Risultati per
You are not a jedi yet !
0%
We not grant u the rank of master !
0%
Ready are u? What knows u of ready?
0%
May the Force be with you !
100%
2 voti
Simulinkモデルを生成AIで自動的に作成できたら便利だと思いませんか？
QiitaのSacredTubesさんは、このアイデアを実験的に試みた記事を公開しています。
その方法は、まず生成AIでVerilogコードを作成し、それをSimulinkに取り込んでモデル化するというものです。（ここではHDL Coderというツールボックスの機能が使われました：importhdl）
まだ実用段階には至っていませんが、モデルベース開発（MBD）と生成AIの可能性を探る上で、非常に興味深い試みです。
生成AIの限界と可能性を考えるきっかけとして、一読の価値があります。
---
もし「Simulink Copilot」のような生成AIツールが登場するとしたら、
どんな機能があったら嬉しいと思いますか？
- 自然言語でブロック図を生成？
- 既存モデルの自動ドキュメント化？
- シミュレーション結果の要約と解釈？
皆さんのアイデアをぜひシェアしてください！
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.
The MATLAB R2025a release gave figures a makeover. @Brian Knolhoff, a developer on the Figure Infrastructure and Services team, reviews the new Figure Container in the Graphics and App Building blog.
Learn the four ways to tile figures, what docking means, and other new features.
Introducing the Tabbed Figure Container
Guest Writer: Brian Knolhoff Brian is a software engineer on the Figure Infrastructure and Services team at MathWorks. After first encountering MATLAB while calibrating radar systems, he now enjoys building and modernizing the tools that support engineers and scientists in their daily work. Outside of the office, Brian enjoys playing guitar, hockey, and board games, and maintains a collection of vintage
- 昨日までちゃんと動いていたのに・・
- ヘルプページ通りに書いているのに・・
MATLAB 関数がエラーを出すようになることありますよね（？）そんな時にみなさんがまず確認するもの、何かありますか？教えてください！
例えば
which -all plot
をコマンドウィンドウで実行して、もともと MATLAB で定義されている plot 関数（MATLAB のインストールフォルダにある plot 関数）がちゃんと頭に出てくるかどうか確認します。
I am deeply honored to announce the official publication of my latest academic volume:
MATLAB for Civil Engineers: From Basics to Advanced Applications
(Springer Nature, 2025).
This work serves as a comprehensive bridge between theoretical civil engineering principles and their practical implementation through MATLAB—a platform essential to the future of computational design, simulation, and optimization in our field.
Structured to serve both academic audiences and practicing engineers, this book progresses from foundational MATLAB programming concepts to highly specialized applications in structural analysis, geotechnical engineering, hydraulic modeling, and finite element methods. Whether you are a student building analytical fluency or a professional seeking computational precision, this volume offers an indispensable resource for mastering MATLAB's full potential in civil engineering contexts.
With rigorously structured examples, case studies, and research-aligned methods, MATLAB for Civil Engineers reflects the convergence of engineering logic with algorithmic innovation—equipping readers to address contemporary challenges with clarity, accuracy, and foresight.
📖 Ideal for:
— Graduate and postgraduate civil engineering students
— University instructors and lecturers seeking a structured teaching companion
— Professionals aiming to integrate MATLAB into complex real-world projects
If you are passionate about engineering resilience, data-informed design, or computational modeling, I invite you to explore the work and share it with your network.
🧠 Let us advance the discipline together through precision, programming, and purpose.
In case you missed it in my overview of the MATLAB R2025a release, Markdown support has been greatly improved. This picture says it all
Untapped Potential for Output-arguments Block
MATLAB has a very powerful feature in its arguments blocks. For example, the following code for a function (or method):
- clearly outlines all the possible inputs
- provides default values for each input
- will produce auto-complete suggestions while typing in the Editor (and Command Window in newer versions)
- checks each input against validation functions to enforce size, shape (e.g., column vs. row vector), type, and other options (e.g., being a member of a set)
function [out] = sample_fcn(in)
arguments(Input)
in.x (:, 1) = []
in.model_type (1, 1) string {mustBeMember(in.model_type, ...
["2-factor", "3-factor", "4-factor"])} = "2-factor"
in.number_of_terms (1, 1) {mustBeMember(in.number_of_terms, 1:5)} = 1
in.normalize_fit (1, 1) logical = false
end
% function logic ...
end
If you do not already use the arguments block for function (or method) inputs, I strongly suggest that you try it out.
