Python DatasetNetCDF.addVariable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 addVariable [as 别名]
def loadGPCC_LTM(
    name=dataset_name, varlist=None, resolution="025", varatts=ltmvaratts, filelist=None, folder=ltmfolder
):
    """ Get a properly formatted dataset the monthly accumulated GPCC precipitation climatology. """
    # prepare input
    if resolution not in ("025", "05", "10", "25"):
        raise DatasetError, "Selected resolution '%s' is not available!" % resolution
    # translate varlist
    if varlist is None:
        varlist = varatts.keys()
    if varlist and varatts:
        varlist = translateVarNames(varlist, varatts)
    # load variables separately
    if "p" in varlist:
        dataset = DatasetNetCDF(
            name=name,
            folder=folder,
            filelist=["normals_v2011_%s.nc" % resolution],
            varlist=["p"],
            varatts=varatts,
            ncformat="NETCDF4_CLASSIC",
        )
    if "s" in varlist:
        gauges = nc.Dataset(folder + "normals_gauges_v2011_%s.nc" % resolution, mode="r", format="NETCDF4_CLASSIC")
        stations = Variable(data=gauges.variables["p"][0, :, :], axes=(dataset.lat, dataset.lon), **varatts["s"])
        # consolidate dataset
        dataset.addVariable(stations, asNC=False, copy=True)
    dataset = addGDALtoDataset(dataset, projection=None, geotransform=None, gridfolder=grid_folder)
    # N.B.: projection should be auto-detected as geographic
    # return formatted dataset
    return dataset

# 需要导入模块: from geodata.netcdf import DatasetNetCDF [as 别名]
# 或者: from geodata.netcdf.DatasetNetCDF import addVariable [as 别名]
def loadCFSR_TS(name=dataset_name, grid=None, varlist=None, varatts=None, resolution='hires', 
                filelist=None, folder=None, lautoregrid=None):
  ''' Get a properly formatted CFSR 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:
      if resolution == 'hires' or resolution == '03' or resolution == '031': varlist = varlist_hires
      elif resolution == 'lowres' or resolution == '05': varlist = varlist_lowres     
    if varlist and varatts: varlist = translateVarNames(varlist, varatts)
    if filelist is None: # generate default filelist
      if resolution == 'hires' or resolution == '03' or resolution == '031': 
        files = [hiresfiles[var] for var in varlist if var in hiresfiles]
      elif resolution == 'lowres' or resolution == '05': 
        files = [lowresfiles[var] for var in varlist if var in lowresfiles]
    # load dataset
    dataset = DatasetNetCDF(name=name, folder=folder, filelist=files, varlist=varlist, varatts=varatts, 
                            check_override=['time'], multifile=False, ncformat='NETCDF4_CLASSIC')
    # load static data
    if filelist is None: # generate default filelist
      if resolution == 'hires' or resolution == '03' or resolution == '031': 
        files = [hiresstatic[var] for var in varlist if var in hiresstatic]
      elif resolution == 'lowres' or resolution == '05': 
        files = [lowresstatic[var] for var in varlist if var in lowresstatic]
      # load constants, if any (and with singleton time axis)
      if len(files) > 0:
        staticdata = DatasetNetCDF(name=name, folder=folder, filelist=files, varlist=varlist, varatts=varatts, 
                                   axes=dict(lon=dataset.lon, lat=dataset.lat), multifile=False, 
                                   check_override=['time'], ncformat='NETCDF4_CLASSIC')
        # N.B.: need to override the axes, so that the datasets are consistent
        if len(staticdata.variables) > 0:
          for var in staticdata.variables.values(): 
            if not dataset.hasVariable(var.name):
              var.squeeze() # remove time dimension
              dataset.addVariable(var, copy=False) # no need to copy... but we can't write to the netcdf file!
    # 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  
    dataset = addGDALtoDataset(dataset, projection=None, geotransform=None, gridfolder=grid_folder)
    # N.B.: projection should be auto-detected as geographic
  else:
    # load from neatly formatted and regridded time-series files
    if folder is None: folder = avgfolder
    grid, resolution = checkGridRes(grid, resolution)
    dataset = loadObservations(name=name, folder=folder, projection=None, resolution=resolution, grid=grid, 
                               period=None, varlist=varlist, varatts=varatts, filepattern=tsfile, 
                               filelist=filelist, lautoregrid=lautoregrid, mode='time-series')
  # return formatted dataset
  return dataset

# 需要导入模块: from geodata.netcdf import DatasetNetCDF [as 别名]
# 或者: from geodata.netcdf.DatasetNetCDF import addVariable [as 别名]
    print('   +++   processing   +++   ') 
    CPU.Climatology(period=period[1]-period[0], offset=offset, flush=False)
    # sync temporary storage with output
    CPU.sync(flush=False)   
    print('\n')

    # add landmask
    print '   ===   landmask   ===   '
    tmpatts = dict(name='landmask', units='', long_name='Landmask for Climatology Fields', 
              description='where this mask is non-zero, no data is available')
    # find a masked variable
    for var in sink.variables.itervalues():
      if var.masked and var.gdal: 
        mask = var.getMapMask(); break
    # add variable to dataset
    sink.addVariable(Variable(name='landmask', units='', axes=(sink.lat,sink.lon), 
                  data=mask, atts=tmpatts), asNC=True)
    sink.mask(sink.landmask)            
    # add names and length of months
    sink.axisAnnotation('name_of_month', name_of_month, 'time', 
                        atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
    #print '   ===   month   ===   '
    sink.addVariable(Variable(name='length_of_month', units='days', axes=(sink.time,), data=days_per_month,
                  atts=dict(name='length_of_month',units='days',long_name='Length of Month')), asNC=True)
    
    # close...
    sink.sync()
    sink.close()
    # print dataset
    print('')
    print(sink)