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Getting Started

Overview and Introduction

Welcome to the EristroPy package documentation! This document provides an overview of EristroPy, a powerful framework for working time series signals via entropy using Python.

The EristroPy package is designed to streamline the variability analysis of signals in Python. It provides end-to-end functionality, starting from constructing stationary signals to determining appropriate metric parameters and efficiently computing entropy and variability measures. By leveraging EristroPy, researchers and practitioners can focus on the analysis and interpretation of time series data, rather than spending time and effort on the intricate details of signal processing and analysis.

To the best of our knowledge, EristroPy is the only existing solution in Python that offers all the necessary functionality for valid and reproducible entropy analysis using novel and scalable heuristics. Its features and benefits enable researchers to perform comprehensive variability analysis, gain valuable insights, and contribute to advancements in the field of cardiopulmonary exercise testing.

Features & Benefits

EristroPy offers a range of features and benefits that facilitate the analysis of time series signals:

  • Automatic Signal Stationarity: EristroPy enables seamless construction of stationary signals, a necessary condition for valid entropy and variability analysis. It incorporates two common techniques, differencing and de-trending, and performs statistical stationarity checks to ensure that the dataset contains valid signals.
  • Scalable Entropy Calculations: EristroPy provides efficient implementations of sample and permutation entropy. Leveraging Numba's just-in-time compilation scheme, EristroPy ensures fast and scalable computations, allowing researchers to focus on the analysis rather than the intricacies of the calculations.
  • Optimal Parameter Selction: Determining appropriate parameter settings for entropy measures can be challenging. EristroPy takes the guesswork out by providing reasonable recommendations based on rigorous, nonparametric statistical approaches. These recommendations empower researchers to confidently choose suitable parameters for their analysis.

Installation

You can install EristroPy by using pip:

pip install eristropy

Usage

To start using EristroPy in your Python project, import it as follows:

import eristropy

License

EristroPy is released under the MIT License.

The MIT License (MIT)

Copyright (c) 2023 Zachary Blanks

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.