How numpy handles numerical exceptions#

The default is to'warn' forinvalid,divide, andoverflowand'ignore' forunderflow. But this can be changed, and it can beset individually for different kinds of exceptions. The different behaviorsare:

• 'ignore' : Take no action when the exception occurs.

• 'warn' : Print aRuntimeWarning (via the Pythonwarnings module).

• 'raise' : Raise aFloatingPointError.

• 'call' : Call a specified function.

• 'print' : Print a warning directly tostdout.

• '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 configurednumpy.errstatecontext manager.

Examples#

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

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

>>>withnp.errstate(invalid='raise'):...np.sqrt(np.array([-1.]))...Traceback (most recent call last):  File"<python-input-1>", line2, in<module>np.sqrt(np.array([-1.]))~~~~~~~^^^^^^^^^^^^^^^^^FloatingPointError:invalid value encountered in sqrt

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

Setting and getting error handling#