Skip to main content

How to change x-axis ticks labels in stackedplot?

  h = stackedplot(rand(6,3));   I want to set x-axis ticks according to my own defined set i.e., instead of 1:6, I want to replace x-axisticks [1, 2,3 ,4,5,6] to ['A', 'S','T', 'AAA', 'BBB', 'ZZZ'] , by rotating it to 90 degree that is vertically insted of horizontally?     NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. There does not seem to be an easy way to set the  XTick  or  XTickLabel  of a  StackedLineChart  object (such as what's created by  stackedplot ):   data = rand(6,3); h = stackedplot(1:6,data); % try a couple of things, neither of which work try set(h,'XTick',1:6,'XTic

How to put a timeout to ginput?

 I want to show user a figure, and ask them to click somewhere within a stipulated time limit, like 2 seconds.

 
I am using ginput as follows, but it seems to be pausing the execution before a button is pressed. I went to check in the ginput function and indeed it was using waitforbuttonpress. I tried some hands in getting a workaround but wasn't much succesfull to interrup ginput (specifically it's wfbp function).
 
I am attaching a sample code what i need.
 
 
I am okay in getting to know some work-arounds, even if they don't use ginput, but i'd still love to know what exactly could ba a solution if I were to use ginput.
 
 
x = linspace(0,4*pi,2000);
y=sin(x);
plot(x,y);

timeout=2;
tic;
[xt,yt]=ginput(1);
if toc>2
    xt=NaN;
    yt=NaN;
end

 


NOTE:-


Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help , Finance Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.


 
I understand that your goal is to show a figure and ask the user to click somewhere within a stipulated time limit (e.g., 2 seconds). The following approach would enable you achieve this:
  1. Plot the Figure: Plot the figure you wish to show the customer.
  2. Initialize Coordinates: Initialize "xt" and "yt" to "NaN".
  3. Create Click Flag: Create a flag "clicked" to check if the user has clicked.
  4. Create Timer: Create a timer object "t" with a "StartDelay" of "timeout" seconds. Set the "TimerFcn" to "uiresume(gcbf)", which resumes the figure's execution when the timer elapses.
  5. Start Timer: Start the timer with "start(t)".
  6. Set Mouse Click Callback: Set the "WindowButtonDownFcn" to "mouseClickFcn", which sets the "clicked" flag to "true" and resumes the figure's execution when the user clicks.
  7. Wait for User Input or Timeout: Use the "uiwait(gcf, timeout)" function to wait for the figure to be resumed by either the timer or the mouse click.
  8. Clean Up Timer: Stop and delete the timer object with "stop(t)" and "delete(t)".
  9. Check Click Flag: Check if the "clicked" flag is "true" to determine if the user clicked within the timeout period.
  10. Display Coordinates: Print the clicked coordinates to the console.
Here is an example:
 
timed_example();

function timed_example()
    x = linspace(0, 4*pi, 2000);
    y = sin(x);
    plot(x, y);

    timeout = 2;
    [xt, yt] = deal(NaN);  % Initialize the coordinates

    % Create a flag to check if the user has clicked
    clicked = false;

    % Create a timer object to interrupt the execution after the timeout
    t = timer('StartDelay', timeout, 'TimerFcn', @(~,~) uiresume(gcbf));
    start(t);

    % Set the WindowButtonDownFcn to capture the click and resume the figure
    set(gcf, 'WindowButtonDownFcn', @mouseClickFcn);


Comments

Popular posts from this blog

What are some good alternatives to Simulink?

Matlabsolutions provide latest  MatLab Homework Help, MatLab Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. SIMULINK is a visual programing environment specially for time transient simulations and ordinary differential equations. Depending on what you need there are plenty of Free, Libre and Open Source Software (FLOSS) available: Modelica language is the most viable alternative and in my opinion it is also a superior option to MathWorks SIMULINK. There are open source implementations  OpenModelica  and  JModelica . One of the main advantages with Modelica that you can code a multidimensional ordinary differential equation with algebraic discrete non-causal equations. With OpenModelica you may create a non-causal model right in the GUI and with
https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing