How can I solve the error 'Too many output arguments' when creating a histogram in Matllab Appdesigner?

I'm trying to create a histogram of the theta and phi orientation of collagen fibers. When I try to plot the histogram I get the following error message:

Error using histogram/meanc

Too many output arguments.

Error in histogram/OrientationhistogramofthetaButtonPushed (line 283)

aSDE = meanc(app)*180/pi;

Error using matlab.ui.control.internal.controller.ComponentController/executeUserCallback (line 410)

Error while evaluating Button PrivateButtonPushedFcn.

Below you can see the code I'm using. I would appreciate your help with finding a solution to this.

classdef histogram < matlab.apps.AppBase

    % Properties that correspond to app components
    properties (Access = public)
        UIFigure        matlab.ui.Figure
        OrientationhistogramofphiButton  matlab.ui.control.Button
        OrientationhistogramofthetaButton  matlab.ui.control.Button
        DcollagenorientationofphiButton  matlab.ui.control.Button
        DcollagenorientationofthetaButton  matlab.ui.control.Button
        LoaddataButton  matlab.ui.control.Button
        UIAxes          matlab.ui.control.UIAxes
    end

    
    methods (Access = private)
        
        function  meanc(app)
                  shgim = mean(double(shgim));3;
                  calcfibangspeed(app,shgim,armd,filton)= [aS];
                  
% find size of image
                  sz=size(shgim,1);
                  sz2=size(shgim,2);

% filters the data using a Gaussian kernel
                  cdata=(shgim)/max(max(shgim));
                  if filton
                    hf = fspecial('gaussian',[9 9],1.2);
                    im=imfilter((double(cdata)+.0000001),hf);
                  else
                    im=cdata;
                  end

% create coordinates of window
                  [xk yk]=meshgrid(1:armd*2+1,1:armd*2+1);

% determine angle of each coordinate relative to center pixels xk and yk
                  xk=xk-armd-1;
                  yk=yk-armd-1;
                  [ang,radd] = cart2pol(xk,-1*yk);

% put all angular data between 0 and pi
                  ang=ang+pi*(ang<0);

% weighting factor 1/radius
                  wei=1./((radd));
                  wei=wei.*(wei>0);

% initialize variables
                  C=zeros(sz,sz2);
                  S=zeros(sz,sz2);


% This set of loops is where vector summation occurs.  For each iteration a
% single directional vector is added to a running total.  
                  for i=-armd:-1
                      for j=-armd:0
                            
                            % creates 3 copies of the image: the original and two copies shifted
                            % in opposite directions by i and j
                          fim = circshift(im,[i j]);
                          bim = circshift(im,[-i -j]);
                          tim(:,:,1)=fim;
                          tim(:,:,2)=im;
                          tim(:,:,3)=bim;
                            
                            % the standard deviation of tim is computed in the 3rd dimension,
                            % which means we're measuring how intensity varies between each
                            % pixel and its neighbors at relative shifts of i,j and -i,-j.
                            % We weight the orientation vector by the max possible standard
                            % deviation and the measured standard deviation
                            
                          dc=sqrt(1/3)-(std(tim,0,3));
                            
                            
                          if ((i~=0)|(j~=0))
                                
                                % we sum the x and y components of each vector and mulitply it
                                % by our weighting factors dc and wei
                                
                                % note that because these are axial data (0deg=180deg), we
                                % multiply the angle by 2 (later we divide by 2)
                              C=C+(cos(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
                              S=S+(sin(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
                                           
                          end
                      end
                  end

                  for i=-armd:0
                      for j=1:armd
                            
                            % creates 3 copies of the image: the original and two copies shifted
                            % in opposite directions by i and j
                          fim = circshift(im,[i j]);
                          bim = circshift(im,[-i -j]);
                          tim(:,:,1)=fim;
                          tim(:,:,2)=im;
                          tim(:,:,3)=bim;
                            
                            % the standard deviation of tim is computed in the 3rd dimension,
                            % which means we're measuring how intensity varies between each
                            % pixel and its neighbors at relative shifts of i,j and -i,-j.
                            % We weight the orientation vector by the max possible standard
                            % deviation and the measured standard deviation
                            
                          dc=sqrt(1/3)-(std(tim,0,3));
                                    
                          if ((i~=0)|(j~=0))
                                
                                % we sum the x and y components of each vector and mulitply it
                                % by our weighting factors dc and wei
                                
                                % note that because these are axial data (0deg=180deg), we
                                % multiply the angle by 2 (later we divide by 2)
                              C=C+(cos(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
                              S=S+(sin(2*ang(armd+1+i,armd+1+j)).*dc.*wei(armd+1+i,armd+1+j));
                                
