Moorea mergeIOP

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</function BVal_mergeIOP() %Mfile to merge LISST, SBE25 data and BB9 data. %first lisst date needs to be converted as it is in sepparate columns %day+hr and min+seconds, then lisst time must be set to zero start. Also %bb9 must be set to zero start, then bb9 and lisst can be merged. CTD and %LISST + BB9 will have to be interpolated to match time sample rates %minilogger sample frequency 1Hz %lisst sample freq 1Hz %ctd sample freq 8Hz %BB9 sample freq 1Hz %ACS sample freq 4 Hz

pathname = pwd; dhnumber = pathname(end-2:end); %=====LISST DATA SET MODULE======== data_lst = load('LISST_indexarr'); lst_depth = .01*data_lst(:,37); lst_date1 = num2str(data_lst(:,39)); lst_date2 = num2str(data_lst(:,40)); lst_day = str2num(lst_date1(:,1:3)); lst_hrs = str2num(lst_date1(:,4:5));

%when lisst minutes reset every 60 sec for ml =1:length(lst_date2) 

   hld = lst_date2(ml,1:2);
   char(hld);
   hld = str2num(hld);
   if isempty(hld)
       lst_min(ml) =0;
       continue
   end
   lst_min(ml) = hld;%str2num(lst_date2(ml,1:2))

end lst_min = (lst_min)'; lst_sec = str2num(lst_date2(:,3:4));

JD = lst_hrs.*3600 + lst_min.*60 + lst_sec; %this is in seconds %start from zero JD = JD-JD(1); %lisst time in seconds starting from zero %======END LISST MODULE========


%=====BB9 MODULE======== % since BB9 time is more accurate, use it for merging ECOname = strcat('archive_23_ECO.',dhnumber); %[BB9_data,BB9_HDR] = hdrload('archive_23_ECO.040'); [BB9_data,BB9_HDR] = hdrload(ECOname); BB9_time = BB9_data(:,1); %(in ms) si BB9_time = BB9_time - BB9_time(1); BB9_time = BB9_time./1000;


%====CTD MODULE======= CTDname = strcat('archive_22_SBE25CTD.',dhnumber)

[CTD_dat,CTD_hdr] = hdrload(CTDname);

%[CTD_dat,CTD_hdr] = hdrload('archive_22_SBE25CTD.040'); ctd_time = CTD_dat(:,1); %ms ctd_time = ctd_time./1000; %seconds %ctd_depth = CTD_dat(:,2); %ctd_temp = CTD_dat(:,3); %ctd_cond = CTD_dat(:,4); %ctd_sal = CTD_dat(:,5);

%====ACS MODULE======= ACSname = strcat('archive_21_ACS.',dhnumber) [ACS_dat,ACS_hdr] = hdrload(ACSname); %ACS_D = interp1(nmini30s_T,nmini30s_D,ctd_time);


%=====MERGING MODULE=====

BB_new = interp1(BB9_time,BB9_data,ctd_time); sample =[]; if issorted(data_lst(:,1)) == 0 

   issorted(JD)
   issorted(ctd_time)
   issorted(data_lst(:,1))
   issorted(data_lst(:,37))
   
   %at this point CTD and BB9 data is parsed.  BB data is matched to CTD time
   
   %======= FILTERING NAN IF TIME RECORDS GO OVER =====
   nn= isnan(BB_new);
   non= find(nn(:,1) ==1);
   CTD_dat(non,:) = [];
   BB_new(non,:) = [];
   %=====
   ctd_time = CTD_dat(:,1); %ms
   ctd_time = ctd_time./1000; %seconds
   ctd_depth = CTD_dat(:,2);
   ctd_temp = CTD_dat(:,3);
   ctd_cond = CTD_dat(:,4);
   ctd_sal = CTD_dat(:,5);
   
   file = CTDname;

