] # 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(
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
hidynfile = dict(pmsl=pmslfile)
hidynvar = dict(pmsl='PRMSL_L101')
# dimensions
#tdim = dict(time='time',tstrlen='tstrlen')
fndim = dict(lat='lat', lon='lon')
hidim = fndim
## start execution
if __name__ == '__main__':
## open input datasets
fnstatset = dict()
fndynset = dict()
hidynset = dict()
for (key, value) in fnstatfile.iteritems():
fnstatset[key] = Dataset(CFSRdata + value, 'r')
for (key, value) in fndynfile.iteritems():
fndynset[key] = Dataset(CFSRdata + value, 'r')
for (key, value) in hidynfile.iteritems():
hidynset[key] = Dataset(CFSRdata + value, 'r')
fnshape = fndynset['rain'].variables[
fndynvar['rain']].shape # (time, lat, lon)
hishape = hidynset['pmsl'].variables[
hidynvar['pmsl']].shape # (time, lat, lon)
## perform actual computation of climatologies
ntime = 12
if finyr:
fnmax = 12 * (finyr - 1) + finmon
himax = fnmax
else:
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)
# files (one per variable)
varlist = dict(rain='pre', T2='tmp', Tmin='tmn', Tmax='tmx', Q2='vap', PET='pet') # for now... there are more variables to come!
filelist = dict() # construct file list from variable list
for (key,value) in varlist.iteritems():
filelist[key] = 'cru_ts3.20.1901.2011.%s.dat.nc'%(value,)
# dimensions
dimlist = dict(lat='lat',lon='lon') # these dimensions will be copied - 'time' is different!
## start execution
if __name__ == '__main__':
## open input datasets
indata = dict()
# loop over all source files and open them
for (key,value) in filelist.iteritems():
indata[key] = Dataset(CRUdata+value, 'r')
indata[key].variables[varlist[key]].set_auto_maskandscale(False)
# get some meta data and create land mask
datashape = indata['rain'].variables[varlist['rain']].shape # (time, lat, lon)
missing_value = indata['rain'].variables[varlist['rain']].getncattr('_FillValue')
dataMask = ( indata['rain'].variables[varlist['rain']][1,:,:] == missing_value )
# # print some meta data
# print indata['rain'].variables[varlist['rain']]
# print indata['rain'].dimensions[dimlist['lat']]
# print indata.variables.keys()
# print indata.dimensions.keys()
## perform actual computation of climatologies
ntime = 12 # 12 month per year...
debmon = max((debyr-1901)*ntime,0) # time begins in 1901
finmon = min((finyr-1901)*ntime,datashape[0]) # time ends in 2010
# pyl.colorbar()
# pyl.show(block=True)
## create NetCDF file
# import netCDF4-python and added functionality
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'
