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data.\n\n[![](https://github.com/dioph/periodicity/workflows/CI/badge.svg)](https://github.com/dioph/periodicity/actions?query=branch%3Amaster)\n[![PyPI version](https://badge.fury.io/py/periodicity.svg)](https://badge.fury.io/py/periodicity)\n[![Downloads](https://pepy.tech/badge/periodicity)](https://pepy.tech/project/periodicity)\n\n__Documentation: https://periodicity.readthedocs.io__\n\nCurrently includes:\n* Auto-Correlation Function (and other general timeseries utilities!)\n* Spectral methods:\n    * Lomb-Scargle periodogram\n    * Bayesian Lomb-Scargle with linear Trend (soon™)\n* Time-frequency methods:\n    * Wavelet Transform\n    * Hilbert-Huang Transform\n    * Composite Spectrum\n* Phase-folding methods:\n    * String Length\n    * Phase Dispersion Minimization\n    * Analysis of Variance (soon™)\n* Decomposition methods:\n    * Empirical Mode Decomposition\n    * Local Mean Decomposition\n    * Variational Mode Decomposition (soon™)\n* Gaussian Processes:\n    * `george` implementation\n    * `celerite2` implementation\n    * `celerite2.theano` implementation\n\n## Installation\n\nThe latest version is available to download via PyPI: __`pip install periodicity`__.\n\nAlternatively, 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`__.\n\n## Development\n\nIf you're interested in contributing to periodicity, install __pipenv__ and you can setup everything you need with __`pipenv install --dev`__.\n\nTo automatically test the project (and also check formatting, coverage, etc.), simply run __`tox`__ within the project's directory.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdioph%2Fperiodicity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdioph%2Fperiodicity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdioph%2Fperiodicity/lists"}