
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_algorithms/plot_algorithm_static_IO.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_algorithms_plot_algorithm_static_IO.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_algorithms_plot_algorithm_static_IO.py:


Algorithm: Static_IO
==============================

.. GENERATED FROM PYTHON SOURCE LINES 5-18

.. code-block:: default

    # Windows users have to encapsulate the code into a main function to avoid multiprocessing errors.
    # def main():
    import pygpc
    import numpy as np
    import matplotlib
    # matplotlib.use("Qt5Agg")

    from collections import OrderedDict

    fn_results = 'tmp/static_IO'   # filename of output
    save_session_format = ".pkl"    # file format of saved gpc session ".hdf5" (slow) or ".pkl" (fast)
    np.random.seed(1)








.. GENERATED FROM PYTHON SOURCE LINES 19-21

Setup input and output data
----------------------------------------------------------------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-43

.. code-block:: default


    # We artificially generate some coordinates for the input data the user has to provide where the model was sampled
    n_grid = 100
    x1 = np.random.rand(n_grid) * 0.8 + 1.2
    x2 = 1.25
    x3 = np.random.rand(n_grid) * 0.6

    # define the properties of the random variables
    parameters = OrderedDict()
    parameters["x1"] = pygpc.Beta(pdf_shape=[1, 1], pdf_limits=[1.2, 2])
    parameters["x3"] = pygpc.Beta(pdf_shape=[1, 1], pdf_limits=[0, 0.6])

    # generate a grid object from the input data
    grid = pygpc.RandomGrid(parameters_random=parameters, coords=np.vstack((x1,x3)).T)

    # get output data (here: Peaks function)
    results = (3.0 * (1 - x1) ** 2. * np.exp(-(x1 ** 2) - (x3 + 1) ** 2)
               - 10.0 * (x1 / 5.0 - x1 ** 3 - x3 ** 5)
               * np.exp(-x1 ** 2 - x3 ** 2) - 1.0 / 3
               * np.exp(-(x1 + 1) ** 2 - x3 ** 2)) +  x2
    results = results[:, np.newaxis]








.. GENERATED FROM PYTHON SOURCE LINES 44-46

Setting up the algorithm
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 46-65

.. code-block:: default


    # gPC options
    options = dict()
    options["method"] = "reg"
    options["solver"] = "LarsLasso"
    options["settings"] = None
    options["order"] = [9, 9]
    options["order_max"] = 9
    options["interaction_order"] = 2
    options["error_type"] = "loocv"
    options["n_samples_validation"] = None
    options["fn_results"] = fn_results
    options["save_session_format"] = save_session_format
    options["backend"] = "omp"
    options["verbose"] = True

    # define algorithm
    algorithm = pygpc.Static_IO(parameters=parameters, options=options, grid=grid, results=results)








.. GENERATED FROM PYTHON SOURCE LINES 66-68

Running the gpc
---------------

.. GENERATED FROM PYTHON SOURCE LINES 68-75

.. code-block:: default


    # initialize gPC Session
    session = pygpc.Session(algorithm=algorithm)

    # run gPC algorithm
    session, coeffs, results = session.run()





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

     > Determining 55 gPC coeffs with 100 simulations!
    Determine gPC coefficients using 'LarsLasso' solver ...

    LOOCV 01 from 25 [=                                       ] 4.0%

    LOOCV 02 from 25 [===                                     ] 8.0%

    LOOCV 03 from 25 [====                                    ] 12.0%

    LOOCV 04 from 25 [======                                  ] 16.0%

    LOOCV 05 from 25 [========                                ] 20.0%

    LOOCV 06 from 25 [=========                               ] 24.0%

    LOOCV 07 from 25 [===========                             ] 28.0%

    LOOCV 08 from 25 [============                            ] 32.0%

    LOOCV 09 from 25 [==============                          ] 36.0%

    LOOCV 10 from 25 [================                        ] 40.0%

    LOOCV 11 from 25 [=================                       ] 44.0%

    LOOCV 12 from 25 [===================                     ] 48.0%

    LOOCV 13 from 25 [====================                    ] 52.0%

    LOOCV 14 from 25 [======================                  ] 56.0%

    LOOCV 15 from 25 [========================                ] 60.0%

    LOOCV 16 from 25 [=========================               ] 64.0%

    LOOCV 17 from 25 [===========================             ] 68.0%

    LOOCV 18 from 25 [============================            ] 72.0%

    LOOCV 19 from 25 [==============================          ] 76.0%

    LOOCV 20 from 25 [================================        ] 80.0%

    LOOCV 21 from 25 [=================================       ] 84.0%

    LOOCV 22 from 25 [===================================     ] 88.0%

    LOOCV 23 from 25 [====================================    ] 92.0%

    LOOCV 24 from 25 [======================================  ] 96.0%

    LOOCV 25 from 25 [========================================] 100.0%
    LOOCV computation time: 0.08343315124511719 sec
    -> relative loocv error = 2.1361275428489573e-05




.. GENERATED FROM PYTHON SOURCE LINES 76-78

Postprocessing
--------------

.. GENERATED FROM PYTHON SOURCE LINES 78-106

.. code-block:: default


    # read session
    session = pygpc.read_session(fname=session.fn_session, folder=session.fn_session_folder)

    # Post-process gPC
    pygpc.get_sensitivities_hdf5(fn_gpc=options["fn_results"],
                                 output_idx=None,
                                 calc_sobol=True,
                                 calc_global_sens=True,
                                 calc_pdf=True,
                                 algorithm="standard")

    # plot gPC approximation and IO data
    pygpc.plot_gpc(session=session,
                   coeffs=coeffs,
                   random_vars=["x1", "x3"],
                   output_idx= 0,
                   n_grid=[100, 100],
                   coords=grid.coords,
                   results=results,
                   fn_out=None)

    # On Windows subprocesses will import (i.e. execute) the main module at start.
    # You need to insert an if __name__ == '__main__': guard in the main module to avoid
    # creating subprocesses recursively.
    #
    # if __name__ == '__main__':
    #     main()



.. image-sg:: /auto_algorithms/images/sphx_glr_plot_algorithm_static_IO_001.png
   :alt: gPC approximation, Probability density
   :srcset: /auto_algorithms/images/sphx_glr_plot_algorithm_static_IO_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    > Loading gpc session object: tmp/static_IO.pkl
    > Loading gpc coeffs: tmp/static_IO.hdf5
    > Adding results to: tmp/static_IO.hdf5





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  2.262 seconds)


.. _sphx_glr_download_auto_algorithms_plot_algorithm_static_IO.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_algorithm_static_IO.py <plot_algorithm_static_IO.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_algorithm_static_IO.ipynb <plot_algorithm_static_IO.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
