合并多个Matplotlib图表：从独立图到统一子图布局

import matplotlib.pyplot as plt
import numpy as np

# 模拟生成第一个图表的函数
def generate_figure_1():
    fig, ax = plt.subplots(figsize=(5, 4))
    x = np.linspace(0, 10, 100)
    y1 = np.sin(x)
    y2 = np.cos(x)
    ax.plot(x, y1, label='sin(x)', color='blue')
    ax.plot(x, y2, label='cos(x)', color='red')
    ax.set_title('Figure 1: Sine and Cosine')
    ax.legend()
    plt.close(fig) # 关闭当前显示，避免重复显示
    return fig

# 模拟生成第二个图表的函数
def generate_figure_2():
    fig, ax = plt.subplots(figsize=(5, 4))
    x = np.random.rand(50)
    y = np.random.rand(50)
    ax.scatter(x, y, color='green', alpha=0.6)
    ax.set_title('Figure 2: Scatter Plot')
    plt.close(fig) # 关闭当前显示，避免重复显示
    return fig

# 获取两个独立的图表对象
fig_1 = generate_figure_1()
fig_2 = generate_figure_2()

# 从图表对象中获取其包含的轴对象列表
# 通常一个简单的图表只有一个轴，但复杂图表可能有多个
axes_1 = fig_1.axes
axes_2 = fig_2.axes

print(f"Figure 1 has {len(axes_1)} axes.")
print(f"Figure 2 has {len(axes_2)} axes.")

# 提取 Figure 1 中第一个轴的线条数据
extracted_data_fig1 = []
if axes_1:
    for line in axes_1[0].lines:
        x_data = line.get_xdata()
        y_data = line.get_ydata()
        color = line.get_color()
        label = line.get_label()
        extracted_data_fig1.append({'x': x_data, 'y': y_data, 'color': color, 'label': label, 'type': 'line'})

# 提取 Figure 2 中第一个轴的散点数据
extracted_data_fig2 = []
if axes_2:
    for collection in axes_2[0].collections: # Scatter plots are collections
        # Scatter data is typically stored in offsets
        # For simplicity, let's assume it's a single scatter plot
        # More robust parsing might be needed for complex collections
        offsets = collection.get_offsets()
        if offsets.size > 0:
            x_data = offsets[:, 0]
            y_data = offsets[:, 1]
            color = collection.get_facecolors()[0] if collection.get_facecolors().size > 0 else 'black'
            extracted_data_fig2.append({'x': x_data, 'y': y_data, 'color': color, 'type': 'scatter'})

print(f"Extracted data from Figure 1: {len(extracted_data_fig1)} plot series.")
print(f"Extracted data from Figure 2: {len(extracted_data_fig2)} plot series.")

# 创建一个新的图表和子图布局
# 这里我们创建了一个1行2列的布局，用于放置两个原始图表的内容
new_fig, new_axes = plt.subplots(1, 2, figsize=(12, 5))

# 将 Figure 1 的内容绘制到第一个子图
ax_combined_1 = new_axes[0]
if extracted_data_fig1:
    for data_item in extracted_data_fig1:
        if data_item['type'] == 'line':
            ax_combined_1.plot(data_item['x'], data_item['y'],
                               color=data_item['color'],
                               label=data_item['label'])
    ax_combined_1.set_title('Combined Subplot 1 (from Figure 1)')
    ax_combined_1.legend()
    # 尝试复制原始轴的标题和标签 (如果需要)
    if axes_1 and axes_1[0].get_title():
        ax_combined_1.set_title(axes_1[0].get_title())
    if axes_1 and axes_1[0].get_xlabel():
        ax_combined_1.set_xlabel(axes_1[0].get_xlabel())
    if axes_1 and axes_1[0].get_ylabel():
        ax_combined_1.set_ylabel(axes_1[0].get_ylabel())


# 将 Figure 2 的内容绘制到第二个子图
ax_combined_2 = new_axes[1]
if extracted_data_fig2:
    for data_item in extracted_data_fig2:
        if data_item['type'] == 'scatter':
            ax_combined_2.scatter(data_item['x'], data_item['y'],
                                  color=data_item['color'])
    ax_combined_2.set_title('Combined Subplot 2 (from Figure 2)')
    # 尝试复制原始轴的标题和标签 (如果需要)
    if axes_2 and axes_2[0].get_title():
        ax_combined_2.set_title(axes_2[0].get_title())
    if axes_2 and axes_2[0].get_xlabel():
        ax_combined_2.set_xlabel(axes_2[0].get_xlabel())
    if axes_2 and axes_2[0].get_ylabel():
        ax_combined_2.set_ylabel(axes_2[0].get_ylabel())

# 调整子图之间的间距
new_fig.tight_layout()

# 显示合并后的图表
plt.show()

# 关闭原始图表以释放内存（如果不再需要）
plt.close(fig_1)
plt.close(fig_2)

# 保存合并后的图表为高分辨率图片
new_fig.savefig("combined_matplotlib_figures.png", dpi=300, bbox_inches='tight')
print("Combined figure saved as 'combined_matplotlib_figures.png'")