filelist = [wrfrgx.match(filename) for filename in os.listdir(folder)] # list folder and match
filelist = [match.group() for match in filelist if match is not None] # assemble valid file list
if len(filelist) == 0:
print("\nWARNING: no matching files found for domain %02i" % (ndom,))
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
wrfrgx = re.compile(wrffiles) # compile regular expression
filelist = [wrfrgx.match(filename) for filename in os.listdir(folder)] # list folder and match
filelist = [match.group() for match in filelist if match is not None] # assemble valid file list
if len(filelist) == 0:
print('\nWARNING: no matching files found for domain %02i'%(ndom,))
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'))
climdata[key] = sumtmp / (finyr-debyr+1)
# climdata[key] = ma.masked_less(sumtmp, 0) / (finyr-debyr+1)
## convert values to mm/day
days_per_month = np.array([31,28.25,31,30,31,30,31,31,30,31,30,31])
climdata['rain'] /= days_per_month.reshape((len(days_per_month),1,1)) # convert to mm/day
## initialize netcdf dataset structure
print('\nWriting data to disk: %s'%(test+outfile,))
# create groups for different resolution
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
break # skip and go to next domain
filelist.sort(
) # sort alphabetically, so that files are in sequence (temporally)
datergx = re.compile(
prdrgx
) # 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
filelist = [cesmrgx.match(filename) for filename in os.listdir(srcdir)] # list folder and match
filelist = [match.group() for match in filelist if match is not None] # assemble valid file list
if len(filelist) == 0:
print('\nWARNING: no matching files found for %s '%(cesmname,))
import sys
sys.exit(1) # exit if there is no match
filelist.sort() # sort alphabetically, so that files are in sequence (temporally)
datergx = re.compile(prdrgx) # 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
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)
'February ',
'March ',
'April ',
'May ',
'June ', #
'July ',
'August ',
'September',
'October ',
'November ',
'December '
]
days = array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
# create time dimensions and coordinate variables
for grp in [fngrp, higrp]:
add_coord(grp, 'time', arange(1, ntime + 1), dtype='i4')
grp.createDimension('tstrlen', 9) # name of month string
grp.createVariable('ndays', 'i4', ('time', ))[:] = days
# 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
## convert values if necessary
days_per_month = np.array([31,28.25,31,30,31,30,31,31,30,31,30,31])
climdata['rain'] /= days_per_month.reshape((len(days_per_month),1,1)) # convert to mm/day
climdata['Tmin'] += 273.15 # convert to Kelvin
climdata['T2'] += 273.15 # convert to Kelvin
climdata['Tmax'] += 273.15 # convert to Kelvin
## initialize netcdf dataset structure
print('\nWriting data to disk:')
# create groups for different resolution
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
import sys
sys.exit(1) # exit if there is no match
filelist.sort(
) # sort alphabetically, so that files are in sequence (temporally)
datergx = re.compile(
prdrgx) # 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
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
from netcdf import Dataset, add_coord, add_var
# settings
outfile = 'prism_clim.nc'
# prefix = 'test_' # development prefix
prefix = 'prismavg/' # production prefix
# initialize netcdf dataset structure
print('\nWriting data to disk: %s'%(prefix+outfile,))
# create groups for different resolution
outdata = Dataset(PRISMroot+prefix+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',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 monthly PRISM data'
outdata.creator = 'Andre R. Erler'
# copy_ncatts(outdata,indata['rain'],prefix='GPCC_')
# create new lat/lon dimensions and coordinate variables
add_coord(outdata, 'lat', values=lat, atts=None)
add_coord(outdata, 'lon', values=lon, atts=None)
# create climatology variables
"February ",
"March ",
"April ",
"May ",
"June ", #
"July ",
"August ",
"September",
"October ",
"November ",
"December ",
]
days = array([31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31])
# create time dimensions and coordinate variables
for grp in [fngrp, higrp]:
add_coord(grp, "time", arange(1, ntime + 1), dtype="i4")
grp.createDimension("tstrlen", 9) # name of month string
grp.createVariable("ndays", "i4", ("time",))[:] = days
# 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)