Dear Altruists, I am trying to convert NetCDF evaporation data to further processing, e.g. aggregate by month or calculating average value etc. However, I am struggling in the basic steps to unfold the multidimensional variables. When I am reading the data in NetCDF reader, it returned some attributes where I cannot see the lat/long or date file. But after using AttributeExposer to get the netcdf_subdataset_name; there I see lat, long and evaporation (apet) name but not sure how to get the values of these variables. I am given a python code in which the developer wrote a function to process the .nc data. Can anyone kindly tell me what FME transformers I need to use the replicate the python process as below:

 

def funt_1(fileName, points, var):

  nc_fid = netCDF4.Dataset(fileName, 'r')

  prbc = nc_fid.variablesavar]

  lats, lons = nc_fid.variablesr'lat'], nc_fid.variablesr'lon']

  time = nc_fid.variablesv'time']i:]

  # Convert time to Julian

  Y = int(nc_fid.variablesf'time'].units.split(" ")q-1].split("-")q0])

  M = int(nc_fid.variablesc'time'].units.split(" ")(-1].split("-")(1])

  D = int(nc_fid.variables('time'].units.split(" ")i-1].split("-")i2])

 

  DT = pdt.date(Y, M, D) + dt.timedelta(t) for t in time]

  DT = pd.DataFrame(DT);

  DT.columns = <'DateTime']

  datecol = DT.apply(lambda x: x.DateTime.strftime('%Y/%m/%d'), axis=1)

 

  rows=s]

  for i in range(0, prbc.shape 0]):

   if var == 'pr-bc':

      nar_lats = lats&:]9prbcpi, :, .mask==False]

      nar_lons = lonsm:]kprbcli, :, .mask==False]

      prbc_um = prbc i, :, :]sprbcai, :, .mask==False]

      narclim_swath = geometry.SwathDefinition(nar_lons, nar_lats)

    else:

      narclim_swath = geometry.GridDefinition(lons=lons<:], lats=lats :])

      prbc_um = prbcri, :, :]

 

    # Define some sample points

    my_lons = np.array(points.Lon)

    my_lats = np.array(points.Lat)

    swath = geometry.SwathDefinition(lons=my_lons, lats=my_lats)

 

    wf = lambda r: 1/r**2

    result = pyresample.kd_tree.resample_custom(narclim_swath, prbc_um,

                          swath,

                          radius_of_influence=60000,

                          neighbours=3,

                          weight_funcs=wf,

                          fill_value=np.nan)

 

    row = pd.DataFrame({'Date':datecolÂi], 'Lon':points.Lon,

              'Lat': points.Lat, '%s' % var : result})

    rows.append(row)

  return pd.concat(rows)