NHP_VisualSearch_Pop-in/CODE/figfunctions/figure_binmod.m

function figure_binmod(fld) 

fprintf('\n======================================================\n');
fprintf('-- Creating Figure binmod --\n');
fprintf('======================================================\n');

loaddir = fullfile(fld.basedir,'results','figure_neurotrace');
savedir = fullfile(fld.basedir,'results','figure_binmod');
mkdir(savedir);

%% settings
green = [23, 105, 13]./255;
red = [201, 0, 34]./255;
cols = [green; 0.3 0.3 0.3; red; 1.00,0.84,0.00];

snrthres = 2.5; mindays = 3;

do_cleantraces = true; % if true we throw out artefact traces

bin.sz = 10; % ms
bin.minmax = [0 250];

%% plots
% load traces
monkeys = {'M1','M2'};

%% plot
f1 = figure;
set(f1,'Position',[500 500 1600 800]);
bonfcorr = true;

if do_cleantraces
    load(fullfile(loaddir,'Traces_SNR_trialbased.mat'),...
        's_traces_ALL','recsites','rsinc');
end

% individual animals
for mi = 1:2
    monkey = monkeys{mi};
    load(fullfile(loaddir, [monkey '_averages_snr' num2str(snrthres) ...
        '_mindays' num2str(mindays) '.mat']),...
        'traces','tracesLUT','env_t');

    % bin these traces
    bb_l = bin.minmax(1):bin.sz:bin.minmax(2);
    bb_r = bb_l+bin.sz;
    bb_c = (bb_l+bb_r)/2;
    
    if do_cleantraces
        tmp_traces=[]; tmp_LUT=[]; 
        for cond=1:3
            tmp_traces = [tmp_traces ; ...
                s_traces_ALL{mi,cond}(rsinc{mi,cond},:)];
            tmp_LUT = [tmp_LUT ; ...
                recsites{mi,cond}(rsinc{mi,cond}) ... 
                cond.*ones(sum(rsinc{mi,cond}),1)];
        end
        traces_ = traces; tracesLUT_ = tracesLUT;
        traces = tmp_traces; tracesLUT = tmp_LUT;
    end

    traces_bin = nan(size(traces,1),length(bb_c));
    for sidx = 1:size(traces,1)
        for bidx = 1:length(bb_l)
            bbool = (env_t.*1e3)>=bb_l(bidx) & (env_t.*1e3)<bb_r(bidx);
            traces_bin(sidx,bidx) = mean(traces(sidx,bbool));
        end
    end

    traces_ALL{mi} = traces_bin;
    tracesLUT_ALL{mi} = tracesLUT;
    env_t_ALL{mi} = bb_c;
    
    figure(f1)
    mns = []; sems = []; trcs = cell(0);
    
    subplot(2,3,mi+1); hold on;
    for cond = 1:3
        incl = tracesLUT(:,2)==cond;
        incl_ALL{mi,cond} = incl;
        
        % calculate the mean across channels
        get_traces = traces_bin(incl,:);
        s_traces = get_traces;
        s_traces_ALL{mi,cond} = s_traces;
        trcs{cond} = s_traces; % for calculating the modulation onset
    end
    df = trcs{1} - trcs{2}; % difference for each channel separately
    mn = mean(df);
    csem = std(trcs{1} - trcs{2})./sqrt(size(trcs{1},1));
    
    % stats (wrong but something)
    p=nan(1,size(df,2));
    for bidx = 1:size(df,2)
        [h,p(bidx)] = ttest(df(:,bidx));
    end

    if bonfcorr; bonf = size(df,2); else; bonf = 1; end

    bar(bb_c,mn,'FaceColor',cols(1,:),'BarWidth',1);
    bar(bb_c(p>0.05/bonf),mn(p>0.05/bonf),...
        'FaceColor',[.5 .5 .5],'BarWidth',1);
    errorbar(bb_c,mn,csem,'LineStyle','none','Color','k');
    title([monkeys{mi} ' TARG']); xlabel('time(ms)'); ylabel('dMUA');
    set(gca,'ylim',[-0.04 0.14]);

    firstsig = find(p<0.05/bonf & mn>0,1,'first');
    fprintf(['M' num2str(mi) '-T: First sign. positive bin at ' ...
        num2str(bb_c(firstsig)) '\n'])

    subplot(2,3,mi+4); hold on;
    df = trcs{3} - trcs{2}; % difference for each channel separately
    mn = mean(df);
    csem = std(trcs{3} - trcs{2})./sqrt(size(trcs{3},1));

