%MAKE_FIGURE Two-panel learning figure (Box-B2 vs Box-A2), saved to results/.
%   Panel A: mean +/- SEM success per training day over the fair 0-13 window,
%            with the fast/slow phase boundary. Panel B: per-animal learning
%            slope (mean +/- 95% CI) by phase. Uses the mergeA2 grouping.
%   Usage: matlab -batch "make_figure"

cfg = tdcs_config();
Sfull = tdcs_scenario_data('mergeA2_full');
T = Sfull(ismember(Sfull.group, {cfg.anchorLow, cfg.anchorHigh}) & Sfull.day <= 13, :);

B2 = [42 120 214]/255;   % #2a78d6  (categorical slot 1)
A2 = [0 131 0]/255;      % #008300  (categorical slot 2)
groups = {cfg.anchorHigh, cfg.anchorLow};
cols   = {B2, A2};
names  = {'Box-B2 (tDCS)', 'Box-A2 (control)'};
mk     = {'o', 's'};
ink    = [0.10 0.10 0.10];

f = figure('Visible', 'off', 'Position', [100 100 1150 460], 'Color', 'w');
tl = tiledlayout(f, 1, 2, 'Padding', 'compact', 'TileSpacing', 'compact');

% ---- Panel A: learning curves ----
ax1 = nexttile(tl); hold(ax1, 'on');
days = 0:13;
hLines = gobjects(1, 2);
for gi = 1:2
    G = T(T.group == groups{gi}, :);
    mu = nan(size(days)); se = nan(size(days));
    for d = days
        v = G.success(G.day == d);
        if ~isempty(v); mu(d+1) = mean(v); se(d+1) = std(v)/sqrt(numel(v)); end
    end
    ok = ~isnan(mu);
    fill(ax1, [days(ok) fliplr(days(ok))], [mu(ok)+se(ok) fliplr(mu(ok)-se(ok))], ...
        cols{gi}, 'FaceAlpha', 0.13, 'EdgeColor', 'none', 'HandleVisibility', 'off');
    hLines(gi) = plot(ax1, days(ok), mu(ok), '-', 'Color', cols{gi}, 'LineWidth', 2, ...
        'Marker', mk{gi}, 'MarkerFaceColor', cols{gi}, 'MarkerEdgeColor', cols{gi}, 'MarkerSize', 5);
end
yl = ylim(ax1);
xline(ax1, 5.5, '--', 'Color', [0.45 0.45 0.45], 'HandleVisibility', 'off');
text(ax1, 2.6, yl(2)*0.97, 'fast (0-5)', 'Color', ink, 'FontSize', 9, 'HorizontalAlignment', 'center');
text(ax1, 9.6, yl(2)*0.97, 'slow (6-13)', 'Color', ink, 'FontSize', 9, 'HorizontalAlignment', 'center');
xlabel(ax1, 'Training day (# Days Reach; day 0 = paper "Day 1")', 'Color', ink);
ylabel(ax1, 'Successful reaches / session', 'Color', ink);
title(ax1, 'A  Learning curves (mean \pm SEM)', 'Color', ink);
legend(ax1, hLines, names, 'Location', 'northwest', 'Box', 'off', 'TextColor', ink);
grid(ax1, 'on'); ax1.GridAlpha = 0.10; ax1.XColor = ink; ax1.YColor = ink; ax1.Box = 'off';
xlim(ax1, [-0.3 13.3]);

% ---- Panel B: per-animal slope by phase ----
ax2 = nexttile(tl); hold(ax2, 'on');
phases = {[0 5], [6 13]}; phaseX = [1 2]; off = 0.14;
hB = gobjects(1, 2);
for gi = 1:2
    for pi = 1:2
        sl = localSlopes(T, groups{gi}, phases{pi});
        m = mean(sl); ci = tinv(0.975, numel(sl)-1) * std(sl)/sqrt(numel(sl));
        x = phaseX(pi) + (2*gi-3)*off;   % gi=1 -> -off, gi=2 -> +off
        h = errorbar(ax2, x, m, ci, 'Marker', mk{gi}, 'Color', cols{gi}, ...
            'MarkerFaceColor', cols{gi}, 'MarkerEdgeColor', cols{gi}, 'MarkerSize', 8, ...
            'LineWidth', 1.8, 'CapSize', 8);
        if pi == 1; hB(gi) = h; end
    end
end
yline(ax2, 0, ':', 'Color', [0.6 0.6 0.6], 'HandleVisibility', 'off');
xticks(ax2, [1 2]); xticklabels(ax2, {'fast (0-5)', 'slow (6-13)'});
xlim(ax2, [0.5 2.5]);
ylabel(ax2, 'Learning slope (reaches / day)', 'Color', ink);
title(ax2, 'B  Per-animal learning rate by phase (mean, 95% CI)', 'Color', ink);
legend(ax2, hB, names, 'Location', 'northeast', 'Box', 'off', 'TextColor', ink);
grid(ax2, 'on'); ax2.GridAlpha = 0.10; ax2.XColor = ink; ax2.YColor = ink; ax2.Box = 'off';

thisDir = fileparts(mfilename('fullpath'));
resDir = fullfile(thisDir, 'results');
if ~exist(resDir, 'dir'); mkdir(resDir); end
outPng = fullfile(resDir, 'figure_learning.png');
exportgraphics(f, outPng, 'Resolution', 200);
fprintf('wrote %s\n', outPng);

function sl = localSlopes(T, grp, ph)
Tg = T(T.group == grp & T.day >= ph(1) & T.day <= ph(2), :);
subs = unique(Tg.subject); sl = [];
for i = 1:numel(subs)
    r = Tg.subject == subs(i);
    if numel(unique(Tg.day(r))) >= 2
        c = polyfit(Tg.day(r), Tg.success(r), 1); sl(end+1,1) = c(1); %#ok<AGROW>
    end
end
end