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ceviche-0.1.2
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Ceviche
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | ceviche-0.1.2 |
| نام | ceviche |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Tyler Hughes |
| ایمیل نویسنده | tylerwhughes91@gmail.com |
| آدرس صفحه اصلی | https://github.com/twhughes/ceviche |
| آدرس اینترنتی | https://pypi.org/project/ceviche/ |
| مجوز | - |
# ceviche [](https://travis-ci.com/twhughes/ceviche)
Electromagnetic Simulation Tools + Automatic Differentiation. Code for paper [Forward-Mode Differentiation of Maxwell's Equations](https://arxiv.org/abs/1908.10507).
<img src="/img/horizontal-color.png" title="ceviche" alt="ceviche">
## 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](https://github.com/HIPS/autograd), 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](https://github.com/fancompute/workshop-invdesign) with several ipython notebook examples:
1. [Running FDFD simulations in ceviche.](https://nbviewer.jupyter.org/github/fancompute/workshop-invdesign/blob/master/01_First_simulation.ipynb)
2. [Performing inverse design of a mode converter.](https://nbviewer.jupyter.org/github/fancompute/workshop-invdesign/blob/master/02_Invdes_intro.ipynb)
3. [Adding fabrication constraints and device parameterizations.](https://nbviewer.jupyter.org/github/fancompute/workshop-invdesign/blob/master/03_Invdes_parameterization.ipynb)
4. [Inverse design of a wavelength-division multiplexer and advanced topics.](https://nbviewer.jupyter.org/github/fancompute/workshop-invdesign/blob/master/04_Invdes_wdm_scheduling.ipynb)
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
```python
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](http://nadinegilmer.com/)
نیازمندی
| مقدار | نام |
|---|---|
| >=1.16.2 | numpy |
| >=1.2.1 | scipy |
| >=3.0.3 | matplotlib |
| >=1.3 | autograd |
| >=0.0.3 | pyMKL |
نحوه نصب
نصب پکیج whl ceviche-0.1.2:
pip install ceviche-0.1.2.whl
نصب پکیج tar.gz ceviche-0.1.2:
pip install ceviche-0.1.2.tar.gz