Python DatasetNetCDF.load方法代碼示例

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import load [as 別名]
def loadObservations(name=None, folder=None, period=None, grid=None, station=None, shape=None, lencl=False, 
                     varlist=None, varatts=None, filepattern=None, filelist=None, resolution=None, 
                     projection=None, geotransform=None, axes=None, lautoregrid=None, mode='climatology'):
  ''' A function to load standardized observational datasets. '''
  # prepare input
  if mode.lower() == 'climatology': # post-processed climatology files
    # transform period
    if period is None or period == '':
      if name not in ('PCIC','PRISM','GPCC','NARR'): 
        raise ValueError("A period is required to load observational climatologies.")
    elif isinstance(period,basestring):
      period = tuple([int(prd) for prd in period.split('-')]) 
    elif not isinstance(period,(int,np.integer)) and ( not isinstance(period,tuple) and len(period) == 2 ): 
      raise TypeError(period)
  elif mode.lower() in ('time-series','timeseries'): # concatenated time-series files
    period = None # to indicate time-series (but for safety, the input must be more explicit)
    if lautoregrid is None: lautoregrid = False # this can take very long!
  # cast/copy varlist
  if isinstance(varlist,basestring): varlist = [varlist] # cast as list
  elif varlist is not None: varlist = list(varlist) # make copy to avoid interference
  # figure out station and shape options
  if station and shape: raise ArgumentError()
  elif station or shape: 
    if grid is not None: raise NotImplementedError('Currently observational station data can only be loaded from the native grid.')
    if lautoregrid: raise GDALError('Station data can not be regridded, since it is not map data.')
    lstation = bool(station); lshape = bool(shape)
    grid = station if lstation else shape
    # add station/shape parameters
    if varlist:
      params = stn_params if lstation else shp_params
      for param in params:
        if param not in varlist: varlist.append(param)    
  else:
    lstation = False; lshape = False
  # varlist (varlist = None means all variables)
  if varatts is None: varatts = default_varatts.copy()
  if varlist is not None: varlist = translateVarNames(varlist, varatts)
  # filelist
  if filelist is None: 
    filename = getFileName(name=name, resolution=resolution, period=period, grid=grid, filepattern=filepattern)
    # check existance
    filepath = '{:s}/{:s}'.format(folder,filename)
    if not os.path.exists(filepath):
      nativename = getFileName(name=name, resolution=resolution, period=period, grid=None, filepattern=filepattern)
      nativepath = '{:s}/{:s}'.format(folder,nativename)
      if os.path.exists(nativepath):
        if lautoregrid: 
          from processing.regrid import performRegridding # causes circular reference if imported earlier
          griddef = loadPickledGridDef(grid=grid, res=None, folder=grid_folder)
          dataargs = dict(period=period, resolution=resolution)
          performRegridding(name, 'climatology',griddef, dataargs) # default kwargs
        else: raise IOError("The dataset '{:s}' for the selected grid ('{:s}') is not available - use the regrid module to generate it.".format(filename,grid) )
      else: raise IOError("The dataset file '{:s}' does not exits!\n('{:s}')".format(filename,filepath))
  # load dataset
  dataset = DatasetNetCDF(name=name, folder=folder, filelist=[filename], varlist=varlist, varatts=varatts, 
                          axes=axes, multifile=False, ncformat='NETCDF4')
  # mask all shapes that are incomplete in dataset
  if shape and lencl and 'shp_encl' in dataset: 
    dataset.load() # need to load data before masking; is cheap for shape averages, anyway
    dataset.mask(mask='shp_encl', invert=True, skiplist=shp_params)
  # correct ordinal number of shape (should start at 1, not 0)
  if lshape:
    if dataset.hasAxis('shapes'): raise AxisError("Axis 'shapes' should be renamed to 'shape'!")
    if not dataset.hasAxis('shape'): 
      raise AxisError()
    if dataset.shape.coord[0] == 0: dataset.shape.coord += 1
# figure out grid
  if not lstation and not lshape:
    if grid is None or grid == name:
      dataset = addGDALtoDataset(dataset, projection=projection, geotransform=geotransform, gridfolder=grid_folder)
    elif isinstance(grid,basestring): # load from pickle file
  #     griddef = loadPickledGridDef(grid=grid, res=None, filename=None, folder=grid_folder)
      # add GDAL functionality to dataset 
      dataset = addGDALtoDataset(dataset, griddef=grid, gridfolder=grid_folder)
    else: raise TypeError(dataset)
    # N.B.: projection should be auto-detected, if geographic (lat/lon)
  return dataset