Hi, I have a function where an array of integers (1-d) is compared element-wise to an integer using the greater-than operator. I noticed that when the integer is 0 it takes about 75% more time than when it's 1 or 2. Is there an explanation?
Here is a stripped-down version which does (sort of)show what I say: def filter_array(array, f1, f2, flag=False): if flag: k = 1 else: k = 0 m1 = reduce(np.add, [(array['f1'] == i).astype(int) for i in f1]) > 0 m2 = reduce(np.add, [(array['f2'] == i).astype(int) for i in f2]) > 0 mask = reduce(np.add, (i.astype(int) for i in (m1, m2))) > k return array[mask] Now let's create an array with two fields: a = np.array(zip( np.random.random_integers(0,10,size=5000), np.random.random_integers(0,10,size=5000)), dtype=[('f1',int),('f2',int)]) Now call the function with flag=True and flag=False, and see what happens: In [29]: %timeit filter_array(a, (6,), (0,), flag=False) 1000 loops, best of 3: 536 us per loop In [30]: %timeit filter_array(a, (6,), (0,), flag=True) 1000 loops, best of 3: 245 us per loop In this example the difference seems to be 1:2. In my program is 1:4. I am at a loss about what causes this. Bye. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion