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jmgoldba
Last activity 2024 年 6 月 21 日

Add Tools->Basic Fitting->...Weighted least squares

An option for 10th degree polynomials but no weighted linear least squares. Seriously? Jesse

Athanasios Paraskevopoulos
Last activity 2024 年 7 月 4 日

Stochastic simulation of a novel pathogen

We are modeling the introduction of a novel pathogen into a completely susceptible population. In the cells below, I have provided you with the Matlab code for a simple stochastic SIR model, implemented using the "GillespieSSA" function

Simulating the stochastic model 100 times for

Since γ is 0.4 per day,

per day

% Define the parameters

beta = 0.36;

gamma = 0.4;

n_sims = 100;

tf = 100; % Time frame changed to 100

% Calculate R0

R0 = beta / gamma

R0 = 0.9000

% Initial state values

initial_state_values = [1000000; 1; 0; 0]; % S, I, R, cum_inc

% Define the propensities and state change matrix

a = @(state) [beta * state(1) * state(2) / 1000000, gamma * state(2)];

nu = [-1, 0; 1, -1; 0, 1; 0, 0];

% Define the Gillespie algorithm function

function [t_values, state_values] = gillespie_ssa(initial_state, a, nu, tf)

t = 0;

state = initial_state(:); % Ensure state is a column vector

t_values = t;

state_values = state';

while t < tf

rates = a(state);

rate_sum = sum(rates);

if rate_sum == 0

break;

end

tau = -log(rand) / rate_sum;

t = t + tau;

r = rand * rate_sum;

cum_sum_rates = cumsum(rates);

reaction_index = find(cum_sum_rates >= r, 1);

state = state + nu(:, reaction_index);

% Update cumulative incidence if infection occurred

if reaction_index == 1

state(4) = state(4) + 1; % Increment cumulative incidence

end

t_values = [t_values; t];

state_values = [state_values; state'];

end

% Function to simulate the stochastic model multiple times and plot results

function simulate_stoch_model(beta, gamma, n_sims, tf, initial_state_values, R0, plot_type)

% Define the propensities and state change matrix

a = @(state) [beta * state(1) * state(2) / 1000000, gamma * state(2)];

nu = [-1, 0; 1, -1; 0, 1; 0, 0];

% Set random seed for reproducibility

rng(11);

% Initialize plot

figure;

hold on;

for i = 1:n_sims

[t, output] = gillespie_ssa(initial_state_values, a, nu, tf);

% Check if the simulation had only one step and re-run if necessary

while length(t) == 1

[t, output] = gillespie_ssa(initial_state_values, a, nu, tf);

end

if strcmp(plot_type, 'cumulative_incidence')

plot(t, output(:, 4), 'LineWidth', 2, 'Color', rand(1, 3));

elseif strcmp(plot_type, 'prevalence')

plot(t, output(:, 2), 'LineWidth', 2, 'Color', rand(1, 3));

end

xlabel('Time (days)');

if strcmp(plot_type, 'cumulative_incidence')

ylabel('Cumulative Incidence');

ylim([0 inf]);

elseif strcmp(plot_type, 'prevalence')

ylabel('Prevalence of Infection');

ylim([0 50]);

end

title(['Stochastic model output for R0 = ', num2str(R0)]);

subtitle([num2str(n_sims), ' simulations']);

xlim([0 tf]);

grid on;

hold off;

end

% Simulate the model 100 times and plot cumulative incidence

simulate_stoch_model(beta, gamma, n_sims, tf, initial_state_values, R0, 'cumulative_incidence');

% Simulate the model 100 times and plot prevalence

simulate_stoch_model(beta, gamma, n_sims, tf, initial_state_values, R0, 'prevalence');

https://blogs.mathworks.com/community/2015/02/26/lets-code-make-a-cody-video/

Ned Gulley
Last activity 2024 年 6 月 13 日

Make a video: "How I Solved It"

Twitch built an entire business around letting you watch over someone's shoulder while they play video games. I feel like we should be able to make at least a few videos where we get to watch over someone's shoulder while they solve Cody problems. I would pay good money for a front-row seat to watch some of my favorite solvers at work. Like, I want to know, did Alfonso Nieto-Castonon just sit down and bang out some of those answers, or did he have to think about it for a while? What was he thinking about while he solved it? What resources was he drawing on? There's nothing like watching a master craftsman at work.

I can imagine a whole category of Cody videos called "How I Solved It". I tried making one of these myself a while back, but as far as I could tell, nobody else made one.

Here's the direct link to the video: https://www.youtube.com/watch?v=hoSmO1XklAQ

I hereby challenge you to make a "How I Solved It" video and post it here. If you make one, I'll make another one.

goc3
Last activity 2024 年 9 月 8 日

+1 for Backwards Loops!

