#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)