% % % file = input('What is the minilogger file name?','s'); % % % fid = fopen(file) % % % %fid = fopen('Asc-5264.IOPBVAL9'); % % % % % % tline = fgetl(fid); % % % %tline = fgetl(fid); %junk line test % % % % Split header % % % A(1,:) = regexp(tline, '\,', 'split'); % % % % % % % Parse and read rest of file % % % ctr = 1; % % % while(~feof(fid)) % % % if ischar(tline) % % % ctr = ctr + 1; % % % tline = fgetl(fid); % % % A(ctr,:) = regexp(tline, '\,', 'split'); % % % else % % % break; % % % end % % % end % % % fclose(fid); % % % % % % %====minilogger parameters====== % % % mini_time = cell2mat(A(:,2)); % % % mni_hr = str2num(mini_time(:,1:2)) + 3; %convert LOCAL BDA to GMT % % % mni_mn = str2num(mini_time(:,4:5)); % % % mni_sc = str2num(mini_time(:,7:8)); % % % mni_depth = str2num(char(A(:,4))); % % % mni_temp = str2num(char(A(:,3))); % % % mini_sc_tot = mni_sc(:,:) + mni_mn.*60 + mni_hr.*3600; %time record from minilogger converted to seconds % % % % % % % % % %=========== laborious part to fix times and depth========= % % % %get run summary info start time to set CTD absolute start time % % % % % % rundir = '/home/eriks/BVAL/BVal47/BV47_IOP/DH-4-113/'; % % % runnum = str2num(dhnumber); % % % runnumch = num2str(runnum); % % % runsubdir = strcat('run',runnumch); % % % sumfile = strcat('/Summary.',dhnumber); % % % % % % sumfid = fopen([rundir,runsubdir,sumfile],'r'); % % % ln = fgetl(sumfid); % % % sumhr = str2num(ln(end-7:end-6)); % % % summin = str2num(ln(end-4:end-3)); % % % sumsec = str2num(ln(end-1:end)); % % % % % % DH4_sc_total = sumhr*3600 + summin*60 + sumsec %sum total seconds of DH4 start time % % % mini_DH4_summary = (abs(mini_sc_tot - DH4_sc_total)); %the absolute min time difference between DH4 start % % % mindex = find(mini_DH4_summary == (min(mini_DH4_summary))); %this is where minilogger time and the DH4 START time intersect. % % % new_mini_time = mini_sc_tot(mindex:end); %starts minilogger record at DH4 start time % % % nmini_depth = mni_depth(mindex:end); % % % nmini_temp = mni_temp(mindex:end); % % % nmini_time = (new_mini_time-new_mini_time(1)); %sets new minilogger time to start at 0 seconds % % % % % % %=== CTD time record starts from zero but is 30 seconds after DH4 is % % % %plugged in. % % % % % % %============== important notes================= % % % %now add 30 seconds to mini start time and that will be % % % %start of recorded data. % % % %Interal LISST time is absolute and will start 10 seconds after time stamp in DH4 % % % %summary file % % % %LISST files recorded in DH4 will start 30s after sart time in summary file % % % %to minimize the amount of data altering, get new depths for each % % % %instrument and process, don't alter data % % % %start time for everything = nmini_time(1) + 30s % % % % % % %================================================== % % % %========================================================================== % % % %================================================== % % % nmini30s_T = nmini_time(30:end); % % % nmini30s_D = nmini_depth(30:end); % % % nmini30s_Temp = nmini_temp(30:end); % % % nmini30s_T = nmini30s_T - nmini30s_T(1); %zeros the mini time in for 30s



set (gcf,'units','inches', 'pos',[2 1 9 5]) % declare variables u=[]; % holds the points d=[]; % holds the index's only cast=0; plot(ctd_time,-ctd_depth,'.');grid; 

 cast=cast+1
 which=['Selecting points for cast: ',num2str(cast)];

% first point 

 disp('select the first region')
 title(which);
 [x,y]=ginput(2);

% zoom to start point of the cast 

 if y(1)<y(2)
   if x(1)<x(2)
     axis([x(1),x(2),y(1),y(2)])
   else
     axis([x(2),x(1),y(1),y(2)])
   end
 else
   if x(1)<x(2)
     axis([x(1),x(2),y(2),y(1)])
   else
     axis([x(2),x(1),y(2),y(1)])
   end					% end if
 end					% end if

% pretty the graph 

 hold on
 plot(ctd_time,-ctd_depth,'ro'); grid; title(which);
 plot([x(1), x(2)], [0, 0], 'g-.'); 
 hold off;

% find specific start point 

 disp('click on start of cast')
 [x,y]=ginput(1);
                                       % add the points to cast_id array
 u=[u;x,y];

% last point 

 plot(ctd_time,-ctd_depth,'.');grid; title(which);
 hold on;
 %d=ceil(u(:,1))
 d=[];

 for id=1:size(u,1),
   dd=find(ctd_time<u(id,1));%___________________________
   d=[d; dd(end)];%-----------------------------------------
 end
 for ip=1:2:size(d,1),
     plot(u(:,1),u(:,2),'ro');
 end

% $$$ plot(mat_in(d(1):d(end),1),... % $$$ mat_in(d(1):d(end),2),'g',u(:,1),u(:,2),'ro'); 

 disp('select the end region')
 [x,y]=ginput(2);

% zoom to end point of the cast 

 if y(1)<y(2)
   if x(1)<x(2)
     axis([x(1),x(2),y(1),y(2)])
   else
     axis([x(2),x(1),y(1),y(2)])
   end
 else
   if x(1)<x(2)
     axis([x(1),x(2),y(2),y(1)])
   else
     axis([x(2),x(1),y(2),y(1)])
   end					% end if
 end					% end if

