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")