Metadata-Version: 2.3
Name: accusleepy
Version: 0.4.4
Summary: Python implementation of AccuSleep
License: GPL-3.0-only
Author: Zeke Barger
Author-email: zekebarger@gmail.com
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: fastparquet (>=2024.11.0,<2025.0.0)
Requires-Dist: joblib (>=1.4.2,<2.0.0)
Requires-Dist: matplotlib (>=3.10.1,<4.0.0)
Requires-Dist: numpy (>=2.2.4,<3.0.0)
Requires-Dist: pandas (>=2.2.3,<3.0.0)
Requires-Dist: pillow (>=11.1.0,<12.0.0)
Requires-Dist: pre-commit (>=4.2.0,<5.0.0)
Requires-Dist: pyside6 (>=6.7.1,<6.8.0)
Requires-Dist: scipy (>=1.15.2,<2.0.0)
Requires-Dist: toml (>=0.10.2,<0.11.0)
Requires-Dist: torch (>=2.6.0,<3.0.0)
Requires-Dist: torchvision (>=0.21.0,<0.22.0)
Requires-Dist: tqdm (>=4.67.1,<5.0.0)
Description-Content-Type: text/markdown

# AccuSleePy

## Description

AccuSleePy is a python implementation of AccuSleep--a set of graphical user interfaces for scoring rodent
sleep using EEG and EMG recordings. It offers several improvements over the original MATLAB version
and is the only version that will be actively maintained.

If you use AccuSleep in your research, please cite our
[publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224642):

Barger, Z., Frye, C. G., Liu, D., Dan, Y., & Bouchard, K. E. (2019). Robust, automated sleep scoring by a compact neural network with distributional shift correction. *PLOS ONE, 14*(12), 1–18.

The data and models associated with AccuSleep are available at https://osf.io/py5eb/

Please contact zekebarger (at) gmail (dot) com with any questions or comments about the software.

## What's new

AccuSleePy offers the following improvements over the MATLAB version:

- Up to 10 brain states can be configured through the user interface
- Models can be trained through the user interface
    - Model files contain useful metadata (brain state configuration,
      epoch length, number of epochs)
    - Models optimized for real-time scoring can be trained
- Lists of recordings can be imported and exported for repeatable batch processing
- Undo/redo functionality in the manual scoring interface

## Installation

- (recommended) create a new virtual environment (using
[venv](https://docs.python.org/3/library/venv.html),
[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html),
etc.) using python >=3.10,<3.13
- (optional) if you have a CUDA device and want to speed up model training, [install PyTorch](https://pytorch.org/)
- `pip install accusleepy`
- (optional) download a classification model from https://osf.io/py5eb/ under /python_format/models/

## Usage

`python -m accusleepy` will open the primary interface.

[Guide to the primary interface](accusleepy/gui/text/main_guide.md)

[Guide to the manual scoring interface](accusleepy/gui/text/manual_scoring_guide.md)

## Screenshots
Primary interface
![AccuSleePy primary interface](accusleepy/gui/images/primary_window.png)

Manual scoring interface
![AccuSleePy manual scoring interface](accusleepy/gui/images/viewer_window.png)

## Acknowledgements

We would like to thank [Franz Weber](https://www.med.upenn.edu/weberlab/) for creating an
early version of the manual labeling interface.
Jim Bohnslav's [deepethogram](https://github.com/jbohnslav/deepethogram) served as an
incredibly useful reference when reimplementing this project in python.

