else:
idx_z1 = zs[data_tag[i]] >= 0.43
idx_z2 = zs[data_tag[i]] <= 0.7
idx_t = idx & idx_z1 & idx_z2
norm = plt.Normalize(vmin=numpy.min(zs[data_tag[i]][idx_t]), vmax=numpy.max(zs[data_tag[i]][idx_t]))
cmap = plt.get_cmap('YlOrRd')
img.axs[j][i].scatter(ras[data_tag[i]][idx_t], decs[data_tag[i]][idx_t], s=5,
color=cmap(zs[data_tag[i]][idx_t]), label=boss_label[data_tag[i]])
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []
num = idx_t.sum()
select_data[j].append([ras[data_tag[i]][idx_t], decs[data_tag[i]][idx_t], zs[data_tag[i]][idx_t]])
img.tick_label(j, i, 1, "R.A.")
img.tick_label(j, i, 0, "DEC.")
img.axs[j][i].set_title(" Total BOSS galaxy: %d" % num)
img.axs[j][i].legend()
plt.colorbar(sm, ax=img.axs[j][i])
# print(shape, block_scale)
# print(dec_bin.min(), dec_bin.max(), dec_bin.shape)
# print(ra_bin.min(), ra_bin.max(), ra_bin.shape)
# print(dec_min, dec_max)
# print(ra_min, ra_max, ra.shape, idx.sum())
# print("\n")
img.subimg_adjust(0.3, 0.2)
img.save_img("/mnt/ddnfs/data_users/hkli/CFHT/gg_lensing/result/pic/areas.pdf")
plt.close()
img.axs[0][1].errorbar(x,
final_signal[:, 4],
final_signal[:, 5],
capsize=5,
c="C1",
label="T",
linestyle="--")
img.axs[0][1].errorbar(x,
final_signal[:, 6],
final_signal[:, 7],
capsize=5,
c="C2",
label="X",
linestyle="--")
img.axs[0][0].legend()
img.axs[0][1].legend()
img.axs[0][0].set_xscale("log")
img.axs[0][1].set_xscale("log")
# img.axs[0][0].set_yscale("symlog")
img.axs[0][1].set_yscale("symlog")
img.axs[0][1].set_ylim(-30, 150)
img.tick_label(0, 0, 0, "$\gamma \\times 10^2$")
img.tick_label(0, 0, 1, "$R \quad Mpc\cdot h^{-1}$")
img.tick_label(0, 1, 0, "$\Delta\Sigma$")
img.tick_label(0, 1, 1, "$R \quad Mpc\cdot h^{-1}$")
if area_num == 1:
img.save_img(data_path + "pic/w_%d/signal.png" % area_ids[0])
else:
img.save_img(data_path + "pic/total/total_signal.png")
