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A set of tools in Python for multiscale graph correlation and other statistical tests

Project description

# mgcpy

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`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.


## Installation Guide:

### Install from PyPi
pip3 install mgcpy

### Install from Github
git clone
cd mgcpy
python3 install
- `sudo`, if required
- `python3 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 20-30 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 --no-browser --allow-root`
- Then copy the url it generates, it looks something like this: `http://(0de284ecf0cd or`
- Edit this: `(0de284ecf0cd or` to: ``, 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
![MGCPY Flow](MGCPY.png)

## License

This project is covered under the **Apache 2.0 License**.

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Files for mgcpy, version 0.0.4
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Filename, size mgcpy-0.0.4-py3.6-macosx-10.13-x86_64.egg (263.9 kB) File type Egg Python version 3.6 Upload date Hashes View

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