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================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib

12 - Customizing Matplotlib: Configurations and Stylesheets

Customizing Matplotlib: Configurations and Stylesheets

Matplotlib’s default plot settings are often the subject of complaint among its users. While much is slated to change in the 2.0 Matplotlib release, the ability to customize default settings helps bring the package in line with your own aesthetic preferences. Here we’ll walk through some of Matplotlib’s runtime configuration (rc) options, and take a look at the newer stylesheets feature, which contains some nice sets of default configurations.

Plot Customization by Hand

Throughout this chapter, we’ve seen how it is possible to tweak individual plot set‐ tings to end up with something that looks a little bit nicer than the default. It’s possi‐ ble to do these customizations for each individual plot. For example, here is a fairly drab default histogram

import matplotlib.pyplot as plt
plt.style.use('classic')
import numpy as np
%matplotlib inline

x=np.random.randn(1000)
plt.hist(x);


# We can adjust this by hand to make it a much more visually pleasing plot
# use  a gray backgoung
ax=plt.axes()
ax.set_axisbelow(True)

# draw solid white grid line
plt.grid(color='w', linestyle='solid')

# hide axis spines
for spine in ax.spines.values():
    spine.set_visible(False)
#hide top and right ticks
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()

#lighten ticks and labels
ax.tick_params(color='gray',direction='out')
for tick in ax.get_xticklabels():
    tick.set_color('gray')

for tick in ax.get_yticklabels():
    tick.set_color('gray')

# control face and edge color of histogram
ax.hist(x,edgecolor='#E6E6E6', color='#EE6666')

(array([ 12.,  37.,  98., 173., 257., 213., 124.,  59.,  23.,   4.]),
 array([-2.90788595, -2.30201003, -1.69613411, -1.09025818, -0.48438226,
         0.12149367,  0.72736959,  1.33324551,  1.93912144,  2.54499736,
         3.15087329]),
 <BarContainer object of 10 artists>)

This looks better, and you may recognize the look as inspired by the look of the R language’s ggplot visualization package. But this took a whole lot of effort! We defi‐ nitely do not want to have to do all that tweaking each time we create a plot. Fortu‐ nately, there is a way to adjust these defaults once in a way that will work for all plots.

Changing the Defaults: rcParams

Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create. You can adjust this configuration at any time using the plt.rc convenience routine. Let’s see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before. We’ll start by saving a copy of the current rcParams dictionary, so we can easily reset these changes in the current session:

Ipython_default=plt.rcParams.copy()

from matplotlib import cycler
colors = cycler('color',
['#EE6666', '#3388BB', '#9988DD',
'#EECC55', '#88BB44', '#FFBBBB'])

plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',
axisbelow=True, grid=True, prop_cycle=colors)

plt.rc('grid', color='w', linestyle='solid')
plt.rc('xtick', direction='out', color='gray')
plt.rc('ytick', direction='out', color='gray')
plt.rc('patch', edgecolor='#E6E6E6')
plt.rc('lines', linewidth=2)

# With these settings defined, we can now create a plot and see our settings in action
plt.hist(x);


# Let’s see what simple line plots look like with these rc parameters
for i in range(4):
    plt.plot(np.random.rand(10))

I find this much more aesthetically pleasing than the default styling. If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes! These settings can be saved in a .matplotlibrc file, which you can read about in the Matplotlib documentation. That said, I prefer to customize Mat‐ plotlib using its stylesheets instead.

Stylesheets

Even if you don’t create your own style, the stylesheets included by default are extremely useful. The available styles are listed in plt.style.available—here I’ll list only the first five for brevity:

plt.style.available[:5]

['Solarize_Light2',
 '_classic_test_patch',
 '_mpl-gallery',
 '_mpl-gallery-nogrid',
 'bmh']

plt.style.use('Solarize_Light2')

with plt.style.context('Solarize_Light2'):
    make_a_plot()

NameError: name 'make_a_plot' is not defined

def hist_and_lines():
    np.random.seed(0)
    fig, ax= plt.subplots(1,2,figsize=(11,4))
    ax[0].hist(np.random.randn(1000))
    for i in range(3):
        ax[1].plot(np.random.rand(100))
        ax[1].legend(['a','b','c'],loc='lower left')

Default style

The default style is what we’ve been seeing so far throughout the book; we’ll start with that. First, let’s reset our runtime configuration to the notebook default:

# reset rcParams
plt.rcParams.update(Ipython_default);

# let's see how it looks
hist_and_lines()


# let use one of the styles avialable
plt.style.available[:]

['Solarize_Light2',
 '_classic_test_patch',
 '_mpl-gallery',
 '_mpl-gallery-nogrid',
 'bmh',
 'classic',
 'dark_background',
 'fast',
 'fivethirtyeight',
 'ggplot',
 'grayscale',
 'seaborn',
 'seaborn-bright',
 'seaborn-colorblind',
 'seaborn-dark',
 'seaborn-dark-palette',
 'seaborn-darkgrid',
 'seaborn-deep',
 'seaborn-muted',
 'seaborn-notebook',
 'seaborn-paper',
 'seaborn-pastel',
 'seaborn-poster',
 'seaborn-talk',
 'seaborn-ticks',
 'seaborn-white',
 'seaborn-whitegrid',
 'tableau-colorblind10']

# Using fivethrityeight
with plt.style.context('fivethirtyeight'):
    hist_and_lines()


# Using ggplot
with plt.style.context('ggplot'):
    hist_and_lines()

Bayesian Methods for Hackers style There is a very nice short online book called Probabilistic Programming and Bayesian Methods for Hackers; it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually appealing style throughout the book. This style is reproduced in the bmh stylesheet

# Using bmh
with plt.style.context('bmh'):
    hist_and_lines()

Dark background For figures used within presentations, it is often useful to have a dark rather than light background. The dark_background style provides this

with plt.style.context('dark_background'):
    hist_and_lines()

Grayscale Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.

with plt.style.context('grayscale'):
    hist_and_lines()

Seaborn style Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in “Visualization with Seaborn” on page 311). As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.

import seaborn
hist_and_lines()


with plt.style.context('seaborn'):
    hist_and_lines()