Similar to ODV, but completely within a Python environment. This was implemented on the OSNAP branch, but wasn't perfect.
Potential value of highlighting before/after processing. Take the after data, subtract the before data, then make a section plot of the difference. When paired with the discrete reference data, this can highlight areas where the processing struggles (such as at boundaries, currents, the surface or clines) and where value is gained by using CTDCAL rather than "industry standard" or a different routine. Basically, "because you used this fitting method, this region here looks better than it otherwise would".
Similar to ODV, but completely within a Python environment. This was implemented on the OSNAP branch, but wasn't perfect.
Potential value of highlighting before/after processing. Take the after data, subtract the before data, then make a section plot of the difference. When paired with the discrete reference data, this can highlight areas where the processing struggles (such as at boundaries, currents, the surface or clines) and where value is gained by using CTDCAL rather than "industry standard" or a different routine. Basically, "because you used this fitting method, this region here looks better than it otherwise would".