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;