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How to create a legend as follows?
Principle Explanation - Graphic Objects
Hidden Properties of Legend are laid as follows
In most cases, legends are drawn using LineLoop and Quadrilateral:
Both of these basic graphic objects are drawn in groups of four points, and the general principle is as follows:
Of course, you can arrange the points in order, or set VertexIndices whitch means the vertex order to obtain the desired quadrilateral shape:
Other objects
Compared to objects that can only be grouped into four points, we also need to introduce more flexible objects. Firstly, LineStrip, a graphical object that draws lines in groups of two points:
And TriangleStrip is a set of three points that draw objects to fill triangles, for example, complex polygons can be filled with multiple triangles:
Principle Explanation - Create and Replace
Let's talk about how to construct basic graphic objects, which are all constructed using undisclosed and very low-level functions, such as LineStrip, not through:
  • LineStrip()
It is built through:
  • matlab.graphics.primitive.world.LineStrip()
After building the object, the following properties must be set to make the hidden object visible:
  • Layer
  • ColorBinding
  • ColorData
  • VertexData
  • PickableParts
The settings of these properties can refer to the original legend to form the object, which will not be elaborated here. You can also refer to the code I wrote.
Afterwards, set the newly constructed object's parent class as the Group parent class of the original component, and then hide the original component
newBoxEdgeHdl.Parent=oriBoxEdgeHdl.Parent;
oriBoxEdgeHdl.Visible='off';
The above is the entire process of component replacement, with two example codes written:
Semi transparent legend
function SPrettyLegend(lgd)
% Semitransparent rounded rectangle legend
% Copyright (c) 2023, Zhaoxu Liu / slandarer
% -------------------------------------------------------------------------
% Zhaoxu Liu / slandarer (2023). pretty legend
% (https://www.mathworks.com/matlabcentral/fileexchange/132128-pretty-legend),
% MATLAB Central File Exchange. 检索来源 2023/7/9.
% =========================================================================
if nargin<1
ax = gca;
lgd = get(ax,'Legend');
end
pause(1e-6)
Ratio = .1;
t1 = linspace(0,pi/2,4); t1 = t1([1,2,2,3,3,4]);
t2 = linspace(pi/2,pi,4); t2 = t2([1,2,2,3,3,4]);
t3 = linspace(pi,3*pi/2,4); t3 = t3([1,2,2,3,3,4]);
t4 = linspace(3*pi/2,2*pi,4); t4 = t4([1,2,2,3,3,4]);
XX = [1,1,1-Ratio+cos(t1).*Ratio,1-Ratio,Ratio,Ratio+cos(t2).*Ratio,...
0,0,Ratio+cos(t3).*Ratio,Ratio,1-Ratio,1-Ratio+cos(t4).*Ratio];
YY = [Ratio,1-Ratio,1-Ratio+sin(t1).*Ratio,1,1,1-Ratio+sin(t2).*Ratio,...
1-Ratio,Ratio,Ratio+sin(t3).*Ratio,0,0,Ratio+sin(t4).*Ratio];
% 圆角边框(border-radius)
oriBoxEdgeHdl = lgd.BoxEdge;
newBoxEdgeHdl = matlab.graphics.primitive.world.LineStrip();
newBoxEdgeHdl.AlignVertexCenters = 'off';
newBoxEdgeHdl.Layer = 'front';
newBoxEdgeHdl.ColorBinding = 'object';
newBoxEdgeHdl.LineWidth = 1;
newBoxEdgeHdl.LineJoin = 'miter';
newBoxEdgeHdl.WideLineRenderingHint = 'software';
newBoxEdgeHdl.ColorData = uint8([38;38;38;0]);
newBoxEdgeHdl.VertexData = single([XX;YY;XX.*0]);
newBoxEdgeHdl.Parent=oriBoxEdgeHdl.Parent;
oriBoxEdgeHdl.Visible='off';
% 半透明圆角背景(Semitransparent rounded background)
oriBoxFaceHdl = lgd.BoxFace;
