#z_bpz cut.............................................. #cat_col, n, N = pt.z_cut(cat_col, z_cut) ###Unificar las funciones de corte en una #print "Num. gal. after z(photo)<%2.2f cut: %d %1.1f%%" % (z_cut, n , 100 * float(n)/float(N) ) #Seeting dictionary before odds cut...................... #cat_before = pt.dict_annz(cat_col) #cat_before = pt.dict(cat_col) #odds cut............................................... #cat_col, n, N = pt.errz_cut(cat_col, errz_cut) #cat_col, n, N = pt.odds_cut(cat_col, odds_cut) #print "Num. gal. after odds> %2.2f cut: %d %1.1f%%" % (odds_cut, n , 100 * float(n)/float(N) ) #Seeting dictionary..................................... #cat = pt.dict_annz(cat_col) cat = pt.dict(cat_col) #Defining binning....................................... binning = pt.set_binning(cat) #errz_binning = np.arange(0., 0.1, 0.01) #print binning #MAIN................................................... os.system("mkdir plots") #zz = np.array([cat['z_phot'], cat['z_true']]) #np.savetxt("zpvszs.txt", zz.T, fmt = '%f' ) #pl.plot_zvsz(cat['z_phot'], cat['z_true'], binning, z_min, z_max) #pl.plot_dzvserrz(cat_before, cat, binning, Dz_range, z_min, z_max) #pl.plot_dzvsodds(cat_before, cat, binning, Dz_range, z_min, z_max, qual_para, od_bin)
shape = len(files) for name in files: cat_file = cat_in_folder + name print cat_file #Reading catalog........................................ cat_col = np.loadtxt(cat_file, unpack = True) #z_bpz cut.............................................. cat_col, n, N = pt.z_cut(cat_col, z_cut) ###Unificar las funciones de corte en una #print "Num. gal. after z(photo)<%2.2f cut: %d %1.1f%%" % (z_cut, n , 100 * float(n)/float(N) ) #Seeting dictionary before odds cut...................... cat_before = pt.dict(cat_col) #odds cut............................................... cat_col, n, N = pt.odds_cut(cat_col, odds_cut) #print "Num. gal. after odds> %2.2f cut: %d %1.1f%%" % (odds_cut, n , 100 * float(n)/float(N) ) #Seeting dictionary..................................... cat = pt.dict(cat_col) #Defining binning....................................... binning = pt.set_binning(cat) z_bin = pt.c_binning(binning) sp_y, err_sp_y = pl.plot_syvsz(cat['z_phot'], cat['z_true'], binning, "sigma", "$\sigma$", "$\sigma_{68}$", "z(true)" , sigma_ref)