clophfit.fitting.error_models
=============================

.. py:module:: clophfit.fitting.error_models

.. autoapi-nested-parse::

   Error models for fitting.



Classes
-------

.. autoapisummary::

   clophfit.fitting.error_models.ErrorModel
   clophfit.fitting.error_models.ConstantErrorModel
   clophfit.fitting.error_models.ProportionalErrorModel
   clophfit.fitting.error_models.ComprehensiveErrorModel


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

.. py:class:: ErrorModel

   Bases: :py:obj:`Protocol`


   Protocol for error models.


   .. py:method:: compute_variance(signal, label = '')

      Compute variance given a signal estimate.



.. py:class:: ConstantErrorModel(sigma_read)

   Homoscedastic error.

   :param sigma_read: Constant error/variance floor.
   :type sigma_read: float | ArrayF | Mapping[int | str, float | ArrayF]


   .. py:method:: compute_variance(signal, label = '')

      Compute variance.



.. py:class:: ProportionalErrorModel(sigma_read, rel_error)

   Simple Heteroscedastic: var = sigma_read^2 + (rel_error * signal)^2.

   Read noise + proportional noise (ignoring Poisson scaling).

   :param sigma_read: Read noise floor.
   :type sigma_read: float | ArrayF | Mapping[int | str, float | ArrayF]
   :param rel_error: Proportional error coefficient.
   :type rel_error: float | Mapping[int | str, float]


   .. py:method:: compute_variance(signal, label = '')

      Compute variance.



.. py:class:: ComprehensiveErrorModel(sigma_read, gain, rel_error)

   Physical error: var = sigma_read^2 + gain * signal + (rel_error * signal)^2.

   Shot Noise + Proportional: Read noise + Poisson shot noise + Scintillation/Proportional noise.

   :param sigma_read: Read noise floor.
   :type sigma_read: float | ArrayF | Mapping[int | str, float | ArrayF]
   :param gain: Instrument gain linking signal to variance (Poisson term).
   :type gain: float | Mapping[int | str, float]
   :param rel_error: Proportional error coefficient.
   :type rel_error: float | Mapping[int | str, float]


   .. py:method:: compute_variance(signal, label = '')

      Compute variance.