Storing For Loop Data For Future Use In A 3D Plot

I wish to store three sets of data (X, Y, Z) acquired through a For loop in an array, but I am unsure of how to do so.

The end goal is to place all data onto a 3-D plot, which I assume I would be able to do once the data is in an array.

I am extremely new to matlab, so any assistance is greatly appreciated!

My current work is below:

data = xlsread('VelocityData.xlsx');
[rowsize, colsize] =size(data);
i=1;
j=10;
k=0;
for ii=2:rowsize
  A=data(ii,1);
  B=data(ii,2);
  C=data(ii,3);

    X=(((A)-((i)/3)));
    Y=(((B)+6)/(j));
    Z=(((C)-(2*(k))/(3)));
    ii=ii+1;
    
    i=i+1;
    j=j+10; 
    k=(k-5);  
end 

Answer 1

[Carter Allen]

data = xlsread('VelocityData.xlsx');
[rowsize, colsize] =size(data);

i=1; j=10; k=0;

for ii=2:rowsize
    A=data(ii,1);  B=data(ii,2); C=data(ii,3);
    
    X(ii-1)=(((A)-((i)/3)));
    Y(ii-1)=(((B)+6)/(j));
    Z(ii-1)=(((C)-(2*(k))/(3)));
    
    i=i+1;
    j=j+10; 
    k=(k-5);  
end 

This seems to accomplish what you wanted. You need to index into a new position of X,Y, and Z for every iteration of the loop, otherwise you will override the previous values and they will remain scalar. Also, you don't need to update the index ii by yourself, the for loop will do that for you.

Alternatively, you could try something like

while ii <= rowsize
    % all of your code goes here
    ii = ii + 1
        
end

Answer 2

[dpb]

What you're written seems like awfully peculiar manipulations to perform on the variables, but

V=[1:rowsize].';                            % temporary vector to build addends
X=data(:,1)-V/3;                            % subtract i/3 from each row i
Y=(data(:,2)+6)./(10*[1:rowsize].');        % and by tens for column 2
X=(data(:,3)+2*5(V-1)/3;                    % and by -5 for column 3 (NB: - a - --> a +)

using MATLAB vector indexing addressing, the underlying reason there is a MATLAB! :)

plot3D should then work directly.

Experiment with the expression using V to see how they reproduce your explicit calculations. Such "tricks" are the key to using MATLAB effectively -- one trades memory for operations and the vectorized code operates very quickly once it is into the core MATLAB compiled code called/executed by the intrinsic math operators, etc.