% pretty the graph 

 hold on
 plot(ctd_time,-ctd_depth,'ro');grid; title(which);
 plot([x(1), x(2)], [0, 0], 'g-.'); 
 hold off;

% find specific end point 

 disp('click on end of cast')
 [x,y]=ginput(1);
                                       % add the points to cast_id array
 u=[u;x,y];

% are we done yet? 

 axis('auto');
 plot(ctd_time,-ctd_depth,'.');grid; title(which);
 hold on;
 %d=ceil(u(:,1))
 d=[];
 for id=1:size(u,1),
   dd=find(ctd_time<u(id,1));
   d=[d; dd(end)];
 end


ctd_depth = ctd_depth(d(1):d(2)); ctd_time = ctd_time(d(1):d(2)); CTD30s_D = ctd_depth;%interp1(nmini30s_T,nmini30s_D,ctd_time); %gets a depth dimention relative to 8Hz ctd time (same dimension as ctd data) CTD_dat = CTD_dat(d(1):d(2),:); 

   ACS_D = interp1(ctd_time,ctd_depth,.001*ACS_dat(:,1));
   BB9_D = interp1(ctd_time,ctd_depth,BB9_time);
   CTD_dat(:,2) = ctd_depth;%CTD30s_D; %repair CTD depth column in data
   
   
   %=========Trim size of all data array to interpolation==========
   
   ACS_dat(isnan(ACS_D),:) = [];
   BB9_data(isnan(BB9_D),:) = [];
   BB9_D(isnan(BB9_D)) =[];
   ACS_D(isnan(ACS_D)) =[];

end

LST_D = interp1(ctd_time,ctd_depth,JD); %size of JD

data_lst(isnan(LST_D),:) = []; LST_D(isnan(LST_D)) = []; LISST_new = [data_lst(:,1:32),.01*LST_D];


% % pause

%filter out bad bb data======= %============================= %badbb = find(bb440 > (mean(bb440)+3*std(bb440)));


% % for t = 2:(size(BB_new,2)) % % badbb = find(abs(BB_new(:,t)) > (mean(BB_new(:,t)) + 3*std(BB_new(:,t)))); % % % % ctd_temp(badbb) = []; % % ctd_depth(badbb) = []; % % ctd_sal(badbb) = []; % % ctd_time(badbb) = []; % % ctd_cond(badbb) = []; % % %bb440(badbb) = []; % % BB_new(badbb,:) =[]; % % CTD30s_D(badbb,:) =[]; % % CTD_dat(badbb,:) =[]; % % end %filter out bad ACS ========= %============================

%TempC(badbb) = []; %Depth(badbb) = []; %Sal(badbb) = []; % ACSC442(badC2,:) = []; % ACS_dat(badC2,:) =[]; % ACS_D(badC2,:) =[];

for t = 2:(size(ACS_dat,2) -5) 

   badC2 = find(ACS_dat(:,t) > 2);
   ACS_dat(badC2,:) =[];
   ACS_D(badC2,:) =[];

end

% for t = 2:(size(ACS_dat,2) -5) % badC = find(abs(ACS_dat(:,t)) > (mean(ACS_dat(:,t)) + 3*std(ACS_dat(:,t)))); % ACS_dat(badC,:) =[]; % ACS_D(badC,:) =[]; % end

%===filter bad lisst============== %==============================


for t = 1:32 

   badC = find(abs(LISST_new(:,t)) > (mean(LISST_new(:,t)) + 3*std(LISST_new(:,t))));
   LISST_new(badC,:) =[];
   LST_D(badC,:) =[];

end sm_LISST = smooth(LISST_new(:,:)); %smoothed lisst data SMLST_D = smooth(LST_D); %smoothed lisst depth %=============================

%=========================================== %========================================================================== %==================LISST====================


% % % % % plot(smooth(ctd_temp),smooth(-CTD30s_D)) % % % % % title('ctd temp ctd depth') % % % % % figure % % % % % plot(ctd_time,-CTD30s_D) % % % % % title('ctd time ctd depth') % % % % % figure % % % % % plot(smooth(nmini30s_Temp),smooth(-nmini30s_D)) % % % % % title('mini temp mini depth') % % % % % figure % % % % % plot(smooth(ctd_sal),smooth(-CTD30s_D)) % % % % % title('ctd sal ctd depth') % % % % % figure % % % % % plot(LISST_new(:,9),-LST_D) %moved below filtering section and changed data_lst to LISST_new % % % % % title('interpolated lisst depth and original data') % % % % % figure % % % % % plot(data_lst(:,9),-lst_depth) % % % % % title('original lisst depth and original data') % % % % % figure % % % % % plot(LISST_new_mn(:,9),-nmini30s_D) % % % % % title('interpolated lisst data and minilogger depth') % % % % % keyboard % % % % % % % % % % for i = 2:size(BB_new,2) % % % % % plot(BB_new(:,i),-CTD30s_D); % % % % % axis([0 .01 -110 0]) % % % % % tline = strcat('bbchan',num2str(i)); % % % % % title(tline) % % % % % figure % % % % % % % % % % end % % % % % % % % % % % for i = 1:size(ACS_dat,2) % % % % % plot(smooth(ACS_dat(:,i)),smooth(-ACS_D)); % % % % % axis([-.05 .1 -110 0]) % % % % % tline = strcat('ACSchan',num2str(i)); % % % % % title(tline) % % % % % figure % % % % % % % % % % end % % % % % for i = 1:size(LISST_new,2) % % % % % plot(smooth(LISST_new(:,i)),-SMLST_D); % % % % % tline = strcat('SMLISSTchan',num2str(i)); % % % % % title(tline) % % % % % figure % % % % % % % % % % end % % % % % pause % % % % % for i = 1:size(LISST_new,2) % % % % % plot(LISST_new(:,i),-LST_D); % % % % % tline = strcat('LISSTchan',num2str(i)); % % % % % title(tline) % % % % % figure % % % % % % % % % % end

