다음에 대한 결과:
Function Syntax Design Conundrum
As a MATLAB enthusiast, I particularly enjoy Steve Eddins' blog and the cool things he explores. MATLAB's new argument blocks are great, but there's one frustrating limitation that Steve outlined beautifully in his blog post "Function Syntax Design Conundrum": cases where an argument should accept both enumerated values AND other data types.
Steve points out this could be done using the input parser, but I prefer having tab completions and I'm not a fan of maintaining function signature JSON files for all my functions.
Personal Context on Enumerations
To be clear: I honestly don't like enumerations in any way, shape, or form. One reason is how awkward they are. I've long suspected they're simply predefined constructor calls with a set argument, and I think that's all but confirmed here. This explains why I've had to fight the enumeration system when trying to take arguments of many types and normalize them to enumerated members, or have numeric values displayed as enumerated members without being recast to the superclass every operation.
The Discovery
While playing around extensively with metadata for another project, I realized (and I'm entirely unsure why it took so long) that the properties of a metaclass object are just, in many cases, the attributes of the classdef. In this realization, I found a solution to Steve's and my problem.
To be clear: I'm not in love with this solution. I would much prefer a better approach for allowing variable sets of membership validation for arguments. But as it stands, we don't have that, so here's an interesting, if incredibly hacky, solution.
If you call struct() on a metaclass object to view its hidden properties, you'll notice that in addition to the public "Enumeration" property, there's a hidden "Enumerable" property. They're both logicals, which implies they're likely functionally distinct. I was curious about that distinction and hoped to find some functionality by intentionally manipulating these values - and I did, solving the exact problem Steve mentions.
The Problem Statement
We have a function with an argument that should allow "dual" input types: enumerated values (Steve's example uses days of the week, mine uses the "all" option available in various dimension-operating functions) AND integers. We want tab completion for the enumerated values while still accepting the numeric inputs.
A Solution for Tab-Completion Supported Arguments
Rather than spoil Steve's blog post, let me use my own example: implementing a none() function. The definition is simple enough tf = ~any(A, dim); but when we wrap this in another function, we lose the tab-completion that any() provides for the dim argument (which gives you "all"). There's no great way to implement this as a function author currently - at least, that's well documented.
So here's my solution:
%% Example Function Implementation
% This is a simple implementation of the DimensionArgument class for implementing dual type inputs that allow enumerated tab-completion.
function tf = none(A, dim)
arguments(Input)
A logical;
dim DimensionArgument = DimensionArgument(A, true);
end
% Simple example (notice the use of uplus to unwrap the hidden property)
tf = ~any(A, +dim);
end
I like this approach because the additional work required to implement it, once the enumeration class is initialized, is minimal. Here are examples of function calls, note that the behavior parallels that of the MATLAB native-style tab-completion:
%% Test Data
% Simple logical array for testing
A = randi([0, 1], [3, 5], "logical");
%% Example function calls
tf = none(A, "all"); % This is the tab-completion it's 1:1 with MATLABs behavior
tf = none(A, [1, 2]); % We can still use valid arguments (validated in the constructor)
tf = none(A); % Showcase of the constructors use as a default argument generator
How It Works
What makes this work is the previously mentioned Enumeration attribute. By setting Enumeration = false while still declaring an enumeration block in the classdef file, we get the suggested members as auto-complete suggestions. As I hinted at, the value of enumerations (if you don't subclass a builtin and define values with the someMember (1) syntax) are simply arguments to constructor calls.
We also get full control over the storage and handling of the class, which means we lose the implicit storage that enumerations normally provide and are responsible for doing so ourselves - but I much prefer this. We can implement internal validation logic to ensure values that aren't in the enumerated set still comply with our constraints, and store the input (whether the enumerated member or alternative type) in an internal property.
As seen in the example class below, this maintains a convenient interface for both the function caller and author the only particuarly verbose portion is the conversion methods... Which if your willing to double down on the uplus unwrapping context can be avoided. What I have personally done is overload the uplus function to return the input (or perform the identity property) this allowss for the uplus to be used universally to unwrap inputs and for those that cant, and dont have a uplus definition, the value itself is just returned:
classdef(Enumeration = false) DimensionArgument % < matlab.mixin.internal.MatrixDisplay
%DimensionArgument Enumeration class to provide auto-complete on functions needing the dimension type seen in all()
% Enumerations are just macros to make constructor calls with a known set of arguments. Declaring the 'all'
% enumeration member means this class can be set as the type for an input and the auto-completion for the given
% argument will show the enumeration members, allowing tab-completion. Declaring the Enumeration attribute of
% the class as false gives us control over the constructor and internal implementation. As such we can use it
% to validate the numeric inputs, in the event the 'all' option was not used, and return an object that will
% then work in place of valid dimension argument options.
%% Enumeration members
% These are the auto-complete options you'd like to make available for the function signature for a given
% argument.
enumeration(Description="Enumerated value for the dimension argument.")
all
end
%% Properties
% The internal property allows the constructor's input to be stored; this ensures that the value is store and
% that the output of the constructor has the class type so that the validation passes.
% (Constructors must return the an object of the class they're a constructor for)
properties(Hidden, Description="Storage of the constructor input for later use.")
