--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/testing/utp_1.1/utps/ao/utp_ao_cohere.m	Tue Dec 06 18:42:11 2011 +0100
@@ -0,0 +1,2323 @@
+% UTP_AO_COHERE a set of UTPs for the ao/cohere method
+%
+% M Hewitson 06-08-08
+%
+% $Id: utp_ao_cohere.m,v 1.44 2011/07/22 12:29:58 mauro Exp $
+%
+
+% <MethodDescription>
+%
+% The cohere method of the ao class computes the coherence between two
+% time-series AOs.
+%
+% </MethodDescription>
+
+function results = utp_ao_cohere(varargin)
+  
+  % Check the inputs
+  if nargin == 0
+    
+    % Some keywords
+    class   = 'ao';
+    mthd    = 'cohere';
+    
+    results = [];
+    disp('******************************************************');
+    disp(['****  Running UTPs for ' class '/' mthd]);
+    disp('******************************************************');
+    
+    % Test AOs
+    [at1,at2,at3,at4,at5,at6] = eval(['get_test_objects_' class]);
+    
+    % Exception list for the UTPs:
+    [ple1,ple2,ple3,ple4,ple5,ple6] = get_test_ples();
+    
+    % Get default window from the preferences
+    prefs = getappdata(0, 'LTPDApreferences');
+    defaultWinType = char(prefs.getMiscPrefs.getDefaultWindow);
+    
+    % Run the tests
+    results = [results utp_01];    % getInfo call
+    results = [results utp_02];    % Vector input          (only with two objects)
+    results = [results utp_03];    % Matrix input          (not possible)
+    results = [results utp_04];    % List input            (only with two objects)
+    results = [results utp_05];    % Test with mixed input (not possible)
+    results = [results utp_06];    % Test history is working
+    results = [results utp_07];    % Test the modify call works
+    results = [results utp_08];    % Test input data shape == output data shape
+    results = [results utp_09];    % Test output of the data
+    results = [results utp_10];    % Test the basic usage against MATLAB mscohere
+    
+    results = [results utp_11(mthd, [at1 at1], ple1)];    % Test plotinfo doesn't disappear
+    
+    results = [results utp_12];    % Test basic symmetry properties of cohere (C)
+    results = [results utp_13];    % Test basic symmetry properties of cohere (MS)
+    results = [results utp_14];    % Test basic symmetry properties of cohere (C)
+    results = [results utp_15];    % Test basic symmetry properties of cohere (MS)
+    results = [results utp_16];    % Test basic relationship (MS) <-> (C)
+    results = [results utp_17];    % Test units handling: complex cohere
+    results = [results utp_18];    % Test units handling: magnitude-squared cohere
+    results = [results utp_19];    % Test data lengths
+    results = [results utp_20];    % Test with single window
+    results = [results utp_21];    % Test number of averages: requested/obtained
+    results = [results utp_22];    % Test number of averages: correct number
+    results = [results utp_23];    % Test number of averages: syntax
+    results = [results utp_24];    % Test the basic usage against MATLAB mscohere
+    results = [results utp_25];    % Test Kaiser win and olap: (C)
+    results = [results utp_26];    % Test Kaiser win and olap: (MS)
+    results = [results utp_30];    % Special cases: same input
+    
+    disp('Done.');
+    disp('******************************************************');
+    
+  elseif nargin == 1 % Check for UTP functions
+    if strcmp(varargin{1}, 'isutp')
+      results = 1;
+    else
+      results = 0;
+    end
+  else
+    error('### Incorrect inputs')
+  end
+  
+  %% UTP_01
+  
+  % <TestDescription>
+  %
+  % Tests that the getInfo call works for this method.
+  %
+  % </TestDescription>
+  function result = utp_01
+    
+    
+    % <SyntaxDescription>
+    %
+    % Test that the getInfo call works for no sets, all sets, and each set
+    % individually.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % Call for no sets
+      io(1) = eval([class '.getInfo(''' mthd ''', ''None'')']);
+      % Call for all sets
+      io(2) = eval([class '.getInfo(''' mthd ''')']);
+      % Call for each set
+      for kk=1:numel(io(2).sets)
+        io(kk+2) = eval([class '.getInfo(''' mthd ''', ''' io(2).sets{kk} ''')']);
+      end
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that getInfo call returned an minfo object in all cases.
