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]))
