https://github.com/dioph/periodicity

Useful tools for periodicity analysis in time series data.
https://github.com/dioph/periodicity

gaussian-processes periodicity-analysis spectral-analysis time-series wavelets

Last synced: about 2 months ago
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Useful tools for periodicity analysis in time series data.

• Host: GitHub
• URL: https://github.com/dioph/periodicity
• Owner: dioph
• License: mit
• Created: 2018-10-26T12:59:27.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2024-01-11T18:42:32.000Z (9 months ago)
• Last Synced: 2024-07-07T10:44:24.278Z (3 months ago)
• Topics: gaussian-processes, periodicity-analysis, spectral-analysis, time-series, wavelets
• Language: Python
• Homepage: https://periodicity.readthedocs.io
• Size: 3.22 MB
• Stars: 33
• Watchers: 2
• Forks: 5
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Periodicity

Useful tools for periodicity analysis in time series data.

[![](https://github.com/dioph/periodicity/workflows/CI/badge.svg)](https://github.com/dioph/periodicity/actions?query=branch%3Amaster)

[![PyPI version](https://badge.fury.io/py/periodicity.svg)](https://badge.fury.io/py/periodicity)

[![Downloads](https://pepy.tech/badge/periodicity)](https://pepy.tech/project/periodicity)

__Documentation: https://periodicity.readthedocs.io__

Currently includes:

* Auto-Correlation Function (and other general timeseries utilities!)

* Spectral methods:

    * Lomb-Scargle periodogram

    * Bayesian Lomb-Scargle with linear Trend (soon™)

* Time-frequency methods:

    * Wavelet Transform

    * Hilbert-Huang Transform

    * Composite Spectrum

* Phase-folding methods:

    * String Length

    * Phase Dispersion Minimization

    * Analysis of Variance (soon™)

* Decomposition methods:

    * Empirical Mode Decomposition

    * Local Mean Decomposition

    * Variational Mode Decomposition (soon™)

* Gaussian Processes:

    * `george` implementation

    * `celerite2` implementation

    * `celerite2.theano` implementation

## Installation

The latest version is available to download via PyPI: __`pip install periodicity`__.

Alternatively, you can build the current development version from source by cloning this repo (__`git clone https://github.com/dioph/periodicity.git`__) and running __`pip install ./periodicity`__.

## Development

If you're interested in contributing to periodicity, install __pipenv__ and you can setup everything you need with __`pipenv install --dev`__.

To automatically test the project (and also check formatting, coverage, etc.), simply run __`tox`__ within the project's directory.