Data = [];
end
%% Constructor method
% By the magic of declaring (Enumeration = false) in our class def arguments we get full control over the
% constructor implementation.
%
% The second argument in this specific instance is to enable the argument's default value to be set in the
% arguments block itself as opposed to doing so in the function body... I like this better but if you didn't
% you could just as easily keep the constructor simple.
methods
function obj = DimensionArgument(A, Adim)
%DimensionArgument Initialize the dimension argument.
arguments
% This will be the enumeration member name from auto-completed entries, or the raw user input if not
% used.
A = [];
% A flag that indicates to create the value using different logic, in this case the first non-singleton
% dimension, because this matches the behavior of functions like, all(), sum() prod(), etc.
Adim (1, 1) logical = false;
end
if(Adim)
% Allows default initialization from an input to match the aforemention function's behavior
obj.Data = firstNonscalarDim(A);
else
% As a convenience for this style of implementation we can validate the input to ensure that since we're
% suppose to be an enumeration, the input is valid
DimensionArgument.mustBeValidMember(A);
% Store the input in a hidden property since declaring ~Enumeration means we are responsible for storing
% it.
obj.Data = A;
end
end
end
%% Conversion methods
% Applies conversion to the data property so that implicit casting of functions works. Unfortunately most of
% the MathWorks defined functions use a different system than that employed by the arguments block, which
% defers to the class defined converter methods... Which is why uplus (+obj) has been defined to unwrap the
% data for ease of use.
methods
function obj = uplus(obj)
obj = obj.Data;
end
function str = char(obj)
str = char(obj.Data);
end
function str = cellstr(obj)
str = cellstr(obj.Data);
end
function str = string(obj)
str = string(obj.Data);
end
function A = double(obj)
A = double(obj.Data);
end
function A = int8(obj)
A = int8(obj.Data);
end
function A = int16(obj)
A = int16(obj.Data);
end
function A = int32(obj)
A = int32(obj.Data);
end
function A = int64(obj)
A = int64(obj.Data);
end
end
%% Validation methods
% These utility methods are for input validation
methods(Static, Access = private)
function tf = isValidMember(obj)
%isValidMember Checks that the input is a valid dimension argument.
tf = (istext(obj) && all(obj == "all", "all")) || (isnumeric(obj) && all(isint(obj) & obj > 0, "all"));
end
function mustBeValidMember(obj)
%mustBeValidMember Validates that the input is a valid dimension argument for the dim/dimVec arguments.
if(~DimensionArgument.isValidMember(obj))
exception("JB:DimensionArgument:InvalidInput", "Input must be an integer value or the term 'all'.")
end
end
end
%% Convenient data display passthrough
methods
function disp(obj, name)
arguments
obj DimensionArgument
name string {mustBeScalarOrEmpty} = [];
end
% Dispatch internal data's display implementation
display(obj.Data, char(name));
end
end
end
In the event you'd actually play with theres here are the function definitions for some of the utility functions I used in them, including my exception would be a pain so i wont, these cases wont use it any...
% Far from my definition isint() but is consistent with mustBeInteger() for real numbers but will suffice for the example
function tf = isint(A)
arguments
A {mustBeNumeric(A)};
end
tf = floor(A) == A
end
% Sort of the same but its fine
function dim = firstNonscalarDim(A)
arguments
A
end
dim = [find(size(A) > 1, 1), 0];
dim(1) = dim(1);
end
I am thrilled python interoperability now seems to work for me with my APPLE M1 MacBookPro and MATLAB V2025a. The available instructions are still, shall we say, cryptic. Here is a summary of my interaction with GPT 4o to get this to work.
===========================================================
MATLAB R2025a + Python (Astropy) Integration on Apple Silicon (M1/M2/M3 Macs)
===========================================================
Author: D. Carlsmith, documented with ChatGPT
Last updated: July 2025
This guide provides full instructions, gotchas, and workarounds to run Python 3.10 with MATLAB R2025a (Apple Silicon/macOS) using native ARM64 Python and calling modules like Astropy, Numpy, etc. from within MATLAB.
===========================================================
Overview
===========================================================
- MATLAB R2025a on Apple Silicon (M1/M2/M3) runs as "maca64" (native ARM64).
- To call Python from MATLAB, the Python interpreter must match that architecture (ARM64).
- Using Intel Python (x86_64) with native MATLAB WILL NOT WORK.
- The cleanest solution: use Miniforge3 (Conda-forge's lightweight ARM64 distribution).
===========================================================
1. Install Miniforge3 (ARM64-native Conda)
===========================================================
In Terminal, run:
curl -LO https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh
Follow prompts:
- Press ENTER to scroll through license.
- Type "yes" when asked to accept the license.
- Press ENTER to accept the default install location: ~/miniforge3
- When asked:
Do you wish to update your shell profile to automatically initialize conda? [yes|no]
Type: yes
===========================================================
2. Restart Terminal and Create a Python Environment for MATLAB
===========================================================
Run the following:
conda create -n matlab python=3.10 astropy numpy -y
conda activate matlab
Verify the Python path:
which python
Expected output:
/Users/YOURNAME/miniforge3/envs/matlab/bin/python
===========================================================
3. Verify Python + Astropy From Terminal
===========================================================
Run:
python -c "import astropy; print(astropy.__version__)"
Expected output:
6.x.x (or similar)
===========================================================
4. Configure MATLAB to Use This Python
===========================================================
In MATLAB R2025a (Apple Silicon):
clear classes
pyenv('Version', '/Users/YOURNAME/miniforge3/envs/matlab/bin/python')
py.sys.version
You should see the Python version printed (e.g. 3.10.18). No error means it's working.
