min}(10^8$ $M_\odot)$':NoH})
data = [n_ion, R_mfp, NoH]
fig = ChainConsumer().add_chain(
data, parameters=["$\zeta$", "$R_{mfp}$", "$Mh_{min}$"]).plotter.plot()
fig.set_size_inches(
3 + fig.get_size_inches()) # Resize fig for doco. You don't need
fig.savefig('plot_nion_{0:.3f}_R_mfp_{1:.3f}_Mhalo_{2:.3f}.png'.format(
n_ion.mean(), R_mfp.mean(), Mh.mean()),
dpi=400)
print('plot_nion_{0:.3f}_R_mfp_{1:.3f}_Mhalo_{2:.3f}.png'.format(
n_ion.mean(), R_mfp.mean(), Mh.mean()))
'''
plt.subplot(312)
plt.ylabel('freq.')
plt.xlabel('$R_{mfp}$')
plt.hist(R_mfp,bins=100,color='orange')
plt.axvline(R_mfp.mean(),color='green',lw=2)
plt.subplot(313)
plt.xlabel('$Mh_{min}$')
plt.ylabel('$freq$')
plt.hist(Mh,bins = 100,color='blue',alpha=0.5)
a=[]
