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"""
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.
"""
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()
###############################################################################
# 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.
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(r'random walkers empirical $\mu$ and $\pm \sigma$ interval')
ax.legend(loc='upper left')
ax.set_xlabel('num steps')
ax.set_ylabel('position')
ax.grid()
###############################################################################
# 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.
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()
###############################################################################
# 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
# :doc:`/gallery/subplots_axes_and_figures/axhspan_demo`.
plt.show()
|