Base case:

Suppose you need to do a computation many times. We are going to assume that this computation cannot be vectorized. The simplest case is to use a for loop:

number_of_elements = 1e6;
test_fcn = @(x) sqrt(x) / x;
tic
for i = 1:number_of_elements
    x(i) = test_fcn(i);
end
t_forward = toc;
disp(t_forward + " seconds")
0.10925 seconds

Preallocation:

This can easily be sped up by preallocating the variable that houses results:

tic
x = zeros(number_of_elements, 1);
for i = 1:number_of_elements
    x(i) = test_fcn(i);
end
t_forward_prealloc = toc;
disp(t_forward_prealloc + " seconds")
0.035106 seconds

In this example, preallocation speeds up the loop by a factor of about three to four (running in R2024a). Comment below if you get dramatically different results.

disp(sprintf("%.1f", t_forward / t_forward_prealloc))
3.1

Run it in reverse:

Is there a way to skip the explicit preallocation and still be fast? Indeed, there is.

clear x
tic
for i = number_of_elements:-1:1
    x(i) = test_fcn(i);
end
t_backward = toc;
disp(t_backward + " seconds")
0.032392 seconds

By running the loop backwards, the preallocation is implicitly performed during the first iteration and the loop runs in about the same time (within statistical noise):

disp(sprintf("%.2f", t_forward_prealloc / t_backward))
1.08

Do you get similar results when running this code? Let us know your thoughts in the comments below.

Beneficial side effect:

Have you ever had to use a for loop to delete elements from a vector? If so, keeping track of index offsets can be tricky, as deleting any element shifts all those that come after. By running the for loop in reverse, you don't need to worry about index offsets while deleting elements.

Ned Gulley
Last activity 2024 年 6 月 25 日

What's your opinion of the Ans Hack?

The Ans Hack is a dubious way to shave a few points off your solution score. Instead of a standard answer like this

function y = times_two(x)
  y = 2*x;
end

you would do this

function ans = times_two(x)
  2*x;
end

The ans variable is automatically created when there is no left-hand side to an evaluated expression. But it makes for an ugly function. I don't think anyone actually defends it as a good practice. The question I would ask is: is it so offensive that it should be specifically disallowed by the rules? Or is it just one of many little hacks that you see in Cody, inelegant but tolerable in the context of the surrounding game?

Incidentally, I wrote about the Ans Hack long ago on the Community Blog. Dealing with user-unfriendly code is also one of the reasons we created the Head-to-Head voting feature. Some techniques are good for your score, and some are good for your code readability. You get to decide with you care about.

Zhaoxu Liu / slandarer
Last activity 2024 年 6 月 9 日

How to set the AlphaData of a colorbar?

Many times when ploting, we not only need to set the color of the plot, but also its

transparency, Then how we set the alphaData of colorbar at the same time ?

It seems easy to do so :

data = rand(12,12); 
% Transparency range 0-1, .3-1 for better appearance here
AData = rescale(- data, .3, 1);
% Draw an imagesc with numerical control over colormap and transparency
imagesc(data, 'AlphaData',AData);
colormap(jet); 
ax = gca;
ax.DataAspectRatio = [1,1,1];
ax.TickDir = 'out';          
ax.Box = 'off';
% get colorbar object
CBarHdl = colorbar;
pause(1e-16)
% Modify the transparency of the colorbar
CData = CBarHdl.Face.Texture.CData;
ALim = [min(min(AData)), max(max(AData))];
CData(4,:) = uint8(255.*rescale(1:size(CData, 2), ALim(1), ALim(2)));
CBarHdl.Face.Texture.ColorType = 'TrueColorAlpha';
CBarHdl.Face.Texture.CData = CData;

But !!!!!!!!!!!!!!! We cannot preserve the changes when saving them as images ：

It seems that when saving plots, the `Texture` will be refresh, but the `Face` will not :

however, object Face only have 4 colors to change(The four corners of a quadrilateral), how

can we set more colors ??

`Face` is a quadrilateral object, and we can change the `VertexData` to draw more than one little quadrilaterals:

data = rand(12,12); 
% Transparency range 0-1, .3-1 for better appearance here
AData = rescale(- data, .3, 1);
%Draw an imagesc with numerical control over colormap and transparency
imagesc(data, 'AlphaData',AData);
colormap(jet); 
 
