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  • Specifying a callback in Matlab after any runtime error

    - by JmG
    Is there a way to specify code to be run whenever an error occurs in Matlab? Googling I came across RunTimeErrorFcn and daqcallback, but I believe these are specific to the Data Acquisition Toolbox. I want something for when I just trip over a bug, like an access to an unassigned variable. (I use a library called PsychToolbox that takes over the GPU, so I want to be able to clear its screen before returning to the command prompt.)

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  • MATLAB plot moving data points in seperate subplots simutaneously

    - by Nate B.
    I wish to visualize the movement of a data point throughout space across a period of time within MATLAB. However, the way I want my figure to display is such that only a single instant is plotted at any given time. That was easy, I simply created a for loop to update my 3D plot display for every set of coordinates (x,y,z) in my data. However, I wish to display 4 different viewing angles of this plot at all times. I am well aware of how to setup subplots within MATLAB, that is not the issue. My issue is getting all 4 of these subplots to execute simultaneously so that all 4 subplots are always displaying the same point in time. I would appreciate if anyone could suggest how to handle this issue. As requested, my code for a figure with a single plot is shown below: datan = DATA; %data in form of x,y,z,a,b,c by column for row# of time points tib=zeros(size(datan,1),12); tib(:,1:3) = datan(:,1:3); tib_ref=tib(1,1:3); for i=1:size(datan,1) tib(i,1:3)=tib(i,1:3)-tib_ref; end angle_to_dircos close all figure('Name','Directions (Individual Cycles)','NumberTitle','off') for cc=1:2 hold off for bb=1:10:size(tib,1); scatter3(tib(bb,1),tib(bb,2),tib(bb,3),'green','filled'); %z and y axes are flipped in polhemus system hold on p0 = [tib(bb,1),tib(bb,2),tib(bb,3)]; p1 = [tib(bb,1)+10*tib(bb,4),tib(bb,2)+10*tib(bb,5),tib(bb,3)+10*tib(bb,6)]; p2 = [tib(bb,1)+10*tib(bb,7),tib(bb,2)+10*tib(bb,8),tib(bb,3)+10*tib(bb,9)]; p3 = [-(tib(bb,1)+100*tib(bb,10)),-(tib(bb,2)+100*tib(bb,11)),-(tib(bb,3)+100*tib(bb,12))]; vectarrow(p0,p1,1,0,0) hold on vectarrow(p0,p2,0,1,0) hold on vectarrow(p0,p3,0,0,1) hold on az = 90; el = 0; view(az, el); xlim([-50,50]); ylim([-50,50]); zlim([-50,50]); xlabel('distance from center in X'); ylabel('distance from center in Y'); zlabel('distance from center in Z'); title('XYZ Scatter Plots of Tracker Position'); hold on plot3(0,0,0,'sk','markerfacecolor',[0,0,0]); p0 = [0,0,0]; p1 = [10,0,0]; p2 = [0,10,0]; p3 = [0,0,100]; vectarrow(p0,p1,1,0,0) hold on vectarrow(p0,p2,0,1,0) hold on vectarrow(p0,p3,1,0,1) drawnow; end end

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  • 3D plotting in Matlab

    - by Jill
    I'm using the subplot and then surf functions to generate images in 3D in Matlab. How do I get rid of the axes and axis' gridlines and change the color to all yellow or all green or something like that? Thanks.

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  • matlab get color

    - by iteratorr
    I am using matlab for cluster visualization. I want to somehow get the color of my current cluster center fill in the plot and draw line of same color to cluster members. How can I get the color?

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  • Matlab code works with one version but not the other