The point of this post, though, is to suggest improvements for the output-arguments block, as it is not nearly as powerful as its input-arguments counterpart. I have included two function examples: the first can work in MATLAB while the second does not, as it includes suggestions for improvements. Commentary specific to each function is provided completely before the code. While this does necessitate navigating back and forth between functions and text, this provides for an easy comparison between the two functions which is my main goal.
Current Implementation
The input-arguments block for sample_fcn begins the function and has already been discussed. A simple output-arguments block is also included. I like to use a single output so that additional fields may be added at a later point. Using this approach simplifies future development, as the function signature, wherever it may be used, does not need to be changed. I can simply add another output field within the function and refer to that additional field wherever the function output is used.
Before beginning any logic, sample_fcn first assigns default values to four fields of out. This is a simple and concise way to ensure that the function will not error when returning early.
The function then performs two checks. The first is for an empty input (x) vector. If that is the case, nothing needs to be done, as the function simply returns early with the default output values that happen to apply to the inability to fit any data.
The second check is for edge cases for which input combinations do not work. In this case, the status is updated, but default values for all other output fields (which are already assigned) still apply, so no additional code is needed.
Then, the function performs the fit based on the specified model_type. Note that an otherwise case is not needed here, since the argument validation for model_type would not allow any other value.
At this point, the total_error is calculated and a check is then made to determine if it is valid. If not, the function again returns early with another specific status value.
Finally, the R^2 value is calculated and a fourth check is performed. If this one fails, another status value is assigned with an early return.
If the function has passed all the checks, then a set of assertions ensure that each of the output fields are valid. In this case, there are eight specific checks, two for each field.
If all of the assertions also pass, then the final (successful) status is assigned and the function returns normally.
function [out] = sample_fcn(in)
arguments(Input)
in.x (:, 1) = []
in.model_type (1, 1) string {mustBeMember(in.model_type, ...
["2-factor", "3-factor", "4-factor"])} = "2-factor"
in.number_of_terms (1, 1) {mustBeMember(in.number_of_terms, 1:5)} = 1
in.normalize_fit (1, 1) logical = false
end
arguments(Output)
out struct
end
%%
out.fit = [];
out.total_error = [];
out.R_squared = NaN;
out.status = "Fit not possible for supplied inputs.";
%%
if isempty(in.x)
return
end
%%
if ((in.model_type == "2-factor") && (in.number_of_terms == 5)) || ... % other possible logic
out.status = "Specified combination of model_type and number_of_terms is not supported.";
return
end
%%
switch in.model_type
case "2-factor"
out.fit = % code for 2-factor fit
case "3-factor"
out.fit = % code for 3-factor fit
case "4-factor"
out.fit = % code for 4-factor fit
end
%%
out.total_error = % calculation of error
if ~isfinite(out.total_error)
out.status = "The total_error could not be calculated.";
return
end
%%
out.R_squared = % calculation of R^2
if out.R_squared > 1
out.status = "The R^2 value is out of bounds.";
return
end
%%
assert(iscolumn(out.fit), "The fit vector is not a column vector.");
assert(size(out.fit) == size(in.x), "The fit vector is not the same size as the input x vector.");
assert(isscalar(out.total_error), "The total_error is not a scalar.");
assert(isfinite(out.total_error), "The total_error is not finite.");
assert(isscalar(out.R_squared), "The R^2 value is not a scalar.");
assert(isfinite(out.R_squared), "The R^2 value is not finite.");
assert(isscalar(out.status), "The status is not a scalar.");
assert(isstring(out.status), "The status is not a string.");
%%
out.status = "The fit was successful.";
end
Potential Implementation
The second function, sample_fcn_output_arguments, provides essentially the same functionality in about half the lines of code. It is also much clearer with respect to the output. As a reminder, this function structure does not currently work in MATLAB, but hopefully it will in the not-too-distant future.
This function uses the same input-arguments block, which is then followed by a comparable output-arguments block. The first unsupported feature here is the use of name-value pairs for outputs. I would much prefer to make these assignments here rather than immediately after the block as in the sample_fcn above, which necessitates four more lines of code.
