[GRASS-user] multiprocessing in python

[Moritz Lennert]

On 06/02/18 12:09, Leonardo Hardtke wrote:
> Dear all,
> I am working on a module to extract the phenological parameters (like 
> timesat) from a time series implemented in python/cython and making use 
> of gscript and other grass stuff.
> It works great on a 256x256 and as the plan is applying it over 
> Australia at 250m over 17 years, I need to split the process in small 
> tiles. The idea is to run this processes in parallel and I am having 
> issues implementing it.
> 
> This would be the first part of the process that runs on each tile:
> 
> def tile_process(tile_index):
>      '''
>      Function for every worker:
>      Applies any function to the sub_region corresponding to the tile_index.
>      '''
>      global Rows
>      global Cols
>      global RowBlockSize
>      global ColBlockSize
>      global full_region
>      global dates
>      global years
>      global indices
>      global data_serie
>      global yr_limits_extra
>      global yr_limits
>      global dbif
> 
>      sub_name='block'
>      TileRow, TileCol, sr = 
> sub_region(tile_index,full_region,RowBlockSize,ColBlockSize)
>      # # Define a temporary region based on the parameters caluculated 
> with the
>      start_row = TileRow * RowBlockSize
>      start_col = TileCol * ColBlockSize
>      n_rows = sr['rows']
>      n_cols = sr['cols']
> 
>      strds = tgis.SpaceTimeRasterDataset(data_serie)
>      strds.select(dbif=dbif)
>      maps = strds.get_registered_maps_as_objects(dbif=dbif)
> 
>      # Numer of time steps
>      steps = len(maps)
>      # Make an empty array
>      #print(steps)
>      EVI = np.empty([steps,n_rows,n_cols])
>      # fill the array
>      for step, map in enumerate(maps):
>           map.select(dbif=dbif)
>           image_name = map.get_name()+'@'+data_serie.split('@')[1]
>           #print("reading: {}".format(image_name))
>           EVI[step,:] = 
> raster2numpy_sub(image_name,start_row,n_rows,start_col,n_cols)
>      mean = EVI.mean()
>      print(mean)
>      ....
>      ....
>      ....
> 
> 
> and this is how I start the multiprocess pool.
> 
>      pool.map(tile_process, xrange(RowBlockN*ColBlockN))
>      pool.close()
>      pool.join()
> 
> and it gives me:
> 
> AssertionError: can only test a child process
> 
> 
> of course if I do: tile_process(0) or tile_process(1) etc ,the right 
> result comes out.
> 
> Does any of you have experience with this? Any suggestion would be welcome!
> Sorry for the messy code. Is still in early stage.

Just a wild guess: have you tried with range (which returns a list) 
instead of xrange (which returns an xrange object) ?

Moritz