% % JD_new = interp1(JD,ctd_time);

%==============End LISST===================


%=========================================== %=========================================== %=========BB9 profile plotting============== h2 = figure; q = jet(9); sepparation_index = find(BB9_D == max(BB9_D)); %where to split cast sep_ind = sepparation_index(1); % % % maxd = find(BB9_D == max(BB9_D)); % % % t = maxd(1); % % % while t>1;%t = maxd:-1:1 %cut out 20m downcast % % % if abs(BB9_D(t-15)) > abs(BB9_D(t)) && BB9_D(t) < 20 %make sure not a false min % % % % plot(depth(t:end),data(t:end,1)) % % % break % % % end % % % t = t -1; % % % end

%BB9_downcast = smooth(BB_new(1:sep_ind,:)); %BB9_downcast(blw0,i) = NaN;

subplot(1,4,1) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,4),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,4)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') ylabel('Depth (m)') title('412'); legend(strcat('mean ',num2str(mean(BB9_data(1:sep_ind,4))))) axis([-.001 0.002 -Inf 0])

subplot(1,4,2) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,8),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,8)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') %ylabel('Depth (m)') title('440');

legend(strcat('mean ',num2str(mean(BB9_data(1:sep_ind,8))))) axis([0 0.002 -Inf 0])

subplot(1,4,3) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,24),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,12)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp 1/m ') % ylabel('Depth (m)') title('488'); legend(strcat('mean ',num2str(mean(BB9_data(1:sep_ind,12))))) axis([0 0.002 -Inf 0])

subplot(1,4,4) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,32),-CTD30s_D); scatter(smooth(BB9_data(1:sep_ind,16)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') legend(strcat('mean ',num2str(mean(BB9_data(1:sep_ind,16))))) % ylabel('Depth (m)') title('510'); axis([0 .002 -Inf 0]) %was .95 suptitle(strcat('BB9 Particle Backscatter',file(end-8:end))); saveas(gcf, ['BBprofile412to510',file(end-7:end)], 'png') %==================================== figure subplot(1,5,1) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,4),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,20)),smooth(-BB9_D(1:sep_ind)),3) refline(0,mean(BB9_data(1:sep_ind,20))) xlabel('bbp (1/m)') ylabel('Depth (m)') title('532'); axis([-.001 0.002 -Inf 0])

subplot(1,5,2) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,8),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,24)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') %ylabel('Depth (m)') title('595'); axis([0 0.002 -Inf 0])

subplot(1,5,3) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,24),-CTD30s_D,'o-'); scatter(smooth(BB9_data(1:sep_ind,28)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp 1/m ') % ylabel('Depth (m)') title('660'); axis([0 0.002 -Inf 0])

subplot(1,5,4) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,32),-CTD30s_D); scatter(smooth(BB9_data(1:sep_ind,32)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') % ylabel('Depth (m)') title('676'); axis([0 .002 -Inf 0]) %was .95 suptitle(strcat('BB9 Particle Backscatter',file(end-8:end)));

subplot(1,5,5) set(gca, 'ColorOrder', q) hold on %plot(BB_new(:,32),-CTD30s_D);

scatter(smooth(BB9_data(1:sep_ind,36)),smooth(-BB9_D(1:sep_ind)),3) xlabel('bbp (1/m)') % ylabel('Depth (m)') title('715'); axis([0 .002 -inf 0]) %was .95 suptitle(strcat('BB9 Particle Backscatter',file(end-8:end))); saveas(gcf, ['BBprofile532to715',file(end-7:end)], 'png')


