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https://github.com/anewusername/meanas
https://github.com/anewusername/meanas
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/anewusername/meanas
- Owner: anewusername
- License: agpl-3.0
- Created: 2024-03-30T23:28:06.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2024-07-29T09:25:24.000Z (4 months ago)
- Last Synced: 2024-09-30T12:36:24.398Z (about 2 months ago)
- Language: Python
- Size: 478 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# meanas
**meanas** is a python package for electromagnetic simulations
** UNSTABLE / WORK IN PROGRESS **
Formerly known as [fdfd_tools](https://mpxd.net/code/jan/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](https://mpxd.net/code/jan/opencl_fdfd) or
those included in [MAGMA](http://icl.cs.utk.edu/magma/index.html). Your
solver will need the ability to solve complex symmetric (non-Hermitian)
linear systems, ideally with double precision.
- [Source repository](https://mpxd.net/code/jan/meanas)
- [PyPI](https://pypi.org/project/meanas)
- [Github mirror](https://github.com/anewusername/meanas)
## Installation
**Requirements:**
* python >=3.11
* numpy
* scipy
Install from PyPI with pip:
```bash
pip3 install 'meanas[dev]'
```
### Development install
Install python3 and git:
```bash
# 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:
```bash
# 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:
- [git book](https://git-scm.com/book/en/v2)
- [venv documentation](https://docs.python.org/3/tutorial/venv.html)
- [python language reference](https://docs.python.org/3/reference/index.html)
- [python standard library](https://docs.python.org/3/library/index.html)
## Use
See `examples/` for some simple examples; you may need additional
packages such as [gridlock](https://mpxd.net/code/jan/gridlock)
to run the examples.