outdata.creator = 'Andre R. Erler'
# create ncatts dictionary from varatts
ncatts = dict()
for var,atts in varatts.iteritems():
newatts = dict() # new atts dictionary
# and iterate over atts dict contents
for key1,val1 in atts.iteritems():
if isinstance(val1,dict): # flatten nested dicts
for key2,val2 in val1.iteritems():
newatts[key2] = val2
else: newatts[key1] = val1
ncatts[var] = newatts
# create time dimensions and coordinate variables
add_coord(outdata, 'time', data=np.arange(1,len(days_per_month)+1), dtype='i4', atts=ncatts['time'])
add_var(outdata, 'length_of_month', ['time'], data=days_per_month,
atts=dict(name='length_of_month',units='days',long_name='Length of Month'))
# names of months (as char array)
add_strvar(outdata, 'name_of_month', name_of_month, 'time',
atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# create new lat/lon dimensions and coordinate variables
add_coord(outdata, 'lat', data=lat, atts=ncatts['lon'])
add_coord(outdata, 'lon', data=lon, atts=ncatts['lat'])
# create climatology variables
fillValue = -9999; axes = ('time','lat','lon')
for name,field in data.iteritems():
add_var(outdata, name, axes, data=field.filled(fillValue), atts=ncatts[name], fillValue=fillValue)
# create land/sea/no-data mask
mask = ma.getmaskarray(data['precip'])[0,:,:]
def creatNetCDF(varname,
varatts=None,
ncatts=None,
data_folder=daily_folder,
fillValue=missing_value):
''' create a NetCDF-4 file for the given variable, create dimensions and variable, and allocate data;
also set options for chunking and compression in a sensible manner '''
if varatts is None: varatts = netcdf_varatts
if ncatts is None: ncatts = netcdf_settings
# create Dataset/file
filename = netcdf_filename.format(varname.lower())
filepath = data_folder + filename
ds = nc.Dataset(filepath, mode='w', format='NETCDF4', clobber=True)
# add coordinate variables
# time is the outer-most (record) dimension
axes = [] # build axes order (need to add in order
atts = varatts['time']
axes.append(atts['name'])
add_coord(
ds,
atts['name'],
data=None,
length=None,
atts=atts,
dtype=np.dtype('i4'),
zlib=True,
fillValue=fillValue,
) # daily
# also add a time-stamp variable
atts = varatts['time_stamp']
add_var(ds,
atts['name'],
dims=axes,
data=None,
shape=(None, ),
atts=atts,
dtype=np.dtype('S'),
zlib=True,
fillValue=None,
lusestr=True) # daily time-stamp
# latitude (intermediate/regular dimension)
atts = varatts['lat']
axes.append(atts['name'])
add_coord(
ds,
atts['name'],
data=SnoDAS_grid.ylat[:],
length=SnoDAS_grid.size[1],
atts=atts,
dtype=netcdf_dtype,
zlib=True,
fillValue=fillValue,
)
# longitude is the inner-most dimension (continuous)
atts = varatts['lon']
axes.append(atts['name'])
add_coord(
ds,
atts['name'],
data=SnoDAS_grid.xlon[:],
length=SnoDAS_grid.size[0],
atts=atts,
dtype=netcdf_dtype,
zlib=True,
fillValue=fillValue,
)
# create NC variable
atts = varatts[varname].copy()
if 'scalefactor' in atts: del atts['scalefactor']
add_var(ds,
atts['name'],
dims=axes,
data=None,
shape=(None, ) + snodas_shape2d,
atts=atts,
dtype=netcdf_dtype,
zlib=True,
fillValue=fillValue,
lusestr=True,
**ncatts)
# return dataset object
return ds
ncatts = dict()
for var, atts in varatts.iteritems():
newatts = dict() # new atts dictionary
# and iterate over atts dict contents
for key1, val1 in atts.iteritems():
if isinstance(val1, dict): # flatten nested dicts
for key2, val2 in val1.iteritems():
newatts[key2] = val2
else:
newatts[key1] = val1
ncatts[var] = newatts
# create time dimensions and coordinate variables
add_coord(outdata,
'time',
data=np.arange(1,
len(days_per_month) + 1),
dtype='i4',
atts=ncatts['time'])
add_var(outdata,
'length_of_month', ['time'],
data=days_per_month,
atts=dict(name='length_of_month',
units='days',
long_name='Length of Month'))
# names of months (as char array)
add_strvar(outdata,
'name_of_month',
name_of_month,
'time',
atts=dict(name='name_of_month',
units='',
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
if os.path.exists(dstdir+climfile):
print(' removing old climatology file \'%s\''%(dstdir+climfile,))
os.remove(dstdir+climfile)
clim = Dataset(dstdir+climfile, 'w', format='NETCDF4')
add_coord(clim, 'time', data=mons, length=len(mons), dtype='i4', atts=dict(units='month of the year')) # month of the year
if dimlist is None:
dimlist = [dim for dim in cesmout.dimensions if dim not in ignorelist]
copy_dims(clim, cesmout, dimlist=dimlist, namemap=dimmap, copy_coords=True, dtype='f4')
# 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'
) # 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
if os.path.exists(dstdir + climfile):
print(' removing old climatology file \'%s\'' % (dstdir + climfile, ))
os.remove(dstdir + climfile)
clim = Dataset(dstdir + climfile, 'w', format='NETCDF4')
add_coord(clim,
'time',
data=mons,
length=len(mons),
dtype='i4',
atts=dict(units='month of the year')) # month of the year
if dimlist is None:
dimlist = [dim for dim in cesmout.dimensions if dim not in ignorelist]
copy_dims(clim,
cesmout,
dimlist=dimlist,
namemap=dimmap,
copy_coords=True,
dtype='f4')
# 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):