User guide

NumPy User Guide, Release 1.9.0

4.2 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 function speciﬁed using the seterrcall function.

• ‘print’ : Print a warning directly to stdout.

• ‘log’ : Record error in a Log object speciﬁed by seterrcall.

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. These behaviors are set on a per-thread basis.

4.3 Examples

>>> oldsettings = np.seterr(all=’warn’)

>>> np.zeros(5,dtype=np.float32)/0.

invalid value encountered in divide

>>> j = np.seterr(under=’ignore’)

>>> np.array([1.e-100])

>>> j = np.seterr(invalid=’raise’)

>>> np.sqrt(np.array([-1.]))

FloatingPointError: invalid value encountered in sqrt

>>> def errorhandler(errstr, errflag):

... print "saw stupid error!"

>>> np.seterrcall(errorhandler)

>>> j = np.seterr(all=’call’)

>>> np.zeros(5, dtype=np.int32)/0

FloatingPointError: invalid value encountered in divide

saw stupid error!

>>> j = np.seterr(

oldsettings) # restore previous

... # error-handling settings

4.4 Interfacing to C

Only a survey of the choices. Little detail on how each works.

1. Bare metal, wrap your own C-code manually.

• Plusses:

48 Chapter 4. Miscellaneous