.. _pylab_examples-boxplot_demo2:

pylab_examples example code: boxplot_demo2.py
=============================================



.. plot:: /home/tcaswell/src/p/matplotlib/doc/mpl_examples/pylab_examples/boxplot_demo2.py

::

    """
    Thanks Josh Hemann for the example
    """
    
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.patches import Polygon
    
    
    # Generate some data from five different probability distributions,
    # each with different characteristics. We want to play with how an IID
    # bootstrap resample of the data preserves the distributional
    # properties of the original sample, and a boxplot is one visual tool
    # to make this assessment
    numDists = 5
    randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
                   'Triangular(2,9,11)']
    N = 500
    np.random.seed(0)
    norm = np.random.normal(1, 1, N)
    logn = np.random.lognormal(1, 1, N)
    expo = np.random.exponential(1, N)
    gumb = np.random.gumbel(6, 4, N)
    tria = np.random.triangular(2, 9, 11, N)
    
    # Generate some random indices that we'll use to resample the original data
    # arrays. For code brevity, just use the same random indices for each array
    bootstrapIndices = np.random.random_integers(0, N - 1, N)
    normBoot = norm[bootstrapIndices]
    expoBoot = expo[bootstrapIndices]
    gumbBoot = gumb[bootstrapIndices]
    lognBoot = logn[bootstrapIndices]
    triaBoot = tria[bootstrapIndices]
    
    data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
            tria, triaBoot]
    
    fig, ax1 = plt.subplots(figsize=(10, 6))
    fig.canvas.set_window_title('A Boxplot Example')
    plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
    
    bp = plt.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
    plt.setp(bp['boxes'], color='black')
    plt.setp(bp['whiskers'], color='black')
    plt.setp(bp['fliers'], color='red', marker='+')
    
    # Add a horizontal grid to the plot, but make it very light in color
    # so we can use it for reading data values but not be distracting
    ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
                   alpha=0.5)
    
    # Hide these grid behind plot objects
    ax1.set_axisbelow(True)
    ax1.set_title('Comparison of IID Bootstrap Resampling Across Five Distributions')
    ax1.set_xlabel('Distribution')
    ax1.set_ylabel('Value')
    
    # Now fill the boxes with desired colors
    boxColors = ['darkkhaki', 'royalblue']
    numBoxes = numDists*2
    medians = list(range(numBoxes))
    for i in range(numBoxes):
        box = bp['boxes'][i]
        boxX = []
        boxY = []
        for j in range(5):
            boxX.append(box.get_xdata()[j])
            boxY.append(box.get_ydata()[j])
        boxCoords = list(zip(boxX, boxY))
        # Alternate between Dark Khaki and Royal Blue
        k = i % 2
        boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
        ax1.add_patch(boxPolygon)
        # Now draw the median lines back over what we just filled in
        med = bp['medians'][i]
        medianX = []
        medianY = []
        for j in range(2):
            medianX.append(med.get_xdata()[j])
            medianY.append(med.get_ydata()[j])
            plt.plot(medianX, medianY, 'k')
            medians[i] = medianY[0]
        # Finally, overplot the sample averages, with horizontal alignment
        # in the center of each box
        plt.plot([np.average(med.get_xdata())], [np.average(data[i])],
                 color='w', marker='*', markeredgecolor='k')
    
    # Set the axes ranges and axes labels
    ax1.set_xlim(0.5, numBoxes + 0.5)
    top = 40
    bottom = -5
    ax1.set_ylim(bottom, top)
    xtickNames = plt.setp(ax1, xticklabels=np.repeat(randomDists, 2))
    plt.setp(xtickNames, rotation=45, fontsize=8)
    
    # Due to the Y-axis scale being different across samples, it can be
    # hard to compare differences in medians across the samples. Add upper
    # X-axis tick labels with the sample medians to aid in comparison
    # (just use two decimal places of precision)
    pos = np.arange(numBoxes) + 1
    upperLabels = [str(np.round(s, 2)) for s in medians]
    weights = ['bold', 'semibold']
    for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
        k = tick % 2
        ax1.text(pos[tick], top - (top*0.05), upperLabels[tick],
                 horizontalalignment='center', size='x-small', weight=weights[k],
                 color=boxColors[k])
    
    # Finally, add a basic legend
    plt.figtext(0.80, 0.08, str(N) + ' Random Numbers',
                backgroundcolor=boxColors[0], color='black', weight='roman',
                size='x-small')
    plt.figtext(0.80, 0.045, 'IID Bootstrap Resample',
                backgroundcolor=boxColors[1],
                color='white', weight='roman', size='x-small')
    plt.figtext(0.80, 0.015, '*', color='white', backgroundcolor='silver',
                weight='roman', size='medium')
    plt.figtext(0.815, 0.013, ' Average Value', color='black', weight='roman',
                size='x-small')
    
    plt.show()
    

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