datergx = re.compile(wrfdate) # compile regular expression, also used to infer month (later)
begindate = datergx.search(filelist[0]).group()
enddate = datergx.search(filelist[-1]).group()
# load first file to copy some meta data
wrfout = Dataset(folder + filelist[0], "r", format="NETCDF4")
# create monthly mean output file
mean = Dataset(folder + meanfile % ndom, "w", format="NETCDF4")
add_coord(
mean, "time", values=None, dtype="i4", atts=dict(units="month since " + begindate)
) # unlimited time dimension
copy_dims(mean, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(mean, wrfout, prefix="WRF_") # copy all attributes and save with prefix WRF
mean.description = "WRF monthly means"
mean.begin_date = begindate
mean.end_date = enddate
mean.experiment = exp
mean.creator = "Andre R. Erler"
# create climatology output file
clim = Dataset(folder + climfile % ndom, "w", format="NETCDF4")
add_coord(clim, "time", values=mons, dtype="i4", atts=dict(units="month of the year")) # month of the year
copy_dims(clim, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension("tstrlen", size=9)
coord = clim.createVariable("month", "S1", ("time", "tstrlen"))
for m in xrange(nmons):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
break # skip and go to next domain
filelist.sort() # sort alphabetically, so that files are in sequence (temporally)
datergx = re.compile(wrfdate) # compile regular expression, also used to infer month (later)
begindate = datergx.search(filelist[0]).group()
enddate = datergx.search(filelist[-1]).group()
# load first file to copy some meta data
wrfout = Dataset(folder+filelist[0], 'r', format='NETCDF4')
# create monthly mean output file
mean = Dataset(folder+meanfile%ndom, 'w', format='NETCDF4')
add_coord(mean, 'time', values=None, dtype='i4', atts=dict(units='month since '+begindate)) # unlimited time dimension
copy_dims(mean, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(mean, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
mean.description = 'WRF monthly means'
mean.begin_date = begindate; mean.end_date = enddate
mean.experiment = exp
mean.creator = 'Andre R. Erler'
# create climatology output file
clim = Dataset(folder+climfile%ndom, 'w', format='NETCDF4')
add_coord(clim, 'time', values=mons, dtype='i4', atts=dict(units='month of the year')) # month of the year
copy_dims(clim, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month','S1',('time','tstrlen'))
for m in xrange(nmons):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
copy_ncatts(clim, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of WRF monthly means'
# load first file to copy some meta data
cesmout = Dataset(srcdir+filelist[0], 'r', format='NETCDF4')
# create climatology output file
clim = Dataset(dstdir+climfile, 'w', format='NETCDF4')
add_coord(clim, 'time', values=mons, dtype='i4', atts=dict(units='month of the year')) # month of the year
copy_dims(clim, cesmout, dimlist=dimlist, namemap=dimmap, copy_coords=True, dtype='f4') # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month','S1',('time','tstrlen'))
for m in xrange(nmons):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
copy_ncatts(clim, cesmout, prefix='CESM_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of CESM monthly means'
clim.begin_date = begindate; clim.end_date = enddate
clim.experiment = cesmname
clim.creator = 'Andre R. Erler'
# copy constant variables (no time dimension)
copy_vars(clim, cesmout, varlist=statlist, namemap=varmap, dimmap=dimmap, remove_dims=['time'], copy_data=True)
# check variable list
for var in varlist:
if not cesmout.variables.has_key(varmap.get(var,var)):
print('\nWARNING: variable %s not found in source file!\n'%(var,))
del var # remove variable if not present in soruce file
# copy variables to new datasets
copy_vars(clim, cesmout, varlist=varlist, namemap=varmap, dimmap=dimmap, copy_data=False)
# length of time, x, and y dimensions
nvar = len(varlist)
nlon = len(cesmout.dimensions['lon'])
'time',
values=None,
dtype='i4',
atts=dict(units='month since ' +
begindate)) # unlimited time dimension
copy_dims(mean,
wrfout,
dimlist=dimlist,
namemap=dimmap,
copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(
mean, wrfout,
prefix='WRF_') # copy all attributes and save with prefix WRF
mean.description = 'WRF monthly means'
mean.begin_date = begindate
mean.end_date = enddate
mean.experiment = exp
mean.creator = 'Andre R. Erler'
# create climatology output file
clim = Dataset(folder + climfile % ndom, 'w', format='NETCDF4')
add_coord(clim,
'time',
values=mons,
dtype='i4',
atts=dict(units='month of the year')) # month of the year
copy_dims(clim,
wrfout,
dimlist=dimlist,
namemap=dimmap,
break # skip and go to next domain
filelist.sort() # sort alphabetically, so that files are in sequence (temporally)
datergx = re.compile(wrfdate) # compile regular expression, also used to infer month (later)
begindate = datergx.search(filelist[0]).group()
enddate = datergx.search(filelist[-1]).group()
# load first file to copy some meta data
wrfout = Dataset(folder+filelist[0], 'r', format='NETCDF4')
# create monthly mean output file
mean = Dataset(folder+meanfile%ndom, 'w', format='NETCDF4')
add_coord(mean, 'time', values=None, dtype='i4', atts=dict(units='month since '+begindate)) # unlimited time dimension
copy_dims(mean, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# global attributes
copy_ncatts(mean, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
mean.description = 'WRF monthly means'
mean.begin_date = begindate; mean.end_date = enddate
mean.experiment = exp
mean.creator = 'Andre R. Erler'
# create climatology output file
clim = Dataset(folder+climfile%ndom, 'w', format='NETCDF4')
add_coord(clim, 'time', values=mons, dtype='i4', atts=dict(units='month of the year')) # month of the year
copy_dims(clim, wrfout, dimlist=dimlist, namemap=dimmap, copy_coords=False) # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month','S1',('time','tstrlen'))
for m in xrange(nmons):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
copy_ncatts(clim, wrfout, prefix='WRF_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of WRF monthly means'
cesmout,
dimlist=dimlist,
namemap=dimmap,
copy_coords=True,
dtype='f4') # don't have coordinate variables
# variable with proper names of the months
clim.createDimension('tstrlen', size=9)
coord = clim.createVariable('month', 'S1', ('time', 'tstrlen'))
for m in xrange(nmons):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
copy_ncatts(clim, cesmout,
prefix='CESM_') # copy all attributes and save with prefix WRF
clim.description = 'climatology of CESM monthly means'
clim.begin_date = begindate
clim.end_date = enddate
clim.experiment = cesmname
clim.creator = 'Andre R. Erler'
# copy constant variables (no time dimension)
copy_vars(clim,
cesmout,
varlist=statlist,
namemap=varmap,
dimmap=dimmap,
remove_dims=['time'],
copy_data=True)
# check variable list
for var in varlist:
if not cesmout.variables.has_key(varmap.get(var, var)):
