Python DatasetNetCDF.replaceAxis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 replaceAxis [as 別名]
def loadGPCC_TS(name=dataset_name, grid=None, varlist=None, resolution='25', varatts=None, filelist=None, 
                folder=None, lautoregrid=None):
  ''' Get a properly formatted dataset with the monthly GPCC time-series. '''
  if grid is None:
    # load from original time-series files 
    if folder is None: folder = orig_ts_folder
    # prepare input  
    if resolution not in ('05', '10', '25'): raise DatasetError, "Selected resolution '%s' is not available!"%resolution
    # translate varlist
    if varatts is None: varatts = tsvaratts.copy()
    if varlist is None: varlist = varatts.keys()
    if varlist and varatts: varlist = translateVarNames(varlist, varatts)
    if filelist is None: # generate default filelist
      filelist = []
      if 'p' in varlist: filelist.append(orig_ts_file.format('precip',resolution))
      if 's' in varlist: filelist.append(orig_ts_file.format('statio',resolution))
    # load dataset
    dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts, 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') + (1901-1979)*12 # 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 GDAL info
    dataset = addGDALtoDataset(dataset, projection=None, geotransform=None)
    # 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, period=None, lclim=False)
    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

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import replaceAxis [as 別名]
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

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import replaceAxis [as 別名]
def loadCRU_TS(name=dataset_name, grid=None, varlist=None, resolution=None, varatts=None, filelist=None, 
               folder=None, lautoregrid=None):
  ''' Get a properly formatted  CRU 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 = varatts.keys()
    if varlist and varatts: varlist = translateVarNames(varlist, varatts)
    # assemble filelist
    if filelist is None: # generate default filelist
      filelist = [orig_ts_file.format(var) for var in varlist if var not in nofile]
    # load dataset
    dataset = DatasetNetCDF(name=name, folder=folder, filelist=filelist, varlist=varlist, varatts=varatts, 
                            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') + (1901-1979)*12 # 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
    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

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import replaceAxis [as 別名]
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

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import replaceAxis [as 別名]

#.........這裏部分代碼省略.........
    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

## Dataset API

dataset_name = 'CMIP5' # dataset name