def plot_hist(run_id, clus, MgII, title):
""" Plot cluster and MgII redshift histograms. """
zbin = Bins(np.arange(-0.1, 1.2, 0.025))
fig = plt.figure(1, figsize=(4.5, 4.5))
fig.clf()
ax = plt.gca()
vals, _ = np.histogram(clus.z, bins=zbin.edges)
y = np.where(vals == 0, -10, np.log10(vals))
ax.plot(zbin.cen,
y,
'r',
lw=2,
ls='steps-mid',
label='clusters (n=%i)' % len(clus),
zorder=10)
vals, _ = np.histogram(MgII['z'], bins=zbin.edges)
label = 'MgII (n={})'.format(len(MgII))
y = np.where(vals == 0, -10, np.log10(vals))
ax.plot(zbin.cen, y, 'b', lw=2, ls='steps-mid', label=label)
ax.set_xlabel('$\mathrm{Redshift}$')
ax.set_ylabel('$\mathrm{Number}$')
y0, y1 = ax.get_ylim()
ax.set_ylim(-0.8, y1)
ax.set_xlim(0.25, 1.05)
make_log_ylabels(ax)
ax.legend(frameon=0, fontsize=8)
ax.set_title(title)
plt.savefig(run_id + '/zhist.png', dpi=200, bbox_inches='tight')
def plot_hist(run_id, clus, MgII, title):
""" Plot cluster and MgII redshift histograms. """
zbin = Bins(np.arange(-0.1, 1.2, 0.025))
fig = plt.figure(1, figsize=(4.5,4.5))
fig.clf()
ax = plt.gca()
vals,_ = np.histogram(clus.z, bins=zbin.edges)
y = np.where(vals == 0, -10, np.log10(vals))
ax.plot(zbin.cen, y, 'r', lw=2, ls='steps-mid',
label='clusters (n=%i)' % len(clus), zorder=10)
vals,_ = np.histogram(MgII['z'], bins=zbin.edges)
label = 'MgII (n={})'.format(len(MgII))
y = np.where(vals == 0, -10, np.log10(vals))
ax.plot(zbin.cen, y, 'b',lw=2, ls='steps-mid', label=label)
ax.set_xlabel('$\mathrm{Redshift}$')
ax.set_ylabel('$\mathrm{Number}$')
y0,y1 = ax.get_ylim()
ax.set_ylim(-0.8, y1)
ax.set_xlim(0.25, 1.05)
make_log_ylabels(ax)
ax.legend(frameon=0, fontsize=8)
ax.set_title(title)
plt.savefig(run_id + '/zhist.png', dpi=200, bbox_inches='tight')
dNdz, dNdz_er, n = find_dndz_vs_rho(
rho, ab['MgII'], iMgII_from_id,
ewbins.edges[i], ewbins.edges[i+1])
y = np.log10(dNdz)
ylo = np.log10(dNdz - dNdz_er)
yhi = np.log10(dNdz + dNdz_er)
errplot(rbin.cen + offsets[i], y, (ylo, yhi), ax=ax,
fmt=colors[i]+symbols[i], label=labels[i])
for j in range(len(n)):
puttext(rbin.cen[j], 0.03 + i*0.03, str(n[j]), ax, color=colors[i],
fontsize=10, xcoord='data', ha='center')
ax.legend(frameon=0)
ax.set_xlabel('Cluster-absorber impact par. (proper Mpc)')
ax.set_ylabel(r'$dN/dz\ (MgII)$')
# skip last bin, where not all pairs are measured.
ax.set_xlim(rbin.edges[0] - rbin.halfwidth[0],
rbin.edges[-2] + rbin.halfwidth[-1])
make_log_xlabels(ax)
make_log_ylabels(ax)
#fig3.savefig(run_id + '/dNdz_vs_rho.png')
plt.show()
if 0:
# check whether QSO properties are consistent across bins
qso_orig = append_QSO_props(ab)
ab['qso'] = qso_orig
fig = plt.figure(figsize=(20,5))
plot_rho_QSO_prop(fig, rho, ab, iqso_from_id)
ax=ax,
fmt=colors[i] + symbols[i],
label=labels[i])
for j in range(len(n)):
puttext(rbin.cen[j],
0.03 + i * 0.03,
str(n[j]),
ax,
color=colors[i],
fontsize=10,
xcoord='data',
ha='center')
ax.legend(frameon=0)
ax.set_xlabel('Cluster-absorber impact par. (proper Mpc)')
ax.set_ylabel(r'$dN/dz\ (MgII)$')
# skip last bin, where not all pairs are measured.
ax.set_xlim(rbin.edges[0] - rbin.halfwidth[0],
rbin.edges[-2] + rbin.halfwidth[-1])
make_log_xlabels(ax)
make_log_ylabels(ax)
#fig3.savefig(run_id + '/dNdz_vs_rho.png')
plt.show()
if 0:
# check whether QSO properties are consistent across bins
qso_orig = append_QSO_props(ab)
ab['qso'] = qso_orig
fig = plt.figure(figsize=(20, 5))
plot_rho_QSO_prop(fig, rho, ab, iqso_from_id)