Make figures more publication ready

In this example, we show several use cases to take MNE plots and customize them for a more publication-ready look.

# Authors: Eric Larson <larson.eric.d@gmail.com>
#          Daniel McCloy <dan.mccloy@gmail.com>
#          Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause

Imports

We are importing everything we need for this example:

import os.path as op

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import (make_axes_locatable, ImageGrid,
                                     inset_locator)

import mne

Evoked plot with brain activation

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 example data.

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)

Out:

Reading /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-ave.fif ...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Found the data of interest:
        t =    -199.80 ...     499.49 ms (Left Auditory)
        0 CTF compensation matrices available
        nave = 55 - aspect type = 100
Projections have already been applied. Setting proj attribute to True.
No baseline correction applied
Removing projector <Projection | PCA-v1, active : True, n_channels : 102>
Removing projector <Projection | PCA-v2, active : True, n_channels : 102>
Removing projector <Projection | PCA-v3, active : True, n_channels : 102>
Removing projector <Projection | Average EEG reference, active : True, n_channels : 60>
Applying baseline correction (mode: mean)

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,
         time_viewer=False, show_traces=False)

Out:

Using control points [ 5.17909658  6.18448887 18.83197989]

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,
                 time_viewer=False, show_traces=False)
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 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 GridSpec keywords. Other approaches (using subplot2grid(), or adding each axes manually) are shown commented out, for reference.

# 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
for text in list(axes[evoked_idx].texts):
    text.remove()
# 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')

Custom timecourse with montage inset

Suppose we want a figure with some mean timecourse extracted from a number of sensors, and we want a smaller panel within the figure to show a head outline with the positions of those sensors clearly marked. If you are familiar with MNE, you know that this is something that mne.viz.plot_compare_evokeds() does, see an example output in HF-SEF dataset at the bottom.

In this part of the example, we will show you how to achieve this result on your own figure, without having to use mne.viz.plot_compare_evokeds()!

Let’s start by loading some example data.

data_path = mne.datasets.sample.data_path()
fname_raw = op.join(data_path, "MEG", "sample", "sample_audvis_raw.fif")
raw = mne.io.read_raw_fif(fname_raw)

# For the sake of the example, we focus on EEG data
raw.pick_types(meg=False, eeg=True)

Out:

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
Removing projector <Projection | PCA-v1, active : False, n_channels : 102>
Removing projector <Projection | PCA-v2, active : False, n_channels : 102>
Removing projector <Projection | PCA-v3, active : False, n_channels : 102>

Measurement date	December 03, 2002 19:01:10 GMT
Experimenter	MEG
Participant	Unknown
Digitized points	146 points
Good channels	59 EEG
Bad channels	None
EOG channels	Not available
ECG channels	Not available
Sampling frequency	600.61 Hz
Highpass	0.10 Hz
Lowpass	172.18 Hz
Filenames	sample_audvis_raw.fif
Duration	00:04:37 (HH:MM:SS)

Let’s make a plot.

# channels to plot:
to_plot = [f"EEG {i:03}" for i in range(1, 5)]

# get the data for plotting in a short time interval from 10 to 20 seconds
start = int(raw.info['sfreq'] * 10)
stop = int(raw.info['sfreq'] * 20)
data, times = raw.get_data(picks=to_plot,
                           start=start, stop=stop, return_times=True)

# Scale the data from the MNE internal unit V to µV
data *= 1e6
# Take the mean of the channels
mean = np.mean(data, axis=0)
# make a figure
fig, ax = plt.subplots(figsize=(4.5, 3))
# plot some EEG data
ax.plot(times, mean)

So far so good. Now let’s add the smaller figure within the figure to show exactly, which sensors we used to make the timecourse. For that, we use an “inset_axes” that we plot into our existing axes. The head outline with the sensor positions can be plotted using the Raw object that is the source of our data. Specifically, that object already contains all the sensor positions, and we can plot them using the plot_sensors method.

# recreate the figure (only necessary for our documentation server)
fig, ax = plt.subplots(figsize=(4.5, 3))
ax.plot(times, mean)
axins = inset_locator.inset_axes(ax, width="30%", height="30%", loc=2)

# pick_channels() edits the raw object in place, so we'll make a copy here
# so that our raw object stays intact for potential later analysis
raw.copy().pick_channels(to_plot).plot_sensors(title="", axes=axins)

That looks nice. But the sensor dots are way too big for our taste. Luckily, all MNE-Python plots use Matplotlib under the hood and we can customize each and every facet of them. To make the sensor dots smaller, we need to first get a handle on them to then apply a *.set_* method on them.

# If we inspect our axes we find the objects contained in our plot:
print(axins.get_children())

Out:

[Text(0, 0, ''), <matplotlib.lines.Line2D object at 0x7fc8b2508670>, <matplotlib.lines.Line2D object at 0x7fc90628a250>, <matplotlib.lines.Line2D object at 0x7fc90628a790>, <matplotlib.lines.Line2D object at 0x7fc90628ab20>, <matplotlib.collections.PathCollection object at 0x7fc90628a310>, <matplotlib.spines.Spine object at 0x7fc90889c2b0>, <matplotlib.spines.Spine object at 0x7fc90889c1c0>, <matplotlib.spines.Spine object at 0x7fc90889c6d0>, <matplotlib.spines.Spine object at 0x7fc90889c250>, <matplotlib.axis.XAxis object at 0x7fc90889cd90>, <matplotlib.axis.YAxis object at 0x7fc90889cd30>, Text(0.5, 1.0, ''), Text(0.0, 1.0, ''), Text(1.0, 1.0, ''), <matplotlib.patches.Rectangle object at 0x7fc9086c8100>]

That’s quite a a lot of objects, but we know that we want to change the sensor dots, and those are most certainly a “PathCollection” object. So let’s have a look at how many “collections” we have in the axes.

print(axins.collections)

Out:

<Axes.ArtistList of 1 collections>

There is only one! Those must be the sensor dots we were looking for. We finally found exactly what we needed. Sometimes this can take a bit of experimentation.

sensor_dots = axins.collections[0]

# Recreate the figure once more; shrink the sensor dots; add axis labels
fig, ax = plt.subplots(figsize=(4.5, 3))
ax.plot(times, mean)
axins = inset_locator.inset_axes(ax, width="30%", height="30%", loc=2)
raw.copy().pick_channels(to_plot).plot_sensors(title="", axes=axins)
sensor_dots = axins.collections[0]
sensor_dots.set_sizes([1])
# add axis labels, and adjust bottom figure margin to make room for them
ax.set(xlabel="Time (s)", ylabel="Amplitude (µV)")
fig.subplots_adjust(bottom=0.2)

Estimated memory usage: 13 MB

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