print(chisqs.min(), chisqs.max())
omega_cm_num = omega_cm_bin.shape[0]
omega_bm_num = omega_bm_bin.shape[0]
sigma8_num = sigma8_bin.shape[0]
my, mx = numpy.zeros((omega_cm_num, sigma8_num)), numpy.zeros(
(omega_cm_num, sigma8_num))
for i in range(omega_cm_num):
my[i] = omega_cm_bin[i]
for j in range(sigma8_num):
mx[:, j] = sigma8_bin[j]
img = Image_Plot(fig_x=4, fig_y=3, xpad=0.25, ypad=0.28)
img.subplots(2, 5)
for i in range(2):
for j in range(5):
tag = i * 2 + j
idx = chisqs[tag] > -200
print(idx.sum())
x = mx[idx].flatten()
y = my[idx].flatten()
z = chisqs[tag][idx].flatten()
img.scatter_pts(i, j, x, y, z, color_map="jet", pts_size=18)
img.axs[i][j].set_title("$\Omega_b=%.3f$" % omega_bm_bin[tag])
img.set_label(i, j, 0, "$\Omega_m$")
img.set_label(i, j, 1, "$\sigma_8$")
img.save_img("%s/%s.png" % (data_path, chisa_name))
img.show_img()
print("Max to %.3f Mpc/h" % (com_dist_len * Rmax / 3600 / 180 * numpy.pi))
if rank == 0:
ra_bin = numpy.linspace(-Rmax, Rmax, bin_num + 1)
dec_bin = numpy.linspace(-Rmax, Rmax, bin_num + 1)
ra, dec = GGLensing_tool.get_pos(ra_bin, dec_bin, bin_num)
shear_data = [GGLensing_tool.cal_shear(ra, dec, nfw, z) for z in src_z]
img = Image_Plot(xpad=0.25, ypad=0.25)
img.subplots(4, src_plane_num)
for i in range(src_plane_num):
kappa, g1, g2 = shear_data[i][0], shear_data[i][1], shear_data[i][2]
g = numpy.sqrt(g1**2 + g2**2)
img.scatter_pts(0, i, ra, dec, kappa)
img.scatter_pts(1, i, ra, dec, g)
img.scatter_pts(2, i, ra, dec, g1)
img.scatter_pts(3, i, ra, dec, g2)
img.axs[0][i].set_title("kappa from source plane at Z=%.3f" % src_z[i])
img.axs[1][i].set_title("g from source plane at Z=%.3f" % src_z[i])
img.axs[2][i].set_title("g1 from source plane at Z=%.3f" % src_z[i])
img.axs[3][i].set_title("g2 from source plane at Z=%.3f" % src_z[i])
for j in range(4):
img.set_label(j, i, 0, "Dec. [arcsec]")
img.set_label(j, i, 1, "R.A. [arcsec]")
img.save_img("./pic/sample.png")
img.close_img()
# img.show_img()
for i in range(len(sig8)):
x[:,i] = sig8[i]
for i in range(len(omg_c)):
y[i] = omg_c[i]
img = Image_Plot(fig_x=5,fig_y=4,xpad=0.25,ypad=0.25)
img.subplots(2,5)
for i in range(2):
for j in range(5):
chisq_i = -chisq[int(i*5 + j)]
idx = chisq_i < 70
xi = x[idx].flatten()
yi = y[idx].flatten()
zi = chisq_i[idx].flatten()
idx = zi == zi.min()
xi_min, yi_min = xi[idx], yi[idx]
print(xi_min, yi_min)
img.scatter_pts(i,j, xi,yi,numpy.log10(zi),color_map='jet',pts_size=5)
img.axs[i][j].scatter(xi_min, yi_min, s=15,c="r", marker="p",label="$\chi^2_{min}=%.3f$"%zi.min())
img.axs[i][j].legend()
# fig = img.axs[i][j].imshow(chisq_i,cmap="jet")
# img.figure.colorbar(fig,ax=img.axs[i][j],)
img.set_label(i,j,0,"$\Omega_m$")
