How numpy handles numerical exceptions

The default is to 'warn' for invalid, divide, and overflow and 'ignore' for underflow. But this can be changed, and it can be set individually for different kinds of exceptions. The different behaviors are:

• 'ignore' : Take no action when the exception occurs.
• 'warn' : Print a RuntimeWarning (via the Python warnings module).
• 'raise' : Raise a FloatingPointError.
• 'call' : Call a specified function.
• 'print' : Print a warning directly to stdout.
• 'log' : Record error in a Log object.

These behaviors can be set for all kinds of errors or specific ones:

• all : apply to all numeric exceptions
• invalid : when NaNs are generated
• divide : divide by zero (for integers as well!)
• overflow : floating point overflows
• underflow : floating point underflows

Note that integer divide-by-zero is handled by the same machinery.

The error handling mode can be configured numpy.errstate context manager.

Examples

>>> with np.errstate(all='warn'):
...     np.zeros(5, dtype=np.float32) / 0.0
<python-input-1>:2: RuntimeWarning: invalid value encountered in divide
array([nan, nan, nan, nan, nan], dtype=float32)

>>> with np.errstate(under='ignore'):
...     np.array([1.e-100])**10
array([0.])

>>> with np.errstate(invalid='raise'):
...     np.sqrt(np.array([-1.]))
...
Traceback (most recent call last):
  File "<python-input-1>", line 2, in <module>
    np.sqrt(np.array([-1.]))
    ~~~~~~~^^^^^^^^^^^^^^^^^
FloatingPointError: invalid value encountered in sqrt

>>> def errorhandler(errstr, errflag):
...     print("saw stupid error!")
>>> with np.errstate(call=errorhandler, all='call'):
...     np.zeros(5, dtype=np.int32) / 0
saw stupid error!
array([nan, nan, nan, nan, nan])

Setting and getting error handling