function [meanlum, stdlum] = get_mean_std_lum_per_neuron(lummat, plotyn)
[Nneuron, Ntime] = size(lummat);
if plotyn
f = figure;
end
meanlum = nan(1,Nneuron);
stdlum = nan(1,Nneuron);
for nn=1:Nneuron
if plotyn
subplot(2,2,1)
hold all
plot(lummat(nn,:))
xlabel('time step')
ylabel('\Delta F / F')
title('\Delta F / F over time')
subplot(2,2,2)
hold all
[N,e] = histcounts(lummat(nn,:), 'Normalization','pdf');
e = e(2:end) - (e(2)-e(1))/2;
plot(e, N);
xlabel('\Delta F / F')
ylabel('# measurements')
title('Distribution \Delta F / F for each neuron')
end
meanlum(nn) = nanmean(lummat(nn,:));
stdlum(nn) = nanstd(lummat(nn,:));
end
if plotyn
subplot(2,2,3)
Y= nanmean(lummat);
x = 1:length(Y);
Y_err = nanstd(lummat);
errmat = [Y'-Y_err' 2*Y_err']; %matrix for errorbars
plot(Y, 'b', 'Linewidth', 2) %main plot
hold on
g=area(x, errmat); %error bars
set(g(1),'FaceColor', 'none', 'EdgeColor', 'none') %make lower area-plot invisible
set(g(2),'FaceColor', 'b', 'EdgeColor', 'none') %colour for main error fill-in
alpha(0.3); %make transparent (alpha 1 makes opaque,0 is completely see-through)
xlabel('time step')
ylabel('\Delta F / F')
title('Mean / std \Delta F / F over time')
subplot(2,4,7)
histogram(meanlum)
title('Distribution (neurons) mean lum value')
xlabel('Mean \Delta F / F value')
subplot(2,4,8)
histogram(stdlum)
title('Distribution (neurons) std lum value')
xlabel('STD \Delta F / F value')
end