% % % % % % % % % %=======BB9 spectra plotting========= % % % sepparation_index = find(CTD30s_D == max(CTD30s_D)); %where to split cast % % % sep_ind = sepparation_index(1); % % % BB9_downcast = smooth(BB_new(1:sep_ind,:)); % % % % % % for i= 1:size(BB9_downcast,2) % % % blw0 = find(BB9_downcast(:,i) < 0); % % % if ~isempty(blw0); % % % BB9_downcast(blw0,i) = NaN; % % % end % % % %BB9_downcast(find(BB9_downcast(:,i) < 0),i) = 'NAN' % % % end % % % %BB9_downcast < 0 == 'NAN' % % % % % % m35_index = find(abs(CTD30s_D(1:sep_ind) -35) == min(abs(CTD30s_D(1:sep_ind) - 35))) % % % m50_index = find(abs(CTD30s_D(1:sep_ind) -50) == min(abs(CTD30s_D(1:sep_ind) - 50))) % % % m80_index = find(abs(CTD30s_D(1:sep_ind) -80) == min(abs(CTD30s_D(1:sep_ind) - 80))) % % % m95_index = find(abs(CTD30s_D(1:sep_ind) -95) == min(abs(CTD30s_D(1:sep_ind) - 95)))


bwindow =5; [binned,stats,stdev,medi]=databinner(BB9_D(1:sep_ind),BB9_data(1:sep_ind,:),bwindow)

header_std = strcat([' stdbbp','412 '],{' '},['stdbbp','440 '],{' '},['stdbbp','488 '],{' '},['stdbbp','510 '],{' '},['stdbbp','532 '],{' '},['stdbbp','595 '],{' '},['stdbbp','660 '],{' '},['stdbbp','676 '],{' '},['stdbbp','715']); header_b = strcat(['binbbp','412 '],{' '},['binbbp','440 '],{' '},['binbbp','488 '],{' '},['binbbp','510 '],{' '},['binbbp','532 '],{' '},['binbbp','595 '],{' '},['binbbp','660 '],{' '},['binbbp','676 '],{' '},['binbbp','715']); header_med = strcat([' median','412 '],{' '},['median','440 '],{' '},['median','488 '],{' '},['median','510 '],{' '},['median','532 '],{' '},['median','595 '],{' '},['median','660 '],{' '},['medianbbp','676 '],{' '},['medianbbp','715']); header_stat = strcat({' '},['pts'],{' '},['meandepth']) total_hdr = char(strcat(header_b,header_std,header_med,header_stat)); total_dat = [binned,stdev,medi,stats];

stid = fopen([file(end-7:end),'BB9stats'],'w+'); fprintf(stid,'%s\n',total_hdr); dlmwrite([file(end-7:end),'BB9stats'],total_dat, '-append','delimiter',' ')


%figure % for pt = 1:length(stats) % figure % plot(412:33.6667:715,binned(pt,4:4:36))%,412:33.6667:715,binned(pt,8),412:33.6667:715,binned(pt,12),412:33.6667:715,binned(pt,16),412:33.6667:715,binned(pt,20),412:33.6667:715,binned(pt,24),412:33.6667:715,binned(pt,28),412:33.6667:715,binned(pt,32),412:33.6667:715,binned(pt,36)) % dph = strcat('depth',num2str(stats(pt,2))); % title(['BB9 spectra',file(end-7:end)]) % ylabel('bbp data 1/m') % xlabel('412-715nm'); % legend(strcat('depth',num2str(dph))) % hold on % % % % end

clear binned stats stdev medi


%=======end BB9 plotting=============



%===================================== %===================================== %========CTD plotting================= %just downcast after 20m debubble maxd = find(CTD30s_D == max(CTD30s_D)); t=1; % % % t = maxd(1); % % % while t>1;%t = maxd:-1:1 %cut out 20m downcast % % % if abs(CTD30s_D(t-15)) > abs(CTD30s_D(t)) && CTD30s_D(t) < 20 %make sure not a false min % % % break % % % end % % % t = t -1; % % % end sepparation_index = find(CTD30s_D == max(CTD30s_D)); %where to split cast sep_ind = sepparation_index(1); ctd_temp = ctd_temp(1:sep_ind); CTD30s_Df = CTD30s_D(1:sep_ind); ctd_sal = ctd_sal(1:sep_ind); ctd_time = ctd_time(1:sep_ind); ctd_cond = ctd_cond(1:sep_ind);

