seterrobj(errobj, /)

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 ).

Notes

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

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

Set the object that defines floating-point error handling.

See Also

getbufsize

geterr

geterrcall

geterrobj

setbufsize

seterr

seterrcall

Examples

This example is valid syntax, but we were not able to check execution

>>> old_errobj = np.geterrobj()  # first get the defaults
... old_errobj
[8192, 521, None]

This example is valid syntax, but we were not able to check execution

>>> def err_handler(type, flag):
...     print("Floating point error (%s), with flag %s" % (type, flag))
...

This example is valid syntax, but we were not able to check execution

>>> 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'

This example is valid syntax, but we were not able to check execution

>>> np.geterr()
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}

This example is valid syntax, but we were not able to check execution

>>> np.geterrcall() is err_handler
True

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.geterrobj numpy.seterrobj

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