mgcpy

mgcpy is a Python package containing tools for multiscale graph correlation and other statistical tests, that is capable of dealing with high dimensional and multivariate data.

Documentation: https://mgcpy.readthedocs.io/en/latest/

Installation Guide:

Install from PyPi

pip3 install mgcpy

Install from Github

git clone https://github.com/NeuroDataDesign/mgcpy
cd mgcpy
python3 setup.py install

• sudo, if required
• python3 setup.py build_ext --inplace # for cython, if you want to test in-place, first execute this

Setting up the development environment:

• To build image and run from scratch:

  • Install docker
  • Build the docker image, docker build -t mgcpy:latest .
    • This takes 10-15 mins to build
  • Launch the container to go into mgcpy's dev env, docker run -it --rm --name mgcpy-env mgcpy:latest

• Pull image from Dockerhub and run:

  • docker pull tpsatish95/mgcpy:latest or docker pull tpsatish95/mgcpy:development
  • docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest

• To run demo notebooks (from within Docker):

  • cd demos
  • jupyter notebook --ip 0.0.0.0 --no-browser --allow-root
  • Then copy the url it generates, it looks something like this: http://(0de284ecf0cd or 127.0.0.1):8888/?token=e5a2541812d85e20026b1d04983dc8380055f2d16c28a6ad
  • Edit this: (0de284ecf0cd or 127.0.0.1) to: 127.0.0.1, in the above link and open it in your browser
  • Then open mgc.ipynb

• To mount/load local files into docker container:

  • Do docker run -it --rm -v <local_dir_path>:/root/workspace/ -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest, replace <local_dir_path> with your local dir path.
  • Do cd ../workspace when you are inside the container to view the mounted files. The mgcpy package code will be in /root/code directory.

MGC Algorithm's Flow

Power Curves

• Recreated Figure 2 in https://arxiv.org/abs/1609.05148, with the addition of MDMR and Fast MGC

License

This project is covered under the Apache 2.0 License.