Set numpy array elements to zero if they are above a specific threshold

Say, I have a numpy array consists of 10 elements, for example:

a = np.array([2, 23, 15, 7, 9, 11, 17, 19, 5, 3])

Now I want to efficiently set all a values higher than 10 to 0, so I’ll get:

[2, 0, 0, 7, 9, 0, 0, 0, 5, 3]

Because I currently use a for loop, which is very slow:

# Zero values below "threshold value".
def flat_values(sig, tv):
    """
    :param sig: signal.
    :param tv: threshold value.
    :return:
    """
    for i in np.arange(np.size(sig)):
        if sig[i] < tv:
            sig[i] = 0
    return sig

How can I achieve that in the most efficient way, having in mind big arrays of, say, 10^6 elements?

Answer

Generally, list comprehensions are faster than for loops in python (because python knows that it doesn’t need to care for a lot of things that might happen in a regular for loop):

a = [0 if a_ > thresh else a_ for a_ in a]

but, as @unutbu correctly pointed out, numpy allows list indexing, and element-wise comparison giving you index lists, so:

super_threshold_indices = a > thresh
a[super_threshold_indices] = 0

would be even faster.

Generally, when applying methods on vectors of data, have a look at numpy.ufuncs, which often perform much better than python functions that you map using any native mechanism.