Latest Contributions
In the latest Graphics and App Building blog article, documentation writer Jasmine Poppick modernized a figure-based bridge analysis app by replacing uicontrol with new UI components and uifigure, resulting in cleaner code, better layouts, and expanded functionality in R2025a.
https://blogs.mathworks.com/graphics-and-apps/2025/08/19/__from-uicontrol-to-ui-components
This article covers the following topics:
Why and when to move from uicontrol and figure to modern UI components and uifigure.
How to replace uicontrol objects with equivalent UI component functions (uicheckbox, uidropdown, uispinner, etc.).
How to update callback code to match new component properties and behaviors.
How to adopt new UI component types (like spinners) to simplify validation and improve usability.
How to configure existing components with modern options (sortable tables, auto-fitting columns, editable data).
How to apply visual styling with uistyle and addStyle to make apps more user-friendly.
How to use uigridlayout to create flexible, adaptive layouts instead of manually managing positions.
The benefits of switching from figure to uifigure for app-building workflows.
A full before-and-after example of modernizing an existing app with incremental, practical updates.
There is a communication regarding "How can I set the text font style of a Data Cursor object interactively on a plot?". But I am not clear on the answer found in this link:
https://www.mathworks.com/matlabcentral/answers/95968-how-can-i-set-the-text-font-style-of-a-data-cursor-object
I do not know how and where to put the recommended commands. Would you please clarfity and give me more details?
Thank you.
Worth the wait: seven new online training courses and one new learning path were released with 25a, covering topics in Controls, Electrification, and Physical Modeling. This release also brings new functionality to support interactions across both MATLAB and Simulink within a single course, beginning with the new Controls courses below:
- Control System Design with MATLAB and Simulink (learning path – includes the 5 controls courses listed below)
- Control System Modeling Essentials
- Linearization of Nonlinear Systems
- Control System Analysis Techniques
- PID Control Techniques
- Classical Controller Design Techniques
- Battery State Estimation
- Motor Modeling with Simscape Electrical
These got released last week and the process for using them on your local machine with MATLAB is very similar to how you use the local deepseek models as I demonstrated in my February blog post How to run local DeepSeek models and use them with MATLAB » The MATLAB Blog - MATLAB & Simulink
You need Ollama and the LLMs with MATLAB package installed (Details on how to do this in the blog post above). Then you run the following in your operating systems' command line
ollama pull gpt-oss:20b
Over to MATLAB and set up a chat session
>> chat = ollamaChat("gpt-oss:20b")
chat =
ollamaChat with properties:
ModelName: "gpt-oss:20b"
Endpoint: "127.0.0.1:11434"
TopK: Inf
MinP: 0
TailFreeSamplingZ: 1
Temperature: 1
TopP: 1
StopSequences: [0×0 string]
TimeOut: 120
SystemPrompt: []
ResponseFormat: "text"
FunctionNames: []
txt = generate(chat,"Who are you?")
txt =
"I’m ChatGPT – a conversational AI developed by OpenAI. My core is the GPT‑4 language model, which has been trained on a massive mix of text from books, websites, articles and other sources to understand and generate human‑like language. I don’t have feelings, consciousness, or a personal identity; I’m a tool that can help answer questions, brainstorm ideas, explain concepts, draft text, and more. My goal is to understand the context you give me and respond in a helpful, accurate and safe way. If there’s something specific you’d like to know or do, just let me know!"
This is the smaller of the two, new open models and it is bringing my aging desktop to its knees. My GPU is too small to do the work so I think everything is happening on the CPU and its slooooow. Will try on my Mac next
Let me know if you try this out!