===========================================================
5. Gotchas and Their Solutions
===========================================================
❌ Error: Python API functions are not available
→ Cause: Wrong architecture or broken .dylib
→ Fix: Use Miniforge ARM64 Python. DO NOT use Intel Anaconda.
❌ Error: Invalid text character (↑ points at __version__)
→ Cause: MATLAB can’t parse double underscores typed or pasted
→ Fix: Use: py.getattr(module, '__version__')
❌ Error: Unrecognized method 'separation' or 'sec'
→ Cause: MATLAB can't reflect dynamic Python methods
→ Fix: Use: py.getattr(obj, 'method')(args)
===========================================================
6. Run Full Verification in MATLAB
===========================================================
Paste this into MATLAB:
% Set environment
clear classes
pyenv('Version', '/Users/YOURNAME/miniforge3/envs/matlab/bin/python');
% Import modules
coords = py.importlib.import_module('astropy.coordinates');
time_mod = py.importlib.import_module('astropy.time');
table_mod = py.importlib.import_module('astropy.table');
% Astropy version
ver = char(py.getattr(py.importlib.import_module('astropy'), '__version__'));
disp(['Astropy version: ', ver]);
% SkyCoord angular separation
c1 = coords.SkyCoord('10h21m00s', '+41d12m00s', pyargs('frame', 'icrs'));
c2 = coords.SkyCoord('10h22m00s', '+41d15m00s', pyargs('frame', 'icrs'));
sep_fn = py.getattr(c1, 'separation');
sep = sep_fn(c2);
arcsec = double(sep.to('arcsec').value);
fprintf('Angular separation = %.3f arcsec\n', arcsec);
% Time difference in seconds
Time = time_mod.Time;
t1 = Time('2025-01-01T00:00:00', pyargs('format','isot','scale','utc'));
t2 = Time('2025-01-02T00:00:00', pyargs('format','isot','scale','utc'));
dt = py.getattr(t2, '__sub__')(t1);
seconds = double(py.getattr(dt, 'sec'));
fprintf('Time difference = %.0f seconds\n', seconds);
% Astropy table display
tbl = table_mod.Table(pyargs('names', {'a','b'}, 'dtype', {'int','float'}));
tbl.add_row({1, 2.5});
tbl.add_row({2, 3.7});
disp(tbl);
===========================================================
7. Optional: Automatically Configure Python in startup.m
===========================================================
To avoid calling pyenv() every time, edit your MATLAB startup:
edit startup.m
Add:
try
pyenv('Version', '/Users/YOURNAME/miniforge3/envs/matlab/bin/python');
catch
warning("Python already loaded.");
end
===========================================================
8. Final Notes
===========================================================
- This setup avoids all architecture mismatches.
- It uses a clean, minimal ARM64 Python that integrates seamlessly with MATLAB.
- Do not mix Anaconda (Intel) with Apple Silicon MATLAB.
- Use py.getattr for any Python attribute containing underscores or that MATLAB can't resolve.
You can now run NumPy, Astropy, Pandas, Astroquery, Matplotlib, and more directly from MATLAB.
===========================================================
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.
What is a rough number? What can they be used for? Today I'll take you down a journey into the land of prime numbers (in MATLAB). But remember that a journey is not always about your destination, but about what you learn along the way. And so, while this will be all about primes, and specifically large primes, before we get there we need some background. That will start with rough numbers.
Rough numbers are what I would describe as wannabe primes. Almost primes, and even sometimes prime, but often not prime. They could've been prime, but may not quite make it to the top. (If you are thinking of Marlon Brando here, telling us he "could've been a contender", you are on the right track.)
Mathematically, we could call a number k-rough if it is evenly divisible by no prime smaller than k. (Some authors will use the term k-rough to denote a number where the smallest prime factor is GREATER than k. The difference here is a minor one, and inconsequential for my purposes.) And there are also smooth numbers, numerical antagonists to the rough ones, those numbers with only small prime factors. They are not relevant to the topic today, even though smooth numbers are terribly valuable tools in mathematics. Please forward my apologies to the smooth numbers.
Have you seen rough numbers in use before? Probably so, at least if you ever learned about the sieve of Eratosthenes for prime numbers, though probably the concept of roughness was never explicitly discussed at the time. The sieve is simple. Suppose you wanted a list of all primes less than 100? (Without using the primes function itself.)
