Useful tools for periodicity analysis in time series data.
Project description
Periodicity
Useful tools for periodicity analysis in time series data.
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
implementationcelerite2
implementationcelerite2.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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for periodicity-1.0b5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | dffb3f17dd8b0513b5e664d5cd8f499b5877e92444d15d388a35e5ab234ca06b |
|
MD5 | 727517eb8f30a0d13417f33cdb216aee |
|
BLAKE2b-256 | 94fe4b41cd91a5ebb4d9b53ea9139c4c6badded8b127021225e173ba0429281b |