【Python】Matplotlib局部放大图画法

迷南。 2024-03-17 21:51 46阅读 0赞

#### 【Python】Matplotlib局部放大图画法 ####

import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.axes_grid1.inset_locator import inset_axes
    from matplotlib.patches import ConnectionPatch
    
    x = np.arange(1,len(LSTM_true)+1)
    
    # 画出测试集的实际结果和预测结果
    fig, ax = plt.subplots(figsize=(30, 12))
    ax.plot(x, LSTM_true,color='black', label='true', alpha=0.7)
    ax.plot(x, LSTM_predict,color='#848484', label='LSTM', alpha=0.7)
    ax.plot(x, MLP_predict, color='#FF774A',label='MLP', alpha=0.7)
    ax.plot(x, RNN_predict,  color='#575B20',label='RNN', alpha=0.7)
    ax.plot(x, TCN_predict,label='TCN', alpha=0.7)
    ax.plot(x, XGBoost_predict,label='XGBoost', alpha=0.7)
    ax.plot(x, CNN_predict,label='CNN', alpha=0.7)
    ax.plot(x, Transformer_predict,label='Transformer', alpha=0.7)
    
    plt.legend(loc='best')
    
    # 嵌入局部放大图的坐标系 
    #ax为父坐标系，后面四个参数同样是（x0, y0, width, height），
    axins = ax.inset_axes((0.2, -0.5, 0.4, 0.3))
    
    #在子坐标系中绘制原始数据
    axins.plot(x, LSTM_true,color='black', label='true', alpha=0.7)
    axins.plot(x, LSTM_predict,color='#848484', label='LSTM', alpha=0.7)
    axins.plot(x, MLP_predict, color='#FF774A',label='MLP', alpha=0.7)
    axins.plot(x, RNN_predict,  color='#575B20',label='RNN', alpha=0.7)
    axins.plot(x, TCN_predict,label='TCN', alpha=0.7)
    axins.plot(x, XGBoost_predict,label='XGBoost', alpha=0.7)
    axins.plot(x, CNN_predict,label='CNN', alpha=0.7)
    axins.plot(x, Transformer_predict,label='Transformer', alpha=0.7)
    
    # 设置放大区间，调整子坐标系的显示范围
    # 设置放大区间
    zone_left = 100
    zone_right = 130
    
    # 坐标轴的扩展比例（根据实际数据调整）
    x_ratio = 0  # x轴显示范围的扩展比例
    y_ratio = 0.05  # y轴显示范围的扩展比例
    
    # X轴的显示范围
    xlim0 = x[zone_left]-(x[zone_right]-x[zone_left])*x_ratio
    xlim1 = x[zone_right]+(x[zone_right]-x[zone_left])*x_ratio
    
    # Y轴的显示范围
    y = np.hstack((LSTM_true[zone_left:zone_right], LSTM_predict[zone_left:zone_right],
                   MLP_predict[zone_left:zone_right],RNN_predict[zone_left:zone_right]))
    ylim0 = np.min(y)-(np.max(y)-np.min(y))*y_ratio
    ylim1 = np.max(y)+(np.max(y)-np.min(y))*y_ratio
    
    # 调整子坐标系的显示范围
    axins.set_xlim(xlim0, xlim1)
    axins.set_ylim(ylim0, ylim1)
    
    # 建立父坐标系与子坐标系的连接线
    # 原图中画方框
    tx0 = xlim0
    tx1 = xlim1
    ty0 = ylim0
    ty1 = ylim1
    sx = [tx0,tx1,tx1,tx0,tx0]
    sy = [ty0,ty0,ty1,ty1,ty0]
    ax.plot(sx,sy,"black")
    
    # 画两条线
    xy = (xlim0,ylim0)
    xy2 = (xlim0,ylim1)
    con = ConnectionPatch(xyA=xy2,xyB=xy,coordsA="data",coordsB="data",
            axesA=axins,axesB=ax)
    axins.add_artist(con)
    
    xy = (xlim1,ylim0)
    xy2 = (xlim1,ylim1)
    con = ConnectionPatch(xyA=xy2,xyB=xy,coordsA="data",coordsB="data",
            axesA=axins,axesB=ax)
    axins.add_artist(con)
    
    plt.savefig(r'C:\Users\ZHUANG\Desktop\pjm_rto_load.png')

![在这里插入图片描述][388f30a3064c49988e313cb7c52bd45a.png_pic_center]  
详细内容看[https://juejin.cn/post/6844904183548608520][https_juejin.cn_post_6844904183548608520]

[388f30a3064c49988e313cb7c52bd45a.png_pic_center]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/14/692af79871e14df6b24545cfd4f9ebe2.png
[https_juejin.cn_post_6844904183548608520]: https://juejin.cn/post/6844904183548608520