.. _api-power_norm_demo:

api example code: power_norm_demo.py
====================================



.. plot:: /home/tcaswell/source/p/matplotlib/doc/mpl_examples/api/power_norm_demo.py

::

    """
    ========================
    Exploring normalizations
    ========================
    
    Let's explore various normalization on a multivariate normal distribution.
    
    """
    
    from matplotlib import 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, axes = plt.subplots(nrows=2, ncols=2)
    
    axes[0, 0].set_title('Linear normalization')
    axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
    
    for ax, gamma in zip(axes.flat[1:], gammas):
        ax.set_title('Power law $(\gamma=%1.1f)$' % gamma)
        ax.hist2d(data[:, 0], data[:, 1],
                  bins=100, norm=mcolors.PowerNorm(gamma))
    
    fig.tight_layout()
    
    plt.show()
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)