% simple sieve of Eratosthenes
Nmax = 100;
N = true(1,Nmax); % A boolean vector which when done, will indicate primes
N(1) = false; % 1 is not a prime by definition
nextP = find(N,1,'first'); % the first prime is 2
while nextP <= sqrt(Nmax)
% flag multiples of nextP as not prime
N(nextP*nextP:nextP:end) = false;
% find the first element after nextP that remains true
nextP = nextP + find(N(nextP+1:end),1,'first');
end
primeList = find(N)
Indeed, that is the set of all 25 primes not exceeding 100. If you think about how the sieve worked, it first found 2 is prime. Then it discarded all integer multiples of 2. The first element after 2 that remains as true is 3. 3 is of course the second prime. At each pass through the loop, the true elements that remain correspond to numbers which are becoming more and more rough. By the time we have eliminated all multiples of 2, 3, 5, and finally 7, everything else that remains below 100 must be prime! The next prime on the list we would find is 11, but we have already removed all multiples of 11 that do not exceed 100, since 11^2=121. For example, 77 is 11*7, but we already removed it, because 77 is a multiple of 7.
Such a simple sieve to find primes is great for small primes. However is not remotely useful in terms of finding primes with many thousands or even millions of decimal digits. And that is where I want to go, eventually. So how might we use roughness in a useful way? You can think of roughness as a way to increase the relative density of primes. That is, all primes are rough numbers. In fact, they are maximally rough. But not all rough numbers are primes. We might think of roughness as a necessary, but not sufficient condition to be prime.
How many primes lie in the interval [1e6,2e6]?
numel(primes(2e6)) - numel(primes(1e6))
There are 70435 primes greater than 1e6, but less than 2e6. Given there are 1 million natural numbers in that set, roughly 7% of those numbers were prime. Next, how many 100-rough numbers lie in that same interval?
N = (1e6:2e6)';
roughInd = all(mod(N,primes(100)) > 0,2);
sum(roughInd)
That is, there are 120571 100-rough numbers in that interval, but all those 70435 primes form a subset of the 100-rough numbers. What does this tell us? Of the 1 million numbers in that interval, approximately 12% of them were 100-rough, but 58% of the rough set were prime.
The point being, if we can efficiently identify a number as being rough, then we can substantially increase the chance it is also prime. Roughness in this sense is a prime densifier. (Is that even a word? It is now.) If we can reduce the number of times we need to perform an explicit isprime test, that will gain greatly because a direct test for primality is often quite costly in CPU time, at least on really large numbers.
In my next post, I'll show some ways we can employ rough numbers to look for some large primes.
I am using the ELECFREAKS IOT:bit Expansion Board with micro:bit to record data, it worked normally but in this week I tested again, and discover no data is uploaded to the channel ie.no entries in channel. The code in makecode are the same as before and the wifi is work, I cannot find the reason that cause this failure.
We are thrilled to announce the grand prize winners of our MATLAB Shorts Mini Hack contest! This year, we invited the MATLAB Graphics and Charting team, the authors of the MATLAB functions used in every entry, to be our judges. After careful consideration, they have selected the top three winners:
Judge comments: Realism & detailed comments; wowed us with Manta Ray
2nd place – Jenny Bosten
Judge comments: Topical hacks : Auroras & Wind turbine; beautiful landscapes & nightscapes
3rd place - Vasilis Bellos
Judge comments: Nice algorithms & extra comments; can’t go wrong with Pumpkins
Judge comments: Impressive spring & cubes!
In addition, after validating the votes, we are pleased to announce the top 10 participants on the leaderboard:
Congratulations to all! Your creativity and skills have inspired many of us to explore and learn new skills, and make this contest a big success!
Dear MATLAB contest enthusiasts,
Welcome to the third installment of our interview series with top contest participants! This time we had the pleasure of talking to our all-time rock star – @Jenny Bosten. Every one of her entries is a masterpiece, demonstrating a deep understanding of the relationship between mathematics and aesthetics. Even Cleve Moler, the original author of MATLAB, is impressed and wrote in his blog: "Her code for Time Lapse of Lake View to the West shows she is also a wizard of coordinate systems and color maps."
you to read it to learn more about Jenny’s journey, her creative process, and her favorite entries.
Question: Who would you like to see featured in our next interview? Let us know your thoughts in the comments!
Welcome to the launch of our new blog area, Semiconductor Design and Verification! The mission is to empower engineers and designers in the semiconductor industry by streamlining architectural exploration, optimizing the post-processing of simulations, and enabling early verification with MATLAB and Simulink.
Meet Our Authors
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@Ganesh Rathinavel and @Cristian Macario Macario have both made significant contributions to the advancement of Analog/Mixed-Signal design and the broader communications, electronics, and semiconductor industries. With impressive engineering backgrounds and extensive experience at leading companies such as IMEC, STMicroelectronics, NXP Semiconductors, LSI Corporation, and ARM, they bring a wealth of knowledge and expertise to our blog. Their work is focused on enhancing MathWorks' tools to better align with industry needs.
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Don’t miss the first post: Accelerating Mixed-Signal Design with Early Behavioral Models, where they explore how early behavioral modeling can accelerate mixed-signal design and enhance system efficiency.
Over the past week, we have seen many creative and compelling short movies! Now, let the voting begin! Cast your votes for the short movies you love. Authors, share your creations with friends, classmates, and colleagues. Let's showcase the beauty of mathematics to the world!
We know that one of the key goals for joining the Mini Hack contest is to LEARN! To celebrate knowledge sharing, we have special prizes—limited-edition MATLAB Shorts—up for grabs!
