[Python知识库]Python&R | nc批量转tif

nc数据：
NetCDF(network Common Data Form)网络通用数据格式。NetCDF 文件中的数据以数组形式存储。例如：某个位置处随时间变化的温度以一维数组的形式存储。某个区域内在指定时间的温度以二维数组的形式存储。

三维 (3D) 数据（如某个区域内随时间变化的温度）

四维 (4D) 数据（如某个区域内随时间和高度变化的温度）以一系列二维数组的形式存储。

本次使用的样例数据为2018-2020年的月均温度netCDF数据

下面主要介绍如何使用Python和R语言批量将上述nc格式的温度数据转为常用的tif格式数据。

1. 基于Python

import os
import netCDF4 as nc
import numpy as np
from osgeo import gdal,osr,ogr
import glob

def nc2tif(data,Output_folder):
    tmp_data = nc.Dataset(data)   #利用.Dataset()方法读取nc数据
    # print(tmp_data)
    # <class 'netCDF4._netCDF4.Dataset'>
    # root group (NETCDF3_CLASSIC data model, file format NETCDF3):
    # dimensions(sizes): lon(7849), lat(5146), time(12)
    # variables(dimensions): float64 lon(lon), float64 lat(lat), float64 time(time), int16 tmp(time, lat, lon)

    # print(tmp_data.variables)   #lon, lat, time, tmp
    # {'lon': <class 'netCDF4._netCDF4.Variable'>
    # float64 lon(lon)
    # long_name: longitude
    # unit: degree
    # unlimited dimensions: 
    # current shape = (7849,)
    # filling on, default _FillValue of 9.969209968386869e+36 used, 'lat': <class 'netCDF4._netCDF4.Variable'>
    # float64 lat(lat)
    # long_name: latitude
    # unit: degree
    # unlimited dimensions: 
    # current shape = (5146,)
    # filling on, default _FillValue of 9.969209968386869e+36 used, 'time': <class 'netCDF4._netCDF4.Variable'>
    # float64 time(time)
    # long_name: time
    # unit: data.01-data.12
    # calendar: gregorian
    # unlimited dimensions: 
    # current shape = (12,)
    # filling on, default _FillValue of 9.969209968386869e+36 used, 'tmp': <class 'netCDF4._netCDF4.Variable'>
    # int16 tmp(time, lat, lon)
    # long_name: 0.1 monthly mean temperature
    # unit: degree centigrade
    # missing_value: -32768.0
    # unlimited dimensions: 
    # current shape = (12, 5146, 7849)
    # filling on, default _FillValue of -32767 used}

    Lat_data = tmp_data.variables['lat'][:]
    Lon_data = tmp_data.variables['lon'][:]
    # print(Lat_data)
    # [58.63129069 58.62295736 58.61462403 ... 15.77295736 15.76462403
    #  15.75629069]
    # print(Lon_data)
    # [ 71.29005534  71.29838867  71.30672201 ... 136.67338867 136.68172201
    #  136.69005534]
    
    tmp_arr = np.asarray(tmp_data.variables['tmp'])
    
    #影像的左上角&右下角坐标
    Lonmin, Latmax, Lonmax, Latmin = [Lon_data.min(), Lat_data.max(), Lon_data.max(), Lat_data.min()]
    # Lonmin, Latmax, Lonmax, Latmin
    # (71.29005533854166, 58.63129069182766, 136.6900553385789, 15.756290691830095)
    
    #分辨率计算
    Num_lat = len(Lat_data)    #5146
    Num_lon = len(Lon_data)  #7849
    Lat_res = (Latmax - Latmin) / (float(Num_lat) - 1)
    Lon_res = (Lonmax - Lonmin) / (float(Num_lon) - 1)
    #print(Num_lat, Num_lon)
    #print(Lat_res, Lon_res)
    # 5146 7849
    # 0.00833333333333286 0.008333333333338078
    
    for i in range(len(tmp_arr[:])):
        #i=0,1,2,3,4,5,6,7,8,9,...
    #创建tif文件
        driver=gdal.GetDriverByName('GTiff')
        out_tif_name = Output_folder +'\\'+ data.split('\\')[-1].split('.')[0] + '_' + str(i+1) + '.tif'
        out_tif = driver.Create(out_tif_name, Num_lon, Num_lat, 1, gdal.GDT_Int16)
        
        #设置影像的显示范围
            #Lat_re前需要添加负号
        geotransform = (Lonmin, Lon_res, 0.0, Latmax, 0.0, -Lat_res)
        out_tif.SetGeoTransform(geotransform)
                
        #定义投影
        prj = osr.SpatialReference()
        prj.ImportFromEPSG(4326)
        out_tif.SetProjection(prj.ExportToWkt())
    
        #数据导出
        out_tif.GetRasterBand(1).WriteArray(tmp_arr[i])  #将数据写入内存，此时没有写入到硬盘
        out_tif.FlushCache()   #将数据写入到硬盘
        out_tif = None #关闭tif文件 

def main():
    Input_folder = 'G:/learnpy/data/'
    Output_folder = 'G:/learnpy/data/nc/nc2tif'
    
    #读取所有数据
    data_list = glob.glob(Input_folder+'*.nc')
    
    for i in range(len(data_list)):
        data = data_list[i]
        nc2tif(data, Output_folder)
        print(data+'转tif成功, 你真棒！')
        
main()

2. 基于R语言

wd <- "G:/Rdata/Tmp/"
setwd(wd)

library(pacman)
p_load(raster, ncdf4,sf,ggplot2,RColorBrewer)

nc_data = list.files(wd,pattern = ".nc")
nc_data
# [1] "tmp_2018.nc" "tmp_2019.nc" "tmp_2020.nc"

for (i in nc_data)
{
  nc_raster <- brick(i,varname = "tmp")
  for(j in 1:12)
  {
    super1 = substr(i, 1, 7)
    super2 = c(1:12)
    super3 = paste(super1, super2, sep = '_')
    
    writeRaster(nc_raster[[j]],filename = super3[[j]], format = "GTiff",overwrite=TRUE, 
                dataType='INT2S')
  }
  print(paste(i,'转换tif成功,你太棒了!'))
}

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