Metadata-Version: 2.3
Name: accusleepy
Version: 0.6.0
Summary: Python implementation of AccuSleep
License: GPL-3.0-only
Author: Zeke Barger
Author-email: zekebarger@gmail.com
Requires-Python: >=3.11,<3.14
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
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 (>=6.9.0,<7.0.0)
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 set of graphical user  interfaces for scoring rodent sleep
using EEG and EMG recordings.
It offers the following improvements over the MATLAB version (AccuSleep):

- Up to 10 brain states can be configured through the user interface
- Classification 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

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.


## 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.) with python >=3.11,<3.14
- (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/

Note that upgrading or reinstalling the package will overwrite any changes
to the [config file](accusleepy/config.json).

## 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)

## Changelog

- 0.6.0: Confidence scores can now be displayed and saved. Retraining your models is recommended
    since the new calibration feature will make the confidence scores more accurate.
- 0.5.0: Performance improvements
- 0.4.5: Added support for python 3.13, **removed support for python 3.10.**
- 0.4.4: Performance improvements
- 0.4.3: Improved unit tests and user manuals
- 0.4.0: Improved visuals and user manuals
- 0.1.0-0.3.1: Early development versions

## 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. The code that
creates spectrograms comes from the
[Prerau lab](https://github.com/preraulab/multitaper_toolbox/blob/master/python/multitaper_spectrogram_python.py)
with only minor modifications.
Jim Bohnslav's [deepethogram](https://github.com/jbohnslav/deepethogram) served as an
incredibly useful reference when reimplementing this project in python.
The model calibration code added in version 0.6.0 comes from Geoff Pleiss'
[temperature scaling repo](https://github.com/gpleiss/temperature_scaling).

