corTsgLinear.m

function [error] = corTsgLinear(hMainFig, PARA, dateMin, dateMax)
% Correct the TSG salinity time series with the Water sample.
% Use a linear fit to the water sample/tsg difference
% 
% Input
% hMainFig ..... Handle to the main GUI
% PARA ......... Parameter
% dateMin ...... the correction is applied between dateMin and date Max
% dateMax ...... the correction is applied between dateMin and date Max
%
% Output
% Error ........  1 everything OK
%       ........ -1 dateMax <= date Min
%
% The error is maximum, equal to 1, if the correction is computed with
% less than 4 samples
%
% The error minimum cannot be lower than 0.01

% Get application data
% --------------------
tsg    = getappdata( hMainFig, 'tsg_data');
SAMPLE = tsg.plot.sample;

% -------------------------------------------------------------------------
% Get from the checkbox the QC code on which the correction will be applied
% -------------------------------------------------------------------------

% get list of keys from hashtable tsg.qc.hash, defined inside
% tsg_initialisation.m
% -----------------------------------------------------------
qc_list = get(tsg.qc.hash);

% iterate (loop) on each key store inside hastable
% ------------------------------------------------
keptCode = [];
nKeptCode = 0;
for i=1:numel(qc_list)

  % get key and some values in hashtable
  % ------------------------------------
  key   = qc_list{i};
  
  % get handle of checkbox
  % ----------------------
  hCb = findobj(hMainFig, 'tag', ['TAG_CHECK_CORRECTION_' key]);
    
  if get( hCb, 'value' )
    nKeptCode = nKeptCode + 1;
    keptCode(nKeptCode) = get(tsg.qc.hash, key, 'code');
  end
end

% Get PROBABLY_GOOD, PROBABLY_BAD and VALUE_CHANGED codes
% -------------------------------------------------------
PROBABLY_GOOD = get(tsg.qc.hash, 'PROBABLY_GOOD', 'code');
PROBABLY_BAD  = get(tsg.qc.hash, 'PROBABLY_BAD',  'code');
VALUE_CHANGED = get(tsg.qc.hash, 'VALUE_CHANGED', 'code');

% Intialisation
% 01/09/2009 : intialisation to NaN
% ---------------------------------
if isempty( tsg.([PARA '_ADJUSTED']) )  
  tsg.([PARA '_ADJUSTED'])       = NaN*ones(size(tsg.(PARA)));
  tsg.([PARA '_ADJUSTED_QC'])    = NaN*ones(size(tsg.([PARA '_QC'])));
  tsg.([PARA '_ADJUSTED_ERROR']) = NaN*ones(size(tsg.(PARA))); 
end

if dateMax > dateMin
 
  % Find samples within TIME_WINDOWS with Good, probably Good, QC
  % -------------------------------------------------------------
  ind = find( tsg.DAYD_EXT    >= dateMin &  tsg.DAYD_EXT    <= dateMax &...
              tsg.([SAMPLE '_EXT_QC']) <= PROBABLY_GOOD);

  if ~isempty(ind)

    % detect NaN in sample.SSPS_DIF due to bad QC code for tsg.SSPS
    % -------------------------------------------------------------
    ind2 = find(~isnan(tsg.EXT_DIF(ind)));

    % Compute linear fit of the TSG/SAMPLE difference
    % -----------------------------------------------
    if ~isempty(ind2) && length(ind2) > 1
      if ~isempty(tsg.EXT_DIF(ind(ind2)))

        % Linear fit applied to the difference tsg-sample
        % -----------------------------------------------
        X = tsg.DAYD_EXT(ind(ind2));
        Y = tsg.EXT_DIF(ind(ind2));

        [p, S, mu] = polyfit( X, Y, 1);

        % The correction is applied to the TSG between dateMin and dateMax using
        % a linear interpolation only on measurements with keptCode Quality
        % Codes
        % ----------------------------------------------------------------------
        for icode = 1 : length( keptCode )
          dtTsg = find( tsg.DAYD    >= dateMin  & tsg.DAYD <= dateMax &...
            tsg.([PARA '_QC']) == keptCode( icode ));

          if ~isempty( dtTsg )
            [tsg.([PARA '_ADJUSTED'])(dtTsg),...
                                    tsg.([PARA '_ADJUSTED_ERROR'])(dtTsg)] =...
                                    polyval( p, tsg.DAYD(dtTsg), S, mu);
            tsg.([PARA '_ADJUSTED'])(dtTsg) = ...
                            tsg.(PARA)(dtTsg) + tsg.([PARA '_ADJUSTED'])(dtTsg);
            tsg.([PARA '_ADJUSTED_QC'])(dtTsg) = tsg.([PARA '_QC'])(dtTsg);

            % The error is maximum, equal to 1, if the median is computed with
            % less than 4 samples
            % -------------------------------------------------------------
            if length( ind2 ) < 4
              tsg.([PARA '_ADJUSTED_ERROR'])(dtTsg) = 1;
            end
          end

        end

        % The error minimum cannot be lower than 0.01 or equal to Inf
        % -----------------------------------------------------------
        tsg.([PARA '_ADJUSTED_ERROR'])...
                            ( tsg.([PARA '_ADJUSTED_ERROR']) < 0.01 ) = 0.01;

        tsg.([PARA '_ADJUSTED_ERROR'])...
                            (isinf(tsg.([PARA '_ADJUSTED_ERROR'])) == 1) = 0.01;
      end
    end
  end

  % Update tsg application data
  % ---------------------------
  setappdata( hMainFig, 'tsg_data', tsg);

  % everything OK
  % -------------
  error = 1;

else

  % DateMax <= DateMin
  % ------------------
  error = -1;

end