%plot temp, sal, cond h2 = figure; q = jet(8);

subplot(1,3,1) set(gca, 'ColorOrder', q) hold on %plot(ctd_temp(t:sep_ind),-CTD30s_Df(t:sep_ind),'o-'); scatter(ctd_temp(t:sep_ind),-CTD30s_Df(t:sep_ind),3); xlabel('Temp (C)') ylabel('Depth (m)') title('CTD'); axis([15 35 -Inf 0])

subplot(1,3,2) set(gca, 'ColorOrder', q) hold on %plot(smooth(ctd_sal(t:sep_ind)),-CTD30s_Df(t:sep_ind),'o-'); scatter(smooth(ctd_sal(t:sep_ind)),-CTD30s_Df(t:sep_ind),3); xlabel('Sal (PSU)') %ylabel('Depth (m)') title('CTD'); axis([34 38 -Inf 0])

subplot(1,3,3) set(gca, 'ColorOrder', q) hold on

%plot(ctd_cond(t:sep_ind),-CTD30s_Df(t:sep_ind),'o-'); scatter(ctd_cond(t:sep_ind),-CTD30s_Df(t:sep_ind),3); xlabel('Conductivity') %ylabel('Depth (m)') title('CTD'); axis([4 7 -Inf 0]) suptitle(strcat('SBE25 Output',file(end-8:end))); saveas(gcf, ['CTD',file(end-7:end)], 'png') %========end CTD plotting=============== %======================================= %=======================================

bwindow =5; disp('doing CTD binning') [binned,stats,stdev,medi]=databinner(CTD30s_Df(1:sep_ind),CTD_dat(1:sep_ind,:),bwindow)

header_std = strcat([' std','time(s)'],{' '},['std','Depth(m) '],{' '},['std','Temp(C) '],{' '},['std','Cond '],{' '},['std','Sal(PSU) ']); header_b = strcat(['bin','time(s) '],{' '},['bin','Depth(m) '],{' '},['bin','Temp(C) '],{' '},['bin','Cond '],{' '},['bin','Sal(PSU) ']); header_med = strcat([' median','time(s) '],{' '},['median','Depth(m) '],{' '},['median','Temp(C) '],{' '},['median','Cond '],{' '},['median','Sal(PSU) ']); header_stat = strcat({' '},['pts'],{' '},['meandepth']) total_hdr = char(strcat(header_b,header_std,header_med,header_stat)); total_dat = [binned,stdev,medi,stats]; stid = fopen([file(end-7:end),'CTDstats'],'w+'); fprintf(stid,'%s\n',total_hdr); dlmwrite([file(end-7:end),'CTDstats'],total_dat, '-append','delimiter',' ')



%=============================== %=============================== %=============================== %====ACS spectra plotting=======

disp('FIX ACS CHANNELS IN SCRIPT TO CONTINUE WITH ACS PROC, THEN COMMENT THIS return LINE') continue

sepparation_index = find(ACS_D == max(ACS_D)); %where to split cast sep_ind = sepparation_index(1); ACS_downcast = (ACS_dat(1:sep_ind,:)); ACS_D = smooth(ACS_D(1:sep_ind,:));

Tref = 22.6 %(from factory cal) header_b = ['Time(ms) c400.9 c404.1 c407.3 c410.3 c413.7 c417.1 c420.5 c424.2 c427.9 c431.7 c435.2 c438.6 c442.5 c446.3 c450.0 c454.3 c458.1 c462.2 c466.2 c470.5 c474.5 c478.5 c482.7 c486.7 c490.6 c494.6 c498.6 c502.4 c506.6 c510.7 c514.7 c518.9 c522.7 c526.8 c530.8 c534.6 c538.4 c542.3 c546.1 c549.9 c553.6 c557.4 c561.0 c564.7 c568.1 c571.3 c574.7 c577.8 c581.2 c584.6 c588.3 c591.8 c595.2 c598.9 c602.6 c606.3 c610.2 c613.9 c617.7 c621.5 c625.2 c629.2 c632.9 c636.8 c640.7 c644.3 c648.4 c652.4 c656.0 c660.0 c663.9 c667.8 c671.3 c674.9 c679.1 c682.7 c686.3 c689.7 c693.4 c697.0 c700.5 c703.9 c707.1 c710.7 c714.2 c717.4 c720.8 c724.1 c727.5 c730.5 c734.2 a402.6 a405.9 a408.9 a411.9 a415.3 a418.7 a422.4 a426.0 a429.5 a433.3 a436.8 a440.3 a444.0 a447.9 a452.0 a455.9 a459.9 a463.8 a468.2 a472.2 a476.4 a480.4 a484.5 a488.5 a492.3 a496.1 a500.1 a504.1 a508.3 a512.5 a516.5 a520.3 a524.4 a528.2 a532.3 a536.1 a539.7 a543.9 a547.6 a551.4 a555.2 a558.7 a562.8 a566.2 a569.0 a572.2 a575.6 a579.0 a582.4 a585.8 a589.3 a592.7 a596.4 a599.9 a603.6 a607.3 a611.3 a615.0 a618.9 a622.7 a626.4 a630.2 a634.0 a638.0 a641.8 a645.6 a649.6 a653.5 a657.5 a661.1 a665.0 a668.8 a672.6 a676.3 a680.1 a683.8 a687.1 a690.8 a694.7 a698.1 a701.5 a705.0 a708.4 a711.8 a715.2 a718.5 a721.7 a725.1 a728.4 a731.8 a735.0 iTemp(C) Conduct Depth XTemp Salinity']; legendvals = regexp(header_b,'\s','split') channels = legendvals(2:end-5); %generating a list of ACS channels for TScal for t2 = 2:length(channels) 

   var = char(channels(t2))
   var  = var(2:end);
   chanlist(t2,:) = str2num(var)

end TScorrected = ACS_TScal(ACS_downcast,Tref,ctd_time,ctd_sal,chanlist);%doing temperature and salinity correction. keyboard

