# check variable list
for var in varlist:
if not wrfout.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(mean, wrfout, varlist=varlist, namemap=varmap, dimmap=dimmap, copy_data=False)
copy_vars(clim, wrfout, varlist=varlist, namemap=varmap, dimmap=dimmap, copy_data=False)
# length of time, x, and y dimensions
nvar = len(varlist)
nx = len(wrfout.dimensions[dimmap["x"]])
ny = len(wrfout.dimensions[dimmap["y"]])
nfiles = len(filelist) # number of files
# close sample input file
wrfout.close()
## compute monthly means and climatology
# allocate arrays
print("\n Computing monthly means from %s to %s (incl);" % (begindate, enddate))
print("%3i fields of shape (%i,%i):\n" % (nvar, nx, ny))
for var in varlist:
print(" %s (%s)" % (var, varmap.get(var, var)))
assert (ny, nx) == mean.variables[var].shape[
1:
], "\nWARNING: variable %s does not conform to assumed shape (%i,%i)!\n" % (var, nx, ny)
# monthly means
meandata = dict()
climdata = dict()
for var in varlist:
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'])
nlat = len(cesmout.dimensions['lat'])
nfiles = len(filelist) # number of files
# close sample input file
cesmout.close()
# monthly means
climdata = dict()
for var in varlist:
climdata[var] = zeros((nmons,nlat,nlon))
xtime = zeros((nfiles,)) # number of month
xmon = zeros((nmons,)) # counter for number of contributions
# loop over input files
print('\n Starting computation: %i iterations (files)\n'%nfiles)
for n in xrange(nfiles):
cesmout = Dataset(srcdir+filelist[n], 'r', format='NETCDF4')
print(' processing file #%3i of %3i:'%(n+1,nfiles))
print(' %s\n'%filelist[n])
# compute monthly averages
m = int(datergx.search(filelist[n]).group()[-2:])-1 # infer month from filename (for climatology)
copy_ncatts(outdata,indata['rain'],prefix='GPCC_')
# create old lat/lon dimensions and coordinate variables
copy_dims(outdata, indata['rain'], dimlist=dimlist.keys(), namemap=dimlist, copy_coords=True)
# create climatology variables
dims = ('time','lat','lon'); fill_value = -9999
# precipitation
atts = dict(long_name='Precipitation', units='mm/day')
add_var(outdata, 'rain', dims, values=climdata['rain'].filled(fill_value), atts=atts, fill_value=fill_value)
# station density
atts = dict(long_name='Station Density', units='#')
add_var(outdata, 'stns', dims, values=climdata['stns'].filled(fill_value), atts=atts, fill_value=fill_value)
# land mask
atts = dict(long_name='Land Mask', units='')
tmp = ma.masked_array(ma.ones((datashape[1],datashape[2])), mask=dataMask)
add_var(outdata, 'landmask', ('lat','lon'), values=tmp.filled(0))
# ## dataset feedback and diagnostics
# # print dataset meta data
# print('\n\n')
# print(outdata)
# # print dimensions meta data
# for dimobj in outdata.dimensions.values():
# print dimobj
# # print variable meta data
# for varobj in outdata.variables.values():
# print varobj
## close netcdf files
for ncset in indata.itervalues(): ncset.close() # input
outdata.close() # output
namemap=varmap,
dimmap=dimmap,
copy_data=False)
copy_vars(clim,
wrfout,
varlist=varlist,
namemap=varmap,
dimmap=dimmap,
copy_data=False)
# length of time, x, and y dimensions
nvar = len(varlist)
nx = len(wrfout.dimensions[dimmap['x']])
ny = len(wrfout.dimensions[dimmap['y']])
nfiles = len(filelist) # number of files
# close sample input file
wrfout.close()
## compute monthly means and climatology
# allocate arrays
print('\n Computing monthly means from %s to %s (incl);' %
(begindate, enddate))
print('%3i fields of shape (%i,%i):\n' % (nvar, nx, ny))
for var in varlist:
print(' %s (%s)' % (var, varmap.get(var, var)))
