

.. _sphx_glr_gallery_pylab_examples_broken_axis.py:


===========
Broken Axis
===========

Broken axis example, where the y-axis will have a portion cut out.




.. image:: /gallery/pylab_examples/images/sphx_glr_broken_axis_001.png
    :align: center





.. code-block:: python

    import matplotlib.pyplot 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    # 30 points between [0, 0.2) originally made using np.random.rand(30)*.2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, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 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 to 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()

**Total running time of the script:** ( 0 minutes  0.135 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: broken_axis.py <broken_axis.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: broken_axis.ipynb <broken_axis.ipynb>`

.. rst-class:: sphx-glr-signature

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
