function z=hilberte(x)
% z=hilberte(x): Hilbert transform with endpoint adjustment
% Input:
%  x: input data
% Output:
%  z: Hilbert transformed output data

%% just do hilbert now, to make it work.
%z=hilbt(x);

[n,k]=size(x);
for c=1:k
    cx = x(:,c);
    % -- predict before real data --
    i=2;
    n_mx=-1; n_mn=-1;
    firstExtremum=0;
    while i<=n-1 & (n_mx < 0 | n_mn < 0)
        prev=cx(i-1);
        cur=cx(i);
        next=cx(i+1);
        if (prev<cur)&(cur>=next)	% max point
            n_mx=i;
            if ~firstExtremum
                firstExtremum=i;
            end
        elseif (prev>=cur)&(cur<next)	% min point
            n_mn=i;
            if ~firstExtremum
                firstExtremum=i;
            end
        end
        i=i+1;
    end
    if i==n
        nPredictHead = 0;
    else
        flipped=0;
        if n_mn>n_mx % flip so that "max" > "min"
            tmp=n_mx;
            n_mx=n_mn;
            m_mn=tmp;
            flipped=1;
        end
        
        % get known half-period
        hp=cx(n_mn:n_mx); % half-period
        hpLen = n_mx-n_mn+1;
        if flipped
            hp = flipud(hp); % so half-period is always increasing
        end
        hpDeriv = diff(hp);
        
        % find where we are at the end in derivative terms
        endDeriv = cx(2)-cx(1);
        
        % find this derivative within the half-period
        if endDeriv<min(hpDeriv)
            
    end
end