%% Code to plot the figures for the elipsa manuscript D'Gama et al
% written by NJY 01.03.2024

%addpath(genpath('X:\Manuscripts\Dgama etal 2023 elipsa\iScience\Revision\Raw data D'Gama etal elipsa manuscript 2024\Figure 6\'));


%% LOAD THE INDIVIDUAL DATASETS FROM THE FOLDER

cfg.pathData='X:\Percival\2photon data\Analysis\elipsa\light_responses\';
[ctrlON,ctrlOFF,mutON,mutOFF,time]=nj_load2pdata_v2(cfg);
numCTRL=size(ctrlOFF,1)-1;
numMUT=size(mutOFF,1)-1;

%% SET SOME PARAMETERS FOR THE PLOTS
close all
clear cfg
cfg.MutID='ELIPSA'; %enter the name of your mutant 'ELIPSA', this is for the title
cfg.Mutcolor='c' %enter the color you want for your mutantd display, 'm'= magenta, 'c'=cyan
cfg.numCTRL=numCTRL;
cfg.numMUT=numMUT;
cfg.label=["tel" "OT" "hind" "habenula"];
cfg.brainRegion=char('telencephalon','TeO/thalamus','Hindbrain', 'Habenula');

%% CALCULATE AVERAGE AND PLOT
[averageON_ALL,averageOFF_ALL]=plot_responseALL_perfish(ctrlON,ctrlOFF,mutON,mutOFF,time,cfg);

%% PLOT THE ROIS AND THE HEATMAPS OF ALL NEURONS FOR THE REPRESENTATIVE CONTROL AND MUTANT
%% For CONTROL ============================================================
% Representative control: 20220308_15_02_50_2022038volelipsactrlF03
load('X:\Percival\2photon data\Analysis\elipsa\light_responses\Best representative\Control\20220308_15_02_50_2022038volelipsactrlF03\Aligned\celldetection_celldetectionpercy2detectiontimepoint\2022038volelipsactrlF03Results_CLUSTERING.mat')
% Plot the ROI for the control---------------------------------------------
%colormap based on colorbrewer
colorM=[166 206 227;
31 120 180;
178 223 138;
51 160 44]/256;

%colormap from matplotlib
cmap = getPyPlot_cMap('seismic', 128);
%all figures will be the same size with a scale bar of 100um
for k=1:7
figure,
for i=1:4
index=results.redoneIndex==i & results.DV_DFF_XYZINDEXPLANE_rawtraces(:,5)==k;
scatter(results.DV_DFF_XYZINDEXPLANE_rawtraces(index,1),results.DV_DFF_XYZINDEXPLANE_rawtraces(index,2),30 ,colorM(i,:),'o','filled'), hold on
end
line([10 110], [10 10],'Color','k','LineWidth',2)
axis image
axis off
xlim([0 1536*results.metadata.voxelSize(1)])
title(results.metadata.name)
end

% plot the clustering with a bar for 5 min --------------------------------
% reodred the cluster so that cluster 1 is ON, cluster 2 is OFF, cluster 3
% is spontaneously active, cluster 4 and 5 is nothing
idx2=results.idx;
idx2(results.idx==4)=10;
idx2(results.idx==2)=11;
idx2(results.idx==5)=12;
idx2(results.idx==1)=13;
idx2(results.idx==3)=14;
% x scalebar is 5 min, y scalebar is 1000 neurons
figure('Position', [100 100 400 650])
imagesc(sortrows(cat(2, idx2, results.DV_DFFaveragetime ))), hold on
colormap(cmap)
caxis([-80 80])
%colorbar
xline(results.cfg.TimeVector.ON,'k--','LineWidth',0.1)
xline(results.cfg.TimeVector.OFF,'k--','LineWidth',0.1)
yline(1+find(diff(sort(idx2))==1),'k--', 'LineWidth',2)
xlim([500 size(results.DV_DFFaveragetime,2)+200])
ylim([0 size(results.DV_DFFaveragetime,1)+200])
line([500 round(500+results.cfg.fps*60*5)], size(results.DV_DFFaveragetime,1)+[200 200],'Color','k','LineWidth', 3)
line(size(results.DV_DFFaveragetime,2)+[200 200], [0 1000],'Color','k','LineWidth',3)
box off
axis off
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
%set(ax,'TickDir','out')
title(results.metadata.name)
%% plot a scatter of the brain areas
load('X:\Percival\2photon data\Analysis\elipsa\light_responses\Best representative\Control\20220308_15_02_50_2022038volelipsactrlF03\Aligned\celldetection_celldetectionpercy2detectiontimepoint\Results_BrainRegions.mat')

