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: 

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 freshlyallocated 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 keywordonly arguments, see the ufunc docs. 
Returns: 

out : ndarray or scalar 
The spacing of values of x . This is a scalar if x is a 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