gravelamps.prior.discrete
=========================

.. py:module:: gravelamps.prior.discrete

.. autoapi-nested-parse::

   Discrete Priors

   Implements priors for discrete distributions as needed for the Morse phase which may take one of
   three distinct values, or the millilensing number of images which must obviously be discrete

   Written by Mick Wright
              Ania Liu
              Justin Janquart

   ..
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Classes
-------

.. autoapisummary::

   gravelamps.prior.discrete.ImageNumber
   gravelamps.prior.discrete.UniformMorse


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

.. py:class:: ImageNumber(ncategories, name='num_images', latex_label='$n_{\\mathrm{signals}}$', unit=None)

   Bases: :py:obj:`bilby.core.prior.Categorical`


   
   Discrete Uniform Prior for handling the number of millilensing image signals.

   Lightly modified such that the minimum must be 1

   Attributes
   ---------
   ncategories : int
       Number of potential images
   name : str
       Name of the parameter used, defaults to 'num_images'
   latex_label : str
       The latex compatible output to be used on plots, etc. Defaults to '$n_{\mathrm{signals}}$'.
   unit : str
       Unit of the parameter















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   .. py:method:: rescale(val)

      
      Rescale a sample from the unit line element to one of the categories


      :Parameters:

          **val** : Union[float, int, array_like]
              Uniform probability between 0 and 1

      :Returns:

          Union[float, array_like]
              ..













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   .. py:method:: cdf(val)

      
      Calculate CDF for sample values


      :Parameters:

          **val** : Union[float, int, array_like]
              Sample values

      :Returns:

          float
              ..













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.. py:class:: UniformMorse(name='morse_phase', latex_label='$n$', unit=None)

   Bases: :py:obj:`bilby.core.prior.Categorical`


   
   Discrete Uniform Prior for handling the Morse phase. 

   This is a restricted subset of the Categorical prior to the cases of 0, 0.5, 1













   :Attributes:

       **name** : str
           Name of the parameter used, defaults to 'morse_phase'

       **latex_label** : str
           The latex compatible output to be used on plots, etc. Defaults to '$n$' 

       **unit** : str
           Unit of the parameter

   .. rubric:: Methods



   ===========  ==========
   **rescale**  Maps the continuous distribution 0 to 1 to discrete distribution of 0, 0.5, 1  
   ===========  ==========

   ..
       !! processed by numpydoc !!

   .. py:method:: rescale(val)

      
      Rescale a sample from the unit line element to one of the categories


      :Parameters:

          **val** : Union[float, int, array_like]
              Uniform proabability between 0 and 1

      :Returns:

          Union[float, array_like]
              ..













      ..
          !! processed by numpydoc !!


   .. py:method:: cdf(val)

      
      Calculate CDF for sample values


      :Parameters:

          **val** : Union[float, int, array_like]
              Sample values

      :Returns:

          float
              ..













      ..
          !! processed by numpydoc !!