g = plots.getSubplotPlotter() g.settings.axes_fontsize = 13 #size of numbers in axes g.settings.legend_fontsize = 30 #size of legend g.settings.lab_fontsize = 30 #size of parameter labels g.triangle_plot([samples], filled=True, colors=['blue'], line_args=[{'color':'blue'}], legend_loc = 'upper right') pl.savefig('BAO_CC_CMB_SN_wis.png') #---------------------------------------------------- PARAMETER ESTIMATION------------------------------------------------------------ #Specify parameter names and labels names = ['ombh2','omch2','ns','As','H0','tau'] labels = ["\Omega_bh^2", "\Omega_ch^2", "n_s", "\ln{(10^{10}A_s)}", "H_0", r"\tau"] samples = MCSamples(samples=chain_cosmo, names = names, labels = labels, label='BAO+CC+CMB+SN') #Return values from chain (limit defines confidence levels: 1-sigma=1, 2-sigma=2, ...) print(samples.getInlineLatex('ombh2', limit=1)+"\\\\") print(samples.getInlineLatex('ombh2', limit=2)+"\\\\") print(samples.getInlineLatex('omch2', limit=1)+"\\\\") print(samples.getInlineLatex('omch2', limit=2)+"\\\\") print(samples.getInlineLatex('ns', limit=1)+"\\\\") print(samples.getInlineLatex('ns', limit=2)+"\\\\") print(samples.getInlineLatex('As', limit=1)+"\\\\") print(samples.getInlineLatex('As', limit=2)+"\\\\") print(samples.getInlineLatex('H0', limit=1)+"\\\\") print(samples.getInlineLatex('H0', limit=2)+"\\\\") print(samples.getInlineLatex('tau', limit=1)+"\\\\") print(samples.getInlineLatex('tau', limit=2)+"\\\\") #Specify parameter names and labels names = ['w1','w2','w3','w4','w5','w6'] labels = ['w_1','w_2','w_3','w_4','w_5','w_6']
#RUN WITH PYTHON3! #Read txt's and selects columns of interest chain = sc.genfromtxt('chain_uncorrelated.txt') chain_cosmo = chain[:, [0, 1, 2, 3, 4, 5]] chain_wis = chain[:, [6, 7, 8, 9, 10, 11]] deriv_param = sc.genfromtxt('deriv_param.txt') #---------------------------------------------------- BEST FIT VALUES ----------------------------------------------------------------- minimum_chi = np.argmin(deriv_param[:, 2]) print('Best fit cosmos: ', chain_cosmo[minimum_chi, :]) print('Best fit wis: ', chain_wis[minimum_chi, :]) print('Best fit derived params: ', deriv_param[minimum_chi, :]) #---------------------------------------------- ESTIMATION OF DERIVED PARAMETERS ----------------------------------------------------- #Specify parameter names and labels names = ['omL', 'omM', 'chi'] labels = ["\Omega_\Lambda", "\Omega_m", "chi"] samples = MCSamples(samples=deriv_param, names=names, labels=labels) #Return values from chain (limit defines confidence levels: 1-sigma=1, 2-sigma=2, ...) print(samples.getInlineLatex('omL', limit=1) + "\\\\") print(samples.getInlineLatex('omL', limit=2) + "\\\\") print(samples.getInlineLatex('omM', limit=1) + "\\\\") print(samples.getInlineLatex('omM', limit=2) + "\\\\") print(samples.getInlineLatex('chi', limit=1) + "\\\\") print(samples.getInlineLatex('chi', limit=2) + "\\\\") #Print size of chain print(len(chain[:, 0]))