不要简单脚本，要完整代码

对于完整的CHM_PRE数据处理代码，我会提供一个包含数据读取、缺失值处理、插值、年度平均值计算以及基本趋势分析的示例。在这个例子中，我们将使用numpy、pandas和matplotlib库。请注意，这只是一个基础示例，你可能需要根据具体需求进行调整。

首先，确保已安装必要的库，如`rasterio`, `numpy`, `pandas` 和 `matplotlib`:

pip install rasterio numpy pandas matplotlib

然后，以下是完整的Python代码：

import numpy as np
import pandas as pd
import rasterio
from rasterio.plot import show
import matplotlib.pyplot as plt

# 读取NetCDF文件
def read_chm_pre(filename):
    with rasterio.open(filename) as src:
        pre_data = src.read(1)  # 降雨量数据
        station_number = src.read(2)
        crs = src.crs
        bounds = src.bounds

    return pre_data, station_number, crs, bounds

# 函数用于处理缺失值和异常值
def clean_and_interpolate(data, missing_val=-99.9):
    # 将缺失值替换为0
    data[data == missing_val] = np.nan
    # 使用线性内插法填充缺失值
    interpolation = Rbf(*np.meshgrid(*data.nonzero()), data.ravel(), function="linear")
    interpolated_data = interpolation(*np.meshgrid(np.arange(data.shape[0]), np.arange(data.shape[1])))

    return interpolated_data

# 读取数据
filename = "your_dataset.nc"
precipitation_data, station_numbers, crs, bounds = read_chm_pre(filename)

# 缺失值处理和插值
cleaned_data = clean_and_interpolate(precipitation_data)

# 计算每年平均降水量
annual_avg_precipitation = cleaned_data.mean(axis=(0, 1)).reshape(-1, 12).mean(axis=1)

# 绘制降水量变化趋势
plt.figure(figsize=(10, 5))
plt.plot(range(len(annual_avg_precipitation)), annual_avg_precipitation, label="Average Monthly Precipitation")
plt.title("Annual Average Precipitation Trend")
plt.xlabel("Year")
plt.ylabel("Precipitation (mm)")
plt.legend()
plt.show()

# 计算趋势斜率
trend_slope = np.polyfit(range(len(annual_avg_precipitation)), annual_avg_precipitation, 1)[0]
print(f"Trend Slope: {trend_slope:.2f} mm/year")

#