I have to make a Lagrange polynomial in Python for a project I'm doing. I'm doing a barycentric style one to avoid using an explicit for-loop as opposed to a Newton's divided difference style one. The problem I have is that I need to catch a division by zero, but Python (or maybe numpy) just makes it a warning instead of a normal exception.
So, what I need to know how to do is to catch this warning as if it were an exception. The related questions to this I found on this site were answered not in the way I needed. Here's my code:
import numpy as np
import matplotlib.pyplot as plt
import warnings
class Lagrange:
def __init__(self, xPts, yPts):
self.xPts = np.array(xPts)
self.yPts = np.array(yPts)
self.degree = len(xPts)-1
self.weights = np.array([np.product([x_j - x_i for x_j in xPts if x_j != x_i]) for x_i in xPts])
def __call__(self, x):
warnings.filterwarnings("error")
try:
bigNumerator = np.product(x - self.xPts)
numerators = np.array([bigNumerator/(x - x_j) for x_j in self.xPts])
return sum(numerators/self.weights*self.yPts)
except Exception, e: # Catch division by 0. Only possible in 'numerators' array
return yPts[np.where(xPts == x)[0][0]]
L = Lagrange([-1,0,1],[1,0,1]) # Creates quadratic poly L(x) = x^2
L(1) # This should catch an error, then return 1.
When this code is executed, the output I get is:
Warning: divide by zero encountered in int_scalars
That's the warning I want to catch. It should occur inside the list comprehension.
To add a little to @Bakuriu's answer:
If you already know where the warning is likely to occur then it's often cleaner to use the
numpy.errstate
context manager, rather thannumpy.seterr
which treats all subsequent warnings of the same type the same regardless of where they occur within your code:Remove warnings.filterwarnings and add:
It seems that your configuration is using the
print
option fornumpy.seterr
:This means that the warning you see is not a real warning, but it's just some characters printed to
stdout
(see the documentation forseterr
). If you want to catch it you can:numpy.seterr(all='raise')
which will directly raise the exception. This however changes the behaviour of all the operations, so it's a pretty big change in behaviour.numpy.seterr(all='warn')
, which will transform the printed warning in a real warning and you'll be able to use the above solution to localize this change in behaviour.Once you actually have a warning, you can use the
warnings
module to control how the warnings should be treated:Read carefully the documentation for
filterwarnings
since it allows you to filter only the warning you want and has other options. I'd also consider looking atcatch_warnings
which is a context manager which automatically resets the originalfilterwarnings
function:To elaborate on @Bakuriu's answer above, I've found that this enables me to catch a runtime warning in a similar fashion to how I would catch an error warning, printing out the warning nicely:
You will probably be able to play around with placing of the warnings.catch_warnings() placement depending on how big of an umbrella you want to cast with catching errors this way.