A set of tools in Python for multiscale graph correlation and other statistical tests
Project description
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/neurodata/mgcpy
cd mgcpy
python3 setup.py install
sudo
, if requiredpython3 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
ordocker 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.
- Do
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.
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