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.

Examples

>>> orig_settings = np.seterr(all='ignore')  # seterr to known value
>>> np.int16(32000) * np.int16(3)
30464
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> 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 scalar multiply

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