Changing value of a netcdf file for a variable

Question

I have a large netcdf file which is three dimensional. I want to replace for the variable LU_INDEX in the netcdf file all the values 10 with 2.

I wrote this python script to do so but it does not seem to work.

filelocation = 'D:/dataset.nc'

ncdataset = nc.Dataset(filelocation,'r')
lat           = ncdataset.variables['XLAT_M'][0,:,:]
lon           = ncdataset.variables['XLONG_M'][0,:,:]
lu_index     = ncdataset.variables['LU_INDEX'][0,:,:]
lu_index_new = lu_index
ncdataset.close()

nlat,nlon=lat.shape

for ilat in range(nlat):
    for ilon in range(lon):
        if lu_index == 10:
          lu_index_new[ilat,ilon] = 2

newfilename = 'D:/dataset.new.nc'
copyfile(ncdataset,newfilename)


newfile     = nc.Dataset(newfilename,'r+')
newfile.variables['LU_INDEX'][0,:,:]   = lu_index_new
newfile.close()

I get the error:

The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I am not a very experienced with python, so if there is a more easier way to do this you are very welcome to comment.

ClimateUnboxed · Accepted Answer · 2022-05-23 04:33:20Z

10

You might try NCO

ncap2 -s 'where(LU_INDEX == 10) LU_INDEX=2' in.nc out.nc

edited May 23, 2022 at 4:33

ClimateUnboxed

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answered Feb 17, 2017 at 18:47

Charlie Zender

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Gijs Koetsenruijter · Accepted Answer · 2017-02-24 08:15:14Z

2

I worked it out as follow:

import netCDF4 as nc
import numpy as np

pathname = 'D:'
filename = '%s/dataset.nc'%pathname
ncfile = nc.Dataset(filename,'r+')
lu_index = ncfile.variables['LU_INDEX'][:]
I = np.where(lu_index == 10)
lu_index[I] = 2
ncfile.variables['LU_INDEX'][:] = lu_index
filename.close()

print 'conversion complete'

answered Feb 24, 2017 at 8:15

Gijs Koetsenruijter

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3 Comments

Charlie Zender Over a year ago

The concision difference between the NCO and Python solutions is striking.

James Adams Over a year ago

NCO is probably 1000x faster too. Pro tip: always leverage NCO where possible.

Saverio Guzzo Over a year ago

not just faster but also memory efficient. It's an awesome set of programs! Thanks Charlie

ClimateUnboxed · Accepted Answer · 2024-02-18 21:48:29Z

2

using np.array might not work for a very large dataset with the following error

"ValueError: array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size."

CDO can be a nice tool other than NCO and for me, it is much faster.

cdo setvals,10,2 in.nc out.nc

It is particularly fast when you have to replace values in more than one variables in the same nc file (e.g. replace missing values representation). One can use setrtoc in place of setval for specifying a range.

edited Feb 18, 2024 at 21:48

ClimateUnboxed

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answered Mar 1, 2018 at 9:49

Vinod Kumar

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