# NumRS2 Implementation Status
This document provides an overview of the current implementation status of NumRS2, highlighting recent progress and next steps.
## Recent Accomplishments
### Documentation
- ✅ Comprehensive API documentation for all functions
- ✅ Detailed guides and tutorials mirroring NumPy's documentation
- User Guide covering all major functionality
- Module-specific guides (Random, Statistics, Linear Algebra, Polynomial, FFT)
- ✅ Project infrastructure documentation
- Contributing guide
- Getting started guide
- Installation guide
- Benchmarking guide
- Release process documentation
- Code of conduct
- Governance model
- NumPy migration guide
### Testing
- ✅ Property-based testing
- Distribution statistical properties
- Relationship tests between distributions
- Linear algebra operation properties
- Special functions properties
- ✅ Reference testing
- Reference value tests for random distributions
- Reference value tests for linear algebra operations
- Reference value tests for special functions
- ✅ Benchmarking
- Linear algebra benchmarks
- Special functions benchmarks
## Current Focus Areas
### Testing
- [ ] Property tests for FFT operations
- [ ] Core array operations benchmarks
- [ ] Comparison benchmarks against NumPy/SciPy
### Development Practices
- [ ] Zero warnings approach with clippy checks
- [ ] Add clippy checks to CI workflow
- [ ] Test-driven development approach
- [ ] Efficient code review process
### Performance Optimization
- [ ] GPU acceleration for applicable operations
## Next Steps
1. **Complete the testing framework**
- Implement property tests for FFT operations
- Create benchmarks for core array operations
- Develop comparison benchmarks against NumPy/SciPy
2. **Improve development processes**
- Establish zero warnings policy with clippy
- Add clippy checks to CI workflow
- Formalize code review process
3. **Explore advanced optimizations**
- Investigate GPU acceleration opportunities
- Research differentiable programming capabilities
- Explore distributed array operations
## Complete Features
NumRS2 has successfully implemented the following major features:
- ✅ N-dimensional array implementation with broadcasting
- ✅ Linear algebra operations with BLAS/LAPACK integration
- ✅ Matrix decompositions (SVD, QR, LU, Cholesky, Schur)
- ✅ Eigenvalue calculations
- ✅ Polynomial functions and interpolation
- ✅ Fast Fourier Transform (FFT) implementation
- ✅ Sparse matrix support
- ✅ SIMD support for vectorized operations
- ✅ Parallel computation with workload-adaptive scheduling
- ✅ Mathematical and statistical functions
- ✅ Modern random number generation with thread safety
- ✅ Special functions (erf, gamma, Bessel functions)
- ✅ Array comparison operations
- ✅ Shape manipulation operations
- ✅ Array serialization/deserialization
- ✅ Masked arrays for handling missing data
- ✅ Matrix library with special matrices
- ✅ Memory-mapped arrays for large datasets
- ✅ Memory layout optimization for cache efficiency
- ✅ Custom memory allocators for numerical workloads
## Contribution Opportunities
If you're interested in contributing to NumRS2, here are some areas where help would be appreciated:
1. **Testing**: Implementing property tests and reference tests for special functions and FFT operations
2. **Benchmarking**: Creating comprehensive benchmarks comparing NumRS2 to NumPy
3. **Documentation**: Writing docstring examples and creating example notebooks
4. **Performance**: Exploring GPU acceleration and other hardware optimizations
5. **Tools**: Developing development tools like clippy checks and CI enhancements
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details on how to contribute.