function []=plotdat_bigred(data)
% close all
N=size(data,2);
T=size(data,1);
calib=500;
freq=1500;


t=1/freq*cumsum(ones(T,1));
p=data(:,2:N);
for k=1:3
    p(:,k)=p(:,k)-p(15000,k);
end
p(:,1:2)=p(:,1:2); %*20 or 40
p_filtered=p;

for k=1:3
    p_filtered(:,k)=filtered(p(:,k),freq,3);
end
% f=freq;                          %resampling frequency
% n=floor(freq/f);
% t=data(1:n:end,1);
% p=zeros(length(t),N);
% pf=zeros(length(t),N);
% fdata=zeros(length(t),N);
% p(:,1)=t;
% pf(:,1)=t;
% fdata(:,1)=t;
% for k=2:N
%     temp=filtered(data(:,k),freq,20);
%     p(:,k)=calib*data(1:n:end,k);
%     pf(:,k)=calib*temp(1:n:end);
% end

% figure()
% % window=ones(floor(2*f),1)/(2*f);
% % for k=2:7
% %     fdata(:,k)=pf(:,k)-convn(pf(:,k),window,'same');%-mean(pf(:,k))
% %     p(:,k)=p(:,k)-mean(p(:,k));
% % end
% plot(t,p(:,1),t,p(:,2),t,p(:,3))
% title('Pressure')
% xlabel('Time (s)')
% % ylabel('Signal (Pa)')
%  legend('1','2','3')
 
 figure()
% window=ones(floor(2*f),1)/(2*f);
% for k=2:7
%     fdata(:,k)=pf(:,k)-convn(pf(:,k),window,'same');%-mean(pf(:,k))
%     p(:,k)=p(:,k)-mean(p(:,k));
% end
plot(t,p_filtered(:,1),t,p_filtered(:,2),t,p_filtered(:,3))
title('Pressure')
xlabel('Time (s)')
% ylabel('Signal (Pa)')
 legend('1','2','3')
 
 
%  figure()
% plot(t,fdata(:,3)-fdata(:,2),t,fdata(:,4)-fdata(:,3),t,fdata(:,5)-fdata(:,4),t,fdata(:,6)-fdata(:,5),t,fdata(:,7)-fdata(:,6))
% title([titledata,',difference filtered'])
% xlabel('Time (s)')
% ylabel('Signal (Pa)')
% legend('2','3','4','5','6')

%  figure()
% plot(t,fdata(:,2)-fdata(:,7),t,fdata(:,3)-fdata(:,7),t,fdata(:,4)-fdata(:,7),t,fdata(:,5)-fdata(:,7),t,fdata(:,6)-fdata(:,7))
% title([titledata,',absolute difference filtered'])
% xlabel('Time (s)')
% ylabel('Signal (Pa)')
% legend('1','2','3','4','5')

% plot_spectrum(data,t(1),t(end))
end