+    % 2) Check that all plists have the correct parameters.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % check we have minfo objects
+      if isa(io, 'minfo')
+        
+        % SET 'None'
+        if ~isempty(io(1).sets), atest = false; end
+        if ~isempty(io(1).plists), atest = false; end
+        % Check all Sets
+        if ~any(strcmpi(io(2).sets, 'Default')), atest = false; end
+        if numel(io(2).plists) ~= numel(io(2).sets), atest = false; end
+        % SET 'Default'
+        if io(3).plists.nparams ~= 9, atest = false; end
+        % Check key
+        if ~io(3).plists.isparam('nfft'), atest = false; end
+        if ~io(3).plists.isparam('win'), atest = false; end
+        if ~io(3).plists.isparam('olap'), atest = false; end
+        if ~io(3).plists.isparam('type'), atest = false; end
+        if ~io(3).plists.isparam('order'), atest = false; end
+        if ~io(3).plists.isparam('navs'), atest = false; end
+        if ~io(3).plists.isparam('times'), atest = false; end
+        if ~io(3).plists.isparam('split'), atest = false; end
+        if ~io(3).plists.isparam('psll'), atest = false; end
+        % Check default value
+        if ~isequal(io(3).plists.find('nfft'), -1), atest = false; end
+        if ~strcmpi(io(3).plists.find('win'), defaultWinType), atest = false; end
+        if ~isequal(io(3).plists.find('olap'), -1), atest = false; end
+        if ~isequal(io(3).plists.find('type'), 'C'), atest = false; end
+        if ~isequal(io(3).plists.find('order'), 0), atest = false; end
+        if ~isequal(io(3).plists.find('navs'), -1), atest = false; end
+        if ~isEmptyDouble(io(3).plists.find('times')), atest = false; end
+        if ~isEmptyDouble(io(3).plists.find('split')), atest = false; end
+        if ~isequal(io(3).plists.find('psll'), 200), atest = false; end
+        % Check options
+        if ~isequal(io(3).plists.getOptionsForParam('nfft'), {-1}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('win'), specwin.getTypes), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('olap'), {-1}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('type'), {'C', 'MS'}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('order'), {-1 0 1 2 3 4 5 6 7 8 9}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('navs'), {-1}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('times'), {[]}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('split'), {[]}), atest = false; end
+        if ~isequal(io(3).plists.getOptionsForParam('psll'), {200}), atest = false; end
+      end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_01
+  
+  %% UTP_02
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method works with a vector of AOs as input. (only
+  % with two objects in the vector)
+  %
+  % </TestDescription>
+  function result = utp_02
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method works for a vector of AOs as input.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      avec = [at1 at5];
+      out  = cohere(avec);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the number of elements in 'out' is equal to 1.
+    % 2) Check that each output AO contains the correct data.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Check we have the correct number of outputs
+      if numel(out) ~= 1, atest = false; end
+      
+      TOL = 1e-13;
+      
+      % Get shortest vector
+      lmin = min([length(at1.y), length(at5.y), length(at6.y)]);
+      % Set Nfft
+      Nfft = lmin;
+      % Get default window
+      if strcmpi(defaultWinType, 'kaiser')
+        win  = specwin(defaultWinType, Nfft, find(ao.getInfo('cohere').plists, 'psll'));
+      else
+        win  = specwin(defaultWinType, Nfft);
+      end
+      % Compute magnitude squared coherence estimate with MATLAB
+      % out: at1->at5
+      [cxy, f] = mscohere(at1.y(1:lmin), at5.y(1:lmin), win.win, Nfft/2, Nfft, at1.fs);
+      if any(abs(out.y-cxy > TOL)), atest = false; end
+      if any(abs(out.x-f   > TOL)), atest = false; end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_02
+  
+  %% UTP_03
+  
+  % <TestDescription>
+  %
+  % Test that the cohere method doesn't work for a matrix of AOs as input.
+  %
+  % </TestDescription>
+  function result = utp_03
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method doesn't work for a matrix of AOs as input.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      amat = [at1 at2;at5 at6];
+      out  = cohere(amat);
+      % </SyntaxCode>
+      stest = false;
+    catch err
+      stest = true;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Nothing to check.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_03
+  
+  %% UTP_04
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method works with a list of AOs as input.
+  %
+  % </TestDescription>
+  function result = utp_04
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method works for a list of AOs as input.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      out = cohere(at1,at5);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the number of elements in 'out' is equal to 1.
+    % 2) Check that each output AO contains the correct data.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Check we have the correct number of outputs
+      if numel(out) ~= 1, atest = false; end
+      
+      TOL = 1e-13;
+      
+      % Get shortest vector
+      lmin = min([length(at1.y), length(at5.y)]);
+      % Set Nfft
+      Nfft = lmin;
+      % Get default window
+      if strcmpi(defaultWinType, 'kaiser')
+        win  = specwin(defaultWinType, Nfft, find(ao.getInfo('cohere').plists, 'psll'));
+      else
+        win  = specwin(defaultWinType, Nfft);
+      end
+      % Compute magnitude squared coherence estimate with MATLAB
+      % out: at1->at5
+      [cxy, f] = mscohere(at1.y(1:lmin), at5.y(1:lmin), win.win, Nfft/2, Nfft, at1.fs);
+      if any(abs(out.y-cxy > TOL)), atest = false; end
+      if any(abs(out.x-f   > TOL)),   atest = false; end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_04
+  
+  %% UTP_05
+  
+  % <TestDescription>
+  %
+  % Test that the cohere method doesn't work with an input of matrices
+  % and vectors and single AOs.
+  %
+  % </TestDescription>
+  function result = utp_05
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method doesn't work with an input of matrices
+    % and vectors and single AOs.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      out = cohere([at5 at6], [at5 at1; at6 at1], at6);
+      stest = false;
+      % </SyntaxCode>
+    catch err
+      stest = true;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Nothing to check
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_05
+  
+  %% UTP_06
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method properly applies history.
+  %
+  % </TestDescription>
+  function result = utp_06
+    
+    % <SyntaxDescription>
+    %
+    % Test that the result of applying the cohere method can be processed back
+    % to an m-file.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      out  = cohere(at5,at6);
+      mout = rebuild(out);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the last entry in the history of 'out' corresponds to
+    %    'cohere'.
