%%
td = [];
td2 = [];
preDist_1 = [];
preDist_2 = [];
postDist_1 = [];
postDist_2 = [];
evalDist = [];
totalDist = [];
occuF = [];
occuR = [];

%%
fwdlrn = [flyStruct.forwardLrn];
revlrn = [flyStruct.reverseLrn];
[lrnFWD,dist,occuF] = lrnScoreUpdate(fwdlrn);
preDist_1 = dist(:,1);
postDist_1 = dist(:,2);
[lrnREV,dist,occuR] = lrnScoreUpdate(revlrn);
preDist_1 = dist(:,1);
postDist_1 = dist(:,2);
%%
ind = isnan(lrnFWD) | isnan(lrnREV);

%%
PCARegressionCI([lrnFWD(~ind)',lrnREV(~ind)']);
axis([-1 1 -1 1]);
xlabel('Learning metric trial 1: CS+ MCH')
ylabel('Learning metric trial 2: CS+ OCT')
[a1,b1] = corrcoef(lrnFWD(~ind),lrnREV(~ind),'rows','pairwise');
temp1 = strcat({'r = '},num2str(round(a1(2),2)));
temp2 = strcat({'p = '},num2str(round(b1(2),2)));
a2 = text(-0.85,0.85,temp1);a2.FontSize = 14;
a2 = text(-0.85,0.75,temp2);a2.FontSize = 14;
cleanFormat
a = title('Correlation of learning and reversal learning');
%% Adding points of example flies
hold on
temp = [lrnFWD(~ind)',lrnREV(~ind)'];
sp1 = find(temp(:,1) == lrnFWD(3));
ex1 = [1-occuF(3,:),1-occuR(3,:)];

sp2 = find(temp(:,2) == lrnREV(17));
ex2 = [1-occuF(17,:),1-occuR(17,:)];

sp3 = find(temp(:,1) == lrnFWD(20));
ex3 = [1-occuF(20,:),1-occuR(20,:)];

temp = temp([sp1,sp2,sp3],:);
a = scatter(temp(1,1),temp(1,2));a.MarkerFaceColor = [0.88 0.8 0.4];a.MarkerEdgeColor = [0 0 0];a.MarkerFaceAlpha = 1;a.SizeData = 75;
a = scatter(temp(2,1),temp(2,2));a.MarkerFaceColor = [0.25 0.25 0.9];a.MarkerEdgeColor = [0 0 0];a.MarkerFaceAlpha = 1;a.SizeData = 75;
a = scatter(temp(3,1),temp(3,2));a.MarkerFaceColor = [0.85 0.3 0.3];a.MarkerEdgeColor = [0 0 0];a.MarkerFaceAlpha = 1;a.SizeData = 75;

%%
PCARegressionCI([postDist_1(~ind),lrnFWD(~ind)']);
xlabel('Learning metric trial 1: CS+ MCH')
ylabel('Learning metric trial 2: CS+ OCT')
[a1,b1] = corrcoef(postDist_1(~ind),lrnFWD(~ind),'rows','pairwise');
temp1 = strcat({'r = '},num2str(round(a1(2),2)));
temp2 = strcat({'p = '},num2str(round(b1(2),2)));
a2 = text(-0.85,0.85,temp1);a2.FontSize = 14;
a2 = text(-0.85,0.75,temp2);a2.FontSize = 14;
cleanFormat
a = title('Correlation of distance traveled and learning score');a.FontWeight = 'normal';
%%
pre = occuF(:,1)';
mattCorr(pre(~ind)',lrnFWD(~ind)');
axis([0 1 -1 1])
xlabel('Learning metric trial 1: CS+ MCH')
ylabel('Learning metric trial 2: CS+ OCT')
[a1,b1] = corrcoef(pre(~ind),lrnFWD(~ind),'rows','pairwise');
temp1 = strcat({'r = '},num2str(round(a1(2),2)));
temp2 = strcat({'p = '},num2str(round(b1(2),2)));
a2 = text(-0.85,0.85,temp1);a2.FontSize = 14;
a2 = text(-0.85,0.75,temp2);a2.FontSize = 14;
cleanFormat
a = title('Relationship of intial odor preference and learning score');a.FontWeight = 'normal';
%%
lrnGraphOld(1-occuF(:,1),1-occuF(:,2))
cleanFormat
ylabel('Octanol preference')
a = title('Shock pre-training vs post-training');
a.FontWeight = 'normal';
temp = 1-occuF(ind,:);
hold on
a2 = plot([0,1],[ex1(1),ex1(2)]);a2.LineWidth = 6;a2.Color = [0.88 0.8 0.4];a2.Marker = '.';a2.MarkerSize = 25;
a2 = plot([0,1],[ex2(1),ex2(2)]);a2.LineWidth = 6;a2.Color = [0.25 0.25 0.9];a2.Marker = '.';a2.MarkerSize = 25;
a2 = plot([0,1],[[ex3(1),ex3(2)]]);a2.LineWidth = 6;a2.Color = [0.85 0.3 0.3];a2.Marker = '.';a2.MarkerSize = 25;

