ans = 
Resultados de
I'm working on training neural networks without backpropagation / automatic differentiation, using locally derived analytic forms of update rules. Given that this allows a direct formula to be derived for the update rule, it removes alot of the overhead that is otherwise required from automatic differentiation.
However, matlab's functionalities for neural networks are currently solely based around backpropagation and automatic differentiation, such as the dlgradient function and requiring everything to be dlarrays during training.
I have two main requests, specifically for functions that perform a single operation within a single layer of a neural network, such as "dlconv", "fullyconnect", "maxpool", "avgpool", "relu", etc:
- these functions should also allow normal gpuArray data instead of requiring everything to be dlarrays.
- these functions are currently designed to only perform the forward pass. I request that these also be designed to perform the backward pass if user requests. There can be another input user flag that can be "forward" (default) or "backward", and then the function should have all the necessary inputs to perform that operation (e.g. for "avgpool" forward pass it only needs the avgpool input data and the avgpool parameters, but for the "avgpool" backward pass it needs the deriviative w.r.t. the avgpool output data, the avgpool parameters, and the original data dimensions). I know that there is a maxunpool function that achieves this for maxpool, but it has significant issues when trying to use it this way instead of by backpropagation in a dlgradient type layer, see (https://www.mathworks.com/matlabcentral/answers/2179587-making-a-custom-way-to-train-cnns-and-i-am-noticing-that-avgpool-is-significantly-faster-than-maxpo?s_tid=srchtitle).
I don't know how many people would benefit from this feature, and someone could always spend their time creating these functionalities themselves by matlab scripts, cuDNN mex, etc., but regardless it would be nice for matlab to have this allowable for more customizable neural net training.
Edit 15 Oct 2025: Removed incorrect code. Replaced symmatrix2sym and symfunmatrix2symfun with sym and symfun respectively (latter supported as of 2024b).
The Symbolic Math Toolbox does not have its own dot and and cross functions. That's o.k. (maybe) for garden variety vectors of sym objects where those operations get shipped off to the base Matlab functions
x = sym('x',[3,1]); y = sym('y',[3,1]);
which dot(x,y)
dot(x,y)
which cross(x,y)
cross(x,y)
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[1,1]);
sympref('AbbreviateOutput',false);
dot() expands the result, which isn't really desirable for exposition.
eqn = z == dot(x,y)
Also, dot() returns the the result in terms of the conjugate of x, which can't be simplifed away at the symmatrix level
assumeAlso(sym(x),'real')
class(eqn)
try
eqn = z == simplify(dot(x,y))
catch ME
ME.message
end
To get rid of the conjugate, we have to resort to sym
eqn = simplify(sym(eqn))
but again we are in expanded form, which defeats the purpose of symmatrix (et. al.)
But at least we can do this to get a nice equation
eqn = z == x.'*y
dot errors with symfunmatrix inputs
clearvars
syms t real
x = symfunmatrix('x(t)',t,[3,1]); y = symfunmatrix('y(t)',t,[3,1]);
try
dot(x,y)
catch ME
ME.message
end
Cross works (accidentally IMO) with symmatrix, but expands the result, which isn't really desirable for exposition
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[3,1]);
eqn = z == cross(x,y)
And it doesn't work at all if an input is a symfunmatrix
syms t
w = symfunmatrix('w(t)',t,[3,1]);
try
eqn = z == cross(x,w);
catch ME
ME.message
end
In the latter case we can expand with
eqn = z == cross(sym(x),symfun(w)) % x has to be converted
But we can't do the same with dot (as shown above, dot doesn't like symfun inputs)
try
eqn = z == dot(sym(x),symfun(w))
catch ME
ME.message
end
Looks like the only choice for dot with symfunmatrix is to write it by hand at the matrix level
x.'*w
or at the sym/symfun level
sym(x).'*symfun(w) % assuming x is real
Ideally, I'd like to see dot and cross implemented for symmatrix and symfunmatrix types where neither function would evaluate, i.e., expand, until both arguments are subs-ed with sym or symfun objects of appropriate dimension.
Also, it would be nice if symmatrix could be assumed to be real. Is there a reason why being able to do so wouldn't make sense?
try
assume(x,'real')
catch ME
ME.message
end
Something that I periodically wonder about is whether an integration with the Rubi integration rules package would improve symbolic integration in Matlab's Symbolic Toolbox. The project is open-source with an MIT-licensed, has a Mathematica implementation, and supposedly SymPy is working on an implementation. Much of my intrigue comes from this 2022 report that compared the previous version of Rubi (4.16.1) against various CAS systems, including Matlab 2021a (Mupad):
While not really an official metric for Rubi, this does "feel" similar to my experience computing symbolic integrals in Matlab Symbolic Toolbox vs Maple/Mathematica. What do y'all think?
