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# numpy.spacing

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

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