lasdi.param
===========

.. py:module:: lasdi.param


Attributes
----------

.. autoapisummary::

   lasdi.param.getParam1DSpace


Classes
-------

.. autoapisummary::

   lasdi.param.ParameterSpace


Functions
---------

.. autoapisummary::

   lasdi.param.get_1dspace_from_list
   lasdi.param.create_uniform_1dspace


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

.. py:function:: get_1dspace_from_list(config)

.. py:function:: create_uniform_1dspace(config)

.. py:data:: getParam1DSpace

.. py:class:: ParameterSpace(config)

   .. py:attribute:: param_list
      :value: []



   .. py:attribute:: param_name
      :value: []



   .. py:attribute:: n_param
      :value: 0



   .. py:attribute:: train_space
      :value: None



   .. py:attribute:: test_space
      :value: None



   .. py:attribute:: n_init
      :value: 0



   .. py:attribute:: test_grid_sizes
      :value: []



   .. py:attribute:: test_meshgrid
      :value: None



   .. py:method:: n_train()


   .. py:method:: n_test()


   .. py:method:: createInitialTrainSpace(param_list)


   .. py:method:: createInitialTrainSpaceForHull(param_list)

      Concatenates the provided lists of training points into a 2D array.

      :Parameters: **param_list** (:obj:`list(dict)`) -- A list of parameter dictionaries

      :returns: **mesh_grids** -- np.array of size [d, k], where d is the number of points provided on the exterior of
                the training space and k is the number of parameters (k == len(param_list)).
      :rtype: :obj:`numpy.array`



   .. py:method:: createTestGridSpace(param_list)


   .. py:method:: createTestGridSpaceForHull(param_list)

      Builds an initial uniform grid for the testing parameters when the test_space is 'hull'.

      :Parameters: **param_list** (:obj:`list(dict)`) -- A list of parameter dictionaries

      :returns: * **gridSizes** (:obj:`list(int)`) -- A list containing the number of elements on the grid in each parameter.
                * **mesh_grids** (:obj:`numpy.array`) -- tuple of numpy nd arrays, corresponding to each parameter.
                  Dimension of the array equals to the number of parameters.
                * **param_grid** (:obj:`numpy.array`) -- numpy 2d array of size (grid size x number of parameters).



   .. py:method:: createTestHullSpace(param_list)

      This function builds an initial uniform grid for the testing parameters, and then
      returns any testing points which are within the convex hull of the provided
      training parameters.

      :Parameters: **param_list** (:obj:`list(dict)`) -- A list of parameter dictionaries

      :returns: * **gridSizes** (:obj:`list(int)`) -- A list containing the number of elements on the grid in each parameter.
                * **mesh_grids** (:obj:`numpy.array`) -- tuple of numpy nd arrays, corresponding to each parameter.
                  Dimension of the array equals to the number of parameters.
                * **test_space** (:obj:`numpy.array`) -- numpy 2d array of size [d, k], where d is the number of testing points within
                  convex hull of the training space and k is the number of parameters (k == len(param_list)).



   .. py:method:: getParameter(param_vector)

      convert numpy array parameter vector to a dict.
      Physics class takes the dict for solve/initial_condition.



   .. py:method:: createHyperMeshGrid(param_ranges)

      param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.
                    The list size is equal to the number of parameters.

      Output: paramSpaces
          - tuple of numpy nd arrays, corresponding to each parameter.
            Dimension of the array equals to the number of parameters



   .. py:method:: createHyperGridSpace(mesh_grids)

      mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.
                  Dimension of the array equals to the number of parameters

      Output: param_grid
          - numpy 2d array of size (grid size x number of parameters).

          grid size is the size of a numpy nd array.



   .. py:method:: appendTrainSpace(param)


   .. py:method:: export()


   .. py:method:: load(dict_)