function[r] = do_all_lev(n,t0,int,duration,n_N,int_N,n_srate,int_srate,rat,MC_L,MC_F)
%do_all_lev explores how filter performance changes with time, sample rate,
%and order of filter. Outputs data,noise estimate, structure of max
%rms error and other info. It uses get_data, miso_filter_lev, and miso_filter_int and retrives
%MC_Ldata or MC_Fdata for multiple times, calculates a miso_filter for initial-time data
%file, applies filter to the other data files, and graphs the rms of the cost
%function vs time. n+1 is number of data files. int is time interval between
%data files, t0 is start time, duration is duration of each data file, srate
%is the sample rate for which filter is calculated, n_N is number of orders
%of the filter you want the program to calculate,int_N is interval by which N
%is varied, n_N is number of srates you want the program to calculate filters
%for, srate will be varied as sample_rate_temp=2^(6+j*int_srate) where j-integer,
%rat is the fraction of the initial data file which is used to calculate
%filter. You have the option of filtering MC_L data with acc and seis data or
%you can filter MC_F data with mic data by typing 1 or 0 for those inputs.

    

%getting data for each time and storing in vector z
 for ii = 0:n
   d_t = -ii*int;
   if MC_L==1
     z_temp = get_z_data(t0,d_t,duration);
   end
   if MC_F==1
     z_temp = get_mic_data(t0,d_t,duration);
   end
   if ii == 0
     z = z_temp;
   else
     z = [z ; z_temp];
   end
   clear d_t z_temp;
 end

%want to loop over various sample rates and various orders
  %looping over various orders of filter 
  for kk=0:n_N-1
  %looping over various sample rates for filter
    %N_temp=2^(8+kk);
    N_temp=(2^8)+kk*int_N
    for jj=0:n_srate-1
      sample_rate_temp=2^(6+jj*int_srate);
      w_temp = miso_filter_lev(N_temp,sample_rate_temp,rat,z(1,:));
%applying filter to data files at all the other times
      s_temp = miso_filter_int(w_temp,z(2:n+1,:));
%calculating rms of error=singal-est_signal for each file and creating
%rms vector and time vector to graph
      x = 0;
      y = 0;
%looping over various times
      for gg=1:n+1
        x_temp = s_temp.t(1,gg);
        x = [x,x_temp];
        y_temp = max(rms(s_temp.f(:,gg),s_temp.perr(:,gg)));
        y = [y, y_temp];
        clear x_temp y_temp
      end
      clear gg
%getting rid of zeroes in x and y row vectors
      x = x(2:end);
      y = y(2:end);
%storing x and y row vectors as rows in matrices x_s, y_s. Also creating column
%vector for corresponding sample rates. 
      if jj==0
        x_s = x
        y_s = y
        s_s = sample_rate_temp
      else
        x_s = [x_s;x];
        y_s = [y_s;y];
        s_s = [s_s;sample_rate_temp];
      end
%clearing variables before looping again
      clear sample_rate_temp x y w_temp s_temp 
    end
%want to store the matrices x_s y_s and s_s for each successive iteration of
%N. one could create a structure for the matrices. Use structure called
%B. Also create a vector of B.N to graph with.
    B_temp = struct('x_s',x_s,'y_s',y_s,'s_s',s_s,'N',N_temp);
    if kk==0
      B = B_temp;
    else
      B = [B,B_temp];
    end
%create a column vector of B.N to graph with.    
    for ii=1:length(B)
     if ii==1
       N_vector = B(ii).N;
     else
       N_vector = [N_vector;B(ii).N];
     end
    end
    r=struct('z',z,'B',B,'N_vector',N_vector)
  end  

 %---------------------------------------------
 %Plotting stuff
 
 
%plot error vs.N for different times at a given srate (for structure type 3)
%if err_srate_time==1
%  B=r.B
%  N_vector=r.B.N
%  u=size(B(kk).y_s)%(number of N's,number of times)
%  leg=''
%  color='bgrcmyk'
%  %looping over N's to make vectors to graph with
%  for ll=1:u(1)
%    q_temp=B(kk).y_s(ll,:)
%    if ll==1
%      q=q_temp
%    else
%      q=[q;q_temp]
%    end
%  end
   %looping over times
%  for ll=1:u(2)
%    plot(N_vector,q(:,ll),color(1+mod(ll-1,7)))
%    hold on 
%    if ll>9
%      b=''
%    else
%      b='0'
%    end
%    leg_temp=['time' b  num2str(ll)]
%    leg=[leg; leg_temp]
%  end
%  hold off
%  ylabel('max rms error(cts/rHz)') 
%  xlabel('srate(Hz)')
%  ti=['error vs srate for different times N=' num2str(r.B(kk).N(kk))]
%  title(ti)
%  legend(leg)
%end
 
