Exemple #1
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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
Exemple #2
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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
Exemple #3
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## 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