Metadata-Version: 2.4
Name: hypline
Version: 0.2.3
Summary: Data Cleaning and Analysis Pipeline for Hyperscanning Research
Project-URL: Repository, https://github.com/princeton-ddss/hypline
Project-URL: Documentation, https://princeton-ddss.github.io/hypline/
Author-email: Sangyoon Park <datumvitae@gmail.com>
License-File: LICENSE
Requires-Python: >=3.10
Requires-Dist: dill>=0.3.9
Requires-Dist: nibabel>=5.3.2
Requires-Dist: nilearn>=0.11.1
Requires-Dist: numpy>=2.2.4
Requires-Dist: polars>=1.26.0
Requires-Dist: pydantic>=2.10.6
Requires-Dist: pyyaml>=6.0.2
Requires-Dist: rich>=14.0.0
Requires-Dist: typer>=0.15.2
Description-Content-Type: text/markdown

# Hypline

Hypline is a Python package that provides a CLI tool for cleaning and analyzing data from hyperscanning studies involving dyadic conversations.

## Installation

Hypline can be installed using `pip`:

```bash
pip install hypline
```

It can also be installed using other package managers such as [`uv`](https://docs.astral.sh/uv/) and [`poetry`](https://python-poetry.org/docs/).

## Quick Start

Once the package is installed, `hypline` command will be available, like so:

```bash
hypline --help
```

Running the above will display an overview of the tool, including supported subcommands.

For instance, `clean` is a subcommand for performing confound regression to clean BOLD outputs from [fMRIPrep](https://fmriprep.org/en/stable/index.html), and its details can be viewed by running:

```bash
hypline clean --help
```

## What Next

If you want to learn more about Hypline, please check out the official project [documentation](https://princeton-ddss.github.io/hypline/).