The mustBeSameSize validation function that I use for fit does not exist, but I really think it should; I would use it a lot. In this case, it provides a very succinct way of ensuring that the function logic did not alter the size of the fit vector from what is expected.
The mustBeFinite validation function for out.total_error does not work here simply because of the limitation on name-value pairs; it does work for regular outputs.
Finally, the assignment of default values to output arguments is not supported.
The next three sections of sample_fcn_output_arguments match those of sample_fcn: check if x is empty, check input combinations, and perform fit logic. Following that, though, the functions diverge heavily, as you might expect. The two checks for total_error and R^2 are not necessary, as those are covered by the output-arguments block. While there is a slight difference, in that the specific status values I assigned in sample_fcn are not possible, I would much prefer to localize all these checks in the arguments block, as is already done for input arguments.
Furthermore, the entire section of eight assertions in sample_fcn is removed, as, again, that would be covered by the output-arguments block.
This function ends with the same status assignment. Again, this is not exactly the same as in sample_fcn, since any failed assertion would prevent that assignment. However, that would also halt execution, so it is a moot point.
function [out] = sample_fcn_output_arguments(in)
arguments(Input)
in.x (:, 1) = []
in.model_type (1, 1) string {mustBeMember(in.model_type, ...
["2-factor", "3-factor", "4-factor"])} = "2-factor"
in.number_of_terms (1, 1) {mustBeMember(in.number_of_terms, 1:5)} = 1
in.normalize_fit (1, 1) logical = false
end
arguments(Output)
out.fit (:, 1) {mustBeSameSize(out.fit, in.x)} = []
out.total_error (1, 1) {mustBeFinite(out.total_error)} = []
out.R_squared (1, 1) {mustBeLessThanOrEqual(out.R_squared, 1)} = NaN
out.status (1, 1) string = "Fit not possible for supplied inputs."
end
%%
if isempty(in.x)
return
end
%%
if ((in.model_type == "2-factor") && (in.number_of_terms == 5)) || ... % other possible logic
out.status = "Specified combination of model_type and number_of_terms is not supported.";
return
end
%%
switch in.model_type
case "2-factor"
out.fit = % code for 2-factor fit
case "3-factor"
out.fit = % code for 3-factor fit
case "4-factor"
out.fit = % code for 4-factor fit
end
%%
out.status = "The fit was successful.";
end
Final Thoughts
There is a significant amount of unrealized potential for the output-arguments block. Hopefully what I have provided is helpful for continued developments in this area.
What are your thoughts? How would you improve arguments blocks for outputs (or inputs)? If you do not already use them, I hope that you start to now.
キーと値の組み合わせでデータを格納できるディクショナリ。R2022bでdictionaryコマンドが登場し、最近のバージョンではreaddictionaryとwritedictionaryでJSONファイルからの読み込み・書き込みにも対応しました。
私はMIDIデータからピアノの演奏動画を作るプログラムで、ディクショナリを使いました。音のノート番号をキーにして、patchで白と黒で鍵盤を塗りつぶしたmatlab.graphics.Graphicsデータ型を値にしたディクショナリで保存して、MIDIで鳴らされた音のノート番号からlookupでグラフのオブジェクトを取得し、FaceColorを変更してハイライトするというもの。
コード例
%% MIDIデータの.matファイルを読み取ってピアノを描画するサンプル
fig = figure('Position', [34 328 1626 524]);
ax = axes;
whiteKeyY = [0 0 150 150];
whiteKeyColor = [1 1 1];
blackKeyY = [50 50 150 150];
blackKeyColor = [0.1 0.1 0.1];
edgeColor = [0 0 0];
% ディクショナリの定義
d = configureDictionary("double", "matlab.graphics.Graphics");
% 白鍵を描画