ACS_newdncast = [ACS_downcast(:,1),TScorrected,ACS_downcast(:,end-4:end)];


bwindow =5; disp('doing ACS binning') [binned,stats,stdev,medi]=databinner(ACS_D(1:sep_ind),ACS_newdncast,bwindow) %[binned,stats,stdev,medi]=databinner(ACS_D(1:sep_ind),ACS_downcast,bwindow) for i=1:length(legendvals) 

   header_std{i} = char([' std',legendvals{i}])';

end header_std = char(header_std)'; for i=1:length(legendvals) 

   header_med{i} = char([' median',legendvals{i}])';

end header_med = char(header_med)';

%header_std = strcat([' stdbbp','412 '],{' '},['stdbbp','440 '],{' '},['stdbbp','488 '],{' '},['stdbbp','510 '],{' '},['stdbbp','532 '],{' '},['stdbbp','595 '],{' '},['stdbbp','660 '],{' '},['stdbbp','676 '],{' '},['stdbbp','715']); %header_b = strcat(['binACS','412 '],{' '},['binACS','440 '],{' '},['binACS','488 '],{' '},['binACS','510 '],{' '},['binACS','532 '],{' '},['binACS','595 '],{' '},['binACS','660 '],{' '},['binACS','676 '],{' '},['binACS','715']); %header_med = strcat([' median','412 '],{' '},['median','440 '],{' '},['median','488 '],{' '},['median','510 '],{' '},['median','532 '],{' '},['median','595 '],{' '},['median','660 '],{' '},['medianbbp','676 '],{' '},['medianbbp','715']); header_stat = strcat({' '},['pts'],{' '},['meandepth']) total_hdr = char(strcat(header_b,header_std(1,:),header_med,header_stat)); total_dat = [binned,stdev,medi,stats]; stid = fopen([file(end-7:end),'ACSstats'],'w+'); fprintf(stid,'%s\n',total_hdr); %dlmwrite([file(end-7:end),'ACSstats'],total_dat, '-append','delimiter',' ')


m35_index = find(abs(ACS_D(1:sep_ind) -35) == min(abs(ACS_D(1:sep_ind) - 35))) m50_index = find(abs(ACS_D(1:sep_ind) -50) == min(abs(ACS_D(1:sep_ind) - 50))) m80_index = find(abs(ACS_D(1:sep_ind) -80) == min(abs(ACS_D(1:sep_ind) - 80))) m95_index = find(abs(ACS_D(1:sep_ind) -95) == min(abs(ACS_D(1:sep_ind) - 95)))

figure plot(400:3.8202:740,ACS_downcast(m35_index(1),2:91),400:3.8202:740,ACS_downcast(m50_index(1),2:91),400:3.8202:740,ACS_downcast(m80_index(1),2:91),400:3.8202:740,ACS_downcast(m95_index(1),2:91))%,400:3.8202:740,avg_spec_001May2012unfilb(:,2:91), 400:3.8202:740,avg_spec_001May2012filc(:,2:91),400:3.8202:740,avg_spec_001May2012unfilc(:,2:91)) title(['ACS 001 spectra',file(end-7:end)]) xlabel('400-740nm'); ylabel('C data 1/m') legend('35m','50m','80m','95m')%,'May2012-unfilter','May2012-newfilter','May2012-newunfilter','May2012-newfilter runII','May2012-newunfilter runII') %saveas(gcf, ['ACS001_C',file(end-7:end)], 'png')

figure plot(400:3.7363:740,ACS_downcast(m35_index(1),92:182),400:3.7363:740,ACS_downcast(m50_index(1),92:182),400:3.7363:740,ACS_downcast(m80_index(1),92:182),400:3.7363:740,ACS_downcast(m95_index(1),92:182))%,400:3.7363:740,avg_spec_001May2012unfilb(:,92:182), 400:3.7363:740,avg_spec_001May2012filc(:,92:182),400:3.7363:740,avg_spec_001May2012unfilc(:,92:182)) title(['ACS 001 spectra',file(end-7:end)]) xlabel('400-740nm'); ylabel('A data 1/m') legend('35m','50m','80m','95m')%'May2012-unfilter','May2012-newfilter','May2012-newunfilter','May2012-newfilter runII','May2012-newunfilter runII') %saveas(gcf, ['ACS001_A',file(end-7:end)], 'png')