+    % 2) Check that the re-built object is the same as 'out'.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Check the last step in the history of 'out'
+      if ~strcmp(out.hist.methodInfo.mname, 'cohere'), atest = false; end
+      % Check the re-built object
+      if ~eq(mout, out, ple2), atest = false; end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_06
+  
+  %% UTP_07
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method can not modify the input AO.
+  %
+  % </TestDescription>
+  function result = utp_07
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method can not modify the input AO.
+    % The method must throw an error for the modifier call.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % copy at1 to work with
+      ain = ao(at1);
+      % modify ain
+      ain.cohere(at5);
+      % </SyntaxCode>
+      stest = false;
+    catch err
+      stest = true;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Nothing to check.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_07
+  
+  %% UTP_08
+  
+  % <TestDescription>
+  %
+  % Test the shape of the output.
+  %
+  % </TestDescription>
+  function result = utp_08
+    
+    % <SyntaxDescription>
+    %
+    % Test that the cohere method keeps the data shape of the input object. The
+    % input AO must be an AO with row data and an AO with column data.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      out1 = cohere(at5, at6);
+      out2 = cohere(at6, at5);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the shpe of the output data doesn't change.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Check the shape of the output data
+      if size(out1.data.y, 2) ~= 1, atest = false; end
+      if size(out2.data.y, 1) ~= 1, atest = false; end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_08
+  
+  %% UTP_09
+  
+  % <TestDescription>
+  %
+  % Check that the cohere method pass back the output objects to a list of
+  % output variables or to a single variable.
+  %
+  % </TestDescription>
+  function result = utp_09
+    
+    % <SyntaxDescription>
+    %
+    % This test is not longer necessary because the cohere method pass back
+    % always only one object.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Nothing to check.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_09
+  
+  %% UTP_10
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method agrees with MATLAB's mscohere when
+  % configured to use the same parameters.
+  %
+  % </TestDescription>
+  function result = utp_10
+    
+    % <SyntaxDescription>
+    %
+    % Test that applying cohere works on two AOs.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % Construct two test AOs
+      nsecs = 10;
+      fs    = 1000;
+      pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'randn(size(t))');
+      a1 = ao(pl); a2 = ao(pl);
+      % Filter one time-series
+      f2 = miir(plist('type', 'bandpass', 'fs', fs, 'order', 3, 'fc', [50 250]));
+      a1f = filter(a1, plist('filter', f2));
+      % make some cross-power
+      a4 = a1f+a2; a4.setName;
+      % Compute coherence
+      Nfft = 2*fs;
+      win  = specwin('Hanning', Nfft);
+      pl = plist('Nfft', Nfft, 'Win', win.type, 'order', -1, 'type', 'MS');
+      out = cohere(a4,a1,pl);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that output agrees with the output of MATLAB's mscohere.
+    % 2) Check that the shape of the output data is equal to the input data
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Compute coherence using MATLAB's cohere
+      [cxy, f] = mscohere(a4.y, a1.y, win.win, Nfft/2, Nfft, a1.fs);
+      if ne(cxy(:), out.y), atest = false; end
+      if ne(f,      out.x), atest = false; end
+      if ne(out, out, ple2), atest = false; end
+      % Check the data shape
+      if size(a4.y,1) == 1
+        if size(out.y,1) ~= 1, atest = false; end
+      else
+        if size(out.y,2) ~= 1, atest = false; end
+      end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_10
+  
+  
+  %% UTP_12
+  
+  % <TestDescription>
+  %
+  % Tests symmetry properties of complex-coherence:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) complex coherence of the white noise series
+  % 4) compare C(x,y) with conj(C(y,x))
+  % 5) compare C(x,x) and C(y,y) with 1
+  %
+  
+  % </TestDescription>
+  function result = utp_12
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) complex coherence of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;10];
+      nsecs_list =          [100:100:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      mu_distr_list =       [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      mu_distr = utils.math.randelement(mu_distr_list, 1);
+      f = [1:5] / 100 * fs;
+      A = sigma_distr + sigma_distr*rand(1,1);
+      phi = 0 + 2*pi*rand(1,1);
+      
+      % White noise
+      type = 'Normal';
+      a_n1 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_n2 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_const = ao(mu_distr);
+      a_wave = ao(plist('waveform', 'sine-wave', ...
+        'fs', fs, 'nsecs', nsecs, 'f', f, 'A', A, 'phi', phi));
+      a_1 = a_n1 + a_const + a_wave;
+      a_2 = a_n2 + a_wave;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the complex coherence of the time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      if strcmp(win_type, 'Kaiser')
+        win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+      else
+        win = specwin(win_type, 1);
+      end
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'C';
+      n_pts = nsecs*fs/10;
+      
+      C12 = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C21 = cohere(a_2, a_1, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C21_cc = conj(C21);
+      C11 = cohere(a_1, a_1, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C22 = cohere(a_2, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that C(x,y) equals conj(C(y,x))
+    % 2) Check that C(x,x) equals 1
+    % 2) Check that C(y,y) equals 1
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C12.data, C21_cc.data, 'dy') || ...
+          ~isequal(C11.y, ones(size(C11.y))) || ...
