outdata = Dataset(GPCCroot+test+outfile, 'w', format='NETCDF4') # outgrp.createGroup('fineres')
# new time dimensions
months = ['January ', 'February ', 'March ', 'April ', 'May ', 'June ', #
'July ', 'August ', 'September', 'October ', 'November ', 'December ']
# create time dimensions and coordinate variables
add_coord(outdata,'time',np.arange(1,ntime+1),dtype='i4')
outdata.createDimension('tstrlen', 9) # name of month string
outdata.createVariable('ndays','i4',('time',))[:] = days_per_month
# names of months (as char array)
coord = outdata.createVariable('month','S1',('time','tstrlen'))
for m in xrange(ntime):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
outdata.description = 'Climatology of GPCC monthly precipitation, averaged from %04i to %04i'%(debyr,finyr)
outdata.creator = 'Andre R. Erler'
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))
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):
# 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,
begindate = datergx.search(filelist[0]).group()
enddate = datergx.search(filelist[-1]).group()
# 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
# names of months (as char array)
coord = grp.createVariable('month', 'S1', ('time', 'tstrlen'))
for m in xrange(ntime):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
if finmon: description = \
'Climatology of CFSR monthly means, averaged from January 1979 to %s %04i'%(months[finmon],1979+finyr-1)
else:
description = 'Climatology of CFSR monthly means, averaged from 1979 to %04i' % (
1979 + finyr - 1)
creator = 'Andre R. Erler'
# fine grid
fngrp.description = description
fngrp.creator = creator
copy_ncatts(fngrp, fndynset['rain'], prefix='CFSR_')
# for att in fndynset['prt'].ncattrs(): fngrp.setncattr('SRC_'+att,fndynset['prt'].getncattr(att))
higrp.description = description
higrp.creator = creator
copy_ncatts(fngrp, hidynset['pmsl'], prefix='CFSR_')
# create old lat/lon dimensions and coordinate variables
copy_dims(fngrp,
fndynset['rain'],
dimlist=fndim.keys(),
namemap=fndim,
copy_coords=True)
copy_dims(higrp,
hidynset['pmsl'],
dimlist=hidim.keys(),
namemap=hidim,
copy_coords=True)
outdata = Dataset(CRUroot+test+outfile, 'w', format='NETCDF4') # outgrp.createGroup('fineres')
# new time dimensions
months = ['January ', 'February ', 'March ', 'April ', 'May ', 'June ', #
'July ', 'August ', 'September', 'October ', 'November ', 'December ']
# create time dimensions and coordinate variables
add_coord(outdata,'time',np.arange(1,ntime+1),dtype='i4')
outdata.createDimension('tstrlen', 9) # name of month string
outdata.createVariable('ndays','i4',('time',))[:] = days_per_month
# names of months (as char array)
coord = outdata.createVariable('month','S1',('time','tstrlen'))
for m in xrange(ntime):
for n in xrange(9): coord[m,n] = months[m][n]
# global attributes
outdata.description = 'Climatology of CRU monthly climate data, averaged from %04i to %04i'%(debyr,finyr)
outdata.creator = 'Andre R. Erler'
copy_ncatts(outdata,indata['rain'],prefix='CRU_')
# 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)
# 2m mean Temperature
atts = dict(long_name='Temperature at 2m', units='K')
add_var(outdata, 'T2', dims, values=climdata['T2'].filled(fill_value), atts=atts, fill_value=fill_value)
# 2m maximum Temperature
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')
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
'December '
]
# create time dimensions and coordinate variables
add_coord(outdata, 'time', np.arange(1, ntime + 1), dtype='i4')
outdata.createDimension('tstrlen', 9) # name of month string
outdata.createVariable('ndays', 'i4', ('time', ))[:] = days_per_month
# names of months (as char array)
coord = outdata.createVariable('month', 'S1', ('time', 'tstrlen'))
for m in xrange(ntime):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
outdata.description = 'Climatology of GPCC monthly precipitation, averaged from %04i to %04i' % (
debyr, finyr)
outdata.creator = 'Andre R. Erler'
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),
for m in xrange(ntime):
for n in xrange(9):
coord[m, n] = months[m][n]
# global attributes
if finmon:
description = "Climatology of CFSR monthly means, averaged from January 1979 to %s %04i" % (
months[finmon],
1979 + finyr - 1,
)
else:
description = "Climatology of CFSR monthly means, averaged from 1979 to %04i" % (1979 + finyr - 1)
creator = "Andre R. Erler"
# fine grid
fngrp.description = description
fngrp.creator = creator
copy_ncatts(fngrp, fndynset["rain"], prefix="CFSR_")
# for att in fndynset['prt'].ncattrs(): fngrp.setncattr('SRC_'+att,fndynset['prt'].getncattr(att))
higrp.description = description
higrp.creator = creator
copy_ncatts(fngrp, hidynset["pmsl"], prefix="CFSR_")
# create old lat/lon dimensions and coordinate variables
copy_dims(fngrp, fndynset["rain"], dimlist=fndim.keys(), namemap=fndim, copy_coords=True)
copy_dims(higrp, hidynset["pmsl"], dimlist=hidim.keys(), namemap=hidim, copy_coords=True)
# copy static variables into new dataset
for (key, value) in fnstatset.iteritems():
copy_vars(fngrp, value, [key], namemap=fnstatvar, remove_dims=["time"], incl_=False) # '_' causes problems
# create dynamic/time-dependent variables
for (key, value) in fndynset.iteritems():
copy_vars(fngrp, value, [key], namemap=fndynvar, copy_data=False)
fngrp.variables[key][:, :, :] = fndynclim[key]
for (key, value) in hidynset.iteritems():