                          end
                      end
                  end

                  meanc = atan2(S,C)/2;
                    
                  meanc=(meanc).*(meanc>=0)+(meanc+pi).*(meanc<0);
                    
                  aS=meanc;
        end
        function calcfibang3D(app)
            function [aSDE,pSDER]=calcfibang3D(app, shgim,armdx,armdz,filton,para)
% This function calculates the voxel-wise orientation. The output and input
% indices are explained in the main program
            im = shgim;
            
            im = double(im);
            sz = size(im,1);
            sz2 = size(im,2);
            sz3 = size(im,3);
            
            % Here starts the determination of theta
            terimc = zeros(sz,sz2,3*sz3);
            terimc(:,:,1:sz3) = im;
            terimc(:,:,(sz3+1):(2*sz3)) = im;
            terimc(:,:,(2*sz3+1):(3*sz3)) = im;
            
            imc = zeros(sz,sz2,sz3);
            
            for i = 1:sz3
            imc(:,:,i) = mean(terimc(:,:,(sz3+i-armdz):(sz3+i+armdz)),3);
            end
            
            clear terimc
            
            meanc = zeros(sz,sz2,sz3);
            h = waitbar(0,'Please Wait');
            ij = 0;
            nij = sz3;
            for i = 1:sz3
                ij = ij+1;
                waitbar(ij/nij)
                meanc(:,:,i) = calcfibangspeed(app,imc(:,:,i),armdx,filton);
            end
            close(h)
            clear imc
            
            % Here starts the determination of beta
            terimj = zeros(3*sz,sz2,sz3);
            terimj(1:sz,:,:) = im;
            terimj((sz+1):(2*sz),:,:) = im;
            terimj((2*sz+1):(3*sz),:,:) = im;
            
            imj = zeros(sz,sz2,sz3);
            for i = 1:sz
                imj(i,:,:) = mean(terimj((sz+i-armdz):(sz+i+armdz),:,:),1);
            end
            clear terimj
            % To calculate the orientation in 2D manner
            meanj = zeros(sz,sz2,sz3);
            h = waitbar(0,'Please Wait')
            ij = 0;
            nij = sz;
            for i = 1:sz
                ij = ij+1;
                waitbar(ij/nij)
                meanj(i,:,:) = calcfibangspeed(squeeze(imj(i,:,:)),armdx,filton);
            end
            close(h)
            clear imj
            meanj = (pi/2-meanj).*(meanj<=pi/2)+(3*pi/2-meanj).*(meanj>pi/2);
            
            % Here starts the determination of gamma
            
            terimg = zeros(sz,3*sz2,sz3);
            terimg(:,1:sz2,:) = im;
            terimg(:,(sz2+1):(2*sz2),:) = im;
            terimg(:,(2*sz2+1):(3*sz2),:) = im;
            
            img = zeros(sz,sz2,sz3);
            for i = 1:sz
                img(:,i,:) = mean(terimg(:,(sz2+i-armdz):(sz2+i+armdz),:),2);
            end
            clear terimg
            % To calculate the orientation in 2D manner
            meang = zeros(sz,sz2,sz3);
            h = waitbar(0,'Please Wait')
            ij = 0;
            nij = sz2;
            for i = 1:sz2
                ij = ij+1;
                waitbar(ij/nij)
                meang(:,i,:) = calcfibangspeed(app,squeeze(img(:,i,:)),armdx,filton);
            end
            close(h)
            clear img
            meang = (pi/2+meang).*(meang<=pi/2)+(meang-(pi/2)).*(meang>pi/2);
            
            % Here to acquire the orientation of phi
            meanp = atan(sqrt(1./(tan(meanj).^2)+1./(tan(meang).^2)));
            meanplim = meanp;
            for i = 1:sz
                for j = 1:sz2
                    for k = 1:sz3
                        if meanj(i,j,k) <= pi/2
                            meanp(i,j,k) = meanp(i,j,k);
                        else
                            meanp(i,j,k) = pi-meanp(i,j,k);
                        end
                    end
                end
            end
            
            % Here to acquire the real phi orientation regardless of Z resolution
            meanpreal = (pi/2)-(atan(para*tan((pi/2)-meanplim)));
            for i = 1:sz
                for j = 1:sz2
                    for k = 1:sz3
                        if meanj(i,j,k) <= pi/2
                            meanpreal(i,j,k) = meanpreal(i,j,k);
                        else
                            meanpreal(i,j,k) = pi-meanpreal(i,j,k);
                        end
                    end
                end
            end
            
            % Here prepares the final output
            aSDE = meanc*180/pi;
            pSDER = meanpreal*180/pi;
            
        end
    end
    end
    

    % Callbacks that handle component events
    methods (Access = private)

