PyData NYC whatsnew NumPy-SciPy 2019
- 1. NumPy & SciPy What’s now? Ralf Gommers PyData NYC 2019
- 2. NumPy 1.17 - the most significant release in years ◎New random number generators (a complete overhaul of numpy.random) ◎New Fourier transform implementations (a complete overhaul of numpy.fft) ◎The __array_function__ protocol became enabled by default. 2
- 3. Vastly improved random number generators Multiple new generators, faster & higher statistical quality. Plus: • Parallel/distributed capabilities (3 methods) • Extensible infrastructure - plug in your own bit-generator • Can be used from Numba, and (in 1.18) from Cython or C 3
- 4. Vastly improved random number generators 4
- 5. Vastly improved random number generators 5
- 6. The NumPy array protocols - goals 6 Separate NumPy API from NumPy “execution engine” Allow other libraries (Dask, CuPy, PyTorch, …) to reuse the NumPy API Bigger picture: avoid or reduce ecosystem fragmentation (we don’t want to see a reimplementation of SciPy for PyTorch, SciPy for Tensorflow, etc.)
- 7. The NumPy array protocols - goals 7
- 8. The NumPy array protocols - goals 8
- 9. Array protocols — using the NumPy API with GPUs 9
- 10. Array protocols — using the NumPy API with distributed arrays via Dask 10
- 11. Fourier transforms - faster & more accurate All FFT functions in NumPy and SciPy reimplemented. 11
- 12. scipy.fft — first new SciPy submodule in a decade • API changes for consistency with NumPy • Based on PyPocketfft (modern C++, close to performance of FFTW) • Bluestein algorithm: accurate & fast also for prime-length arrays • Context managers for backend selection and multi-threading support 12
- 13. scipy.fft — first new SciPy submodule in a decade 13
- 14. More robust new global optimizers optimize.shgo, optimize.dual_annealing 14