.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.py:


Create a gaussian process from a cov. model using HMatrix
=========================================================

In this basic example we are going to build a gaussian process from its covariance model and using the `HMatrix` as sampling method.


.. code-block:: default

    from __future__ import print_function
    import openturns as ot
    import openturns.viewer as viewer
    from matplotlib import pylab as plt
    ot.Log.Show(ot.Log.NONE)








Define the covariance model :


.. code-block:: default

    dimension = 1
    amplitude = [1.0] * dimension
    scale = [10] * dimension
    covarianceModel = ot.AbsoluteExponential(scale, amplitude)








Define the time grid on which we want to sample the gaussian process :

define a mesh


.. code-block:: default

    tmin = 0.0
    step = 0.01
    n = 10001
    timeGrid = ot.RegularGrid(tmin, step, n)








Finally define the gaussian process :

create the process


.. code-block:: default

    process = ot.GaussianProcess(covarianceModel, timeGrid)
    print(process)





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

 Out:

 .. code-block:: none

    GaussianProcess(trend=[x0]->[0.0], covariance=AbsoluteExponential(scale=[10], amplitude=[1]))




We set the sampling method to `HMAT`


.. code-block:: default

    process.setSamplingMethod(1)








We sample the process : 

draw a sample


.. code-block:: default

    sample = process.getSample(6)
    graph = sample.drawMarginal(0)
    view = viewer.View(graph)
    plt.show()




.. image:: /auto_probabilistic_modeling/stochastic_processes/images/sphx_glr_plot_gaussian_process_covariance_hmat_001.png
    :alt: Unnamed - 0 marginal
    :class: sphx-glr-single-img





We notice here that we are able to sample the covariance model over a mesh of size `10000`, which is usually tricky on laptop. This is mainly due to the compression.


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

   **Total running time of the script:** ( 0 minutes  0.893 seconds)


.. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_gaussian_process_covariance_hmat.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_gaussian_process_covariance_hmat.py <plot_gaussian_process_covariance_hmat.py>`



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

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


.. only:: html

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

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