Slicing in PyTables

What is the fastest way to slice through arrays stored in h5 using PyTables?

The scenario is as follows:

• The data has already been saved (no need to optimize here):

  filters = tables.Filters(complib='blosc', complevel=5)
  h5file = tables.open_file(hd5_filename, mode='w',
                            title='My Data',
                            filters=filters)
  group = h5file.create_group(h5file.root, 'Data', 'Data')
  
  X_atom = tables.Float32Atom(shape=[50,50,50])
  X = h5file.create_carray(group, 'X', atom=X_atom, title='XData',
                       shape=(1000,), filters=filters)
  
        
  
          
          
          
        
  
      

• Data is opened:

  h5file = tables.openFile(hd5_filename, mode="r")
  node = h5file.getNode('/', data_node)
  X = getattr(node, X_str)
  
        
  
          
          
          
        
  
      

• This is where I need optimization, I need to do many of the following kinds of array slice, which cannot be sorted, for many indices and different min / max locations:

  for index, min_x, min_y, min_z, max_x, max_y, max_z in my_very_long_list:    
      current_item = X[index][min_x:max_x,min_y:max_y,min_z:max_z]
      do_something(current_item)
  
        
  
          
          
          
        
  
      

Question: Is this the fastest way to complete the task?