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Chen Lin
Last activity on 22 May 2024

Celebrating Spring: MATLAB Tulip

Bringing the beauty of MathWorks Natick's tulips to life through code!

Get the code from @Eric Ludlam.

Remix challenge: create and share with us your new breeds of MATLAB tulips!

Hans Scharler
Last activity on 17 May 2024

The Office Words Spoken by Episode

I found this plot of words said by different characters on the US version of The Office sitcom. There's a sparkline for each character from pilot to finale episode.

lazymatlab
Last activity on 18 May 2024

Helped a high school student for a physics problem with this.

A high school student called for help with this physics problem:

Car A moves with constant velocity v.
Car B starts to move when Car A passes through the point P.
Car B undergoes...
uniform acc. motion from P to Q.
uniform velocity motion from Q to R.
uniform acc. motion from R to S.
Car A and B pass through the point R simultaneously.
Car A and B arrive at the point S simultaneously.

Q1. When car A passes the point Q, which is moving faster?

Q2. Solve the time duration for car B to move from P to Q using L and v.

Q3. Magnitude of acc. of car B from P to Q, and from R to S: which is bigger?

Well, it can be solved with a series of tedious equations. But... how about this?

Code below:

%% get images and prepare stuffs
figure(WindowStyle="docked"), 
ax1 = subplot(2,1,1);
hold on, box on
ax1.XTick = [];
ax1.YTick = [];
A = plot(0, 1, 'ro', MarkerSize=10, MarkerFaceColor='r');
B = plot(0, 0, 'bo', MarkerSize=10, MarkerFaceColor='b');
[carA, ~, alphaA] = imread('https://cdn.pixabay.com/photo/2013/07/12/11/58/car-145008_960_720.png');
[carB, ~, alphaB] = imread('https://cdn.pixabay.com/photo/2014/04/03/10/54/car-311712_960_720.png');
carA = imrotate(imresize(carA, 0.1), -90);
carB = imrotate(imresize(carB, 0.1), 180);
alphaA = imrotate(imresize(alphaA, 0.1), -90);
alphaB = imrotate(imresize(alphaB, 0.1), 180);
carA = imagesc(carA, AlphaData=alphaA, XData=[-0.1, 0.1], YData=[0.9, 1.1]);
carB = imagesc(carB, AlphaData=alphaB, XData=[-0.1, 0.1], YData=[-0.1, 0.1]);
txtA = text(0, 0.85, 'A', FontSize=12);
txtB = text(0, 0.17, 'B', FontSize=12);
yline(1, 'r--')
yline(0, 'b--')
xline(1, 'k--')
xline(2, 'k--')
text(1, -0.2, 'Q', FontSize=20, HorizontalAlignment='center')
text(2, -0.2, 'R', FontSize=20, HorizontalAlignment='center')
% legend('A', 'B') % this make the animation slow. why?
xlim([0, 3])
ylim([-.3, 1.3])
%% axes2: plots velocity graph
ax2 = subplot(2,1,2);
box on, hold on
xlabel('t'), ylabel('v')
vA = plot(0, 1, 'r.-');
vB = plot(0, 0, 'b.-');
xline(1, 'k--')
xline(2, 'k--')
xlim([0, 3])
ylim([-.3, 1.8])
p1 = patch([0, 0, 0, 0], [0, 1, 1, 0], [248, 209, 188]/255, ...
    EdgeColor = 'none', ...
    FaceAlpha = 0.3);
%% solution
v = 1; % car A moves with constant speed.
L = 1; % distances of P-Q, Q-R, R-S
% acc. of car B for three intervals
a(1) = 9*v^2/8/L;
a(2) = 0;
a(3) = -1;
t_BatQ = sqrt(2*L/a(1)); % time when car B arrives at Q
v_B2 = a(1) * t_BatQ; % speed of car B between Q-R
%% patches for velocity graph
p2 = patch([t_BatQ, t_BatQ, t_BatQ, t_BatQ], [1, 1, v_B2, v_B2], ...
    [248, 209, 188]/255, ...
    EdgeColor = 'none', ...
    FaceAlpha = 0.3);
p3 = patch([2, 2, 2, 2], [1, v_B2, v_B2, 1], [194, 234, 179]/255, ...
    EdgeColor = 'none', ...
    FaceAlpha = 0.3);
%% animation
tt = linspace(0, 3, 2000);
for t = tt
    A.XData = v * t;
    vA.XData = [vA.XData, t];
    vA.YData = [vA.YData, 1];
    if t < t_BatQ
        B.XData = 1/2 * a(1) * t^2;
        vB.XData = [vB.XData, t];
        vB.YData = [vB.YData, a(1) * t];
        p1.XData = [0, t, t, 0];
        p1.YData = [0, vB.YData(end), 1, 1];
    elseif t >= t_BatQ && t < 2
        B.XData = L + (t - t_BatQ) * v_B2;
        vB.XData = [vB.XData, t];
        vB.YData = [vB.YData, v_B2];
        p2.XData = [t_BatQ, t, t, t_BatQ];
        p2.YData = [1, 1, vB.YData(end), vB.YData(end)];
    else
        B.XData = 2*L + v_B2 * (t - 2) + 1/2 * a(3) * (t-2)^2;
        vB.XData = [vB.XData, t];
        vB.YData = [vB.YData, v_B2 + a(3) * (t - 2)];
        p3.XData = [2, t, t, 2];
        p3.YData = [1, 1, vB.YData(end), v_B2];
    end
    txtA.Position(1) = A.XData(end);
    txtB.Position(1) = B.XData(end);
    carA.XData = A.XData(end) + [-.1, .1];
    carB.XData = B.XData(end) + [-.1, .1];
    drawnow
end

Build Simulink Apps with App Designer

Adam Danz
Last activity on 15 May 2024

Simulink and App Designer integration in R2024a

Are you a Simulink user eager to learn how to create apps with App Designer? Or an App Designer enthusiast looking to dive into Simulink?

Don't miss today's article on the Graphics and App Building Blog by @Robert Philbrick! Discover how to build Simulink Apps with App Designer, streamlining control of your simulations!

Robert
Last activity on 13 May 2024

Alpha Vantage: Easy tutorial on how to get data into Matlab from Alpha Vantage

From Alpha Vantage's website: API Documentation | Alpha Vantage

Try using the built-in Matlab function webread(URL)... for example:

% copy a URL from the examples on the site
URL = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=IBM&apikey=demo'
% or use the pattern to create one
tickers = [{'IBM'} {'SPY'} {'DJI'} {'QQQ'}]; i = 1;
URL = ...
['https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&outputsize=full&symbol=', ...
        + tickers{i}, ...
        + '&apikey=***Put Your API Key here***'];
X = webread(URL);

You can access any of the data available on the site as per the Alpha Vantage documentation using these two lines of code but with different designations for the requested data as per the documentation.

It's fun!

https://www.mathworks.com/matlabcentral/fileexchange/165846-matlab-plot-cheat-sheet

Zhaoxu Liu / slandarer
Last activity on 13 May 2024

Latest version of MATLAB-PLOT-CHEAT-SHEET

This cheat sheet is here:

reference:

MATLAB used to have official visualization-cheat-sheet, but there have been quite a few new updates in MATLAB versions recently. Therefore, I made my own cheat sheet and marked the versions of each new thing that were released :

Daniele Lupo
Last activity on 12 May 2024

Which toolboxes for crypto trading?

Hi to all.

I'm trying to learn a bit about trading with cryptovalues. At the moment I'm using Freqtrade (in dry-run mode of course) for automatic trading. The tool is written in python and it allows to create custom strategies in python classes and then run them.

I've written some strategy just to learn how to do, but now I'd like to create some interesting algorithm. I've a matlab license, and I'd like to know what are suggested tollboxes for following work:

Create a criptocurrency strategy algorythm (for buying and selling some crypto like BTC, ETH etc).
Backtesting the strategy with historical data (I've a bunch of json files with different timeframes, downloaded with freqtrade from binance).
Optimize the strategy given some parameters (they can be numeric, like ROI, some kind of enumeration, like "selltype" and so on).
Convert the strategy algorithm in python, so I can use it with Freqtrade without worrying of manually copying formulas and parameters that's error prone.
I'd like to write both classic algorithm and some deep neural one, that try to find best strategy with little neural network (they should run on my pc with 32gb of ram and a 3080RTX if it can be gpu accelerated).

What do you suggest?

Athanasios Paraskevopoulos
Last activity on 5 May 2024

Dynamic Simulation of DNA Base Pair Displacements with Non-linearity and Damping Effects

The study of the dynamics of the discrete Klein - Gordon equation (DKG) with friction is given by the equation :

above equation, W describes the potential function :

The objective of this simulation is to model the dynamics of a segment of DNA under thermal fluctuations with fixed boundaries using a modified discrete Klein-Gordon equation. The model incorporates elasticity, nonlinearity, and damping to provide insights into the mechanical behavior of DNA under various conditions.

% Parameters
numBases = 200; % Number of base pairs, representing a segment of DNA
kappa = 0.1; % Elasticity constant
omegaD = 0.2; % Frequency term
beta = 0.05; % Nonlinearity coefficient
delta = 0.01; % Damping coefficient

Position: Random initial perturbations between 0.01 and 0.02 to simulate the thermal fluctuations at the start.
Velocity: All bases start from rest, assuming no initial movement except for the thermal perturbations.

% Random initial perturbations to simulate thermal fluctuations
initialPositions = 0.01 + (0.02-0.01).*rand(numBases,1);
initialVelocities = zeros(numBases,1); % Assuming initial rest state

The simulation uses fixed ends to model the DNA segment being anchored at both ends, which is typical in experimental setups for studying DNA mechanics. The equations of motion for each base are derived from a modified discrete Klein-Gordon equation with the inclusion of damping:

% Define the differential equations
dt = 0.05; % Time step
tmax = 50; % Maximum time
tspan = 0:dt:tmax; % Time vector
x = zeros(numBases, length(tspan)); % Displacement matrix
x(:,1) = initialPositions; % Initial positions
% Velocity-Verlet algorithm for numerical integration
for i = 2:length(tspan)
    % Compute acceleration for internal bases
    acceleration = zeros(numBases,1);
    for n = 2:numBases-1
        acceleration(n) = kappa * (x(n+1, i-1) - 2 * x(n, i-1) + x(n-1, i-1)) ...
            - delta * initialVelocities(n) - omegaD^2 * (x(n, i-1) - beta * x(n, i-1)^3);
    end
    % positions for internal bases
    x(2:numBases-1, i) = x(2:numBases-1, i-1) + dt * initialVelocities(2:numBases-1) ...
        + 0.5 * dt^2 * acceleration(2:numBases-1);
    % velocities using new accelerations
    newAcceleration = zeros(numBases,1);
    for n = 2:numBases-1
        newAcceleration(n) = kappa * (x(n+1, i) - 2 * x(n, i) + x(n-1, i)) ...
            - delta * initialVelocities(n) - omegaD^2 * (x(n, i) - beta * x(n, i)^3);
    end
    initialVelocities(2:numBases-1) = initialVelocities(2:numBases-1) + 0.5 * dt * (acceleration(2:numBases-1) + newAcceleration(2:numBases-1));
end
% Visualization of displacement over time for each base pair
figure;
hold on;
for n = 2:numBases-1
    plot(tspan, x(n, :));
end
xlabel('Time');
ylabel('Displacement');
legend(arrayfun(@(n) ['Base ' num2str(n)], 2:numBases-1, 'UniformOutput', false));
title('Displacement of DNA Bases Over Time');
hold off;

The results are visualized using a plot that shows the displacements of each base over time . Key observations from the simulation include :

Wave Propagation: The initial perturbations lead to wave-like dynamics along the segment, with visible propagation and reflection at the boundaries.
Damping Effects: The inclusion of damping leads to a gradual reduction in the amplitude of the oscillations, indicating energy dissipation over time.
Nonlinear Behavior: The nonlinear term influences the response, potentially stabilizing the system against large displacements or leading to complex dynamic patterns.

