function out = bbopbin(in, cfl, dww)
% BBOPBIN   - Bin bbop data
% Bins the data in depth bins and returns the mean for each bin
%
% Use as: out = bbopbin(in, cfl, dww)
%
% Inputs: in  = column oriented data (from GETPRRDATA)
%         cfl = configration data (from READCFL)
%         dww = depth window width (in m)
%
% The center of each depth bins start from the minimum depth rounded 
% towards zero to the maximum depth rounded towards infinity. The spacing
% between each bin center is specified by dww.

% Brian Schlining
% 28 Jan 2000

%===================================
% Find the column containing depth
%===================================
fields = lower(strvcat(cfl.info.description));
d      = find(strcmp(lower({cfl.info.description}), 'depth'));
depth  = in(:,d);

%===============================
% Create a vector of depth bins
%===============================
minDepth  = floor(nanmin(depth));
maxDepth  = ceil(nanmax(depth));
binCenter = [minDepth:dww:maxDepth+dww]';
numBins   = length(binCenter);
[r c]     = size(in);

%==================
% Initalize memory
%==================
out = ones(numBins, c)*NaN;

%===============================
% Calculate a mean for each min
%===============================
for i = 1:length(binCenter)
   top      = binCenter(i) - dww/2;
   bottom   = binCenter(i) + dww/2;
   good     = find(depth > top & depth <= bottom);
   if isempty(good)
      out(i,:) = NaN;
   elseif length(good) == 1
      out(i,:) = in(good,:);
   else
      out(i,:) = mean(in(good,:));
   end
   
end

%=================================================================
% Replace the depth column with the bins and remove rows of NaN's
%=================================================================
out(:,d) = binCenter;
out      = excise(out);