density_data = [[dens_t, dens_x, sigma_mean], [dens_t_sig, dens_x_sig, num]]
dens_titles = [["$< g_1\Sigma_c>$", "$< g_2\Sigma_c>$", "$<\Sigma_c>$"],
["$\delta <g_1\Sigma_c>$","$\delta <g_2\Sigma_c>$","$number$"]]
gamma_data = [[gamma_t, gamma_x, gamma], [gamma_t_sig, gamma_x_sig, num],[gamma_tc, gamma_xc, num]]
gamma_titles = [["$g_1$", "$g_2$", "$g$"],
["$\delta g_1$", "$\delta g_2$", "$number$"],
["$< g_1\Sigma_c>/\Sigma_{z=%.2f}$"%crit_z,"$< g_2\Sigma_c>/\Sigma_{z=%.2f}$"%crit_z,"nothing"]]
kappa_data = [[kappa_recon_ks95, kappa_recon_ks95_crit], [kappa_recon, kappa_recon_crit]]
kappa_titles = [["$\kappa KS\_95 Fourier$ ", "$\kappa KS\_95 Fourier critical$"],
["$\kappa real$", "$\kappa real criticak$"]]
# show density map
img = plot_tool.Image_Plot(fig_x=8, fig_y=8)
img.subplots(2,3)
cmap = plt.get_cmap('YlOrRd')
sm = plt.cm.ScalarMappable(cmap=cmap)
for i in range(2):
for j in range(3):
density_data[i][j][idx] = 0
ax = img.axs[i][j].imshow(density_data[i][j][inverse],cmap=cmap)
img.set_label(i,j, 1, "RA")
img.set_label(i,j, 0, "DEC")
img.axs[i][j].set_title(dens_titles[i][j],fontsize=img.xy_lb_size)
img.figure.colorbar(ax, ax=img.axs[i][j])
img.save_img(data_path + "density_%d.png"%ir)
if platform.system() != 'Linux':
# img.axs[0][0].legend()
# img.save_img()
# coeff_t = tool_box.fit_1d(gh, chi_t, 2, "scipy")
# corr_sig_t = numpy.sqrt(1 / 2. / coeff_t[2])
# g_corr_t = -coeff_t[1] / 2. / coeff_t[2]
#
# coeff_x = tool_box.fit_1d(gh, chi_x, 2, "scipy")
# corr_sig_x = numpy.sqrt(1 / 2. / coeff_x[2])
# g_corr_x = -coeff_x[1] / 2. / coeff_x[2]
comm.Barrier()
# plt.close()
if rank == 0:
numpy.savez(data_path + "result_%d.npz"%area_id, result)
img = plot_tool.Image_Plot()
img.create_subfig(1, 2)
gh = 10**numpy.linspace(numpy.log10(0.04), numpy.log10(15), 13)
img.axs[0][0].errorbar(gh, result[0], result[1], label="T",capsize=img.cap_size)
img.axs[0][0].errorbar(gh, result[2], result[3], label="X",capsize=img.cap_size)
img.axs[0][0].set_xscale("log")
img.axs[0][0].legend()
img.axs[0][1].errorbar(gh, numpy.abs(result[0]), result[1], label="T",capsize=img.cap_size)
img.axs[0][1].errorbar(gh, numpy.abs(result[2]), result[3], label="X",capsize=img.cap_size)
img.axs[0][1].set_xscale("log")
img.axs[0][1].set_yscale("log")
img.axs[0][1].set_ylim(0.01, 200)
img.axs[0][1].legend()
img.save_img(data_path + "result_%d.pdf"%area_id)
ch_num = 8
cuts_num = 20
x_coord = [i*2/cuts_num*100 for i in range(ch_num)]
print(x_coord)
ch = [i*2 for i in range(ch_num)]
ylabels = ["m$_1 \\times 10^2$", "m$_2 \\times 10^2$", "m$_1 \\times 10^2$", "m$_2 \\times 10^2$"]
fmt = '%2.f%%'
xticks = mtick.FormatStrFormatter(fmt)
npz = numpy.load("E:/works/CFHT_tomo/all/cut_ext/flux_alt_s12_a1/total.npz")
mc1 = npz["arr_0"][:,ch]
mc2 = npz["arr_1"][:,ch]
img = plot_tool.Image_Plot()
img.subplots(1, 2)
img.axs[0][0].errorbar(x_coord, mc1[0]-1, mc1[1],marker="s", mfc="none",linewidth=img.plt_line_width,capsize=img.cap_size, label="$m_1$")
