interp_utils
======================

.. py:module:: jaxns.internals.interp_utils

.. rubric:: :code:`jaxns.internals.interp_utils`


.. rubric:: Module Contents

.. py:function:: apply_interp(x, i0, alpha0, i1, alpha1, axis = 0)

   Apply interpolation alpha given axis.

   :param x: nd-array
   :param i0: [N] or scalar
   :param alpha0: [N] or scalar
   :param i1: [N] or scalar
   :param alpha1: [N] or scalar
   :param axis: axis to take along

   :returns: [N] or scalar interpolated along axis


.. py:function:: left_broadcast_multiply(x, y, axis = 0)

   Left broadcast multiply of two arrays.
   Equivalent to right-padding before multiply

   :param x: [..., a,b,c,...]
   :param y: [a, b]

   :returns: [..., a, b, c, ...]


.. py:function:: get_interp_indices_and_weights(x, xp, regular_grid = False)

   One-dimensional linear interpolation. Outside bounds is also linear from nearest two points.

   :param x: the x-coordinates at which to evaluate the interpolated values
   :param xp: the x-coordinates of the data points, must be increasing

   :returns: the interpolated values, same shape as `x`


.. py:class:: InterpolatedArray

   .. py:attribute:: x
      :type:  jax.Array


   .. py:attribute:: values
      :type:  jax.Array


   .. py:attribute:: axis
      :type:  int
      :value: 0



   .. py:attribute:: regular_grid
      :type:  bool
      :value: False



   .. py:method:: __post_init__()


   .. py:property:: shape


   .. py:method:: __call__(time)

      Interpolate at time based on input times.

      :param time: time to evaluate at.

      :returns: value at given time