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"""
.. _ex-publication-figure:
===================================
Make figures more publication ready
===================================
In this example, we take some MNE plots and make some changes to make
a figure closer to publication-ready.
"""
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Daniel McCloy <dan.mccloy@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, ImageGrid
import mne
###############################################################################
# Suppose we want a figure with an evoked plot on top, and the brain activation
# below, with the brain subplot slightly bigger than the evoked plot. Let's
# start by loading some :ref:`example data <sample-dataset>`.
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_stc = op.join(data_path, 'MEG', 'sample', 'sample_audvis-meg-eeg-lh.stc')
fname_evoked = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evoked = mne.read_evokeds(fname_evoked, 'Left Auditory')
evoked.pick_types(meg='grad').apply_baseline((None, 0.))
max_t = evoked.get_peak()[1]
stc = mne.read_source_estimate(fname_stc)
###############################################################################
# During interactive plotting, we might see figures like this:
evoked.plot()
stc.plot(views='lat', hemi='split', size=(800, 400), subject='sample',
subjects_dir=subjects_dir, initial_time=max_t)
###############################################################################
# To make a publication-ready figure, first we'll re-plot the brain on a white
# background, take a screenshot of it, and then crop out the white margins.
# While we're at it, let's change the colormap, set custom colormap limits, and
# remove the default colorbar (so we can add a smaller, vertical one later):
colormap = 'viridis'
clim = dict(kind='value', lims=[4, 8, 12])
# Plot the STC, get the brain image, crop it
brain = stc.plot(views='lat', hemi='split', size=(800, 400), subject='sample',
subjects_dir=subjects_dir, initial_time=max_t,
background='w', colorbar=False, clim=clim, colormap=colormap)
screenshot = brain.screenshot()
brain.close()
###############################################################################
# Now let's crop out the white margins and the white gap between hemispheres.
# The screenshot has dimensions ``(h, w, 3)``, with the last axis being R, G, B
# values for each pixel, encoded as integers between ``0`` and ``255``. ``(255,
# 255, 255)`` encodes a white pixel, so we'll detect any pixels that differ
# from that:
nonwhite_pix = (screenshot != 255).any(-1)
nonwhite_row = nonwhite_pix.any(1)
nonwhite_col = nonwhite_pix.any(0)
cropped_screenshot = screenshot[nonwhite_row][:, nonwhite_col]
# before/after results
fig = plt.figure(figsize=(4, 4))
axes = ImageGrid(fig, 111, nrows_ncols=(2, 1), axes_pad=0.5)
for ax, image, title in zip(axes, [screenshot, cropped_screenshot],
['Before', 'After']):
ax.imshow(image)
ax.set_title('{} cropping'.format(title))
###############################################################################
# A lot of figure settings can be adjusted after the figure is created, but
# many can also be adjusted in advance by updating the
# :data:`~matplotlib.rcParams` dictionary. This is especially useful when your
# script generates several figures that you want to all have the same style:
# Tweak the figure style
plt.rcParams.update({
'ytick.labelsize': 'small',
'xtick.labelsize': 'small',
'axes.labelsize': 'small',
'axes.titlesize': 'medium',
'grid.color': '0.75',
'grid.linestyle': ':',
})
###############################################################################
# Now let's create our custom figure. There are lots of ways to do this step.
# Here we'll create the figure and the subplot axes in one step, specifying
# overall figure size, number and arrangement of subplots, and the ratio of
# subplot heights for each row using :mod:`GridSpec keywords
# <matplotlib.gridspec>`. Other approaches (using
# :func:`~matplotlib.pyplot.subplot2grid`, or adding each axes manually) are
# shown commented out, for reference.
# sphinx_gallery_thumbnail_number = 5
# figsize unit is inches
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(4.5, 3.),
gridspec_kw=dict(height_ratios=[3, 4]))
# alternate way #1: using subplot2grid
# fig = plt.figure(figsize=(4.5, 3.))
# axes = [plt.subplot2grid((7, 1), (0, 0), rowspan=3),
# plt.subplot2grid((7, 1), (3, 0), rowspan=4)]
# alternate way #2: using figure-relative coordinates
# fig = plt.figure(figsize=(4.5, 3.))
# axes = [fig.add_axes([0.125, 0.58, 0.775, 0.3]), # left, bot., width, height
# fig.add_axes([0.125, 0.11, 0.775, 0.4])]
# we'll put the evoked plot in the upper axes, and the brain below
evoked_idx = 0
brain_idx = 1
# plot the evoked in the desired subplot, and add a line at peak activation
evoked.plot(axes=axes[evoked_idx])
peak_line = axes[evoked_idx].axvline(max_t, color='#66CCEE', ls='--')
# custom legend
axes[evoked_idx].legend(
[axes[evoked_idx].lines[0], peak_line], ['MEG data', 'Peak time'],
frameon=True, columnspacing=0.1, labelspacing=0.1,
fontsize=8, fancybox=True, handlelength=1.8)
# remove the "N_ave" annotation
axes[evoked_idx].texts = []
# Remove spines and add grid
axes[evoked_idx].grid(True)
axes[evoked_idx].set_axisbelow(True)
for key in ('top', 'right'):
axes[evoked_idx].spines[key].set(visible=False)
# Tweak the ticks and limits
axes[evoked_idx].set(
yticks=np.arange(-200, 201, 100), xticks=np.arange(-0.2, 0.51, 0.1))
axes[evoked_idx].set(
ylim=[-225, 225], xlim=[-0.2, 0.5])
# now add the brain to the lower axes
axes[brain_idx].imshow(cropped_screenshot)
axes[brain_idx].axis('off')
# add a vertical colorbar with the same properties as the 3D one
divider = make_axes_locatable(axes[brain_idx])
cax = divider.append_axes('right', size='5%', pad=0.2)
cbar = mne.viz.plot_brain_colorbar(cax, clim, colormap, label='Activation (F)')
# tweak margins and spacing
fig.subplots_adjust(
left=0.15, right=0.9, bottom=0.01, top=0.9, wspace=0.1, hspace=0.5)
# add subplot labels
for ax, label in zip(axes, 'AB'):
ax.text(0.03, ax.get_position().ymax, label, transform=fig.transFigure,
fontsize=12, fontweight='bold', va='top', ha='left')
|