% introduce colorscale to be used
colorM=[166 206 227;
31 120 180;
178 223 138;
51 160 44]/256;
% plot 3D figure
figure
set(gcf,  'Position',[50 50 800 500])
hold on
for k=[1,3,4,2]
index=results.redoneIndex==k;
scatter3(-results.positions_nodoubles(index,1),results.positions_nodoubles(index,2),-results.positions_nodoubles(index,3),30,results.redoneIndex(index,:),'filled','o'), 
colormap (colorM), caxis([1 4])
alpha(.8)
end
axis image
%axis off
view (-15,45)


%% PLOT THE ROIS IN DIFFERENT REGION========================================
% introduce colorscale to be used
colorM=[166 206 227;
31 120 180;
178 223 138;
51 160 44]/256;
% plot 3D figure
figure
set(gcf,  'Position',[50 50 800 500])
hold on
for k=[1,3,4,2]
index=results.redoneIndex==k;
scatter3(-results.positions_nodoubles(index,1),results.positions_nodoubles(index,2),-results.positions_nodoubles(index,3),30,results.redoneIndex(index,:),'filled','o'), 
colormap (colorM), caxis([1 4])
alpha(.8)
end
axis image
%axis off
view (-15,45)

%% For MUTANT ============================================================
% Representative control: 20220308_15_02_50_2022038volelipsactrlF03
load('X:\Percival\2photon data\Analysis\elipsa\light_responses\Best representative\Mutant\20220308_20_38_11_2022038volelipsamutantF02\Aligned\celldetection_celldetectionpercy2detectiontimepoint\2022038volelipsamutantF02Results_CLUSTERING.mat')

% Plot the ROI for the control --------------------------------------------
%colormap
colorM=[166 206 227;
31 120 180;
178 223 138;
51 160 44]/256;
%all figures will be the same size with a scale bar of 100um
for k=1:7
figure,
for i=1:4
index=results.redoneIndex==i & results.DV_DFF_XYZINDEXPLANE_rawtraces(:,5)==k;
scatter(results.DV_DFF_XYZINDEXPLANE_rawtraces(index,1),results.DV_DFF_XYZINDEXPLANE_rawtraces(index,2),30 ,colorM(i,:),'o','filled'), hold on
end
line([10 110], [10 10],'Color','k','LineWidth',2)
axis image
axis off
xlim([0 1536*results.metadata.voxelSize(1)])
title(results.metadata.name)
end

% plot the clustering with a bar for 5 min --------------------------------
% reodred the cluster so that cluster 1 is ON, cluster 2 is OFF, cluster 3
% is spontaneously active, cluster 4 and 5 is nothing
idx2=results.idx;
idx2(results.idx==4)=10;
idx2(results.idx==3)=11;
idx2(results.idx==2)=12;
idx2(results.idx==1)=13;
idx2(results.idx==5)=14;
% x scalebar is 5 min, y scalebar is 1000 neurons
figure('Position', [100 100 400 650])
imagesc(sortrows(cat(2, idx2, results.DV_DFFaveragetime ))), hold on
colormap(cmap)
caxis([-80 80])
%colorbar
xline(results.cfg.TimeVector.ON,'k--','LineWidth',0.1)
xline(results.cfg.TimeVector.OFF,'k--','LineWidth',0.1)
yline(1+find(diff(sort(idx2))==1),'k--', 'LineWidth',2)
xlim([500 size(results.DV_DFFaveragetime,2)+200])
ylim([0 size(results.DV_DFFaveragetime,1)+200])
line([500 round(500+results.cfg.fps*60*5)], size(results.DV_DFFaveragetime,1)+[200 200],'Color','k','LineWidth', 3)
line(size(results.DV_DFFaveragetime,2)+[200 200], [0 1000],'Color','k','LineWidth',3)
box off
axis off
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
%set(ax,'TickDir','out')
title(results.metadata.name)

%% PLOT THE SORTED RESPONSE TO LIGHT ON AND OFF STIMULUS FOR THE BEST REPRESENTATIVE EXAMPLE
% ===========================================================0
%% Load data from all control and all mutants
load('X:\Percival\2photon data\Analysis\elipsa\light_responses\Elipsa_All_light_response.mat')
cfg.onset=465;
cfg.offset=503;
cfg.brainRegion=char('telencephalon','TeO/thalamus','Hindbrain', 'Habenula');