% 3D plot for displacement
figure;
[X, T] = meshgrid(1:numBases, tspan);
surf(X', T', x);
xlabel('Base Pair');
ylabel('Time');
zlabel('Displacement');
title('3D View of DNA Base Displacements');
colormap('jet');
shading interp;
colorbar; % Adds a color bar to indicate displacement magnitude

% Snapshot visualization at a specific time
snapshotTime = 40; % Desired time for the snapshot
[~, snapshotIndex] = min(abs(tspan - snapshotTime)); % Find closest index
snapshotSolution = x(:, snapshotIndex); % Extract displacement at the snapshot time
% Plotting the snapshot
figure;
stem(1:numBases, snapshotSolution, 'filled'); % Discrete plot using stem
title(sprintf('DNA Model Displacement at t = %d seconds', snapshotTime));
xlabel('Base Pair Index');
ylabel('Displacement');

% Time vector for detailed sampling
tDetailed = 0:0.5:50; % Detailed time steps
% Initialize an empty array to hold the data
data = [];
% Generate the data for 3D plotting
for i = 1:numBases
    % Interpolate to get detailed solution data for each base pair
    detailedSolution = interp1(tspan, x(i, :), tDetailed);
    % Concatenate the current base pair's data to the main data array
    data = [data; repmat(i, length(tDetailed), 1), tDetailed', detailedSolution'];
end
% 3D Plot
figure;
scatter3(data(:,1), data(:,2), data(:,3), 10, data(:,3), 'filled');
xlabel('Base Pair');
ylabel('Time');
zlabel('Displacement');
title('3D Plot of DNA Base Pair Displacements Over Time');
colorbar; % Adds a color bar to indicate displacement magnitude

Gregory Vernon
Last activity on 6 May 2024

Support "hidden blocks" in Livescripts

Updating some of my educational Livescripts to 2024a, really love the new "define a function anywhere" feature, and have a "new" idea for improving Livescripts -- support "hidden" code blocks similar to the Jupyter Notebooks functionality.

For example, I often create "complicated" plots with a bunch of ancillary items and I don't want this code exposed to the reader by default, as it might confuse the reader. For example, consider a Livescript that might read like this:

-----

Noting the similar structure of these two mappings, let's now write a function that simply maps from some domain to some other domain using change of variable.

function x = ChangeOfVariable( x, from_domain, to_domain )
x = x - from_domain(1);
x = x * ( ( to_domain(2) - to_domain(1) ) / ( from_domain(2) - from_domain(1) ) );
x = x + to_domain(1);
end

Let's see this function in action

% HIDE CELL
clear
close all

from_domain = [-1, 1];
to_domain = [2, 7];
from_values = [-1, -0.5, 0, 0.5, 1];
to_values = ChangeOfVariable( from_values, from_domain, to_domain )

to_values = 1×5

2.0000 3.2500 4.5000 5.7500 7.0000

We can plot the values of from_values and to_values, showing how they're connected to each other:

