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You've spent hours designing the perfect figure and now it's time to add it to a presentation or publication but the font sizes in the figure are too small to see for the people in the back of the room or too large for the figure space in the publication. You've got titles, subtitles, axis labels, legends, text objects, and other labels but their handles are inaccessible or scattered between several blocks of code. Making your figure readable no longer requires digging through your code and setting each text object's font size manually.

Starting in MATLAB R2022a, you have full control over a figure's font sizes and font units using the new fontsize function (see release notes ).

Use fontsize() to

• Set FontSize and FontUnits properties for all text within specified graphics objects
• Incrementally increase or decrease font sizes
• Specify a scaling factor to maintain relative font sizes
• Reset font sizes and font units to their default values . Note that the default font size and units may not be the same as the font sizes/units set directly with your code.

When specifying an object handle or an array of object handles, fontsize affects the font sizes and font units of text within all nested objects.

While you're at it, also check out the new fontname function that allows you to change the font name of objects in a figure!

Give the new fontsize function a test drive using the following demo figure in MATLAB R2022a or later and try the following commands:

% Increase all font sizes within the figure by a factor of 1.5
fontsize(fig, scale=1.5)

% Set all font sizes in the uipanel to 16
fontsize(uip, 16, "pixels")

% Incrementally increase the font sizes of the left two axes (x1.1)
% and incrementally decrease the font size of the legend (x0.9)
fontsize([ax1, ax2], "increase")
fontsize(leg, "decrease")

% Reset the font sizes within the entire figure to default values
fontsize(fig, "default")

% Create fake behavioral data
rng('default')
fy = @(a,x)a*exp(-(((x-8).^2)/(2*3.^2)));
x = 1 : 0.5 : 20;
y = fy(32,x);
ynoise = y+8*rand(size(y))-4;
selectedTrial = 13;

% Plot behavioral data
fig = figure('Units','normalized','Position',[0.1, 0.1, 0.4, 0.5]);
movegui(fig, 'center')
tcl = tiledlayout(fig,2,2); 
ax1 = nexttile(tcl); 
hold(ax1,'on')
h1 = plot(ax1, x, ynoise, 'bo', 'DisplayName', 'Response');
h2 = plot(ax1, x, y, 'r-', 'DisplayName', 'Expected');
grid(ax1, 'on')
title(ax1, 'Behavioral Results')
subtitle(ax1, sprintf('Trial %d', selectedTrial))
xlabel(ax1, 'Time (seconds)','Interpreter','Latex')
ylabel(ax1, 'Responds ($\frac{deg}{sec}$)','Interpreter','Latex')
leg = legend([h1,h2]);

% Plot behavioral error
ax2 = nexttile(tcl,3);
behavioralError = ynoise-y; 
stem(ax2, x, behavioralError)
yline(ax2, mean(behavioralError), 'r--', 'Mean', ...
    'LabelVerticalAlignment','bottom')
grid(ax2, 'on')
title(ax2, 'Behavioral Error')
subtitle(ax2, ax1.Subtitle.String)
xlabel(ax2, ax1.XLabel.String,'Interpreter','Latex')
ylabel(ax2, 'Response - Expected ($\frac{deg}{sec}$)','Interpreter','Latex')

% Simulate spike train data
ntrials = 25; 
nSamplesPerSecond = 3; 
nSeconds = max(x) - min(x); 
nSamples = ceil(nSeconds*nSamplesPerSecond);
xTime = linspace(min(x),max(x), nSamples);
spiketrain = round(fy(1, xTime)+(rand(ntrials,nSamples)-.5));
[trial, sample] = find(spiketrain);
time = xTime(sample);

% Spike raster plot
axTemp = nexttile(tcl, 2, [2,1]);
uip = uipanel(fig, 'Units', axTemp.Units, ...
    'Position', axTemp.Position, ...
    'Title', 'Neural activity', ...
    'BackgroundColor', 'W');
delete(axTemp)
tcl2 = tiledlayout(uip, 3, 1);
pax1 = nexttile(tcl2); 
plot(pax1, time, trial, 'b.', 'MarkerSize', 4)
yline(pax1, selectedTrial-0.5, 'r-', ...
    ['\leftarrow Trial ',num2str(selectedTrial)], ...
    'LabelHorizontalAlignment','right', ...
    'FontSize', 8); 
linkaxes([ax1, ax2, pax1], 'x')
pax1.YLimitMethod = 'tight';
title(pax1, 'Spike train')
xlabel(pax1, ax1.XLabel.String)
ylabel(pax1, 'Trial #')

% Show MRI
pax2 = nexttile(tcl2,2,[2,1]); 
[I, cmap] = imread('mri.tif');
imshow(I,cmap,'Parent',pax2)
hold(pax2, 'on')
th = 0:0.1:2*pi; 
plot(pax2, 7*sin(th)+84, 5*cos(th)+90, 'r-','LineWidth',2)
text(pax2, pax2.XLim(2), pax2.YLim(1), 'ML22a',...
    'FontWeight', 'bold', ...
    'Color','r', ...
    'VerticalAlignment', 'top', ...
    'HorizontalAlignment', 'right', ...
    'BackgroundColor',[1 0.95 0.95])
title(pax2, 'Area of activation')

% Overall figure title
title(tcl, 'Single trial responses')

This Community Highlight is attached as a live script.

