pyextal.LucyRichardson
======================

.. py:module:: pyextal.LucyRichardson


Functions
---------

.. autoapisummary::

   pyextal.LucyRichardson.sgnoise
   pyextal.LucyRichardson.DQE
   pyextal.LucyRichardson.chisq2d
   pyextal.LucyRichardson.convolved2d
   pyextal.LucyRichardson.lucy_Richardson


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

.. py:function:: sgnoise(img, m, g, A, delta, varB)

   Calculates the signal-dependent noise.

   :param img: The input image.
   :type img: np.ndarray
   :param m: Mixing factor.
   :type m: float
   :param g: Gain.
   :type g: float
   :param A: A parameter related to Fano noise and conversion gain.
   :type A: float
   :param delta: Squared percentage error in the gain image.
   :type delta: float
   :param varB: Variance of the background.
   :type varB: float

   :returns: The calculated signal-dependent noise.
   :rtype: np.ndarray


.. py:function:: DQE(img, varB, delta, A)

   Calculates the Detective Quantum Efficiency (DQE).

   :param img: The input image.
   :type img: np.ndarray
   :param varB: Variance of the background.
   :type varB: float
   :param delta: Squared percentage error in the gain image.
   :type delta: float
   :param A: A parameter related to Fano noise and conversion gain.
   :type A: float

   :returns: The calculated DQE.
   :rtype: np.ndarray


.. py:function:: chisq2d(exp, convolved, bval, m, g, A, delta, varB)

   Calculates the 2D chi-squared value.

   :param exp: The experimental data.
   :type exp: np.ndarray
   :param convolved: The convolved model.
   :type convolved: np.ndarray
   :param bval: The background values.
   :type bval: np.ndarray
   :param m: Mixing factor.
   :type m: float
   :param g: Gain.
   :type g: float
   :param A: A parameter related to Fano noise and conversion gain.
   :type A: float
   :param delta: Squared percentage error in the gain image.
   :type delta: float
   :param varB: Variance of the background.
   :type varB: float

   :returns: The calculated chi-squared value.
   :rtype: float


.. py:function:: convolved2d(img, mtf)

   Performs a 2D convolution using FFT.

   :param img: The input image.
   :type img: np.ndarray
   :param mtf: The modulation transfer function.
   :type mtf: np.ndarray

   :returns: The convolved image.
   :rtype: np.ndarray


.. py:function:: lucy_Richardson(dp, mtf, background, niter, varB=0, delta=0, A=1, g=1, m=1)

   Performs Lucy-Richardson deconvolution.

   :param dp: The diffraction pattern.
   :type dp: np.ndarray
   :param mtf: The modulation transfer function.
   :type mtf: np.ndarray
   :param background: The background of the diffraction pattern.
   :type background: np.ndarray
   :param niter: The number of iterations.
   :type niter: int
   :param varB: Variance of the background. Defaults to 0.
   :type varB: float, optional
   :param delta: The squared percentage error in the gain image.
                 Defaults to 0.
   :type delta: float, optional
   :param A: A parameter related to Fano noise and conversion
             gain, given by :math:`1 + F + \frac{1}{mG}`. Defaults to 1.
   :type A: float, optional
   :param g: The gain. Defaults to 1.
   :type g: float, optional
   :param m: The mixing factor. Defaults to 1.
   :type m: float, optional

   :returns: The deconvolved image.
   :rtype: np.ndarray