+          ~isequal(C22.y, ones(size(C22.y)))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_12
+  
+  %% UTP_13
+  
+  % <TestDescription>
+  %
+  % Tests symmetry properties of complex-coherence:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) magnitude-squared coherence of the white noise series
+  % 4) compare C(x,y) with C(y,x)
+  % 5) compare C(x,x) and C(y,y) with 1
+  %
+  
+  % </TestDescription>
+  function result = utp_13
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) magnitude-squared coherence of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;10];
+      nsecs_list =          [100:100:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      mu_distr_list =       [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      mu_distr = utils.math.randelement(mu_distr_list, 1);
+      f = [1:5] / 100 * fs;
+      A = sigma_distr + sigma_distr*rand(1,1);
+      phi = 0 + 2*pi*rand(1,1);
+      
+      % White noise
+      type = 'Normal';
+      a_n1 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_n2 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_const = ao(mu_distr);
+      a_wave = ao(plist('waveform', 'sine-wave', ...
+        'fs', fs, 'nsecs', nsecs, 'f', f, 'A', A, 'phi', phi));
+      a_1 = a_n1 + a_const + a_wave;
+      a_2 = a_n2 + a_wave;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the magnitude-squared coherence of the time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      if strcmp(win_type, 'Kaiser')
+        win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+      else
+        win = specwin(win_type, 1);
+      end
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'MS';
+      n_pts = nsecs*fs/10;
+      
+      C12 = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C21 = cohere(a_2, a_1, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C11 = cohere(a_1, a_1, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      C22 = cohere(a_2, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that C(x,y) equals C(y,x)
+    % 1) Check that C(x,x) equals 1
+    % 1) Check that C(y,y) equals 1
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~isequal(C12.data, C21.data) || ...
+          ~isequal(C11.y, ones(size(C11.y))) ...
+          || ~isequal(C22.y, ones(size(C22.y)))
+        atest = false;
+      end
+      if atest == false
+        fs
+        nsecs
+        sigma_distr
+        mu_distr
+        f
+        A
+        phi
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_13
+  
+  %% UTP_14
+  
+  % <TestDescription>
+  %
+  % Tests symmetry properties of complex-coherence:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) complex coherence of the combination of white noise series
+  % 4) compare C(x,y) with 1
+  %
+  
+  % </TestDescription>
+  function result = utp_14
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) complex coherence of the combination of noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;10];
+      nsecs_list =          [100:100:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      mu_distr_list =       [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      mu_distr = utils.math.randelement(mu_distr_list, 1);
+      f = [1:5] / 100 * fs;
+      A = sigma_distr + sigma_distr*rand(1,1);
+      phi = 0 + 2*pi*rand(1,1);
+      
+      % White noise
+      type = 'Normal';
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_const = ao(mu_distr);
+      % Sinusoidal signal
+      a_wave = ao(plist('waveform', 'sine-wave', ...
+        'fs', fs, 'nsecs', nsecs, 'f', f, 'A', A, 'phi', phi));
+      a_1 = a_n + a_wave;
+      % Linear combination (totally correlated time series)
+      a_2 = a_1 + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the complex coherence of the time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      if strcmp(win_type, 'Kaiser')
+        win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+      else
+        win = specwin(win_type, 1);
+      end
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'C';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the complex coherence equals 1
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    TOL = 1e-12;
+    
+    if stest
+      if any(abs((C.y - 1)) > TOL)
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_14
+  
+  %% UTP_15
+  
+  % <TestDescription>
+  %
+  % Tests symmetry properties of complex-coherence:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) magnitude-squared coherence of the combination of white noise series
+  % 4) compare C(x,y) with 1
+  %
+  
+  % </TestDescription>
+  function result = utp_15
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) magnitude-squared coherence of the combination of noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;10];
+      nsecs_list =          [100:100:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      mu_distr_list =       [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      mu_distr = utils.math.randelement(mu_distr_list, 1);
+      f = [1:5] / 100 * fs;
+      A = sigma_distr + sigma_distr*rand(1,1);
+      phi = 0 + 2*pi*rand(1,1);
+      
+      % White noise
+      type = 'Normal';
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_const = ao(mu_distr);
+      % Sinusoidal signal
+      a_wave = ao(plist('waveform', 'sine-wave', ...
+        'fs', fs, 'nsecs', nsecs, 'f', f, 'A', A, 'phi', phi));
+      a_1 = a_n + a_wave;
+      % Linear combination (totally correlated time series)
+      a_2 = a_1 + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the complex coherence of the time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      if strcmp(win_type, 'Kaiser')
+        win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+      else
+        win = specwin(win_type, 1);
+      end
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'MS';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the magnitude-squared coherence equals 1
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C.y, ones(size(C.y)))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_15
+  
+  %% UTP_16
+  
+  % <TestDescription>
+  %
+  % Tests symmetry properties of complex-coherence:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) magnitude-squared coherence M of the combination of white noise series
+  % 4) complex coherence C of the combination of white noise series
+  % 5) compare abs(C)^2 with M
+  %
+  
+  % </TestDescription>
+  function result = utp_16
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) magnitude-squared coherence of the combination of noise
+    % 6) complex coherence of the combination of noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;10];
+      nsecs_list =          [100:100:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      mu_distr_list =       [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      mu_distr = utils.math.randelement(mu_distr_list, 1);
+      f = [1:5] / 100 * fs;
+      A = sigma_distr + sigma_distr*rand(1,1);
+      phi = 0 + 2*pi*rand(1,1);
+      
+      % White noise
+      type = 'Normal';
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_const = ao(mu_distr);
+      % Sinusoidal signal
+      a_wave = ao(plist('waveform', 'sine-wave', ...