ax = gca;
ax.DataAspectRatio = [1,1,1];  
ax.TickDir = 'out';           
ax.Box = 'off';
% get colorbar object
CBarHdl = colorbar;
pause(1e-16)
% Modify the transparency of the colorbar
CData = CBarHdl.Face.Texture.CData;
ALim = [min(min(AData)), max(max(AData))];
CData(4,:) = uint8(255.*rescale(1:size(CData, 2), ALim(1), ALim(2)));
warning off
CBarHdl.Face.ColorType = 'TrueColorAlpha';
VertexData = CBarHdl.Face.VertexData;
tY = repmat((1:size(CData,2))./size(CData,2), [4,1]);
tY1 = tY(:).'; tY2 = tY  - tY(1,1); tY2(3:4,:) = 0; tY2 = tY2(:).';
tM1 = [tY1.*0 + 1; tY1; tY1.*0 + 1];
tM2 = [tY1.*0; tY2; tY1.*0];
CBarHdl.Face.VertexData = repmat(VertexData, [1,size(CData,2)]).*tM1 + tM2;
CBarHdl.Face.ColorData = reshape(repmat(CData, [4,1]), 4, []);

The higher the value, the more transparent it becomes

data = rand(12,12); 
AData = rescale(- data, .3, 1);
imagesc(data, 'AlphaData',AData);
colormap(jet); 
ax = gca;
ax.DataAspectRatio = [1,1,1];
ax.TickDir = 'out';          
ax.Box = 'off';
CBarHdl = colorbar;
pause(1e-16)
CData = CBarHdl.Face.Texture.CData;
ALim = [min(min(AData)), max(max(AData))];
CData(4,:) = uint8(255.*rescale(size(CData, 2):-1:1, ALim(1), ALim(2)));
warning off
CBarHdl.Face.ColorType = 'TrueColorAlpha';
VertexData = CBarHdl.Face.VertexData;
tY = repmat((1:size(CData,2))./size(CData,2), [4,1]);
tY1 = tY(:).'; tY2 = tY  - tY(1,1); tY2(3:4,:) = 0; tY2 = tY2(:).';
tM1 = [tY1.*0 + 1; tY1; tY1.*0 + 1];
tM2 = [tY1.*0; tY2; tY1.*0];
CBarHdl.Face.VertexData = repmat(VertexData, [1,size(CData,2)]).*tM1 + tM2;
CBarHdl.Face.ColorData = reshape(repmat(CData, [4,1]), 4, []);

More transparent in the middle

data = rand(12,12) - .5; 
AData = rescale(abs(data), .1, .9);
imagesc(data, 'AlphaData',AData);
colormap(jet); 
ax = gca;
ax.DataAspectRatio = [1,1,1];
ax.TickDir = 'out';          
ax.Box = 'off';
CBarHdl = colorbar;
pause(1e-16)
CData = CBarHdl.Face.Texture.CData;
ALim = [min(min(AData)), max(max(AData))];
CData(4,:) = uint8(255.*rescale(abs((1:size(CData, 2)) - (1 + size(CData, 2))/2), ALim(1), ALim(2)));
warning off
CBarHdl.Face.ColorType = 'TrueColorAlpha';
VertexData = CBarHdl.Face.VertexData;
tY = repmat((1:size(CData,2))./size(CData,2), [4,1]);
tY1 = tY(:).'; tY2 = tY  - tY(1,1); tY2(3:4,:) = 0; tY2 = tY2(:).';
tM1 = [tY1.*0 + 1; tY1; tY1.*0 + 1];
tM2 = [tY1.*0; tY2; tY1.*0];
CBarHdl.Face.VertexData = repmat(VertexData, [1,size(CData,2)]).*tM1 + tM2;
CBarHdl.Face.ColorData = reshape(repmat(CData, [4,1]), 4, []);

The code will work if the plot have AlphaData property

data = peaks(30);
AData = rescale(data, .2, 1);
surface(data, 'FaceAlpha','flat','AlphaData',AData);
colormap(jet(100)); 
ax = gca;
ax.DataAspectRatio = [1,1,1];
ax.TickDir = 'out';          
ax.Box = 'off';
view(3)
CBarHdl = colorbar;
pause(1e-16)
CData = CBarHdl.Face.Texture.CData;
ALim = [min(min(AData)), max(max(AData))];
CData(4,:) = uint8(255.*rescale(1:size(CData, 2), ALim(1), ALim(2)));
warning off
CBarHdl.Face.ColorType = 'TrueColorAlpha';
VertexData = CBarHdl.Face.VertexData;
tY = repmat((1:size(CData,2))./size(CData,2), [4,1]);
tY1 = tY(:).'; tY2 = tY  - tY(1,1); tY2(3:4,:) = 0; tY2 = tY2(:).';
tM1 = [tY1.*0 + 1; tY1; tY1.*0 + 1];
tM2 = [tY1.*0; tY2; tY1.*0];
CBarHdl.Face.VertexData = repmat(VertexData, [1,size(CData,2)]).*tM1 + tM2;
CBarHdl.Face.ColorData = reshape(repmat(CData, [4,1]), 4, []);

goc3
Last activity 2024 年 6 月 7 日

Trick: Easy Digit Manipulation

There are a host of problems on Cody that require manipulation of the digits of a number. Examples include summing the digits of a number, separating the number into its powers, and adding very large numbers together.

