[Numpy-discussion] Catching and dealing with floating point errors

[Skipper Seabold]

I am doing some optimizations on random samples.  In a small number of
cases, the objective is not well-defined for a given sample (it's not
possible to tell beforehand and hopefully won't happen much in
practice).  What is the most numpythonic way to handle this?  It
doesn't look like I can use np.seterrcall in this case (without
ignoring its actual intent).  Here's a toy example of the method I
have come up with.

import numpy as np

def reset_seterr(d):
    """
    Helper function to reset FP error-handling to user's original settings
    """
    for action in [i+'='+"'"+d[i]+"'" for i in d]:
        exec(action)
    np.seterr(over=over, divide=divide, invalid=invalid, under=under)

def log_random_sample(X):
    """
    Toy example to catch a FP error, re-sample, and return objective
    """
    d = np.seterr() # get original values to reset
    np.seterr('raise') # set to raise on fp error in order to catch
    try:
        ret = np.log(X)
        reset_seterr(d)
        return ret
    except:
        lb,ub = -1,1  # includes bad domain to test recursion
        X = np.random.uniform(lb,ub)
        reset_seterr(d)
        return log_random_sample(X)

lb,ub = 0,0
orig_setting = np.seterr()
X = np.random.uniform(lb,ub)
log_random_sample(X)
assert(orig_setting == np.seterr())

This seems to work, but I'm not sure it's as transparent as it could
be.  If it is, then maybe it will be useful to others.

Skipper