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,:,:]
atts = dict(name='datamask', units='', long_name='Mask for Climatology Fields',
description='where this mask is non-zero, no data is available')
add_var(outdata, 'datamask', ['lat','lon'], data=mask, atts=atts)
# dataset feedback and diagnostics
# load data into memory
dataset.load()
# # add landmask
# addLandMask(dataset) # create landmask from precip mask
# dataset.mask(dataset.landmask) # mask all fields using the new landmask
# add length and names of month
addLengthAndNamesOfMonth(dataset, noleap=False)
# figure out a different filename
filename = getFileName(grid='NARR', period=None, name='NARR', filepattern=avgfile)
print('\n'+filename+'\n')
if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)
# write data and some annotation
ncset = writeNetCDF(dataset, avgfolder+filename, close=False)
add_strvar(ncset,'name_of_month', name_of_month, 'time', # add names of month
atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
# close...
ncset.close()
dataset.close()
# print dataset before
print(dataset)
print('')
# generate averaged climatology
elif mode == 'average_timeseries':
# load source
periodstr = '%4i-%4i'%period
print('\n')
'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),
