.. | ||
fdfd | ||
fdmath | ||
fdtd | ||
test | ||
__init__.py | ||
eigensolvers.py | ||
LICENSE.md | ||
py.typed | ||
README.md |
meanas
meanas is a python package for electromagnetic simulations
** UNSTABLE / WORK IN PROGRESS **
Formerly known as fdfd_tools.
This package is intended for building simulation inputs, analyzing simulation outputs, and running short simulations on unspecialized hardware. It is designed to provide tooling and a baseline for other, high-performance purpose- and hardware-specific solvers.
Contents
- Finite difference frequency domain (FDFD)
- Library of sparse matrices for representing the electromagnetic wave equation in 3D, as well as auxiliary matrices for conversion between fields
- Waveguide mode operators
- Waveguide mode eigensolver
- Stretched-coordinate PML boundaries (SCPML)
- Functional versions of most operators
- Anisotropic media (limited to diagonal elements eps_xx, eps_yy, eps_zz, mu_xx, ...)
- Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
- Finite difference time domain (FDTD)
- Basic Maxwell time-steps
- Poynting vector and energy calculation
- Convolutional PMLs
This package does not provide a fast matrix solver, though by default
meanas.fdfd.solvers.generic(...)
will call
scipy.sparse.linalg.qmr(...)
to perform a solve.
For 2D FDFD problems this should be fine; likewise, the waveguide mode
solver uses scipy's eigenvalue solver, with reasonable results.
For solving large (or 3D) FDFD problems, I recommend a GPU-based iterative solver, such as opencl_fdfd or those included in MAGMA. Your solver will need the ability to solve complex symmetric (non-Hermitian) linear systems, ideally with double precision.
- Source repository
- PyPI TBD
Installation
Requirements:
- python >=3.8
- numpy
- scipy
Install from PyPI with pip:
pip3 install 'meanas[dev]'
Development install
Install python3 and git:
# This is for Debian/Ubuntu/other-apt-based systems; you may need an alternative command
sudo apt install python3 build-essential python3-dev git
In-place development install:
# Download using git
git clone https://mpxd.net/code/jan/meanas.git
# If you'd like to create a virtualenv, do so:
python3 -m venv my_venv
# If you are using a virtualenv, activate it
source my_venv/bin/activate
# Install in-place (-e, editable) from ./meanas, including development dependencies ([dev])
pip3 install --user -e './meanas[dev]'
# Run tests
cd meanas
python3 -m pytest -rsxX | tee test_results.txt
See also:
Use
See examples/
for some simple examples; you may need additional
packages such as gridlock
to run the examples.