:py:mod:`gnome.utilities.distributions`
=======================================

.. py:module:: gnome.utilities.distributions

.. autoapi-nested-parse::

   Classes that generate various types of probability distributions



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

Classes
~~~~~~~

.. autoapisummary::

   gnome.utilities.distributions.DistributionBase
   gnome.utilities.distributions.UniformDistribution
   gnome.utilities.distributions.NormalDistribution
   gnome.utilities.distributions.LogNormalDistribution
   gnome.utilities.distributions.WeibullDistribution
   gnome.utilities.distributions.RayleighDistribution



Functions
~~~~~~~~~

.. autoapisummary::

   gnome.utilities.distributions.get_distribution_by_name



Attributes
~~~~~~~~~~

.. autoapisummary::

   gnome.utilities.distributions.ALL_DISTS


.. py:function:: get_distribution_by_name(dist_name)

   Return a distribution object corresponding to its name

   note: this isn't really helpful until / unless we
   standardize the initialization interface.


.. py:class:: DistributionBase(name=None, _appearance=None, *args, **kwargs)


   Bases: :py:obj:`gnome.gnomeobject.GnomeId`

   Base class for distributions, just to make it easier to know
   what is a distribution class

   At them moment, all they need is a set_values method

   NOTE: if possible, it would be good to have the same
   interface to all distributions. For example, see: the
   distributions in numpy.random. for example:

   `random.normal(loc=0.0, scale=1.0, size=None)`

   all distribution have a location and a scale,
   which have different meanings depending on the distribution.

   .. py:method:: set_values(np_array)
      :abstractmethod:



.. py:class:: UniformDistribution(low=0.0, high=0.1, **kwargs)


   Bases: :py:obj:`DistributionBase`

   Base class for distributions, just to make it easier to know
   what is a distribution class

   At them moment, all they need is a set_values method

   NOTE: if possible, it would be good to have the same
   interface to all distributions. For example, see: the
   distributions in numpy.random. for example:

   `random.normal(loc=0.0, scale=1.0, size=None)`

   all distribution have a location and a scale,
   which have different meanings depending on the distribution.

   :param low: For the Uniform distribution, it is lower bound.
   :param high: For the Uniform distribution, it is upper bound.

   .. py:attribute:: _schema

      Uniform Probability Distribution


   .. py:method:: _check_uniform_args()


   .. py:method:: _uniform(np_array)


   .. py:method:: set_values(np_array)



.. py:class:: NormalDistribution(mean=0.0, sigma=0.1, **kwargs)


   Bases: :py:obj:`DistributionBase`

   Base class for distributions, just to make it easier to know
   what is a distribution class

   At them moment, all they need is a set_values method

   NOTE: if possible, it would be good to have the same
   interface to all distributions. For example, see: the
   distributions in numpy.random. for example:

   `random.normal(loc=0.0, scale=1.0, size=None)`

   all distribution have a location and a scale,
   which have different meanings depending on the distribution.

   :param mean: The mean of the normal distribution
   :param sigma: The standard deviation of normal distribution

   .. py:attribute:: _schema

      Normal Probability Distribution


   .. py:method:: _check_normal_args()


   .. py:method:: _normal(np_array)


   .. py:method:: set_values(np_array)



.. py:class:: LogNormalDistribution(mean=0.0, sigma=0.1, **kwargs)


   Bases: :py:obj:`DistributionBase`

   Base class for distributions, just to make it easier to know
   what is a distribution class

   At them moment, all they need is a set_values method

   NOTE: if possible, it would be good to have the same
   interface to all distributions. For example, see: the
   distributions in numpy.random. for example:

   `random.normal(loc=0.0, scale=1.0, size=None)`

   all distribution have a location and a scale,
   which have different meanings depending on the distribution.

   :param mean: The mean of the normal distribution
   :param sigma: The standard deviation of normal distribution

   .. py:attribute:: _schema

      Log Normal Probability Distribution


   .. py:method:: _check_lognormal_args()


   .. py:method:: _lognormal(np_array)


   .. py:method:: set_values(np_array)



.. py:class:: WeibullDistribution(alpha=None, lambda_=1.0, min_=None, max_=None, **kwargs)


   Bases: :py:obj:`DistributionBase`

   Base class for distributions, just to make it easier to know
   what is a distribution class

   At them moment, all they need is a set_values method

   NOTE: if possible, it would be good to have the same
   interface to all distributions. For example, see: the
   distributions in numpy.random. for example:

   `random.normal(loc=0.0, scale=1.0, size=None)`

   all distribution have a location and a scale,
   which have different meanings depending on the distribution.

   :param alpha: The shape parameter 'alpha' - labeled as 'a' in
                 numpy.random.weibull distribution
   :param lambda_: The scale parameter for the distribution - required for
                   2-parameter weibull distribution (Rosin-Rammler).

   NOTE: underscore names because `min`, `max`, and `lambda` are Python
         builtins

   .. py:attribute:: _schema

      Weibull Probability Distribution, also known as
      Rosin Rammler distribution


   .. py:method:: _check_weibull_args()


   .. py:method:: _weibull(np_array)


   .. py:method:: set_values(np_array)



.. py:class:: RayleighDistribution


   .. py:method:: sigma_from_wind(avg_speed)
      :classmethod:


   .. py:method:: pdf(x, sigma)
      :classmethod:


   .. py:method:: cdf(x, sigma)
      :classmethod:


   .. py:method:: quantile(f, sigma)
      :classmethod:



.. py:data:: ALL_DISTS