Xdas: a Python Framework for Distributed Acoustic Sensing

Alister Trabattoni , Marie Baillet, Martijn van den Ende, Diane Rivet, Eleonore Stutzman, Claudio Strumia, Francesco Biagioli

Xdas is a Python library designed to manipulate Distributed Acoustic Sensing (DAS) data. It provides a unified abstraction for reading any DAS file format into a standardized Python object, streamlining data handling across different acquisition systems. To address the challenge of massive, multi-file datasets, Xdas aggregates data chunks into virtually contiguous arrays organized by instrument and acquisition. This structure allows for efficient spatial and temporal slicing while minimizing overhead. To enable scalable offline processing of massive DAS datasets, Xdas process data in manageable chunks. To ensure processing continuity, Xdas uses a stateful pipes-and-filters architecture. Most Xdas operations are multithreaded by default to take full advantage of multicore systems. This approach also enables real-time data processing. Its built-in network streaming capabilities allow Xdas to be deployed on DAS instruments for custom, real-time workflows at the point of data generation. At its core, Xdas uses a labeled N-dimensional array structure that encapsulates both data values and coordinate metadata and can be used to handle any kind of dataset (not just time-space DAS records). This data model adheres to the established standards provided by the NetCDF4/HDF5 formats and the Climate and Forecast (CF) conventions. Designed to mirror the APIs of popular libraries such as NumPy, SciPy, and Xarray, Xdas minimizes the learning curve for new users. Its modular and extensible design means that adding support for a new file format or integrating a processing function typically requires less than ten lines of code.