+        'fs', fs, 'nsecs', nsecs, 'f', f, 'A', A, 'phi', phi));
+      a_1 = a_n + a_wave;
+      % Linear combination (totally correlated time series)
+      a_2 = a_1 + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the complex coherence of the time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      if strcmp(win_type, 'Kaiser')
+        win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+      else
+        win = specwin(win_type, 1);
+      end
+      olap = win.rov;
+      detrend = 0;
+      n_pts = nsecs*fs/10;
+      
+      M = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', 'MS'));
+      C = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', 'C'));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the magnitude-squared coherence equals the square
+    % modulus of the complex coherence
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    TOL = 1e-15;
+    
+    if stest
+      if any(abs(M.y - abs(C.y).^2) > TOL)
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_16
+  
+  %% UTP_17
+  
+  % <TestDescription>
+  %
+  % Tests handling of units:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) complex coherence of the white noise series
+  % 4) compares the units of the input and output
+  %
+  
+  % </TestDescription>
+  function result = utp_17
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) complex cohere of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Build time-series test data
+      fs = 1;
+      nsecs = 86400;
+      sigma_distr_1 = 4.69e-12;
+      mu_distr_1 = -5.11e-14;
+      sigma_distr_2 = 6.04e-9;
+      mu_distr_2 = 1.5e-10;
+      
+      % White noise
+      type = 'Normal';
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
+      a_const = ao(mu_distr_1);
+      a_1 = a_n + a_const;
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_2));
+      a_const = ao(mu_distr_2);
+      a_2 = a_n + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the coherence of the time-series data
+      win = specwin('BH92');
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'C';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that (complex coherence yunits) equals [1]
+    % 2) Check that (complex coherence xunits) equals [Hz]
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C.yunits, unit('')) || ~eq(C.xunits, unit('Hz'))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_17
+  
+  %% UTP_18
+  
+  % <TestDescription>
+  %
+  % Tests handling of units:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) magnitude-squared coherence of the white noise series
+  % 4) compares the units of the input and output
+  %
+  
+  % </TestDescription>
+  function result = utp_18
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) magnitude-squared cohere of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Build time-series test data
+      fs = 1;
+      nsecs = 86400;
+      sigma_distr_1 = 4.69e-12;
+      mu_distr_1 = -5.11e-14;
+      sigma_distr_2 = 6.04e-9;
+      mu_distr_2 = 1.5e-10;
+      
+      % White noise
+      type = 'Normal';
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
+      a_const = ao(mu_distr_1);
+      a_1 = a_n + a_const;
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_2));
+      a_const = ao(mu_distr_2);
+      a_2 = a_n + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the coherence of the time-series data
+      win = specwin('BH92');
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'MS';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win.type, 'olap', olap, 'Nfft', n_pts, 'order', detrend,'type', scale_type));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that (magnitude-squared coherence yunits) equals [1]
+    % 2) Check that (magnitude-squared coherence xunits) equals [Hz]
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C.yunits, unit('')) || ~eq(C.xunits, unit('Hz'))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_18
+  
+  %% UTP_19
+  
+  % <TestDescription>
+  %
+  % Tests that differently sized data sets are treated properly
+  %
+  % </TestDescription>
+  function result = utp_19
+    
+    % <SyntaxDescription>
+    %
+    % Test that applying cohere works on two AOs.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % Construct two test AOs
+      nsecs = [10000:1:20000];
+      fs    = 1;
+      pl = plist('fs', fs, 'tsfcn', 'randn(size(t))');
+      a1 = ao(pl.pset('nsecs', utils.math.randelement(nsecs, 1)));
+      a2 = ao(pl.pset('nsecs', utils.math.randelement(nsecs, 1)));
+      len_1 = a1.len;
+      len_2 = a2.len;
+      % Filter one time-series
+      f2 = miir(plist('type', 'bandpass', 'fs', fs, 'order', 3, 'fc', [.050 .25]));
+      a1f = filter(a1, plist('filter', f2));
+      % Compute cohere
+      Nfft = -1;
+      win  = 'Hanning';
+      pl = plist('Nfft', Nfft, 'Win', win, 'order', -1);
+      out = cohere(a2,a1f,pl);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that cohere used the length of the shortest ao.
