numpy: Efficiently avoid 0s when taking log(matrix)

Question

from numpy import *

m = array([[1,0],
           [2,3]])

I would like to compute the element-wise log2(m), but only in the places whe

Answer 1

Problem

Questions: Feb 2014, May 2012

For an array containing zeros or negatives we get the respective errors.

y = np.log(x)
# RuntimeWarning: divide by zero encountered in log
# RuntimeWarning: invalid value encountered in log

Solution

markroxor suggests np.clip, in my example this creates a horizontal floor. gg349 and others use np.errstate and np.seterr, I think these are clunky and does not solve the problem. As a note np.complex doesn't work for zeros. user3315095 uses indexing p=0, and NumPy.log has this functionality built in, where/out. mdeff demonstrates this, but replaces the -inf with 0 which for me was insufficient, and doesn't solve for negatives.

I suggest 0 and np.nan (or if needed np.NINF/-np.inf).

y = np.log(x, where=0

John Zwinck uses mask matrix np.ma.log this works but is computationally slower, try App:timeit.

Example

import numpy as np
x = np.linspace(-10, 10, 300)

# y = np.log(x)                         # Old
y = np.log(x, where=0

App:timeit

Time Comparison for mask and where

import numpy as np
import time
def timeit(fun, xs):
    t = time.time()
    for i in range(len(xs)):
        fun(xs[i])
    print(time.time() - t)

xs = np.random.randint(-10,+10, (1000,10000))
timeit(lambda x: np.ma.log(x).filled(np.nan), xs)
timeit(lambda x: np.log(x, where=0