test of shared memory array / numpy integration

test_sm.py

import multiprocessing
import numpy

#Demonstrates shared memory numpy arrays with no synchronization between processes

def increment(s_arr):
    #A function for a single process
    #increment every element in s_arr by 1.0
    #s_arr is a shared array from multiprocessing

    #Let's make s_arr into numpy array
    np_array=numpy.frombuffer(s_arr,numpy.double)

    for idx in xrange(len(np_array)):
        np_array[idx]+=1.0
    #Done

#test this thing

arr=numpy.zeros(10,numpy.double) # Numpy array (presumably we'll start with that)
s_arr=multiprocessing.RawArray('d',arr) # Make a shared memory array -> copy of arr; arr is useless after this
arr=None 

#Launch a bunch of processes, each will independently increment the array
processes=[]
for x in range(100):
    p=multiprocessing.Process(target=increment, args=(s_arr,)) # launch a process which will increment every value of s_arr
    processes.append(p) # remember it
    p.start() #...and run!

#wait for every process to end
for p in processes:
    p.join()

#If all went well, the array should have values near 100's in it now. Not quite hitting 100 because of the lack of synchronization
arr=numpy.frombuffer(s_arr,numpy.double) #make it back to numpy array so we can print easily
print arr