Long before I joined MathWorks, I was a member of the academic Research Software Engineering (RSE) community where part of my mission was to introduce basic software engineering concepts to the research community. Things like version control, testing and even simply writing code instead of using only pointy-clicky GUIs before copying and pasting the results plot into a word document. I've seen things..........*shudders*
The RSE movement is still going very strong and I am elated that MathWorks is increasingly interacting with it. One example of such interaction is a video tutorial contributed by my colleauge @Mihaela Jarema to a comminity seminar series called 'A summer of Testing' It's linked to below
The video assumes you've never run a test before and gently guides you through the principles. Along the way you'll learn about some of MATLAB's superb testing capabilities. Things like
- Unit testing Framework
- Test Browser App
- Code Coverage
- Test Fixtures (Setup and teardown)
- Test driven devellopment
- Function argument validation
- CI/CD using GitHub actions
Go check out out.
I have started a blog series on the history of image display in MATLAB. If this topic interests you, and if there is something in particular you would like me to address in the series, let me know.
Hello to all!
I would like to share with the Matlab and Simulink community this video about Neural Networks in Simulink.
This is a series of videos that use a multilayer perceptron implemented in Simulink as a case study. Why Simulink? Because it's a visual and intuitive modeling tool, you can see the forward propagation of this network and better understand the flow. The objective of this series is to show the implementation using Simulink for both simulation and Arduino, as well as its training using Matlab and Matlab with Deep Learning Toolbox, and a video of training with Python.
The video is in Spanish, but the Simulink model is available in English for the entire community; subtitles are also available.
The files are located in the first comment of each video. We hope you find it interesting and enjoyable. Best regards!
Here I share the link to the first video.
In case you missed it in my overview of the MATLAB R2025a release, Markdown support has been greatly improved. This picture says it all
In many parts of Africa, particularly in technical universities and engineering institutes, physical laboratories are scarce or poorly equipped. This reality deeply limits the hands-on experience students deserve, especially in fields like control systems, signal processing, power electronics, and fluid mechanics.
But MATLAB and Simulink can fill part of this gap.
As an educator and researcher, I’ve made it my mission to promote MATLAB as a didactic simulation environment that brings real-world experimentation into the virtual space—affordable, accessible, and scalable. Whether simulating dynamic systems, visualizing electromagnetic fields, or tuning PID controllers interactively, students can develop strong intuition without needing costly hardware.
🔧 I’ve used MATLAB to teach:
- Power systems and control theory without needing real generators or oscilloscopes,
- Hydrology and environmental modeling without field sensors,
- Robotics and AI concepts even where no robot is available.
🌍 This is more than a tool for me. It’s a bridge between educational ambition and limited infrastructure.
I dream of creating MATLAB-based virtual laboratories across African institutions. And I know I’m not alone.
Is anyone else here working on similar goals in under-resourced regions? Let’s connect and make it real.
— Patrick K.N.
As someone who grew up programming in C#, I often find myself wishing for a tighter, more native integration between MATLAB and C#.
There’s so much I dream of doing—leveraging the power of Simulink models or MATLAB’s advanced numerical libraries inside my .NET desktop or web applications. Of course, I know there are some workarounds: COM automation, MATLAB Engine API for .NET, or using MATLAB Compiler SDK… but let’s be honest: it’s not quite as seamless as I’d hope.
I imagine a world where:
- I could directly call MATLAB functions from C# as if they were .NET assemblies, without middleware.
- Simulink blocks could generate portable C# code (not just C/C++).
- MATLAB UI components could be embedded in WPF/WinForms apps natively.
Until then... we make do with what we have. But the vision remains.
Anyone else here trying to bridge MATLAB and C# in their workflow? I’d love to hear your experiences or ideas!
— Patrick K.N.
I found some beautiful computational art made by a developer called @yuruyurau who used a language called Processing. Unfortunately, I know very little about this language so I asked Claude to convert it to MATLAB for me.
Give it a try yourself and show me what you come up with.
Details here: Pair programming with Claude to produce computational art in MATLAB » The MATLAB Blog - MATLAB & Simulink
In a discussion on LInkedin about my recent blog post, Do these 3 things to increase the reach of your open source MATLAB toolbox, I was asked by "Could you elaborate on why someone might consider opening/sharing their code? Thinking of early-career researchers, what might be in it for them?"
I'll give my answer here but I'm more interested in yours. How would you have answered this?
