目录
一、使用ArcMap提取出第一期数据
1.使用工具箱中的“Make NetCDF Raster Layer”工具,提取出一个数据
可以发现该数据有正确的像元大小、坐标系等
2.导出该数据作为标准数据
二、使用python批量提取所有数据
1. 查看数据属性
from netCDF4 import Dataset,num2date
infile = "../01Data/Runoff1992-2014/GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"
data_set = Dataset(infile) # 读取nc文件信息
print(data_set)
输出为
<class 'netCDF4._netCDF4.Dataset'>
root group (NETCDF3_CLASSIC data model, file format NETCDF3):
title: GRUN
version: GRUN 1.0
meteorological_forcing: GSWP3
temporal_resolution: monthly
spatial_resolution: 0.5x0.5
crs: WGS84
proj4: +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
EPSG: 4326
references: Ghiggi et al.,2019. GRUN: An observation-based global gridded runoff dataset from 1902 to 2014. ESSD, doi: https://doi.org/10.5194/essd-2019-32
authors: Gionata Ghiggi; Lukas Gudmundsson
contacts: gionata.ghiggi@gmail.com; lukas.gudmundsson@env.ethz.ch
institution: Land-Climate Dynamics, Institute for Atmospheric and Climate Science, ETH Zürich
institution_id: IAC ETHZ
dimensions(sizes): X(720), Y(360), time(1356)
variables(dimensions): float64 X(X), float64 Y(Y), float64 time(time), float32 Runoff(time, Y, X)
groups:
可以看到variables
变量X、Y为经纬度,time为时间,Runoff
为需要的结果
2.批量导出结果
from osgeo import gdal
from netCDF4 import Dataset,num2date
import numpy as np
def WriteTiff(im_data,inputdir, path):
raster = gdal.Open(inputdir)
im_width = raster.RasterXSize #栅格矩阵的列数
im_height = raster.RasterYSize #栅格矩阵的行数
im_bands = raster.RasterCount #波段数
im_geotrans = raster.GetGeoTransform()#获取仿射矩阵信息
im_proj = raster.GetProjection()#获取投影信息
if 'int8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
elif 'int16' in im_data.dtype.name:
datatype = gdal.GDT_UInt16
else:
datatype = gdal.GDT_Float32
if len(im_data.shape) == 3:
im_bands, im_height, im_width = im_data.shape
elif len(im_data.shape) == 2:
im_data = np.array([im_data])
im_bands, (im_height, im_width) = 1, im_data.shape
# 创建文件
driver = gdal.GetDriverByName("GTiff")
dataset = driver.Create(path, im_width, im_height, im_bands, datatype)
if (dataset != None):
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
dataset.SetProjection(im_proj) # 写入投影
for i in range(im_bands):
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
del dataset
infile = "../01Data/Runoff1992-2014/GRUN_v1_GSWP3_WGS84_05_1902_2014.nc"
data_set = Dataset(infile) # 读取nc文件信息
time = data_set.variables["time"][:] # 获取时间一列
units = data_set.variables["time"].units # 获取第一期时间
#读取样本tif文件的地理信息
intif = "../03ProcessData/runoff_example.tif"
for i in range(0,len(time)):
yr = num2date(time[i],units).year # 提取年份
mon = num2date(time[i],units).month # 提取月份
value_data = data_set.variables['Runoff'][i]
# 将缺失值改为0
data = value_data.data
mask = value_data.mask
data[np.where(mask == True)] = 0
outputname = "../01Data/Runoff1992-2014/tif/" + str(yr) + str(mon).zfill(2) + ".tif"
WriteTiff(data,intif , outputname)
print(outputname)
!注意事项
1.使用时候请自行修改修改输入输出文件路径与变量名称
2.根据需要处理缺失值
到此这篇关于python的netCDF4批量处理NC格式文件的操作方法的文章就介绍到这了,更多相关python netCDF4处理NC格式文件内容请搜索三水点靠木以前的文章或继续浏览下面的相关文章希望大家以后多多支持三水点靠木!
python的netCDF4批量处理NC格式文件的操作方法
- Author -
skypanxh- Original Sources -
声明:登载此文出于传递更多信息之目的,并不意味着赞同其观点或证实其描述。
Reply on: @reply_date@
@reply_contents@