.. _pylab_examples-broken_axis:

pylab_examples example code: broken_axis.py
===========================================



.. plot:: /home/mandrake/rpm/BUILD/matplotlib-1.1.1/doc/mpl_examples/pylab_examples/broken_axis.py

::

    """
    Broken axis example, where the y-axis will have a portion cut out.
    """
    import matplotlib.pylab as plt
    import numpy as np
    
    
    # 30 points between 0 0.2] originally made using np.random.rand(30)*.2
    pts = np.array([ 0.015,  0.166,  0.133,  0.159,  0.041,  0.024,  0.195,
        0.039, 0.161,  0.018,  0.143,  0.056,  0.125,  0.096,  0.094, 0.051,
        0.043,  0.021,  0.138,  0.075,  0.109,  0.195,  0.05 , 0.074, 0.079,
        0.155,  0.02 ,  0.01 ,  0.061,  0.008])
    
    # Now let's make two outlier points which are far away from everything.
    pts[[3,14]] += .8
    
    # If we were to simply plot pts, we'd lose most of the interesting
    # details due to the outliers. So let's 'break' or 'cut-out' the y-axis
    # into two portions - use the top (ax) for the outliers, and the bottom
    # (ax2) for the details of the majority of our data
    f,(ax,ax2) = plt.subplots(2,1,sharex=True)
    
    # plot the same data on both axes
    ax.plot(pts)
    ax2.plot(pts)
    
    # zoom-in / limit the view to different portions of the data
    ax.set_ylim(.78,1.) # outliers only
    ax2.set_ylim(0,.22) # most of the data
    
    # hide the spines between ax and ax2
    ax.spines['bottom'].set_visible(False)
    ax2.spines['top'].set_visible(False)
    ax.xaxis.tick_top()
    ax.tick_params(labeltop='off') # don't put tick labels at the top
    ax2.xaxis.tick_bottom()
    
    # This looks pretty good, and was fairly painless, but you can get that
    # cut-out diagonal lines look with just a bit more work. The important
    # thing to know here is that in axes coordinates, which are always
    # between 0-1, spine endpoints are at these locations (0,0), (0,1),
    # (1,0), and (1,1).  Thus, we just need to put the diagonals in the
    # appropriate corners of each of our axes, and so long as we use the
    # right transform and disable clipping.
    
    d = .015 # how big to make the diagonal lines in axes coordinates
    # arguments to pass plot, just so we don't keep repeating them
    kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
    ax.plot((-d,+d),(-d,+d), **kwargs)      # top-left diagonal
    ax.plot((1-d,1+d),(-d,+d), **kwargs)    # top-right diagonal
    
    kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes
    ax2.plot((-d,+d),(1-d,1+d), **kwargs)   # bottom-left diagonal
    ax2.plot((1-d,1+d),(1-d,1+d), **kwargs) # bottom-right diagonal
    
    # What's cool about this is that now if we vary the distance between
    # ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(),
    # the diagonal lines will move accordingly, and stay right at the tips
    # of the spines they are 'breaking'
    
    plt.show()
    

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