'K'# 书写属性# 全局属性rootgrp.description="bogus example script"rootgrp.history="Created "+Time.ctime(Time.time())rootgrp.source="netCDF4 python module tutorial"latitudes.units="degrees north"longitudes.units="degrees east"levels.units="hPa"temp.units="K"times.units="hours since 0001-01-0...
ds.to_netcdf("air_temperature.nc") python读写NETCDF文件(进阶版本)mp.weixin.qq.com/s/iSE8tIMhrVAxPfVlRPMprQ
Global attr source = netCDF4 python module tutorial 4 NetCDF文件的读写操作 4.1 NetCDF文件的写入操作 我们现在有了一个NetCDF变量实例,那么如何将数据放入其中呢?你可以把它当作一个numpy数组,然后使用类似于numpy数组切片的方式给变量赋值。 >>> import numpy as np>>> lats = np.arange(-90, 91, ...
>>>level=rootgrp.createDimension("level",None)>>>time=rootgrp.createDimension("time",None)>>>lat=rootgrp.createDimension("lat",73)>>>lon=rootgrp.createDimension("lon",144) 所有的 Dimension 实例都被存储在一个 python 字典中。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>>print r...
("time",)) ndvi = ndvi_nc.createVariable("ndvi", "f4", ("y", "x")) ndvi_nc.description = "NDVI dataset" ndvi_nc.source = "netCDF4 python module tutorial" ndvi.units = "dimensionless" times.units = "seconds since 1970-1-1" ndvi[:] = NDVInew times[:] = 1303939211 ndvi_nc...
test.source = 'netCDF4 python module tutorial' # 设置变量属性 lats.units = 'degrees north' lons.units = 'degrees east' levels.units = 'hPa' temp.units = 'K' times.units = 'hours since 0001-01-01 00:00:00.0' times.calendar = 'gregorian' ...
netcdf-c and hdf5 be built with MPI support, andmpi4py. To open a file for parallel access in a program running in an MPI environment using mpi4py, just useparallel=Truewhen creating theDatasetinstance. Seeexamples/mpi_example.pyfor a demonstration. For more info, see the tutorialsection...
importtime# 设置对文件的描述test.description ='bogus example script'# 设置文件的历史信息test.history ='Created'+ time.ctime(time.time())# 设置文件的来源信息test.source ='netCDF4 python module tutorial'# 设置变量属性lats.units ='degrees north'lons.units ='degrees east'levels.units ='hPa'temp...
sudo apt install python3 一般Ubuntu会默认安装,但也许是在安装geant4过程中装好的 python3 --version 检查Python的版本 which gcc g++ gfortran python3 查看所需要的编译器安装位置 先看看并仔细阅读手动编译要求 https://docs.abinit.org/tutorial/
ds=xr.tutorial.open_dataset('rasm',chunks={'time':12}) 此数据集为xarray官方提供的示例数据。这里设置的 time 维度的块大小为12。然后,对上述数据集执行相关计算操作: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 result=np.sqrt(np.sin(ds)**2+np.cos(ds)**2) ...