Echopype: A Python library for interoperable and scalable processing of water column sonar data for biological information
arxiv(2021)
摘要
High-frequency sonar systems deployed on a wide array of ocean observing
platforms are creating a deluge of water column sonar data at an unprecedented
speed from all corners of the ocean. Efficient and integrative analysis of
these data, either across different sonar instruments or with other
oceanographic datasets, holds the key to monitoring and understanding the
response of marine ecosystems to the rapidly changing climate. In this paper we
present Echopype, an open-source Python software library designed to address
this need. By standardizing water column sonar data from diverse instrument
sources following a community convention and utilizing the widely embraced
netCDF data model to encode sonar data as labeled, multi-dimensional arrays,
Echopype facilitates intuitive, user-friendly exploration and use of sonar data
in an instrument-agnostic manner. By leveraging existing open-source Python
libraries optimized for distributed computing, Echopype directly enables
computational interoperability and scalability in both local and cloud
computing environments. Echopype's modularized package structure further
provides a conceptually unified implementation framework for expanding its
support for additional instrument raw data formats and incorporating new data
analysis functionalities. We envision the continued development of Echopype as
a catalyst for making information derived from water column sonar data an
integrated component of regional and global ocean observation strategies.
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