コード例 #1
0
ファイル: PRISM.py プロジェクト: xiefengy/GeoPy
 for var,atts in varatts.iteritems():
   newatts = dict() # new atts dictionary
   # and iterate over atts dict contents
   for key1,val1 in atts.iteritems():
     if isinstance(val1,dict): # flatten nested dicts
       for key2,val2 in val1.iteritems():
         newatts[key2] = val2 
     else: newatts[key1] = val1
   ncatts[var] = newatts      
 
 # create time dimensions and coordinate variables
 add_coord(outdata, 'time', data=np.arange(1,len(days_per_month)+1), dtype='i4', atts=ncatts['time'])
 add_var(outdata, 'length_of_month', ['time'], data=days_per_month, 
         atts=dict(name='length_of_month',units='days',long_name='Length of Month'))
 # names of months (as char array)
 add_strvar(outdata, 'name_of_month', name_of_month, 'time', 
            atts=dict(name='name_of_month', units='', long_name='Name of the Month'))
 
 # create new lat/lon dimensions and coordinate variables
 add_coord(outdata, 'lat', data=lat, atts=ncatts['lon'])
 add_coord(outdata, 'lon', data=lon, atts=ncatts['lat'])
 # create climatology variables  
 fillValue = -9999; axes = ('time','lat','lon')
 for name,field in data.iteritems():
   add_var(outdata, name, axes, data=field.filled(fillValue), atts=ncatts[name], fillValue=fillValue)
 # create land/sea/no-data mask
 mask = ma.getmaskarray(data['precip'])[0,:,:]
 atts = dict(name='datamask', units='', long_name='Mask for Climatology Fields', 
             description='where this mask is non-zero, no data is available')
 add_var(outdata, 'datamask', ['lat','lon'], data=mask, atts=atts)
 
 # dataset feedback and diagnostics
コード例 #2
0
ファイル: NARR.py プロジェクト: zhenkunl/GeoPy
      # load data into memory
      dataset.load()

#       # add landmask
#       addLandMask(dataset) # create landmask from precip mask
#       dataset.mask(dataset.landmask) # mask all fields using the new landmask      
      # add length and names of month
      addLengthAndNamesOfMonth(dataset, noleap=False) 
      
      # figure out a different filename
      filename = getFileName(grid='NARR', period=None, name='NARR', filepattern=avgfile)
      print('\n'+filename+'\n')      
      if os.path.exists(avgfolder+filename): os.remove(avgfolder+filename)      
      # write data and some annotation
      ncset = writeNetCDF(dataset, avgfolder+filename, close=False)
      add_strvar(ncset,'name_of_month', name_of_month, 'time', # add names of month
                 atts=dict(name='name_of_month', units='', long_name='Name of the Month')) 
       
      # close...
      ncset.close()
      dataset.close()
      # print dataset before
      print(dataset)
      print('')           
      
   
  # generate averaged climatology
  elif mode == 'average_timeseries':
    
    # load source
    periodstr = '%4i-%4i'%period
    print('\n')
コード例 #3
0
                  'time',
                  data=np.arange(1,
                                 len(days_per_month) + 1),
                  dtype='i4',
                  atts=ncatts['time'])
        add_var(outdata,
                'length_of_month', ['time'],
                data=days_per_month,
                atts=dict(name='length_of_month',
                          units='days',
                          long_name='Length of Month'))
        # names of months (as char array)
        add_strvar(outdata,
                   'name_of_month',
                   name_of_month,
                   'time',
                   atts=dict(name='name_of_month',
                             units='',
                             long_name='Name of the Month'))

        # create new lat/lon dimensions and coordinate variables
        add_coord(outdata, 'lat', data=lat, atts=ncatts['lon'])
        add_coord(outdata, 'lon', data=lon, atts=ncatts['lat'])
        # create climatology variables
        fillValue = -9999
        axes = ('time', 'lat', 'lon')
        for name, field in data.iteritems():
            add_var(outdata,
                    name,
                    axes,
                    data=field.filled(fillValue),