Python DatasetNetCDF.hasAxis方法代码示例

# 需要导入模块: from geodata.netcdf import DatasetNetCDF [as 别名]
# 或者: from geodata.netcdf.DatasetNetCDF import hasAxis [as 别名]
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

# 需要导入模块: from geodata.netcdf import DatasetNetCDF [as 别名]
# 或者: from geodata.netcdf.DatasetNetCDF import hasAxis [as 别名]

#.........这里部分代码省略.........
    elif ldiag: raise NotImplementedError
    else: raise DatasetError
    filenames.append(filename) # append to list (passed to DatasetNetCDF later)
    # check existance
    filepath = '{:s}/{:s}'.format(folder,filename)
    if not os.path.exists(filepath):
      nativename = fileformat.format('',periodstr) # original filename (before regridding)
      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(experiment=experiment, filetypes=[filetype], period=period)
          print("The '{:s}' (CESM) dataset for the grid ('{:s}') is not available:\n Attempting regridding on-the-fly.".format(name,filename,grid))
          if performRegridding('CESM','climatology' if lclim else 'time-series', griddef, dataargs): # default kwargs
            raise IOError, "Automatic regridding failed!"
          print("Output: '{:s}'".format(name,filename,grid,filepath))            
        else: raise IOError, "The '{:s}' (CESM) dataset '{:s}' for the selected grid ('{:s}') is not available - use the regrid module to generate it.".format(name,filename,grid) 
      else: raise IOError, "The '{:s}' (CESM) dataset file '{:s}' does not exits!\n({:s})".format(name,filename,folder)
   
  # load dataset
  #print varlist, filenames
  if experiment: title = experiment.title
  else: title = name
  dataset = DatasetNetCDF(name=name, folder=folder, filelist=filenames, varlist=varlist, axes=None, 
                          varatts=atts, title=title, multifile=False, ignore_list=ignore_list, 
                          ncformat='NETCDF4', squeeze=True, mode=ncmode, check_vars=check_vars)
  # replace time axis
  if lreplaceTime:
    if lts or lcvdp:
      # check time axis and center at 1979-01 (zero-based)
      if experiment is None: ys = period[0]; ms = 1
      else: ys,ms,ds = [int(t) for t in experiment.begindate.split('-')]; assert ds == 1
      if dataset.hasAxis('time'):
        ts = (ys-1979)*12 + (ms-1); te = ts+len(dataset.time) # month since 1979 (Jan 1979 = 0)
        atts = dict(long_name='Month since 1979-01')
        timeAxis = Axis(name='time', units='month', coord=np.arange(ts,te,1, dtype='int16'), atts=atts)
        dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
      if dataset.hasAxis('year'):
        ts = ys-1979; te = ts+len(dataset.year) # month since 1979 (Jan 1979 = 0)
        atts = dict(long_name='Years since 1979-01')
        yearAxis = Axis(name='year', units='year', coord=np.arange(ts,te,1, dtype='int16'), atts=atts)
        dataset.replaceAxis(dataset.year, yearAxis, asNC=False, deepcopy=False)
    elif lclim:
      if dataset.hasAxis('time') and not dataset.time.units.lower() in monthlyUnitsList:
        atts = dict(long_name='Month of the Year')
        timeAxis = Axis(name='time', units='month', coord=np.arange(1,13, dtype='int16'), atts=atts)
        assert len(dataset.time) == len(timeAxis), dataset.time
        dataset.replaceAxis(dataset.time, timeAxis, asNC=False, deepcopy=False)
      elif dataset.hasAxis('year'): raise NotImplementedError, dataset
  # rename SST
  if lSST: dataset['SST'] = dataset.Ts
  # correct ordinal number of shape (should start at 1, not 0)
  if lshape:
    # mask all shapes that are incomplete in dataset
    if lencl and 'shp_encl' in dataset: dataset.mask(mask='shp_encl', invert=True)   
    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
  # check
  if len(dataset) == 0: raise DatasetError, 'Dataset is empty - check source file or variable list!'
  # add projection, if applicable
  if not ( lstation or lshape ):
    dataset = addGDALtoDataset(dataset, griddef=griddef, gridfolder=grid_folder, lwrap360=True, geolocator=True)
  # return formatted dataset
  return dataset