A Python-based converter for sonar logs used by PINGMapper
Project description
A Python-based convertor for sonar logs collected with consumer-grade fishfinders.
PINGVerter is designed to pre-process sonar logs for PINGMapper post-processing. It is not intended to function as a stand-alone package.
Compatibility
- Humminbird ( *.DAT/ *.SON/ *.IDX)
- Lowrance ( *.sl2 / *.sl3)
- Cerulean (Beta) (Omniscan 450 SS *.svlog)
Installation
pip install pingverter
Usage - Example Only
PINGVerter can be used as a stand-alone engine for decoding sonar logs and exporting ping attributes to CSV. While this functionality is described here, please use PINGMapper for all sonar log processing.
Humminbird
# Import
from pingverter import hum2pingmapper
# Parameters
inFile = r'C:\Path\To\Recording\Rec00001.DAT'
projDir = r'C:\Path\To\Outputs\MyProject'
sonar_object = hum2pingmapper(inFile, projDir)
Lowrance
# Import
from pingverter import low2pingmapper
# Parameters
inFile = r'C:\Path\To\Recording\Log.sl2'
projDir = r'C:\Path\To\Outputs\MyProject'
sonar_object = low2pingmapper(inFile, projDir)
Cerulean (Beta)
# Import
from pingverter import low2pingmapper
# Parameters
inFile = r'C:\Path\To\Recording\Log.svlog'
projDir = r'C:\Path\To\Outputs\MyProject'
sonar_object = cerul2pingmapper(inFile, projDir)
Outputs from the above examples will be exported to C:\Path\To\Outputs\MyProject\meta.
Acknowledgments
PINGVerter has been made possible through mentorship, partnerships, financial support, open-source software, manuscripts, and documentation linked below.
NOTE: The contents of this repository are those of the author(s) and do not necessarily represent the views of the individuals and organizations specifically mentioned here.
-
Dr. Arthur Trembanis (Post-Doc Advisor) & Dr. Daniel Buscombe (PhD Advisor)
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Coastal Sediments, Hydrodynamics and Engineering Lab (CSHEL), College of Earth, Ocean, & Environment (CEOE), University of Delaware
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- Bodine, C. S., Buscombe, D., Best, R. J., Redner, J. A., & Kaeser, A. J. (2022). PING-Mapper: Open-source software for automated benthic imaging and mapping using recreation-grade sonar. Earth and Space Science, 9, e2022EA002469. https://doi.org/10.1029/2022EA002469
- Bodine, C. S., Buscombe, D., & Hocking, T. D. (2024). Automated river substrate mapping from sonar imagery with machine learning. Journal of Geophysical Research: Machine Learning and Computation, 1, e2024JH000135. https://doi.org/10.1029/2024JH000135
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- Buscombe, D., Grams, P.E., and Smith, S. (2015) "Automated riverbed sediment classification using low-cost sidescan sonar", Journal of Hydraulic Engineering, https://doi.org/10.1061/(ASCE)HY.1943-7900.0001079, 06015019.
- Buscombe, D., 2017, Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar. ENVIRONMENTAL MODELLING & SOFTWARE 89, 1-18.
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- Halmai, Akos; Gradwohl Valkay, Alexandra; Czigany, Szabolcs; Ficsor, Johanna; Liptay, ZoltAn Arpad; Kiss, Kinga; Loczy, Denes and Pirkhoffer, Ervin. 2020. "Applicability of a Recreational-Grade Interferometric Sonar for the Bathymetric Survey and Monitoring of the Drava River" ISPRS International Journal of Geo-Information 9, no. 3: 149. https://doi.org/10.3390/ijgi9030149
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sonarlight - Kenneth Thoro Martinsen
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Navico (Lowrance, Simrad, B&G) Sonar Log File Format - Herbert Oppmann
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Vincent Capone - Black Laser Learning
Future Development, Collaborations, & Partnerships
If you are interested in partnering on future developments, please reach out to Cameron Bodine.
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