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

.. only:: html

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

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

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

.. _sphx_glr_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_non_parametric_copula.py:


Fit a non parametric copula
===========================

.. GENERATED FROM PYTHON SOURCE LINES 7-8

In this example we are going to estimate a normal copula from a sample using non parametric representations.

.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 16-17

Create data

.. GENERATED FROM PYTHON SOURCE LINES 17-22

.. code-block:: default

    R = ot.CorrelationMatrix(2)
    R[1, 0] = 0.4
    copula = ot.NormalCopula(R)
    sample = copula.getSample(30)








.. GENERATED FROM PYTHON SOURCE LINES 23-24

Estimate a normal copula using BernsteinCopulaFactory

.. GENERATED FROM PYTHON SOURCE LINES 24-26

.. code-block:: default

    distribution = ot.BernsteinCopulaFactory().build(sample)








.. GENERATED FROM PYTHON SOURCE LINES 27-28

Draw fitted distribution

.. GENERATED FROM PYTHON SOURCE LINES 28-31

.. code-block:: default

    graph = distribution.drawPDF()
    view = viewer.View(graph)




.. image-sg:: /auto_data_analysis/estimate_dependency_and_copulas/images/sphx_glr_plot_estimate_non_parametric_copula_001.png
   :alt: [X0,X1] iso-PDF
   :srcset: /auto_data_analysis/estimate_dependency_and_copulas/images/sphx_glr_plot_estimate_non_parametric_copula_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 32-33

Estimate a normal copula using KernelSmoothing

.. GENERATED FROM PYTHON SOURCE LINES 33-37

.. code-block:: default

    distribution = ot.KernelSmoothing().build(sample).getCopula()
    graph = distribution.drawPDF()
    view = viewer.View(graph)
    plt.show()



.. image-sg:: /auto_data_analysis/estimate_dependency_and_copulas/images/sphx_glr_plot_estimate_non_parametric_copula_002.png
   :alt: [X0,X1] iso-PDF
   :srcset: /auto_data_analysis/estimate_dependency_and_copulas/images/sphx_glr_plot_estimate_non_parametric_copula_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.344 seconds)


.. _sphx_glr_download_auto_data_analysis_estimate_dependency_and_copulas_plot_estimate_non_parametric_copula.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_estimate_non_parametric_copula.py <plot_estimate_non_parametric_copula.py>`



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

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


.. only:: html

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

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