def plot_dzvsodds(cat_before, cat, binning, Dz_range, z_min, z_max, qual_para, od_bin):
if(delta_z_str == "$\Delta z$"): delta_z = cat_before['z_phot'] - cat_before['z_true']
if(delta_z_str == "$\Delta z/(1+z)$"): delta_z = (cat_before['z_phot'] - cat_before['z_true']) / (1 + cat_before['z_true'])
od_min = cat_before['odds'].min()
od_max = cat_before['odds'].max()
#if(od_max > 1.0): od_max = 1.
odds_binning = np.arange(od_min, od_max, od_bin)
odds_bin = tool.c_binning(odds_binning)
s_y, err_s_y = tool.sigmavsodds(cat_before['odds'], delta_z, odds_bin = odds_binning)
mult = 1
plt.figure(figsize=(13, 7))
plt.subplot(121)
plt.plot(cat_before['odds'], delta_z, 'o', markersize = 1)
plt.errorbar(odds_bin, mult*s_y, mult*err_s_y, color = 'green', label = "cumulative RMSx" + str(mult))
plt.axhline( 0, linewidth = 1, color = 'red')
plt.axvline( odds_cut, linewidth = 1, color = 'blue', label = qual_para + " cut = " + str(odds_cut))
plt.xlim(xmin = 0, xmax = 1)
if (qual_para == 'odds'): plt.xlim(xmin = 0, xmax = 1)
if (qual_para == 'PDZbest'): plt.xlim(xmin = 0, xmax = 100)
plt.ylim(ymin = -Dz_range, ymax = Dz_range)
plt.xlabel(qual_para)
plt.ylabel(delta_z_str)
z_bin = tool.c_binning(binning)
n_true, N_true = tool.hist_oddscut(cat_before['z_true'], cat['z_true'], binning)
n_phot, N_phot = tool.hist_oddscut(cat_before['z_phot'], cat['z_phot'], binning)
#c:completeness
c_true = np.array([])
c_true_high = np.array([])
c_true_low = np.array([])
c_phot = np.array([])
c_phot_high = np.array([])
c_phot_low = np.array([])
for i in range(len(binning)-1):
if N_true[i] > 1: c, c_low, c_high = tool.Completeness(0, n_true[i], N_true[i])
else:
c = nan
c_high = nan
c_low = nan
c_true = np.append(c_true, c)
c_true_high = np.append(c_true_high, c_high)
c_true_low = np.append(c_true_low, c_low)
if N_phot[i] > 1: c, c_low, c_high = tool.Completeness(0, n_phot[i], N_phot[i])
else:
c = nan
c_high = nan
c_low = nan
c_phot = np.append(c_phot, c)
c_phot_high = np.append(c_phot_high, c_high)
c_phot_low = np.append(c_phot_low, c_low)
err_c_true = [c_true_low, c_true_high]
err_c_phot = [c_phot_low, c_phot_high]
plt.subplot(122)
plt.errorbar(z_bin, c_true, err_c_true, color = 'blue', label = "z(true)")
plt.errorbar(z_bin, c_phot, err_c_phot, color = 'red', label = "z(phot)")
plt.ylabel("Completeness")
plt.xlabel("z")
plt.xlim(xmin = z_min, xmax = z_max)
plt.ylim(ymin = 0, ymax = 1.)
plt.legend(loc = 'best')
plt.savefig(plot_folder + "odds_cut.png", bbox_inches='tight')
plt.close()
def plot_dzvserrz(cat_before, cat, binning, Dz_range, z_min, z_max):
if(delta_z_str == "$\Delta z$"): delta_z = cat_before['z_phot'] - cat_before['z_true']
if(delta_z_str == "$\Delta z/(1+z)$"): delta_z = (cat_before['z_phot'] - cat_before['z_true']) / (1 + cat_before['z_true'])
errz_bin = tool.c_binning(errz_binning)
s_y, err_s_y = tool.sigmavserrz(cat_before['err_z_phot'], delta_z)
mult = 1
plt.figure(figsize=(13, 7))
plt.subplot(121)
plt.plot(cat_before['err_z_phot'], delta_z, 'o', markersize = 1)
plt.errorbar(errz_bin, mult*s_y, mult*err_s_y, color = 'green', label = "cumulative RMSx" + str(mult))
plt.axhline( 0, linewidth = 1, color = 'red')
plt.axvline( errz_cut, linewidth = 1, color = 'blue', label = "ERR z(phot) cut = " + str(errz_cut))
plt.xlim(xmin = 0, xmax = yrange_sigma * sigma_ref)
plt.ylim(ymin = -Dz_range, ymax = Dz_range)
plt.xlabel("ERR z(phot)")
plt.ylabel(delta_z_str)
plt.legend(loc = 'best')
z_bin = tool.c_binning(binning)
n_true, N_true = tool.hist_oddscut(cat_before['z_true'], cat['z_true'], binning)
n_phot, N_phot = tool.hist_oddscut(cat_before['z_phot'], cat['z_phot'], binning)
#c:completeness
c_true = np.array([])
c_true_high = np.array([])
c_true_low = np.array([])
c_phot = np.array([])
c_phot_high = np.array([])
c_phot_low = np.array([])
for i in range(len(binning)-1):
if N_true[i] > 1: c, c_low, c_high = tool.Completeness(0, n_true[i], N_true[i])
else:
c = nan
c_high = nan
c_low = nan
c_true = np.append(c_true, c)
c_true_high = np.append(c_true_high, c_high)
c_true_low = np.append(c_true_low, c_low)
if N_phot[i] > 1: c, c_low, c_high = tool.Completeness(0, n_phot[i], N_phot[i])
else:
c = nan
c_high = nan
c_low = nan
c_phot = np.append(c_phot, c)
c_phot_high = np.append(c_phot_high, c_high)
c_phot_low = np.append(c_phot_low, c_low)
err_c_true = [c_true_low, c_true_high]
err_c_phot = [c_phot_low, c_phot_high]
plt.subplot(122)
plt.errorbar(z_bin, c_true, err_c_true, color = 'blue', label = "z(true)")
plt.errorbar(z_bin, c_phot, err_c_phot, color = 'red', label = "z(phot)")
plt.ylabel("Completeness")
plt.xlabel("z")
plt.xlim(xmin = z_min, xmax = z_max)
plt.ylim(ymin = 0, ymax = 1.)
plt.legend(loc = 'best')
plt.savefig(plot_folder + "errz_cut.png")
plt.close()
