The project consists of next folders:
- data contains all of the data on which our models were built. On how to obtain this data, refer to Data section,
- docs contains documentation, built with 'sphinx',
- src/pdgrapher contains the source code for PDGrapher,
- examples contains some examples that demonstrate the use of PDGrapher library, read the README.md in examples folder for more instructions,
- tests contains unit and integration tests.
conda env create -f conda-env.yml
conda activate pdgrapher
pip install pip==23.2.1
pip install -r requirements.txt
pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.1+cu111.html
pip install torchmetrics==0.9.3
pip install lightning==1.9.5
pip install torch-sparse==0.6.13
For processed data, download the compressed folders and place them in data/processed/ with the following commands:
cd data
mkdir processed
cd processed
wget https://figshare.com/ndownloader/files/43624557
tar -xzvf torch_data.tar.gz
cd ../
wget https://figshare.com/ndownloader/files/43632327
tar -xzvf splits.tar.gz
This project can be build as a Python library by running pip install -e . in the root of this repository.
Documentation can be build with next two commands:
sphinx-apidoc -fe -o docs/source/ src/pdgrapher/updates the source files from which the documentation is built,docs/make htmlbuilds the documentation
Then, the documentation can be accessed locally by going to docs/build/html/index.html
All of the settings along with links to instructions can be found and modified in docs/source/conf.py.
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