newBoxFaceHdl = matlab.graphics.primitive.world.TriangleStrip();
Ind = [1:(length(XX)-1);ones(1,length(XX)-1).*(length(XX)+1);2:length(XX)];
Ind = Ind(:).';
newBoxFaceHdl.PickableParts = 'all';
newBoxFaceHdl.Layer = 'back';
newBoxFaceHdl.ColorBinding = 'object';
newBoxFaceHdl.ColorType = 'truecoloralpha';
newBoxFaceHdl.ColorData = uint8(255*[1;1;1;.6]);
newBoxFaceHdl.VertexData = single([XX,.5;YY,.5;XX.*0,0]);
newBoxFaceHdl.VertexIndices = uint32(Ind);
newBoxFaceHdl.Parent = oriBoxFaceHdl.Parent;
oriBoxFaceHdl.Visible = 'off';
end
Usage examples
clc; clear; close all
rng(12)
% 生成随机点(Generate random points)
mu = [2 3; 6 7; 8 9];
S = cat(3,[1 0; 0 2],[1 0; 0 2],[1 0; 0 1]);
r1 = abs(mvnrnd(mu(1,:),S(:,:,1),100));
r2 = abs(mvnrnd(mu(2,:),S(:,:,2),100));
r3 = abs(mvnrnd(mu(3,:),S(:,:,3),100));
% 绘制散点图(Draw scatter chart)
hold on
propCell = {'LineWidth',1.2,'MarkerEdgeColor',[.3,.3,.3],'SizeData',60};
scatter(r1(:,1),r1(:,2),'filled','CData',[0.40 0.76 0.60],propCell{:});
scatter(r2(:,1),r2(:,2),'filled','CData',[0.99 0.55 0.38],propCell{:});
scatter(r3(:,1),r3(:,2),'filled','CData',[0.55 0.63 0.80],propCell{:});
% 增添图例(Draw legend)
lgd = legend('scatter1','scatter2','scatter3');
lgd.Location = 'northwest';
lgd.FontSize = 14;
% 坐标区域基础修饰(Axes basic decoration)
ax=gca; grid on
ax.FontName = 'Cambria';
ax.Color = [0.9,0.9,0.9];
ax.Box = 'off';
ax.TickDir = 'out';
ax.GridColor = [1 1 1];
ax.GridAlpha = 1;
ax.LineWidth = 1;
ax.XColor = [0.2,0.2,0.2];
ax.YColor = [0.2,0.2,0.2];
ax.TickLength = [0.015 0.025];
% 隐藏轴线(Hide XY-Ruler)
pause(1e-6)
ax.XRuler.Axle.LineStyle = 'none';
ax.YRuler.Axle.LineStyle = 'none';
SPrettyLegend(lgd)
Heart shaped legend (exclusive to pie charts)
function pie2HeartLegend(lgd)
% Heart shaped legend for pie chart
% Copyright (c) 2023, Zhaoxu Liu / slandarer
% -------------------------------------------------------------------------
% Zhaoxu Liu / slandarer (2023). pretty legend
% (https://www.mathworks.com/matlabcentral/fileexchange/132128-pretty-legend),
% MATLAB Central File Exchange. 检索来源 2023/7/9.
% =========================================================================
if nargin<1
ax = gca;
lgd = get(ax,'Legend');
end
pause(1e-6)
% 心形曲线(Heart curve)
x = -1:1/100:1;
y1 = 0.6 * abs(x) .^ 0.5 + ((1 - x .^ 2) / 2) .^ 0.5;
y2 = 0.6 * abs(x) .^ 0.5 - ((1 - x .^ 2) / 2) .^ 0.5;
XX = [x, flip(x),x(1)]./3.4+.5;
YY = ([y1, y2,y1(1)]-.2)./2+.5;
Ind = [1:(length(XX)-1);2:length(XX)];
Ind = Ind(:).';
% 获取图例图标(Get Legend Icon)
lgdEntryChild = lgd.EntryContainer.NodeChildren;
iconSet = arrayfun(@(lgdEntryChild)lgdEntryChild.Icon.Transform.Children.Children,lgdEntryChild,UniformOutput=false);
% 基础边框句柄(Base Border Handle)
newEdgeHdl = matlab.graphics.primitive.world.LineStrip();
newEdgeHdl.AlignVertexCenters = 'off';
newEdgeHdl.Layer = 'front';
newEdgeHdl.ColorBinding = 'object';
newEdgeHdl.LineWidth = .8;
newEdgeHdl.LineJoin = 'miter';
newEdgeHdl.WideLineRenderingHint = 'software';
newEdgeHdl.ColorData = uint8([38;38;38;0]);
newEdgeHdl.VertexData = single([XX;YY;XX.*0]);
newEdgeHdl.VertexIndices = uint32(Ind);
% 基础多边形面句柄(Base Patch Handle)
newFaceHdl = matlab.graphics.primitive.world.TriangleStrip();
Ind = [1:(length(XX)-1);ones(1,length(XX)-1).*(length(XX)+1);2:length(XX)];
Ind = Ind(:).';
newFaceHdl.PickableParts = 'all';
newFaceHdl.Layer = 'middle';
newFaceHdl.ColorBinding = 'object';
newFaceHdl.ColorType = 'truecoloralpha';
newFaceHdl.ColorData = uint8(255*[1;1;1;.6]);