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      % Compare the nfft with the length of the input data
+      
+      if out.x(2) ~= 1/min(len_1,len_2)
+        atest = false;
+      end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_19
+  
+  %% UTP_20
+  
+  % <TestDescription>
+  %
+  % Tests that applying a single window the coherence is 1
+  %
+  % </TestDescription>
+  function result = utp_20
+    
+    % <SyntaxDescription>
+    %
+    % Test that applying cohere works on two AOs.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % Construct two test AOs
+      nsecs = [10000:100:20000];
+      fs    = 1;
+      pl = plist('fs', fs, 'tsfcn', 'randn(size(t))');
+      a1 = ao(pl.pset('nsecs', utils.math.randelement(nsecs, 1)));
+      a2 = ao(pl.pset('nsecs', utils.math.randelement(nsecs, 1)));
+      % Filter one time-series
+      f2 = miir(plist('type', 'bandpass', 'fs', fs, 'order', 3, 'fc', [.050 .25]));
+      a1f = filter(a1, plist('filter', f2));
+      % Compute cohere
+      Nfft = -1;
+      win  = 'Hanning';
+      pl = plist('Nfft', Nfft, 'Win', win, 'order', -1);
+      out_c = cohere(a2, a1f, pl.pset('type', 'C'));
+      out_ms = cohere(a2, a1f, pl.pset('type', 'MS'));
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that the calculated cohere is 1
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    TOL = 1e-12;
+    if stest
+      % <AlgoCode>
+      % Compare the calculated cohere with 1
+      
+      if any(abs(abs(out_c.y) - 1) > TOL)
+        atest = false;
+      end
+      if any(abs(abs(out_ms.y) - 1) > TOL)
+        atest = false;
+      end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_20
+  
+  %% UTP_21
+  
+  % <TestDescription>
+  %
+  % Tests the possibility to set the number of averages rather than setting the Nfft:
+  % 1) white noise produced from normal pdf, with:
+  %   a given mean value and sigma (distribution's 1st and 2nd order)
+  % 2) cohere of the noise, without detrending, random window, set number of
+  %   averages
+  % 3) check the effective number of averages
+  %
+  
+  % </TestDescription>
+  function result = utp_21
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) cohere of the noise, without detrending, random window, set number of
+    %   averages
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;2;5;10];
+      nsecs_list =          [2000:1000:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      trend_0_list =        [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      trend_0 = utils.math.randelement(trend_0_list, 1);
+      
+      % White noise
+      type = 'Normal';
+      a_n1 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_n2 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      
+      % Constant signal
+      a_c = ao(trend_0);
+      
+      % Total signals
+      a1 = a_n1 + a_c;
+      a2 = a_n2 + a_c;
+      
+      % Evaluate the complex coherence of the white noise time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      switch win_type
+        case 'Kaiser'
+          win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+        otherwise
+          win = specwin(win_type, 1);
+      end
+      
+      olap = win.rov;
+      detrend = 0;
+      n_pts = -1;
+      scale_type = 'C';
+      navs = utils.math.randelement([1:100],1);
+      
+      % Evaluates the coherence asking for the number of averages
+      C = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', n_pts, 'order', detrend, 'type', scale_type, 'navs', navs));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that calculated navs are identical to those requested
+    %
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      % Compare the navs written in the output object with the requested one
+      if ne(navs, C.data.navs)
+        if ne(find(C.hist.plistUsed, 'navs'), C.data.navs)
+          atest = false;
+        end
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_21
+  
+  %% UTP_22
+  
+  % <TestDescription>
+  %
+  % Tests the possibility to set the number of averages rather than setting the Nfft:
+  % 1) white noise produced from uniform pdf, with:
+  %   a given mean value and sigma (distribution's 1st and 2nd order)
+  % 2) cohere of the noise, without detrending, random window, random navs
+  % 3) get the number of averages
+  % 4) get the nfft used
+  % 5) run cohere again, with the nfft used
+  % 6) compare the calculated objects
+  %
+  
+  % </TestDescription>
+  function result = utp_22
+    
+    % <SyntaxDescription>
+    %
+    % 1) white noise produced from uniform pdf, with:
+    %   a given mean value and sigma (distribution's 1st and 2nd order)
+    % 2) cohere of the noise, without detrending, random window, random navs
+    % 3) get the number of averages
+    % 4) get the nfft used
+    % 5) run cohere again, with the nfft used
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;2;5;10];
+      nsecs_list =          [20 100 1000:1000:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      trend_0_list =        [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      trend_0 = utils.math.randelement(trend_0_list, 1);
+      
+      % White noise
+      type = 'Uniform';
+      a_n1 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_n2 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      
+      % Constant signal
+      a_c = ao(trend_0);
+      
+      % Total signals
+      a1 = a_n1 + a_c;
+      a2 = a_n2 + a_c;
+      
+      % Evaluate the complex coherence of the white noise time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      switch win_type
+        case 'Kaiser'
+          win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+        otherwise
+          win = specwin(win_type, 1);
+      end
+      
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'MS';
+      navs = fix(utils.math.randelement(logspace(0,log10(max(0,a1.len/10)),50),1));
+      
+      % Calculates the coherence asking for the number of averages
+      C1 = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', -1, 'order', detrend, 'type', scale_type, ...
