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

.. only:: html

    .. meta::
        :keywords: codex

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

        :ref:`Go to the end <sphx_glr_download_gallery_scales_power_norm.py>`
        to download the full example code.

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

.. _sphx_glr_gallery_scales_power_norm.py:


========================
Exploring normalizations
========================

Various normalization on a multivariate normal distribution.

.. GENERATED FROM PYTHON SOURCE LINES 9-39

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from numpy.random import multivariate_normal

    import matplotlib.colors as mcolors

    # Fixing random state for reproducibility.
    np.random.seed(19680801)

    data = np.vstack([
        multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
        multivariate_normal([30, 20], [[3, 1], [1, 3]], size=1000)
    ])

    gammas = [0.8, 0.5, 0.3]

    fig, axs = plt.subplots(nrows=2, ncols=2)

    axs[0, 0].set_title('Linear normalization')
    axs[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)

    for ax, gamma in zip(axs.flat[1:], gammas):
        ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
        ax.hist2d(data[:, 0], data[:, 1], bins=100, norm=mcolors.PowerNorm(gamma))

    fig.tight_layout()

    plt.show()




.. image-sg:: /gallery/scales/images/sphx_glr_power_norm_001.png
   :alt: Linear normalization, Power law $(\gamma=0.8)$, Power law $(\gamma=0.5)$, Power law $(\gamma=0.3)$
   :srcset: /gallery/scales/images/sphx_glr_power_norm_001.png, /gallery/scales/images/sphx_glr_power_norm_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 40-49

.. admonition:: References

   The use of the following functions, methods, classes and modules is shown
   in this example:

   - `matplotlib.colors`
   - `matplotlib.colors.PowerNorm`
   - `matplotlib.axes.Axes.hist2d`
   - `matplotlib.pyplot.hist2d`


.. _sphx_glr_download_gallery_scales_power_norm.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: power_norm.zip <power_norm.zip>`


.. only:: html

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

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