numpy.seterr#

numpy.seterr(all=None, divide=None, over=None, under=None, invalid=None)[source]#

Set how floating-point errors are handled.

Note that operations on integer scalar types (such as int16) are handled like floating point, and are affected by these settings.

Parameters

all{‘ignore’, ‘warn’, ‘raise’, ‘call’, ‘print’, ‘log’}, optional

Set treatment for all types of floating-point errors at once:

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.

The default is not to change the current behavior.

divide{‘ignore’, ‘warn’, ‘raise’, ‘call’, ‘print’, ‘log’}, optional

Treatment for division by zero.

over{‘ignore’, ‘warn’, ‘raise’, ‘call’, ‘print’, ‘log’}, optional

Treatment for floating-point overflow.

under{‘ignore’, ‘warn’, ‘raise’, ‘call’, ‘print’, ‘log’}, optional

Treatment for floating-point underflow.

invalid{‘ignore’, ‘warn’, ‘raise’, ‘call’, ‘print’, ‘log’}, optional

Treatment for invalid floating-point operation.

Returns

old_settingsdict: Dictionary containing the old settings.

See also

seterrcall: Set a callback function for the ‘call’ mode.
geterr, geterrcall, errstate

Notes

The floating-point exceptions are defined in the IEEE 754 standard [1]:

Division by zero: infinite result obtained from finite numbers.
Overflow: result too large to be expressed.
Underflow: result so close to zero that some precision was lost.
Invalid operation: result is not an expressible number, typically indicates that a NaN was produced.

1: https://en.wikipedia.org/wiki/IEEE_754

Examples

>>> old_settings = np.seterr(all='ignore')  #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings)  # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}

>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars

>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
30464

Floating point error handling

numpy.geterr