Python DatasetNetCDF.axisAnnotation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 axisAnnotation [as 別名]

#.........這裏部分代碼省略.........
        assert os.path.exists(expfolder)
        filepath = expfolder+filename
        tmpfilepath = expfolder+tmpfilename
        lskip = False # else just go ahead
        if os.path.exists(filepath): 
          if not loverwrite: 
            age = datetime.fromtimestamp(os.path.getmtime(filepath))
            # if sink file is newer than source file, skip (do not recompute)
            if age > sourceage and os.path.getsize(filepath) > 1e6: lskip = True
            # N.B.: NetCDF files smaller than 1MB are usually incomplete header fragments from a previous crash
            #print sourceage, age
          if not lskip: os.remove(filepath) 
        
        # depending on last modification time of file or overwrite setting, start computation, or skip
        if lskip:        
          # print message
          skipmsg =  "\n{:s}   >>>   Skipping: file '{:s}' in dataset '{:s}' already exists and is newer than source file.".format(pidstr,filename,dataset_name)
          skipmsg += "\n{:s}   >>>   ('{:s}')\n".format(pidstr,filepath)
          logger.info(skipmsg)              
        else:
           
          ## begin actual computation
          beginmsg = "\n{:s}   <<<   Computing '{:s}' (d{:02d}) Climatology from {:s}".format(
                      pidstr,dataset_name,domain,periodstr)
          if griddef is None: beginmsg += "  >>>   \n" 
          else: beginmsg += " ('{:s}' grid)  >>>   \n".format(griddef.name)
          logger.info(beginmsg)
  
          ## actually load datasets
          if source is None:
            source = loadWRF_TS(experiment=experiment, filetypes=[filetype], domains=domain) # comes out as a tuple... 
          if not lparallel and ldebug: logger.info('\n'+str(source)+'\n')
  
          # prepare sink
          if os.path.exists(tmpfilepath): os.remove(tmpfilepath) # remove old temp files
          sink = DatasetNetCDF(name='WRF Climatology', folder=expfolder, filelist=[tmpfilename], atts=source.atts.copy(), mode='w')
          sink.atts.period = periodstr 
          
          # initialize processing
          if griddef is None: lregrid = False
          else: lregrid = True
          CPU = CentralProcessingUnit(source, sink, varlist=varlist, tmp=lregrid, feedback=ldebug) # no need for lat/lon
          
          # start processing climatology
          if shift != 0: 
            logger.info('{0:s}   (shifting climatology by {1:d} month, to start with January)   \n'.format(pidstr,shift))
          CPU.Climatology(period=period, offset=offset, shift=shift, flush=False)
          # N.B.: immediate flushing should not be necessary for climatologies, since they are much smaller!
          
          # reproject and resample (regrid) dataset
          if lregrid:
            CPU.Regrid(griddef=griddef, flush=True)
            logger.info('%s    ---   '+str(griddef.geotansform)+'   ---   \n'%(pidstr))              
          
          # sync temporary storage with output dataset (sink)
          CPU.sync(flush=True)
          
          # add Geopotential Height Variance
          if 'GHT_Var' in sink and 'Z_var' not in sink:
            data_array = ( sink['GHT_Var'].data_array - sink['Z'].data_array**2 )**0.5
            atts = dict(name='Z_var',units='m',long_name='Square Root of Geopotential Height Variance')
            sink += Variable(axes=sink['Z'].axes, data=data_array, atts=atts)
            
          # add (relative) Vorticity Variance
          if 'Vorticity_Var' in sink and 'zeta_var' not in sink:
            data_array = ( sink['Vorticity_Var'].data_array - sink['zeta'].data_array**2 )**0.5
            atts = dict(name='zeta_var',units='1/s',long_name='Square Root of Relative Vorticity Variance')
            sink += Variable(axes=sink['zeta'].axes, data=data_array, atts=atts)
            
          # 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'))        
          if not sink.hasVariable('length_of_month'):
            sink += 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'))
          
          # close... and write results to file
          sink.sync()
          sink.close()
          writemsg =  "\n{:s}   >>>   Writing to file '{:s}' in dataset {:s}".format(pidstr,filename,dataset_name)
          writemsg += "\n{:s}   >>>   ('{:s}')\n".format(pidstr,filepath)
          logger.info(writemsg)      
          # rename file to proper name
          if os.path.exists(filepath): os.remove(filepath) # remove old file
          os.rename(tmpfilepath,filepath) # this will overwrite the old file
          
          # print dataset
          if not lparallel and ldebug:
            logger.info('\n'+str(sink)+'\n')
          
          # clean up (not sure if this is necessary, but there seems to be a memory leak...   
          del sink, CPU; gc.collect() # get rid of these guys immediately
          
    # clean up and return
    if source is not None: source.unload(); del source
    # N.B.: source is only loaded once for all periods    

  # N.B.: garbage is collected in multi-processing wrapper as well
  # return
  return 0 # so far, there is no measure of success, hence, if there is no crash...

# 需要導入模塊: from geodata.netcdf import DatasetNetCDF [as 別名]
# 或者: from geodata.netcdf.DatasetNetCDF import axisAnnotation [as 別名]
    sink.atts.period = periodstr 
    
    # determine averaging interval
    offset = source.time.getIndex(period[0]-1979)/12 # origin of monthly time-series is at January 1979 
    # initialize processing
#     CPU = CentralProcessingUnit(source, sink, varlist=['precip', 'T2'], tmp=True) # no need for lat/lon
    CPU = CentralProcessingUnit(source, sink, varlist=None, tmp=True) # no need for lat/lon
    
    # start processing climatology
    CPU.Climatology(period=period[1]-period[0], offset=offset, flush=False)
    
    # sync temporary storage with output
    CPU.sync(flush=True)

#     # make new masks
#     sink.mask(sink.landmask, maskSelf=False, varlist=['snow','snowh','zs'], invert=True, merge=False)

    # 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 += VarNC(sink.dataset, 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'))
    
    # close...
    sink.sync()
    sink.close()
    # print dataset
    print('')
    print(sink)