If you are interested in live script lecture notes in the following areas, take a look at the short course ( Advanced MATLAB for Scientific Computing ) developed at Stanford. You can also download the required data for the examples from the course GitHub page.

• MATLAB Fundamentals
• Graphics and Data Visualization
• Efficient Code Writing
• System and File Manipulation
• Big Data Handling
• Numerical Linear Algebra
• Numerical Optimization
• Symbolic Toolbox, ODE, and PDE
• Statistical and Machine Learning
• Deep Learning
• Object-Oriented Programming
• Using MATLAB with Other Programming Languages
• Image Processing, Computer Vision, and Image Acquisition
• Signal Processing, Audio, and DSP System

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Cody is a useful tool to practice MATLAB skills not only by solving the problems but also learn from each other’s solutions. Sometimes you see subpar solutions that are cheats and hacks. With the flagging feature we released recently, you can help us identify solutions that administrators, including Community Advisory Board members, can review and delete.

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In many universities, introductory programming is taught as a foundation course. Students from different departments are usually brought together to learn to program in these foundation courses. Their home departments may have a programming language preference and that preference may change from department to department. Some universities either strictly teach one language in a single course, some of them teach multiple languages in the same course and give students the flexibility to choose their language for the assignments and projects. How can we make students multilingual when it comes to programming? Is there a way to teach multiple languages in a fair light, side by side without creating a new course or sacrificing one language to teach the other one? Dr. Nathan Kutz from the University of Washington found a creative way to teach MATLAB and Python side by side in his AMATH 301 course. This course is an introductory programming course at the University of Washington and almost all engineering students take it. Do you wonder how Dr. Kutz taught this course? Check out these recordings and course resources! They can be utilized in an in-person or a distance learning setting:

• Main Course Page
• Sample course videos (MATLAB and Python side by side)

Are you looking for ways to keep your students engaged in a virtual setting? Would you like to spice up your courses with hands-on projects? Using Arduino Engineering Kit, you can achieve these. Due to COVID-19, many instructors started to look for creative ways of giving students a lab experience. Some of them chose to create virtual labs, some of them designed hardware projects with low-cost hardware or integrated hardware projects kits to their curriculum. If you are interested in how Dr. Azadi from San Francisco State University used Arduino Engineering Kit during the pandemic to teach his Mechatronics course, check out these articles:

• Creating a Virtual Hands-on Mechatronics Course with MATLAB, Simulink, and the Arduino Engineering Kit
• Keeping things creative: Real-world learning from a remote perspective

I need to model a brushless motor for which I only have the data of voltage, power, speed, nominal torque, starting torque, max current and total weight, which moves a bicycle. I have studied the Permanent Magnet Synchronous Machine power_pmmotor Simulink example, but I do not have all the required data. My question is whether it is possible to make an approximate model with my few data. I guess some data could be assumed, but I don't know what typical values ​​would be correct. I would greatly appreciate any suggestion. My best regards.

Every day, thousands of people ask questions on MATLAB Answers and many of these are about their code. Questions such as “How can I make this faster?”, “Why do I get this error message?” or “Why don’t I get the answer I expect?”. There’s often one crucial thing missing though – the code in question!

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Make it easier to help me help you.

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In my case, I almost always focus on problems that I can easily reproduce. I rarely know the answer to any question off the top of my head and so what I like to do is start off with the problem you are facing and use the various tools available to me such as the profiler or debugger to figure it out. This is the fun of it all for me – I almost always learn something by doing this and you get helped out as a side effect!

The easier I can reproduce your issue, the more likely I am to get started. If I can’t reproduce anything and the answer isn’t immediately obvious to me I’ll just move onto the next question. One example that demonstrates this perfectly is a case where someone’s MATLAB code was running too slowly. All of the code was available so I could run it on my machine, profile it and provide a speed-up of almost 150x.

It's not always feasible or desirable to post all of your code in which case you need to come up with a minimal, reproducible example. What’s the smallest amount of code and data you can post that I can run on my machine and see what you see? This may be more work for you but it will greatly increase your chances of receiving an answer to your question.