ABOUT_FLOATS is the jdata module of jLab.

 ABOUT_FLOATS  Historical dataset of eddy-resolving subsurface floats. 
    FLOATS.MAT and FLOATS.NC contain all available historical eddy-
    resolving subsurface float data, as distributed online at NOAA's 
    Atlantic Oceanographic and Meteorological Laboratory (AOML)
    and described by Ramsey, Furey, and Bower (2018) at
    A list of experiments can be found at 
    FLOATS is a slightly reprocessed version of the Matlab file distributed
    by AOML, modified to make the dataset more amenable to time series
    analysis, which require continuous, uniformly spaced fields. 
    FLOATS.MAT is a Matlab version, while FLOATS.NC is a NetCDF version.
    FLOATS is distributed as a part of JDATA, a supplement to the software
    toolbox JLAB, and is available at http://www.jmlilly.net.
    Additional processing
    The following processing steps have been applied to AOML's version:
        1. All data points lacking a valid position are removed
        2. Trajectories are split at gaps exceeding two days
        3. Trajectory segments less than two weeks in duration are removed
        4. Data are interpolated onto a uniformly spaced time grid 
        5. Trajectory segments with 12.5% or more missing data are removed
        6. Velocities are computed by central differencing position
    Step 4 handles the fact that not all data are uniformly spaced.  The 
    interpolation onto a uniformly spaced time grid using the median
    value of the sample interval for each data segment as the time spacing.
    Step 5 removes 7 floats, all from the from the same experiment, which
    have nearly half of their data missing within the uniform time grid.
    Step 6 forms velocities by calling the jLab function LATLON2UV.
    Matlab version
    LOAD FLOATS loads the structure FLOATS, with the following fields:
        floats.about         Pointer to this document       
        floats.experiment    Experiment names            {52 x 1 cell}
        floats.expid         Experiment id number        [3091 x 1 array] 
        floats.type          Float types                 {5 x 1 cell}
        floats.typeid        Type id number              [3091 x 1 array] 
        floats.id            Float id number             [3091 x 1 array] 
        floats.dt            Sample interval in days     [3091 x 1 array] 
        floats.num           Date in DATENUM format      {3091 x 1 cell} 
        floats.lat           Latitude                    {3091 x 1 cell} 
        floats.lon           Longitude                   {3091 x 1 cell} 
        floats.u             Eastward velocity in cm/s   {3091 x 1 cell} 
        floats.v             Northward velocity in cm/s  {3091 x 1 cell} 
        floats.p             Pressure in decibar         {3091 x 1 cell} 
        floats.t             Temperature in centigrade   {3091 x 1 cell} 
        floats.filled        True if gap was filled      {3091 x 1 cell} 
    ID is the float number, the same as 'indexFlt' in AOML's dataset.
    EXPID is the experiment number, the same as 'expNum' in AOML's dataset.
    Unlike in AOML's dataset, here floats trajectories may be split into
    multiple segements in order to avoid having long gaps.  Thus, there are 
    3091 trajectory segments, but only 2193 different floats. 
    Missing data points in P and T are filled with INFs.  There is no 
    missing data in the other fields. 
    The field FILLED is true if a gap was filled during processing Step 4
    listed above. Information on gaps filled during upstream processing in 
    forming the fields that compose the AOML dataset is not available. 
    The last eight field are all cell arrays, with one drifter per cell. 
    See JCELL for a number of tools for working with this type of data. In
    particular, CELLPLOT will plot such data.
    After 'load floats, type 'use floats' to map the structure fields 
    into named variables in the workspace--id, num, lat, lon, etc.
    To convert the cell arrays into one long column vector, with a NAN 
    separating each float, type 'cell2col(num,lat,lon,u,v,t,p);'.
    The experiment names are found by experiment(expid(100)), for example,
    which returns the experiment name of the 100th trajectory segment.
    This will match the listing in the table FloatDatabaseTable.pdf.
    The float type for a particular segment is similarly returned by e.g. 
    type(typeid(100)).  The types are as follows: 1) SOFAR 2) Bobber SOFAR
    3) RAFOS 4) RAFOS - VOCHA and 5) APEX.
    To generate the above figure of the dataset, type 'about_floats --f'. 
    FLOATS.MAT is distributed as a part of JDATA, a supplement to the 
    software toolbox JLAB, and is available at http://www.jmlilly.net.
    NetCDF version
    The NetCDF version is basically the same as the mat-file version, but
    (i) lacking the 'experiment' and 'type' variables, which are cell 
    arrays of strings, and (ii) having all the other cell array fields  
    stored as column arrays with separating NaNs, see COL2CELL.
    The experiment names corresponding to 'expid' can be found in the
    document FloatDatabaseTable.pdf, while the type names are given above.
    Use the JLAB routine NCLOAD to load the NetCDF file and convert all the
    NaN-separated columns into cells, i.e. 'ncload floats'.
    Dataset creation
    For completeness, the m-file ABOUT_FLOATS also contains the processing 
    steps used in the creation of FLOATS.MAT and FLOATS.NC.
    If you wish to recreate these yourself, with JLAB on your search path, 
    'about floats --create' will create FLOATS.MAT and FLOATS.NC by 
    reading in allFloats_12122017.mat as downloaded from AOML.  This 
    dataset therefore needs to be on your search path. 
    'about_floats --f' generates the sample figure shown above.
    Usage: about_floats
           about_floats --create     
    This is part of JLAB --- type 'help jlab' for more information
    (C) 2014--2019 J.M. Lilly --- type 'help jlab_license' for details

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