On 2009-10-08 15:14 PM, Felix wrote:
I am trying to create a shared, read-only numpy.ndarray between several processes. After some googling the basic idea is:sarr = mp.Array('i',1000) ndarr = scipy.frombuffer(sarr._obj,dtype='int32') Since it will be read only (after being filled once in a single process) I don't think I need any locking mechanism. However is this really true given garbage collection, reference counts and other implicit things going on? Or is there a recommended better way to do this?
I recommend using memory-mapped arrays for such a purpose. You will want to ask further numpy questions on the numpy mailing list: http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
