nlcpy.seterr

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

Sets how floating-point errors are handled.

Parameters

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

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

• ignore: Take no action when the exception occurs.

• warn: Print a RuntimeWarning.

• raise: Raise a FloatingPointError.

• print: Print a warning directly to stdout.

The default is not to change the current behavior.

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

Treatment for division by zero.

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

Treatment for floating-point overflow.

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

Treatment for floating-point underflow.

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

Treatment for invalid floating-point operation.

Returns

old_settingsdict

Dictionary containing the old settings.

See also

geterr

Gets the current way of handling floating-point errors.

errstate

Context manager for floating-point error handling.

Note

• The floating-point exceptions are defined in the IEEE 754 standard:

  • 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.

Restriction

• If the ‘call’ mode or the ‘log’ mode is specified for each parameter, KeyError occurs.

Examples

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