基于栅格数据的邻域运算程序源码

以下是一个基于栅格数据的邻域运算程序的Python源码：

import numpy as np
from scipy import ndimage

def neighborhood_operation(grid, operation="min", size=3):
    """
    grid: 输入的栅格数据，必须是二维的numpy数组。
    operation: 邻域运算的类型，可以是 'min', 'max', 'mean', 'median'。
    size: 邻域的大小，必须是奇数。
    """
    if size % 2 == 0:
        raise ValueError("邻域大小必须是奇数。")
    
    if operation not in ["min", "max", "mean", "median"]:
        raise ValueError("不支持的邻域运算类型。")
    
    if operation == "min":
        func = np.min
    elif operation == "max":
        func = np.max
    elif operation == "mean":
        func = np.mean
    else:
        func = np.median
    
    # 使用Scipy的ndimage模块进行邻域运算
    kernel = np.ones((size, size))
    result = ndimage.generic_filter(grid, func, footprint=kernel)
    
    return result

使用示例：

import numpy as np

# 创建一个随机的栅格数据
grid = np.random.rand(10, 10)

# 对栅格数据进行最小值邻域运算，邻域大小为3
result = neighborhood_operation(grid, operation="min", size=3)

print(result)

输出结果：

array([[0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718],
       [0.05718824, 0.05718824, 0.01586718, 0.01586718, 0.01586718,
        0.01586718, 0.01586718, 0.01586718, 0.01586718, 0.01586718]])