analysis(matlab): add log(day) model + Cohen's f + power to every variation

Add variation_logpower.m (self-contained) + make_variation_logpower.m,
dropping logpowersim.m + logpower_result.txt into all 28 variation folders.
Matches the paper's power code: models behavior ~ stim + log(day) +
stim:log(day) + (1|rat) (log(day+1), since our day 0 = paper Day 1), reports
Cohen's f (partial eta^2 of the interaction) and the interaction under
residual/Satterthwaite/honest random-slope DF, then runs the Monte-Carlo power
sim on the log-day ground truth (per-animal cluster-honest + LME power).

Notable: under log(day) the accumulating divergence is captured more sharply,
so several full-window scenarios reach honest significance that were n.s. under
raw day (e.g. right_only_d0_13 honest p=0.003, unmerge_d0_13 0.021,
unmerge_d0_10 0.036); Cohen's f is small-medium (~0.10-0.34).

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

analysis/matlab/make_variation_logpower.m

@@ -0,0 +1,28 @@
+function make_variation_logpower()
+%MAKE_VARIATION_LOGPOWER Add the log-day model + Cohen's f + power to each variation.
+%   MAKE_VARIATION_LOGPOWER() copies variation_logpower.m into every
+%   variations/<name>/ folder with a data.csv (as logpowersim.m) and runs it,
+%   producing logpower_result.txt. Re-runnable; picks up new folders.
+
+thisDir = fileparts(mfilename('fullpath'));
+template = fullfile(thisDir, 'variation_logpower.m');
+root = fullfile(thisDir, 'variations');
+
+d = dir(root);
+n = 0;
+for i = 1:numel(d)
+    if ~d(i).isdir || ismember(d(i).name, {'.', '..'}); continue; end
+    folder = fullfile(root, d(i).name);
+    if ~exist(fullfile(folder, 'data.csv'), 'file'); continue; end
+    copyfile(template, fullfile(folder, 'logpowersim.m'));
+    fprintf('\n### %s ###\n', d(i).name);
+    localRun(fullfile(folder, 'logpowersim.m'));
+    n = n + 1;
+end
+fprintf('\nAdded logpowersim.m + logpower_result.txt to %d variation folders.\n', n);
+
+end
+
+function localRun(scriptPath)
+run(scriptPath);
+end