newFaceHdl.VertexData = single([XX,.5;YY,.5;XX.*0,0]);
newFaceHdl.VertexIndices = uint32(Ind);
% 替换图例图标(Replace Legend Icon)
for i = 1:length(iconSet)
oriEdgeHdl = iconSet{i}(1);
tNewEdgeHdl = copy(newEdgeHdl);
tNewEdgeHdl.ColorData = oriEdgeHdl.ColorData;
tNewEdgeHdl.Parent = oriEdgeHdl.Parent;
oriEdgeHdl.Visible = 'off';
oriFaceHdl = iconSet{i}(2);
tNewFaceHdl = copy(newFaceHdl);
tNewFaceHdl.ColorData = oriFaceHdl.ColorData;
tNewFaceHdl.Parent = oriFaceHdl.Parent;
oriFaceHdl.Visible = 'off';
end
end
Usage examples
clc; clear; close all
% 生成随机点(Generate random points)
X = [1 3 0.5 2.5 2];
pieHdl = pie(X);
% 修饰饼状图(Decorate pie chart)
colorList=[0.4941 0.5490 0.4118
0.9059 0.6510 0.3333
0.8980 0.6157 0.4980
0.8902 0.5137 0.4667
0.4275 0.2824 0.2784];
for i = 1:2:length(pieHdl)
pieHdl(i).FaceColor=colorList((i+1)/2,:);
pieHdl(i).EdgeColor=colorList((i+1)/2,:);
pieHdl(i).LineWidth=1;
pieHdl(i).FaceAlpha=.6;
end
for i = 2:2:length(pieHdl)
pieHdl(i).FontSize=13;
pieHdl(i).FontName='Times New Roman';
end
lgd=legend('FontSize',13,'FontName','Times New Roman','TextColor',[1,1,1].*.3);
pie2HeartLegend(lgd)
I recently discovered a 2-minute video that introduces MatGPT, and I believe it's a resource worth sharing. The creator highlights MatGPT's impressive capabilities by demonstrating how it tackles the classic Travelling Salesman Problem.
With more than 13,000 downloads on File Exchange, MatGPT is gaining traction among users. I strongly recommend taking it for a spin to experience its potential firsthand.
I found this list on Book Authority about the top MATLAB books: https://bookauthority.org/books/best-matlab-books
My favorite book is Accelerating MATLAB Performance - 1001 tips to speed up MATLAB programs. I always pick something up from the book that helps me out.
how accurate are the answers of the AI Playground regarding information that are not specifiyed in the documentation?
A key aspect to masting MATLAB Graphics is getting a hang of the MATLAB Graphics Object Hierarchy which is essentially the structure of MATLAB figures that is used in the rendering pipeline. The base object is the Graphics Root (see groot) which contains the Figure. The Figure contains Axes or other containers such as a Tiled Chart Layout (see tiledlayout). Then these Axes can contain graphics primatives (the objects that contain data and get rendered) such as Lines or Patches.
Every graphics object has two important properties, the "Parent" and "Children" properties which can be used to access other objects in the tree. This can be very useful when trying to customize a pre-built chart (such as adding grid lines to both axes in an eye diagram chart) or when trying to access the axes of a non-current figure via a primative (so "gca" doesn't help out).
One last Tip and Trick with this is that you can declare graphics primatives without putting them on or creating an Axes by setting the first input argument to "gobjects(0)" which is an empty array of placeholder graphics objects. Then, when you have an Axes to plot the primitive on and are ready to render it, you can set the "Parent" of the object to your new Axes.
For Example:
l = line(gobjects(0), 1:10, 1:10);
...
...
...
l.Parent = gca;
Practicing navigating and exploring this tree will help propel your understanding of plotting in MATLAB.
Mike Croucher
Mike Croucher
Last activity am 29 Jan. 2024

