function [cont_times,cont_values,cont_names,d,dd,md_max,cont_values2,variance]=resample_params(time_series_names,times,values,names,des,des_names)


cont_names=time_series_names;

num_params=max(size(time_series_names));
num_samples=24*2*2;

cont_times=zeros(num_params,num_samples);
cont_values=zeros(num_params,num_samples);
cont_values2=zeros(num_params,num_samples);

xx=linspace(0,num_samples*30,96); %minutes


% cubic spline interolation of time series parameters

for param_id=1:num_params
    selector=strcmp(time_series_names(param_id),names);
    
    if sum(selector)==1
        Y=values{selector};
        x=times{selector};

        limits=get_param_limits_by_name(time_series_names{param_id});
        
        for y_idx=1:length(Y)
            if  Y(y_idx) < limits(1) ||  Y(y_idx)>limits(2)
                 Y(y_idx)=NaN;
            end
        end
        
        x=x(~isnan(Y(:)));
        Y=Y(~isnan(Y(:)));
        
        for x_idx=1:length(x)-1
            if  x(x_idx)==x(x_idx+1)
                 Y(x_idx)=(Y(x_idx)+Y(x_idx+1))/2;
                 x(x_idx)=NaN;
                 Y(x_idx)=NaN;
            end
        end
        
        x=x(~isnan(Y(:)));
        Y=Y(~isnan(Y(:)));
        
        if isempty(Y)
            cont_times(param_id,:)=NaN;
            cont_values(param_id,:)=NaN;
            continue
        end
        
        if max(size(Y))==1
            cont_times(param_id,:)=xx;
            cont_values(param_id,:)=Y;
            continue
        end
        
        if x(1) > 0
            Y=[Y(1) Y];
            x=[xx(1) x];
        end
        
        if x(end) < xx(end)
            Y=[Y Y(end)];
            x=[x xx(end)];
        end
        
%         size(x)
%         if max(size(Y))==1
%             pause(1)
%         end
%         size(xx)
        

    % spline resampling
       % yy = spline(x,Y,xx);
        

        
        % linear resampling
        ts1 = timeseries(Y,x,'Name','speed');
        res_ts=resample(ts1,xx);
        yy=squeeze(res_ts.Data);
        
        for y_idx=1:length(yy)
            if  yy(y_idx) < limits(1)-0.1*abs(limits(2)-limits(1)) ||  yy(y_idx)>limits(2)+0.1*abs(limits(2)-limits(1))
                 yy(y_idx)=median(Y);
            end
        end
        
% View the time, data. amd interpolation method:



% Resample ts1 using its default interpolation method:



        cont_times(param_id,:)=xx;
        cont_values(param_id,:)=yy;
        
%         hold off
%         plot(cont_times(param_id,:),cont_values(param_id,:))
%         hold on
%         scatter(x,Y)
%         title(time_series_names{param_id})
%          pause(.1)
        
    else
        cont_times(param_id,:)=NaN;
        cont_values(param_id,:)=NaN;
    end
    
end

% loop goes through time and adds missing params

if des{:,strcmp(des_names,'Gender')}==0

  savefile='dynamic_gender0.mat'; 
  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
   
elseif des{:,strcmp(des_names,'Gender')}==1
  savefile='dynamic_gender1.mat'; 
  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
   
else
  savefile='dynamic_gender1.mat'; 
  load(savefile,'N2','PHAT_all','best_th','A','S','Mu','V', 'CV', 'HP', 'LC','N','pc1','mu','sigma','B');
   
end

for sample_id=1:num_samples
    X=cont_values(:,sample_id);
    S_new=calc_S_new_data(X,A,V,Mu,N,CV);
    X_new_rec =( repmat(Mu,1,1) + A*S_new)';
   % cont_values(:,sample_id)=X_new_rec';
    
    sigma0 = sigma;
    sigma0(sigma0==0) = 1;
    z = bsxfun(@minus,X_new_rec', mu');
    z = bsxfun(@rdivide, z, sigma0');
             x_in=pinv(pc1)*z;
                x_in=x_in(1:N2);
    
   % cont_values(1:N2,sample_id)=z;
   cont_values(:,sample_id)=z;
    cont_values2(:,sample_id)=z;

end

if sum(sum(isnan(cont_values)))>0
    cont_values(isnan(cont_values))=0;
end

%if sum(c)

%cont_values=cont_values(1:N2,:);

variance=var(cont_values2,0,2);


data_width=96;
window=24;
step=1;

md_max=-1;
d=[];
dd=[];
%C=cov(cont_values(:,1:end)');
% c = rcond(C);
% if c < 1e-5
%     pause(1)
%     
% end

for md_idx=window:data_width-window

    m1=median(cont_values(:,md_idx-window+1:md_idx),2);
    m2=median(cont_values(:,md_idx+1:md_idx+window),2);
  
    md=(m1-m2)'*(m1-m2);
    if md > md_max
        md_max=md;
        d=m1;
        dd=m1-m2;
    end



end

% if md_max > 1e2
%    
%     
%     for p_idx=1:min(size(cont_values2))
%         
%         figure(p_idx)
%         hold off
%         
%         scatter(cont_times(p_idx,:),cont_values2(p_idx,:))
%         hold on
%         %scatter(x,Y)
%         title(time_series_names{p_idx})
%        
%         
%         
%         
%         
%         
%     end
%          pause(.1)
% end


% for idx=learning_width:data_width-test_width
%     % For matrix input X, where each row is an observation, and each column is a variable, cov(X) is the covariance matrix
%     
%     %near history
%    % C=cov(selected_data(:,(idx-learning_width+1):idx)');
%     
%     % all history
%     C=cov(selected_data(:,1:idx)');
%     
%     history_mean=mean(selected_data(:,idx-learning_width+1:idx),2);
%     test_mean=mean(selected_data(:,idx+1:idx+test_width),2);
%     Md(idx)=(history_mean-test_mean)'*inv(C)*(history_mean-test_mean);
% end

end