img.axs[0][0].errorbar(x_coord, mc2[0]-1, mc2[1], marker="s", mfc="none",linewidth=img.plt_line_width,capsize=img.cap_size, label="$m_2$")
img.axs[0][1].errorbar(x_coord, mc1[2], mc1[3], marker="s", mfc="none",linewidth=img.plt_line_width,capsize=img.cap_size, label="$c_1$")
img.axs[0][1].errorbar(x_coord, mc2[2], mc2[3], marker="s", mfc="none",linewidth=img.plt_line_width,capsize=img.cap_size, label="$c_2$")
for i in range(2):
img.axs[0][i].xaxis.set_major_formatter(xticks)
xs = img.axs[0][i].set_xlim()
img.axs[0][i].plot([xs[0], xs[1]],[0,0],linestyle="--",c="grey")
img.axs[0][i].legend(fontsize=img.legend_size)
img.set_label(0,0,0,"m")
img.set_label(0,1,0,"c")
img.set_label(0,0,1,"Cutoff percentage")
idx_dec_s2 = dec <= dec_max + radius
idx_p = idx_ra_s1 & idx_ra_s2 & idx_dec_s1 & idx_dec_s2
for ir in range(len(redshift_bin) - 1):
idx_z_b1 = redshift >= redshift_bin[ir]
idx_z_b2 = redshift < redshift_bin[ir + 1]
idx_z_sub = idx_z_b1 & idx_z_b2
idx_s = idx_p & idx_z_sub
if rank == 0:
img = plot_tool.Image_Plot(fig_x=30, fig_y=30)
img.subplots(1, 2)
img.axs[0][0].scatter(fore_ra[igal],
fore_dec[igal],
s=200,
facecolors="none",
edgecolors="r",
marker="*")
for i in range(ny + 1):
img.axs[0][0].plot([ra_min, ra_max], [dec_bin[i], dec_bin[i]],
c="black",
linestyle="--",
alpha=0.5,
linewidth=0.3)
for j in range(nx + 1):
idx_dec_s2 = dec <= dec_max + radius
idx_p = idx_ra_s1 & idx_ra_s2 & idx_dec_s1 & idx_dec_s2
for ir in range(len(redshift_bin) - 1):
idx_z_b1 = redshift >= redshift_bin[ir]
idx_z_b2 = redshift < redshift_bin[ir + 1]
idx_z_sub = idx_z_b1 & idx_z_b2
idx_s = idx_p & idx_z_sub
if rank == 0:
img = plot_tool.Image_Plot(fig_x=12, fig_y=12)
img.create_subfig(1, 2)
img.axs[0][0].scatter(fore_ra[igal],
fore_dec[igal],
s=200,
facecolors="none",
edgecolors="r",
marker="*")
for i in range(ny + 1):
img.axs[0][0].plot([ra_min, ra_max], [dec_bin[i], dec_bin[i]],
c="black",
linestyle="--",
alpha=0.5,
linewidth=0.3)
for j in range(nx + 1):
ra_min, ra_max = ra_bin.min(), ra_bin.max()
dec_min, dec_max = dec_bin.min(), dec_bin.max()
h5f.close()
print(ra, dec, ra_bin[1] - ra_bin[0] - scale, dec_bin[1] - dec_bin[0] - scale)
mask = fits.open("mask.fits")[0].data
target_blocks = []
for i in range(ny):
for j in range(nx):
if mask[i, j] > -1:
m, n = divmod(mask[i, j], nx)
target_blocks.append((m, n))
print(m, n, dec_bin[m], dec_bin[m + 1], ra_bin[n], ra_bin[n + 1])
print(len(target_blocks))
img = plot_tool.Image_Plot(fig_x=20, fig_y=int(20. * ny / nx))
img.plot_img(1, 1)
for i in range(ny + 1):
img.axs[0][0].plot([ra_min, ra_max], [dec_bin[i], dec_bin[i]],
c="black",
linewidth=0.5)
for j in range(nx + 1):
img.axs[0][0].plot([ra_bin[j], ra_bin[j]], [dec_min, dec_max],
c="black",
linewidth=0.5)
for blks in target_blocks:
blk_x, blks_y = ra_bin[blks[1]] + 0.5 * scale, dec_bin[
blks[0]] + 0.5 * scale
img.axs[0][0].scatter(blk_x, blks_y, s=5, c="blue")