scPhyloX.estimation.mutation_rate
=================================

.. py:module:: scPhyloX.estimation.mutation_rate


Classes
-------

.. autoapisummary::

   scPhyloX.estimation.mutation_rate.GenerationEst
   scPhyloX.estimation.mutation_rate.BranchLength
   scPhyloX.estimation.mutation_rate.LogLike


Functions
---------

.. autoapisummary::

   scPhyloX.estimation.mutation_rate.mutation_rate_de
   scPhyloX.estimation.mutation_rate.my_loglike
   scPhyloX.estimation.mutation_rate.mutation_rate_mcmc


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

.. py:class:: GenerationEst(lr_dist, mu: float, gennum=None)

   Estimation of generations with given LR distance distribution and mutaion rate

   Args:
       lr_dist: 
           distribution of LR distance
       mu: 
           mutation rate


   .. py:attribute:: lr_dist


   .. py:attribute:: mu


   .. py:attribute:: u1


   .. py:attribute:: s1


   .. py:attribute:: generation
      :value: None



   .. py:method:: generation_map(mn: float)

      MAP estimation of given lr-dist

      Args:
          mn: 
              lr-distance
          
      Returns:
          float:
              Estimated generation



   .. py:method:: estimate(cell_number=None)

      Generation estimation of all given lr distance in self.lr_dist




.. py:class:: BranchLength(mu, delta)

   Probability distribution of LP distance

   Args:
       mu:
           mutation rate
       delta:
           Probability of branching division


   .. py:attribute:: mu


   .. py:attribute:: delta


   .. py:method:: prob(x)

      Probability density function of lp-dist

      Args:
          x:
              lp-dist
      Return:
          float:
              probability density



   .. py:method:: likelihood(data)

      Likelihood of lp-dist

      Args:
          data:
              Observed lp dist
      Return:
          float:
              Sum of log-likelihood of given lp dist



.. py:function:: mutation_rate_de(data, n_iter: int = 100, bootstrape: int = 0)

   Mutation rate estimation using DE

   Args:
       data:
           lp-dist
       n_iter:
           Iterations of de estimation
       bootstrape:
           Weather using bootstrape to accuratly estimate mutation rate, 0 to turn off.
   Return:
       tuple:
           (accepted parameters, loss, de-estimator)


.. py:class:: LogLike(loglike, data)

   Bases: :py:obj:`pytensor.tensor.Op`


   .. py:attribute:: itypes


   .. py:attribute:: otypes


   .. py:attribute:: likelihood


   .. py:attribute:: data


   .. py:method:: perform(node, inputs, outputs)


.. py:function:: my_loglike(theta, data)

.. py:function:: mutation_rate_mcmc(data, draw=1000, tune=1000, chain=4, mu0=2, sigma=0.2)

   Mutation rate estimation using DE-MCMC

   Args:
       data:
           Observed lp-dist
       draw:
           Number of smaples to draw
       tune:
           Number of iterations to tune
       chain:
           number of chains to sample
       mu0:
           mean of prior distribution of mutation rate
       sigma:
           variation of prior distribution of mutation rate