Have you ever been enrolled in a course that uses an LMS and there is an assignment that invovles posting a question to, or answering a question in, a discussion group? This discussion group is meant to simulate that experience.
The functionality would allow report generation straight from live scripts that could be shared without exposing the code. This could be useful for cases where the recipient of the report only cares about the results and not the code details, or when the methodology is part of a company know how, e.g. Engineering services companies.
In order for it to be practical for use it would also require that variable values could be inserted into the text blocks, e.g. #var_name# would insert the value of the variable "var_name" and possibly selecting which code blocks to be hidden.
Modern engineering requires both robust hardware and powerful simulation tools. MATLAB and Simulink are widely used for data analysis, control design, and embedded system development. At the same time, Kasuo offers a wide range of components—from sensors and connectors to circuit protection devices—that engineers rely on to build real-world systems.
By combining these tools, developers can bridge the gap between simulation and implementation, ensuring their designs are reliable and ready for deployment.
Example Use Case: Sensor Data Acquisition and Processing
- Kasuo Hardware Setup
- Select a Kasuo sensor (e.g., temperature, microphone, or motion sensor).
- Connect it to a DAQ or microcontroller board for data collection.
- Data Acquisition in MATLAB
- Use MATLAB’s Data Acquisition Toolbox to stream sensor data directly.
- Example snippet:
s = daq("ni");
addinput(s,
"Dev1", "ai0", "Voltage");
data = read(s, seconds(
5), "OutputFormat", "Matrix");
plot(data);
- Signal Processing with Simulink
- Build a Simulink model to filter noise, detect anomalies, or design control logic.
- Simulink enables real-time visualization and iterative tuning.
- Validation & Protection Simulation
- Add Kasuo’s circuit protection components (e.g., TVS diodes, surge suppressors) in the physical design.
- Use Simulink to simulate stress conditions, validating system robustness before hardware testing.
Benefits of the Workflow
- Faster prototyping with MATLAB & Simulink.
- Greater reliability by incorporating Kasuo protection devices.
- Seamless transition from model to hardware implementation.
Conclusion
Kasuo’s electronic components provide the hardware foundation for many embedded and signal processing applications. When combined with MATLAB and Simulink, engineers can design, simulate, and validate systems more efficiently—reducing risks and development time.
With AI agents dev coding on other languages has become so easy.
Im waiting for matlab to build something like warp but for matlab.
I know they have the current ai but with all respect it's rubbish compared to vibe coding tools in others sectors.
Matlab leads AI so it really should be leading this space.
When you compare MATLAB Plot Gallery with matplotlib gallery, you can see that matplotlib gallery contains a lot of nice graphs which are easy to create in MATLAB but not listed in MATLAB Plot Gallery.
For example, "Data Distribution Plots" section in the MATLAB Plot Gallery includes example for pie function instead of examples for piechart and donutchart functions, etc.
mlapp being a binary is a pain point for source control. It means that you either have to:
- have hooks in your source control system to zip/unzip a mlapp. However, The Mathworks have informed users not to rely on this as the mlapp format may change.
- do all your source control in MATLAB. This is non standard behaviour. Source code and source control should be independent of each other. Web front-ends to source control systems, 3rd party source control apps, CI/CD systems and much more are extremely limited in what they can do with mlapps.
I wish an mlapp could just be a directory full of the required text/other files.
Requested to post this here from reddit.
There is no call to rescan audio devices in audioPlayerRecorder, even though PortAudio has such a call. I have a measurement environment that takes a long time to initialise. If I forget to plug in my audio device, I have to do it all over again...
Share your ideas, suggestions, and wishlists for improving MathWorks products. What would make the software absolutely perfect for you? Discuss your idea(s) with other community users.
Guidelines & Tips
We encourage all ideas, big or small! To help everyone understand and discuss your suggestion, please include as much detail as possible in your post:
- Product or Feature: Clearly state which product (e.g., MATLAB, Simulink, a toolbox, etc.) or specific feature your idea relates to.
- The Problem or Opportunity: Briefly describe what challenge you’re facing or what opportunity you see for improvement.
- Your Idea: Explain your suggestion in detail. What would you like to see added, changed, or improved? How would it help you and other users?
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Ready to share your idea?
Click here and then "Start a Discussion”, and let the community know how MATLAB could be even better for you!
Thank you for your contributions and for helping make MATLAB Central a vibrant place for sharing and improving ideas.