%% Plot scatter response for representative CONTROL per brain region-------
%colormap from matplotlib
cmap = getPyPlot_cMap('seismic', 128);

%this is the ID of the right control---------------------------------------
CTRL=9;
%scale the graph so that they are visible
for k=1:4
sizedata(k)=size(find(ctrlON{CTRL+1,7}(:,4)==k),1);
end
sizeimage=[sizedata(1)/12 sizedata(2)/25 sizedata(3)/25 sizedata(4)/5];

% Plot scatter response for representative CONTROL lightON----------------
for i=CTRL
for k=1:4
figure
set(gcf,  'Position',[50 50 200 sizeimage(k)])
matrix=ctrlON{i+1,6}(ctrlON{i+1,7}(:,4)==k,cfg.onset-10:cfg.offset);
imagesc(sortrows([mean(matrix(:,10:end),2) matrix],1,'descend')), colormap(cmap), caxis([-80 80]), hold on
xlim([2 53])
xline(11,'k--','LineWidth',1)
line([53  53], [0 200],'Color','k','LineWidth', 3)
ylabel('neurons')
title([cfg.brainRegion(k,:) ' ctrlON'])
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
box off
axis off
end
end

% Plot scatter response for representative CONTROL lightOFF----------------
for i=CTRL
for k=1:4
figure
set(gcf,  'Position',[50 50 200 sizeimage(k)])
matrix=ctrlOFF{i+1,6}(ctrlOFF{i+1,7}(:,4)==k,cfg.onset-10:cfg.offset);
imagesc(sortrows([mean(matrix(:,10:end),2) matrix],1,'descend')), colormap(cmap), caxis([-80 80]), hold on
xlim([2 53])
xline(11,'k--','LineWidth',1)
line([53  53], [0 200],'Color','k','LineWidth', 3)
ylabel('neurons')
title([cfg.brainRegion(k,:) ' ctrlOFF'])
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
box off
axis off
end
end


%% Plot scatter response for representative MUTANT per brain region
%colormap from matplotlib
cmap = redblue;

%scale the graph so that they are visible
for k=1:4
sizedata(k)=size(find(ctrlON{CTRL+1,7}(:,4)==k),1);
end
sizeimage=[sizedata(1)/12 sizedata(2)/25 sizedata(3)/25 sizedata(4)/5];

%this is the ID of the right mutant---------------------------------------
MUT=9;

% Plot scatter response for representative MUTANT lightON----------------
for i=MUT
for k=1:4
figure
set(gcf,  'Position',[50 50 200 sizeimage(k)])
matrix=mutON{i+1,6}(mutON{i+1,7}(:,4)==k,cfg.onset-10:cfg.offset);
imagesc(sortrows([mean(matrix(:,10:end),2) matrix],1,'descend')), colormap(cmap), caxis([-80 80]), hold on
xlim([2 53])
xline(11,'k--','LineWidth',1)
line([53  53], [0 200],'Color','k','LineWidth', 3)
ylabel('neurons')
title([cfg.brainRegion(k,:) ' mutON'])
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
box off
axis off
end
end

% Plot scatter response for representative MUTANT lightOFF----------------
for i=MUT
for k=1:4
matrix=mutOFF{i+1,6}(mutOFF{i+1,7}(:,4)==k,cfg.onset-10:cfg.offset);
imagesc(sortrows([mean(matrix(:,10:end),2) matrix],1,'descend')), colormap(cmap), caxis([-80 80]), hold on
xlim([2 53])
xline(11,'k--','LineWidth',1)
line([53  53], [0 200],'Color','k','LineWidth', 3)
ylabel('neurons')
title([cfg.brainRegion(k,:) ' mutOFF'])
ax=gca;
set(ax,'XTick',[])
set(ax,'YTick',[])
box off
axis off
end
end


