for loop executing else statement every time!

Hello. Right now, the else branch of the if/then statement is executing every single time so the probability is always 100% for each beetle in one of the behavior categories, regardless of the values placed in the if statement resulting in the graph below.

Based off of the slope of figure 2 which is TT(k)/msd(:,1), I would like to make a bar graph that shows the results if the slope is between 0.75-1.5 (ballistic behavior)-- otherwise, the behavior is diffusive. Here is the error I keep getting:

Index exceeds the number of array elements (5).

Error in MeanSquaredDispCharTime1new (line 58)

behaviorSum(2*i)=behaviorSum(2*i)+1; %incrementing diffusive bin

Here is an excerpt of my code. Thank you so much!

LET = ['ABCDE']; %# letters represent # of beetles; ie ABC means there are 3 beetles

for e = 1:2

%%%% 0 frequency

if e == 1, freq = 0;

% figure(e*10+1); clf; hold on;

% figure(e*10+2); clf; hold on;

figure(e*10+3); clf; hold on;

for j = 1:5

clear data

data = csvread(['Control4' LET(j) '.csv']); %yes

nData = size(data,1); %# number of data points

numberOfDeltaT = floor(nData); %# for MSD, dt should be up to 1/4 of number of data points

msd = zeros(numberOfDeltaT,3); %# We'll store [mean, std, n]

%# calculate msd for all deltaT's

for dt = 1:numberOfDeltaT

deltaCoords = data(1+dt:end,1:2) - data(1:end-dt,1:2);

squaredDisplacement = sum(deltaCoords.^2,2); %# dx^2+dy^2+dz^2

msd(dt,1) = mean(squaredDisplacement); %# average

msd(dt,2) = std(squaredDisplacement); %# std

msd(dt,3) = length(squaredDisplacement); %# n

end

% figure(e*10+1);

% plot(data(:,1), data(:,2)); axis equal;

%

% figure(e*10+2);

TT = (1:numberOfDeltaT);

% h = errorbar(TT, msd(:,1), msd(:,2), 'o', 'markersize', 12);

% h = plot(TT, msd(:,1), 'o', 'markersize', 12);

% h.LineWidth = 2;

% set(gca, 'xscale', 'log', 'yscale', 'log');

% plot(xlim, ylim, '--k', 'markersize', 4) %GOAL: place a line in the middle so we know where slope = 1 as a reference

% xlabel('i\deltat(s)');

% ylabel('\langle\Deltax_{i}^{2}\rangle/\langle\Deltax_{1}^{2}\rangle');

% title('Normalized Mean Square Displacements vs. Time Difference for Control 4')

figure(e*10+3);

behaviors= 2; % 2 behaviors

beetles=5; %ideally will use LET so the loop worked regardless of if 4 or 5 beetles and not a manual input

count= zeros(1,beetles*behaviors); % creates 2 empty 'bins' or storage areas for ballistic or diffusive

yValues=nData*beetles;

% Count outcomes of behavior of beetles

behaviorSum=zeros(1,beetles*behaviors);

for k= 1:nData

for i=1:beetles

if (1.5 >= TT(k)/(msd(k,1))) && (TT(k)/(msd(k,1)) >= 0.75) %ballistic

behaviorSum(2*i-1)=behaviorSum(2*i-1)+1; %incrementing ballistic bin

else

behaviorSum(2*i)=behaviorSum(2*i)+1; %incrementing diffusive bin

end

% increment appropriate bin

end

%this is storing a value in one of the bins created

end

for n=1:beetles*behaviors

count(n)=behaviorSum(n)/nData;

end

end

% Show bar graph of outcome

bar(1:beetles*behaviors, count) %this is creating the histogram with 1:behaviors as number of bars and count as y values for bars

title(sprintf('Characteristic time \deltat_{c} vs. Probability', yValues), 'Fontsize',14)

xlabel('Characteristic time \deltat_{c}', 'Fontsize',14)

ylabel('Probability', 'Fontsize',14)

shg %show graph window

else

fprintf('Thanks for helping!');

end

end

Attached below is an example csv file:

