# numpy.seterr

numpy.seterr(all=None, divide=None, over=None, under=None, invalid=None)

  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_settings dict

      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 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