This is what I said:
- It's the right thing to do scientifically. A computational paper is essentially just an advertisement of what you've done. The code contains vital details about how you actually did it. A computational paper is incomplete without the code.
- If you only describe your algorithm in a paper, I have to implement it before I can apply your research to my problem. If you share the code, I can get started much more quickly using your research. This means I publish faster and since I am a good scientist, this means you get cited faster.
- Other scientists start off as users of your code. This leads to citations. Over time, some of them start deeply using and modifying your code, this leads to collaborators.
- Once you decide to share code via something like GitHub, you quickly start adopting good software engineering practices without initially realizing it. This improves the quality of your research since adopting good software practices makes it more likely that your software will give the right answers.
That last point can be a little hard to get your head around sometimes. Even if all you do is use file upload to get your stuff onto GitHub (i.e. you're not using git properly yet) you will start to naturally converge towards better code.
Why? Because as soon as you share code, you have to solve the problem of getting it to run on someone else's machine.
A trivial example concerns hard coded paths, for example. If you only ever run it on your machine then having a line like datafile = "C:\Mystuff\data.csv" always works but it breaks as soon as I try to run it on my machine. You'll look at this and think "Maybe there's a better way to do that".
Similarly dependencies. Your Path may be full of stuff that isn't present on my machine. As soon as I try to run your code, it won't work and you'll have to figure out how to handle dependencies in a reproducible way.
Documentation! An empty README.md is no good if you expect me to know how to use your code. You at least have to say something like "To run this, type runme(N) into MATLAB where N is the size of the model...etc etc)
The act of sharing, and dealing with the consequences, leads to much better code than if you keep it to yourself.
Due to MATLAB being banned in some mainland Chinese universities in 2020, in recent years a Chinese company called "Suzhou Tongyuan SoftControl" has completely imitated MATLAB’s behavior. Below are some screenshots as evidence. What is your opinion on this issue?
Source: Syslab 帮助文档-苏州同元软控
This week's Graphics and App Building blog article guides chart authors and app builders through the process of designing for a specific theme or creating theme-responsive charts and apps.
- Learn how dark theme may impacts charts and apps
- Discover best practices for theme-adaptive workflows
- Step-by-step examples for both script-based plots and advanced custom charts and UI components
- Discover new tools like ThemeChangedFcn, getTheme, and fliplightness
So you've downloaded the R2025a pre-release, tried Dark mode and are wondering what else is new. A lot! A lot is new!
One thing I am particularly happy about is the fact that Apple Accelerate is now the default BLAS on Apple Silicon machines. Check it out by doing
>> version -blas
ans =
'Apple Accelerate BLAS (ILP64)'
If you compare this to R2024b that is using OpenBLAS you'll see some dramatic speed-ups in some areas. For example, I saw up to 3.7x speed-up for matrix-matrix multiplication on my M2 Mabook Pro and 2x faster LU factorisation.
Details regarding my experiments are in this blog post Life in the fast lane: Making MATLAB even faster on Apple Silicon with Apple Accelerate » The MATLAB Blog - MATLAB & Simulink . Back then you had to to some trickery to switch to Apple Accelerate, now its the default.
I’m quite curious as to why this particular email was sent directly to my personal inbox. I have never actively subscribed to any online or offline training services nor clicked on any related marketing links. Could it be that because I frequently visit the official MATLAB forums, someone has identified me as a potential customer for their targeted promotions?
I’d love to hear your thoughts and start a discussion on this!
One of MATLAB's strengths is how easy it is to document a custom function/class. The first continuous comment block is automatially displayed by the help and doc functions, with some neat automatic formatting. For example:
% myFunc My example function
%
% This function does not do anything yet, but one day will be great. For
% example, you will be able to type:
% out=myFunc(in1,in2);
% and something cool will happen.
%
% See also otherFunc
function varargout=myFunc(varargin)
% actual code
end
will have a link to the documentation for "otherFunc", assuming that file exists. Class documentation is nicely broken up into a header (with "See also" support) followed by a property and method summary.
