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https://github.com/BerkeleyPhotonicsGenerator/BPG
The Berkeley Photonics Generator (BPG) is a Python framework that enables the generation and simulation of photonics layout
https://github.com/BerkeleyPhotonicsGenerator/BPG
Last synced: 2 months ago
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The Berkeley Photonics Generator (BPG) is a Python framework that enables the generation and simulation of photonics layout
- Host: GitHub
- URL: https://github.com/BerkeleyPhotonicsGenerator/BPG
- Owner: BerkeleyPhotonicsGenerator
- License: bsd-3-clause
- Created: 2018-06-19T17:52:00.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-10T02:35:49.000Z (over 1 year ago)
- Last Synced: 2024-04-15T03:03:55.216Z (3 months ago)
- Language: Python
- Size: 2.41 MB
- Stars: 35
- Watchers: 17
- Forks: 8
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome_photonics - BerkeleyPhotonicsGenerator
README
# Berkeley Photonics Generator
[](https://dev.azure.com/pvnbhargava/BPG_CICD/_build/latest?definitionId=2&branchName=master)
[](https://bpg.readthedocs.io/en/latest/?badge=latest)
[](https://zenodo.org/badge/latestdoi/137926394)
The Berkeley Photonics Generator (BPG) is a Python framework that enables you to generate and simulate silicon photonics
layout. BPG leverages the [BAG 2.0 framework](https://github.com/ucb-art/BAG_framework), and has similar syntax and
file structure. If you would like to cite this software in a publication, please use the following link:
[](https://zenodo.org/badge/latestdoi/137926394)
## Table of Contents
- [Documentation](#Documentation)
- [Installation](#Installation)
- [Usage](#Usage)
- [Contributing](#Contributing)
- [Credits](#Credits)
## Documentation
The most up to date BPG documentation can be found [here](https://bpg.readthedocs.io)
## Installation
WARNING: Installation instructions are currently in flux.
We highly recommend you use an [Anaconda](https://www.anaconda.com/distribution/) environment with a Python version
greater than 3.6. BPG generally will not function with Python versions less than 3.6, and requires packages with
C/C++ dependencies that are most easily installed using Anaconda.
Once Anaconda is set up, please run the following commands to install packages with C/C++ dependencies:
```bash
conda install numpy
conda install rtree
conda install shapely
```
Then clone and install BAG with in any folder with:
```bash
git clone [email protected]:ucb-art/BAG_Framework.git
cd BAG_Framework
pip install .
```
Finally clone and install BPG in any folder with:
```bash
git clone [email protected]:BerkeleyPhotonicsGenerator/BPG.git
cd BPG
pip install .
```
BPG generally generates output layouts in the GDSII format. To view these layouts, we recommend you install and use the
free open-source software package, [Klayout](https://klayout.de).
## Usage
1) In order to setup a brand new workspace, navigate to a clean folder and run `bpg setup_workspace`. This will copy in
a directory with sample technology information, and a new file called `sourceme.sh`
2) Next run `source sourceme.sh` to set up all of the environment variables needed. This soruce file assumes that you
are using a bash/sh/zsh shell.
3) At this point any layout generator can be run by executing `python `
For more information check out the [getting started guide](https://bpg.readthedocs.io/en/latest/getting_started/root.html)
## Contributing
We'd love your help in building and improving BPG! Please create an issue and contact Pavan at
[email protected] before sending your first pull request. Please make sure to run the test suite (run_tests.py)
in the Photonics_Dev repo prior to making your pull request.
## Credits
Thanks to [Sidney Buchbinder](https://github.com/sbuchbinder),
[Ruocheng Wang](https://github.com/Ruocheng-Wang),
and [Brian Sun](https://github.com/bsun598) for building many of the core features in BPG!