    - by user1325655
    I have a code that works in Matlab version R2010a but shows errors in matlab R2008a. I am trying to implement a self organizing fuzzy neural network with extended kalman filter. I have the code running but it only works in matlab version R2010a. It doesn't work with other versions. Any help? Code attach function [ c, sigma , W_output ] = SOFNN( X, d, Kd ) %SOFNN Self-Organizing Fuzzy Neural Networks %Input Parameters % X(r,n) - rth traning data from nth observation % d(n) - the desired output of the network (must be a row vector) % Kd(r) - predefined distance threshold for the rth input %Output Parameters % c(IndexInputVariable,IndexNeuron) % sigma(IndexInputVariable,IndexNeuron) % W_output is a vector %Setting up Parameters for SOFNN SigmaZero=4; delta=0.12; threshold=0.1354; k_sigma=1.12; %For more accurate results uncomment the following %format long; %Implementation of a SOFNN model [size_R,size_N]=size(X); %size_R - the number of input variables c=[]; sigma=[]; W_output=[]; u=0; % the number of neurons in the structure Q=[]; O=[]; Psi=[]; for n=1:size_N x=X(:,n); if u==0 % No neuron in the structure? c=x; sigma=SigmaZero*ones(size_R,1); u=1; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else [Q,O,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n); end; KeepSpinning=true; while KeepSpinning %Calculate the error and if-part criteria ae=abs(d(n)-pT_n*O); %approximation error [phi,~]=GetMePhi(x,c,sigma); [maxphi,maxindex]=max(phi); % maxindex refers to the neuron's index if ae>delta if maxphi<threshold %enlarge width [minsigma,minindex]=min(sigma(:,maxindex)); sigma(minindex,maxindex)=k_sigma*minsigma; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else %Add a new neuron and update structure ctemp=[]; sigmatemp=[]; dist=0; for r=1:size_R dist=abs(x(r)-c(r,1)); distIndex=1; for j=2:u if abs(x(r)-c(r,j))<dist distIndex=j; dist=abs(x(r)-c(r,j)); end; end; if dist<=Kd(r) ctemp=[ctemp; c(r,distIndex)]; sigmatemp=[sigmatemp ; sigma(r,distIndex)]; else ctemp=[ctemp; x(r)]; sigmatemp=[sigmatemp ; dist]; end; end; c=[c ctemp]; sigma=[sigma sigmatemp]; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); KeepSpinning=false; u=u+1; end; else if maxphi<threshold %enlarge width [minsigma,minindex]=min(sigma(:,maxindex)); sigma(minindex,maxindex)=k_sigma*minsigma; Psi=GetMePsi(X,c,sigma); [Q,O] = UpdateStructure(X,Psi,d); pT_n=GetMeGreatPsi(x,Psi(n,:))'; else %Do nothing and exit the while KeepSpinning=false; end; end; end; end; W_output=O; end function [Q_next, O_next,pT_n] = UpdateStructureRecursively(X,Psi,Q,O,d,n) %O=O(t-1) O_next=O(t) p_n=GetMeGreatPsi(X(:,n),Psi(n,:)); pT_n=p_n'; ee=abs(d(n)-pT_n*O); %|e(t)| temp=1+pT_n*Q*p_n; ae=abs(ee/temp); if ee>=ae L=Q*p_n*(temp)^(-1); Q_next=(eye(length(Q))-L*pT_n)*Q; O_next=O + L*ee; else Q_next=eye(length(Q))*Q; O_next=O; end; end function [ Q , O ] = UpdateStructure(X,Psi,d) GreatPsiBig = GetMeGreatPsi(X,Psi); %M=u*(r+1) %n - the number of observations [M,~]=size(GreatPsiBig); %Others Ways of getting Q=[P^T(t)*P(t)]^-1 %************************************************************************** %opts.SYM = true; %Q = linsolve(GreatPsiBig*GreatPsiBig',eye(M),opts); % %Q = inv(GreatPsiBig*GreatPsiBig'); %Q = pinv(GreatPsiBig*GreatPsiBig'); %************************************************************************** Y=GreatPsiBig\eye(M); Q=GreatPsiBig'\Y; O=Q*GreatPsiBig*d'; end %This function works too with x % (X=X and Psi is a Matrix) - Gets you the whole GreatPsi % (X=x and Psi is the row related to x) - Gets you just the column related with the observation function [GreatPsi] = GetMeGreatPsi(X,Psi) %Psi - In a row you go through the neurons and in a column you go through number of %observations **** Psi(#obs,IndexNeuron) **** GreatPsi=[]; [N,U]=size(Psi); for n=1:N x=X(:,n); GreatPsiCol=[]; for u=1:U GreatPsiCol=[ GreatPsiCol ; Psi(n,u)*[1; x] ]; end; GreatPsi=[GreatPsi GreatPsiCol]; end; end function [phi, SumPhi]=GetMePhi(x,c,sigma) [r,u]=size(c); %u - the number of neurons in the structure %r - the number of input variables phi=[]; SumPhi=0; for j=1:u % moving through the neurons S=0; for i=1:r % moving through the input variables S = S + ((x(i) - c(i,j))^2) / (2*sigma(i,j)^2); end; phi = [phi exp(-S)]; SumPhi = SumPhi + phi(j); %phi(u)=exp(-S) end; end %This function works too with x, it will give you the row related to x function [Psi] = GetMePsi(X,c,sigma) [~,u]=size(c); [~,size_N]=size(X); %u - the number of neurons in the structure %size_N - the number of observations Psi=[]; for n=1:size_N [phi, SumPhi]=GetMePhi(X(:,n),c,sigma); PsiTemp=[]; for j=1:u %PsiTemp is a row vector ex: [1 2 3] PsiTemp(j)=phi(j)/SumPhi; end; Psi=[Psi; PsiTemp]; %Psi - In a row you go through the neurons and in a column you go through number of %observations **** Psi(#obs,IndexNeuron) **** end; end

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  • MATLAB : frequency distribution

    - by Arkapravo
    I have raw observations of 500 numeric values (ranging from 1 to 25000) in a text file, I wish to make a frequency distribution in MATLAB. I did try the histogram (hist), however I would prefer a frequency distribution curve than blocks and bars. Any help is appreciated !