%% plot amplitude of elipsa and control
cfg.MutID='elipsa';
figure (40)
set(gcf,  'Position',[100 100 400 600])
str= {[cfg.MutID ' Amplitude of ON and OFF responses, SEM']};
annotation('textbox',[0.05 0.95 0.9 0.05],'String',str, 'FontSize',14, 'FontWeight', 'bold', 'LineStyle', 'none');

subplot(2,1,1)
rectangle('Position',[0 -2 60 1],'FaceColor',[0.99 0.99 0.75], 'EdgeColor',[0.99 0.99 0.75]), hold on
shadedErrorBar(time,mean(averageON_ALL.ctrl,1),std(averageON_ALL.ctrl,[],1)/sqrt(size(averageON_ALL.ctrl,1)),'lineprops', 'k')
shadedErrorBar(time,mean(averageON_ALL.mut,1),std(averageON_ALL.mut,[],1)/sqrt(size(averageON_ALL.mut,1)),'lineprops', cfg.Mutcolor)
title(['Average ON for all cells in' num2str(cfg.numCTRL) ' ctrl and ' num2str(cfg.numMUT) ' mut fish '])
xlim([-10 60])
ylim([-5 20])
xlabel('seconds')
ylabel('dF/F')

subplot(2,1,2)
rectangle('Position',[-10 -2 10 1],'FaceColor',[0.99 0.99 0.75], 'EdgeColor',[0.99 0.99 0.75]), hold on
shadedErrorBar(time,mean(averageOFF_ALL.ctrl,1),std(averageOFF_ALL.ctrl,[],1)/sqrt(size(averageOFF_ALL.ctrl,1)),'lineprops', 'k')
shadedErrorBar(time,mean(averageOFF_ALL.mut,1),std(averageOFF_ALL.mut,[],1)/sqrt(size(averageOFF_ALL.mut,1)),'lineprops', cfg.Mutcolor)
title(['Average OFF for all cells in' num2str(cfg.numCTRL) ' ctrl and ' num2str(cfg.numMUT) ' mut fish '])
xlim([-10 60])
ylim([-5 30])
xlabel('seconds')
ylabel('dF/F')

%% amplitude cells per brain region
%close all
onset=465;
offset=503;

jj=[1,4,2,3]; %reorder so that Hb is between Tel and OT, added 14.03.2023


figure (52)
for k=1:4
    j=jj(k);
set(gcf,  'Position',[100 100 300 400])
str= {[cfg.MutID ' Amplitude ALL cells ON, from ' num2str(time(onset)) 's to ' num2str(time(offset)) 's']};
annotation('textbox',[0.05 0.95 0.9 0.05],'String',str, 'FontSize',10, 'FontWeight', 'bold', 'LineStyle', 'none');

for i=1:cfg.numCTRL 
matrixC(i,:)=mean(ctrlON{i+1,6}(ctrlON{i+1,7}(:,4)==j,:),1);
end
for i=1:cfg.numMUT 
matrixM(i,:)=mean(mutON{i+1,6}(mutON{i+1,7}(:,4)==j,:),1);
end
toplotC=matrixC(:,onset:offset);
toplotM=matrixM(:,onset:offset);
swarmchart((2*k-2)+ones(cfg.numCTRL,1), mean(toplotC,2),80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
line((2*k-2)+[0.6 1.4], repmat(mean(mean(toplotC,2)),1,2),'Color','k')
errorbar((2*k-2)+1,mean(mean(toplotC,2)), std(mean(toplotC,2)),'Color','k')
swarmchart((2*k-2)+2*ones(cfg.numMUT,1), mean(toplotM,2),80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
line((2*k-2)+[1.6 2.4], repmat(mean(mean(toplotM,2)),1,2),'Color','k')
errorbar((2*k-2)+2,mean(mean(toplotM,2)), std(mean(toplotM,2)),'Color','k')
%xlim([0 3]), 
ylim([-10 40])
ylabel('amplitude')
text((2*k-2)+0.25,-5,['p= '...
    num2str(ranksum(mean(toplotC,2),mean(toplotM,2)),'%.4f')]);
xticks([1.5 3.5 5.5 7.5])
xticklabels({'Tel', 'Hab','OT/Thal', 'Hind'})
%title([cfg.brainRegion(k,:) 'ON response'])
clear toplotC toplotM matrixC matrixM
end

figure (53)
for k=1:4
    j=jj(k);
set(gcf,  'Position',[100 100 300 400])
str= {[cfg.MutID ' Amplitude ALL cells OFF, from ' num2str(time(onset)) 's to ' num2str(time(offset)) 's']};
annotation('textbox',[0.05 0.95 0.9 0.05],'String',str, 'FontSize',10, 'FontWeight', 'bold', 'LineStyle', 'none');

