A set of tools in Python for multiscale graph correlation and other statistical tests
mgcpy is a Python package containing tools for independence testing using multiscale graph correlation and other statistical tests, that is capable of dealing with high dimensional and multivariate data.
- System Requirements
- Installation Guide
- Setting up the development environment
mgcpy aims to be a comprehensive independence testing package including all of the commonly used independence tests as mentioned above and additional functionality such as two sample independence testing and a novel random forest-based independence test. These tests are not only included to benchmark MGC but to have a convenient location for users if they would prefer to utilize those tests instead. The package utilizes a simple class structure to enhance usability while also allowing easy extension of the package for developers. The package can be installed on all major platforms (e.g. BSD, GNU/Linux, OS X, Windows)from Python Package Index (PyPI) and GitHub.
mgcpy package requires only a standard computer with enough RAM to support the in-memory operations.
This package is supported for macOS and Linux. The package has been tested on the following systems:
- macOS: Mojave (10.14.1)
- Linux: Ubuntu 16.04
mgcpy mainly depends on the Python scientific stack.
numpy scipy Cython scikit-learn pandas seaborn
Install from PyPi
pip3 install mgcpy
Install from Github
git clone https://github.com/neurodata/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:latestor
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):
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
To mount/load local files into docker container:
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.
cd ../workspacewhen you are inside the container to view the mounted files. The mgcpy package code will be in
MGC Algorithm's Flow
- Recreated Figure 2 in https://arxiv.org/abs/1609.05148, with the addition of MDMR and Fast MGC
This project is covered under the Apache 2.0 License.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|mgcpy-0.3.0-cp36-cp36m-macosx_10_14_x86_64.whl (261.1 kB) Copy SHA256 hash SHA256||Wheel||cp36|
|mgcpy-0.3.0-py3.6-macosx-10.14-x86_64.egg (349.1 kB) Copy SHA256 hash SHA256||Egg||3.6|
|mgcpy-0.3.0.tar.gz (316.7 kB) Copy SHA256 hash SHA256||Source||None|