If you haven't come across this trick yet, you might want to write it down (or save it electronically):

digits = num2str(4207) - '0'

That code results in the following:

digits =
     4     2     0     7

Now, summing the digits of the number is easy:

sum(digits)
ans =
    13

Chen Lin
Last activity 2024 年 10 月 1 日

Welcome to the Cody Discussion Channel! Please Read Before Posting

Hello and a warm welcome to everyone! We're excited to have you in the Cody Discussion Channel. To ensure the best possible experience for everyone, it's important to understand the types of content that are most suitable for this channel.

Content that belongs in the Cody Discussion Channel:

Tips & tricks: Discuss strategies for solving Cody problems that you've found effective.
Ideas or suggestions for improvement: Have thoughts on how to make Cody better? We'd love to hear them.
Issues: Encountering difficulties or bugs with Cody? Let us know so we can address them.
Requests for guidance: Stuck on a Cody problem? Ask for advice or hints, but make sure to show your efforts in attempting to solve the problem first.
General discussions: Anything else related to Cody that doesn't fit into the above categories.

Content that does not belong in the Cody Discussion Channel:

Comments on specific Cody problems: Examples include unclear problem descriptions or incorrect testing suites.
Comments on specific Cody solutions: For example, you find a solution creative or helpful.

Please direct such comments to the Comments section on the problem or solution page itself.

We hope the Cody discussion channel becomes a vibrant space for sharing expertise, learning new skills, and connecting with others.

Adam Danz
Last activity 2024 年 5 月 30 日

How to leave feedback for documentation pages

How to leave feedback on a doc page

Leaving feedback is a two-step process. At the bottom of most pages in the MATLAB documentation is a star rating.

Start by selecting a star that best answers the question. After selecting a star rating, an edit box appears where you can offer specific feedback.

When you press "Submit" you'll see the confirmation dialog below. You cannot go back and edit your content, although you can refresh the page to go through that process again.

Tips on leaving feedback

Be productive. The reader should clearly understand what action you'd like to see, what was unclear, what you think needs work, or what areas were really helpful.
Positive feedback is also helpful. By nature, feedback often focuses on suggestions for changes but it also helps to know what was clear and what worked well.
Point to specific areas of the page. This helps the reader to narrow the focus of the page to the area described by your feedback.

What happens to that feedback?

Before working at MathWorks I often left feedback on documentation pages but I never knew what happens after that. One day in 2021 I shared my speculation on the process:

> That feedback is received by MathWorks Gnomes which are never seen nor heard but visit the MathWorks documentation team at night while they are sleeping and whisper selected suggestions into their ears to manipulate their dreams. Occassionally this causes them to wake up with a Eureka moment that leads to changes in the documentation.

I'd like to let you in on the secret which is much less fanciful. Feedback left in the star rating and edit box are collected and periodically reviewed by the doc writers who look for trends on highly trafficked pages and finer grain feedback on less visited pages. Your feedback is important and often results in improvements.

David
Last activity 2024 年 5 月 23 日

Introduced in...

A colleague said that you can search the Help Center using the phrase 'Introduced in' followed by a release version. Such as, 'Introduced in R2022a'. Doing this yeilds search results specific for that release.

Seems pretty handy so I thought I'd share.

Chen Lin
Last activity 2024 年 5 月 22 日

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!

lazymatlab
Last activity 2024 年 5 月 18 日

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

Robert
Last activity 2024 年 5 月 13 日

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 2024 年 5 月 13 日

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 :

Contests

Chen Lin
Last activity 2024 年 5 月 9 日

Unveiling Creativity: An Interview with Zhaoxu Liu / slanderer

Dear MATLAB contest enthusiasts,

I believe many of you have been captivated by the innovative entries from Zhaoxu Liu / slanderer, in the 2023 MATLAB Flipbook Mini Hack contest.

Ever wondered about the person behind these creative entries? What drives a MATLAB user to such levels of skill? And what inspired his participation in the contest? We were just as curious as you are!

We were delighted to catch up with him and learn more about his use of MATLAB. The interview has recently been published in MathWorks Blogs. For an in-depth look into his insights and experiences, be sure to read our latest blog post: Community Q&A – Zhaoxu Liu.

But the conversation doesn't end here! Who would you like to see featured in our next interview? Drop their name in the comments section below and let us know who we should reach out to next!

Gregory Vernon
Last activity 2024 年 5 月 6 日

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 2024 年 5 月 6 日

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 2024 年 4 月 29 日

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 2024 年 4 月 22 日

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