for j in range(i + 1, 6):
print(titles[i], titles[j])
data_2 = all_para_2[:, j][idx1]
chisq = chisq_2[idx]
norm = img.figure.Normalize(vmin=numpy.min(chisq),
vmax=numpy.max(chisq))
cmap = img.figure.get_cmap('YlOrRd')
img.axs[i][j - i - 1].scatter(data_2, data_1, s=10)
img.set_label(i, j - i - 1, 0, titles[i])
img.set_label(i, j - i - 1, 1, titles[j])
for i in range(5):
for j in range(4, 4 - i, -1):
print(i, j)
img.del_tick(i, j, [0, 1], [0, 1, 2, 3])
img.subimg_adjust(0.27, 0.27)
img.save_img(total_path + "para_contour_.png")
img.show_img()
# print(idx1.sum(), idx2.sum())
# print(chisq_1[idx1].min(), chisq_1[idx1].max())
# print(chisq_1[idx2].min(), chisq_1[idx2].max())
# print(chisq_2.min())
# idx1 = chisq_2 <= 0.03
# idx21 = chisq_2 > 0.03
# idx22 = chisq_2 < 0.2
# idx2 = idx21 & idx22
# idx3 = chisq_2 > 10
# img = Image_Plot()
# img.subplots(1, 1)
# img.axs[0][0].hist(chisq_2[idx1], 2)
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()
h5f.close()
# The foreground
# write to the final hdf5 file for calculation
# the CMASS catalog
h5f = h5py.File("/mnt/ddnfs/data_users/hkli/CFHT/gg_lensing/data/redshift.hdf5","r")
redshift = h5f["/redshift"].value
distance = h5f["/distance"].value
h5f.close()
img.subplots(1, 2)
img.axs[0][0].plot(x, signals, color='k', label="True")
img.axs[0][0].errorbar(x, mcmc_shear, mcmc_sig, label="MCMC Recovered")
img.axs[0][0].errorbar(x, fq_shear[0], fq_shear[1], label="FQ Recovered")
img.set_label(0, 0, 0, "g")
img.set_label(0, 0, 1, "X")
img.axs[0][0].legend()
img.axs[0][1].plot(x,
100 * (signals[:, 0] - mcmc_shear[:, 0]),
label="MCMC: True - Recovered")
img.axs[0][1].plot(x,
100 * (signals[:, 0] - fq_shear[0]),
label="FQ: True - Recovered")
img.set_label(0, 1, 0, "$10^2 \\times\Delta g$")
img.set_label(0, 1, 1, "X")
img.axs[0][1].legend()
img.subimg_adjust(0, 0.25)
img.save_img("./pic/mcmc_recover_nw_%d_stp_%d.png" % (nwalkers, step))
img.close_img()
# for i in range(ndim):
# img = Image_Plot()
# img.subplots(1, 1)
# img.axs[0][0].hist(sampler.flatchain[:, 0], 100, histtype="step", color='k')
# img.save_img("mcmc_chisq.png")
# # img.show_img()
# img.close_img()
# pool.close()