    % stats (wrong but something)
    p=nan(1,size(df,2));
    for bidx = 1:size(df,2)
        [h,p(bidx)] = ttest(df(:,bidx));
    end

    if bonfcorr; bonf = size(df,2); else; bonf = 1; end

    bar(bb_c,mn,'FaceColor',cols(3,:),'BarWidth',1); 
    bar(bb_c(p>0.05/bonf),mn(p>0.05/bonf),...
        'FaceColor',[.5 .5 .5],'BarWidth',1);
    errorbar(bb_c,mn,csem,'LineStyle','none','Color','k');
    title([monkeys{mi} ' SD']); xlabel('time(ms)'); ylabel('dMUA');
    set(gca,'ylim',[-0.12 0.05]);

    firstsig = find(p<0.05/bonf & mn>0,1,'first');
    fprintf(['M' num2str(mi) '-SD: First sign. positive bin at ' ...
        num2str(bb_c(firstsig)) '\n'])
end

% pooled animals
for cond = 1:3
    incl = [incl_ALL{1,cond}; incl_ALL{2,cond}];
    
    % calculate the mean across channels
    traces = [traces_ALL{1}; traces_ALL{2}];
    get_traces = traces(incl,:);
    s_traces = get_traces;
    trcs{cond} = s_traces; % for calculating the modulation onset
end

inRF = {'target','','distractor'};
    
subplot(2,3,1);hold on
df = trcs{1} - trcs{2}; % difference for each channel separately
mn = mean(df);
csem = std(trcs{1} - trcs{2})./sqrt(size(trcs{1},1));

% stats (wrong but something)
p=nan(1,size(df,2));
for bidx = 1:size(df,2)
    [h,p(bidx)] = ttest(df(:,bidx));
end

if bonfcorr; bonf = size(df,2); else; bonf = 1; end

bar(bb_c,mn,'FaceColor',cols(1,:),'BarWidth',1);
bar(bb_c(p>0.05/bonf),mn(p>0.05/bonf),...
    'FaceColor',[.5 .5 .5],'BarWidth',1);
errorbar(bb_c,mn,csem,'LineStyle','none','Color','k');
title('BOTH monkeys TARG'); xlabel('time(ms)'); ylabel('dMUA');
set(gca,'ylim',[-0.04 0.14]);

firstsig = find(p<0.05/bonf & mn>0,1,'first');
fprintf(['POOLED-T: First sign. positive bin at ' ...
    num2str(bb_c(firstsig)) '\n'])

subplot(2,3,4);hold on
df = trcs{3} - trcs{2}; % difference for each channel separately
mn = mean(df);
csem = std(trcs{3} - trcs{2})./sqrt(size(trcs{3},1));

% stats (wrong but something)
p=nan(1,size(df,2));
for bidx = 1:size(df,2)
    [h,p(bidx)] = ttest(df(:,bidx));
end

if bonfcorr; bonf = size(df,2); else; bonf = 1; end

bar(bb_c,mn,'FaceColor',cols(3,:),'BarWidth',1);
bar(bb_c(p>0.05/bonf),mn(p>0.05/bonf),...
    'FaceColor',[.5 .5 .5],'BarWidth',1);
errorbar(bb_c,mn,csem,'LineStyle','none','Color','k');
title('BOTH monkeys SD'); xlabel('time(ms)'); ylabel('dMUA');
set(gca,'ylim',[-0.12 0.05]);
text(gca,10,-0.1,'Colored bars: p<0.05, Bonf. corr.')

firstsig = find(p<0.05/bonf & mn>0,1,'first');
fprintf(['POOLED-SD: First sign. positive bin at ' ...
    num2str(bb_c(firstsig)) '\n'])

%% save figure
figure(f1)
saveas(gcf,fullfile(savedir,'figure_binmod.svg'));