Experiments DB Dev
0e9263b958
Add tdcs_model_summary as the single place that renders a fitted model verbatim (disp(model) with the Command-Window <strong> markup stripped), and route every report through it so all scenarios emit the complete model-fitting output: - tdcs_report: adds the learning-rate GLMM's full summary (count/rate already had theirs); localCleanDisp now delegates to the shared helper. - tdcs_lme_report / tdcs_paper_lme: embed the full fitlme summary before the curated effect table. - tdcs_phase_lme: appends each phase's full fitlme summary. Tests: tReport asserts 3 native summaries in a GLMM report; tLme asserts the lme_*/paper_*/phase_* reports embed 1/1/3 summaries with markup stripped. Suite 39/39. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
56 lines
2.5 KiB
Matlab
56 lines
2.5 KiB
Matlab
classdef tReport < matlab.unittest.TestCase
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%TREPORT Tests for tdcs_glm / tdcs_report (Task 4: entry point + report).
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%
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% MATLAB R2025b + Statistics and Machine Learning Toolbox IS licensed
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% in this environment, so this suite is actually run. It does not
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% re-check golden values (tModels already does that); it checks the
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% wiring: tdcs_glm(scenario) runs end-to-end without error and writes
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% results/<scenario>.txt, and the otherwise-branch errors on an
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% unrecognized scenario name.
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methods (Test)
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function testRunsScenarioAndWritesResultFile(testCase)
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thisDir = fileparts(mfilename('fullpath'));
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resultsDir = fullfile(fileparts(thisDir), 'results');
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outFile = fullfile(resultsDir, 'mergeB2_full.txt');
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if exist(outFile, 'file')
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delete(outFile);
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end
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tdcs_glm('mergeB2_full');
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testCase.verifyTrue(exist(outFile, 'file') == 2, ...
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'tdcs_glm did not write results/mergeB2_full.txt.');
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txt = fileread(outFile);
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testCase.verifyNotEmpty(txt);
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testCase.verifyTrue(contains(txt, 'DESCRIPTIVES'));
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testCase.verifyTrue(contains(txt, 'INTERPRETATION'));
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testCase.verifyTrue(contains(txt, 'CAVEATS'));
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% Command-Window-only markup must be stripped from the report.
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testCase.verifyFalse(contains(txt, '<strong>'));
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% Full native GLMM summary is present for all three models
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% (count, rate, learning-rate): each disp() prints this header.
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testCase.verifyEqual(count(txt, 'Fixed effects coefficients (95% CIs):'), 3, ...
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'Expected the full fitglme summary for count, rate, and learning GLMMs.');
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end
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function testUnmergedScenarioIncludesClassification(testCase)
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thisDir = fileparts(mfilename('fullpath'));
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resultsDir = fullfile(fileparts(thisDir), 'results');
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outFile = fullfile(resultsDir, 'unmerged_full.txt');
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tdcs_glm('unmerged_full');
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txt = fileread(outFile);
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testCase.verifyTrue(contains(txt, 'Classification'));
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testCase.verifyTrue(contains(txt, 'Electrode-Box-A'));
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testCase.verifyTrue(contains(txt, 'Right-Electrode'));
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end
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function testBadScenarioErrors(testCase)
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testCase.verifyError(@() tdcs_glm('not_a_real_scenario'), ...
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'tdcs_glm:badScenario');
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end
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end
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end
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