def newGraph_Confidence(result): H_0 = [ii[0] for ii in result] omega_m0 = [ii[1] for ii in result] omega_q0 = [ii[2] for ii in result] alpha = [ii[3] for ii in result] alpha_x = [ii[4] for ii in result] m = [ii[5] for ii in result] names = [ "H_0", "\Omega_{m_0}", "\Omega_{Q_0}", r"\tilde{\alpha}", r"\tilde{\alpha}_x", "m" ] #names = ["H_0"] labels = names newShape = [H_0, omega_m0, omega_q0, alpha, alpha_x, m] #newShape = [H_0] values = np.array(newShape).T #print(values.shape) s1 = MCSamples(samples=values, names=names, labels=labels) g = plots.get_subplot_plotter() s1.updateSettings({'contours': [0.68, 0.95, 0.99]}) g.settings.colormap = "binary" g.settings.num_plot_contours = 3 g.triangle_plot([s1], shaded=True, title_limit=1) plt.savefig("result.pdf")
def _triangle_plot(self, these_samples, these_y, plotname): names = self._parameters_list labels = [] level_lines = [0.2, 0.4, 0.6, 0.8, 0.95, 0.98] num_level_lines = len(level_lines) g = plots.getSubplotPlotter(width_inch=9) g.settings.num_plot_contours = num_level_lines mcsamples = MCSamples(samples=these_samples, names=names, labels=names) mcsamples.updateSettings({'contours': level_lines}) g.triangle_plot( [mcsamples], names, # filled_compare=True, legend_labels=labels, legend_loc='upper right', # filled=False, contour_colors=['darkblue', 'green'], # filled=True, # contour_lws=[.2, .4, .68, .95, .98] ) n_params = len(names) for i in range(n_params): for j in range(n_params): if j > i: continue ax = g.subplots[i, j] ax.axvline(these_y[j], color='black', ls='--', alpha=0.4) if i != j: ax.axhline(these_y[i], color='black', ls='-.', alpha=0.4) g.export(plotname)
#data.reshape(7,7) cov = data ndim = data.shape[0] mean_ = [0.01, 0.082, -2.6, -3.14, 20 * 0.53, -2.54, 1.53] samps = np.random.multivariate_normal(mean_, cov, size=nsamp) #samps = np.random.multivariate_normal([0]*ndim, cov, size=nsamp) #A = np.random.rand(ndim,ndim) #cov = np.dot(A, A.T) #samps2 = np.random.multivariate_normal([0]*ndim, cov, size=nsamp) #names = ['r','As','$\alpha_s$','$\beta_s$','Ad','$\alpha_d$','$\beta_d$'] #labels = ['r','As','$\alpha_s$','$\beta_s$','Ad','$\alpha_d$','$\beta_d$'] names = ['r', 'As', 'alpha_s', 'beta_s', 'Ad', 'alpha_d', 'beta_d'] #labels = ['r','As','alpha_s','beta_s','Ad','alpha_d','beta_d'] labels = ['r', 'A_s', '\\alpha_s', 'beta_s', 'Ad', 'alpha_d', 'beta_d'] samples = MCSamples(samples=samps, names=names, labels=labels, ranges={ 'r': (0, None), 'As': (0, None) }) #samples2 = MCSamples(samples=samps2,names = names, labels = labels, label='Second set') g = plots.getSubplotPlotter() samples.updateSettings({'contours': [0.68, 0.95, 0.99]}) g.settings.num_plot_contours = 3 g.triangle_plot(samples, filled=True) #g.triangle_plot([samples, samples2], filled=True) g.export('output_file.pdf')
usecols=[2, 3], sep='\s+', skiprows=1, names=colname) from getdist import plots, MCSamples labels = ['\\mathrm{X}', '\\mathrm{Y}'] samples1 = MCSamples(samples=chains1[['Separation', 'Flux_Ratio']].values, names=colname, labels=labels) samples2 = MCSamples(samples=chains2[['Separation', 'Flux_Ratio']].values, names=colname, labels=labels) conf = [0.683, 0.955, 0.997] samples1.updateSettings({'contours': conf}) samples2.updateSettings({'contours': conf}) g = plots.getSubplotPlotter() g.settings.num_plot_contours = 3 g.settings.shade_level_scale = 3 g.settings.fig_width_inch = 8.5 g.settings.linewidth = 0.3 g.plot_2d(samples1, 'Separation', 'Flux_Ratio', filled=False, lims=[881, 911, 807, 837]) #30 x 30 pixels g.plot_2d(samples2, 'Separation', 'Flux_Ratio',