for i=1:cfg.numCTRL 
matrixC(i,:)=mean(ctrlOFF{i+1,6}(ctrlOFF{i+1,7}(:,4)==j,:),1);
end
for i=1:cfg.numMUT 
matrixM(i,:)=mean(mutOFF{i+1,6}(mutOFF{i+1,7}(:,4)==j,:),1);
end
toplotC=matrixC(:,onset:offset);
toplotM=matrixM(:,onset:offset);
swarmchart((2*k-2)+ones(cfg.numCTRL,1), mean(toplotC,2),80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
line((2*k-2)+[0.6 1.4], repmat(mean(mean(toplotC,2)),1,2),'Color','k')
errorbar((2*k-2)+1,mean(mean(toplotC,2)), std(mean(toplotC,2)),'Color','k')
swarmchart((2*k-2)+2*ones(cfg.numMUT,1), mean(toplotM,2),80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
line((2*k-2)+[1.6 2.4], repmat(mean(mean(toplotM,2)),1,2),'Color','k')
errorbar((2*k-2)+2,mean(mean(toplotM,2)), std(mean(toplotM,2)),'Color','k')
%xlim([0 3]), 
ylim([-10 60])
ylabel('amplitude')
text((2*k-2)+0.25,-5,['p= '...
    num2str(ranksum(mean(toplotC,2),mean(toplotM,2)),'%.4f')]);
xticks([1.5 3.5 5.5 7.5])
xticklabels({'Tel', 'Hab','OT/Thal', 'Hind'})
%title([cfg.brainRegion(k,:) 'ON response'])
clear toplotC toplotM matrixC matrixM
end
%close all
%% calculate number of responding cells per region
%close all
NumCtrl=[];
NumMut=[];
jj=[1,4,2,3]; %reorder so that Hb is between Tel and OT, added 14.03.2023

% calculate responding cells per brain region
for k=1:cfg.numCTRL
for ik=1:4; %there are four brain region
    j=jj(ik);
NumCtrl.ON.activated(k,ik)=length(find(ctrlON{k+1, 4}(:,4)==j))/sum(ctrlON{k+1, 7}(:,4)==j)*100;  
NumCtrl.OFF.activated(k,ik)=length(find(ctrlOFF{k+1, 4}(:,4)==j))/sum(ctrlOFF{k+1, 7}(:,4)==j)*100;
NumCtrl.ON.inhibited(k,ik)=length(find(ctrlON{k+1, 5}(:,4)==j))/sum(ctrlON{k+1, 7}(:,4)==j)*100;
NumCtrl.OFF.inhibited(k,ik)=length(find(ctrlOFF{k+1, 5}(:,4)==j))/sum(ctrlOFF{k+1, 7}(:,4)==j)*100;

end
end

for k=1:cfg.numMUT
for ik=1:4; %there are four brain region
    j=jj(ik);
NumMut.ON.activated(k,ik)=length(find(mutON{k+1, 4}(:,4)==j))/sum(mutON{k+1, 7}(:,4)==j)*100;  
NumMut.OFF.activated(k,ik)=length(find(mutOFF{k+1, 4}(:,4)==j))/sum(mutOFF{k+1, 7}(:,4)==j)*100;  
NumMut.ON.inhibited(k,ik)=length(find(mutON{k+1, 5}(:,4)==j))/sum(mutON{k+1, 7}(:,4)==j)*100;  
NumMut.OFF.inhibited(k,ik)=length(find(mutOFF{k+1, 5}(:,4)==j))/sum(mutOFF{k+1, 7}(:,4)==j)*100;
end
end

figure (57),
set(gcf,  'Position',[100 100 300 400])
toplotC=NumCtrl.ON.activated;
toplotM=NumMut.ON.activated;
swarmchart(repmat([1 2 3 4], cfg.numCTRL,1), toplotC,80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii,mean(toplotC(:,ii)), std(toplotC(:,ii)),'Color','k', 'Marker','_')
end
swarmchart(repmat([1.5 2.5 3.5 4.5], cfg.numMUT,1), toplotM,80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii+0.5,mean(toplotM(:,ii)), std(toplotM(:,ii)),'Color','k', 'Marker','_')
end
xlim([0 5]), ylim([-10 80])
ylabel('% of responding cells')
for i=1:4
p(i)=ranksum(toplotC(:,i),toplotM(:,i));
end
text(0,-5,num2str(['Tel:' sprintf('%.3f',p(1))...
    ' Hb:' sprintf('%.3f',p(2)) ' OT:' sprintf('%.3f',p(3))...
    ' Hind:' sprintf('%.3f',p(4))]))
xticks([1.25 2.25 3.25 4.25])
xticklabels({'Tel', 'Hab','OT', 'Hind'})
title('ON activated')
clear toplotC toplotM p
hold off

