dz_all = np.copy(dz) _, X = np.loadtxt(file, usecols= (0, col_list[c]), unpack = True) X_all = np.copy(X) od = tls.od_renorm(od) od = tls.od2eff(od) mask = (od > 0.5) dz = dz[mask] X = X[mask] print 'Completness=', 100*float(len(X))/float(len(X_all)) print len(dz), len(dz_all) dz_bin = pztls.binsplit(dz, X, binning[c]) dz_bin_all = pztls.binsplit(dz_all, X_all, binning[c]) k = 0 for e in estim_list: print " %s" % e val, err_val = pztls.estim(dz_bin,dz_bin_all,e) index = 1 + j + n_col * k print index a = plt.subplot(n_estim, n_col, index) x = binning[c][:-1] + 0.5 * (binning[c][1:] - binning[c][:-1]) #plt.plot(x, val, color = colors[i]) if(f != '_default'): plt.errorbar(x, val, err_val, color = colors[i], label = filt_label[i])
#print "\nResults using the entire catalog:" #print "Sigma68 = %.5f +/- %.5f" %(sigma68_all,errsigma68_all) #print "Bias = %.5f +/- %.5f" %(bias_all,errbias_all) #print "Completeness = %.5f +%.5f/-%.5f" %(comp_all, errcomp1_all, errcomp2_all) #print "2sig Outliers fraction (rms) = %.5f +%.5f/-%.5f" %(out_frac_all_rms_2, errout_frac1_all_rms_2, errout_frac2_all_rms_2) #print "3sig Outliers fraction (rms) = %.5f +%.5f/-%.5f\n" %(out_frac_all_rms_3, errout_frac1_all_rms_3, errout_frac2_all_rms_3) print "Generation of plots...\n" #Bin splitting...................... i = 1 for l in col_list: print " %s" % l cat[l] = cat_all[l][mask] dz_bin_all = pztls.binsplit(dz_all, cat_all[l], binning[l]) dz_bin = pztls.binsplit(dz, cat[l], binning[l]) j=0 for e in estim_list: print " %s" % e if(e == 'scatter'): id_od = np.argsort(od_all) a = plt.subplot(n_estim, n_val, i + n_val*j) #plt.scatter(cat_all[l][id_od],dz_all[id_od], c=cmap( (od_all[id_od] - od_cut.min()) / (od_cut.max() - od_cut.min()) ), marker = 'o', lw = 0, rasterized=True) plt.scatter(cat_all[l][id_od],dz_all[id_od], c=cmap(tls.cmapind(od_all[id_od],eff_lim, id_lim)), marker = 'o', s = 2.5, lw = 0, rasterized=True) else: val, err_val = pztls.estim(dz_bin, dz_bin_all, e) a = plt.subplot(n_estim, n_val, i + n_val*j)
