edges = -2:1/fs:3;
figure
boundedline(edges(1:end-1), mean(prePSTCa,2), std(prePSTCa,[],2)/sqrt(size(prePSTCa,2)), 'k-', ...,
    edges(1:end-1), mean(postPSTCa,2), std(postPSTCa,[],2)/sqrt(size(postPSTCa,2)), 'r-')
xlim([-1 2])
legend('prelearning', 'postlearning')
ylabel('dF/F0 (%)')
xlabel('Time (s)')
title('Mouse 16D')


% Reward only
figure
boundedline(edges(1:end-1), mean(rewardPSTCa,2), std(rewardPSTCa,[],2)/sqrt(size(rewardPSTCa,2)), 'g-')
legend('Reward only')
ylabel('dF/F0 (%)')
xlabel('Time (s)')
title('Mouse 16D')
xlim([-1 2])







%% Statistics
% Mean calcium amplitudes (between t = 0 and 1.5 s after stimulus) before and after learning were compared by Mann-Whitney U test.

[p h] = ranksum(mean(Mouse15C.prePSTCa(61:105,:)), mean(Mouse15C.postPSTCa(61:105,:)))

[p h] = ranksum(mean(Mouse16D.prePSTCa(61:105,:)), mean(Mouse16D.postPSTCa(61:105,:)))


[p h] = ranksum(mean(Mouse31F.prePSTCa(61:105,:)), mean(Mouse31F.postPSTCa(61:105,:)))







%% Pooled
prePSTCa = [Mouse15C.prePSTCa Mouse16D.prePSTCa Mouse31F.prePSTCa];
postPSTCa = [Mouse15C.postPSTCa Mouse16D.postPSTCa Mouse31F.postPSTCa];
[p h] = ranksum(mean(prePSTCa(61:105,:)), mean(postPSTCa(61:105,:)))

rewardPSTCa = [Mouse15C.rewardPSTCa Mouse16D.rewardPSTCa Mouse31F.rewardPSTCa];
[p h] = signrank(mean(rewardPSTCa(31:60,:)), mean(rewardPSTCa(61:105,:)))