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=[]