These exclusive prizes can only be earned through the MATLAB Shorts Mini Hack contest. Interested? Share your knowledge in the Discussions' general channel (be sure to add the tag 'contest2024') to earn opportunities to win the coveted MATLAB Shorts. You can share various types of content, such as tips and tricks for creating animations, background stories of your entry, or learnings you've gained from the contest. We will select different types of winners each week.
We also have an exciting feature announcement: you can now experiment with code in MATLAB Online. Simply click the 'Open in MATLAB Online' button above the movie preview section. Even better! ‘Open in MATLAB Online’ is also available in previous Mini Hack contests!
We look forward to seeing more amazing short movies in Week 2!
We are thrilled to announce the redesign of the Discussions leaf page, with a new user-focused right-hand column!
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Previously, the right-hand column displayed related content, but feedback from our community indicated that this wasn't meeting your needs. Many of you expressed a desire to read more posts from the same author but found it challenging to locate them.
With the new design, readers can easily learn more about the author, explore their other posts, and follow them to receive notifications on new content.
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Since the launch of the Discussions area earlier this year, we've seen an influx of community members sharing insightful technical articles, use cases, and ideas. The new design aims to help you grow your followers and organize your content more effectively by editing tags. We highly encourage you to use the Discussions area as your community blogging platform.
We hope you enjoy the new design of the right-hand column. Please feel free to share your thoughts and experiences by leaving a comment below.
Hello, everyone!
Over the past few weeks, our community has been buzzing with activity, showcasing the incredible depth of knowledge, creativity, and innovation that makes this forum such a vibrant place. Today, we're excited to highlight some of the noteworthy contributions that have sparked discussions, offered insights, and shared knowledge across various topics. Let's dive in!
Interesting Questions
Fatima Majeed brings us a thought-provoking mathematical challenge, delving into inequalities and the realms beyond (e^e). If you're up for a mathematical journey, this question is a must-see!
lil brain tackles a practical problem many of us have faced: efficiently segmenting a CSV file based on specific criteria. This post is not only a query but a learning opportunity for anyone dealing with similar data manipulation challenges.
Popular Discussions
Discover a simple yet effective trick for digit manipulation from goc3. This tip is especially handy for those frequenting Cody challenges or anyone interested in enhancing their number handling skills in MATLAB.
Chen Lin shares an exciting update about the 'Run Code' feature in the Discussions area, highlighting how our community can now directly execute and share code snippets within discussions. This feature marks a significant enhancement in how we interact and solve problems together.
From the Blogs
A Deep Dive into EEG Analysis for Predicting Neurological Outcomes By Tanya Kuruvilla
Connell D`Souza, alongside Team Swarthbeat, explores the cutting-edge application of EEG analysis in predicting neurological outcomes post-cardiac arrest. This blog post offers an in-depth look into the challenges and methodologies of modern medical data analysis.
Mihir Acharya discusses the pivotal role of MATLAB and Simulink in the future of robotics simulation. Through an engaging conversation with industry analyst George Chowdhury, this post sheds light on overcoming simulation challenges and the exciting possibilities that lie ahead.
We encourage everyone to explore these contributions further and engage with the authors and the community. Your participation is what fuels this community's continual growth and innovation.
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Englisch Translate Französisch Deutsch Deutsch Russisch PONS Deepl übersetzer Spanisch Deutsch DeepL kostenlos Deutsch Englisch hallo, ich bitte um Hilfe! Seit Jahren habe ich Konto auf Thingspeak, den ich für meinen Zweck nicht richtig nutzen kann. Ich kann "Upoad to meine private Chanel", aber das lesen funktioniert nicht, weder in ArdunioIDE, noch Ardunio iot kann ich die gesendete Daten (Temperatur) von einem anderen Board lesen, es ist egal, ob ESP8266, ESP32, MKR, oder UnoWifi Rev2. Nichts! Die Beispiele im Bibliotek sind eine Katastrophe, "wetter-chanel" und diese funktionieren auch nicht. Auch nicht die von GitHub. Es sollte aber einfach sein, denn auf meiner Seite sehe ich ja "GET"+ url. inkl json+result.
Wo gibt es eine richtige sketch für "read private chanel/field" , welche funktioniert? Man braucht nicht die Wifi-Enstellungen, sondern den code für die Abfrage, "Serial.print"(value)"
Bitte um Hilfe, danke schön.
hello, I ask for help! I've had an account on Thingspeak for years, but I can't use it properly for my purpose. I can "Upoad to my private Chanel", but reading doesn't work, neither in ArdunioIDE nor Ardunio iot can I read the sent data (temperature) from another board, it doesn't matter whether ESP8266, ESP32, MKR, or UnoWifi Rev2. Nothing! The examples in the library are a disaster, "weather-chanel" and they don't work either. Not even the one from GitHub. But it should be easy, because on my site I see “GET”+ url. including json+result.
Where is there a proper sketch for "read private chanel/field" that works? You don't need the WiFi settings, but the code for the query, "Serial.print"(value)"
Please help, thank you very much.
Several of the colormaps are great for a 256 color surface plot, but aren't well optimized for extracting m colors for plotting several independent lines. The issue is that many colormaps have start/end colors that are too similar or are suboptimal colors for lines. There are certainly many workarounds for this, but it would be a great quality of life to adjust that directly when calling this.
