numpy.seterrobj¶

numpy.seterrobj(errobj)¶

Set the object that defines floating-point error handling.

The error object contains all information that defines the error handling behavior in Numpy. seterrobj is used internally by the other functions that set error handling behavior (seterr, seterrcall).

Parameters:

Parameters:	errobj : list The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function]. The error mask is a single integer that holds the treatment information on all four floating point errors. The information for each error type is contained in three bits of the integer. If we print it in base 8, we can see what treatment is set for “invalid”, “under”, “over”, and “divide” (in that order). The printed string can be interpreted with 0 : ‘ignore’ 1 : ‘warn’ 2 : ‘raise’ 3 : ‘call’ 4 : ‘print’ 5 : ‘log’

errobj : list

The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function].

The error mask is a single integer that holds the treatment information on all four floating point errors. The information for each error type is contained in three bits of the integer. If we print it in base 8, we can see what treatment is set for “invalid”, “under”, “over”, and “divide” (in that order). The printed string can be interpreted with

0 : ‘ignore’

1 : ‘warn’

2 : ‘raise’

3 : ‘call’

4 : ‘print’

5 : ‘log’

Notes

For complete documentation of the types of floating-point exceptions and treatment options, see seterr.

Examples

>>> old_errobj = np.geterrobj()  # first get the defaults
>>> old_errobj
[10000, 0, None]

>>> def err_handler(type, flag):
...     print "Floating point error (%s), with flag %s" % (type, flag)
...
>>> new_errobj = [20000, 12, err_handler]
>>> np.seterrobj(new_errobj)
>>> np.base_repr(12, 8)  # int for divide=4 ('print') and over=1 ('warn')
'14'
>>> np.geterr()
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
>>> np.geterrcall() is err_handler
True