All the above works great with one big exception: apart from highlighting uses of the file's name, there is no way to display anything but pure text. No Markdown, no LateX, and so forth. It is possible to sneak an HTML link into the comment block, calling a MATLAB command that can display a live script with fancy formatting. I have done this in the past, although it can be a little tricky for files inside a package/namespace (folders beginning with '+').
I can envision a system where fancy documentation would be buried inside an "example" subfolder where "myFunc.m" is located. Invoking "showExample myFunc", where "showExample" is a to-be-written utility, would look for a live script inside the appropriate subfolder, make a local copy for the user to tinker with, and then display that local copy in the MATLAB editor. Since the actual function function and its example woulld obviously inside a Git repository, text-based live scripts would be used instead of an *.mlx file.
Again, this all works fine on its fine on its own, but it would be very difficult to replicate the "See also" capability or the other features of the standard doc function. So what are we to do? Is there a clever way to add another block to a standard comment block "See examples" that would automatically detect scripts in a subfolder of function/class being queried?
I know there is a way to incorporate custom documentation into MATLAB help system. This is much too cumbersome for my purposes, where many functions/classes are being added/edited all the time. The existing doc system covers maybe 80% of my needs, but sometimes a little math (LaTeX) would go a long way on explaining how things work.
T < 2 years
38%
2 years < T < 5 years
26%
5 years < T < 10 years
18%
10 years < T < 20 years
11%
T > 20 years
8%
10172 votes
The attached code is an animated solution of the three body problem. On 2024b it runs perfectly fine. When we tried it on 2025a, the animation constantly hitches, the CPU usage is almost double and the runtime is much slower. The curves also look less detailed and jagged in some places. When we run it without drawing anything, the performance seems comparable between versions, but still slightly slower. All of this behavior persists across different hardware. Anybody else having this kind of problem with the new release? I'm suspecting the graphics backend changes may be the culprit here...
clc
clear
close
syms t x1(t) y1(t) x2(t) y2(t) x3(t) y3(t)
G = 6.6743 * 10^-11;
%epsilon = 1e-4
m1 = 10^12;
m2 = 1*10^12;
m3 = 1*10^12;
r1 = [x1(t),y1(t)];
K1 = 1/2 * m1 * (diff(x1(t),t)^2 + diff(y1(t),t)^2);
r2 = [x2(t),y2(t)];
K2 = 1/2 * m2 * (diff(x2(t),t)^2 + diff(y2(t),t)^2);
r3 = [x3(t),y3(t)];
K3 = 1/2 * m3 * (diff(x3(t),t)^2 + diff(y3(t),t)^2);
L1x = diff(diff(K1,diff(x1(t),t)) , t);
L1y = diff(diff(K1,diff(y1(t),t)) , t);
L2x = diff(diff(K2,diff(x2(t),t)) , t);
L2y = diff(diff(K2,diff(y2(t),t)) , t);
L3x = diff(diff(K3,diff(x3(t),t)) , t);
L3y = diff(diff(K3,diff(y3(t),t)) , t);
r12 = r2 - r1;
r13 = r3 - r1;
