fix(matlab): adopt user's compact plot layout for all variations

Port the unmerge_d0_5 fix (b6dc69c) into variation_plot.m: tight centred plot
box (Position [0.2 0.30 0.60 0.60]), TickDir out, and xticklabels('auto') --
which keeps x labels horizontal when they fit (<=~10 days) and auto-rotates
them when crowded (e.g. 14-day windows). Replaces the manual rotated-text hack.
Regenerated all learning-curve figures; removed the stray plotcurve.asv and
gitignored *.asv.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

.gitignore

@@ -29,3 +29,4 @@ playwright-report/
 # OS
 .DS_Store
 Thumbs.db
+*.asv

analysis/matlab/variation_plot.m

@@ -38,21 +38,12 @@ legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)},
     'Location', 'northwest', 'Box', 'off');
 xlabel('training day'); ylabel(ylab);
 title(vname, 'Interpreter', 'none');
-% Vertical x-axis tick labels. XTickLabelRotation / xtickangle are NOT honored
+% Compact publication layout with a tight centred plot box and ticks pointing
-% by exportgraphics on a headless invisible figure, so draw the labels manually
+% out. Setting XTickLabel {} then xticklabels('auto') resets the labels cleanly
-% as rotated text objects (which always render).
+% after the Position change.
-set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.13 0.20 0.80 0.70], ...
+set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.2 0.30 0.60 0.60], ...
-    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off');
+    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
-drawnow;
+xticklabels('auto')
-yl = ylim(gca);
-yb = yl(1) - 0.015 * (yl(2) - yl(1));
-for k = 1:numel(xd)
-    text(xd(k), yb, num2str(xd(k)), 'Parent', gca, 'Rotation', 90, ...
-        'HorizontalAlignment', 'right', 'VerticalAlignment', 'middle', ...
-        'FontName', 'Arial', 'FontSize', 13, 'Clipping', 'off');
-end
-xlh = get(gca, 'XLabel');            % push the axis label below the vertical ticks
-set(xlh, 'Units', 'normalized', 'Position', [0.5 -0.15 0]);
 drawnow;
 exportgraphics(fig, outFile, 'Resolution', 150);
 close(fig);

analysis/matlab/variations/unmerge_d0_5/plotcurve.asv

@@ -1,79 +0,0 @@
-% Variation learning-curve plots, in the style of the paper:
-%   "Lines indicate mean (and SEM) across animals in the anodal (red) and
-%    control (blue) groups."
-% Produces TWO figures from this folder's data.csv:
-%   learning_curve.png       # successes (count)   per training day
-%   learning_curve_rate.png  success rate (success/attempts) per training day
-% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
-% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
-% the x-axis tick labels are drawn vertically.
-% Run:  matlab -batch "plotcurve"
-% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
-
-here = fileparts(mfilename('fullpath'));
-if isempty(here); here = pwd; end
-vname = regexprep(here, '.*[/\\]', '');
-
-D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
-days = unique(D.day);
-xd = days + 1;                    % plot as 1-indexed training day (paper axis)
-red = [0.85 0.10 0.10];
-blue = [0.10 0.30 0.85];
-
-localPlot(D, days, xd, 'count', '# successes', ...
-    fullfile(here, 'learning_curve.png'), vname, red, blue);
-localPlot(D, days, xd, 'rate', 'success rate', ...
-    fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
-
-% ------------------------------------------------------------------ helpers
-function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
-[Ma, Sa, na] = localCurve(D, 1, days, metric);   % anodal / treatment
-[Mc, Sc, nc] = localCurve(D, 0, days, metric);   % control
-fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
-hold on
-e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
-e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWidth', 2);
-hold off
-legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
-    'Location', 'northwest', 'Box', 'off');
-xlabel('training day'); ylabel(ylab);
-title(vname, 'Interpreter', 'none');
-
-set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.13 0.20 0.80 0.70], ...
-    'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
-drawnow;
-yl = ylim(gca);
-yb = yl(1) - 0.015 * (yl(2) - yl(1));
-for k = 1:numel(xd)
-    text(xd(k), yb, num2str(xd(k)), 'Parent', gca, ...
-        'HorizontalAlignment', 'right', 'VerticalAlignment', 'middle', ...
-        'FontName', 'Arial', 'FontSize', 13, 'Clipping', 'off');
-end
-xlh = get(gca, 'XLabel');            % push the axis label below the vertical ticks
-set(xlh, 'Units', 'normalized', 'Position', [0.5 -0.15 0]);
-drawnow;
-exportgraphics(fig, outFile, 'Resolution', 150);
-close(fig);
-fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
-end
-
-function [M, S, n] = localCurve(D, stimVal, days, metric)
-%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
-subs = unique(D.subject(D.stim == stimVal));
-n = numel(subs);
-X = nan(numel(days), n);
-for j = 1:n
-    for i = 1:numel(days)
-        r = D.subject == subs(j) & D.day == days(i);
-        if ~any(r); continue; end
-        if strcmp(metric, 'rate')
-            tot = sum(D.total(r));
-            if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
-        else
-            X(i, j) = mean(D.success(r));
-        end
-    end
-end
-M = mean(X, 2, 'omitnan');
-S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
-end