xorbits.numpy.spacing#

xorbits.numpy.spacing(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])[source]#

Return the distance between x and the nearest adjacent number.

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) – This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

  • **kwargs – For other keyword-only arguments, see the ufunc docs.

Returns

out – The spacing of values of x. This is a scalar if x is a scalar.

Return type

ndarray or scalar

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

This docstring was copied from numpy.