Customizing plots

With the exception of plot.brain plots, Eelbrain’s plots are all based on matplotlib. A lot of fine control over the plots can be achieved through two means:

• Customizing Matplotlib globally, before calling Eelbrain plotting functions, through styles or ``rcParams` <https://matplotlib.org/tutorials/ introductory/customizing.html#customizing-matplotlib-with-style-sheets-and-rcparams>`_

• Accessing and modifying components of the plots after calling Eelbrain plotting functions

# sphinx_gallery_thumbnail_number = 3
from eelbrain import *
import matplotlib.style

ds = datasets.get_uv()

Styles

Matplotlib offers several styles

p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

# Apply a style
matplotlib.style.use('ggplot')
p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

matplotlib.style.use('bmh')
p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

matplotlib.style.use('seaborn')
p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

• 
• 
• 
• 

rcParams

Individual styles parameters can be modified directly in``rcParams``

matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.size'] = 8
p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

# revert back to the default style
matplotlib.style.use('default')

Modifying components

Matplotlib can be used to fully customize a plot’s appearance by accessing the underlying matplotlib.figure.Figure object through the plot’s figure aatribute.

p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2)

p = plot.Boxplot('fltvar', 'A % B', match='rm', data=ds, w=2, h=3, xlabel=False)
ax = p.figure.axes[0]
ax.set_xticklabels(['A long label', 'An even longer label', 'Another label', 'And yet another one'], rotation=45, ha='right')
ax.grid(axis='y')
ax.set_yticks([-2, 0, 2])
ax.tick_params('y', left=False)
for spine in ax.spines.values():
    spine.set_visible(False)
p.figure.tight_layout()

• 
•