Metadata-Version: 2.1
Name: periodicity
Version: 1.0b3
Summary: Useful tools for periodicity analysis in time series data.
Home-page: https://github.com/dioph/periodicity
Author: Eduardo Nunes
Author-email: dioph@pm.me
License: MIT
Description: # 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 (WIP):
            * Wavelet Transform
            * Hilbert-Huang Transform
        * 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
            * `celerite` implementation
            * `pymc3` implementation (soon™)
        
        ## 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.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: docs