for n = 1:9
pos = 23*7*(n-1);
d = insert(d, 21 + (n-1)*12, patch([pos+5 pos+28 pos+28 pos+5],whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 21 + (n-1)*12));
d = insert(d, 23 + (n-1)*12, patch([pos+28 pos+51 pos+51 pos+28], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 23 + (n-1)*12));
d = insert(d, 24 + (n-1)*12, patch([pos+51 pos+74 pos+74 pos+51], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 24 + (n-1)*12));
if n < 9
d = insert(d, 26 + (n-1)*12, patch([pos+74 pos+97 pos+97 pos+74], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 26 + (n-1)*12));
d = insert(d, 28 + (n-1)*12, patch([pos+97 pos+120 pos+120 pos+97], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 28 + (n-1)*12));
d = insert(d, 29 + (n-1)*12, patch([pos+120 pos+143 pos+143 pos+120], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 29 + (n-1)*12));
d = insert(d, 31 + (n-1)*12, patch([pos+143 pos+166 pos+166 pos+143], whiteKeyY, whiteKeyColor, 'EdgeColor', edgeColor, 'UserData', 31 + (n-1)*12));
end
end
% 黒鍵を描画。白鍵の上になるようにループを分けています
for n = 1:9
pos = 23*7*(n-1);
d = insert(d, 22 + (n-1)*12, patch([pos+23 pos+33 pos+33 pos+23], blackKeyY, blackKeyColor, 'EdgeColor', [0 0 0], 'UserData', 22 + (n-1)*12));
if n < 9
d = insert(d, 25 + (n-1)*12, patch([pos+69 pos+79 pos+79 pos+69], blackKeyY, blackKeyColor, 'EdgeColor', [0 0 0], 'UserData', 25 + (n-1)*12));
d = insert(d, 27 + (n-1)*12, patch([pos+92 pos+102 pos+102 pos+92], blackKeyY, blackKeyColor, 'EdgeColor', [0 0 0], 'UserData', 27 + (n-1)*12));
d = insert(d, 30 + (n-1)*12, patch([pos+138 pos+148 pos+148 pos+138], blackKeyY, blackKeyColor, 'EdgeColor', [0 0 0], 'UserData', 30 + (n-1)*12));
d = insert(d, 32 + (n-1)*12, patch([pos+161 pos+171 pos+171 pos+161], blackKeyY, blackKeyColor, 'EdgeColor', [0 0 0], 'UserData', 32 + (n-1)*12));
end
end
xticklabels({})
yticklabels({})
xlim([5 1362])
drawnow
%% MIDI音源の.matファイルを読み込み
matData = load('fur-elise.mat');
msg = matData.receivedMessages;
eventTimes = [msg.Timestamp] - msg(1).Timestamp;
n = 1;
numNotes = 0;
lastNote = 0;
highlightedCircles = cell(1, 127);
% 音が鳴った鍵盤だけハイライトする
tic
while toc < max(eventTimes)
if toc > eventTimes(n)
thisMsg = msg(n);
if thisMsg.Type == "NoteOn"
numNotes = numNotes + 1;
lastNote = thisMsg.Note;
thisPatch = lookup(d, thisMsg.Note);
thisPatch.FaceColor = '#CCFFCC';
drawnow
elseif thisMsg.Type == "NoteOff"
numNotes = 0;
thisPatch = lookup(d, thisMsg.Note);
[~, ~, wOrB] = calcNotePos(thisMsg.Note);
if wOrB == "w"
thisPatch.FaceColor = 'white';
else
thisPatch.FaceColor = 'black';
end
drawnow
end
n = n+1;
end
end
%% サブ関数
function [pianoPos, centerPos, wOrB] = calcNotePos(note)
tempVar = idivide(int64(note), int64(12)); % 12で割った商
pos = 23*7*(tempVar-1);
switch mod(note, 12)
case 0 % C
pianoPos = pos + 62.5;
centerPos = 30;
wOrB = "w";
case 2 % D
pianoPos = pos + 85.5;
centerPos = 30;
wOrB = "w";
case 4 % E
pianoPos = pos + 108.5;
centerPos = 30;
wOrB = "w";
case 5 % F
pianoPos = pos + 131.5;
centerPos = 30;
wOrB = "w";
case 7 % G
pianoPos = pos + 154.5;
centerPos = 30;
wOrB = "w";
case 9 % A
pianoPos = pos + 177.5;
centerPos = 30;
wOrB = "w";
case 11 % B
pianoPos = pos + 200.5;
centerPos = 30;
wOrB = "w";
case 1 % C#
pianoPos = pos + 69;
centerPos = 100;
wOrB = "b";
case 3 % D#
pianoPos = pos + 92;
centerPos = 100;
wOrB = "b";
case 6 % F#
pianoPos = pos + 138;
centerPos = 100;
wOrB = "b";
case 8 % G#
pianoPos = pos + 161;
centerPos = 100;
wOrB = "b";
case 10 % A#
pianoPos = pos + 184;
