Approximate fuzzy subgraph matching in polynomial time
Project description
Fornax
An implementation of NeMa: Fast Graph Search with Label Similarity using python3 and sqlite or postgres.
Install
Via pip:
pip install fornax
Note that Fornax requires numpy to be installed (part of the SciPy ecosystem) which in turn has non python dependencies. The available options for installing SciPy packages are listed here.
We recomend installing fornax via anaconda
conda create --name myenv python=3.6
source activate myenv
pip install fornax
Install (Dev)
From the root directory:
# install dev dependencies
pip install -r requirements/dev.txt
# install fornax
pip install -e .
View the Docs
View the docs at: fornax.readthedocs.io
Test
From the root directory
python run_test.py
Tutorials
See the tutorials for a full working example.
Install Tutorial Dependencies (using conda)
The following tutorials use jupyter notebooks to create a worked example. We recommend you use the anaconda python distribution to run the notebooks.
conda env create -f environment.yml
Run the Tutorials
source activate fornax_tutorial
cd docs/tutorial
jupyter-notebook
Documentation
Build the Docs
# install docs dependencies
pip install -r requirements/docs.txt
# install fornax
pip install .
# build
cd docs
make html
View the Docs Locally
cd _build/html
python3 -m http.server
navigate to 0.0.0.0:8000
in your browser.
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