--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/spikecleaning.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,214 @@
+% spikecleaning detects and corrects possible spikes in analysis objects
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: SPIKECLEANING detects spikes in the temperature data and
+%	           replaces them by artificial values depending on the method chosen ('random',
+%	           'mean', 'previous').
+%	           Spikes are defined as singular samples with an (absolute) value
+%	           higher than kspike times the standard deviation of the high-pass
+%	           filtered (IIR filter) input AO.
+%
+% CALL:        b = spikecleaning(a1, a2, ..., an, pl)
+%
+% INPUTS:    aN - a list of analysis objects
+%	           pl - parameter list
+%
+% OUTPUTS:     b - a list of analysis objects with "spike values" removed
+%              and corrected
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'spikecleaning')">Parameters Description</a>
+% 
+% VERSION:     $Id: spikecleaning.m,v 1.17 2011/04/08 08:56:16 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout=spikecleaning(varargin)
+
+  %%% Check if this is a call for parameters
+  if utils.helper.isinfocall(varargin{:})
+    varargout{1} = getInfo(varargin{3});
+    return
+  end
+
+  if nargout == 0
+    error('### cat cannot be used as a modifier. Please give an output variable.');
+  end
+
+  % Collect input variable names
+  in_names = cell(size(varargin));
+  for ii = 1:nargin,in_names{ii} = inputname(ii);end
+
+  % Collect all AOs
+  [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+  pli             = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+
+  pls = parse(pli, getDefaultPlist());
+
+  % initialise output array
+  bo = [];
+
+  % go through each input AO
+  for i=1:numel(as)
+    a = as(i);
+    d = a.data;
+
+    % check this is a time-series object
+    if ~isa(d, 'tsdata')
+      error(' ### temperature spike detection requires tsdata (time-series) inputs.')
+    end
+
+    %--- check input parameters
+    kspike = find(pls, 'kspike'); % kspike*sigma definition
+    method = find(pls, 'method'); % method of spike-values substitution
+    pls.pset('gain', 1);          % gain of the filter
+    pls.pset('type', 'highpass'); % type of the filter
+    fs = plist();
+    fs.append('fs', d.fs);
+    pls.combine(fs);              % determination of the sampling frequency of the input AO
+
+    % high-pass filtering data
+    xfiltered = filtfilt(a, miir(pls));
+
+    % standard deviation of the filtered data is calculated
+    nxfiltered = find(abs(xfiltered) < kspike*std(xfiltered));
+
+    xfiltered_2 = xfiltered.data.y(nxfiltered);
+
+    std_xfiltered_2 = std(xfiltered_2);
+
+    % spikes vector position is determined
+    nspike = find(abs(xfiltered) > kspike*std_xfiltered_2);
+
+    % substitution of spike values starts here
+    xcleaned = a.data.y;
+    for j=1:length(nspike)
+      if nspike(j) <=2 % just in case a spike is detected in the 1st or 2nd sample
+        xcleaned(nspike(j)) = mean(xcleaned(1:50));
+      else
+        if strcmp(method, 'random') % spike is substituted by a random value: N(0,std_xfiltered)
+          xcleaned(nspike(j)) = xcleaned(nspike(j)-1) + randn(1)*std_xfiltered_2;
+        elseif strcmp(method, 'mean') % spike is substituted by the mean if the two previous values
+          xcleaned(nspike(j)) = (xcleaned(nspike(j)-1) + xcleaned(nspike(j)-2))/2;
+        elseif strcmp(method, 'previous') % spike is substituted by the pervious value
+          xcleaned(nspike(j)) = xcleaned(nspike(j)-1);
+        end
+      end
+    end
+
+    % create new output tsdata
+    ts = tsdata(xcleaned, d.fs);
+    ts.setYunits(d.yunits);
+    ts.setXunits(d.xunits);
+
+    % % create new output history
+    % h = history(ALGONAME, VERSION, pls, a.hist);
+    % h = set(h, 'invars', invars);
+
+    % make output analysis object
+    b = ao(ts);
+    b.name = sprintf('spikecleaning(%s)', ao_invars{i});
+    b.addHistory(getInfo('None'), pls, ao_invars(i), as(i).hist);
+
+    % add to output array
+    bo = [bo b];
+
+  end
+
+  % Set output
+  if nargout == numel(bo)
+    % List of outputs
+    for ii = 1:numel(bo)
+      varargout{ii} = bo(ii);
+    end
+  else
+    % Single output
+    varargout{1} = bo;
+  end
+
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                               Local Functions                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% FUNCTION:    getInfo
+%
+% DESCRIPTION: Get Info Object
+%
+% HISTORY:     11-07-07 M Hewitson
+%                Creation.
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function ii = getInfo(varargin)
+  if nargin == 1 && strcmpi(varargin{1}, 'None')
+    sets = {};
+    pl   = [];
+  else
+    sets = {'Default'};
+    pl   = getDefaultPlist();
+  end
+  % Build info object
+  ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: spikecleaning.m,v 1.17 2011/04/08 08:56:16 hewitson Exp $', sets, pl);
+  ii.setModifier(false);
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% FUNCTION:    getDefaultPlist
+%
+% DESCRIPTION: Get Default Plist
+%
+% HISTORY:     11-07-07 M Hewitson
+%                Creation.
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function plout = getDefaultPlist()
+  persistent pl;  
+  if exist('pl', 'var')==0 || isempty(pl)
+    pl = buildplist();
+  end
+  plout = pl;  
+end
+
+function pl = buildplist()
+
+  pl = plist();
+  
+  % kspike
+  p = param({'kspike', 'High values imply no correction of relative low amplitude spikes.'}, paramValue.DOUBLE_VALUE(3.3));
+  pl.append(p);
+  
+  % fc
+  p = param({'fc', 'Frequency cut-off of the IIR filter.'}, paramValue.DOUBLE_VALUE(0.025));
+  pl.append(p);
+  
+  % Order
+  p = param({'order', 'The order of the IIR filter.'}, paramValue.DOUBLE_VALUE(2));
+  pl.append(p);
+  
+  % Ripple
+  p = param({'ripple', 'Specify the pass/stop-band ripple for bandpass/bandreject filters'}, ...
+    paramValue.DOUBLE_VALUE(0.5));
+  pl.append(p);
+  
+  % Method
+  p = param({'method', 'The method used to replace the spike value.'}, {1, {'random', 'mean'}, paramValue.SINGLE});
+  pl.append(p);
+  
+end
+
+% PARAMETERES: 'kspike' - set the kspike value. High values imply
+%                         not correction of relative low amplitude spike
+%                         [default: 3.3]
+%	           'method' - method used to replace the spike value: 'random,
+%                         'mean', 'previous' [default:random]
+%	           'fc' - frequency cut-off of the IIR filter [default: 0.025]
+%	           'order' - order of the IIR filter [default: 2]
+%	           'ripple' - specify pass/stop-band ripple for bandpass
+%                         and bandreject filters
+%                         <<default: 0.5>>
+%