def nccopyvariable(ncin,ncout,varin,varout=None,copyvalues=False,copyattr=True):
''' create a new netcdf variable with the same dimensions and attributes as the original variable. Optionally, the values itself can be copied as well
ncin: input netcdf file
ncout output netcdf file
varin: input variable
varout (optional): output variable. If None, it is equal to varin
copyvalues: copy values (only possible if the dimensions of source and target variable are of the same size)
'''
if varout == None:
varoutdef = varin
else:
varoutdef = varout
if varoutdef not in ncout.variables:
for edimension in ncin.variables[varin].dimensions:
if edimension not in ncout.dimensions:
nccopydimension(ncin,ncout,edimension,copyvalues=True)
#ncout.createVariable(variable,ncin.variables[variable].typecode(),ncin.variables[variable].dimensions)
# workaround: functions are now considered an output to be float64: better make the function make arrays
# that have the same output type!! create variable according to the output of dataouttemp!!
ncout.createVariable(varoutdef,ncin.variables[varin].typecode(),ncin.variables[varin].dimensions)
if copyattr:
nccopyattrvar(ncin,ncout,varin=varin,varout=varoutdef)
# for attr in dir(ncin.variables[varin]):
# if attr not in ['assignValue', 'getValue', 'typecode']:
# setattr(ncout.variables[varoutdef],attr,getattr(ncin.variables[varin],attr))
if copyvalues == True:
if ncin.variables[varin].dimensions != tuple():
pcd2.pcd(lambda x: x,[],[{'file':ncin,'varname':varin}],[{'file':ncout,'varname':varoutdef}],appenddim=True)
# cdo selindexbox (Abort): Unsupported grid type!
# on the contrary, this functionality now works with pynacolada.pcd(): they are no restrictions on the grid type.
# example 1: crop the domain for the variable 'T'
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_sel.nc','w')
func = lambda x: x[20:90,30:100]
dnamsel = ['rlon','rlat']
evar='T'
print ('processing variable: ',evar)
datin = [{'file':fin,'varname':evar,}]
datout = [{'file':fout,'varname':evar},]
pcd.pcd(func,dnamsel,datin,datout,appenddim=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.
# example 2: crop the domain to all variables that involve the lat-lon grid, and just copy any other variable
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_sel2.nc','w')
func = lambda x: x[20:90,30:100]
dnamsel = [['rlon','srlon'],['rlat','srlat']]
for evar in fin.variables:
# apply the domain selection on all variables that involve these dimensions
procvar = True
def avgcycle(x,n):
xout = np.zeros([n+1]+list(np.shape(x)[1:]))
for i in range(n):
xout[i] = np.mean(x[i::n],axis=0)
xout[n] = xout[0]
return xout
dts = dtrange(dt.datetime.strptime('2009040100','%Y%m%d%H'),dt.datetime.strptime('2009050100','%Y%m%d%H'),dt.timedelta(hours=1))
for RUN in ['aq09ec_td','aq09_td','aq09ec','aq09']:
path = '/media/URB_AQ_1/data/belgium_aq/rcm/'+RUN+'/stage1/aurora/au3d/'
fins = [ path+'/au3d'+time.strftime('%Y%m%d%H')+'.nc' for time in dts]
fout = NetCDF.NetCDFFile('/media/URB_AQ_1/data/belgium_aq/rcm/'+RUN+'_au3d200904_diucycle.nc','w')
pcd.pcd(lambda x: avgcycle(x,24),('time',),\
[{'file': fins, 'varname': 'O3', 'predim': 'time'}],\
[{'file': fout, 'varname': 'O3'}],appenddim=True)
fout.close(); print('data written to: ',fout)
fin = NetCDF.NetCDFFile('/media/URB_AQ_1/data/belgium_aq/rcm/'+RUN+'_au3d200904_diucycle.nc','r')
fout = NetCDF.NetCDFFile('/media/URB_AQ_1/data/belgium_aq/rcm/'+RUN+'_au3d200904_diucycle_spatvar.nc','w')
pcd.pcd(lambda x: x - np.mean(x),('jy','kx'),\
[{'file': fin, 'varname': 'O3'}],\
[{'file': fout, 'varname': 'O3'}])
fout.close(); print('data written to: ',fout)
fina = NetCDF.NetCDFFile('/media/URB_AQ_1/data/belgium_aq/rcm/aq09ec_td_au3d200904_diucycle_spatvar.nc','r')
finb = NetCDF.NetCDFFile('/media/URB_AQ_1/data/belgium_aq/rcm/aq09_td_au3d200904_diucycle_spatvar.nc','r')
def avgglide(x,avlen):
xout = np.zeros_like(x)
xwork = np.zeros_like(x)
if np.mod(avlen,2) == 0:
xwork[0] = np.nan
xwork[:-1] = (x[1:] + x[:-1])/2.
else:
xwork = x
lenxout = xout.shape[0]
avlen2 = int(avlen/2.)
xout[:avlen2] = np.nan
xout[-avlen2:] = np.nan
for i in range(0,avlen,1):
xout[avlen2:(-avlen+avlen2)] = xout[avlen2:(-avlen+avlen2)] + xwork[i:(-avlen+i)]
return xout/avlen
# now, apply weekly cycle to a netcdf file
ncin = NetCDF.NetCDFFile('input.nc','r')
ncglide = NetCDF.NetCDFFile('output_glide.nc','w')
# we can cycle through the netcdf variables that have the 'time'-dimension
for evar in ncin.variables:
if ((evar not in ncin.dimensions) & ('time' in ncin.variables[evar].dimensions):
pcd.pcd(lambda x: avgglide(x,168),\
('time',),\
[{'file' : ncin, 'varname': evar}],\
[{'file' : ncglide, 'varname': evar}])
ncglide.close(); print('data written to:', ncglide)
ncin.close()
