pystencils
Run blazingly fast stencil codes on numpy arrays.
pystencils uses sympy to define stencil operations, that can be executed on numpy array. It runs faster than normal numpy code and even as Cython and numba.
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:
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)Look at the documentation to learn more.
Installation
pip install pystencils[interactive]Without [interactive] you get a minimal version with very little dependencies.
All options:
-
gpu: use this if 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]Documentation
Read the docs here and
check out the Jupyter notebooks in doc/notebooks.