Roadmap for Brain Networks In Python
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BrainNetworksInPython is a toolkit to analyse structural covariance brain networks using python.
The project aims to:
- unlock the specialised knowledge required to conduct graph theoretic analyses on brain imaging data by providing clear documentation
- make an existing code base more modular and therefore flexible enough to be used for a wider range of applications
- promote free and open source community driven scientific code
What do we need to do?
Milestone: Refactor Brain Networks In Python code into modules
We will consider issues within this milestone our first priority
Brain Networks In Python needs to be refactored to break down the conceptual steps for a network analysis to allow the user to re-combine them as they see fit and, as we are fond of repeating, "The second best time to add in tests for software is when refactoring. (The optimum time would have been when the code was written in the first place)".
#22 Document the new regression tests
#45 Use Sphinx to document code
#70 Use flake8 to lint code
#10 Imitate the shablona python package framework
#40 Speed up creation of random graphs
#39 Shadow Brain Connectivity Toolbox
#75 Add command line examples to readme
testing : maximise coverage of code, especially for core modules (these are modules in the top level of the BrainNetworksInPython folder, or its wrappers subfolder
Milestone: Add new functionality to the code
#13 Introduce way to investigate differences between two groups
#37 Add diverse club to network measures we can compute
#41 Bootstrap resampling for correlation matrices
Milestone: Release as a Python package
In the fullness of time, we plan to release Brain Networks In Python as a Python package. Let's keep track of what needs to be done here.
#19 Choose a package name
#10 Restructure to python package framework
Finally...
Milestone: Make Brain Networks In Python accessible for newbies
Grow our community!
We try to make Brain Networks In Python a pleasant environment for contributors. This starts with our code of conduct and contributing guidelines. What else can we do for our users and contributors?