Example:
x = linspace(0,2*pi,101)';
y = [1:6].*cos(x);
figure; plot(x,y,'LineWidth',2); grid on; axis tight;
And now if I wanted to color these lines, I could use something like turbo(6) or gray(6) and then apply it using colororder.
colororder(turbo(6))
But my issue is that the ends of the colormap are too similar. For other colormaps, you may get lines that are too light to be visible against the white background. There are plenty of workarounds, with my preference being to create extra colors and truncate that before using colororder.
cmap = turbo(8); cmap = cmap(2:end-1,:); % Truncate the end colors
figure; plot(x,y,'LineWidth',2); grid on; axis tight;
colororder(cmap)
I think it would be really awesome to add some name-argument input pair to these colormaps that can specify the range you want so this could even be done inside the colororder calling if desired. An example of my proposed solution would look something like this:
cmap = turbo(6,'Range',[0.1 0.8]); % Proposed idea to add functionality
Where in this scenario, the resulting colormap would be 6 equally spaced colors that range from 10% to 80% of the total color range. This would be especially nice because you could more quickly modify the range of colors, or you could set the limits regardless of whether you need to plot 3, 6, or 20 lines.
There will be a warning when we try to solve equations with piecewise:
syms x y
a = x+y;
b = 1.*(x > 0) + 2.*(x <= 0);
eqns = [a + b*x == 1, a - b == 2];
S = solve(eqns, [x y]);
% 错误使用 mupadengine/feval_internal
% System contains an equation of an unknown type.
%
% 出错 sym/solve (第 293 行)
% sol = eng.feval_internal('solve', eqns, vars, solveOptions);
%
% 出错 demo3 (第 5 行)
% S=solve(eqns,[x y]);
But I found that the solve function can include functions such as heaviside to indicate positive and negative：
syms x y
a = x+y;
b = floor(heaviside(x)) - 2*abs(2*heaviside(x) - 1) + 2*floor(-heaviside(x)) + 4;
eqns = [a + b*x == 1, a - b == 2];
S = solve(eqns, [x y])
% S =
% 包含以下字段的 struct:
%
% x: -3/2
% y: 11/2
The piecewise function is divided into two sections, which is so complex, so this work must be encapsulated as a function to complete：
function pwFunc=piecewiseSym(x,waypoint,func,pfunc)
% @author : slandarer
gSign=[1,heaviside(x-waypoint)*2-1];
lSign=[heaviside(waypoint-x)*2-1,1];
inSign=floor((gSign+lSign)/2);
onSign=1-abs(gSign(2:end));
inFunc=inSign.*func;
onFunc=onSign.*pfunc;
pwFunc=simplify(sum(inFunc)+sum(onFunc));
end
Function Introduction
- x : Argument
- waypoint : Segmentation point of piecewise function
- func : Functions on each segment
- pfunc : The value at the segmentation point
example
syms x
% x waypoint func pfunc
f=piecewiseSym(x,[-1,1],[-x-1,-x^2+1,(x-1)^3],[-x-1,(x-1)^3]);
For example, find the analytical solution of the intersection point between the piecewise function and f=0.4 and plot it:
syms x
% x waypoint func pfunc
f=piecewiseSym(x,[-1,1],[-x-1,-x^2+1,(x-1)^3],[-x-1,(x-1)^3]);
% solve
S=solve(f==.4,x)
% S =
%
% -7/5
% (2^(1/3)*5^(2/3))/5 + 1
% -15^(1/2)/5
% 15^(1/2)/5
% draw
xx=linspace(-2,2,500);
f=matlabFunction(f);
yy=f(xx);
plot(xx,yy,'LineWidth',2);
hold on
scatter(double(S),.4.*ones(length(S),1),50,'filled')
precedent
syms x y
a=x+y;
b=piecewiseSym(x,0,[2,1],2);
eqns = [a + b*x == 1, a - b == 2];
S=solve(eqns,[x y])
% S =
% 包含以下字段的 struct:
%
% x: -3/2
% y: 11/2
Hello, Community Members!
Every day, we witness the incredible exchange of knowledge as over 100,000 users visit our community for answers or to get some code. We have such a vibrant community because of the dedicated group of contributors who volunteer their time and expertise to help one another.
We learned that many community users are looking for different ways to show their appreciation to contributors. In response, we're thrilled to announce the launch of our latest feature – Skill Endorsements.
When you visit a contributor's profile page, you'll notice a brand-new 'Endorsements' tab. Here, you have the power to acknowledge the skills of your fellow members by either endorsing a new skill or bolstering existing ones.
But it's more than just saying "thank you." By highlighting the strengths of our members, you're contributing to an environment of trust and making it easier for users to connect with experts in specific areas.
So, take a moment to reflect: Who has made a difference in your community experience? Whose expertise has guided you through a challenge? Show your appreciation and support their contributions – start endorsing skills today!
Your participation makes all the difference.
Warm regards,
MATLAB Central Community Team
Hello, an intern working at MathWorks is finishing up his program soon and he would like to interview some MATLAB users. He is looking for people who can give their perspective on the question:
"What makes MATLAB and Simulink special in comparison to other languages?"
