numpy - python: getting around division by zero

Question

I have a big data set of floating point numbers. I iterate through them and evaluate np.log(x) for each of them. I get

RuntimeWarning: divide by zero encountered in log

I would like to get around this and return 0 if this error occurs.

I am thinking of defining a new function:

def safe_ln(x):
    #returns: ln(x) but replaces -inf with 0
    l = np.log(x)
    #if l = -inf:
    l = 0
    return l

Basically,I need a way of testing that the output is -inf but I don't know how to proceed. Thank you for your help!

Answer 1

You are using a np function, so I can safely guess that you are working on a numpy array? Then the most efficient way to do this is to use the where function instead of a for loop

myarray= np.random.randint(10,size=10)
result = np.where(myarray>0, np.log(myarray), 0)

otherwise you can simply use the log function and then patch the hole:

myarray= np.random.randint(10,size=10)
result = np.log(myarray)
result[result==-np.inf]=0

The np.log function return correctly -inf when used on a value of 0, so are you sure that you want to return a 0? if somewhere you have to revert to the original value, you are going to experience some problem, changing zeros into ones...