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        "author": "Martin Bauer",
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            "Framework :: Jupyter",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Code Generators"
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        "description": "pystencils\n==========\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/mabau/pystencils/master?filepath=doc%2Fnotebooks)\n[![Docs](https://img.shields.io/badge/read-the_docs-brightgreen.svg)](http://pycodegen.pages.walberla.net/pystencils)\n[![pipeline status](https://i10git.cs.fau.de/pycodegen/pystencils/badges/master/pipeline.svg)](https://i10git.cs.fau.de/pycodegen/pystencils/commits/master)\n[![coverage report](https://i10git.cs.fau.de/pycodegen/pystencils/badges/master/coverage.svg)](http://pycodegen.pages.walberla.net/pystencils/coverage_report)\n\nRun blazingly fast stencil codes on numpy arrays.\n\n*pystencils* uses sympy to define stencil operations, that can be executed on numpy arrays.\nExploiting the stencil structure makes *pystencils* run faster than normal numpy code and even as Cython and numba,\n[as demonstrated in this notebook](http://pycodegen.pages.walberla.net/pystencils/notebooks/demo_benchmark.html).\n\n\nHere is a code snippet that computes the average of neighboring cells:\n```python\nimport pystencils as ps\nimport numpy as np\n\nf, g = ps.fields(\"f, g : [2D]\")\nstencil = ps.Assignment(g[0, 0],\n                        (f[1, 0] + f[-1, 0] + f[0, 1] + f[0, -1]) / 4)\nkernel = ps.create_kernel(stencil).compile()\n\nf_arr = np.random.rand(1000, 1000)\ng_arr = np.empty_like(f_arr)\nkernel(f=f_arr, g=g_arr)\n```\n\n*pystencils* is mostly used for numerical simulations using finite difference or finite volume methods.\nIt comes with automatic finite difference discretization for PDEs:\n\n```python\nc, v = ps.fields(\"c, v(2): [2D]\")\nadv_diff_pde = ps.fd.transient(c) - ps.fd.diffusion(c, sp.symbols(\"D\")) + ps.fd.advection(c, v)\ndiscretize = ps.fd.Discretization2ndOrder(dx=1, dt=0.01)\ndiscretization = discretize(adv_diff_pde)\n```\n\nLook at the [documentation](http://pycodegen.pages.walberla.net/pystencils) to learn more.\n\n\nInstallation\n------------\n\n```bash\npip install pystencils[interactive]\n```\n\nWithout `[interactive]` you get a minimal version with very little dependencies.\n\nAll options:\n-  `gpu`: use this if an Nvidia GPU is available and CUDA is installed\n-  `pyopencl`: basic OpenCL support (experimental)\n- `alltrafos`: pulls in additional dependencies for loop simplification e.g. libisl\n- `bench_db`: functionality to store benchmark result in object databases\n- `interactive`: installs dependencies to work in Jupyter including image I/O, plotting etc.\n- `autodiff`: enable derivation of adjoint kernels and generation of Torch/Tensorflow operations\n- `doc`: packages to build documentation\n\nOptions can be combined e.g.\n```bash\npip install pystencils[interactive,gpu,doc]\n```    \n\n\nDocumentation\n-------------\n\nRead the docs [here](http://pycodegen.pages.walberla.net/pystencils) and\ncheck out the Jupyter notebooks in `doc/notebooks`.",
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