Update 'crosscorr.m'

crosscorr.m

@@ -1,8 +1,126 @@
 %% This function was automatically generated. When modifying its signature, take care to apply
 %% modifications also to the descriptor files in the repository.
-function crosscorr(Please provide a time series file (1), Please provide a time series file (2), Number of lags, Number of STD)
 
 %% MOD_CROSS_CORRELATION_1
 %% TODO: Put your application code here
 
+function [xcf,lags,bounds,numLags,numSTD] = ...
+          crosscorr(ts1,ts2,numLags,numSTD)
+%%
+
+if nargin < 2
+    
+   error('Not enough input parameters');
+     
+end
+
+% Ensure the sample data are vectors:
+
+[rows,columns] = size(ts1);
+
+if ((rows ~= 1) && (columns ~= 1)) || (rows*columns < 2)
+    
+   error('Time series 1 must be a vector');
+     
+end
+
+[rows,columns] = size(ts2);
+
+if ((rows ~= 1) && (columns ~= 1)) || (rows*columns < 2)
+    
+   error('Time series 2 must be a vector');
+     
+else
+
+end
+
+ts1 = ts1(:); % Ensure a column vector
+ts2 = ts2(:); % Ensure a column vector
+
+N = min(length(ts1),length(ts2)); % Sample size
+defaultLags = 20; % Recommendation of [1]
+
+% Ensure numLags is a positive integer or set default:
+
+if (nargin >= 3) && ~isempty(numLags)
+    
+   if numel(numLags) > 1
+       
+      error('Number of lags must be a scalar value');
+        
+   end
+   
+   if (round(numLags) ~= numLags) || (numLags <= 0)
+       
+      error('Number of lags must be a positive integer');
+        
+   end
+   
+   if numLags > (N-1)
+       
+      error('Number of XCF lags must not exceed the number of observations minus one');
+   %  numLags = min(defaultLags,N-1); % Default
+        
+   end
+   
+else
+    
+   numLags = min(defaultLags,N-1); % Default
+   
+end
+
+% Ensure numSTD is a positive scalar or set default:
+
+if (nargin >= 4) && ~isempty(numSTD)
+    
+   if numel(numSTD) > 1
+       
+      error('Number of standard deviations must be a scalar value');
+        
+   end
+   
+   if numSTD < 0
+       
+      error('Number of standard deviations cannot be negative');
+        
+   end
+   
+else
+    
+   numSTD = 2; % Default
+   
+end
+
+% The FILTER command could be used to compute the XCF, but FFT-based 
+% computation is significantly faster for large data sets.
+
+ts1 = ts1-mean(ts1);
+ts2 = ts2-mean(ts2);
+L1 = length(ts1);
+L2 = length(ts2);
+
+if L1 > L2
+    
+   ts2(L1) = 0;
+   
+elseif L1 < L2
+    
+   ts1(L2) = 0;
+   
+end
+
+nFFT = 2^(nextpow2(max([L1 L2]))+1);
+F = fft([ts1(:) ts2(:)],nFFT); 
+
+ACF1 = ifft(F(:,1).*conj(F(:,1)));
+ACF2 = ifft(F(:,2).*conj(F(:,2)));
+
+xcf = ifft(F(:,1).*conj(F(:,2)));
+xcf = xcf([(numLags+1:-1:1) (nFFT:-1:(nFFT-numLags+1))]);
+xcf = real(xcf)/(sqrt(ACF1(1))*sqrt(ACF2(1)));
+
+lags = (-numLags:numLags)';
+bounds = [numSTD;-numSTD]/sqrt(N);
+
+end
 end