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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

Added

  • Rust FFI documentation with PyO3 binding patterns and memory management examples
  • Jupyter integration guide with interactive visualization examples
  • Visualization examples using matplotlib for manifold analysis
  • Data export utilities for web (JSON, WebGL, Three.js formats)
  • Comprehensive CI/CD workflows for multi-platform builds
  • Release checklist and version management documentation
  • Ecosystem diagram showing repository relationships
  • Use case examples for games, ML, science, robotics, CAD, and finance
  • Quick reference card for rapid API lookup
  • Research and publications documentation

Changed

  • Enhanced production documentation with additional debugging guides

[0.1.0] - 2024-01-15

Added

  • Initial release of Python bindings for Constraint Theory
  • PythagoreanManifold class with snap() and snap_batch() methods
  • generate_triples() function for Pythagorean triple generation
  • snap() convenience function for one-off operations
  • NumPy array support for batch operations
  • Type hints for all public APIs
  • Protocol classes for duck typing support
  • Comprehensive test suite with compatibility tests
  • Basic documentation and examples

Features

  • O(log n) KD-tree lookup for single vector snapping (~100ns)
  • SIMD-optimized batch processing
  • GIL release for multi-threaded batch operations
  • Cross-platform deterministic results
  • Exact unit norm guarantee for snapped vectors

Supported Platforms

  • Linux x86_64
  • macOS ARM64 and x86_64
  • Windows x86_64

Python Support

  • Python 3.8, 3.9, 3.10, 3.11, 3.12

Release Notes

Version 0.1.0

Initial release providing deterministic geometric snapping for Python applications.

Key Features:

  • Snap 2D vectors to exact Pythagorean triple coordinates
  • Cross-platform reproducible results
  • High performance with Rust core engine
  • Easy NumPy integration

Performance:

  • Single snap: ~100 nanoseconds
  • Batch snap: ~30 nanoseconds per vector
  • Memory efficient: ~80KB for density=200

Use Cases:

  • Game physics (networked determinism)
  • Machine learning (reproducible augmentation)
  • Scientific computing (Monte Carlo reproducibility)
  • CAD/CAM (geometric precision)

Version History

Version Date Summary
0.1.0 2024-01-15 Initial release

Upgrade Guide

Upgrading to 0.1.0

This is the initial release. No upgrade path needed.

pip install constraint-theory

Roadmap

Planned for 0.2.0

  • 3D manifold support
  • Async/await API
  • Streaming batch processing
  • Memory-mapped large dataset support

Planned for 0.3.0

  • Extended quantization methods
  • GPU acceleration (optional)
  • Additional language bindings

Planned for 1.0.0

  • Stable API guarantee
  • Full documentation coverage
  • Enterprise support options