Experiments DB Dev
78 lines
3.3 KiB
Matlab
78 lines
3.3 KiB
Matlab
% Variation learning-curve plots, in the style of the paper:
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% "Lines indicate mean (and SEM) across animals in the anodal (red) and
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% control (blue) groups."
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% Produces TWO figures from this folder's data.csv:
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% learning_curve.png # successes (count) per training day
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% learning_curve_rate.png success rate (success/attempts) per training day
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% anodal / treatment = stim 1 (red); control = stim 0 (blue). Per-group N is
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% read from the data. Training day is 1-indexed (our day 0 = paper "Day 1") and
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% the x-axis tick labels are drawn vertically.
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% Run: matlab -batch "plotcurve"
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% (Copy of analysis/matlab/variation_plot.m; see make_variation_plot.m.)
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here = fileparts(mfilename('fullpath'));
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if isempty(here); here = pwd; end
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vname = regexprep(here, '.*[/\\]', '');
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D = readtable(fullfile(here, 'data.csv'), 'TextType', 'string');
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days = unique(D.day);
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xd = days + 1; % plot as 1-indexed training day (paper axis)
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red = [0.85 0.10 0.10];
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blue = [0.10 0.30 0.85];
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localPlot(D, days, xd, 'count', '# successes', ...
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fullfile(here, 'learning_curve.png'), vname, red, blue);
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localPlot(D, days, xd, 'rate', 'success rate', ...
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fullfile(here, 'learning_curve_rate.png'), vname, red, blue);
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% ------------------------------------------------------------------ helpers
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function localPlot(D, days, xd, metric, ylab, outFile, vname, red, blue)
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[Ma, Sa, na] = localCurve(D, 1, days, metric); % anodal / treatment
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[Mc, Sc, nc] = localCurve(D, 0, days, metric); % control
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fig = figure('Visible', 'off', 'Color', 'w', 'Position', [100 100 560 460]);
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hold on
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e1 = errorbar(xd, Ma, Sa, '-o', 'Color', red, 'MarkerFaceColor', red, 'LineWidth', 2);
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e2 = errorbar(xd, Mc, Sc, '-o', 'Color', blue, 'MarkerFaceColor', blue, 'LineWidth', 2);
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hold off
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legend([e1 e2], {sprintf('anodal, N = %d', na), sprintf('control, N = %d', nc)}, ...
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'Location', 'northwest', 'Box', 'off');
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xlabel('training day'); ylabel(ylab);
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title(vname, 'Interpreter', 'none');
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% Compact publication layout with a tight centred plot box and ticks pointing
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% out. Setting XTickLabel {} then xticklabels('auto') resets the labels cleanly
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% after the Position change.
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set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.2 0.30 0.60 0.60], ...
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'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
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xticklabels('auto')
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if strcmp(metric, 'count') % success count: y-ticks every 5 units
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yl = ylim(gca);
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set(gca, 'YTick', floor(yl(1) / 5) * 5 : 5 : ceil(yl(2) / 5) * 5);
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end
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grid on
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drawnow;
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exportgraphics(fig, outFile, 'Resolution', 150);
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close(fig);
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fprintf('%s: wrote %s (anodal N=%d, control N=%d)\n', vname, outFile, na, nc);
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end
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function [M, S, n] = localCurve(D, stimVal, days, metric)
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%LOCALCURVE Per-day mean and SEM across the animals in a group, for a metric.
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subs = unique(D.subject(D.stim == stimVal));
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n = numel(subs);
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X = nan(numel(days), n);
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for j = 1:n
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for i = 1:numel(days)
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r = D.subject == subs(j) & D.day == days(i);
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if ~any(r); continue; end
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if strcmp(metric, 'rate')
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tot = sum(D.total(r));
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if tot > 0; X(i, j) = sum(D.success(r)) / tot; end
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else
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X(i, j) = mean(D.success(r));
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end
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end
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end
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M = mean(X, 2, 'omitnan');
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S = std(X, [], 2, 'omitnan') ./ sqrt(sum(~isnan(X), 2));
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end
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