poplar.distributions
====================

.. py:module:: poplar.distributions

.. autoapi-nested-parse::

   Some standard distributions with vectorised probability density functions (pdfs) and a routine for numerical cumulative density function (CDF) computation and distribution sampling.

   If the user requires other distributions, they can implement them as their own class by subclassing the Distribution base class and adding their own methods as required.

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Classes
-------

.. autoapisummary::

   poplar.distributions.FixedLimitsPowerLaw
   poplar.distributions.VariableLimitsPowerLaw
   poplar.distributions.FixedLimitsTruncatedGaussian
   poplar.distributions.UniformDistribution
   poplar.distributions.FixedLimitBetaDistribution
   poplar.distributions.FixedLimitTruncatedBetaDistribution
   poplar.distributions.FixedLimits_PowerLawTruncatedGaussian


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

.. py:class:: FixedLimitsPowerLaw(limits, npoints=1000, grid_spacing='logarithmic', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A power law distribution with fixed limits (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: VariableLimitsPowerLaw(limits, npoints=1000, grid_spacing='logarithmic', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A power law distribution with variable limits (i.e. the pdf takes the limits as arguments).


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: FixedLimitsTruncatedGaussian(limits, npoints=1000, grid_spacing='linear', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A truncated normal distribution with fixed limits (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).

   Ref : http://parker.ad.siu.edu/Olive/ch4.pdf

   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: UniformDistribution(limits, npoints=1000, grid_spacing='linear', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A uniform distribution with fixed limits (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: FixedLimitBetaDistribution(limits, npoints=1000, grid_spacing='linear', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A Beta distribution with fixed limits (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: FixedLimitTruncatedBetaDistribution(limits, npoints=1000, grid_spacing='linear', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A Truncated Beta distribution with fixed limits (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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.. py:class:: FixedLimits_PowerLawTruncatedGaussian(limits, npoints=1000, grid_spacing='linear', device='cpu')

   Bases: :py:obj:`Distribution`


   
   A distribution similar to Peak+PowerLaw with fixed bounds (i.e. the pdf does not take the limits as arguments, they are fixed at initialisation).
   Ref : https://arxiv.org/abs/2010.14533


   :Parameters:

       **limits** : list
           [Lower, Upper] limits for this distribution. Used to initialise the CDF computational grid.

       **npoints** : int, optional
           number of points in CDF computational grid, by default 1000

       **grid_spacing** : str, optional
           specifies either linear or logarithmic spacing for CDF computational grid, by default 'linear'

       **device** : str, optional
           pytorch device this distribution operates using, by default "cpu"














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