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feat(matlab): learning figure + methods write-up + organized CSV data export

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Adds make_figure (2-panel learning curves + per-phase slopes, colorblind-safe
Box-B2/Box-A2 palette) -> results/figure_learning.png; analysis/writeup.md
(figure + methods + results summary); and tdcs_export_all/run_export exporting
every data variation into an organized export/ tree (curated_all, scenarios/9,
anchors/3 with tDCS factor, phases/9 + MANIFEST). Adds tExport tests. Suite 36/36.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Experiments DB Dev
2026-07-20 12:11:37 -04:00
parent e730aa0020
commit 800189371b
30 changed files with 2535 additions and 0 deletions
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analysis/matlab/tests/tExport.m
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classdef tExport < matlab.unittest.TestCase
%TEXPORT Tests for tdcs_export_all (organized CSV export of all variations).
methods (Test)
function testExportTreeAndContents(testCase)
outRoot = tempname;
files = tdcs_export_all(outRoot);
testCase.verifyGreaterThanOrEqual(numel(files), 22); % 1 + 9 + 3 + 9
% curated: 12 animals
Tc = readtable(fullfile(outRoot, 'curated_all.csv'), 'TextType', 'string');
testCase.verifyEqual(numel(unique(Tc.subject)), 12);
% scenario mergeB2_full: Box-B2 has 5 subjects
Sb = readtable(fullfile(outRoot, 'scenarios', 'mergeB2_full.csv'), 'TextType', 'string');
nB2 = numel(unique(Sb.subject(Sb.group == "Electrode-Box-B2")));
testCase.verifyEqual(nB2, 5);
% anchor file carries a binary tDCS factor with both levels
A = readtable(fullfile(outRoot, 'anchors', 'mergeA2_B2vsA2.csv'));
testCase.verifyEqual(sort(unique(A.tDCS))', [0 1]);
% phases folder holds 3 merges x 3 phases = 9 files, each with dayp
ph = dir(fullfile(outRoot, 'phases', '*.csv'));
testCase.verifyEqual(numel(ph), 9);
P = readtable(fullfile(outRoot, 'phases', 'mergeA2_phase_6-13.csv'));
testCase.verifyTrue(ismember('dayp', P.Properties.VariableNames));
testCase.verifyGreaterThanOrEqual(min(P.dayp), 0);
end
end
end