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"description": "\n\n\n# angler\n\n`angler` (named for '**a**djoint **n**onlinear **g**radients') is a package for simulating and optimizing optical structures.\n\nIt provides a finite-difference frequency-domain (FDFD) solver for simulating for linear and nonlinear devices in the frequency domain.\n\nIt 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:\n\n\n\n`angler` is released as part of a paper `Adjoint method and inverse design for nonlinear optical devices`, which can be viewed [here](https://arxiv.org/abs/1811.01255).\n\n## Installation\n\nOne can install the most stable version of `angler` and all of its dependencies (apart from MKL) using\n\n\tpip install angler\n\t\nAlternatively, to use the most current version\n\n\tgit clone https://github.com/fancompute/angler.git\n\tpip install -e angler\n\nAnd then this directory can be added to path to import angler, i.e.\n\n\timport sys\n\tsys.path.append('path/to/angler')\n\n\n## Make angler faster\n\nThe most computationally expensive operation in `angler` is the sparse linear system solve. This is done with [`scipy.sparse.linalg.spsolve()`](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.linalg.spsolve.html) by default. If MKL is installed, `angler` instead uses this with a python wrapper [`pyMKL`](https://github.com/dwfmarchant/pyMKL), which makes things significantly faster, depending on the problem. The best way to install MKL, if using anaconda, is\n\n\tconda install MKL\n\t\n(pyMKL does not work when MKL is pip installed.)\n\n## Examples / Quickstart\n\nThere are several jupyter notebook examples in the `Notebooks/` directory.\n\nFor a good introduction, try:\n\n\tNotebooks/Splitter.ipynb\n\nFor more specific applications:\n\n#### Electromagnetic simulations\n\nFor modeling linear devices with our FDFD solver (no optimization), see\n\n\tNotebooks/Linear_system.ipynb\n\nFor modeling nonlinear devices with FDFD (no optimization), see \n\n\tNotebooks/Nonlinear_system.ipynb\n\n#### Inverse design & optimization\n\nFor examples of optimizing linear devices, see \n\n\tNotebooks/Splitter.ipynb\n\tNotebooks/Accelerator.ipynb\n\nFor examples of optimizing nonlinear devices, see\n\n\tNotebooks/2_port.ipynb\n\tNotebooks/3_port.ipynb\n\tNotebooks/T_port.ipynb\n\n## Package Structure\n\n`angler` provides two main classes, `Simulation` and `Optimization`, which perform most of the functionality.\n\nGenerally, `Simulation` objects are used to perform FDFD simulations, and `Optimization` classes run inverse design and optimization algorithms over `Simulation`s. To learn more about how `angler` works and how to use it, please take a look at [angler/README.md](angler/README.md) for a more detailed explanation.\n\n## Tests\n\nTo run all tests:\n\n\tpython -m unittest discover tests\n\nOr to run individually:\n\t\n\tpython tests/individual_test.py\n\n## Contributing\n\n`angler` is under development and we welcome suggestions, pull-requests, feature-requests, etc.\n\nIf you contribute a new feature, please also write a few tests and document your changes in [angler/README.md](angler/README.md) or the wiki.\n\n## Authors\n\n`angler` was written by Tyler Hughes, Momchil Minkov, and Ian Williamson.\n\n## Citing\n\nIf you use `angler`, please cite us using\n\n\t@misc{hughes2018adjoint,\n\tAuthor = {Tyler W. Hughes and Momchil Minkov and Ian A. D. Williamson and Shanhui Fan},\n\tTitle = {Adjoint method and inverse design for nonlinear nanophotonic devices},\n\tYear = {2018},\n\tEprint = {arXiv:1811.01255},\n\t}\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details. Copyright 2018 Tyler Hughes.\n\n## Acknowledgments\n\n* our logo was made by [Nadine Gilmer](http://nadinegilmer.com/)\n* RIP Ian's contributions before the code merge\n* We made use of a lot of code snippets (and advice) from [Jerry Shi](https://yujerryshi.github.io/index.html)",
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![\"Angler\"](\"/img/anglerlogos/rainbow.png\" "\\"Angler\\"")
