Metadata-Version: 2.1
Name: photontorch
Version: 0.4.0
Summary:  PhotonTorch: a photonic simulation framework based on the deep learning framework PyTorch.
Home-page: http://github.com/flaport/photontorch
Author: Floris Laporte
Author-email: floris.laporte@ugent.be
License: UNKNOWN
Description: # Photontorch
        
        Photontorch is a photonic simulator for highly parallel simulation and
        optimization of photonic circuits in time and frequency domain.
        Photontorch features CUDA enabled simulation and optimization of
        photonic circuits. It leverages the deep learning framework PyTorch to
        view the photonic circuit as essentially a recurrent neural network.
        This enables the use of native PyTorch optimizers to optimize the
        (physical) parameters of the circuit.
        
        - Floris Laporte [[floris.laporte@ugent.be](mailto:floris.laporte@gmail.com)]
        - Website: [photontorch.com](http://photontorch.com)
        
        ## Installation
        
        ### Stable version
        
        Photontorch can be installed with pip:
        
        ```
        pip install photontorch
        ```
        
        ### Development version
        
        During development or to use the most recent Photontorch version,
        clone the repository and link with pip:
        
        ```
        git clone https://git.photontorch.com/photontorch.git
        ./install-git-hooks.sh # Unix [Linux/Mac/BSD/...]
        install-git-hooks.bat  # Windows
        pip install -e photontorch
        ```
        
        During development, use pytest to run the tests from within the root
        of the git-repository:
        
        ```
        pytest tests
        ```
        
        ## Documentation
        
        Read the full documentation here: [https://docs.photontorch.com](https://docs.photontorch.com)
        
        ## Dependencies
        
        ### Required dependencies
        
        - Python 2.7 (Linux only) or 3.6+. It's recommended to use the [Anaconda](http://www.anaconda.com/download) distribution.
        - [`pytorch>=1.5.0`](http://pytorch.org): `conda install pytorch` (see [pytorch.org](https://pytorch.org) for more installation options for your CUDA version)
        - [`numpy`](http://www.numpy.org): `conda install numpy`
        - [`scipy`](http://www.scipy.org): `conda install scipy`
        
        ### Optional (but recommended) dependencies
        
        - [`tqdm`](https://github.com/tqdm/tqdm): `conda install tqdm` [progress bars]
        - [`networkx`](http://networkx.github.io): `conda install networkx` [network visualization]
        - [`matplotlib`](http://matplotlib.org): `conda install matplotlib` [visualization]
        - [`pytest`](http://docs.pytest.org): `conda install pytest` [to run tests]
        - [`pandoc`](https://pandoc.org): `conda install pandoc` [to generate docs]
        - [`sphinx`](https://www.sphinx-doc.org): `pip install sphinx nbsphinx` [to generate docs]
        - [`torch-lfilter`](https://github.com/flaport/torch_lfilter): `pip install torch-lfilter` [faster lfilter for detectors]
        
        ## Reference
        
        If you're using Photontorch in your work or feel in any way inspired by it,
        we ask you to cite us in your work:
        
        Floris Laporte, Joni Dambre, and Peter Bienstman. _"Highly parallel simulation
        and optimization of photonic circuits in time and frequency domain based on the
        deep-learning framework PyTorch."_ Scientific reports 9.1 (2019): 5918.
        
        ## Known issues
        
        - Complex tensor support. Complex tensors are not supported in
          PyTorch/Photontorch. Wherever complex tensors would be applicable,
          Photontorch expects a real-valued tensor with the real and imag part
          stacked in the first dimension. The Photontorch issue can be
          followed [here](https://github.com/flaport/photontorch/issues/4) and
          the PyTorch issue [here](https://github.com/pytorch/pytorch/issues/755).
        - Sparse tensor support. A lot of memory usage can probably be avoided
          when transitioning to a sparse tensor representation for the connection matrices and
          scatter matrices. The Photontorch issue can be followed [here](https://github.com/flaport/photontorch/issues/5)
        
        ## License
        
        Photontorch used to be available under a custom Academic License, but Since October
        2020, Photontorch is now fully open source and available under the [AGPLv3](LICENSE). 
        
        Copyright © 2020, Floris Laporte - UGent - [AGPLv3](LICENSE)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
