Metadata-Version: 2.1
Name: ecgmentations
Version: 0.0.4
Summary: Ecg augmentation library and easy to use wrapper around other libraries
Home-page: https://github.com/rostepifanov/egcmentations
Author: Rostislav Epifanov
License: MIT
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=1.20.0
Requires-Dist: opencv-python-headless >=4.1.1

# Ecgmentations

Ecgmentations is a Python library for ecg augmentation. Ecg augmentation is used in deep learning to increase the quality of trained models. The purpose of ecg augmentation is to create new training samples from the existing data.

## Table of contents
- [Authors](#authors)
- [Installation](#installation)
- [A simple example](#a-simple-example)
- [Citing](#citing)

## Authors
[**Rostislav Epifanov** — Researcher at Novosibirsk State University]()

## Installation
Installation from PyPI:

```
pip install ecgmentations
```

Installation from GitHub:

```
pip install git+https://github.com/rostepifanov/ecgmentations
```

## A simple example
```python
import numpy as np
import ecgmentations as E

# Declare an augmentation pipeline
transform = E.Sequential([
    E.Reverse(p=0.5),
    E.ChannelShuffle(p=0.06),
])

# Create example ecg
ecg = np.ones((12, 5000)).T

# Augment an ecg
transformed = transform(ecg=ecg)
transformed_ecg = transformed['ecg']
```

# Citing

If you find this library useful for your research, please consider citing:

```
@misc{epifanov2023ecgmentations,
  Author = {Rostislav Epifanov},
  Title = {Ecgmentations},
  Year = {2023},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/rostepifanov/ecgmentations}}
}
```