assert (ny,nx) == mean.variables[var].shape[1:], \
'\nWARNING: variable %s does not conform to assumed shape (%i,%i)!\n'%(var,nx,ny)
# monthly means
meandata = dict()
climdata = dict()
for var in varlist:
# print higrp
# dimensions
# for dimobj in outgrp.dimensions.values():
# print dimobj
# for dimobj in fngrp.dimensions.values():
# print dimobj
# for dimobj in higrp.dimensions.values():
# print dimobj
# variables
# for varobj in outgrp.variables.values():
# print varobj
# for varobj in fngrp.variables.values():
# print varobj
# for varobj in higrp.variables.values():
# print varobj
## close
# input
for ncset in fnstatset.itervalues():
ncset.close()
for ncset in fndynset.itervalues():
ncset.close()
for ncset in hidynset.itervalues():
ncset.close()
# output
# outgrp.close()
fngrp.close()
print(' %s' % (test + fnoutfile, ))
higrp.close()
print(' %s' % (test + hioutfile, ))
atts = dict(long_name='Maximum 2m Temperature', units='K')
add_var(outdata, 'Tmax', dims, values=climdata['Tmax'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m minimum Temperature
atts = dict(long_name='Minimum 2m Temperature', units='K')
add_var(outdata, 'Tmin', dims, values=climdata['Tmin'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m water vapor
atts = dict(long_name='Water Vapor Pressure at 2m', units='hPa')
add_var(outdata, 'Q2', dims, values=climdata['Q2'].filled(fill_value), atts=atts, fill_value=fill_value)
# land mask
atts = dict(long_name='Land Mask', units='')
tmp = ma.masked_array(ma.ones((datashape[1],datashape[2])), mask=dataMask)
add_var(outdata, 'landmask', ('lat','lon'), values=tmp.filled(0)) # create climatology variables
# for (key,value) in indata.iteritems():
# copy_vars(outdata, value, [key], namemap=varlist, copy_data=False, fill_value=) # , incl_=True
# outdata.variables[key][:,:,:] = climdata[key]
# ## dataset feedback and diagnostics
# # print dataset meta data
# print outdata
# # print dimensions meta data
# for dimobj in outdata.dimensions.values():
# print dimobj
# # print variable meta data
# for varobj in outdata.variables.values():
# print varobj
## close netcdf files
for ncset in indata.itervalues(): ncset.close() # input
outdata.close() # output
print(' %s'%(test+outfile,))
(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'])
nlat = len(cesmout.dimensions['lat'])
nfiles = len(filelist) # number of files
# close sample input file
cesmout.close()
# monthly means
climdata = dict()
for var in varlist:
climdata[var] = zeros((nmons, nlat, nlon))
xtime = zeros((nfiles, )) # number of month
xmon = zeros((nmons, )) # counter for number of contributions
# loop over input files
print('\n Starting computation: %i iterations (files)\n' % nfiles)
for n in xrange(nfiles):
cesmout = Dataset(srcdir + filelist[n], 'r', format='NETCDF4')
print(' processing file #%3i of %3i:' % (n + 1, nfiles))
print(' %s\n' % filelist[n])
# compute monthly averages
m = int(datergx.search(filelist[n]).group()
# print higrp
# dimensions
# for dimobj in outgrp.dimensions.values():
# print dimobj
# for dimobj in fngrp.dimensions.values():
# print dimobj
# for dimobj in higrp.dimensions.values():
# print dimobj
# variables
# for varobj in outgrp.variables.values():
# print varobj
# for varobj in fngrp.variables.values():
# print varobj
# for varobj in higrp.variables.values():
# print varobj
## close
# input
for ncset in fnstatset.itervalues():
ncset.close()
for ncset in fndynset.itervalues():
ncset.close()
for ncset in hidynset.itervalues():
ncset.close()
# output
# outgrp.close()
fngrp.close()
print(" %s" % (test + fnoutfile,))
higrp.close()
print(" %s" % (test + hioutfile,))
