PyMPDATA | Numba-accelerated Pythonic implementation of MPDATA

PyMPDATA is a Python library. PyMPDATA has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install PyMPDATA' or download it from GitHub, PyPI.

PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA algorithm of Smolarkiewicz et al. used in geophysical fluid dynamics and beyond. MPDATA numerically solves generalised transport equations - partial differential equations used to model conservation/balance laws, scalar-transport problems, convection-diffusion phenomena. As of the current version, PyMPDATA supports homogeneous transport in 1D, 2D and 3D using structured meshes, optionally generalised by employment of a Jacobian of coordinate transformation. PyMPDATA includes implementation of a set of MPDATA variants including the non-oscillatory option, infinite-gauge, divergent-flow, double-pass donor cell (DPDC) and third-order-terms options. It also features support for integration of Fickian-terms in advection-diffusion problems using the pseudo-transport velocity approach. In 2D and 3D simulations, domain-decomposition is used for multi-threaded parallelism. PyMPDATA is engineered purely in Python targeting both performance and usability, the latter encompassing research users', developers' and maintainers' perspectives. From researcher's perspective, PyMPDATA offers hassle-free installation on multitude of platforms including Linux, OSX and Windows, and eliminates compilation stage from the perspective of the user. From developers' and maintainers' perspective, PyMPDATA offers a suite of unit tests, multi-platform continuous integration setup, seamless integration with Python development aids including debuggers and profilers. PyMPDATA design features a custom-built multi-dimensional Arakawa-C grid layer allowing to concisely represent multi-dimensional stencil operations on both scalar and vector fields. The grid layer is built on top of NumPy's ndarrays (using "C" ordering) using the Numba's @njit functionality for high-performance array traversals. It enables one to code once for multiple dimensions, and automatically handles (and hides from the user) any halo-filling logic related with boundary conditions. Numba prange() functionality is used for implementing multi-threading (it offers analogous functionality to OpenMP parallel loop execution directives). The Numba's deviation from Python semantics rendering closure variables as compile-time constants is extensively exploited within PyMPDATA code base enabling the just-in-time compilation to benefit from information on domain extents, algorithm variant used and problem characteristics (e.g., coordinate transformation used, or lack thereof). A separate project called numba-mpi has been developed with the intention to set the stage for future MPI distributed memory parallelism in PyMPDATA. The PyMPDATA-examples package covers a set of examples presented in the form of Jupyer notebooks offering single-click deployment in the cloud using mybinder.org or using colab.research.google.com. The examples reproduce results from several published works on MPDATA and its applications, and provide a validation of the implementation and its performance.