def loadNARR_TS(name=dataset_name, grid=None, varlist=None, resolution=None, varatts=None, filelist=None,
folder=None, lautoregrid=None):
''' Get a properly formatted NARR dataset with monthly mean time-series. '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# translate varlist
if varatts is None: varatts = tsvaratts.copy()
if varlist is None: varlist = tsvarlist
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
if filelist is None: # generate default filelist
filelist = [orig_ts_file.format(special[var]) if var in special else orig_ts_file.format(var) for var in varlist
if var not in nofile and var in varatts]
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts,
atts=projdict, multifile=False, ncformat='NETCDF4_CLASSIC')
# replace time axis with number of month since Jan 1979
data = np.arange(0,len(dataset.time),1, dtype='int16') # month since 1979 (Jan 1979 = 0)
timeAxis = Axis(name='time', units='month', coord=data, atts=dict(long_name='Month since 1979-01'))
dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
# add projection
projection = getProjFromDict(projdict, name='{0:s} Coordinate System'.format(name))
dataset = addGDALtoDataset(dataset, projection=projection, geotransform=None, gridfolder=grid_folder)
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
dataset = loadObservations(name=name, folder=folder, projection=None, resolution=None, grid=grid,
period=None, varlist=varlist, varatts=varatts, filepattern=tsfile,
filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
# return formatted dataset
return dataset
def loadNARR_LTM(name=dataset_name, varlist=None, grid=None, interval='monthly', varatts=None, filelist=None, folder=ltmfolder):
''' Get a properly formatted dataset of daily or monthly NARR climatologies (LTM). '''
if grid is None:
# load from original time-series files
if folder is None: folder = orig_ts_folder
# prepare input
if varatts is None: varatts = ltmvaratts.copy()
if varlist is None: varlist = ltmvarlist
if interval == 'monthly':
pfx = '.mon.ltm.nc'; tlen = 12
elif interval == 'daily':
pfx = '.day.ltm.nc'; tlen = 365
else: raise DatasetError, "Selected interval '%s' is not supported!"%interval
# translate varlist
if varlist and varatts: varlist = translateVarNames(varlist, varatts)
# axes dictionary, primarily to override time axis
axes = dict(time=Axis(name='time',units='day',coord=(1,tlen,tlen)),load=True)
if filelist is None: # generate default filelist
filelist = [special[var]+pfx if var in special else var+pfx for var in varlist if var not in nofile]
# load dataset
dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts,
axes=axes, atts=projdict, multifile=False, ncformat='NETCDF4_CLASSIC')
# add projection
projection = getProjFromDict(projdict, name='{0:s} Coordinate System'.format(name))
dataset = addGDALtoDataset(dataset, projection=projection, geotransform=None, folder=grid_folder)
else:
# load from neatly formatted and regridded time-series files
if folder is None: folder = avgfolder
raise NotImplementedError, "Need to implement loading neatly formatted and regridded time-series!"
# return formatted dataset
return dataset
## NARR Meta-data
dataset_name = 'NARR'
root_folder = getRootFolder(dataset_name=dataset_name) # get dataset root folder based on environment variables
# NARR projection
projdict = dict(proj = 'lcc', # Lambert Conformal Conic
lat_1 = 50., # Latitude of first standard parallel
lat_2 = 50., # Latitude of second standard parallel
lat_0 = 50., # Latitude of natural origin
lon_0 = -107.) # Longitude of natural origin
#
# x_0 = 5632642.22547, # False Origin Easting
# y_0 = 4612545.65137) # False Origin Northing
# NARR grid definition
projection = getProjFromDict(projdict)
geotransform = (-5648873.5, 32463.0, 0.0, -4628776.5, 0.0, 32463.0)
size = (349, 277) # (x,y) map size of NARR grid
# make GridDefinition instance
NARR_grid = GridDefinition(name=dataset_name, projection=projdict, geotransform=geotransform, size=size)
# variable attributes and name
varatts = dict(air = dict(name='T2', units='K'), # 2m Temperature
prate = dict(name='precip', units='kg/m^2/s'), # total precipitation rate (kg/m^2/s)
# LTM-only variables (currently...)
prmsl = dict(name='pmsl', units='Pa'), # sea-level pressure
pevap = dict(name='pet', units='kg/m^2'), # monthly accumulated PET (kg/m^2)
pr_wtr = dict(name='pwtr', units='kg/m^2'), # total precipitable water (kg/m^2)
# axes (don't have their own file; listed in axes)
lon = dict(name='lon2D', units='deg E'), # geographic longitude field
lat = dict(name='lat2D', units='deg N'), # geographic latitude field