img.set_label(i,j,1,"$\sigma_8$")
img.axs[i][j].set_title("$\Omega_b=%.2f$"%omg_b[int(i*5 + j)])
# img.save_img(data_path +"/brutal.pdf")
img.show_img()
pic_nm = "./result/galsim/mc_%s_ellip_mix.png"%psf_tag
pic_nm_pdf = "./result/galsim/mc_%s_ellip_mix.pdf"%psf_tag
else:
numpy.savez("./result/galsim/cache_%s_ellip_%.2f.npz"%(psf_tag,ellip), mcs)
pic_nm = "./result/galsim/mc_%s_ellip_%.2f.png"%(psf_tag, ellip)
pic_nm_pdf = "./result/galsim/mc_%s_ellip_%.2f.pdf"%(psf_tag, ellip)
y = numpy.zeros((si_num*sr_num,))
x = numpy.zeros((si_num*sr_num,))
for i in range(si_num):
for j in range(sr_num):
tag = int(i*sr_num + j)
y[tag] = sersic_index[i]
x[tag] = scale_radius[j]
titles = ["$m_1$","$c_1$","$m_2$", "$c_2$"]
img = Image_Plot(xpad=0.3,ypad=0.3,fig_x=4,fig_y=3)
img.subplots(2, 2)
for i in range(2):
for j in range(2):
tag = int(i*2 + j)
z = mcs[tag].flatten()
img.scatter_pts(i,j,x,y,z,pts_size=30,color_map="bwr")
img.set_label(i,j,0,"Sersic Index")
img.set_label(i,j,1,"Scale Radius")
img.axs[i][j].set_title(titles[tag],fontsize=img.xy_lb_size)
img.save_img(pic_nm)
img.save_img(pic_nm_pdf)
# img.show_img()
mcs[0, i, j] = mc1[1] -1 # m1
mcs[1, i, j] = mc1[0] # c1
mcs[2, i, j] = mc2[1]-1 # m2
mcs[3, i, j] = mc2[0] # c2
if rank == 0:
numpy.savez("./cache_%s.npz"%psf_tag, mcs)
y = numpy.zeros((si_num*sr_num,))
x = numpy.zeros((si_num*sr_num,))
for i in range(si_num):
for j in range(sr_num):
tag = int(i*sr_num + j)
y[tag] = sersic_index[i]
x[tag] = scale_radius[j]
titles = ["$m_1$","$c_1$","$m_2$", "$c_2$"]
img = Image_Plot(xpad=0.25,ypad=0.25,fig_x=4,fig_y=3)
img.subplots(2, 2)
for i in range(2):
for j in range(2):
tag = int(i*2 + j)
z = mcs[tag].flatten()
img.scatter_pts(i,j,x,y,z,pts_size=30)
img.set_label(i,j,0,"Sersic Index")
img.set_label(i,j,1,"Scale Radius")
img.axs[i][j].set_title(titles[tag])
img.save_img("./mc_%s.png"%psf_tag)
# img.show_img()
], [
"Hist after PDF_SYM", "$\Delta N$ after PDF_SYM", "$\chi^2$ after PDF_SYM"
]]
for i in range(2):
hist2d = numpy.histogram2d(check_mnu, check_mg - i * sigma * check_mnu,
[mnur_bin, mg_bin])[0]
chisq, hist2d_left, hist2d_right, hist2d_diff, hist2d_sum = get_diff(
hist2d)
numpy.savez("./cache_%d_%s.npz" % (i, cmd), x, y, xh, yh, hist2d, chisq,
hist2d_left, hist2d_right, hist2d_diff, hist2d_sum)
idx = hist2d > 0
img.scatter_pts(i, 0, xlog[idx], xlog[idx], hist2d[idx], color_map="jet")
idx = hist2d_sum > 0
img.scatter_pts(i,
1,
xhlog[idx],
yhlog[idx],
hist2d_diff[idx],
color_map="jet")
img.scatter_pts(i, 2, xhlog[idx], yhlog[idx], chisq[idx], color_map="jet")
for j in range(3):
img.set_label(i, j, 0, "N+U bin [log]")
img.set_label(i, j, 1, "$G_t$ bin [log]")
img.axs[i][j].set_title(titles[i][j])
