MATLAB: Matrix evaluation at a collection of indices

matrix evaluation

Hello,

I would like to do multiple matrix evaluations without the use of a loop. In particular, suppose Z is a p-by-q matrix, and X and Y are m-by-n integer-valued matrices.

I would like to construct an m-by-n matrix U defined as

U(i,j) = Z(X(i,j),Y(i,j))

Is there a simple way to do this without using the loop? Thanks a lot!

Best Answer

idx = sub2ind([m n],i1,j1);
U = zeros([m n]);
U(idx) = Z(X(idx),Y(idx));

Related Solutions

MATLAB: Sparse Matrix indexing

Hi Samir,

more or less that's the way to go. Take a look at the function speye, spdiags and the others under "See also" that help build up your matrix (in your example "eye" is a full matrix, whereas you want only to set the diagonal).

Titus

MATLAB: Condition for Specific Data collection

hello

see example below

this could be interesting for data 1 example

but the two other data it's not feasible unless someone come's with a clever condtion equation to select such fraction of data

 [num,txt,raw] = xlsread('Data 1.xlsx') ;
 
 time = num(:,1);       % s 
 current = num(:,2);    % A
 voltage = num(:,3);     % V
 
 samples = length(time);
 dt = (time(end)-time(1))/(samples-1);
 Fs = 1/dt;
 
 % main loop : extract min max vales for every 5 min data %
 [dvoltage, ddvoltage] = firstsecondderivatives(time,voltage);
figure(1);
subplot(2,1,1),plot(time,voltage,'-b');grid
subplot(2,1,2),plot(time,dvoltage,'-b');grid
% selected buffer is defined between the two positive peaks of dvoltage
dvoltage_max = max(dvoltage(dvoltage>0));
threshold = dvoltage_max/2;
ind = find(dvoltage > threshold);
ind_start = ind(1)-100;
ind_stop = ind(end)-100;
% plot selected portion of data
figure(2);
plot(time,voltage,'-b',time(ind_start:ind_stop),voltage(ind_start:ind_stop),'or');grid
function [dy, ddy] = firstsecondderivatives(x,y)
% The function calculates the first & second derivative of a function that is given by a set
% of points. The first derivatives at the first and last points are calculated by
% the 3 point forward and 3 point backward finite difference scheme respectively.
% The first derivatives at all the other points are calculated by the 2 point
% central approach.

% The second derivatives at the first and last points are calculated by
% the 4 point forward and 4 point backward finite difference scheme respectively.
% The second derivatives at all the other points are calculated by the 3 point
% central approach.
n = length (x);
dy = zeros;
ddy = zeros;
% Input variables:
% x: vector with the x the data points.
% y: vector with the f(x) data points.
% Output variable:
% dy: Vector with first derivative at each point.
% ddy: Vector with second derivative at each point.
dy(1) = (-3*y(1) + 4*y(2) - y(3)) / 2*(x(2) - x(1)); % First derivative
ddy(1) = (2*y(1) - 5*y(2) + 4*y(3) - y(4)) / (x(2) - x(1))^2; % Second derivative
for i = 2:n-1
   
dy(i) = (y(i+1) - y(i-1)) / 2*(x(i+1) - x(i-1));
ddy(i) = (y(i-1) - 2*y(i) + y(i+1)) / (x(i-1) - x(i))^2;
end
dy(n) = (y(n-2) - 4*y(n-1) + 3*y(n)) / 2*(x(n) - x(n-1));
ddy(n) = (-y(n-3) + 4*y(n-2) - 5*y(n-1) + 2*y(n)) / (x(n) - x(n-1))^2;
end

Best Answer

Related Solutions

MATLAB: Sparse Matrix indexing

MATLAB: Condition for Specific Data collection

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