+        'navs', navs));
+      
+      % Calculates the coherence asking for the number of points just evaluated
+      C2 = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', find(C1.hist.plistUsed, 'Nfft'), 'order', detrend, 'type', scale_type));
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that calculated objects C1 and C2 are identical
+    %
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      % Compare the output objects
+      if ne(C1,C2,ple3)
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_22
+  
+  %% UTP_23
+  
+  % <TestDescription>
+  %
+  % Tests the possibility to set the number of averages rather than setting the Nfft:
+  % 1) white noise produced from normal pdf, with:
+  %   a given mean value and sigma (distribution's 1st and 2nd order)
+  % 2) cohere of the noise, without detrending, random window, random navs
+  % 3) get the number of averages
+  % 4) get the nfft used
+  % 5) run cohere again, with the nfft used
+  % 6) compare navs, nfft, coheres
+  %
+  
+  % </TestDescription>
+  function result = utp_23
+    
+    % <SyntaxDescription>
+    %
+    % 1) white noise produced from normal pdf, with:
+    %   a given mean value and sigma (distribution's 1st and 2nd order)
+    % 2) cohere of the noise, without detrending, random window, random navs
+    % 3) get the number of averages
+    % 4) get the nfft used
+    % 5) run cohere again, with the nfft used
+    % 6) run cohere again, with conflicting parameters, and verify it uses
+    %     nfft rather than navs
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      % Array of parameters to pick from
+      fs_list =             [0.1;1;2;5;10];
+      nsecs_list =          [1000:1000:10000]';
+      sigma_distr_list =    [1e-6 2e-3 0.25 1:0.1:10]';
+      trend_0_list =        [1e-6 2e-3 0.25 1:0.1:10]';
+      
+      % Build time-series test data
+      
+      % Picks the values at random from the list
+      fs = utils.math.randelement(fs_list, 1);
+      nsecs = utils.math.randelement(nsecs_list, 1);
+      sigma_distr = utils.math.randelement(sigma_distr_list, 1);
+      trend_0 = utils.math.randelement(trend_0_list, 1);
+      
+      % White noise
+      type = 'Normal';
+      a_n1 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      a_n2 = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
+      
+      % Constant signal
+      a_c = ao(trend_0);
+      
+      % Total signals
+      a1 = a_n1 + a_c;
+      a2 = a_n2 + a_c;
+      
+      % Evaluate the complex coherence of the white noise time-series data
+      win_list = specwin.getTypes;
+      win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
+      win_type = win_type{1};
+      switch win_type
+        case 'Kaiser'
+          win = specwin(win_type, 1, find(ao.getInfo('psd').plists, 'psll'));
+        otherwise
+          win = specwin(win_type, 1);
+      end
+      
+      olap = win.rov;
+      detrend = 0;
+      scale_type = 'C';
+      navs = fix(utils.math.randelement(logspace(0,log10(max(a1.len/10,0)),50),1));
+      
+      % Calculates the coherence asking for the number of averages
+      C1 = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', -1, 'order', detrend, 'type', scale_type, ...
+        'navs', navs));
+      
+      npts_2 = find(C1.hist.plistUsed, 'Nfft');
+      % Calculates the coherence asking for the number of points
+      C2 = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', npts_2, 'order', detrend, 'type', scale_type));
+      
+      npts_3 = fix(npts_2/2);
+      % Calculates the coherence asking for the number of points AND the window length
+      C3 = cohere(a1, a2, plist('Win', win.type, 'olap', olap, ...
+        'Nfft', npts_3, ...
+        'order', detrend, 'type', scale_type, ...
+        'navs', navs));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that calculated objects C1 and C2 are identical
+    % 2) Check that C3 used different values
+    %
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      % Compare the navs written in the output object with the requested one
+      if  ne(C1,C2,ple3) || ...
+          ne(find(C3.hist.plistUsed, 'Nfft'), npts_3) || eq(C3.data.navs, navs)
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_23
+  
+  %% UTP_24
+  
+  % <TestDescription>
+  %
+  % Tests that the cohere method agrees with MATLAB's mscohere when
+  % configured to use the same parameters.
+  %
+  % </TestDescription>
+  function result = utp_24
+    
+    % <SyntaxDescription>
+    %
+    % Test that the applying cohere works on two AOs.
+    %
+    % </SyntaxDescription>
+    
+    try
+      % <SyntaxCode>
+      % Construct two test AOs
+      nsecs = 10;
+      fs    = 1000;
+      pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'randn(size(t))');
+      a1 = ao(pl); a2 = ao(pl);
+      % Filter one time-series
+      f2 = miir(plist('type', 'bandpass', 'fs', fs, 'order', 3, 'fc', [50 250]));
+      a1f = filter(a1, plist('filter', f2));
+      % make some cross-power
+      a4 = a1f+a2; a4.setName;
+      % Create the transpose of a4 to check the output data shape
+      a4 = a4.';
+      % Compute coherence
+      Nfft = 2*fs;
+      % Use different windows size as Nfft
+      win  = specwin('Hanning', 1000);
+      pl = plist('Nfft', Nfft, 'Win', win.type, 'order', 0, 'type', 'MS');
+      out = cohere(a4,a1,pl);
+      % </SyntaxCode>
+      stest = true;
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that output agrees with the output of MATLAB's mscohere.
