Experiments DB Dev
a2a812acc8
Add variation_plot.m + make_variation_plot.m, dropping plotcurve.m +
learning_curve.png into all 28 variation folders. Each figure plots mean +/-
SEM successful reaches per training day for the anodal/treatment group (red)
vs control (blue), in the style of the paper ("Lines indicate mean (and SEM)
across animals in the anodal (red) and control (blue) groups"). Per-group N is
read from the data and shown in the legend; training day is 1-indexed (our day
0 = paper Day 1). Headless via exportgraphics. prev_f_full reproduces the
paper's own figure (anodal N=12 vs control N=12).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
29 lines
1008 B
Matlab
29 lines
1008 B
Matlab
function make_variation_plot()
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%MAKE_VARIATION_PLOT Add a paper-style learning-curve figure to each variation.
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% MAKE_VARIATION_PLOT() copies variation_plot.m into every variations/<name>/
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% folder with a data.csv (as plotcurve.m) and runs it, producing
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% learning_curve.png (mean +/- SEM successes per training day, anodal red vs
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% control blue). Re-runnable; picks up new folders.
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thisDir = fileparts(mfilename('fullpath'));
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template = fullfile(thisDir, 'variation_plot.m');
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root = fullfile(thisDir, 'variations');
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d = dir(root);
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n = 0;
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for i = 1:numel(d)
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if ~d(i).isdir || ismember(d(i).name, {'.', '..'}); continue; end
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folder = fullfile(root, d(i).name);
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if ~exist(fullfile(folder, 'data.csv'), 'file'); continue; end
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copyfile(template, fullfile(folder, 'plotcurve.m'));
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localRun(fullfile(folder, 'plotcurve.m'));
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n = n + 1;
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end
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fprintf('\nAdded plotcurve.m + learning_curve.png to %d variation folders.\n', n);
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end
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function localRun(scriptPath)
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run(scriptPath);
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end
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