\n\n# angler\n\n`angler` (named for '**a**djoint **n**onlinear **g**radients') is a package for simulating and optimizing optical structures.\n\nIt provides a finite-difference frequency-domain (FDFD) solver for simulating for linear and nonlinear devices in the frequency domain.\n\nIt 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:\n\n![\"Fields\"](\"/img/Tport.gif\" "\\"Fields\\"")
\n\n`angler` is released as part of a paper `Adjoint method and inverse design for nonlinear optical devices`, which can be viewed [here](https://arxiv.org/abs/1811.01255).\n\n## Installation\n\nOne can install the most stable version of `angler` and all of its dependencies (apart from MKL) using\n\n\tpip install angler\n\t\nAlternatively, to use the most current version\n\n\tgit clone https://github.com/fancompute/angler.git\n\tpip install -e angler\n\nAnd then this directory can be added to path to import angler, i.e.\n\n\timport sys\n\tsys.path.append('path/to/angler')\n\n\n## Make angler faster\n\nThe most computationally expensive operation in `angler` is the sparse linear system solve. This is done with [`scipy.sparse.linalg.spsolve()`](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.linalg.spsolve.html) by default. If MKL is installed, `angler` instead uses this with a python wrapper [`pyMKL`](https://github.com/dwfmarchant/pyMKL), which makes things significantly faster, depending on the problem. The best way to install MKL, if using anaconda, is\n\n\tconda install MKL\n\t\n(pyMKL does not work when MKL is pip installed.)\n\n## Examples / Quickstart\n\nThere are several jupyter notebook examples in the `Notebooks/` directory.\n\nFor a good introduction, try:\n\n\tNotebooks/Splitter.ipynb\n\nFor more specific applications:\n\n#### Electromagnetic simulations\n\nFor modeling linear devices with our FDFD solver (no optimization), see\n\n\tNotebooks/Linear_system.ipynb\n\nFor modeling nonlinear devices with FDFD (no optimization), see \n\n\tNotebooks/Nonlinear_system.ipynb\n\n#### Inverse design & optimization\n\nFor examples of optimizing linear devices, see \n\n\tNotebooks/Splitter.ipynb\n\tNotebooks/Accelerator.ipynb\n\nFor examples of optimizing nonlinear devices, see\n\n\tNotebooks/2_port.ipynb\n\tNotebooks/3_port.ipynb\n\tNotebooks/T_port.ipynb\n\n## Package Structure\n\n`angler` provides two main classes, `Simulation` and `Optimization`, which perform most of the functionality.\n\nGenerally, `Simulation` objects are used to perform FDFD simulations, and `Optimization` classes run inverse design and optimization algorithms over `Simulation`s. To learn more about how `angler` works and how to use it, please take a look at [angler/README.md](angler/README.md) for a more detailed explanation.\n\n## Tests\n\nTo run all tests:\n\n\tpython -m unittest discover tests\n\nOr to run individually:\n\t\n\tpython tests/individual_test.py\n\n## Contributing\n\n`angler` is under development and we welcome suggestions, pull-requests, feature-requests, etc.\n\nIf you contribute a new feature, please also write a few tests and document your changes in [angler/README.md](angler/README.md) or the wiki.\n\n## Authors\n\n`angler` was written by Tyler Hughes, Momchil Minkov, and Ian Williamson.\n\n## Citing\n\nIf you use `angler`, please cite us using\n\n\t@misc{hughes2018adjoint,\n\tAuthor = {Tyler W. Hughes and Momchil Minkov and Ian A. D. Williamson and Shanhui Fan},\n\tTitle = {Adjoint method and inverse design for nonlinear nanophotonic devices},\n\tYear = {2018},\n\tEprint = {arXiv:1811.01255},\n\t}\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details. Copyright 2018 Tyler Hughes.\n\n## Acknowledgments\n\n* our logo was made by [Nadine Gilmer](http://nadinegilmer.com/)\n* RIP Ian's contributions before the code merge\n* We made use of a lot of code snippets (and advice) from [Jerry Shi](https://yujerryshi.github.io/index.html)",
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