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  • MATLAB query about for loop, reading in data and plotting

    - by mp7
    Hi there, I am a complete novice at using matlab and am trying to work out if there is a way of optimising my code. Essentially I have data from model outputs and I need to plot them using matlab. In addition I have reference data (with 95% confidence intervals) which I plot on the same graph to get a visual idea on how close the model outputs and reference data is. In terms of the model outputs I have several thousand files (number sequentially) which I open in a loop and plot. The problem/question I have is whether I can preprocess the data and then plot later - to save time. The issue I seem to be having when I try this is that I have a legend which either does not appear or is inaccurate. My code (apolgies if it not elegant): fn= xlsread(['tbobserved' '.xls']); time= fn(:,1); totalreference=fn(:,4); totalreferencelowerci=fn(:,6); totalreferenceupperci=fn(:,7); figure plot(time,totalrefrence,'-', time, totalreferencelowerci,'--', time, totalreferenceupperci,'--'); xlabel('Year'); ylabel('Reference incidence per 100,000 population'); title ('Total'); clickableLegend('Observed reference data', 'Totalreferencelowerci', 'Totalreferenceupperci','Location','BestOutside'); xlim([1910 1970]); hold on start_sim=10000; end_sim=10005; h = zeros (1,1000); for i=start_sim:end_sim %is there any way of doing this earlier to save time? a=int2str(i); incidenceFile =strcat('result_', 'Sim', '_', a, 'I_byCal_total.xls'); est_tot=importdata(incidenceFile, '\t', 1); cal_tot=est_tot.data; magnitude=1; t1=cal_tot(:,1)+1750; totalmodel=cal_tot(:,3)+cal_tot(:,5); h(a)=plot(t1,totalmodel); xlim([1910 1970]); ylim([0 500]); hold all clickableLegend(h(a),a,'Location','BestOutside') end Essentially I was hoping to have a way of reading in the data and then plot later - ie. optimise the code. I hope you might be able to help. Thanks. mp

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  • Working with a figure file in Matlab

    - by 49er
    I've got a .fig file showing the intensities of 2d data. How do I obtain the values when I load it in Matlab? Is it even possible? When I try playing with the children properties I am able to obtain only the size of my x and y vectors, not the values themselves which I don't know how to extract.

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  • compilation of image stitching code in matlab

    - by chee
    i am facing lots of problems while running code for image stitching given at this link http://se.cs.ait.ac.th/cvwiki/matlab:tutorial:image_stitching_from_high-view_images_using_homography may i get help regarding this type of problems here. EDIT: Image stitching code fails with the following message: ??? Undefined function or variable 'x2'. Error in ==compute_direct_homography at 26 amplified_x2=x2.*repmat([diagonal_ratio(x1,x2) diagonal_ratio(x1,x2) 1]',1,size(x2,2)); %assumption 1degree of lat and long =110,000 meters refer wiki Error in == project at 3 compute_direct_homography;

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  • MATLAB clear current figure

    - by rlbond
    I want to clear MATLAB's global CurrentFigure property, because I need a plot that I make to not be overwritten if a careless user uses plot without opening a new figure. I tried set(0, 'CurrentFigure', []); But it doesn't seem to work. Is this impossible?

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  • Matlab regex if statement

    - by Dan
    I want to have matlab take user input but accept both cases of a letter. For example I have: function nothing = checkGC(gcfile) if exist(gcfile) reply = input('file exists, would you like to overwrite? [Y/N]: ', 's'); if (reply == [Yy]) display('You have chosen to overwrite!') else $ Do nothing end end The if statement obviously doesn't work, but basically I want to accept a lowercase or uppcase Y. Whats the best way to do this?

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  • Evaluating and graphing functions in Matlab

    - by thiol3
    New to programming, I am trying to graph the following Gaussian function in Matlab (should graph in 3 dimensions) but am making some mistakes somewhere. What is wrong? sigma = 1 for i = 1:20 for j = 1:20 z(i,j) = (1/(2*pi*sigma^2))*exp(-(i^2+j^2)/(2*sigma^2)); end end surf(z)

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  • Funny plots in MATLAB

    - by Arkapravo
    I recently learned the ezplot function in MATLAB. Recently I typed in ezplot('x^y - y^x', [-100 100 -100 100]); and this is what I got; Can anyone please tell me whatever is happening ? for lower scaling of x and y ( [ -10 10 -10 10]) there are more patterns in the 2nd 3rd and 4th quadrants. I was not very sure of the shape of curve, but I did not expect this !

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