=============================================================
    Demo of the histogram (hist) function with multiple data sets
    =============================================================

    Plot histogram with multiple sample sets and demonstrate:

        * Use of legend with multiple sample sets
        * Stacked bars
        * Step curve with no fill
        * Data sets of different sample sizes

    Selecting different bin counts and sizes can significantly affect the
    shape of a histogram. The Astropy docs have a great section on how to
    select these parameters:
    http://docs.astropy.org/en/stable/visualization/histogram.html

def PyplotHistogram():
    """
    =============================================================
    Demo of the histogram (hist) function with multiple data sets
    =============================================================

    Plot histogram with multiple sample sets and demonstrate:

        * Use of legend with multiple sample sets
        * Stacked bars
        * Step curve with no fill
        * Data sets of different sample sizes

    Selecting different bin counts and sizes can significantly affect the
    shape of a histogram. The Astropy docs have a great section on how to
    select these parameters:
    http://docs.astropy.org/en/stable/visualization/histogram.html
    """

    import numpy as np
    import matplotlib.pyplot as plt

    np.random.seed(0)

    n_bins = 10
    x = np.random.randn(1000, 3)

    fig, axes = plt.subplots(nrows=2, ncols=2)
    ax0, ax1, ax2, ax3 = axes.flatten()

    colors = ['red', 'tan', 'lime']
    ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors)
    ax0.legend(prop={'size': 10})
    ax0.set_title('bars with legend')

    ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True)
    ax1.set_title('stacked bar')

    ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
    ax2.set_title('stack step (unfilled)')

    # Make a multiple-histogram of data-sets with different length.
    x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
    ax3.hist(x_multi, n_bins, histtype='bar')
    ax3.set_title('different sample sizes')

    fig.tight_layout()
    return fig