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

Question

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

User · Accepted Answer

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:

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.
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:

User · Answer

Remove warnings filterwarnings and add   numpy seterr all  raise

User · Answer

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

User · Answer

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

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

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