I'm trying to vectorize a simple function in numpy and getting inconsistent behavior. I expect my code to return 0 for values < 0.5 and the unchanged value otherwise. Strangely, different runs of the script from the command line yield varying results: sometimes it works correctly, and sometimes I get all 0's. It doesn't matter which of the three lines I use for the case when d <= T. It does seem to be correlated with whether the first value to be returned is 0. Any ideas? Thanks.

import numpy as np

def my_func(d, T=0.5):
    if d > T:   return d
    #if d <= T:  return 0
    else:  return 0
    #return 0

N = 4
A = np.random.uniform(size=N**2)
A.shape = (N,N)
print A
f = np.vectorize(my_func)
print f(A)

$ python x.py
[[ 0.86913815  0.96833127  0.54539153  0.46184594]
 [ 0.46550903  0.24645558  0.26988519  0.0959257 ]
 [ 0.73356391  0.69363161  0.57222389  0.98214089]
 [ 0.15789303  0.06803493  0.01601389  0.04735725]]
[[ 0.86913815  0.96833127  0.54539153  0.        ]
 [ 0.          0.          0.          0.        ]
 [ 0.73356391  0.69363161  0.57222389  0.98214089]
 [ 0.          0.          0.          0.        ]]
$ python x.py
[[ 0.37127366  0.77935622  0.74392301  0.92626644]
 [ 0.61639086  0.32584431  0.12345342  0.17392298]
 [ 0.03679475  0.00536863  0.60936931  0.12761859]
 [ 0.49091897  0.21261635  0.37063752  0.23578082]]
[[0 0 0 0]
 [0 0 0 0]
 [0 0 0 0]
 [0 0 0 0]]