function sigma = nonst_stat(data,A,sumV,params) % Nonstationarity test - continuous process % % Usage: % % sigma=nonst_test(data,A,sumV,params) % Input: % Note units have to be consistent. See chronux.m for more information. % data (1d array in samples) -- required % A quadratic coefficient matrix - (Compute this separately since % the computation is time consuming - [A,sumV]=quadcof(N,NW,order). order % has to < 4NW.) % sumV sum of the quadratic inverse basis vectors % params: structure with fields tapers, pad, Fs, fpass, err, trialave % -optional % tapers : precalculated tapers from dpss or in the one of the following % forms: % (1) A numeric vector [TW K] where TW is the % time-bandwidth product and K is the number of % tapers to be used (less than or equal to % 2TW-1). % (2) A numeric vector [W T p] where W is the % bandwidth, T is the duration of the data and p % is an integer such that 2TW-p tapers are used. In % this form there is no default i.e. to specify % the bandwidth, you have to specify T and p as % well. Note that the units of W and T have to be % consistent: if W is in Hz, T must be in seconds % and vice versa. Note that these units must also % be consistent with the units of params.Fs: W can % be in Hz if and only if params.Fs is in Hz. % The default is to use form 1 with TW=3 and K=5 % % pad (padding factor for the FFT) - optional (can take values -1,0,1,2...). % -1 corresponds to no padding, 0 corresponds to padding % to the next highest power of 2 etc. % e.g. For N = 500, if PAD = -1, we do not pad; if PAD = 0, we pad the FFT % to 512 points, if pad=1, we pad to 1024 points etc. % Defaults to 0. % Fs (sampling frequency) - optional. Default 1. % Output: % sigma (nonstationarity index Thomson, 2000) if nargin < 1; error('Need data'); end; if nargin < 2; params=[]; end; order = length(A); N = length(data); %nfft=max(2^(nextpow2(N)+pad),N); [tapers,pad,Fs]=getparams(params); tapers=dpsschk(tapers,N,Fs); % check tapers alpha=zeros(1,order); for j=1:order alpha(j) = trace(squeeze(A(:,:,j))*squeeze(A(:,:,j))); end; tmp=mtfftc(data,tapers,N,Fs); %tmp=mtfftc(data,tapers,nfft,Fs); sigma = zeros(length(data),1); % Pbar = sum(abs(tmp).^2,2)./sum(weights.^2,2); Pbar=mean(abs(tmp).^2,2); for ii=1:order a0=real(sum(tmp'.*(squeeze(A(:,:,ii))*tmp.')))'/alpha(ii); sigma=sigma+alpha(ii)*(a0./Pbar-sumV(ii)).^2; end;