Metadata-Version: 2.1
Name: host-picker
Version: 2.4.3
Summary: High Order STatisics picking algorithms
Home-page: https://github.com/mbagagli/host-picker
Author: Matteo Bagagli
Author-email: matteo.bagagli@erdw.ethz.com
License: UNKNOWN
Description: [![DOI](https://zenodo.org/badge/418490530.svg)](https://zenodo.org/badge/latestdoi/418490530)
        [![Build Status](https://app.travis-ci.com/mbagagli/host-picker.svg?branch=main)](https://app.travis-ci.com/mbagagli/host-picker)
        
        # HOST
        
        **Author:** _Matteo Bagagli_
        **Date:** _10/2021_
        **Version:** _2.4.3_
        
        ## What is it?
        The **HOST** acronym stands for **H**igh-**O**rder-**ST**atistics seismic pickers.
        This algorithm took inspiration from the one described in [_Baillard et al. 2014_](10.1785/0120120347).
        The idea behind this package is to provide user-friendly classes for seismic phase picking with
        _skewness_ and _kurtosis_ characteristic-functions.
        Originally applied to seismograms by [_Saragiotis et al._](10.1109/TGRS.2002.800438),
        the HOS-CF are able to detect energic transient when the statistical properties of a seismogram
        (or time series) change abruptly. These CF are calculated on moving window with fixed window.
        
        Measurements of statistical properties in a moving window are suitable for frequency-specific
        (or expected) transient. For this reason, the picker support a multi-window analysis,
        in order to collect more information or even increase the quality of detections.
        Both _skewness_ and _kurtosis_ are calculated with C-routine under the hood,
        making this package fast and suitable also for realtime picking porpouses.
        
        
        ## Standard installation
        In general, the usage of virtual env is a good habit for python users.
        Although this package's dependencies is not For separated **conda** environment is recommended.
        Since version `v2.4.1` the picker can be found in PyPI repository. Therefore to install the latest
        stable version you may simply type:
        ```bash
        $ pip install host-picker
        ```
        and be ready to go.
        
        ## Developer installation
        If you want to be updated with the latest patches or even contribute to
        the project (yes, really your PR are welcome!), fork-me and clone it
        to your device:
        ```bash
        $ git clone https://github.com/mbagagli/host host
        $ cd host/
        $ # conda activate myenv (optional)
        $ pip install .
        ```
        ... et voila'!
        
        ## Additional infos
        Although the package testing is in [Travis-CI](https://app.travis-ci.com/mbagagli/host-picker.svg?branch=main), you could still install `pytest` and
        inside the cloned folder type:
        ```bash
        $ pytest
        ```
        to check everything is fine
        
        The package comes with jupyter-notebooks (under `books` subdir) where you
        can test and understand this picking-algorithm.
        
        ## Citing
        
        If you use this code for your science, please consider citing it in your final results or manuscripts.
        ```
        M. Bagagli (2021). mbagagli/host-picker: host-picker. Zenodo. https://doi.org/10.5281/zenodo.5579271
        ```
        
        ## Reference
        - Baillard, C., Crawford, W.C., Ballu, V., Hibert, C. and Mangeney, A., 2014. An automatic kurtosis‐based P‐and S‐phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104(1), pp.394-409.
        - Saragiotis, C.D., Hadjileontiadis, L.J. & Panas, S.M., 2002. PAI-S/K: a robust automatic seismic P phase arrival identification scheme, IEEE Trans. Geosci. Remote Sens. 40, 1395–1404.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.5
Description-Content-Type: text/markdown
