angler (named for 'adjoint nonlinear gradients') is a package for simulating and optimizing optical structures.

It provides a finite-difference frequency-domain (FDFD) solver for simulating for linear and nonlinear devices in the frequency domain.

It also provides an easy to use package for adjoint-based inverse design and optimization of linear and nonlinear devices. For example, you can inverse design optical switches to transport power to different ports for different input powers:

angler is released as part of a paper Adjoint method and inverse design for nonlinear optical devices, which can be viewed here.

One can install the most stable version of angler and all of its dependencies (apart from MKL) using

pip install angler

Alternatively, to use the most current version

git clone https://github.com/fancompute/angler.git
pip install -e angler

And then this directory can be added to path to import angler, i.e.

import sys
sys.path.append('path/to/angler')

The most computationally expensive operation in angler is the sparse linear system solve. This is done with scipy.sparse.linalg.spsolve() by default. If MKL is installed, angler instead uses this with a python wrapper pyMKL, which makes things significantly faster, depending on the problem. The best way to install MKL, if using anaconda, is

conda install MKL

(pyMKL does not work when MKL is pip installed.)

There are several jupyter notebook examples in the Notebooks/ directory.

For a good introduction, try:

Notebooks/Splitter.ipynb

For more specific applications:

For modeling linear devices with our FDFD solver (no optimization), see

Notebooks/Linear_system.ipynb

For modeling nonlinear devices with FDFD (no optimization), see

Notebooks/Nonlinear_system.ipynb

For examples of optimizing linear devices, see

Notebooks/Splitter.ipynb
Notebooks/Accelerator.ipynb

For examples of optimizing nonlinear devices, see

Notebooks/2_port.ipynb
Notebooks/3_port.ipynb
Notebooks/T_port.ipynb

angler provides two main classes, Simulation and Optimization, which perform most of the functionality.

Generally, Simulation objects are used to perform FDFD simulations, and Optimization classes run inverse design and optimization algorithms over Simulations. To learn more about how angler works and how to use it, please take a look at angler/README.md for a more detailed explanation.

To run all tests:

python -m unittest discover tests

Or to run individually:

python tests/individual_test.py

angler is under development and we welcome suggestions, pull-requests, feature-requests, etc.

If you contribute a new feature, please also write a few tests and document your changes in angler/README.md or the wiki.

angler was written by Tyler Hughes, Momchil Minkov, and Ian Williamson.

If you use angler, please cite us using

@article{Hughes2018,
author = {Hughes, Tyler W. and Minkov, Momchil and Williamson, Ian A. D. and Fan, Shanhui},
title = {Adjoint Method and Inverse Design for Nonlinear Nanophotonic Devices},
journal = {ACS Photonics},
volume = {5},
number = {12},
pages = {4781-4787},
year = {2018},
doi = {10.1021/acsphotonics.8b01522}
}

This project is licensed under the MIT License - see the LICENSE.md file for details. Copyright 2018 Tyler Hughes.

• our logo was made by Nadine Gilmer :)
• RIP Ian's contributions before the code merge
• We made use of a lot of code snippets (and advice) from Jerry Shi