content: #from dask.distributed import Client
  import xarray as xr
  import numpy as np
  import pandas as pd
  
  import importlib
  import ELMlib
  importlib.reload(ELMlib)
  
  #client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')
  #client
  
  #ds = xr.open_dataset('../Data/14C_spinup_holger_fire.2x2_small.nc')
  from netCDF4 import Dataset
  ds = Dataset('../Data/14C_spinup_holger_fire.2x2_small.nc')
  
  
  #lat, lon = ds.coords['lat'], ds.coords['lon']
  lat, lon = ds['lat'][:], ds['lon'][:]
  lat_indices, lon_indices = np.meshgrid(
      range(len(lat)),
      range(len(lon)),
      indexing='ij'
  )
  
  lats, lons = np.meshgrid(lat, lon, indexing='ij')
  df_pd = pd.DataFrame(
      {
          'cell_nr': range(len(lat)*len(lon)),
          'lat_index': lat_indices.flatten(),
          'lon_index': lon_indices.flatten(),
          'lat': lats.flatten(),
          'lon': lons.flatten()
      }
  )
  
  import dask.array as da
  
  import dask.dataframe as dask_df
  
  df_dask = dask_df.from_pandas(df_pd, npartitions=4)
  df_dask
  
  parameter_set = ELMlib.load_parameter_set(
      ds_filename = '../Data/14C_spinup_holger_fire.2x2_small.nc',
      time_shift  = -198*365,
      nstep       = 10
  )
  
  def func(line):
      location_dict = {
          'cell_nr':   int(line.cell_nr),
          'lat_index': int(line.lat_index),
          'lon_index': int(line.lon_index)    
      }
      
      cell_nr, log, xs_12C_data, us_12C_data, rs_12C_data= ELMlib.load_model_12C_data(parameter_set, location_dict)
      return cell_nr, log, xs_12C_data, us_12C_data, rs_12C_data
  
  df_dask_2 = df_dask.apply(func, axis=1, meta=('A', 'object'))
  
  df_dask_2.compute()
  type(df_dask_2)
  
  df_dask_2
  
  list(df_dask_2)
  
  pd.DataFrame(list(df_dask_2), columns=('cell_nr', 'log', 'xs_12C_data', 'us_12C_data', 'rs_12C_data'))
  
  
  