%TS corrected ACS data

figure

plot(400:3.8202:740,ACS_newdncast(m35_index(1),2:91),400:3.8202:740,ACS_newdncast(m50_index(1),2:91),400:3.8202:740,ACS_newdncast(m80_index(1),2:91),400:3.8202:740,ACS_newdncast(m95_index(1),2:91))%,400:3.8202:740,avg_spec_001May2012unfilb(:,2:91), 400:3.8202:740,avg_spec_001May2012filc(:,2:91),400:3.8202:740,avg_spec_001May2012unfilc(:,2:91)) title(['ACS 001 TScor spectra',file(end-7:end)]) xlabel('400-740nm'); ylabel('C data 1/m') legend('35m','50m','80m','95m')%,'May2012-unfilter','May2012-newfilter','May2012-newunfilter','May2012-newfilter runII','May2012-newunfilter runII') %saveas(gcf, ['ACS001TSCOR_C',file(end-7:end)], 'png')

figure plot(400:3.7363:740,ACS_newdncast(m35_index(1),92:182),400:3.7363:740,ACS_newdncast(m50_index(1),92:182),400:3.7363:740,ACS_newdncast(m80_index(1),92:182),400:3.7363:740,ACS_newdncast(m95_index(1),92:182))%,400:3.7363:740,avg_spec_001May2012unfilb(:,92:182), 400:3.7363:740,avg_spec_001May2012filc(:,92:182),400:3.7363:740,avg_spec_001May2012unfilc(:,92:182)) title(['ACS 001 TScor spectra',file(end-7:end)]) xlabel('400-740nm'); ylabel('A data 1/m') legend('35m','50m','80m','95m')%'May2012-unfilter','May2012-newfilter','May2012-newunfilter','May2012-newfilter runII','May2012-newunfilter runII') %saveas(gcf, ['ACS001TSCOR_A',file(end-7:end)], 'png')


%========end ACS plotting=============



%====ACS profile plotting====== % % % % % % % % % % % % h3 = figure; % % % q = jet(4); % % % % % % subplot(1,4,1) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,4),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,6)),-ACS_D,3) % % % xlabel('c (1/m)') % % % ylabel('Depth (m)') % % % title('413.7'); % % % axis([-.1 0.1 -Inf Inf]) % % % % % % subplot(1,4,2) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,8),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,14)),-ACS_D,3) % % % xlabel('c (1/m)') % % % %ylabel('Depth (m)') % % % title('442.5'); % % % axis([0 0.1 -Inf Inf]) % % % % % % subplot(1,4,3) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,24),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,54)),-ACS_D,3) % % % xlabel('c 1/m ') % % % % ylabel('Depth (m)') % % % title('595.2'); % % % axis([0 0.1 -Inf Inf]) % % % % % % subplot(1,4,4) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,32),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,75)),-ACS_D,3) % % % xlabel('c (1/m)') % % % % ylabel('Depth (m)') % % % title('674.9'); % % % axis([0 .1 -Inf Inf]) %was .95 % % % suptitle(strcat('ACS attenuation',file(end-8:end))); % % % saveas(gcf, ['ACSCprofile',file(end-7:end)], 'png') % % % % % % h3 = figure; % % % q = jet(4); % % % % % % subplot(1,4,1) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,4),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,96)),-ACS_D,3) % % % xlabel('a (1/m)') % % % ylabel('Depth (m)') % % % title('411.9'); % % % axis([-.1 0.1 -Inf Inf]) % % % % % % subplot(1,4,2) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,8),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,105)),-ACS_D,3) % % % xlabel('a (1/m)') % % % %ylabel('Depth (m)') % % % title('444'); % % % axis([0 0.1 -Inf Inf]) % % % % % % subplot(1,4,3) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,24),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,145)),-ACS_D,3) % % % xlabel('a 1/m ') % % % % ylabel('Depth (m)') % % % title('596.4'); % % % axis([0 0.1 -Inf Inf]) % % % % % % subplot(1,4,4) % % % set(gca, 'ColorOrder', q) % % % hold on % % % %plot(BB_new(:,32),-CTD30s_D,'o-'); % % % scatter(smooth(ACS_downcast(:,166)),-ACS_D,3) % % % xlabel('a (1/m)') % % % % ylabel('Depth (m)') % % % title('676.3'); % % % axis([0 .1 -Inf Inf]) %was .95 % % % suptitle(strcat('ACS absorption',file(end-8:end))); % % % saveas(gcf, ['ACSAprofile',file(end-7:end)], 'png')

%======= FINDING/FILTERING NAN IF TIME RECORDS GO OVER ===== nn= isnan(BB_new); non= find(nn(:,1) ==1); CTD_dat(non,:) = []; BB_new(non,:) = [];


return>

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