% HIDE CELL
figure
hold on
for n = 1 : 5
    plot( [from_values(n) to_values(n)], [1 0], Color="k", LineWidth=1 )
end
ax = gca;
ax.YTick = [];
ax.XLim = [ min( [from_domain, to_domain] ) - 1,  max( [from_domain, to_domain] ) + 1 ];
ax.YLim = [-0.5, 1.5];
ax.XGrid = "on";
scatter( from_values, ones( 5, 1 ), Marker="s", MarkerFaceColor="flat", MarkerEdgeColor="k", SizeData=120, LineWidth=1, SeriesIndex=1 )
text( mean( from_domain ), 1.25, "$\xi$", Interpreter="latex", HorizontalAlignment="center", VerticalAlignment="middle" )
scatter( to_values, zeros( 5, 1 ), Marker="o", MarkerFaceColor="flat", MarkerEdgeColor="k", SizeData=120, LineWidth=1, SeriesIndex=2 )
text( mean( to_domain ), -0.25, "$x$", Interpreter="latex", HorizontalAlignment="center", VerticalAlignment="middle" )
scaled_arrow( ax, [mean( [from_domain(1), to_domain(1) ] ) - 1, 0.5], ( 1 - 0 ) / ( from_domain(1) - to_domain(1) ), 1 )
scaled_arrow( ax, [mean( [from_domain(end), to_domain(end)] ) + 1, 0.5], ( 1 - 0 ) / ( from_domain(end) - to_domain(end) ), -1 )
text( mean( [from_domain(1), to_domain(1) ] ) - 1.5, 0.5, "$x(\xi)$", Interpreter="latex", HorizontalAlignment="center", VerticalAlignment="middle" )
text( mean( [from_domain(end), to_domain(end)] ) + 1.5, 0.5, "$\xi(x)$", Interpreter="latex", HorizontalAlignment="center", VerticalAlignment="middle" )

-----

Where scaled_arrow is some utility function I've defined elsewhere... See how a majority of the code is simply "drivel" to create the plot, clear and close? I'd like to be able to hide those cells so that it would look more like this:

-----

Noting the similar structure of these two mappings, let's now write a function that simply maps from some domain to some other domain using change of variable.

function x = ChangeOfVariable( x, from_domain, to_domain )
x = x - from_domain(1);
x = x * ( ( to_domain(2) - to_domain(1) ) / ( from_domain(2) - from_domain(1) ) );
x = x + to_domain(1);
end

Let's see this function in action

▶ Show code cell

from_domain = [-1, 1];
to_domain = [2, 7];
from_values = [-1, -0.5, 0, 0.5, 1];
to_values = ChangeOfVariable( from_values, from_domain, to_domain )

to_values = 1×5

2.0000 3.2500 4.5000 5.7500 7.0000

We can plot the values of from_values and to_values, showing how they're connected to each other:

▶ Show code cell

-----

Thoughts?

Emma Farnan
Last activity on 6 May 2024

Include information about the "Margin" toggles in Preferences

I recently had issues with code folding seeming to disappear and it turns out that I had unknowingly disabled the "show code folding margin" option by accident. Despite using MATLAB for several years, I had no idea this was an option, especially since there seemed to be no references to it in the code folding part of the "Preferences" menu.

It would be great if in the future, there was a warning that told you about this when you try enable/disable folding in the Preferences.

I am using 2023b by the way.

Jérôme
Last activity on 29 Apr 2024

Highlighting structure fields in MATLAB editor

In the MATLAB editor, when clicking on a variable name, all the other instances of the variable name will be highlighted.

But this does not work for structure fields, which is a pity. Such feature would be quite often useful for me.

I show an illustration below, and compare it with Visual Studio Code that does it. ;-)

I am using MATLAB R2023a, sorry if it has been added to newer versions, but I didn't see it in the release notes.

Adam Danz
Last activity on 22 Apr 2024

Debugging Using Conditional Breakpoints & fprintf

Temporary print statements are often helpful during debugging but it's easy to forget to remove the statements or sometimes you may not have writing privileges for the file. This tip uses conditional breakpoints to add print statements without ever editing the file!

What are conditional breakpoints?

Conditional breakpoints allow you to write a conditional statement that is executed when the selected line is hit and if the condition returns true, MATLAB pauses at that line. Otherwise, it continues.

The Hack: use ~fprintf() as the condition

fprintf prints information to the command window and returns the size of the message in bytes. The message size will always be greater than 0 which will always evaluate as true when converted to logical. Therefore, by negating an fprintf statement within a conditional breakpoint, the fprintf command will execute, print to the command window, and evalute as false which means the execution will continue uninterupted!

How to set a conditional break point

1. Right click the line number where you want the condition to be evaluated and select "Set Conditional Breakpoint"

2. Enter a valid MATLAB expression that returns a logical scalar value in the editor dialog.

Handy one-liners

Check if a line is reached: Don't forget the negation (~) and the line break (\n)!

