Package: mgcpy
The R version is available on CRAN andhttps://github.com/neurodata/r-mgc.
The MATLAB version is available athttps://github.com/neurodata/mgc-matlab.mgcpymgcpyis 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.OverviewDocumentationSystem RequirementsInstallation GuideSetting up the development environmentLicenseIssuesOverviewmgcpyaims to be a comprehensive independence testing package including commonly used independence tests 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.DocumenationThe official documentation with usage is at:https://mgc.neurodata.io/ReadTheDocs:https://mgcpy.readthedocs.io/en/latest/System RequirementsHardware requirementsmgcpypackage requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS RequirementsThis package is supported formacOSandLinux. The package has been tested on the following systems:macOS: Mojave (10.14.1)Linux: Ubuntu 16.04Python Dependenciesmgcpymainly depends on the Python scientific stack.numpy
scipy
Cython
scikit-learn
pandas
seabornInstallation Guide:Install from PyPipip3 install mgcpyInstall from Githubgit clone https://github.com/neurodata/mgcpy
cd mgcpy
python3 setup.py installsudo, if requiredpython3 setup.py build_ext --inplace  # for cython, if you want to test in-place, first execute thisSetting up the development environment:To build image and run from scratch:InstalldockerBuild the docker image,docker build -t mgcpy:latest .This takes 10-15 mins to buildLaunch the container to go into mgcpy's dev env,docker run -it --rm --name mgcpy-env mgcpy:latestPull image from Dockerhub and run:docker pull tpsatish95/mgcpy:latestordocker pull tpsatish95/mgcpy:developmentdocker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latestordocker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:developmentTo run demo notebooks (from within Docker):cd demosjupyter notebook --ip 0.0.0.0 --no-browser --allow-rootThen copy the url it generates, it looks something like this:http://(0de284ecf0cd or 127.0.0.1):8888/?token=e5a2541812d85e20026b1d04983dc8380055f2d16c28a6adEdit this:(0de284ecf0cd or 127.0.0.1)to:127.0.0.1, in the above link and open it in your browserThen openmgc.ipynbTo mount/load local files into docker container:Dodocker 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.Docd ../workspacewhen you are inside the container to view the mounted files. Themgcpypackage code will be in/root/codedirectory.MGC Algorithm's FlowPower CurvesRecreated Figure 2 inhttps://arxiv.org/abs/1609.05148, with the addition of MDMR and Fast MGCLicenseThis project is covered under theApache 2.0 License.