data_1 = all_para_2[:, i][idx1]
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)
data = h5f["/data"][()]
img.axs[0][i].errorbar(data[1, :data_num],
y,
xerr=data[2, :data_num],
capsize=img.cap_size - 1)
ys = img.axs[0][i].set_ylim()
img.axs[0][i].plot([signal, signal], [ys[0], ys[1]],
ls="dotted",
c="grey",
alpha=0.5)
img.set_label(0, i, 1, "g")
for j in range(data_num):
if i < 2:
text_str = "%d bins, $10^2\sigma=%.3f$, $N\sigma^2=%.3f$" % (
data[4, j], scale[i] * data[2, j], data[3, j])
else:
text_str = "%d bins, $10^3\sigma=%.3f$, $N\sigma^2=%.3f$" % (
data[4, j], scale[i] * data[2, j], data[3, j])
img.axs_text(0,
i,
y[j],
-0.019,
text_str,
text_fontsize=img.legend_size - 4,
ax_trans=False)
img.axs[0][i].set_xlim(signal - 0.031, signal + 0.007)
img.axs[0][i].set_title(titles[i], fontsize=img.legend_size)
img.del_tick(0, i, [0])
img.axs[0][i].set_xticks(x_ticks)
img.save_img("D:/sigma.png")
img.show_img()
c="grey")
img.axs[0][i].plot([xs[0], xs[1]], [-0.1, -0.1],
ls="--",
alpha=0.5,
c="grey")
img.axs[0][i].plot([xs[0], xs[1]], [-0.2, -0.2],
ls="--",
alpha=0.5,
c="grey")
img.axs[0][i].plot([xs[0], xs[1]], [-0.3, -0.3],
ls="--",
alpha=0.5,
c="grey")
y1 = mc1[:, 0] - mc1[:, 1] * 1.5
y2 = mc2[:, 0] - mc2[:, 1] * 1.5
for i in range(len(bin_num)):
img.axs[0][0].text(i,
min(y1[i], y2[i]) * scale[0],
"%d bins" % bin_num[i],
color="green",
ha="left",
va="center",
rotation=60,
fontsize=img.legend_size - 4)
for i in range(2):
img.del_tick(0, i, [1])
img.save_img(parent_path +
"/result_noisy_bin_num_compare_%s.png" % fit_label)
img.close_img()
# img.show_img()