~fprintf('Entered callback function\n')

Display the call stack from the break point line: one of my favorites!

~fprintf('%s\n',formattedDisplayText(struct2table(dbstack)))

Inspect variable values: For scalar values,

~fprintf('v = %.5f\n', v)

Use formattedDisplayText to convert more complex data to a string

~fprintf('%s\n', formattedDisplayText(v)).

Make sense of frequent hits: In some situations such as responses to listeners or interactive callbacks, a line can be executed 100s of times per second. Incorporate a timestamp to differentiate messages during rapid execution.

~fprintf('WindowButtonDownFcn - %s\n', datetime('now'))

Closing

This tip not only keeps your code clean but also offers a dynamic way to monitor code execution and variable states without permanent modifications. Interested in digging deeper? @Steve Eddins takes this tip to the next level with his Code Trace for MATLAB tool available on the File Exchange (read more).

Summary animation

To reproduce the events in this animation:

% buttonDownFcnDemo.m
fig = figure();
tcl = tiledlayout(4,4,'TileSpacing','compact');
for i = 1:16
    ax = nexttile(tcl); 
    title(ax,"#"+string(i))
    ax.ButtonDownFcn = @axesButtonDownFcn;
    xlim(ax,[-1 1])
    ylim(ax,[-1,1])
    hold(ax,'on')
end
function axesButtonDownFcn(obj,event)
    colors = lines(16); 
    plot(obj,event.IntersectionPoint(1),event.IntersectionPoint(2),...
        'ko','MarkerFaceColor',colors(obj.Layout.Tile,:))
end

cui,xingxing
Last activity on 23 Apr 2024

Is there a position for Matlab Community remote support work?

As far as I know, the MATLAB Community (including Matlab Central and Mathworks' official GitHub repository) has always been a vibrant and diverse professional and amateur community of MATLAB users from various fields globally. Being a part of it myself, especially in recent years, I have not only benefited continuously from the community but also tried to give back by helping other users in need.

I am a senior MATLAB user from Shenzhen, China, and I have a deep passion for MATLAB, applying it in various scenarios. Due to the less than ideal job market in my current social environment, I am hoping to find a position for remote support work within the Matlab Community. I wonder if this is realistic. For instance, Mathworks has been open-sourcing many repositories in recent years, especially in the field of deep learning with typical applications across industries. I am eager to use the latest MATLAB features to implement state-of-the-art algorithms. Additionally, I occasionally contribute through GitHub issues and pull requests.

In conclusion, I am looking forward to the opportunity to formally join the Matlab Community in a remote support role, dedicating more energy to giving back to the community and making the world a better place! (If a Mathworks employer can contact me, all the better~)

Athanasios Paraskevopoulos
Last activity on 14 Apr 2024

How do mathematicians walk their dogs?

What's your way?

https://www.davidyding.com/navPages/Ellipse

David Ding
Last activity on 12 Apr 2024

Ellipse Visualizer using MATLAB App Designer--Inspired by the Total Solar Eclipse 2024!

I created an ellipse visualizer in #MATLAB using App Designer! To read more about it, and how it ties to the recent total solar eclipse, check out my latest blog post:

Github Repo of the app (you can open it on MATLAB Online!):

https://github.com/dvfire123/EllipseVisualizer/tree/main

Vonny Groose
Last activity on 8 Nov 2024 at 21:03

Pet Day with PetWorks photo contest

Mari is helping Dad work.

Tom Sleeper
Last activity on 9 Jun 2024

#nationalpetday Tucker (a.k.a Tuckaroo) never ceases to amaze me

Today, he got dressed for work to design some new dog toy-making algorithms. #nationalpetday

RAGHUVEER RAJESH
Last activity on 11 Apr 2024

Paws and Pixels: Image Processing using PETLAB..Sorry MATLAB

Transforming my furry friend into a grayscale masterpiece with MATLAB! 🐾 #MATLABPetsDay ✌️

Hans Scharler
Last activity on 11 Apr 2024

Stella while compiling the project...

This is Stella while waiting to see if the code works...