How do I catch a numpy warning like it's an exception (not just for testing)?

It seems that your configuration is using the print option for numpy.seterr:

>>> import numpy as np
>>> np.array([1])/0   #'warn' mode
__main__:1: RuntimeWarning: divide by zero encountered in divide
array([0])
>>> np.seterr(all='print')
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn', 'under': 'ignore'}
>>> np.array([1])/0   #'print' mode
Warning: divide by zero encountered in divide
array([0])

This means that the warning you see is not a real warning, but it's just some characters printed to stdout(see the documentation for seterr). If you want to catch it you can:

1. Use 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.
2. Use 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:

>>> import warnings
>>> 
>>> warnings.filterwarnings('error')
>>> 
>>> try:
...     warnings.warn(Warning())
... except Warning:
...     print 'Warning was raised as an exception!'
... 
Warning was raised as an exception!

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 at catch_warnings which is a context manager which automatically resets the original filterwarnings function:

>>> import warnings
>>> with warnings.catch_warnings():
...     warnings.filterwarnings('error')
...     try:
...         warnings.warn(Warning())
...     except Warning: print 'Raised!'
... 
Raised!
>>> try:
...     warnings.warn(Warning())
... except Warning: print 'Not raised!'
... 
__main__:2: Warning: 

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 than numpy.seterr which treats all subsequent warnings of the same type the same regardless of where they occur within your code:

import numpy as np

a = np.r_[1.]
with np.errstate(divide='raise'):
    try:
        a / 0   # this gets caught and handled as an exception
    except FloatingPointError:
        print('oh no!')
a / 0           # this prints a RuntimeWarning as usual

---

Edit:

In my original example I had a = np.r_[0], but apparently there was a change in numpy's behaviour such that division-by-zero is handled differently in cases where the numerator is all-zeros. For example, in numpy 1.16.4:

all_zeros = np.array([0., 0.])
not_all_zeros = np.array([1., 0.])

with np.errstate(divide='raise'):
    not_all_zeros / 0.  # Raises FloatingPointError

with np.errstate(divide='raise'):
    all_zeros / 0.  # No exception raised

with np.errstate(invalid='raise'):
    all_zeros / 0.  # Raises FloatingPointError

The corresponding warning messages are also different: 1. / 0. is logged as RuntimeWarning: divide by zero encountered in true_divide, whereas 0. / 0. is logged as RuntimeWarning: invalid value encountered in true_divide. I'm not sure why exactly this change was made, but I suspect it has to do with the fact that the result of 0. / 0. is not representable as a number (numpy returns a NaN in this case) whereas 1. / 0. and -1. / 0. return +Inf and -Inf respectively, per the IEE 754 standard.

If you want to catch both types of error you can always pass np.errstate(divide='raise', invalid='raise'), or all='raise' if you want to raise an exception on any kind of floating point error.

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:

import warnings

with warnings.catch_warnings():
    warnings.filterwarnings('error')
    try:
        answer = 1 / 0
    except Warning as e:
        print('error found:', e)

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.