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# meanas
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2016-04-13 04:05:08 -07:00
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2019-08-04 13:48:41 -07:00
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**meanas** is a python package for electromagnetic simulations
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2019-10-08 23:56:33 -07:00
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** UNSTABLE / WORK IN PROGRESS **
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Formerly known as [fdfd_tools](https://mpxd.net/code/jan/fdfd_tools).
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2019-08-04 13:48:41 -07:00
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This package is intended for building simulation inputs, analyzing
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simulation outputs, and running short simulations on unspecialized hardware.
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It is designed to provide tooling and a baseline for other, high-performance
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purpose- and hardware-specific solvers.
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2016-05-30 22:30:45 -07:00
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**Contents**
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- Finite difference frequency domain (FDFD)
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* Library of sparse matrices for representing the electromagnetic wave
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equation in 3D, as well as auxiliary matrices for conversion between fields
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* Waveguide mode operators
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* Waveguide mode eigensolver
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* Stretched-coordinate PML boundaries (SCPML)
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* Functional versions of most operators
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* Anisotropic media (limited to diagonal elements eps_xx, eps_yy, eps_zz, mu_xx, ...)
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* Arbitrary distributions of perfect electric and magnetic conductors (PEC / PMC)
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- Finite difference time domain (FDTD)
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* Basic Maxwell time-steps
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* Poynting vector and energy calculation
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* Convolutional PMLs
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2016-08-04 22:55:24 -07:00
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This package does *not* provide a fast matrix solver, though by default
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`meanas.fdfd.solvers.generic(...)` will call
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`scipy.sparse.linalg.qmr(...)` to perform a solve.
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For 2D FDFD problems this should be fine; likewise, the waveguide mode
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solver uses scipy's eigenvalue solver, with reasonable results.
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For solving large (or 3D) FDFD problems, I recommend a GPU-based iterative
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solver, such as [opencl_fdfd](https://mpxd.net/code/jan/opencl_fdfd) or
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2019-10-08 23:56:33 -07:00
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those included in [MAGMA](http://icl.cs.utk.edu/magma/index.html). Your
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solver will need the ability to solve complex symmetric (non-Hermitian)
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linear systems, ideally with double precision.
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2020-02-19 18:56:56 -08:00
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- [Source repository](https://mpxd.net/code/jan/meanas)
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2024-03-30 18:06:41 -07:00
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- [PyPI](https://pypi.org/project/meanas)
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- [Github mirror](https://github.com/anewusername/meanas)
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2016-05-30 22:30:45 -07:00
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## Installation
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**Requirements:**
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2024-03-30 18:06:31 -07:00
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* python >=3.11
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* numpy
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* scipy
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2019-10-08 23:56:33 -07:00
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Install from PyPI with pip:
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```bash
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pip3 install 'meanas[dev]'
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```
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### Development install
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Install python3 and git:
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```bash
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# This is for Debian/Ubuntu/other-apt-based systems; you may need an alternative command
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sudo apt install python3 build-essential python3-dev git
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```
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In-place development install:
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```bash
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# Download using git
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git clone https://mpxd.net/code/jan/meanas.git
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# If you'd like to create a virtualenv, do so:
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python3 -m venv my_venv
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# If you are using a virtualenv, activate it
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source my_venv/bin/activate
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# Install in-place (-e, editable) from ./meanas, including development dependencies ([dev])
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pip3 install --user -e './meanas[dev]'
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# Run tests
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2019-11-05 19:02:40 -08:00
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cd meanas
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python3 -m pytest -rsxX | tee test_results.txt
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```
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2019-10-09 00:11:27 -07:00
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#### See also:
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- [git book](https://git-scm.com/book/en/v2)
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- [venv documentation](https://docs.python.org/3/tutorial/venv.html)
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- [python language reference](https://docs.python.org/3/reference/index.html)
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- [python standard library](https://docs.python.org/3/library/index.html)
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2019-10-08 23:56:33 -07:00
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2016-08-04 22:55:24 -07:00
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## Use
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See `examples/` for some simple examples; you may need additional
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packages such as [gridlock](https://mpxd.net/code/jan/gridlock)
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to run the examples.
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