even_sample.m

%***********************************************************************
%
%       CONVERTS A RANDOMLY SAMPLED SIGNAL SET INTO AN EVENLY SAMPLED
%       SIGNAL SET (by interpolation)
%       
%	By	:	Haldun KOMSUOGLU
%	Start	:	07/23/1999
%	Last	:	07/23/1999
%	Statue  :	Neural Model Research Material
%	
%	Inputs:	
%              t : A column vector that contains the time values for the
%                  corresponding computed state trajectory points
%              x : A matrix in which each row is a state value on the 
%                  solution trajectory that corresponds to the time value in
%                  vector t with the same row value. Format of each row:
%                          row = [x1 x2 ... xN]
%              Fs: The sampling frequency (1/sec) for the evenly sampled 
%                  set to be generated.
%
%       Outputs:
%              Et : Even sampling instants. This is a column vector in the
%                   same format with "t".
%              Ex : A matrix of the same form with "x" that contains the
%                   state values corresponding to the time instants in Et
%
%***********************************************************************
function [Et, Ex] = even_sample(t, x, Fs, type)

if nargin < 4, type = 'linear'; end

dt = diff(t);
dt = dt + (dt==0)*1e-5;
t = [0;cumsum(dt)];

% Obtain the process related parameters
N = size(x, 2);    % number of signals to be interpolated
M = size(t, 1);    % Number of samples provided
t0 = t(1,1);       % Initial time
tf = t(M,1);       % Final time
EM = (tf-t0)*Fs;   % Number of samples in the evenly sampled case with
                   % the specified sampling frequency
Et = linspace(t0, tf, round(EM))';

% Using linear interpolation (used to be cubic spline interpolation)
% and re-sample each signal to obtain the evenly sampled forms
for s = 1:N,
  Ex(:,s) = interp1(t(:,1), x(:,s), Et(:,1),type); 
end;