scdn is a Python-based package implementing sparse causal dynamic network analysis for convolution model, particular for Functional magnetic resonance imaging (fMRI) in our paper. It aims to provide a sparse dynamic network estimation not only for fMRI data but for other possible data that can well represented by convolution model. The introduciton and explaination of parameters and ODE models can be found in (1). For more details of convolution model, see (2)
This package supports both python 2.7 and python 3.6.
Example provided in the repo has been tested in Mac os and Linux environment.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
This package is also published in pypi. For a quick installation, try
pip install scdn
What things you need to install the software and how to install them
See setup.py for details of packages requirements.
Download the packages by using git clone https://github.com/xuefeicao/scdn.git
python setup.py install
If you experience problems related to installing the dependency Matplotlib on OSX, please see https://matplotlib.org/faq/osx_framework.html
After installing our package locally, try to import scdn in your python environment and learn about package's function.
The examples subfolder includes a basic analysis of our sample data.
The test is going to be added in the future.
- Python 2.7
- python 2.7
- python 3.6
- Xuefei Cao - Maintainer - (https://github.com/xuefeicao)
- Xi Luo (http://bigcomplexdata.com/)
- Björn Sandstede (http://www.dam.brown.edu/people/sandsted/)
This project is licensed under the MIT License - see the LICENSE file for details