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Efficient implementation of the 2D translation-invariant bispectrum in PyTorch

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Bispectrum

This repository provides simple implementations and demos of the 2-dimensional translation-invariant bispectrum

Installation

To install the requirements and package, run:

pip install -r requirements.txt
python setup.py install

Dataset

To download the dataset used in the demo, run:

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1tTj_bJ9nnc2ZfGB3cGZ_I3A5wqWK3aaD' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1tTj_bJ9nnc2ZfGB3cGZ_I3A5wqWK3aaD" -O van-hateren.zip 
rm -rf /tmp/cookies.txt
unzip van-hateren.zip
rm -r van-hateren.zip
mkdir datasets
mv van-hateren datasets

If your machine doesn't have wget, follow these steps:

  1. Download the zip file here.
  2. Place the file in the top node of this directory, i.e. in bispectral-networks/.
  3. Run:
    unzip van-hateren.zip
    rm -r van-hateren.zip
    mkdir datasets
    mv van-hateren datasets
    

Demo

To view a simple demo, open the notebook at:

demo.ipynb

License

This repository is licensed under the MIT License.

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Efficient implementation of the 2D translation-invariant bispectrum in PyTorch

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