Experiments DB Dev
83 lines
3.5 KiB
Matlab
83 lines
3.5 KiB
Matlab
% 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'); ylh = ylabel(ylab);
|
|
title(vname, 'Interpreter', 'none');
|
|
% Compact publication layout with a tight centred plot box and ticks pointing
|
|
% out. Setting XTickLabel {} then xticklabels('auto') resets the labels cleanly
|
|
% after the Position change.
|
|
set(gca, 'XTick', xd, 'XTickLabel', {}, 'Position', [0.2 0.30 0.60 0.60], ...
|
|
'FontName', 'Arial', 'FontSize', 13, 'LineWidth', 1.5, 'Box', 'off', 'TickDir', 'out');
|
|
xticklabels('auto')
|
|
set(gca, 'XTickLabelRotation', 0); % keep x labels horizontal (no auto-rotate)
|
|
if strcmp(metric, 'count') % success count: y-ticks every 5 units
|
|
yl = ylim(gca);
|
|
set(gca, 'YTick', floor(yl(1) / 5) * 5 : 5 : ceil(yl(2) / 5) * 5);
|
|
end
|
|
grid on
|
|
% Add breathing room between the y-axis label and the tick numbers.
|
|
set(ylh, 'Units', 'normalized');
|
|
yp = get(ylh, 'Position');
|
|
set(ylh, 'Position', [yp(1) - 0.03, yp(2), 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
|