        % Button pushed function: LoaddataButton
        function LoaddataButtonPushed(app, event)
            sz = 512 ;
            sz2 = 512  ;
            sz3 = 30 ; % 46; % modify 1, define sz, sz2, and sz3 according to the size of the 3D image

            shgstack = zeros(sz,sz2,sz3);

            for i = 1:sz3
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%7  modify
    % shgstack(:,:,i) = imread(['C:\Users\myy15\Desktop\human500um1um\humanskin250umz1um\noshift2\',num2str(i),'.tif']); % modify 2 
                shgstack(:,:,i) = imread(['/Users/kaitlynn/Desktop/Matlab/imaging/',num2str(i),'.tif']); % modify 2 
    % shgstack(:,:,i) = imread(['C:\Users\myy15\Desktop\human500um1um\stack2\',num2str(i),'.tif']); % modify 2 
    % 'shgstack' is the 3D image used for directional variance analysis.
    % Define the path of the folder where the images are saved
            end
        end

        % Button pushed function: DcollagenorientationofthetaButton
        function DcollagenorientationofthetaButtonPushed(app, event)
            
        end

        % Button pushed function: OrientationhistogramofthetaButton
        function OrientationhistogramofthetaButtonPushed(app, event)
           aSDE = meanc(app)*180/pi;
            
            histogram(app.UIAxes,aSDE, sz,sz2, sz3) ;
        end
    end

    % Component initialization
    methods (Access = private)

        % Create UIFigure and components
        function createComponents(app)

            % Create UIFigure and hide until all components are created
            app.UIFigure = uifigure('Visible', 'off');
            app.UIFigure.Position = [100 100 640 480];
            app.UIFigure.Name = 'MATLAB App';

            % Create UIAxes
            app.UIAxes = uiaxes(app.UIFigure);
            title(app.UIAxes, 'Title')
            xlabel(app.UIAxes, 'X')
            ylabel(app.UIAxes, 'Y')
            zlabel(app.UIAxes, 'Z')
            app.UIAxes.Position = [297 240 300 185];

            % Create LoaddataButton
            app.LoaddataButton = uibutton(app.UIFigure, 'push');
            app.LoaddataButton.ButtonPushedFcn = createCallbackFcn(app, @LoaddataButtonPushed, true);
            app.LoaddataButton.Position = [52 424 100 22];
            app.LoaddataButton.Text = 'Load data';

            % Create DcollagenorientationofthetaButton
            app.DcollagenorientationofthetaButton = uibutton(app.UIFigure, 'push');
            app.DcollagenorientationofthetaButton.ButtonPushedFcn = createCallbackFcn(app, @DcollagenorientationofthetaButtonPushed, true);
            app.DcollagenorientationofthetaButton.Position = [10 364 184 22];
            app.DcollagenorientationofthetaButton.Text = '2D collagen orientation of theta';

            % Create DcollagenorientationofphiButton
            app.DcollagenorientationofphiButton = uibutton(app.UIFigure, 'push');
            app.DcollagenorientationofphiButton.Position = [16 314 173 22];
            app.DcollagenorientationofphiButton.Text = '2D collagen orientation of phi';

            % Create OrientationhistogramofthetaButton
            app.OrientationhistogramofthetaButton = uibutton(app.UIFigure, 'push');
            app.OrientationhistogramofthetaButton.ButtonPushedFcn = createCallbackFcn(app, @OrientationhistogramofthetaButtonPushed, true);
            app.OrientationhistogramofthetaButton.Position = [13 190 176 22];
            app.OrientationhistogramofthetaButton.Text = 'Orientation histogram of theta';

            % Create OrientationhistogramofphiButton
            app.OrientationhistogramofphiButton = uibutton(app.UIFigure, 'push');
            app.OrientationhistogramofphiButton.Position = [19 130 165 22];
            app.OrientationhistogramofphiButton.Text = 'Orientation histogram of phi';

            % Show the figure after all components are created
            app.UIFigure.Visible = 'on';
        end
    end

    % App creation and deletion
    methods (Access = public)

        % Construct app
        function app = histogram

            % Create UIFigure and components
            createComponents(app)

            % Register the app with App Designer
            registerApp(app, app.UIFigure)

            if nargout == 0
                clear app
            end
        end

        % Code that executes before app deletion
        function delete(app)

            % Delete UIFigure when app is deleted
            delete(app.UIFigure)
        end
    end
end

Answer 1

[Vidhi Agarwal]

Hi Kaitlynn,

I understand you've encountered a challenge with an error stating "Too many output arguments" within your MATLAB application. It seems this difficulty may arise from the way the “meanc” function has been defined in the code.

To address this issue, it is suggested to revise the function's signature to enable it to return an output, as shown below:

function aS = meanc(app) 
%%function as it is.
end

By implementing this adjustment, the “meanc” function will be configured to return a value, aligning with the original intention. This modification might help in resolving the encountered error.

For a better understanding of functions in MATLAB, you can refer to the following documentation:

• https://www.mathworks.com/help/matlab/ref/function.html?