作ったコードは公開して使ってもらいましょう！ということでその方法をブログで紹介します。
GitHub や File Exchange で公開しているコードがあれば、ぜひこのスレで教えてください！
ブログで紹介している大まかな３ステップをここにまとめます。
1. GitHub でコードを公開・開発する
- GitHub 上でのリポジトリ公開はコミュニティ形成にもつながります。
- R2025a 以降は MATLAB の Markdown サポートも強化されており、README.md を充実させると理解や導入が促進されます。
2. File Exchange に展開（GitHub と連携して自動同期）
- File Exchangeで公開することで MATLAB 内から検索・インストールが可能になります。
- GitHub と File Exchange の連携設定により、GitHub の更新を自動的に File Exchange に反映させることも可能です。
3. 「Open in MATLAB Online」ボタンやリンクを追加
- GitHub リポジトリに「Open in MATLAB Online」リンクやボタンを埋め込むことで、ブラウザ上でコードを試せます。
Nice to have - function output argument provide code assist when said function is called
This is a feature which doesn't apear to currently exist, but I think alot of matlab users would like, particularly ones who write alot of custom classes.
Imagine i have a custom class with some properties:
classdef CustomClass < handle
properties
name (1,1) string = "default name"
varOne (1,1) double = 0
end
methods
function obj = CustomClass(name,varOne)
obj.name = name;
obj.VarOne = varOne;
end
end
end
Then imagine I have a function which returns one of these custom class objects:
function [obj] = Calculation(Var1,Var2,name)
arguments (Input)
Var1 (1,1) double
Var2 (1,1) double
end
arguments (Output)
obj (1,1) CustomClass
end
results = Var1 + Var2;
obj = CustomClass(name,result);
end
With this class and this function which returns one of these class objects, I would like the fact that I provided "(1,1) CustomClass" in the output arguemnts block of function "Calculation(Var1,Var2,name)" to trigger code assist automaticaly show me, when writing code that the retuned value from this funciton has properties "name" and "varOne" in the object.
For istance, if I write the following code with this function and the class in the Matlab search path
testObj = Calculation(1,1,"test");
testObj.varOne = 10; %the property "varOne" doesn't apear in code assist when writing this line of code
I would like that the fact function "Calcuation(Var1,Var2,name) has the output arguments block enforcing that this function must return an object of "CustomClass" to make code assist recognise that "testObj" is a "CustomClass" object, just as if testObj was an input argument to another function which had an input argument requiring that "testObj" was a "CustomClass" object.
Maybe this is a feature that may be added to matlab in future releases? (please and thank you LOL)
This is a feature which doesn't apear to currently exist, but I think alot of matlab users would like, particularly ones who write alot of custom classes.
Imagine i have a custom class with some properties:
classdef CustomClass < handle
properties
name (1,1) string = "default name"
varOne (1,1) double = 0
end
methods
function obj = CustomClass(name,varOne)
obj.name = name;
obj.VarOne = varOne;
end
end
end
Then imagine I have a function which returns one of these custom class objects:
function [obj] = Calculation(Var1,Var2,name)
arguments (Input)
Var1 (1,1) double
Var2 (1,1) double
end
arguments (Output)
obj (1,1) CustomClass
end
results = Var1 + Var2;
obj = CustomClass(name,result);
end
With this class and this function which returns one of these class objects, I would like the fact that I provided "(1,1) CustomClass" in the output arguemnts block of function "Calculation(Var1,Var2,name)" to trigger code assist automaticaly show me, when writing code that the retuned value from this funciton has properties "name" and "varOne" in the object.
For istance, if I write the following code with this function and the class in the Matlab search path
testObj = Calculation(1,1,"test");
testObj.varOne = 10; %the property "varOne" doesn't apear in code assist when writing this line of code
I would like that the fact function "Calcuation(Var1,Var2,name) has the output arguments block enforcing that this function must return an object of "CustomClass" to make code assist recognise that "testObj" is a "CustomClass" object, just as if testObj was an input argument to another function which had an input argument requiring that "testObj" was a "CustomClass" object.
Maybe this is a feature that may be added to matlab in future releases? (please and thank you LOL)
群馬産業技術センター様をお招きし、製造現場での異常検知の取り組みについてご紹介いただくオンラインセミナーを開催します。
実際の開発事例を通して、MATLABを使った「教師なし」異常検知の進め方や、予知保全に役立つ最新機能もご紹介します。
✅ 異常検知・予知保全に興味がある方
✅ データ活用を何から始めればいいか迷っている方
✅ 実際の現場事例を知りたい方
ぜひお気軽にご参加ください！
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！
- 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。
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！
