pyBOA
Published:
Detection algorithm for oceanographic data (specifically chlorophyl / temperature but can be used for others) accessible on Zenodo.
Original pseudo-code: Belkin, I.M., O’Reilly, J.E., 2009. An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. Journal of Marine Systems, Special Issue on Observational Studies of Oceanic Fronts 78, 319–326_ (doi).
Transcription of the work from: Lin et al. (2019) - Matlab, Lin, L., Liu, D., Luo, C., Xie, L., 2019. Double fronts in the Yellow Sea in summertime identified using sea surface temperature data of multi-scale ultra-high resolution analysis. Continental Shelf Research 175, 76–86. (doi). Ben Galuardi, boaR - R package.
Additions: Generalized contextual filter, rolling percentile selection, morphological thinning for single lines.
What to get: The sample netcdf file, the stnd_alone file, and pyBOA.py.
Important This works as an extension of the xarray packages and was built under python 3.9.
Example of output, overlaying chlorophyll-a (viridis collormap) and fronts (red)
Recommended citation: Lhériau-Nice, A. (2023). pyBOA: Contextual front detection (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.8135921.