sccala.interplib.interpolators
==============================

.. py:module:: sccala.interplib.interpolators


Classes
-------

.. autoapisummary::

   sccala.interplib.interpolators.Interpolator
   sccala.interplib.interpolators.LC_Interpolator
   sccala.interplib.interpolators.AKS_Interpolator
   sccala.interplib.interpolators.Vel_Interpolator
   sccala.interplib.interpolators.AE_Interpolator


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

.. py:class:: Interpolator

   .. py:method:: get_toe_rnd(tkde, toe_samples, toe=0.0)


   .. py:method:: sample_posterior(num_live_points=800)


   .. py:method:: log_likelihood(params)


   .. py:method:: reject_invalid(data_int, dint, no_reject)


   .. py:method:: check_sample_size(size, toe_samples)


   .. py:method:: predict_from_posterior(t_pred, tkde=None, toe=0.0, size=100, no_reject=False, toe_samples=25)


   .. py:method:: _print_predicting()


.. py:class:: LC_Interpolator(data, data_error, t_grid, num_live_points=800, disable_mean_fit=False, disable_white_noise_fit=False)

   Bases: :py:obj:`Interpolator`


   .. py:attribute:: data


   .. py:attribute:: data_error


   .. py:attribute:: t_grid


   .. py:attribute:: num_live_points
      :value: 800



   .. py:attribute:: disable_mean_fit
      :value: False



   .. py:attribute:: disable_white_noise_fit
      :value: False



   .. py:attribute:: parameters
      :value: []



   .. py:attribute:: gp


   .. py:method:: prior_transform(cube)


   .. py:method:: _print_predicting()


.. py:class:: AKS_Interpolator(data, t_grid, uncertainty=0.004, num_live_points=400, disable_mean_fit=False)

   Bases: :py:obj:`Interpolator`


   .. py:attribute:: data


   .. py:attribute:: t_grid


   .. py:attribute:: uncertainty
      :value: 0.004



   .. py:attribute:: num_live_points
      :value: 400



   .. py:attribute:: disable_mean_fit
      :value: False



   .. py:attribute:: parameters
      :value: []



   .. py:attribute:: gp


   .. py:method:: prior_transform(cube)


   .. py:method:: _print_predicting()


   .. py:method:: predict_from_posterior(t_pred, size=1000)


.. py:class:: Vel_Interpolator(data, data_error, t_grid, num_live_points=800, disable_mean_fit=True, disable_white_noise_fit=True)

   Bases: :py:obj:`Interpolator`


   .. py:attribute:: data


   .. py:attribute:: data_error


   .. py:attribute:: t_grid


   .. py:attribute:: num_live_points
      :value: 800



   .. py:attribute:: disable_mean_fit
      :value: True



   .. py:attribute:: disable_white_noise_fit
      :value: True



   .. py:attribute:: parameters
      :value: []



   .. py:attribute:: gp


   .. py:method:: prior_transform(cube)


   .. py:method:: reject_invalid(data_int, dint, no_reject)


   .. py:method:: _print_predicting()


.. py:class:: AE_Interpolator(data, data_error, t_grid, num_live_points=800, disable_mean_fit=True, disable_white_noise_fit=True)

   Bases: :py:obj:`Interpolator`


   .. py:attribute:: data


   .. py:attribute:: data_error


   .. py:attribute:: t_grid


   .. py:attribute:: num_live_points
      :value: 800



   .. py:attribute:: disable_mean_fit
      :value: True



   .. py:attribute:: disable_white_noise_fit
      :value: True



   .. py:attribute:: parameters
      :value: []



   .. py:attribute:: gp


   .. py:method:: reject_invalid(data_int, dint, no_reject)


   .. py:method:: prior_transform(cube)


   .. py:method:: _print_predicting()