clear
% Images of the representative videos. Using the 2/80 bandpass filter and
% okada filtered videos to make these images.

%% Generate projection images
okadafiltfold = '..\datasets\videos\okada\';
video = h5read(strcat(okadafiltfold, '031220-10x-2xbin-p40-chatgc3-%5B2.041-0.06%5DA30stim006_Green_okada1.h5'), '/video');
regfiltfold = '..\datasets\videos\registered\';
regvid = h5read(strcat(regfiltfold, '031220-10x-2xbin-p40-chatgc3-%5B2.041-0.06%5DA30stim006_Green_okada1cubic_registered.h5'), '/video3');
load(strcat(regfiltfold, '031220-10x-2xbin-p40-chatgc3-%5B2.041-0.06%5DA30stim006_Green_okada1cubic_registered_associated.mat'), 'maxmove');

simfield = sum(maxmove(1:3, :));
svid = video(simfield(3)+1:end-simfield(4), simfield(1)+1:end-simfield(2), :);
sframe = 600;
eframe = 750;
subplot(2, 2, 1)
imshowpair(svid(:, :, sframe), svid(:, :, eframe));
title('Overlap before registration')
axis equal
axis normal
subplot(2, 2, 2)
imshowpair(regvid(:, :, sframe), regvid(:, :, eframe));
title('Overlap following registration')
axis equal
axis normal
subplot(2, 2, 3)
imagesc(mean(svid(:, :, sframe:eframe), 3), [0 60])
title('Unregistered average projection')
colormap gray
axis equal
axis normal
subplot(2, 2, 4)
imagesc(mean(regvid(:, :, sframe:eframe), 3), [0 60])
title('Registered average projection')
colormap gray
axis equal
axis normal

figure
subplot(2, 2, 1)
imagesc(max(svid(:, :, sframe:eframe), [], 3), [0 80])
title('Unregistered maximum projection')
colormap gray
axis equal
axis normal
subplot(2, 2, 2)
imagesc(max(regvid(:, :, sframe:eframe), [], 3), [0 80])
title('Registered maximum projection')
colormap gray
axis equal
axis normal
subplot(2, 2, 3)
imagesc(min(svid(:, :, sframe:eframe), [], 3), [0 40])
title('Unregistered minimum projection')
colormap gray
axis equal
axis normal
subplot(2, 2, 4)
imagesc(min(regvid(:, :, sframe:eframe), [], 3), [0 40])
title('Registered minimum projection')
colormap gray
axis equal
axis normal

%% Correlation comparison between different fields of view
load('..\datasets\figure_1-3\comparison-of-correlation.mat')
logcorr = zeros(35, 18);
mcorr = zeros(3, 18);
stdcorr = zeros(3, 18);
type = {'300', '300+150', '300+150+100'};

color = [0.5 0.2 0.2; 0.2 0.2 0.8; 0.5 0.1 0.5; 0.2 0.2 0.2];
%Figure used in manuscript is the 23 generated in this list
f = figure;
f.Position(1) = f.Position(1) - f.Position(3)/2;
f.Position(3) = f.Position(3)*2;
tg = uitabgroup;
for nn = 1:length(okC)
    thistab = uitab(tg);
    for tt = 1:size(okC{nn}, 1)
        logcorr(nn, tt) = nanmean(unmC{nn}(tt, :)-compC{nn}(tt, :));  
        logcorr(nn, tt+3) = nanmean(bpnmC{nn}(tt, :)-compC{nn}(tt, :));  
        logcorr(nn, tt+6) = nanmean(oknmC{nn}(tt, :)-compC{nn}(tt, :));  
        logcorr(nn, tt+9) = nanmean(uC{nn}(tt, :)-compC{nn}(tt, :));
        logcorr(nn, tt+12) = nanmean(bpC{nn}(tt, :)-compC{nn}(tt, :));
        logcorr(nn, tt+15) = nanmean(okC{nn}(tt, :)-compC{nn}(tt, :));
        axes('Parent',thistab);
        subplot(1, 3, tt)
        plot(compC{nn}(tt, :), 'k')
        hold on
        plot(unmC{nn}(tt, :), '--')
        plot(bpnmC{nn}(tt, :), '--')
        plot(oknmC{nn}(tt, :), '--')
        plot(uC{nn}(tt, :))
        plot(bpC{nn}(tt, :))
        plot(okC{nn}(tt, :))
        legend({'Unregistered', 'Unfiltered no memory', 'BP no mem', 'BP+OK no mem', 'Unfiltered', 'Bandpass', 'Bandpass+Okada'},'Location','South')
        title(type{tt})
        xlabel('Frame')
        ylabel('Correlation')
        
        ylim([-1 1])
    end
    thistab.Title = num2str(nn);
end

figure
%Make the barplot to show the change in correlation for the 6 different
%filtering and memory vs no memory conditions for a fixed field of view
mcorr = mean(logcorr);
mcorr = reshape(mcorr, [3 6])';
stdcorr = std(logcorr);
stdcorr = reshape(stdcorr, [3 6])';

dist = [0.4 0.6 0.8 1.2 1.4 1.6];
dist = dist'+[0 2 4];

order = [1 2 3];
for fn = 1:6
    b = bar(dist(fn, :), mcorr(fn, :)', 0.1);
    b.FaceColor = 'flat';
    hold on
end
xticks([1 3 5])
xticklabels({'300', '300+150', '300+150+100'})
xlabel('Patch size (pixels)')
ylabel('\Delta Correlation')
legend({'Unfiltered no memory', 'BP no memory', 'BP+OK no memory', 'Unfiltered', 'Bandpass', 'Bandpass+Okada'}, 'AutoUpdate', 'off')
for fn = 1:6
    er = errorbar(dist(fn, :), mcorr(fn,:),stdcorr(fn, :));  
    er.Color = [0 0 0];                            
    er.LineStyle = 'none';  
end
plot(dist(1:3, 1), logcorr(:, [1 4 7]), 'k.', 'MarkerSize', 10);
plot(dist(1:3, 2), logcorr(:, [1 4 7]+1), 'k.', 'MarkerSize', 10);
plot(dist(1:3, 3), logcorr(:, [1 4 7]+2), 'k.',  'MarkerSize', 10);

plot(dist(4:6, 1), logcorr(:, [10 13 16]), 'k.', 'MarkerSize', 10);
plot(dist(4:6, 2), logcorr(:, [10 13 16]+1), 'k.', 'MarkerSize', 10);
plot(dist(4:6, 3), logcorr(:, [10 13 16]+2), 'k.',  'MarkerSize', 10);

%% Get the path finding example with Dijkstra's
clearvars -except regfiltfold
hmfolder = pwd;
cd(regfiltfold)
load('050720-10x-2xbin-Mp50-chatgc3-%5B2.041-0.06%5D30Astim004_Green_okada1cubic_registered_associated.mat');
cd(hmfolder)

NCparamStruct.maxshift = [200 75 40];
NCparamStruct.distthresh = [200 40 20];
NCparamStruct.fillthresh = [60 30 30];
NCparamStruct.buffer = [40 40 40];

fn = 1;

%Plot used in the figure is the 4th plot generated
peakshift = DijkstraPeakSearch(peaks{fn+1}, shifts2{fn+1}, transKey{fn+1}, NCparamStruct.distthresh(fn), NCparamStruct.fillthresh(fn), NCparamStruct.buffer(fn), NCparamStruct.maxshift(fn), 1);