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Description
Hello - thanks for developing and sharing this awesome toolbox.
My group and I (at the NOAA/SWFSC) are looking to switch over to pyglider for our slocum glider data processing. I've been playing with outputs (from slocum.binary_to_timeseries
) relative to our past outputs (from SOCIB Matlab toolbox), and I have a question about how to best use pyglider with our data. Also, off the top, happy to move this to a Discussion if that's more appropriate.
For a number of our past deployments, our science sampling strategy has been to only sample on dives. Thus, if we pass a sci variables (eg "sci_water_temp") to binary_to_timeseries()
as 'time_base', then our timeseries only consists of the dives. Also, on these deployments the glider computer only samples every 4-5s, and so using eg "m_depth" as 'time_base' means we'd be downsampling the science data, which we'd like to avoid.
An additional factor is that we often have a variety of additional sensors on our gliders, often either a Nortek echosounder or shadowgraph camera in addition to oxygen/eco puck, and so we want to stay flexible to whatever sampling strategies we decide to use across instruments in the future.
(A brief note recognizing that using sci_m_present_time for all sensors means slightly interpolating values from other sci sensors, eg oxygen, but as y'all have chosen we're ok with this)
Do you have thoughts/suggestions for how to use pyglider.slocum and 1) ensure the timeseries has both the glider's dives and climbs and 2) not downsample the science sensors?
Per the dbdreader backend thread, perhaps the best option for what we're looking for is to use something like @smerckel's suggestion from that thread of x=[v for _,v in dbd.get(*dbd.parameterNames, return_nans=True)]
, put the engineering and science variables are each put in their own data frames, and then do a pd.merge(df_eng, df_sci, "outer", left_index=True, right_index=True)
. If you agree, is this something you'd be open to as a pr?