numpy.spacing¶

numpy.spacing(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'spacing'>¶

Return the distance between x and the nearest adjacent number.

Parameters:

Parameters:	x : array_like Values to find the spacing of. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see the ufunc docs.
Returns:	out : array_like The spacing of values of x1.

x : array_like

Values to find the spacing of.

out : ndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

where : array_like, optional

Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

**kwargs

For other keyword-only arguments, see the ufunc docs.

Returns:

out : array_like

The spacing of values of x1.

Notes

It can be considered as a generalization of EPS: spacing(np.float64(1)) == np.finfo(np.float64).eps, and there should not be any representable number between x + spacing(x) and x for any finite x.

Spacing of +- inf and NaN is NaN.

Examples

>>> np.spacing(1) == np.finfo(np.float64).eps
True