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Electromagnetic simulations in python
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Jan Petykiewicz 4e240988c9 use "is None" to check for default args 
numpy.any(numpy.equal(x, None)) is more general, because
`numpy.array(None) is not None`, but checking for that doesn't make much
sense if you're already type-checking
2022-10-04 17:27:44 -07:00
examples just check if file exists rather than trying to open 2022-10-04 17:09:14 -07:00
meanas use "is None" to check for default args 2022-10-04 17:27:44 -07:00
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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.

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.