Ceviche
Project description
ceviche 
Electromagnetic Simulation Tools + Automatic Differentiation. Code for paper Forward-Mode Differentiation of Maxwell's Equations.
What is ceviche?
ceviche
provides two core electromagnetic simulation tools for solving Maxwell's equations:
-
finite-difference frequency-domain (FDFD)
-
finite-difference time-domain (FDTD)
Both are written in numpy
/ scipy
and are compatible with the HIPS autograd package, supporting forward-mode and reverse-mode automatic differentiation.
This allows you to write code to solve your E&M problem, and then use automatic differentiation on your results.
As a result, you can do gradient-based optimization, sensitivity analysis, or plug your E&M solver into a machine learning model without having to go through the tedious process of deriving your derivatives by hand.
Examples
There is a comprehensive ceviche tutorial available at this link with several ipython notebook examples:
- Running FDFD simulations in ceviche.
- Performing inverse design of a mode converter.
- Adding fabrication constraints and device parameterizations.
- Inverse design of a wavelength-division multiplexer and advanced topics.
There are also a few examples in the examples/*
directory.
Installation
There are many ways to install ceviche
.
The easiest is by
pip install ceviche
But to install from a local copy, one can instead do
git clone https://github.com/twhughes/ceviche.git
pip install -e ceviche
pip install -r ceviche/requirements.txt
from the main directory.
Alternatively, just download it:
git clone https://github.com/twhughes/ceviche.git
and then import the package from within your python script
import sys
sys.path.append('path/to/ceviche')
Package Structure
Ceviche
The ceviche
directory contains everything needed.
To get the FDFD and FDTD simulators, import directly from ceviche import fdtd, fdfd_ez, fdfd_hz
To get the differentiation, import from ceviche import jacobian
.
constants.py
contains some constants EPSILON_0
, C_0
, ETA_0
, Q_E
, which are needed throughout the package
utils.py
contains a few useful functions for plotting, autogradding, and various other things.
optimizers.py
contains optimizer functions for doing inverse design.
viz.py
are functions that help with plotting fields and sructures.
modes.py
contains a mode sorter (WIP) that can be used to create waveguide mode profiles for the simulation, for example.
Examples
There are many demos in the examples
directory, which will give you a good sense of how to use the package.
Tests
Tests are located in tests
. To run, cd
into tests
and
python -m unittest
to run all or
python specific_test.py
to run a specific one. Some of these tests involve visual inspection of the field plots rather than error checking on values.
To run all of the gradient checking functions, run
chmod +x test/test_all_gradients.sh
tests/test_all_gradients.sh
Credits
If you use this for your research or work, please cite
@article{hughes2019forward,
title={Forward-Mode Differentiation of Maxwell’s Equations},
author={Hughes, Tyler W and Williamson, Ian AD and Minkov, Momchil and Fan, Shanhui},
journal={ACS Photonics},
volume={6},
number={11},
pages={3010--3016},
year={2019},
publisher={ACS Publications}
}
Our logo was created by @nagilmer
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file ceviche-0.1.3.tar.gz
.
File metadata
- Download URL: ceviche-0.1.3.tar.gz
- Upload date:
- Size: 30.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
7481ec6891944c7121d982c58d10ae5f1a71e057b7f890fb42a90b8383547b55
|
|
MD5 |
034af82d2f9e76e5c3b619f65925bf58
|
|
BLAKE2b-256 |
22f6e804f69ff6401d4c37802f2f346dbc74d71e47af801ef02aa80b5fa1a7da
|
File details
Details for the file ceviche-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: ceviche-0.1.3-py3-none-any.whl
- Upload date:
- Size: 32.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.9
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
5d64e9e7f6bbe2fb3c6cc1057cc091173e3e8267777c8f73ed454d33e9557285
|
|
MD5 |
bffea7e4db87cda55eceea169d65c107
|
|
BLAKE2b-256 |
c87b14fbe8ecfbff579511af47da8d29068bff1ee6d8b5e150621675f105dad3
|