jexplore.sampling.space
=======================

.. py:module:: jexplore.sampling.space

.. autoapi-nested-parse::

   This module provides the definitions for the base target space classes.
   The prototyping class :py:attr:`jexplore.sampling.space.Space` and the class
   defining a simple box space :py:attr:`jexplore.sampling.space.Box`



Classes
-------

.. autoapisummary::

   jexplore.sampling.space.Space
   jexplore.sampling.space.Box


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

.. py:class:: Space

   Bases: :py:obj:`Protocol`


   Markov sampling target space prototype class.
   Child space classes should implement an `inspace`
   method and a `wrap` method.

   :param dim: dimension of the target space.


   .. py:attribute:: dim
      :type:  int

      Dimension of the target space


   .. py:method:: inspace(points)

      Check which of a set of point lay in the defined space.

      :param points: set of points to be checked, it can have the `state`
                     shape :math:`(N_{chains}, D)` or the `samples` shape
                     :math:`(N_{chains}, D, S)`.

      :return: a boolean mask of shape :math:`(N_{chains})` or :math:`(N_{chains}, S)`
               selecting points that lay in the defined space.



   .. py:method:: wrap(points)

      folds the wrapped dimensions of the target space.

      :param points: set of points to be processed, it can have the `state`
                     shape :math:`(N_{chains}, D)` or the `samples` shape
                     :math:`(N_{chains}, D, S)`.

      return: the same set of points with the wrapped dimensions folded.



.. py:class:: Box(dim = None, size = jnp.inf, box = None, wrapped = None)

   Bases: :py:obj:`Space`


   Simple rectangular box space.

   :param dim: dimension of the box. Only used if the `box` parameter is not
               provided.
   :param size: size of the box (assumed having equal size in all dimensions)
                only used if the `box` parameter is not provided.
                Default: infinity.
   :param box: list of lists defining the segments boundaries for each box
               dimensions. If not provided a box with `dim` dimensions of equal
               size `size` is considered.
   :param wrapped: list of periodic dimensions indexes. These dimensions will be
                   considererd unbound and the corresponding box `intervals` will
                   be interpreted as principal domain intervals. Default: empty list.


   .. py:attribute:: bounds
      :type:  jax.Array

      Box bounds


   .. py:attribute:: wrap_dims
      :type:  list[int]

      Wrapped dimensions indexes


   .. py:attribute:: wrap_domain
      :type:  jax.Array

      wrapped dimensions principal domain


   .. py:attribute:: dim

      Dimension of the target space


   .. py:method:: inspace(points)

      Check which of a set of point lay in the defined space.

      :param points: set of points to be checked, it can have the `state`
                     shape :math:`(N_{chains}, D)` or the `samples` shape
                     :math:`(N_{chains}, D, S)`.

      :return: a boolean mask of shape :math:`(N_{chains})` or :math:`(N_{chains}, S)`
               selecting points that lay in the defined space.



   .. py:method:: wrap(points)

      folds the wrapped dimensions of the target space.

      :param points: set of points to be processed, it can have the `state`
                     shape :math:`(N_{chains}, D)` or the `samples` shape
                     :math:`(N_{chains}, D, S)`.

      return: the same set of points with the wrapped dimensions folded.