trainlytics 0.1.0

Open-source Python package for workout analysis and recommendations.

trainlytics

Overview

Open-source Python package for physical training analysis and recommendations. Ingests workout logs, computes metrics, detects plateaus, and suggests weekly progressions. Professional structure, automation, tests, and robust documentation.

• Target audience: Data scientists, fitness professionals, enthusiasts, and developers.
• Highlights: Professional structure, strong documentation, tests, CI/CD, reproducible data and reports.

Quick Demo

Installation

pip install trainlytics

Quickstart

from trainlytics import ingest, clean, features, model, recommender
# See notebooks/ for complete examples

Project Structure

• src/trainlytics/: Core package modules
• notebooks/: EDA, modeling, reporting
• tests/: Unit and integration tests
• docs/: Documentation, changelog, references
• .github/workflows/: CI/CD
• data/: Sample data

Sample Data

• CSV format: data/sample_workout_log.csv
• Schema: date,exercise,sets,reps,weight
• Note: Example data only, for educational purposes.

How to Use

1. Install dependencies: pip install -r requirements.txt
2. Activate the virtual environment: source .venv/bin/activate (Linux/Mac) or .venv\Scripts\Activate.ps1 (Windows)
3. Run notebooks in notebooks/ for full examples.
4. Run tests: PYTHONPATH=src pytest tests (Linux/Mac) or $env:PYTHONPATH="$(Resolve-Path .\src)"; pytest tests (Windows)

Automation & Tasks

• CI/CD: Lint, test, build, and publish artifacts via GitHub Actions
• VS Code tasks: Lint, format, test (see .vscode/tasks.json)

FAIR & Reproducibility

• Versioned data and code
• Notebooks with controlled outputs
• Metadata and citation in CITATION.cff

Roadmap

• Initial structure
• Ingestion and cleaning modules
• Modeling and recommendation
• Micro-app and reporting
• Tests and coverage

How to Cite

See CITATION.cff for BibTeX/APA.

How to Contribute

See CONTRIBUTING.md and open a PR!

License

MIT