xs = img.axs[2 * i + 1][0].set_xlim()
img.axs[2 * i + 1][0].plot([xs[0], xs[1]], [1, 1],
ls="--",
alpha=0.4,
c="k")
img.axs[2 * i + 1][0].plot([xs[0], xs[1]], [0, 0],
ls="--",
alpha=0.4,
c="k")
img.axs[2 * i + 1][0].plot([xs[0], xs[1]], [-1, -1],
ls="--",
alpha=0.4,
c="k")
labels = [
"noise_free\n$10^2m_{1/2}$", "noise_free\n$10^4c_{1/2}$",
"noisy\n$10^2m_{1/2}$", "noisy\n$10^4c_{1/2}$"
]
for i in range(4):
img.set_label(i, 0, 0, labels[i], fontsize=img.xy_lb_size - 2)
psf_scale = [
"$r_d=1.6$", "$r_d=2$", "$r_d=2.5$", "$r_d=3$", "$r_d=4$", "$r_d=6$",
"$r_d=7$"
]
# img.axs[3][0].set_xticks(x_tick_pos)
# img.axs[3][0].set_xticklabels(psf_scale)
img.set_ticklabel_str(3, 0, 1, x_tick_pos, psf_scale)
img.save_img(parent_path + "/result.png")
marker="s")
img.axs[1][i].errorbar(x + 0.15,
pdf_mcs[2] * c_scale,
pdf_mcs[3] * c_scale,
label="PDF $10^4c1$",
capsize=img.cap_size,
marker="o",
ls="--")
img.axs[1][i].errorbar(x + 0.15,
pdf_mcs[6] * c_scale,
pdf_mcs[7] * c_scale,
label="PDF $10^4c2$",
capsize=img.cap_size,
marker="o",
ls="--")
img.axs[0][i].legend(ncol=2,
fontsize=img.legend_size - 8,
loc="upper left")
img.axs[1][i].legend(ncol=2,
fontsize=img.legend_size - 8,
loc="upper left")
img.set_ticklabel_str(0, i, 1, x, xlabels)
img.set_ticklabel_str(1, i, 1, x, xlabels)
img.axs_text(0, i, 0.1, 0.1, weight[i], text_color="k")
img.axs_text(1, i, 0.1, 0.1, weight[i], text_color="k")
img.axs[0][i].set_ylim(-1.5, 1.5)
img.axs[1][i].set_ylim(-0.5, 0.5)
img.save_img(pic_nm)
# img.show_img()
img.close_img()
scale = [24.2423, 23.4742]
line_labels = ["$30%$ cutoff", "$60%$ cutoff"]
img = Image_Plot(legend_size=14,
fig_x=6,
fig_y=4,
plt_line_width=2,
axis_linewidth=2,
xpad=0.2)
img.subplots(1, 1)
# img.set_style()
img.axis_type(0, "major", tick_len=6, tick_width=1.2)
img.axis_type(1, "major", tick_len=6, tick_width=1.2)
bins = img.axs[0][0].hist(-mag_true, 50, ec="gray", label="Entire sample")[1]
img.axs[0][0].hist(-mag_true[idx], bins, ec="gray", label="Detected sample")
ys = img.axs[0][0].set_ylim()
for i, s in enumerate(scale):
img.axs[0][0].plot([s, s], [ys[0], ys[1]],
lw=img.plt_line_width,
ls="--",
label=line_labels[i])
img.axs[0][0].legend(fontsize=img.legend_size, frameon=False)
img.set_ticklabel_str(0, 0, 0, [2 * i * 100000 for i in range(1, 7)],
["%d" % (2 * i) for i in range(1, 7)])
img.set_label(0, 0, 0, "$10^{-5}N$")
img.set_label(0, 0, 1, "Magnitude")
# img.axs[0][0].set_xlim(22, 25)
img.save_img("E:/cutoff_line.pdf")
img.show_img()
ls=ls[0],
c=ls[1])
ys = img.axs[0][0].set_ylim()
img.axs[0][0].plot([chi_guess[i], chi_guess[i]], [ys[0], ys[1]],
c=ls[1])
img.axs[0][0].legend(ncol=2, fontsize=12, loc="lower center")
ys = img.axs[0][0].set_ylim()
img.axs[0][0].set_xscale("symlog")
img.axs[0][0].set_ylim(1, 170)
# img.axs[0][0].set_xlim(-4*10**(-6),8*10**(-6))
img.axs[0][0].set_yscale("log")
img.axs[0][0].set_title(titles[j])
img.set_label(0, 0, 0, "$\\chi^2$")
img.set_label(0, 0, 1, "$\\xi_1$ guess")
img.set_ticklabel_str(0, 0, 1, [1.e-7, 1.e-6, 1.e-5],
["$10^{-%d}$" % i for i in range(7, 4, -1)])
img.save_img(data_path + "/chisq_%s.png" % data_type[j])
# img.show_img()
img.close_img()
numpy.savez(data_path + "/xi_cache_%s.npz" % data_type[0], chi_measures[0])
numpy.savez(data_path + "/xi_cache_%s.npz" % data_type[1], chi_measures[1])
cs = ["C2", "C1"]
img = Image_Plot(fig_x=5, fig_y=5, xpad=0.15, ypad=0.15)
img.subplots(1, 1)
for i in range(1, -1, -1):
img.axs[0][0].errorbar(chi_guess,
chi_measures[i][0],
chi_measures[i][1],
marker="s",
