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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_ex_descriptive_statistics.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_ex_descriptive_statistics.py:


**********************
Descriptive Statistics
**********************

Numpy offers many statistical functions, but if you want to obtain several
statistical variables from the same array, it's necessary to process the data
several times to calculate the various parameters. This example shows how to use
the DescriptiveStatistics class to obtain several statistical variables with a
single calculation. Also, the calculation algorithm is incremental and is more
numerically stable.

.. note::

    Pébay, P., Terriberry, T.B., Kolla, H. et al.
    Numerically stable, scalable formulas for parallel and online
    computation of higher-order multivariate central moments
    with arbitrary weights.
    Comput Stat 31, 1305–1325,
    2016,
    https://doi.org/10.1007/s00180-015-0637-z

.. GENERATED FROM PYTHON SOURCE LINES 24-29

.. code-block:: Python

    import dask.array
    import numpy

    import pyinterp








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Create a random array

.. GENERATED FROM PYTHON SOURCE LINES 31-33

.. code-block:: Python

    values = numpy.random.random_sample((2, 4, 6, 8))








.. GENERATED FROM PYTHON SOURCE LINES 34-35

Create a DescriptiveStatistics object.

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

    ds = pyinterp.DescriptiveStatistics(values)








.. GENERATED FROM PYTHON SOURCE LINES 38-52

The constructor will calculate the statistical variables on the provided data.
The calculated variables are stored in the instance and can be accessed using
different methods:

* mean
* var
* std
* skewness
* kurtosis
* min
* max
* sum
* sum_of_weights
* count

.. GENERATED FROM PYTHON SOURCE LINES 52-54

.. code-block:: Python

    ds.count()





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

 .. code-block:: none


    array([384], dtype=uint64)



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.. code-block:: Python

    ds.mean()





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

 .. code-block:: none


    array([0.50151422])



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It's possible to get a structured numpy array containing the different
statistical variables calculated.

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.. code-block:: Python

    ds.array()





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

 .. code-block:: none


    array([(384, -1.17292331, 0.99964469, 0.50151422, 0.00674238, 0.00654089, 384., 192.58146049, 0.07907892)],
          dtype=[('count', '<u8'), ('kurtosis', '<f8'), ('max', '<f8'), ('mean', '<f8'), ('min', '<f8'), ('skewness', '<f8'), ('sum_of_weights', '<f8'), ('sum', '<f8'), ('var', '<f8')])



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Like numpy, it's possible to compute statistics along axis.

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.. code-block:: Python

    ds = pyinterp.DescriptiveStatistics(values, axis=(1, 2))
    ds.mean()





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

 .. code-block:: none


    array([[0.55284495, 0.50918919, 0.58101145, 0.54275717, 0.54968826,
            0.52358159, 0.47940404, 0.48739702],
           [0.51255507, 0.38284679, 0.42390928, 0.49230764, 0.43535806,
            0.49225299, 0.65549639, 0.40362762]])



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The class can also process a dask array. In this case, the call to the
constructor triggers the calculation.

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.. code-block:: Python

    ds = pyinterp.DescriptiveStatistics(dask.array.from_array(values,
                                                              chunks=(2, 2, 2, 2)),
                                        axis=(1, 2))
    ds.mean()





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

 .. code-block:: none


    array([[0.55284495, 0.50918919, 0.58101145, 0.54275717, 0.54968826,
            0.52358159, 0.47940404, 0.48739702],
           [0.51255507, 0.38284679, 0.42390928, 0.49230764, 0.43535806,
            0.49225299, 0.65549639, 0.40362762]])



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Finally, it's possible to calculate weighted statistics.

.. GENERATED FROM PYTHON SOURCE LINES 77-80

.. code-block:: Python

    weights = numpy.random.random_sample((2, 4, 6, 8))
    ds = pyinterp.DescriptiveStatistics(values, weights=weights, axis=(1, 2))
    ds.mean()




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

 .. code-block:: none


    array([[0.55204361, 0.48518751, 0.57168112, 0.59331051, 0.57745313,
            0.52974562, 0.45314877, 0.48891758],
           [0.51717951, 0.4383929 , 0.43142146, 0.46124402, 0.4340834 ,
            0.48024679, 0.64057294, 0.39312551]])




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

   **Total running time of the script:** (0 minutes 0.193 seconds)


.. _sphx_glr_download_auto_examples_ex_descriptive_statistics.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/CNES/pangeo-pyinterp/master?urlpath=lab/tree/notebooks/auto_examples/ex_descriptive_statistics.ipynb
        :alt: Launch binder
        :width: 150 px

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

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


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