Electromagnetic simulations in python
Go to file
2019-11-25 00:04:53 -08:00
examples prettyify example plots 2019-10-27 12:46:12 -07:00
meanas use fdmath derivatives where possible 2019-11-25 00:04:53 -08:00
pdoc_templates Big documentation and structure updates 2019-11-24 23:47:31 -08:00
.gitignore ignore vim swap files 2019-11-24 23:54:24 -08:00
LICENSE.md add license 2016-04-13 04:06:15 -07:00
make_docs.sh Big documentation and structure updates 2019-11-24 23:47:31 -08:00
MANIFEST.in Switch to file-based version number 2019-09-27 20:44:31 -07:00
README.md Readme updates 2019-11-24 22:46:36 -08:00
setup.py Update repo location 2019-10-08 23:59:22 -07:00

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.

Installation

Requirements:

  • python 3 (tests require 3.7)
  • numpy
  • scipy

Install from PyPI with pip:

pip3 install 'meanas[test,examples]'

Development install

Install python3.7, virtualenv, and git:

# This is for Debian/Ubuntu/other-apt-based systems; you may need an alternative command
sudo apt install python3.7 virtualenv build-essential python3.7-dev git

If python 3.7 is not your default python3 version, create a virtualenv:

# Check python3 version:
python3 --version
# output on my system: Python 3.7.5rc1
# If this indicates a version >= 3.7, you can skip all
#  the steps involving virtualenv or referencing the venv/ directory

# Create a virtual environment using python3.7 and place it in the directory `venv/`
virtualenv -p python3.7 venv

In-place development install:

# Download using git
git clone --branch wip https://mpxd.net/code/jan/fdfd_tools.git meanas/

# NOTE: In the future this will become
#git clone https://mpxd.net/code/jan/meanas.git

# If you are using a virtualenv, activate it
source venv/bin/activate

# Install in-place (-e, editable) from ./meanas, including testing and example dependencies ([test, examples])
pip3 install --user -e './meanas[test,examples]'

# 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.