dnamsel = [['rlon','srlon'],['rlat','srlat']]
for evar in fin.variables:
# apply the domain selection on all variables that involve these dimensions
procvar = True
for ednamsel in dnamsel:
found = False
if type(ednamsel).__name__ == 'list':
for eednamsel in ednamsel:
if eednamsel in fin.variables[evar].dimensions:
found = True
elif ednamsel in fin.variables[evar].dimensions:
found = True
if found == False:
procvar = False
if procvar:
print ('processing variable: ',evar)
datin = [{'file':fin,'varname':evar,}]
datout = [{'file':fout,'varname':evar},]
pcd.pcd(func,dnamsel,datin,datout,appenddim=True)
else:
if evar not in fin.dimensions:
print ('copying variable: ',evar)
pcd.nccopyvariable(fin,fout,evar,copyvalues=True)
fout.close();print('output file written to:',fout )
fin.close()
# Unfortunatly, It not superfast. The reason is that the 'preperations' in pcd needs to be redone
# for each variable.
import pynacolada as pcd
from Scientific.IO import NetCDF
fin = NetCDF.NetCDFFile('/home/hendrik/data/belgium_aq/rcm/aq09/stage1/int2lm/laf2009010100_urb_ahf.nc','r')
fout = NetCDF.NetCDFFile('/home/hendrik/data/belgium_aq/rcm/aq09/stage1/int2lm/laf2009010100_urb_ahf_test.nc','w')
pcd.nccopyvariable(fin,fout,'lat',copyvalues=True)
pcd.nccopyvariable(fin,fout,'lon',copyvalues=True)
pcd.nccopyvariable(fin,fout,'rlon',copyvalues=True)
pcd.nccopyvariable(fin,fout,'rlat',copyvalues=True)
pcd.nccopyvariable(fin,fout,'T',copyvalues=True)
pcd.nccopyvariable(fin,fout,'U',copyvalues=True)
pcd.nccopyvariable(fin,fout,'V',copyvalues=True)
pcd.nccopyattrfile(fin,fout)
fin.close()
fout.close()