Pong Wars in MATLAB

One of my colleauges, Michio, recently posted an implementation of Pong Wars in MATLAB
Making me wonder about variations. What might the resulting patterns look with differing numbers of balls? Different physics etc?
We're thrilled to announce the roll-out of some new features that are going to supercharge your Playground experience! Here's what's new:
Copy/Download code from the script area
You can now effortlessly Copy/Download code from the script area with just a single click. Copy code or Download your script directly as .m files and keep your work organized and portable.We hope this will allow you to effortlessly transfer your work from Playground to MATLAB Desktop/Online.
Run Code directly from the Chat panel
Execute code snippets from the chat section with a single click. This new affordance means saving a step since you no longer have to insert code and then hit run from the toolstrip to execute instead just hit run in the chat panel to see the output immediately in the script area
Enhanced visual Experience
Customize your Playground workspace by expanding or collapsing the chat and script sections. Focus on what matters most to you, whether it's AI chat or working on your script.
We hope you will love these updates. Try them out and let us know your feedback.
Mike Croucher
Mike Croucher
Last activity am 22 Okt. 2024

Multiple AIs in the chat playground

When I want to understand a problem, I'll often use different sources. I'll read different textbooks, blog posts, research papers and ask the same question to different people. The differences in the solutions are almost always illuminating.
I feel the same way about AIs. Sometimes, I don't want to ask *THE* AI...I want to ask a bunch of them. They'll have different strengths and weaknesses..different personalities if you want to think of it that way.
I've been playing with the AI chat arena and there really is a lot of difference between the answers returned by different models. https://lmarena.ai/?arena
I think it would be great if the MATLAB Chat playgroundwere to allow the user to change which AI they were talking with.
What does everyone else think?
NODIRABDUSHAKHIDOV
NODIRABDUSHAKHIDOV
Last activity am 1 Apr. 2024

help, how can i use this AI?

how can i use this AI?
Hans Scharler
Hans Scharler
Last activity am 29 Jan. 2024

TIL spy

spy
I have been finding the AI Chat Playground very useful for daily MATLAB use. In particular it has been very useful for me in basically replacing or supplementing dives into MATLAB documentation. The documentation for MATLAB is in my experience uniformly excellent and thorough but it is sometimes lengthy and hard to parse and the AI Chat is a great one stop shop for many questions I have. However, I would find it very useful if the AI Chat could answer my queries and then also supply a link directly to the documentation. E.g. a box at the bottom of the answer that is basically
"Here is the documentation on the functions AI Chat referred to in this response"
could be neat.
Over at Reddit, a MATLAB user asked about when to use a script vs. a live script. How would you answer this?
Hi
I am using simulink for the frequency response analysis of the three phase induction motor stator winding.
The problem is that i can't optimise the pramaeter values manually, for this i have to use genetic algrothem. But iam stucked how to use genetic algorithum to optimise my circuit paramter values like RLC. Any guidence will be highly appreciated.
Starting with MATLAB can be daunting, but the right resources make all the difference. In my experience, the combination of MATLAB Onramp and Cody offers an engaging start.
MATLAB Onramp introduces you to MATLAB's basic features and workflows. Then practice your coding skill on Cody. Challenge yourself to solve 1 basic problem every day for a month! This consistent practice can significantly enhance your proficiency.
What other resources have helped you on your MATLAB journey? Share your recommendations and let's create a comprehensive learning path for beginners!
sky
sky
Last activity am 18 Jan. 2024

Relative pose in 2D: problem 1

I'm having problem in its test 6 ... passing 5/6 what would be the real issue..
am wring Transformation matrix correct.. as question said SSW should be 202.5 degree...
so what is the issue..
I am a beginner of deep learning, and meet with some problems in learning the MATLAB example "Denoise Signals with Adversarial Learning Denoiser Model", hope very much to get help!
Denoise Signals with Adversarial Learning Denoiser Model - MATLAB & Simulink - MathWorks China
1. visualizaition of the features
It is my understanding that the encoded representation of the autoencoder is the features of the original signal. However in this example, the output dimension of the encoder is 64xSignalLength. Does it mean that every sample point of the signal has 64 features?
2. usage of the residual blocks
The encoder-decoder model uses residual blocks (which contribute to reconstructing the denoised signal from the latent space, ). However, only the encoder output is connected to the discriminator. Doesn't it cause the prolem that most features will be learned by the residual blocks, and only a few features that could confuse the discriminator will be learned by the encoder and sent to the discriminator?
I would tell myself to understand vectorization. MATLAB is designed for operating on whole arrays and matrices at once. This is often more efficient than using loops.
James Prestegard
James Prestegard
Last activity am 12 Jan. 2024

Training a deep neural net

I have been developing a neural net to extract a set of generative parameters from an image of a 2-D NMR spectrum. I use a pair of convolution layers each followed by a fullyconnected layer; the pair are joined by an addtion layer and that fed to a regression layer. This trains fine, but answers are sub-optimal. I woudl like to add a fully connected layer between the addtion layer and regression, but training using default training scripts simply won't converge. Any suggestions? Maybe I can start with the pre-trained weights for the convolution layers, but I don't know how to do this.
JHP