figure (58)
set(gcf,  'Position',[100 100 300 400])
str= {[cfg.MutID ' % ON inhibited']};
toplotC=NumCtrl.ON.inhibited;
toplotM=NumMut.ON.inhibited;
swarmchart(repmat([1 2 3 4], cfg.numCTRL,1), toplotC,80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii,mean(toplotC(:,ii)), std(toplotC(:,ii)),'Color','k', 'Marker','_')
end
swarmchart(repmat([1.5 2.5 3.5 4.5], cfg.numMUT,1), toplotM,80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii+0.5,mean(toplotM(:,ii)), std(toplotM(:,ii)),'Color','k', 'Marker','_')
end
xlim([0 5]), ylim([-2 10])
ylabel('% of responding cells')

for i=1:4
p(i)=ranksum(toplotC(:,i),toplotM(:,i));
end
text(0,-1,num2str(['Tel:' sprintf('%.3f',p(1))...
    ' Hb:' sprintf('%.3f',p(2)) ' OT:' sprintf('%.3f',p(3))...
    ' Hind:' sprintf('%.3f',p(4))]))
xticks([1.25 2.25 3.25 4.25])
xticklabels({'Tel', 'Hab','OT', 'Hind'})
title('ON inhibited')
clear toplotC toplotM p
hold off


figure (59)
set(gcf,  'Position',[100 100 300 400])
str= {[cfg.MutID ' % OFF activated']};
toplotC=NumCtrl.OFF.activated;
toplotM=NumMut.OFF.activated;
swarmchart(repmat([1 2 3 4], cfg.numCTRL,1), toplotC,80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii,mean(toplotC(:,ii)), std(toplotC(:,ii)),'Color','k', 'Marker','_')
end
swarmchart(repmat([1.5 2.5 3.5 4.5], cfg.numMUT,1), toplotM,80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii+0.5,mean(toplotM(:,ii)), std(toplotM(:,ii)),'Color','k', 'Marker','_')
end
xlim([0 5]), ylim([-10 80])
ylabel('% of responding cells')
for i=1:4
p(i)=ranksum(toplotC(:,i),toplotM(:,i));
end
text(0,-5,num2str(['Tel:' sprintf('%.3f',p(1))...
    ' Hb:' sprintf('%.3f',p(2)) ' OT:' sprintf('%.3f',p(3))...
    ' Hind:' sprintf('%.3f',p(4))]))
xticks([1.25 2.25 3.25 4.25])
xticklabels({'Tel', 'Hab','OT', 'Hind'})
title('OFF activated')
clear toplotC toplotM p
hold off


figure (60)
set(gcf,  'Position',[100 100 300 400])
str= {[cfg.MutID ' % OFF inhibited']};
toplotC=NumCtrl.OFF.inhibited;
toplotM=NumMut.OFF.inhibited;
swarmchart(repmat([1 2 3 4], cfg.numCTRL,1), toplotC,80,'k','filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii,mean(toplotC(:,ii)), std(toplotC(:,ii)),'Color','k', 'Marker','_')
end
swarmchart(repmat([1.5 2.5 3.5 4.5], cfg.numMUT,1), toplotM,80,cfg.Mutcolor,'filled','MarkerFaceAlpha',0.5,'MarkerEdgeAlpha',0.5,'XJitterWidth',0.3), hold on
for ii=1:4
errorbar(ii+0.5,mean(toplotM(:,ii)), std(toplotM(:,ii)),'Color','k', 'Marker','_')
end
xlim([-0 5]), ylim([-2 25])
ylabel('% of responding cells')
for i=1:4
p(i)=ranksum(toplotC(:,i),toplotM(:,i));
end
text(0,-1,num2str(['Tel:' sprintf('%.3f',p(1))...
    ' Hb:' sprintf('%.3f',p(2)) ' OT:' sprintf('%.3f',p(3))...
    ' Hind:' sprintf('%.3f',p(4))]))
xticks([1.25 2.25 3.25 4.25])
xticklabels({'Tel', 'Hab','OT', 'Hind'})
title('OFF inhibited')
clear toplotC toplotM p
hold off