ObsPy: a bridge for seismology into the scientific Python ecosystem. (18th May 2015)

• Record Type: Journal Article
• Authors: Krischer, Lion; Megies, Tobias; Barsch, Robert; Beyreuther, Moritz; Lecocq, Thomas; Caudron, Corentin; Wassermann, Joachim
• Is Part Of: Computational science & discovery. Volume 8:Number 1(2015)
• Publication Date: 2015-05-18
• Subjects: seismology -- time series analysis -- Python -- earthquakes -- signal processing -- NumPy -- SciPy; Science -- Computer simulation -- Periodicals; Technology -- Computer simulation -- Periodicals; Science -- Data processing -- Periodicals; Technology -- Data processing -- Periodicals; Research -- Methodology -- Periodicals; Research -- Periodicals; Periodicals; 501.13
• DOI: 10.1088/1749-4699/8/1/014003
• Languages: English
• ISSNs: 1749-4699

Abstract

Abstract: The Python libraries NumPy and SciPy are extremely powerful tools for numerical processing and analysis well suited to a large variety of applications. We developed ObsPy (http://obspy.org ), a Python library for seismology intended to facilitate the development of seismological software packages and workflows, to utilize these abilities and provide a bridge for seismology into the larger scientific Python ecosystem. Scientists in many domains who wish to convert their existing tools and applications to take advantage of a platform like the one Python provides are confronted with several hurdles such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software. We present an approach to implement a domain-specific time series library on top of the scientific NumPy stack. In so doing, we show a realization of an abstract internal representation of time series data permitting I/O support for a diverse collection of file formats. Then we detail the integration and repurposing of well established legacy codes, enabling them to be used in modern workflows composed in Python. Finally we present a case study on how to integrate research code into ObsPy, opening it to the broader community. While the implementations presented in this work are specific to seismology, many of the described concepts and abstractions are directly applicable to other sciences, especially to those with an emphasis on time series analysis.