+    % 2) Check that the shape of the output data is equal to the input data
+    %
+    % </AlgoDescription>
+    
+    atest = true;
+    if stest
+      % <AlgoCode>
+      TOL = 1e-12;
+      
+      % Redesign the window
+      win = specwin('Hanning', Nfft);
+      % Compute coherence using MATLAB's cohere
+      [cxy, f] = mscohere(a4.y, a1.y, win.win, Nfft/2, Nfft, a1.fs);
+      if  any(abs(cxy(4:end)-out.y(4:end))>TOL), atest = false; end
+      if ne(f,      out.x), atest = false; end
+      if ne(out, out, ple2), atest = false; end
+      % Check the data shape
+      if size(a4.y,1) == 1
+        if size(out.y,1) ~= 1, atest = false; end
+      else
+        if size(out.y,2) ~= 1, atest = false; end
+      end
+      % </AlgoCode>
+    else
+      atest = false;
+    end
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_24
+  
+  %% UTP_25
+  
+  % <TestDescription>
+  %
+  % Tests handling of units:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) complex coherence of the white noise series
+  % 4) compares the units of the input and output
+  %
+  
+  % </TestDescription>
+  function result = utp_25
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) complex cohere of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Build time-series test data
+      fs = 1;
+      nsecs = 86400;
+      sigma_distr_1 = 4.69e-12;
+      mu_distr_1 = -5.11e-14;
+      sigma_distr_2 = 6.04e-9;
+      mu_distr_2 = 1.5e-10;
+      
+      % White noise
+      type = 'Normal';
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
+      a_const = ao(mu_distr_1);
+      a_1 = a_n + a_const;
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_2));
+      a_const = ao(mu_distr_2);
+      a_2 = a_n + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the coherence of the time-series data
+      win = 'Kaiser';
+      psll = utils.math.randelement([10:10:200], 1);
+      detrend = 0;
+      scale_type = 'C';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win, 'psll', psll, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that (complex coherence yunits) equals [1]
+    % 2) Check that (complex coherence xunits) equals [Hz]
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C.yunits, unit('')) || ~eq(C.xunits, unit('Hz'))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_25
+  
+  %% UTP_26
+  
+  % <TestDescription>
+  %
+  % Tests handling of units:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 3) complex coherence of the white noise series
+  % 4) compares the units of the input and output
+  %
+  
+  % </TestDescription>
+  function result = utp_26
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 4) Assign a random unit
+    % 5) complex cohere of the white noise
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Build time-series test data
+      fs = 1;
+      nsecs = 86400;
+      sigma_distr_1 = 4.69e-12;
+      mu_distr_1 = -5.11e-14;
+      sigma_distr_2 = 6.04e-9;
+      mu_distr_2 = 1.5e-10;
+      
+      % White noise
+      type = 'Normal';
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
+      a_const = ao(mu_distr_1);
+      a_1 = a_n + a_const;
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_2));
+      a_const = ao(mu_distr_2);
+      a_2 = a_n + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      a_2.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Evaluate the coherence of the time-series data
+      win = 'Kaiser';
+      psll = utils.math.randelement([10:10:200], 1);
+      detrend = 0;
+      scale_type = 'C';
+      n_pts = nsecs*fs/10;
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win, 'psll', psll, 'Nfft', n_pts, 'order', detrend, 'type', scale_type));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that (complex coherence yunits) equals [1]
+    % 2) Check that (complex coherence xunits) equals [Hz]
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if ~eq(C.yunits, unit('')) || ~eq(C.xunits, unit('Hz'))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_26
+  
+  %% UTP_30
+  
+  % <TestDescription>
+  %
+  % Tests handling of special cases:
+  % 1) white noise produced from normal pdf, with a given mean value and
+  % sigma (distribution's 1st and 2nd orders)
+  % 2) the same noise series
+  % 3) cohere of the white noise series
+  % 4) compares the output to unity
+  %
+  
+  % </TestDescription>
+  function result = utp_30
+    
+    % <SyntaxDescription>
+    %
+    % 1) Prepare the test tsdata:
+    %   white noise from normal distribution + offset
+    % 2) Assign a random unit
+    % 3) Prepare the test tsdata:
+    %   the same data as 1) and 2)
+    % 4) cohere of the series
+    %
+    % </SyntaxDescription>
+    
+    % <SyntaxCode>
+    try
+      
+      % Build time-series test data
+      fs = 1;
+      nsecs = 86400;
+      sigma_distr_1 = 4.69e-12;
+      mu_distr_1 = -5.11e-14;
+      
+      % White noise
+      type = 'Normal';
+      
+      a_n = ao(plist('waveform', 'noise', ...
+        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
+      a_const = ao(mu_distr_1);
+      a_1 = a_n + a_const;
+      
+      % Set units and prefix from those supported
+      unit_list = unit.supportedUnits;
+      % remove the first empty unit '' from the list, because then is it
+      % possible that we add a prefix to an empty unit
+      unit_list = unit_list(2:end);
+      prefix_list = unit.supportedPrefixes;
+      a_1.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
+      
+      % Build the second object as a copy of the first
+      a_2 = a_1;
+      
+      % Evaluate the cohere of the time-series data
+      win = specwin('BH92');
+      olap = win.rov;
+      detrend = 0;
+      n_pts = nsecs*fs/10;
+      scale_type = 'C';
+      
+      C = cohere(a_1, a_2, ...
+        plist('Win', win, 'Nfft', n_pts, 'order', detrend, 'type', scale_type, 'olap', olap));
+      
+      stest = true;
+      
+    catch err
+      disp(err.message)
+      stest = false;
+    end
+    % </SyntaxCode>
+    
+    % <AlgoDescription>
+    %
+    % 1) Check that calculated cohere equals 1
+    
+    % </AlgoDescription>
+    
+    % <AlgoCode>
+    atest = true;
+    
+    if stest
+      if sum(ne(C.y, 1))
+        atest = false;
+      end
+    else
+      atest = false;
+    end
+    % </AlgoCode>
+    
+    % Return a result structure
+    result = utp_prepare_result(atest, stest, dbstack, mfilename);
+  end % END UTP_30
+  
+end