numpy.geterrobj

numpy.geterrobj()

Return the current object that defines floating-point error handling.

The error object contains all information that defines the error handling behavior in NumPy. geterrobj is used internally by the other functions that get and set error handling behavior (geterr, seterr, geterrcall, seterrcall).

Returns:

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’

See also

seterrobj, seterr, geterr, seterrcall, geterrcall, getbufsize, setbufsize

Notes

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

Examples

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

>>> def err_handler(type, flag):
...     print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> old_bufsize = np.setbufsize(20000)
>>> old_err = np.seterr(divide='raise')
>>> old_handler = np.seterrcall(err_handler)
>>> np.geterrobj()
[20000, 2, <function err_handler at 0x91dcaac>]

>>> old_err = np.seterr(all='ignore')
>>> np.base_repr(np.geterrobj()[1], 8)
'0'
>>> old_err = np.seterr(divide='warn', over='log', under='call',
                        invalid='print')
>>> np.base_repr(np.geterrobj()[1], 8)
'4351'