centerPos = 100;
wOrB = "b";
end
end
皆さんはディクショナリを使ってますか？ もし使っていたら、どういう活用をしているか、聞かせてください！
どの方法を使う事が多いですか？他によく使う方法があれば教えてくださいー。
方法①
Livescript 上で for ループ内で描画を編集させて描いた動画は「アニメーションのエクスポート」から動画ファイルに出力するのが一番簡単ですね。再生速度やら細かい設定ができない点は要注意。
方法②
exportgraphics 関数で "Append" オプション指定で実現できるようになった（R2022a から）のでこれも便利ですね。
N = 100;
x = linspace(0,4*pi,N);
y = sin(x);
filename = 'animation_sample.gif'; % Specify the output file name
if exist(filename,'file')
delete(filename)
end
h = animatedline;
axis([0,4*pi,-1,1]) % x軸の表示範囲を固定
for k = 1:length(x)
addpoints(h,x(k),y(k)); % ループでデータを追加
exportgraphics(gca,filename,"Append",true)
end
方法③
R2021b 以前のバージョンだとこんな感じ。
各ループで画面キャプチャして、imwrite で動画ファイルにフレーム追加していくイメージです。"DelayTime" を使って細かい指定ができるので、必要に応じて今でも利用します。
for k = 1:length(x)
addpoints(h,x(k),y(k)); % ループでデータを追加
drawnow % グラフアップデート
frame = getframe(gcf); % Figure 画面をムービーフレーム（構造体）としてキャプチャ
tmp = frame2im(frame); % 画像に変更
[A,map] = rgb2ind(tmp,256); % RGB -> インデックス画像に
if k == 1 % 新規 gif ファイル作成
imwrite(A,map,filename,'gif','LoopCount',Inf,'DelayTime',0.2);
else % 以降、画像をアペンド
imwrite(A,map,filename,'gif','WriteMode','append','DelayTime',0.2);
end
end
I saw this on Reddit and thought of the past mini-hack contests. We have a few folks here who can do something similar with MATLAB.
これからは生成AIでコードを１から書くという事が減ってくるのかと思いますが，皆さんがMATLABのコードを書く時に意識しているご自身のルールのようなものがあれば教えてください．
MATLAB言語は柔軟に書けますが，自然と個人個人のルールというものが出来上がってきているのでは，と思います．
私はParameter, Valueペアの引数がある関数はそれぞれのペアを新しい行に書く，というのをよくやります．
h = plot(x, y, "ro-", ...
"LineWidth", 2, ...
"MarkerSize", 10, ...
"MarkerFaceColor", "g");
Parameter=Valueでも同じです．
h = plot(x, y, "ro-", ...
LineWidth = 2, ...
MarkerSize = 10, ...
MarkerFaceColor = "g");
また，一時期は "=" を揃えることもやってました（今はやってませんが）．
h = plot(x, y, "ro-", ...
LineWidth = 2, ...
MarkerSize = 10, ...
MarkerFaceColor = "g");
皆さんにはどのようなルールがありますか？
The Graphics and App Building Blog just launched its first article on R2025a features, authored by Chris Portal, the director of engineering for the MATLAB graphics and app building teams.
Over the next few months, we'll publish a series of articles that showcase our updated graphics system, introduce new tools and features, and provide valuable references enriched by the perspectives of those involved in their development.
To stay updated, you can subscribe to the blog (look for the option in the upper left corner of the blog page). We also encourage you to join the conversation—your comments and questions under each article help shape the discussion and guide future content.
It seems like the financial news is always saying the stock market is especially volatile now. But is it really? This code will show you the daily variation from the prior day. You can see that the average daily change from one day to the next is 0.69%. So any change in the stock market from the prior day less than about 0.7% or 1% is just normal "noise"/typical variation. You can modify the code to adjust the starting date for the analysis. Data file (Excel workbook) is attached (hopefully - I attached it twice but it's not showing up yet).
% Program to plot the Dow Jones Industrial Average from 1928 to May 2025, and compute the standard deviation.
% Data available for download at https://finance.yahoo.com/quote/%5EDJI/history?p=%5EDJI
% Just set the Time Period, then find and click the download link, but you ned a paid version of Yahoo.
%
% If you have a subscription for Microsoft Office 365, you can also get historical stock prices.