Ultimately he plans to condense the answers into 15-second videos or sound bites.
If people are willing to participate but want more time to talk about their experience with MATLAB, he doesn't have time left for in-depth interviews but he can find someone else to take over the project.
Please send me an email via my profle if you are interested.
The 2022 community contests have concluded! During the 4 weeks, we hope you had a lot of fun and learned some MATLAB skills. Together, we've achieved amazing milestones:
- 500+ entries and 5,000+ votes created in the Mini Hack 2022 contest.
- 100,000+ solutions submitted in Cody 10th Anniversary contest
- 2,000+ participants in both contests
Now, it's time to announce weekly winners and grand prize winners!
Mini Hack - Pick of the MATLAB Graphics Team
We invited the MATLAB Graphics team, the authors of the MATLAB functions used in every entry, to be our Mini Hack judges. Here are their picks in 3 categories:
- Our Top 3:
Rising Hand by Augusto Mazzei
Object used: fill; Judge comments: Clever code, nice metaphor, kind comments
Above the clouds by Tim
Object used: patch (via VOXview); Judge comments: Very pretty, looks like it could be in a video game
Monocular rivalry by Jenny Bosten
Object used: Image; Judge comments: We spent a too much time trying to figure out what this illusion was doing to our brains
- Clever use of Charts:
Object used: Bar3; Judge comments: Cute! Nice fireworks!
Object used: histogram2; Judge comments: We didn't realize there was a ship in the flow data set
Object used: plot; Judge comments: Nifty and very different from other entries.
Object used: scatter; Judge comments: Overlapping markers make for a very cool tunnel-like effect
- Things we still loved:
Object used: image; Judge comments: Domain Warping FTW
Object used: surface; Judge comments: Clever use of transparency
Congratulations and you should be very proud of yourself! It's a huge achievement that your entry is recognized by the MATLAB Graphics team!
Mini Hack - special category for Week 4
Mini Hack - grand prize winners
After an intensive (and very time-consuming) review of votes on winning entries, we have finalized the list of grand prize winners. Huge congratulations! We appreciate the time and effort you spent and the awesome entries you created. Each of you won an Amazon gift card.
Anton Kogios, Brandon Caasenbrood, KARUPPASAMYPANDIYAN M, Teodo, Jenny Bosten, MvLevi, Abdullah Caliskan, Stewart Thomas, Jonas Schlatter, and Tim Davis
Cody 10th Anniversary - surprise prize for 28-day streak winners
We are thrilled to see that 37 players have built a streak of 28 days! Coming back every day to solve problems is an incredible achievement. We decided to show our appreciation by awarding a surprise prize to those 37 players. Congratulations! Each of you will get a MathWorks T-shirt.
Christian Schröder, Stefan Abendroth, Mohammed, Victoria, Vasileios Pasialis, Gerardo Domínguez Ramírez, HH, Anton Kogios, Lizhi Zhu, Marco Fuscà, Armando Longobardi, Monica, Rithik KRT, Ayman, Teodo, Lincoln Poon, Elijah Keifert, siranjeevi gurumani, kazuyoshi kouno, Ryan Koh, Manuela Kaiser, Mehmet OZC, Dyuman Joshi, KOTHAPALLI SRI BRINDA, Gergely Patay, abyss, Takumi, Keita Abe, Petr Cerny, Shubham Shubham, Meredith, Andrew K, Atsushi Ueno, Peter Orthmann, Armando Longobardi, Chuang Tao, and David Romero
Cody 10th Anniversary winners – Week 4
The top 3 players for solving the most problems in week 4 are Christian Schröder, Gerardo Dominguez Ramirez, and Stefan Abendroth, Congratulations! Each of you won an Amazon gift card.
Week 4 lucky winners are Qingrui Liu and Basant Ale.
Cody 10th Anniversary winners – grand prize winners
We know how hard it is to be a top-10 leader in the contest leaderboard! It requires a huge time commitment and advanced MATLAB skills. Congratulations! Each of you will win an Amazon gift card.
Christian Schröder, Stefan Abendroth, Mohammed, Victoria, Vasileios Pasialis, Gerardo Domínguez Ramírez, HH, Anton Kogios, Lizhi Zhu, and Marco Fuscà
Lucky voters and participants
Thank you for your participation in our 2022 contests. You don’t need to be on the top of the leaderboards to win. As we announced, we would give out 20 MathWorks T-shirts to lucky voters and participants of the 2 contests.
Ismail Bera Altan, Robin Stolz, Michael Mellin, Kellan Smith, Neha Shaah, Siranjeevi gurumani, Paul Villain, Andrew Horchler, Meg Noah, Saurabh Chaudhary, Pakize erdogmus, Godfrey Ojerheghan, Selena Mastrodonato, Damir Rasic, Thomas Kjeldsen, Meredith, John Noah Ramiterre, Patience Oliveira, Panda, and Sujeet Kumar Choudhary
On behalf of the MATLAB Central community team, we thank you for joining our celebration of the MATLAB Central community 2022 contests. We hope you enjoyed these contests and look forward to seeing you in next year’s contests.
Two fun community contests: MATLAB Mini Hack 2022 and Cody 10th Anniversary start today on Oct. 3rd!
