python画图局部放大图代码

# 不同模型的ROC曲线
lable_names = ["逻辑回归","SVM","神经网络","随机森林","决策树"] # 模型名称
colors = ["r","b","g","m","k",] # 不同曲线颜色
linestyles =["-", "--", "-.", ":", "-"] #不同曲线

fig  = plt.figure(figsize=(8,7),dpi=150)
for n in range(5):
    ## 计算绘制ROC曲线的取值
        plt.plot(fpr[n], tpr[n],color=colors[n],linewidth = 2,
                 linestyle = linestyles[n],
                 label = f'AUC={auc[n]} '+lable_names[n])
plt.plot([0, 1], [0, 1], 'k--')
plt.xlabel("假正率"); plt.ylabel("真正率")
plt.xlim(0, 1); plt.ylim(0, 1)
plt.grid()
plt.legend()
plt.title("不同模型的ROC曲线")
## 放大的图片
inset_ax = fig.add_axes([0.3, 0.45, 0.4, 0.4],facecolor="white")
for n in range(5):
    ## 放大的图
    inset_ax.plot(fpr[n], tpr[n],color=colors[n],linewidth = 2,linestyle = linestyles[n],
                  label = f'AUC={auc[n]} '+lable_names[n])
    inset_ax.set_xlim([-0.1,1])
    inset_ax.set_ylim([0.7,1.01])
    inset_ax.grid()
plt.show()

# 残差图——检验自相关
    def e_picture_auto(self):
        #创建画布
        fig = plt.figure(figsize=(8, 8))
        #使用axisartist.Subplot方法创建一个绘图区对象ax
        ax = axisartist.Subplot(fig, 111)  
        #将绘图区对象添加到画布中
        fig.add_axes(ax)
        #通过set_visible方法设置绘图区所有坐标轴隐藏
        ax.axis[:].set_visible(False)
        #ax.new_floating_axis代表添加新的坐标轴
        ax.axis["x"] = ax.new_floating_axis(0,0)
        #给x坐标轴加上箭头
        ax.axis["x"].set_axisline_style("->", size = 1.0)
        #添加y坐标轴，且加上箭头
        ax.axis["y"] = ax.new_floating_axis(1,0)
        ax.axis["y"].set_axisline_style("-|>", size = 1.0)
        #设置x、y轴上刻度显示方向
        ax.axis["x"].set_axis_direction("top")
        ax.axis["y"].set_axis_direction("right")
        
        plt.scatter(self.e.values[:-1],self.e.values[1:], marker='.')
        # 标注某个点
        plt.annotate("异常值", (-7,63), xycoords='data',xytext=(-20, 55),arrowprops=dict(arrowstyle='->'),fontsize=15) 
        plt.annotate("异常值", (63,-8), xycoords='data',xytext=(45, -8),arrowprops=dict(arrowstyle='->'),fontsize=15)
        plt.annotate(r"$e_t$", (-3,67), xycoords='data',xytext=(-4,67),fontsize=15) # ,arrowprops=dict(arrowstyle='-') # y标签
        plt.annotate(r"$e_{t-1}$", (67,5), xycoords='data',xytext=(67,-4),fontsize=15) # x标签
        plt.annotate(r"$O$", (67,5), xycoords='data',xytext=(-3,2),fontsize=15) # 原坐标标签
        
        # 局部放大图
        inset_ax = fig.add_axes([0.45, 0.45, 0.4, 0.4],facecolor="white")
        ## 放大的图
        inset_ax.scatter(self.e.values[:-1],self.e.values[1:])
        inset_ax.set_xlim([-12,5])
        inset_ax.set_ylim([-12,5])
        inset_ax.annotate("", (-11,0), xycoords='data',arrowprops=dict(arrowstyle='<-'),xytext=(5,0),fontsize=12) # 添加原坐标标签# inset_ax.axhline(y=0,c="black", lw=0.5) # 添加水平线
        inset_ax.annotate("", (0,-11), xycoords='data',arrowprops=dict(arrowstyle='<-'),xytext=(0,4),fontsize=12)# inset_ax.axvline(x=0,c="black", lw=0.5) # 添加纵轴线
        inset_ax.annotate(r"$O$", (-1,0.5), xycoords='data',xytext=(-1,0.5),fontsize=12) # 添加原坐标标签
        inset_ax.spines['right'].set_visible(False)
        inset_ax.spines['top'].set_visible(False)
        inset_ax.text(-10,4, '局部放大图', bbox=dict(facecolor='yellow', alpha=0.5), fontsize=15) # 添加文本