如何将多个Matplotlib图表合并为一个综合图表

import matplotlib.pyplot as plt
import numpy as np

def generate_figure_1():
    """生成第一个图表，包含一条正弦曲线。"""
    fig = plt.figure(figsize=(6, 4))
    ax = fig.add_subplot(111)
    x = np.linspace(0, 2 * np.pi, 100)
    y = np.sin(x)
    ax.plot(x, y, label='Sin(x)', color='blue')
    ax.set_title('Figure 1: Sine Wave')
    ax.set_xlabel('X-axis')
    ax.set_ylabel('Y-axis')
    ax.legend()
    plt.close(fig) # 关闭当前Figure，避免在notebook中显示
    return fig

def generate_figure_2():
    """生成第二个图表，包含一条余弦曲线和一个散点图。"""
    fig = plt.figure(figsize=(6, 4))
    ax1 = fig.add_subplot(211) # 第一个子图
    ax2 = fig.add_subplot(212) # 第二个子图

    x = np.linspace(0, 2 * np.pi, 100)
    y_cos = np.cos(x)
    ax1.plot(x, y_cos, label='Cos(x)', color='red')
    ax1.set_title('Figure 2, Subplot 1: Cosine Wave')
    ax1.legend()

    x_scatter = np.random.rand(50) * 10
    y_scatter = np.random.rand(50) * 10
    ax2.scatter(x_scatter, y_scatter, label='Random Scatter', color='green', marker='o')
    ax2.set_title('Figure 2, Subplot 2: Scatter Plot')
    ax2.legend()

    fig.tight_layout()
    plt.close(fig) # 关闭当前Figure
    return fig

# 生成两个独立的Figure对象
fig_a = generate_figure_1()
fig_b = generate_figure_2()

def extract_plot_data(figure):
    """从给定的Figure对象中提取所有Axes及其上的绘制数据。"""
    extracted_data = []
    for ax in figure.axes:
        ax_data = {'lines': [], 'scatter': [], 'bars': [], 'title': ax.get_title(), 'xlabel': ax.get_xlabel(), 'ylabel': ax.get_ylabel(), 'legend_handles_labels': ([], [])}

        # 提取线条数据
        for line in ax.lines:
            ax_data['lines'].append({
                'xdata': line.get_xdata(),
                'ydata': line.get_ydata(),
                'color': line.get_color(),
                'linestyle': line.get_linestyle(),
                'marker': line.get_marker(),
                'label': line.get_label()
            })

        # 提取散点数据 (通常是PathCollection)
        for collection in ax.collections:
            if isinstance(collection, plt.cm.ScalarMappable): # 排除colorbar等
                continue
            if hasattr(collection, 'get_offsets') and hasattr(collection, 'get_facecolors'):
                # 简单处理散点图，可能需要更复杂的逻辑处理颜色映射等
                offsets = collection.get_offsets()
                ax_data['scatter'].append({
                    'xdata': offsets[:, 0],
                    'ydata': offsets[:, 1],
                    'color': collection.get_facecolors()[0] if collection.get_facecolors().size > 0 else 'black',
                    'marker': collection.get_paths()[0].vertices[0] if collection.get_paths() else 'o', # 尝试获取marker
                    'label': collection.get_label()
                })

        # 提取柱状图数据 (通常是Rectangle对象)
        for container in ax.containers:
            if isinstance(container, plt.BarContainer):
                for bar in container.patches:
                    ax_data['bars'].append({
                        'x': bar.get_x(),
                        'y': bar.get_height(),
                        'width': bar.get_width(),
                        'color': bar.get_facecolor(),
                        'label': container.get_label() # BarContainer的label
                    })

        # 提取图例信息
        if ax.get_legend() is not None:
            handles, labels = ax.get_legend_handles_labels()
            ax_data['legend_handles_labels'] = (handles, labels)

        extracted_data.append(ax_data)
    return extracted_data

# 提取数据
data_from_fig_a = extract_plot_data(fig_a)
data_from_fig_b = extract_plot_data(fig_b)

all_extracted_data = data_from_fig_a + data_from_fig_b

# 计算总共需要多少个子图
num_subplots = len(all_extracted_data)

# 确定子图布局 (例如，两列布局)
rows = int(np.ceil(num_subplots / 2))
cols = 2 if num_subplots > 1 else 1

# 创建新的主图表和子图
new_fig, new_axes = plt.subplots(rows, cols, figsize=(cols * 7, rows * 5))
new_axes = new_axes.flatten() # 将axes数组展平，方便迭代

# 遍历所有提取的数据，并在新的子图中重新绘制
for i, ax_data in enumerate(all_extracted_data):
    current_ax = new_axes[i]

    # 重新绘制线条
    for line_info in ax_data['lines']:
        current_ax.plot(line_info['xdata'], line_info['ydata'],
                        color=line_info['color'],
                        linestyle=line_info['linestyle'],
                        marker=line_info['marker'],
                        label=line_info['label'])

    # 重新绘制散点
    for scatter_info in ax_data['scatter']:
        current_ax.scatter(scatter_info['xdata'], scatter_info['ydata'],
                           color=scatter_info['color'],
                           marker=scatter_info['marker'],
                           label=scatter_info['label'])

    # 重新绘制柱状图 (这里只是一个简单示例，可能需要更多参数)
    for bar_info in ax_data['bars']:
        current_ax.bar(bar_info['x'], bar_info['y'],
                       width=bar_info['width'],
                       color=bar_info['color'],
                       label=bar_info['label'])

    # 设置标题和轴标签
    current_ax.set_title(ax_data['title'])
    current_ax.set_xlabel(ax_data['xlabel'])
    current_ax.set_ylabel(ax_data['ylabel'])

    # 添加图例
    if ax_data['legend_handles_labels'][1]: # 如果有标签
        current_ax.legend()

# 调整布局，确保所有元素可见
new_fig.tight_layout()

# 显示最终合并的图表
plt.show()

# 保存合并后的图表
plt.savefig("combined_matplotlib_figures.png", dpi=300, bbox_inches='tight')
print("合并图表已保存为 combined_matplotlib_figures.png")