netsse.tools.misc_func.weighted_quantile
========================================

.. py:function:: netsse.tools.misc_func.weighted_quantile(x, q, w, a=0.5, b=0.5, axis=-1)

   Computes the `q`-th quantiles of weighted data.

   The quantiles are computed along one of the axes of the input array
   ``x``.

   :param x: Input data array for which the quantiles must be computed.
   :type x: array_like
   :param q: Sequence of quantiles to compute, which must be between 0 and 1.
   :type q: 1d-array of shape (Nq,)
   :param w: Array of weights. The arrays ``x`` and ``w`` must be broadcastable.
   :type w: array_like
   :param a: User-defined constant used in the computation of the quantiles.
             The default is 0.5 to minimize biases (Rogers, 2003).
   :type a: floats, default 0.5
   :param b: User-defined constant used in the computation of the quantiles.
             The default is 0.5 to minimize biases (Rogers, 2003).
   :type b: floats, default 0.5
   :param axis: Axis of ``x`` along which the ``q``-quantiles are computed.
   :type axis: integer, default -1

   :returns: **v** -- Values of the ``q``-quantiles computed along the specified ``axis``
             of the input array ``x`` with weights ``w``.
   :rtype: array_like

   .. seealso::

      :py:obj:`weighted_std`
          Computes the weighted standard deviation of some data.

   .. rubric:: References

   Rogers, J.W., 2003. Estimating the variance of percentiles using replicate
   weights, in: Proc. of the Joint Statistical Meetings, Survey Research
   Methods Section, American Statistical Association. pp. 3525–3532.
   URL: http://www.asasrms.org/Proceedings/y2003/Files/JSM2003-000742.pdf

   .. rubric:: Example

   >>> v = weighted_quantile(x, q, w, a=0.5, b=0.5, axis=-1)