.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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.
.. code-block:: default
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal
data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[2, 3], [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:: /gallery/scales/images/sphx_glr_power_norm_001.png
:alt: Linear normalization, Power law $(\gamma=0.8)$, Power law $(\gamma=0.5)$, Power law $(\gamma=0.3)$
:class: sphx-glr-single-img
------------
References
""""""""""
The use of the following functions, methods, classes and modules is shown
in this example:
.. code-block:: default
import matplotlib
matplotlib.colors
matplotlib.colors.PowerNorm
matplotlib.axes.Axes.hist2d
matplotlib.pyplot.hist2d
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
.. _sphx_glr_download_gallery_scales_power_norm.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: power_norm.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: power_norm.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
Keywords: matplotlib code example, codex, python plot, pyplot
`Gallery generated by Sphinx-Gallery
`_