Python Cdo.ensmean方法代码示例

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import ensmean [as 别名]
def main_improved(in_folder=""):
    print("Processing {}".format(in_folder))
    out_folder = in_folder + "_clim"

    if not os.path.isfile(out_folder):
        os.makedirs(out_folder)

    cdo_obj = Cdo()
    opath = os.path.join(out_folder, "mean_year.nc")
    inpaths = os.path.join(in_folder, "*")
    cdo_obj.ensmean(input=inpaths, output=opath, options="-f nc")

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import ensmean [as 别名]
def main(in_folder = "/skynet1_rech3/huziy/EXP_0.1deg/DFS4.3_interpolated",
         out_folder = "/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK/DFS4.3_clim"):

    create_links_to_forcing_files.create_links(expdir=out_folder, forcing_dir=in_folder)
    cdo_obj = Cdo()
    for vname in varnames:
        opath = os.path.join(out_folder, "{0}.nc".format(vname))
        inpaths = os.path.join(out_folder, "{0}_y*.nc".format(vname))
        cdo_obj.ensmean(input = inpaths, output = opath, options = "-f nc")

        print("processed: {0}".format(vname))

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import ensmean [as 别名]
def modelUncertaintyWorker(resource):
  """retuns the result
  
  :param resource: list of pathes to netCDF files
  """
  from cdo import Cdo
  cdo = Cdo()
  from flyingpigeon.utils import check_timestepps

  # check resource for consistency
  resource_qc =  check_timestepps(resource)  
  
  try: 
    # ensemble mean and magnitude
    nc_ensmean = cdo.ensmean(input = resource_qc, output = 'nc_ensmean.nc')    
    logger.info('ensmean calculations done')
  except Exception as e: 
    logger.error('ensmean calculations failed: %s ' % e )

  try:     
    #nc_delta = nc_ensmean(lastpt) - nc_ensmean(firstpt)    
    nc_laststep = cdo.seltimestep('-1', input = nc_ensmean, output = 'nc_laststep.nc')
    nc_firststep = cdo.seltimestep(1, input = nc_ensmean, output = 'nc_firststep.nc')
    nc_delta = cdo.sub(input = [nc_laststep, nc_firststep], output = 'nc_delta.nc')
    nc_absdelta = cdo.abs(input = nc_delta, output = 'nc_absdelta.nc')
    logger.info('delta calculation done')
  except Exception as e: 
    logger.error('delta calculation failed: %s ' % e )  

  try: 
    # ensemble std    
    nc_ensstd = cdo.ensstd(input = resource_qc, output = 'nc_ensstd.nc')
    logger.info('std calculation done')
  except Exception as e: 
    logger.error('std calculation failed: %s ' % e )

  try:
    # compute mask: if nc_absdelta > nc_enssstd
    nc_level = cdo.mulc(1, input = nc_ensstd, output = 'nc_level.nc')
    nc_binmask = cdo.gt(input = [nc_absdelta, nc_level], output = 'nc_binmask.nc')
    logger.info('calculated mask')
  except Exception as e: 
    logger.error('mask calculation failed: %s ' % e )

  
  #if > const
  #cdo.gtc('arg', input=infile, ontput=outfile)

  # mean + sigma as a mask 
  # sigma1 = cdo.fldstd(input = nc_ensmean, output = 'nc_sigma1.nc')
  # mask 
  
  # mean + 2* sigma as a mask
  
  # merge to on result netCDF
  # cdo.merge(input=[file1, file2], output='result.nc')

  result = nc_ensmean    #ensemble mean
  result2 = nc_ensstd    #ensemble std
  result3 = nc_absdelta  #magnitude of model change  
  result4 = nc_binmask   #absdelta > std
  
  return result, result2, result3, result4

# 需要导入模块: from cdo import Cdo [as 别名]
# 或者: from cdo.Cdo import ensmean [as 别名]

#.........这里部分代码省略.........

  # set the periodes: 
  try: 
    start = int(start)
    end = int(end)
    if timeslice == None: 
      timeslice = int((end - start) / 3)
      if timeslice == 0: 
        timeslice = 1
    else: 
      timeslice = int(timeslice)
    start1 = start
    start2 = start1 + timeslice - 1 
    end1 = end - timeslice + 1
    end2 = end
    logger.info('timeslice and periodes set')
  except Exception as e:
    msg = 'failed to set the periodes'
    logger.exception(msg)
    raise Exception(msg)

  try:
    files = []
    for i, mf in enumerate(mergefiles):
      files.append(cdo.selyear('{0}/{1}'.format(start1,end2), input=[mf.replace(' ','\ ')] , output='file_{0}_.nc'.format(i) )) #python version
    logger.info('timeseries selected from defined start to end year')
  except Exception as e:
    msg = 'seltime and mergetime failed'
    logger.exception(msg)
    raise Exception(msg)    

  try: 
    # ensemble mean 
    nc_ensmean = cdo.ensmean(input=files , output='nc_ensmean.nc')
    logger.info('ensemble mean calculation done')
  except Exception as e:
    msg = 'ensemble mean failed'
    logger.exception(msg)
    raise Exception(msg)
  
  try: 
    # ensemble std 
    nc_ensstd  = cdo.ensstd(input=files , output='nc_ensstd.nc')
    logger.info('ensemble std and calculation done')
  except Exception as e:
    msg = 'ensemble std or failed'
    logger.exception(msg)
    raise Exception(msg)
  
  # get the get the signal as difference from the beginning (first years) and end period (last years), :
  try:
    selyearstart = cdo.selyear('%s/%s' % (start1,start2), input = nc_ensmean, output = 'selyearstart.nc' ) 
    selyearend = cdo.selyear('%s/%s' % (end1,end2), input = nc_ensmean, output = 'selyearend.nc' )
    meanyearst = cdo.timmean(input = selyearstart, output= 'meanyearst.nc')
    meanyearend = cdo.timmean(input = selyearend, output= 'meanyearend.nc')
    signal = cdo.sub(input=[meanyearend, meanyearst], output = 'signal.nc')
    logger.info('Signal calculation done')
  except Exception as e:
    msg = 'calculation of signal failed'
    logger.exception(msg)
    raise Exception(msg)
  
  # get the intermodel standard deviation (mean over whole period)
  try:
    #std_selyear = cdo.selyear('%s/%s' % (end1,end2), input=nc_ensstd, output='std_selyear.nc')
    #std = cdo.timmean(input = std_selyear, output = 'std.nc')