[Mathieu NOE]

hello

with the supplied csv file there is no error poping up , at least on my R2020b

also there is no figure 2 showing up, so I ended up ploting myself the TT(k)/(msd(k,1)) array , which, for the csv file provided, is above 5 ; the if statement (ballistic) is never met

                    if (1.5 >= TT(k)/(msd(k,1))) && (TT(k)/(msd(k,1)) >= 0.75) %ballistic 

- is that your issue ?

clc
clearvars

% LET = ['ABCDE']; %# letters represent # of beetles; ie ABC means there are 3 beetles
LET = ['A']; %# letters represent # of beetles; ie ABC means there are 3 beetles

for e = 1:2
    %%%% 0 frequency
    if e == 1, freq = 0;
%         figure(e*10+1); clf; hold on;
%         figure(e*10+2); clf; hold on;
        figure(e*10+3); clf; hold on;
        
        for j = 1:1%5
            clear data
            data = csvread(['Control4' LET(j)  '.csv']); %yes
            nData = size(data,1); %# number of data points
            numberOfDeltaT = floor(nData); %# for MSD, dt should be up to 1/4 of number of data points
            
            msd = zeros(numberOfDeltaT,3); %# We'll store [mean, std, n]
            
            %# calculate msd for all deltaT's
            
            for dt = 1:numberOfDeltaT
                deltaCoords = data(1+dt:end,1:2) - data(1:end-dt,1:2);
                squaredDisplacement = sum(deltaCoords.^2,2); %# dx^2+dy^2+dz^2
                
                msd(dt,1) = mean(squaredDisplacement); %# average
                msd(dt,2) = std(squaredDisplacement); %# std
                msd(dt,3) = length(squaredDisplacement); %# n
            end
            
%             figure(e*10+1);
%             plot(data(:,1), data(:,2)); axis equal;
%             
%             figure(e*10+2);
            TT = (1:numberOfDeltaT);
            % h = errorbar(TT, msd(:,1), msd(:,2), 'o', 'markersize', 12);
%             h = plot(TT, msd(:,1), 'o', 'markersize', 12);
%             h.LineWidth = 2;
%             set(gca, 'xscale', 'log', 'yscale', 'log');
%             plot(xlim, ylim, '--k', 'markersize', 4) %GOAL: place a line in the middle so we know where slope = 1 as a reference
%             xlabel('i\deltat(s)');
%             ylabel('\langle\Deltax_{i}^{2}\rangle/\langle\Deltax_{1}^{2}\rangle');
%             title('Normalized Mean Square Displacements vs. Time Difference for Control 4')
            
            figure(e*10+3);
            behaviors= 2;                % 2 behaviors
            beetles=5;      %ideally will use LET so the loop worked regardless of if 4 or 5 beetles and not a manual input
            count= zeros(1,beetles*behaviors);  % creates 2 empty 'bins' or storage areas for ballistic or diffusive
            yValues=nData*beetles;
            % Count outcomes of behavior of beetles
            behaviorSum=zeros(1,beetles*behaviors);
            for k= 1:nData
                for i=1:beetles
                    my_test_data(k) = TT(k)/(msd(k,1));
                    if (1.5 >= TT(k)/(msd(k,1))) && (TT(k)/(msd(k,1)) >= 0.75) %ballistic 
                        behaviorSum(2*i-1)=behaviorSum(2*i-1)+1; %incrementing ballistic bin
                    else
                        behaviorSum(2*i)=behaviorSum(2*i)+1; %incrementing diffusive bin
                    end
                    % increment appropriate bin
                end
                %this is storing a value in one of the bins created
            end
            for n=1:beetles*behaviors
                count(n)=behaviorSum(n)/nData;
            end
        end
            
            % Show bar graph of outcome
            bar(1:beetles*behaviors, count) %this is creating the histogram with 1:behaviors as number of bars and count as y values for bars
            title(sprintf('Characteristic time \deltat_{c} vs. Probability', yValues), 'Fontsize',14)
            xlabel('Characteristic time \deltat_{c}', 'Fontsize',14)
            ylabel('Probability', 'Fontsize',14)
            shg %show graph window
    else 
        fprintf('Thanks for helping!');
    end
end

figure
semilogy(my_test_data)