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    "info": {
        "author": "Satish Palaniappan, Bear Xiong, Sambit Panda, Sandhya Ramachandran, Ananya Swaminathan, Richard Guo",
        "author_email": "spalani2@jhu.edu",
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            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
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        "description": "The R version is available on CRAN and https://github.com/neurodata/r-mgc.\nThe MATLAB version is available at https://github.com/neurodata/mgc-matlab.\n\n# mgcpy\n\n[![Coverage Status](https://coveralls.io/repos/github/neurodata/mgcpy/badge.svg?branch=master)](https://coveralls.io/github/neurodata/mgcpy?branch=master)\n[![Build Status](https://travis-ci.com/neurodata/mgcpy.svg?branch=master)](https://travis-ci.com/neurodata/mgcpy)\n[![PyPI](https://img.shields.io/pypi/v/mgcpy.svg)](https://pypi.org/project/mgcpy/)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/mgcpy.svg)](https://pypi.org/project/mgcpy/)\n[![DockerHub](https://img.shields.io/docker/automated/tpsatish95/mgcpy.svg)](https://hub.docker.com/r/tpsatish95/mgcpy/)\n[![DOI](https://zenodo.org/badge/147731955.svg)](https://zenodo.org/badge/latestdoi/147731955)\n[![Documentation Status](https://readthedocs.org/projects/mgcpy/badge/?version=latest)](https://mgcpy.readthedocs.io/en/latest/?badge=latest)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/)\n[![Code Climate](https://api.codeclimate.com/v1/badges/51ac28d51f1474bf3567/maintainability)](https://codeclimate.com/github/neurodata/mgcpy/maintainability)\n\n`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.\n\n- [Overview](#overview)\n- [Documentation](#documentation)\n- [System Requirements](#system-requirements)\n- [Installation Guide](#installation-guide)\n- [Setting up the development environment](#setting-up-the-development-environment)\n- [License](#license)\n- [Issues](https://github.com/neurodata/mgcpy/issues)\n\n# Overview\n``mgcpy`` aims 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.\n\n# Documenation\nThe official documentation with usage is at: https://mgc.neurodata.io/\nReadTheDocs: https://mgcpy.readthedocs.io/en/latest/\n\n# System Requirements\n## Hardware requirements\n`mgcpy` package requires only a standard computer with enough RAM to support the in-memory operations.\n\n## Software requirements\n### OS Requirements\nThis package is supported for *macOS* and *Linux*. The package has been tested on the following systems:\n+ macOS: Mojave (10.14.1)\n+ Linux: Ubuntu 16.04\n\n### Python Dependencies\n`mgcpy` mainly depends on the Python scientific stack.\n\n```\nnumpy\nscipy\nCython\nscikit-learn\npandas\nseaborn\n```\n\n# Installation Guide:\n\n### Install from PyPi\n```\npip3 install mgcpy\n```\n\n### Install from Github\n```\ngit clone https://github.com/neurodata/mgcpy\ncd mgcpy\npython3 setup.py install\n```\n- `sudo`, if required\n- `python3 setup.py build_ext --inplace  # for cython`, if you want to test in-place, first execute this\n\n# Setting up the development environment:\n- To build image and run from scratch:\n  - Install [docker](https://docs.docker.com/install/)\n  - Build the docker image, `docker build -t mgcpy:latest .`\n    - This takes 10-15 mins to build\n  - Launch the container to go into mgcpy's dev env, `docker run -it --rm --name mgcpy-env mgcpy:latest`\n- Pull image from Dockerhub and run:\n  - `docker pull tpsatish95/mgcpy:latest` or `docker pull tpsatish95/mgcpy:development`\n  - `docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:latest` or `docker run -it --rm -p 8888:8888 --name mgcpy-env tpsatish95/mgcpy:development`\n\n\n- To run demo notebooks (from within Docker):\n  - `cd demos`\n  - `jupyter notebook --ip 0.0.0.0 --no-browser --allow-root`\n  - Then copy the url it generates, it looks something like this: `http://(0de284ecf0cd or 127.0.0.1):8888/?token=e5a2541812d85e20026b1d04983dc8380055f2d16c28a6ad`\n  - Edit this: `(0de284ecf0cd or 127.0.0.1)` to: `127.0.0.1`, in the above link and open it in your browser\n  - Then open `mgc.ipynb`\n\n- To mount/load local files into docker container:\n  - 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.\n  - Do `cd ../workspace` when you are inside the container to view the mounted files. The **mgcpy** package code will be in `/root/code` directory.\n\n\n## MGC Algorithm's Flow\n![MGCPY Flow](https://raw.githubusercontent.com/neurodata/mgcpy/master/MGCPY.png)\n\n## Power Curves\n- Recreated Figure 2 in https://arxiv.org/abs/1609.05148, with the addition of MDMR and Fast MGC\n![Power Curves](https://raw.githubusercontent.com/neurodata/mgcpy/master/power_curves_dimensions.png)\n\n# License\n\nThis project is covered under the **Apache 2.0 License**.\n\n\n",
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