 
  %err vs N for diff times at given srate
%if err_N_time==1
%  B=r.B
%  N_vector=r.N_vector
%  v=size(B)
%  v=v(2)%number of srate's
%  u=size(B(kk).y_s)%(number of N's,number of times)
%  leg=''
%  color='bgrcmyk'
  %looping over srate's to make vectors to graph with
%  for ll=1:v
%    q_temp=B(ll).y_s(kk,:)
%    if ll==1
%      q=q_temp
%    else
%      q=[q;q_temp]
%    end
%  end
  
   %looping over times
%  for ll=1:u(2)
%    plot(N_vector,q(:,ll),color(1+mod(ll-1,7)))
%    hold on 
%    if ll>9
%      b=''
%    else
%      b='0'
%    end
%    leg_temp=['time' b  num2str(ll)]
%    leg=[leg; leg_temp]
%  end
%  hold off
%  ylabel('max rms error(cts/rHz)') 
%  xlabel('srate(Hz)')
%  ti=['error vs srate for different times N=' num2str(r.B(kk).N(kk))]
%  title(ti)
%  legend(leg)
  
  
  %The following doesn't yet work err vs N for diff times
%if err_time_N==1
%  B=r256L.B
%  N_vector=r256L.N_vector
%  u=size(B(kk).y_s)
%  leg=''
%  color='bgrcmyk'

  %looping over N's
%  for ll=1:length(N_vector)
%    q_temp=B(ll).y_s(kk,:)
%    if ll==1
%      q=q_temp
%    else
%      q=[q;q_temp]
%    end
%  end
  
  %looping over times
%  for ll=1:u(2)
%    plot(N_vector,q(:,ll),color(1+mod(ll,8)))
%    leg_temp=['time' num2str(ll)]
%    leg=[leg; leg_temp] 
%    hold on
%  end
%  hold off
%  legend([leg])
%  ylabel('max rms error(cts/rHz)') 
%  xlabel('N(order)')
%  title(['error vs N for different times srate=' num2str(r.B(1).s_s(kk))])
%end  
%plot(N_vector,q1,N_vector,q2,N_vector,q3,N_vector,q4,N_vector,q5,N_vector,q6,N_vector,q7)if
%err_time_N==1


 %err vs N for different srates
%  B=r256L.B
%  N_vector=r256L.N_vector
%  u=size(B(kk).y_s)
%  leg=''
%  color='bgrcmyk'

  %looping over N's
 % for ll=1:length(N_vector)
%  for ll=1:5
%    q_temp=B(ll).y_s(:,kk)
%    if ll==1
%      q=q_temp'
%    else
%      q=[q;q_temp']
%    end
%  end
  
   %looping over srates
%  for ll=1:u(1)
%    %plot(N_vector,q(:,ll),color(1+mod(ll,8)))
%    temp_srate=B(kk).s_s(ll)
%    plot(N_vector(1:5),q(:,ll),color(1+mod(ll,8)))
%    if ll==1
%      t='0'
%    else
%      t=''
%    end
%    leg_temp=['srate ' t num2str(B(kk).s_s(ll)) 'Hz']
%    leg=[leg; leg_temp] 
%    hold on
%  end
%  hold off
%  legend([leg])
%  ylabel('max rms error(cts/rHz)') 
%  xlabel('N(order)')
%  title(['error vs N for different srates, time1'])
%end  
  
    
  
%plot err vs IR for different srates
%  B=r256L.B
%  N_vector=r256L.N_vector
%  u=size(B(kk).y_s)
%  leg=''
%  color='bgrcmyk'

  %looping over N's
 % for ll=1:length(N_vector)
%  for ll=1:5
%    q_temp=B(ll).y_s(:,kk)
%    if ll==1
%      q=q_temp'
%    else
%      q=[q;q_temp']
%    end
%  end
  
    
  %looping over srates
%  for ll=1:u(1)
    %plot(N_vector,q(:,ll),color(1+mod(ll,8)))
%    temp_srate=B(kk).s_s(ll)
%    plot((N_vector(1:5)*(1/temp_srate)),q(:,ll),color(1+mod(ll,8)))
%    if ll==1
%      t='0'
%    else
%      t=''
%    end
%    leg_temp=['srate ' t num2str(temp_srate) 'Hz']
%    leg=[leg; leg_temp] 
%    hold on
%  end
%  hold off
%  legend([leg])
%  ylabel('max rms error(cts/rHz)') 
%  xlabel('Impulse Response(seconds)')
%  title(['error vs IR for different srates, time1'])
%end