Fill Between and Alpha#
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 used to enhance a graph's
visual appearance. Let's compare two graphs of financial data 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 r = (cbook.get_sample_data('goog.npz', np_load=True)['price_data'] .view(np.recarray)) # 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(r.date, r.close, lw=2) ax2.fill_between(r.date, pricemin, r.close, alpha=0.7) for ax in ax1, ax2: ax.grid(True) ax.label_outer() ax1.set_ylabel('price') 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 filled regions to plot +/- one standard deviation of the mean position of the population. Here the alpha channel is useful, not just aesthetic.
# Fixing random state for reproducibility np.random.seed(19680801) Nsteps, Nwalkers = 100, 250 t = np.arange(Nsteps) # an (Nsteps x Nwalkers) array of random walk steps S1 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers) S2 = 0.002 + 0.01*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') ax.plot(t, mu2, lw=2, label='mean population 2') ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='C0', alpha=0.4) ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='C1', alpha=0.4) 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()
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 dashed
line, and the plus/minus one sigma deviation from the mean is shown
as the filled region. We use the where mask
X > upper_bound to
find the region where the walker is outside the one sigma boundary,
and shade that region red.
# Fixing random state for reproducibility np.random.seed(1) 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') ax.plot(t, mu*t, lw=1, label='population mean', color='C0', ls='--') ax.fill_between(t, lower_bound, upper_bound, facecolor='C0', alpha=0.4, 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, fc='red', alpha=0.4) ax.fill_between(t, lower_bound, X, where=X < lower_bound, fc='red', alpha=0.4) ax.set_xlabel('num steps') ax.set_ylabel('position') ax.grid()
Total running time of the script: ( 0 minutes 1.622 seconds)