README.md

pystencils
==========

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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](https://pycodegen.pages.i10git.cs.fau.de/pystencils/notebooks/demo_benchmark.html).


Here is a code snippet that computes the average of neighboring cells:
```python
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:

```python
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)
```

Installation
------------

```bash
pip install pystencils[interactive]
```

Without `[interactive]` you get a minimal version with very little dependencies.

All options:
-  `gpu`: use this if an NVIDIA GPU is available and CUDA is installed
-  `opencl`: basic OpenCL support (experimental)
- `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.
- `autodiff`: enable derivation of adjoint kernels and generation of Torch/Tensorflow operations
- `doc`: packages to build documentation

Options can be combined e.g.
```bash
pip install pystencils[interactive, gpu, doc]
```

pystencils is also fully compatible with Windows machines. If working with visual studio, a pycuda makes sure to run example files first to ensure that pycuda can find the compiler's executable.

Documentation
-------------

Read the docs [here](https://pycodegen.pages.i10git.cs.fau.de/pystencils) and
check out the Jupyter notebooks in `doc/notebooks`.

Authors
-------

Many thanks go to the [contributors](AUTHORS.txt) of pystencils.

### Please cite us

If you use pystencils in a publication, please cite the following articles:

Overview:
  - M. Bauer et al, Code Generation for Massively Parallel Phase-Field Simulations. Association for Computing Machinery, 2019. https://doi.org/10.1145/3295500.3356186