% Reference: https://support.microsoft.com/en-us/office/stockhistory-function-1ac8b5b3-5f62-4d94-8ab8-7504ec7239a8#:~:text=The%20STOCKHISTORY%20function%20retrieves%20historical,Microsoft%20365%20Business%20Premium%20subscription.
% For example put this in an Excel Cell
% =STOCKHISTORY("^DJI", "1/1/2000", "5/10/2025", 0, 1, 0, 1,2,3,4, 5)
% and it will fill out a table in Excel
%====================================================================================================================
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
imtool close all; % Close all imtool figures if you have the Image Processing Toolbox.
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 14;
filename = 'Dow Jones Industrial Index.xlsx';
data = readtable(filename);
% Date,Close,Open,High,Low,Volume
dates = data.Date;
closing = data.Close;
volume = data.Volume;
% Define start date and stop date
startDate = datetime(2011,1,1)
stopDate = dates(end)
selectedDates = dates > startDate;
% Extract those dates:
dates = dates(selectedDates);
closing = closing(selectedDates);
volume = volume(selectedDates);
% Plot Volume
hFigVolume = figure('Name', 'Daily Volume');
plot(dates, volume, 'b-');
grid on;
xticks(startDate:calendarDuration(5,0,0):stopDate)
title('Dow Jones Industrial Average Volume', 'FontSize', fontSize);
hFig = figure('Name', 'Daily Standard Deviation');
subplot(3, 1, 1);
plot(dates, closing, 'b-');
xticks(startDate:calendarDuration(5,0,0):stopDate)
drawnow;
grid on;
caption = sprintf('Dow Jones Industrial Average from %s through %s', dates(1), dates(end));
title(caption, 'FontSize', fontSize);
% Get the average change from one trading day to the next.
diffs = 100 * abs(closing(2:end) - closing(1:end-1)) ./ closing(1:end-1);
subplot(3, 1, 2);
averageDailyChange = mean(diffs)
% Looks pretty noisy so let's smooth it for a nicer display.
numWeeks = 4;
diffs = sgolayfilt(diffs, 2, 5*numWeeks+1);
plot(dates(2:end), diffs, 'b-');
grid on;
xticks(startDate:calendarDuration(5,0,0):stopDate)
hold on;
line(xlim, [averageDailyChange, averageDailyChange], 'Color', 'r', 'LineWidth', 2);
ylabel('Percentage', 'FontSize', fontSize);
caption = sprintf('Day-to-Day Change Percentage. Average Daily Change (from prior day) = %.2f%%', averageDailyChange);
title(caption, 'FontSize', fontSize);
drawnow;
% Get the stddev over a 5 trading day window.
sd = stdfilt(closing, ones(5, 1));
% Get it relative to the magnitude.
sd = sd ./ closing * 100;
averageVariation = mean(sd)
numWeeks = 2;
% Looks pretty noisy so let's smooth it for a nicer display.
sd = sgolayfilt(sd, 2, 5*numWeeks+1);
% Plot it.
subplot(3, 1, 3);
plot(dates, sd, 'b-');
grid on;
xticks(startDate:calendarDuration(5,0,0):stopDate)
hold on;
line(xlim, [averageVariation, averageVariation], 'Color', 'r', 'LineWidth', 2);
ylabel('Percentage', 'FontSize', fontSize);
caption = sprintf('Weekly Standard Deviation, Averaged Over %d Weeks (%d trading days). Mean SD = %.2f', ...
numWeeks, 5*numWeeks+1, averageVariation);
title(caption, 'FontSize', fontSize);
% Maximize figure window.
g = gcf;
g.WindowState = 'maximized';
The following lines were added to the subplot function in version 2025a (line 291):
if ancestorFigure.Units == "normalized"
waitfor(ancestorFigure,'FigureViewReady',true);
end
That code isn't in version 2024a.
Because of this, I'm experiencing issues that cause the code to stop running when using subplot in this way:
figure('Units','normalized','Position',[0 0 0.3 0.3])
subplot(1,2,1)
...
Has anyone else encountered this error?
Does anyone understand the need for those lines of code?
As far as I can tell, there is still no official support for creating publication-ready tables from regression output, either as latex or natively. Although MATLAB isn't primarily statistical software, this still seems like an oversight, as almost any similar software has this capability built-in or as a package.
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