# pip install netcdf4 --prefer-binary

import netCDF4 as nc
 
def inspect_nc_file(nc_file_path):
    """
    詳細(xì)查看NC文件結(jié)構(gòu)
    """
    try:
        with nc.Dataset(nc_file_path, 'r') as nc_file:
            print("=" * 50)
            print("NC文件基本信息:")
            print("=" * 50)
            
            # 文件格式和維度
            print(f"文件格式: {nc_file.data_model}")
            print(f"文件維度: {nc_file.dimensions}")
            print(f"文件變量: {list(nc_file.variables.keys())}")
            
            print("\n" + "=" * 50)
            print("維度詳細(xì)信息:")
            print("=" * 50)
            for dim_name, dim in nc_file.dimensions.items():
                print(f"{dim_name}: {len(dim)} 個(gè)元素")
            
            print("\n" + "=" * 50)
            print("變量詳細(xì)信息:")
            print("=" * 50)
            for var_name, var in nc_file.variables.items():
                print(f"\n變量名: {var_name}")
                print(f"  維度: {var.dimensions}")
                print(f"  形狀: {var.shape}")
                print(f"  數(shù)據(jù)類型: {var.dtype}")
                print(f"  屬性: {dict(var.__dict__)}")
                
                # 顯示部分?jǐn)?shù)據(jù)樣本
                if len(var.shape) <= 2:  # 只顯示低維數(shù)據(jù)的樣本
                    sample_data = var[:]
                    if hasattr(sample_data, 'flatten'):
                        sample_data = sample_data.flatten()
                    print(f"  數(shù)據(jù)樣本: {sample_data[:5]}...")  # 顯示前5個(gè)值
            
            print("\n" + "=" * 50)
            print("全局屬性:")
            print("=" * 50)
            for attr_name in nc_file.ncattrs():
                print(f"{attr_name}: {getattr(nc_file, attr_name)}")
                
    except Exception as e:
        print(f"錯(cuò)誤: {e}")
 
# 使用示例
inspect_nc_file('wspd_2024.nc')

import netCDF4 as nc
import pandas as pd
import numpy as np
 
def nc_to_csv(nc_file_path, csv_file_path):
    """
    將NC文件轉(zhuǎn)換為CSV文件
    
    參數(shù):
    nc_file_path: NC文件路徑
    csv_file_path: 輸出的CSV文件路徑
    """
    try:
        # 讀取NC文件
        with nc.Dataset(nc_file_path, 'r') as nc_file:
            # 提取變量數(shù)據(jù)
            lat = nc_file.variables['lat'][:]
            lon = nc_file.variables['lon'][:]
            data = nc_file.variables['data'][:]
            
            print(f"數(shù)據(jù)維度: lat={lat.shape}, lon={lon.shape}, data={data.shape}")
            
            # 創(chuàng)建網(wǎng)格坐標(biāo)
            lon_grid, lat_grid = np.meshgrid(lon, lat)
            
            # 展平數(shù)據(jù)
            df = pd.DataFrame({
                'latitude': lat_grid.flatten(),
                'longitude': lon_grid.flatten(),
                'data_value': data.flatten()
            })
            
            # 保存為CSV
            df.to_csv(csv_file_path, index=False)
            print(f"文件已保存到: {csv_file_path}")
            print(f"共 {len(df)} 行數(shù)據(jù)")
            
    except Exception as e:
        print(f"錯(cuò)誤: {e}")
 
# 使用示例
if __name__ == "__main__":
    nc_to_csv('wspd_2024.nc', 'wspd_2024.csv')