%~~~~~~ Adding mean trend line
a = plot([0,1],[nanmean(1-occuF(ind,1)),nanmean(1-occuF(ind,2))],'Color',[0.2 0.8 0.5]);
a.LineWidth = 4;
a.Marker = '.';
a.MarkerSize = 25;
a1 = axis;a1(4) = 1.2;axis(a1)
a = plot([0,1],[1.1,1.1]);a.LineWidth = 1;a.Color = [0,0,0];
a = plot([0,0],[1.1,1.075]);a.LineWidth = 1;a.Color = [0,0,0];
a = plot([1,1],[1.1,1.075]);a.LineWidth = 1;a.Color = [0,0,0];
%~~~~~~~~~~~~~~~~~~~~

%%
mattbar_norm({1-occuF(:,1)',1-occuF(:,2)'})
a1 = axis;a1(3) = 0;a1(4) = 1;
axis(a1)
xticks([1,2])
xticklabels({'pre-training','post-training'})

%% adding bar graphs

lrnGraph(1-occuR(:,1),1-occuR(:,2))
cleanFormat
ylabel('Octanol preference')
a = title('Shock pre-training vs post-training');
a.FontWeight = 'normal';
temp = 1-occuR(ind,:);
hold on
a2 = plot([0,1],[ex1(3),ex1(4)]);a2.LineWidth = 6;a2.Color = [0.88 0.8 0.4];a2.Marker = '.';a2.MarkerSize = 25;
a2 = plot([0,1],[ex2(3),ex2(4)]);a2.LineWidth = 6;a2.Color = [0.25 0.25 0.9];a2.Marker = '.';a2.MarkerSize = 25;
a2 = plot([0,1],[[ex3(3),ex3(4)]]);a2.LineWidth = 6;a2.Color = [0.85 0.3 0.3];a2.Marker = '.';a2.MarkerSize = 25;
%~~~~~~ Adding mean trend line
a = plot([0,1],[nanmean(1-occuR(:,1)),nanmean(1-occuR(:,2))],'Color',[0.2 0.8 0.5]);
a.LineWidth = 4;
a.Marker = '.';
a.MarkerSize = 25;
a1 = axis;a1(4) = 1.2;axis(a1)
a = plot([0,1],[1.1,1.1]);a.LineWidth = 1;a.Color = [0,0,0];
a = plot([0,0],[1.1,1.075]);a.LineWidth = 1;a.Color = [0,0,0];
a = plot([1,1],[1.1,1.075]);a.LineWidth = 1;a.Color = [0,0,0];
%~~~~~~~~~~~~~~~~~~~~

mattbar_norm({1-occuR(:,1)',1-occuR(:,2)'})
a1 = axis;a1(3) = 0;a1(4) = 1;
axis(a1)
xticks([1,2])
xticklabels({'pre-training','post-training'})