Run blazingly fast stencil codes on numpy arrays.
pystencils uses sympy to define stencil operations, that can be executed on numpy arrays. Exploiting the stencil structure makes pystencils run faster than normal numpy code and even as Cython and numba, as demonstrated in this notebook.
Here is a code snippet that computes the average of neighboring cells:
import pystencils as ps import numpy as np f, g = ps.fields("f, g : [2D]") stencil = ps.Assignment(g[0, 0], (f[1, 0] + f[-1, 0] + f[0, 1] + f[0, -1]) / 4) kernel = ps.create_kernel(stencil).compile() f_arr = np.random.rand(1000, 1000) g_arr = np.empty_like(f_arr) kernel(f=f_arr, g=g_arr)
pystencils is mostly used for numerical simulations using finite difference or finite volume methods. It comes with automatic finite difference discretization for PDEs:
import pystencils as ps import sympy as sp c, v = ps.fields("c, v(2): [2D]") adv_diff_pde = ps.fd.transient(c) - ps.fd.diffusion(c, sp.symbols("D")) + ps.fd.advection(c, v) discretize = ps.fd.Discretization2ndOrder(dx=1, dt=0.01) discretization = discretize(adv_diff_pde)
pip install pystencils[interactive]
[interactive] you get a minimal version with very little dependencies.
gpu: use this if an NVIDIA GPU is available and CUDA is installed
alltrafos: pulls in additional dependencies for loop simplification e.g. libisl
bench_db: functionality to store benchmark result in object databases
interactive: installs dependencies to work in Jupyter including image I/O, plotting etc.
doc: packages to build documentation
Options can be combined e.g.
pip install pystencils[interactive, gpu, doc]
pystencils is also fully compatible with Windows machines. If working with visual studio and pycuda makes sure to run example files first to ensure that pycuda can find the compiler's executable.
Read the docs here and
check out the Jupyter notebooks in
doc/notebooks. The Changelog of pystencils can be found here.
Many thanks go to the contributors of pystencils.
Please cite us
If you use pystencils in a publication, please cite the following articles:
- M. Bauer et al, Code Generation for Massively Parallel Phase-Field Simulations. Association for Computing Machinery, 2019. https://doi.org/10.1145/3295500.3356186