% generate bit matrix based on groupname_Bsize.mat
clear;
load 'hassebrook_Bsize'; % get sizes
load 'hassebrook_r';
[M,N]=size(r)
figure(1)
plot(r);
% demodulate first sequence, threshold with 0 level, (pos=1,neg=0)
if max(r)>abs(min(r))
    range=max(r)
else
    range=abs(min(r))
end
scale=1/(range*1.1);
r1=r.*scale; % scale in such a way as not to move the zero level
b1=ceil(r1);
n=1:N;
figure(2)
plot(n,r1,n,b1);
% demodulate second sequence
r2=abs(r); % flip negative values to positive values
% threshold at about 1.5, use peak value to estimate this
%r2=r2-(max(r2)*(1.5/2.0));
r2=r2-1.5; % assume accurate estimate of quantization levels
if max(r2)>abs(min(r2))
    range=max(r2)
else
    range=abs(min(r2))
end
scale=1/(range*1.1);
r2=r2.*scale; % scale in such a way as not to move the zero level
b2=ceil(r2);

figure(3)
plot(n,r2,n,b2);
Bs=zeros(Nseq,N);
Bs(1,1:N)=b1(1:N);
Bs(2,1:N)=b2(1:N);
figure(4)
imagesc(Bs);
colormap gray
save 'hassebrook_Bs' Bs;