RuntimeWarning: Divide by Zero error: How to avoid? PYTHON, NUMPY

Question

I am running in to RuntimeWarning: Invalid value encountered in divide

 import numpy
 a = numpy.random.rand((1000000, 100))
 b = numpy.random.rand((1,100))
 dots = numpy.dot(b,a.T)/numpy.dot(b,b)
 norms = numpy.linalg.norm(a, axis =1)
 angles = dots/norms ### Basically I am calculating angle between 2 vectors 

There are some vectors in my a which have norm as 0. so while calculating angles it is giving runtime warning.

Is there a one line pythonic way to compute angles while taking into account norms which are 0?

angles =[i/j if j!=0 else -2 for i,j in zip(dots, norms)] # takes 10.6 seconds

But it takes a lot of time. Since all angles will have values between 1 and -1 and I need only 10 max values this will help me. This takes around 10.6 seconds which is insane.

Answer 1

you can ignore warings with the np.errstate context manager and later replace nans with what you want:

import numpy as np
angle = np.arange(-5., 5.) 
norm = np.arange(10.)
with np.errstate(divide='ignore'):
    print np.where(norm != 0., angle / norm, -2)
# or:
with np.errstate(divide='ignore'):
    res = angle/norm
res[np.isnan(res)] = -2

Answer 2

In newer versions of numpy there is a third alternative option that avoids needing to use the errstate context manager.

All Numpy ufuncs accept an optional "where" argument. This acts slightly differently than the np.where function, in that it only evaluates the function "where" the mask is true. When the mask is False, it doesn't change the value, so using the "out" argument allows us to preallocate any default we want.

import numpy as np

angle = np.arange(-5., 5.)
norm = np.arange(10.)

# version 1
with np.errstate(divide='ignore'):
    res1 = np.where(norm != 0., angle / norm, -2)

# version 2
with np.errstate(divide='ignore'):
    res2 = angle/norm
res2[np.isinf(res2)] = -2

# version 3
res3 = -2. * np.ones(angle.shape)
np.divide(angle, norm, out=res3, where=norm != 0)

print(res1)
print(res2)
print(res3)

np.testing.assert_array_almost_equal(res1, res2)
np.testing.assert_array_almost_equal(res1, res3)