eztaox.kernels.direct
=====================

.. py:module:: eztaox.kernels.direct

.. autoapi-nested-parse::

   Kernels evaluated using a direct apporach.

   The likelihood computation follows O(N^3) scaling.



Classes
-------

.. autoapisummary::

   eztaox.kernels.direct.MultibandLowRank
   eztaox.kernels.direct.MultibandFullRank


Module Contents
---------------

.. py:class:: MultibandLowRank

   Bases: :py:obj:`tinygp.kernels.Kernel`


   A multiband kernel implementating a low-rank Kronecker covariance structure.

   The specific form of the cross-band Kronecker covariance matrix is
   given by Equation 13 of `Gordon et al. (2020)
   <https://arxiv.org/pdf/2007.05799>`_. The implementation is inspired
   by
   `this tinygp tutorial <https://tinygp.readthedocs.io/en/stable/tutorials/quasisep-custom.html#multivariate-quasiseparable-kernels>`_.


   .. py:method:: coord_to_sortable(X) -> tinygp.helpers.JAXArray

      Extract the time-sortable component of the coordinates.



   .. py:method:: evaluate(X1, X2) -> tinygp.helpers.JAXArray

      Evaluate the kernel at a pair of input coordinates.



.. py:class:: MultibandFullRank(kernel, diagonal, off_diagonal)

   Bases: :py:obj:`tinygp.kernels.Kernel`


   A multiband kernel implementating the full-rank Kronecker covariance structure.

   The specific form of the cross-band Kronecker covariance matrix is given by
   Equation 18-20 of `Gordon et al. (2020) <https://arxiv.org/pdf/2007.05799>`_.
   The implementation is inspired by `this tinygp tutorial <https://tinygp.readthedocs.io/en/stable/tutorials/quasisep-custom.html#multivariate-quasiseparable-kernels>`_.


   .. note::
       This kernel is still in development, please use with caution.


   .. py:method:: coord_to_sortable(X) -> tinygp.helpers.JAXArray

      Extract the time-sortable component of the coordinates.



   .. py:method:: evaluate(X1, X2) -> tinygp.helpers.JAXArray

      Evaluate the kernel at a pair of input coordinates.