music-computing/amads
Jun 2026 – Jun 2026
1 Month active
Languages Used
MarkdownPythonTOML
Technical Skills
DocumentationGitHub ActionsJupyter NotebookJupyter NotebooksPythonPython programming
markgotham
PROFILE
Markgotham
Developed core music-analysis features for the music-computing/amads repository, focusing on modules such as Syncopation Span and Chord Bigram, each accompanied by robust tests and Jupyter Notebook demonstrations. Enhanced data visualization using matplotlib, refining plotting behavior for clarity and usability. Improved project documentation and onboarding through updated docstrings, style corrections, and expanded technical guides. Addressed bugs related to chord reversal rules, MIDI value handling, and absolute value computations, ensuring reliable backend functionality. Leveraged Python and Markdown to implement new discovery features, including degradation experiments and pitch analysis tools, while maintaining strong test coverage and project scaffolding for long-term maintainability.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
34Total
Bugs
6
Commits
34
Features
14
Lines of code
66,828
Activity Months1
Your Network
23 peopleWork History
June 2026
34 Commits • 14 Features
Jun 1, 2026June 2026 focused on delivering core music-analysis features, strengthening test coverage and documentation, and improving visualization for faster insight generation in music-computing/amads. Key outcomes include a new Syncopation Span module with tests, an initial chord_bigram prototype with tests and notebooks, and substantial plotting and UX refinements, underpinned by improved docs, scaffolding, and maintainability work.
34 Commits • 14 Features
Jun 1, 2026June 2026 focused on delivering core music-analysis features, strengthening test coverage and documentation, and improving visualization for faster insight generation in music-computing/amads. Key outcomes include a new Syncopation Span module with tests, an initial chord_bigram prototype with tests and notebooks, and substantial plotting and UX refinements, underpinned by improved docs, scaffolding, and maintainability work.
June 2026
Activity
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Quality Metrics
Correctness94.2%
Maintainability92.4%
Architecture93.0%
Performance91.8%
AI Usage21.8%
Skills & Technologies
Programming Languages
MarkdownPythonTOML
Technical Skills
DocumentationGitHub ActionsJupyter NotebookJupyter NotebooksPythonPython programmingPython scriptingTestingalgorithm designbackend developmentdata analysisdata structuresdata visualizationdocumentationmatplotlib
Repositories Contributed To
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