matlab-data-analysis

MATLAB Data Analysis

Use this skill for MATLAB analysis pipelines that start with data and end with validated numbers, figures, or reports.

Workflow

1. Inventory input files and schemas before coding.
2. Load data with typed APIs: readtable, readtimetable, matfile, datastore, or toolbox importers.
3. Normalize units, time zones, missing values, categorical fields, and outliers explicitly.
4. Build analysis as functions plus a thin runner script.
5. Save clean data, figures, and summary metrics under artifacts/.
6. Validate with shape checks, range checks, and at least one numerical assertion.

API Preferences

• Tables and timetables for labeled data.
• groupsummary, rowfun, varfun, synchronize, and retime for structured transformations.
• fitlm, fitnlm, fitrgp, fitcsvm, or fitcensemble only after checking toolbox availability.
• optimproblem or solver APIs for constrained fitting when simple regression is not enough.
• Export figures with explicit size, resolution, and format.

Reproducible Figures

Every reproduced paper figure should include:

• Source data provenance.
• Script name and command used.
• Random seed if applicable.
• Axis labels, units, legend, and saved image path.
• A simple metric comparing reproduced result with reference when possible.

Validation

Use checks such as:

assert(height(T) > 0)
assert(all(isfinite(metrics.rmse)))
assert(isfile(fullfile(outDir, "figure_1.png")))

Do not accept a plot as "done" unless the underlying numerical summary also looks sane.