Participants across all skill levels are welcome to join! Even if you have limited time, you still have opportunities to win as long as you participate.
Want to challenge yourself and win Amazon gift Cards and limited-edition Badges?
1. MATLAB Mini Hack 2022: Create your best entry (either a new or a remixed entry).
2. Cody 10th Anniversary: Solve your 1st Cody problem today!
If you have any questions about the contest rules or prizes, let us know by replying to this thread.
We hope you enjoy the contests, improve your MATLAB skills, and win prizes! Now, let the party begin!
In MATLAB Answers, oftentimes we see good comments that provide solutions in a question thread. Those comments should really be answers. On the other hand, there are some answers that do not offer solutions. Those answers should actually be comments. The answer/comment issue makes it harder for readers of a question thread to quickly identify useful information. To tackle this issue, the community team just released the MOVE feature!
What can be moved?
Answers and comments can now be moved in 4 ways within the same question thread:
- Change an answer to a comment
- Change an answer with comments to a group of comments
- Change a comment to an answer
- Move a comment
Who can move answers and comments?
New privileges have been awarded to contributors with 2000 reputation points or more. Privileged contributors will see move icons added in the list of actions available for answers and comments.
After an answer or comment is moved, an indication of the move will be displayed with the content.
As always, please let us know your thoughts by leaving a reply below.
After 7 weeks of fun, the MATLAB Central community 20th anniversary contests have concluded! Together, we shared the art of MATLAB and contributed to the battle against the global pandemic. See the fantastic stats below.
- 1,700 entries created and 24,000 votes cast in MATLAB Mini Hack
- 6,300 tasks completed in Treasure Hunt
- $17,000 raised for Direct Relief
MATLAB Mini Hack Winners - Week 4
In Week 4, we invited the MATLAB Graphics team to help judge the entries. As the authors of the MATLAB functions used in every entry, they made sure every entry selected used a unique graphics function or technique from the other winners. Here are their choices:
1. Umbrellas by Shanshan Wang
Comment: Cool use of 'swarmchart' to make art from distributions; Only use of one of our newest graphics functions
2. Happy Sheep by Victoria
Comment: Cute!
3. Alien Giant by Jenny Bosten
Comment: Original idea, well textured, and efficient code
4. 3D Ultrasound by Adam Danz
Comment: Replicate source material very well. Effective use of lighting and material. Overall, impressive to produce this image given the limitation
5. Sunset in the Savanna by Sebastian Kraemer
Comment: Looks cool! Nice mix of 'image', 'fill' and 'scatter' commands.
6. Night Flight by Ratul Das
Comment: original; clever use of 'rectangle'
7. Lantern #2 by Tim
Comment: Nice use of 'getframe' to create a texture for 'surf' for a compelling picture
8. Geometric Design (6) by Daniel Pereira
Comment: Looks like some walls at MathWorks
9. Rosette 1313 by Alex P
Comment: Looks cool! Nice use of 'pcolor'
10. Mandelbrot contour by Sumihiro
Comment: Best use of contour!
11. Aim High by Murty PLN
Comment: Largest number of unique graphics objects for the Mini Hack (plot, patch,stairs,stem,text)
In the spirit of Mini Hack, the MATLAB Graphics team also created several cool graphs about the contest. Facing a similar space limit, I have picked only 2.
Bonus Prize Winners - Week 4
Congratulations to our 5 winners for their dual participation in the Treasure Hunt and the MATLAB Mini Hack. Yogiraj Bhagavatula, Pramod Devireddy, Devika U, FruitsLord, and Augusto Mazzei.
Lucky voters - Week 4
Congratulations to the lucky voters who cast the 12000th vote (Gordg Garin), 12500th vote (Eder Esteban Reyes), 13000th vote (Peram Balakrishna), 13500th vote (Emerson Nithiyaraj), 14000th vote(Sekar Naai), 14500th vote (Arika Amasarao), 15000th vote (Nikita Yakovlev), 15500th vote (Kesava Rao), and 16000th vote (Kundi Chandra Sekhar).
Grant Prize Winners
Finally, after validating entries and votes, we have picked the grand prize winners. We appreciate the time and effort you spent and the awesome entries you created. Huge congratulations!
1. Top 10 Authors of most voted entries
Each author will receive 5 customized T-shirts with the winning image and your name on the back of the T-shirts. You can choose the sizes and share them with your family or friends.
- Ciro Bermudez
- Jenny Bosten
- Adam Danz
- Jr
- Maximilian Schönau
- KSSV
- Dr Raveendranathan K C
- taoizm
- Sebastian Kraemer
- Victoria
2. Top 10 Authors with most total votes
Top 10 contestants on the leaderboard will each get an Amazon gift card. The top 3 winners on the leaderboard will also earn special virtual badges.
- Ciro Bermudez
- KSSV
- Juan Villacrés
- Murty PLN
- Pink_panther
- Jenny Bosten
- KARUPPASAMYPANDIYAN M
- Jr
- Adam Danz
- Victoria
On behalf of the MATLAB Central community team, we thank you for joining our celebration of the MATLAB Central community’s 20th anniversary with us in the past 7 weeks. We hope you enjoyed these contests and look forward to seeing you in next year’s contests. Question: “What contests would you like to see next?”