r23 = r3 - r2;
dlugosc_r12 = sqrt(r12(1)^2 + r12(2)^2);
dlugosc_r13 = sqrt(r13(1)^2 + r13(2)^2);
dlugosc_r23 = sqrt(r23(1)^2 + r23(2)^2);
Q12 = G * m1 * m2 / dlugosc_r12^2 * (r2-r1)/dlugosc_r12;
Q13 = G * m1 * m3 / dlugosc_r13^2 * (r3-r1)/dlugosc_r13;
Q23 = G * m2 * m3 / dlugosc_r23^2 * (r3-r2)/dlugosc_r23;
Q21 = -Q12;
Q32 = -Q23;
Q31 = -Q13;
Q1 = Q12 + Q13;
Q2 = Q21 + Q23;
Q3 = Q31 + Q32;
eqn_1_x = L1x == Q1(1);
eqn_1_y = L1y == Q1(2);
eqn_2_x = L2x == Q2(1);
eqn_2_y = L2y == Q2(2);
eqn_3_x = L3x == Q3(1);
eqn_3_y = L3y == Q3(2);
syms X1 Y1 X2 Y2 X3 Y3
Q1_num = subs(Q1,[x1(t), y1(t), x2(t), y2(t), x3(t), y3(t)],[X1, Y1, X2, Y2, X3, Y3]);
Q2_num = subs(Q2,[x1(t), y1(t), x2(t), y2(t), x3(t), y3(t)],[X1, Y1, X2, Y2, X3, Y3]);
Q3_num = subs(Q3,[x1(t), y1(t), x2(t), y2(t), x3(t), y3(t)],[X1, Y1, X2, Y2, X3, Y3]);
syms vx1 vy1 vx2 vy2 vx3 vy3
state_dot = [
vx1;
vy1;
vx2;
vy2;
vx3;
vy3;
Q1_num(1)/m1;
Q1_num(2)/m1;
Q2_num(1)/m2;
Q2_num(2)/m2;
Q3_num(1)/m3;
Q3_num(2)/m3
];
f = matlabFunction(state_dot, 'Vars', {sym('t'), [X1; Y1; X2; Y2; X3; Y3; vx1; vy1; vx2; vy2; vx3; vy3]});
u0 = [-1e5; %x1
0; %y1
1e5; %x2
0; %y2
0; %x3
sqrt(3)*1e5; %y3
-11/2 * 1e-3; %vx1
11/2*sqrt(3)*1e-3; %vy1
-11/2 * 1e-3; %vx2
-11/2*sqrt(3)*1e-3; %vy2
11e-3; %vx3
0]; %vy3
tspan = [0, 1e9];
%options = odeset('RelTol', 1e-15, 'AbsTol', 1e-20);
[t_sol, u_sol] = ode45(f, tspan, u0);
t_anim = linspace(t_sol(1), t_sol(end), 5000);
u_anim = interp1(t_sol, u_sol, t_anim);
%%
% figure;
tor_1 = plot(u_anim(:,1), u_anim(:,2), 'r', 'LineWidth',1.5); hold on;
tor_2 = plot(u_anim(:,3), u_anim(:,4), 'g', 'LineWidth',1.5);
tor_3 = plot(u_anim(:,5), u_anim(:,6), 'b', 'LineWidth',1.5);
% xlabel('x [m]');
% ylabel('y [m]');
% legend('Ciało 1', 'Ciało 2', 'Ciało 3');
% title('Trajektorie ciał w układzie trzech ciał');
% axis equal
% grid on;
pozycja_1 = plot(u_anim(1,1),u_anim(1,2),'ro','markersize',10,'markerface','r'); hold on
pozycja_2 = plot(u_anim(1,3),u_anim(1,4),'go','markersize',10,'markerface','g');
pozycja_3 = plot(u_anim(1,5),u_anim(1,6),'bo','markersize',10,'markerface','b');
% xlim([-2e5,2e5])
% ylim([-2e5,2e5])
axis equal
for i = 1 : 1 : length(t_sol)
set(pozycja_1,'XData', u_anim(i,1),'YData', u_anim(i,2));
set(pozycja_2,'XData', u_anim(i,3),'YData', u_anim(i,4));
set(pozycja_3,'XData', u_anim(i,5),'YData', u_anim(i,6));
set(tor_1,'XData', u_anim(1:i,1),'YData', u_anim(1:i,2));
set(tor_2,'XData', u_anim(1:i,3),'YData', u_anim(1:i,4));
set(tor_3,'XData', u_anim(1:i,5),'YData', u_anim(1:i,6));
drawnow;
% pause(0.001);
end
Are you a dark mode enthusiast or are you curious about how it’s shaping MATLAB graphics? Check out the latest article in the MATLAB Graphics and App Building blog.
🔹 User Insights: find out how user surveys influenced the development of graphics themes
🔹 Learn three ways to switch between light and dark themes for figures
🔹 Understand how custom and default colors behave across themes
🔹 Download a handy cheat sheet for working with themes in your graphics and apps.
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