spacetop preprocessing

Code for preprocessing fMRI, physiogical and behavioral data

About
Prerequisites
Usage
Getting help
Contributing
License
Contributors
Acknowledgments

About

spacetop-prep is a repository that hosts a number of codes for preprocessing data, collected under the Spatial Topology grant.

Preprocessing entails data cleaning of fMRI, physiological, and behavioral data, using both open-source containers such as fmriprep or custom built code specifically for the Spatial Topology dataset.
The goal of this repository is two-folds: reproducibility of the preprocessing steps in the spacetop universe, and generalizatbility for other users planning to preprocess BIDS-formatted fMRI, physio, and behavioral data.
Each folder is intended to serve as an independent module of its own. Please go through each README underneath each modular folder, which will walk you through the prerequisites, usage, and relevant questions.

Prerequisites

For the physiological data preprocessing, please install the following conda environment, physio.yaml

Usage

git clone the repository
Run the following code to enable a spacetop_prep module.

``` python setup.py sdist pip install -e . ```

Import a module. For example

from spacetop_prep.physio import utils

*  [physio](https://github.com/spatialtopology/spacetop-prep/tree/master/spacetop_prep/physio) @jungheejung
*  [redcap](https://github.com/spatialtopology/spacetop-prep/tree/master/spacetop_prep/redcap) @jungheejung

Getting help

If you have issues or any suggestions, please use our Issues tab. We have sections for

🐞 bug report,
💎 feature request
📚 documentation request
🧠 logging in duplicate scans for datalad users.

Based on your input, we plan to compile a generic Wiki with Q&As for future users.

Contributing

Thank you for your interest in making spacetop-prep a better resource.

Submitting changes

Please send a GitHub Pull Request to spacetop-prep with a clear list of what you've done (read more about pull requests).
Please follow our coding conventions (below) and make sure all of your commits are atomic (one feature per commit).
Always write a clear log message for your commits. One-line messages are fine for small changes, but bigger changes should look like this:
```
$ git commit -m "${TAG}: A brief summary of the commit
> 
> A paragraph describing what changed and its impact."
```
Coding conventions
We indent using four spaces
We ALWAYS put spaces after list items and method parameters ([1, 2, 3], not [1,2,3]), around operators (x += 1, not x+=1), and around hash arrows.
We strive to use relative paths instead of absolute paths. In cases where absolute paths are needed, consider using arguments outside of python script, using argparse or click.
For commit messages, we use tags, follow the convention of Numpy: check out documentation ["writing the commit message"] (https://numpy.org/doc/stable/dev/development_workflow.html?highlight=maint#writing-the-commit-message)