ObsPy: A Python Toolbox for Seismology

TLDR: ObsPy as mentioned in this paper is a Python toolbox that simplifies the usage of Python programming for seismologists by providing direct access to the actual time series, allowing the use of powerful numerical array-programming modules like NumPy (http://numpy.mathworks.org) or SciPy(http://scipy.org).

Abstract: The wide variety of computer platforms, file formats, and methods to access seismological data often requires considerable effort in preprocessing such data. Although preprocessing work-flows are mostly very similar, few software standards exist to accomplish this task. The objective of ObsPy is to provide a Python toolbox that simplifies the usage of Python programming for seismologists. It is conceptually similar to SEATREE (Milner and Thorsten 2009) or the exploration seismic software project MADAGASCAR (http://www.reproducibility.org). In ObsPy the following essential seismological processing routines are implemented and ready to use: reading and writing data only SEED/MiniSEED and Dataless SEED (http:// www.iris.edu/manuals/SEEDManual_V2.4.pdf), XML-SEED (Tsuboi et al. 2004), GSE2 (http://www.seismo.ethz.ch/autodrm/downloads/provisional_GSE2.1.pdf) and SAC (http:// www.iris.edu/manuals/sac/manual.html), as well as filtering, instrument simulation, triggering, and plotting. There is also support to retrieve data from ArcLink (a distributed data request protocol for accessing archived waveform data, see Hanka and Kind 1994) or a SeisHub database (Barsch 2009). Just recently, modules were added to read SEISAN data files (Havskov and Ottemoller 1999) and to retrieve data with the IRIS/FISSURES data handling interface (DHI) protocol (Malone 1997). Python gives the user all the features of a full-fledged programming language including a large collection of scientific open-source modules. ObsPy extends Python by providing direct access to the actual time series, allowing the use of powerful numerical array-programming modules like NumPy (http://numpy.scipy.org) or SciPy (http://scipy.org). Results can be visualized using modules such as matplotlib (2D) (Hunter 2007) or MayaVi (3D) (http://code.enthought.com/ projects/mayavi/). This is an advantage over the most commonly used seismological analysis packages SAC, SEISAN, SeismicHandler (Stammler 1993), or PITSA (Scherbaum and Johnson 1992), which do not provide methods for general numerical array manipulation. Because Python and its previously mentioned modules are open-source, there are no restrictions due to licensing. This is a clear advantage over the proprietary product MATLAB (http://www.mathworks.com) in combination with MatSeis (Creager 1997) or CORAL (Harris and Young 1997), where the number of concurrent processes is limited by a costly and restricting license policy. Additionally, Python is known for its intuitive syntax. It is platform independent, and its rapidly growing popularity extends beyond the seismological community (see, e.g., Olsen and Ely 2009). Python is used in various fields because its comprehensive standard library provides tools for all kinds of tasks (e.g., complete Web servers can be written in a few lines with standard modules). It has excellent features for wrapping external shared C or FORTRAN libraries, which are used within ObsPy to access libraries for manipulating MiniSEED (libmseed; http://www.iris.edu/pub/programs) and GSE2 (gse_util; http://www.orfeus-eu.org/Software/softwarelib.html#gse) volumes. Similarly, seismologists may wrap their own C or FORTRAN code and thus are able to quickly develop powerful and efficient software. In the next section we will briefly introduce the capabilities of ObsPy by demonstrating the data conversion of SAC files to MiniSEED volumes, removing the instrument response, applying a low-pass filter, and plotting the resulting trace. We then give an overview on how to access an external C or FORTRAN library from within Python.