''' 2D contours ''' xstep1, ystep1 = loadtxt('flat-LCDM_steps.dat', unpack=True) xstep = int(xstep1) ystep = int(ystep1) print('check 6') z2 = loadtxt('flat_LCDM_chi2_H(z)only.dat', unpack=True) y2 = loadtxt('flat_LCDM_Omega_m0.dat', unpack=True) x2 = loadtxt('flat_LCDM_H0.dat', unpack=True) zmin = z2.tolist().index(min(z2)) xmin2 = x2[zmin] ymin2 = y2[zmin] c2 = z2[zmin] print(c2, xmin2, ymin2, "2D best fit points with grid") g = plots.getSinglePlotter(width_inch=4, ratio=1) samples.updateSettings({'contours': [0.6827, 0.9545, 0.9973]}) g.settings.num_plot_contours = 3 g.plot_2d([samples],['H','m'],filled_compare=False, line_args=[{'ls':'solid','color':'k'}]) x2_2D_mcase3 = reshape(x2, (xstep, ystep)) y2_2D_mcase3 = reshape(y2, (xstep, ystep)) z2_2D_mcase3 = reshape(z2, (xstep, ystep)) plt.contour(x2_2D_mcase3, y2_2D_mcase3, z2_2D_mcase3, [c2, c2 + 2.3, c2 + 6.17, c2 + 11.8], colors='blue', linestyles=':') plt.plot([xmin2, xmin2], [ymin2, ymin2], marker='+', color='blue') #Best fit parameters g.export('flat_LCDM_H0_Om_2D_MCMC_vs_grid_my_data.pdf') print("--- %s seconds ---" % (time.time() - start_time))
"N_{mu}" ] gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu, something_1, something_2, \ something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat') samps = np.array([gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu]).T # names = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"] # labels = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"] # gamma, N_a, N_conv, N_pr, N_mu, something_1, something_2, \ # something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat') # samps = np.array([gamma, N_a, N_conv, N_pr, N_mu]).T samples = MCSamples(samples=samps, names=names, labels=labels) plt.figure() g = plots.get_subplot_plotter() samples.updateSettings({ 'contours': [0.68, 0.90], 'fine_bins_2D': 40, 'smooth_scale_2D': 0.6 }) g.settings.num_plot_contours = 3 g.triangle_plot(samples, shaded=True) #plt.plot(-2.37, -1.80, 'X', color = 'black', markersize=14) # g.export(os.getcwd()+'/out/plots/all_param_marg.pdf') g.export(os.getcwd() + '/out/plots/all_param_marg.png')
def plot_triangle(sim_samples, gp_samples, burnin_frac=0.1, emulator=True, gp_error=False): if gp_error and emulator: label_gp = 'GP (Error)' elif emulator and not gp_error: label_gp = 'GP (Mean)' else: label_gp = 'Simulator (MOPED)' ndim = sim_samples.shape[-1] names = ["x%s" % i for i in range(ndim)] labels = [ r"\Omega_{m}", r"w_{0}", r"M_{B}", r"\delta M", r"\alpha", r"\beta" ] # for the simulator burnin = int(burnin_frac * sim_samples.shape[1]) samples_exact = sim_samples[:, burnin:, :].reshape((-1, ndim)) cut_samps = samples_exact[samples_exact[:, 0] >= 0.0, :] samples1 = MCSamples(samples=cut_samps, names=names, labels=labels, ranges={'x0': (0.0, None)}) # for the emulator burnin = int(burnin_frac * gp_samples.chain.shape[1]) samples_emu = gp_samples.chain[:, burnin:, :].reshape((-1, ndim)) cut_samps = samples_emu[samples_emu[:, 0] >= 0.0, :] samples2 = MCSamples(samples=cut_samps, names=names, labels=labels, ranges={'x0': (0.0, None)}) # setups for plotting sim_color = '#EEC591' gp_color = 'Blue' alpha_tri = 0.1 red_patch = mpatches.Patch(color=sim_color, label='Simulator', alpha=alpha_tri) gp_line = Line2D([0], [0], color=gp_color, linewidth=3, linestyle='--', label=label_gp) rec_leg = [red_patch, gp_line] contours = np.array([0.68, 0.95]) G = plots.getSubplotPlotter(subplot_size=3.5) samples1.updateSettings({'contours': [0.68, 0.95]}) G.triangle_plot([samples1], filled=True, line_args={ 'lw': 3, 'color': sim_color }, contour_colors=[sim_color]) G.settings.num_plot_contours = 2 plt.legend(handles=rec_leg, loc='best', prop={'size': 25}, bbox_to_anchor=(0.7, 6.0), borderaxespad=0.) G.settings.alpha_filled_add = alpha_tri for i in range(0, 6): for j in range(0, i + 1): if i != j: ax = G.subplots[i, j] a, b = G.get_param_array(samples2, ['x' + str(j), 'x' + str(i)]) density = G.sample_analyser.get_density_grid(samples2, a, b) density.contours = density.getContourLevels(contours) contour_levels = density.contours ax.contour(density.x, density.y, density.P, sorted(contour_levels), colors=gp_color, linewidths=3, linestyles='--') ax.tick_params(labelsize=20) ax.yaxis.label.set_size(20) ax.xaxis.label.set_size(20) else: ax = G.subplots[i, j] dense = samples2.get1DDensity('x' + str(i)) dense.normalize(by='max') ax.plot(dense.x, dense.P, lw=3, c=gp_color, linestyle='--') ax.tick_params(labelsize=20) ax.yaxis.label.set_size(20) ax.xaxis.label.set_size(20) plt.savefig('images/triangle_plot.jpg', bbox_inches='tight', transparent=False) plt.close()