

.. _sphx_glr_gallery_recipes_fill_between_alpha.py:


Fill Between and Alpha
======================

The :meth:`~matplotlib.axes.Axes.fill_between` function generates a
shaded region between a min and max boundary that is useful for
illustrating ranges.  It has a very handy ``where`` argument to
combine filling with logical ranges, e.g., to just fill in a curve over
some threshold value.

At its most basic level, ``fill_between`` can be use to enhance a
graphs visual appearance. Let's compare two graphs of a financial
times with a simple line plot on the left and a filled line on the
right.



.. code-block:: python


    import matplotlib.pyplot as plt
    import numpy as np
    import matplotlib.cbook as cbook

    # load up some sample financial data
    with cbook.get_sample_data('goog.npz') as datafile:
        r = np.load(datafile)['price_data'].view(np.recarray)
    # Matplotlib prefers datetime instead of np.datetime64.
    date = r.date.astype('O')
    # create two subplots with the shared x and y axes
    fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)

    pricemin = r.close.min()

    ax1.plot(date, r.close, lw=2)
    ax2.fill_between(date, pricemin, r.close, facecolor='blue', alpha=0.5)

    for ax in ax1, ax2:
        ax.grid(True)

    ax1.set_ylabel('price')
    for label in ax2.get_yticklabels():
        label.set_visible(False)

    fig.suptitle('Google (GOOG) daily closing price')
    fig.autofmt_xdate()




.. image:: /gallery/recipes/images/sphx_glr_fill_between_alpha_001.png
    :align: center




The alpha channel is not necessary here, but it can be used to soften
colors for more visually appealing plots.  In other examples, as we'll
see below, the alpha channel is functionally useful as the shaded
regions can overlap and alpha allows you to see both.  Note that the
postscript format does not support alpha (this is a postscript
limitation, not a matplotlib limitation), so when using alpha save
your figures in PNG, PDF or SVG.

Our next example computes two populations of random walkers with a
different mean and standard deviation of the normal distributions from
which the steps are drawn.  We use shared regions to plot +/- one
standard deviation of the mean position of the population.  Here the
alpha channel is useful, not just aesthetic.



.. code-block:: python


    Nsteps, Nwalkers = 100, 250
    t = np.arange(Nsteps)

    # an (Nsteps x Nwalkers) array of random walk steps
    S1 = 0.002 + 0.01*np.random.randn(Nsteps, Nwalkers)
    S2 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers)

    # an (Nsteps x Nwalkers) array of random walker positions
    X1 = S1.cumsum(axis=0)
    X2 = S2.cumsum(axis=0)


    # Nsteps length arrays empirical means and standard deviations of both
    # populations over time
    mu1 = X1.mean(axis=1)
    sigma1 = X1.std(axis=1)
    mu2 = X2.mean(axis=1)
    sigma2 = X2.std(axis=1)

    # plot it!
    fig, ax = plt.subplots(1)
    ax.plot(t, mu1, lw=2, label='mean population 1', color='blue')
    ax.plot(t, mu2, lw=2, label='mean population 2', color='yellow')
    ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='blue', alpha=0.5)
    ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='yellow', alpha=0.5)
    ax.set_title('random walkers empirical $\mu$ and $\pm \sigma$ interval')
    ax.legend(loc='upper left')
    ax.set_xlabel('num steps')
    ax.set_ylabel('position')
    ax.grid()




.. image:: /gallery/recipes/images/sphx_glr_fill_between_alpha_002.png
    :align: center




The ``where`` keyword argument is very handy for highlighting certain
regions of the graph.  ``where`` takes a boolean mask the same length
as the x, ymin and ymax arguments, and only fills in the region where
the boolean mask is True.  In the example below, we simulate a single
random walker and compute the analytic mean and standard deviation of
the population positions.  The population mean is shown as the black
dashed line, and the plus/minus one sigma deviation from the mean is
shown as the yellow filled region.  We use the where mask
``X > upper_bound`` to find the region where the walker is above the one
sigma boundary, and shade that region blue.



.. code-block:: python


    Nsteps = 500
    t = np.arange(Nsteps)

    mu = 0.002
    sigma = 0.01

    # the steps and position
    S = mu + sigma*np.random.randn(Nsteps)
    X = S.cumsum()

    # the 1 sigma upper and lower analytic population bounds
    lower_bound = mu*t - sigma*np.sqrt(t)
    upper_bound = mu*t + sigma*np.sqrt(t)

    fig, ax = plt.subplots(1)
    ax.plot(t, X, lw=2, label='walker position', color='blue')
    ax.plot(t, mu*t, lw=1, label='population mean', color='black', ls='--')
    ax.fill_between(t, lower_bound, upper_bound, facecolor='yellow', alpha=0.5,
                    label='1 sigma range')
    ax.legend(loc='upper left')

    # here we use the where argument to only fill the region where the
    # walker is above the population 1 sigma boundary
    ax.fill_between(t, upper_bound, X, where=X > upper_bound, facecolor='blue',
                    alpha=0.5)
    ax.set_xlabel('num steps')
    ax.set_ylabel('position')
    ax.grid()




.. image:: /gallery/recipes/images/sphx_glr_fill_between_alpha_003.png
    :align: center




Another handy use of filled regions is to highlight horizontal or
vertical spans of an axes -- for that matplotlib has some helper
functions :meth:`~matplotlib.axes.Axes.axhspan` and
:meth:`~matplotlib.axes.Axes.axvspan` and example
:ref:`sphx_glr_gallery_pylab_examples_axhspan_demo.py`.


**Total running time of the script:** ( 0 minutes  0.139 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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