编写函数画P维变量的两两散点图，Python代码。

在Python中，我们可以使用matplotlib库以及numpy库来绘制高维空间中的散点图。以下是一个简单的例子，展示如何编写一个函数，用于生成P维空间中每对变量之间的二维散点图：

import matplotlib.pyplot as plt
import numpy as np

def plot.pairplot_high_dim(data, dimensions, labels=None):
    # 创建一个P x P的子图网格
    fig, axs = plt.subplots(nrows=len(dimensions), ncols=len(dimensions), figsize=(8 * len(dimensions), 6 * len(dimensions)))

    if labels is not None:
        assert len(labels) == len(dimensions), "Number of labels must match the number of dimensions"

    for i in range(len(dimensions)):
        for j in range(i, len(dimensions)):
            ax = axs[i, j] if i < j else axs[j, i]
            
            # 如果是主对角线，只显示数据范围
            if i == j:
                ax.set_title(f'Dimension {i+1}')
                ax.scatter(*data[:, dimensions[i]].T)
                ax.axis('equal')
                ax.set_yticklabels([])
                ax.set_xticklabels([])
            else:
                # 绘制一对变量的散点图
                ax.scatter(data[:, dimensions[i]], data[:, dimensions[j]])
                ax.set_xlabel(labels[dimensions[i]] if labels else f'Dimension {i+1}')
                ax.set_ylabel(labels[dimensions[j]] if labels else f'Dimension {j+1}')

    # 调整子图间的间距
    plt.tight_layout()

# 使用示例
data = np.random.rand(100, 5)  # 假设我们有5维数据
dimensions = [0, 1, 2, 3, 4]  # 我们要画出所有维度对
labels = ['Dimension ' + str(i) for i in range(1, len(dimensions)+1)] if labels is None else labels
plot.pairplot_high_dim(data, dimensions, labels)

# 显示图形
plt.show()