% UTP_AO_CPSD a set of UTPs for the ao/cpsd method
%
% M Hewitson 06-08-08
%
% $Id: utp_ao_cpsd.m,v 1.43 2011/07/22 11:51:46 mauro Exp $
%

% <MethodDescription>
%
% The cpsd method of the ao class computes the cross-spectral density between two
% time-series AOs.
%
% </MethodDescription>

function results = utp_ao_cpsd(varargin)
  
  % Check the inputs
  if nargin == 0
    
    % Some keywords
    class   = 'ao';
    mthd    = 'cpsd';
    
    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 against MATLAB's cpsd()
    
    results = [results utp_11(mthd, [at1 at1], ple1)];    % Test plotinfo doesn't disappear
    
    results = [results utp_17];    % Test units handling: CPSD
    results = [results utp_18];    % Comparison with PSD
    results = [results utp_24];    % Test data lengths
    results = [results utp_25];    % Test Kaiser win and olap: CPSD
    results = [results utp_51];    % Test number of averages: requested/obtained
    results = [results utp_52];    % Test number of averages: correct number
    results = [results utp_53];    % Test number of averages: syntax
    
    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 ~= 8, 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('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('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; disp('1'); end
        if ~isequal(io(3).plists.getOptionsForParam('win'), specwin.getTypes), atest = false;disp('2'); end
        if ~isequal(io(3).plists.getOptionsForParam('olap'), {-1}), atest = false; disp('3');end
        if ~isequal(io(3).plists.getOptionsForParam('order'), {-1 0 1 2 3 4 5 6 7 8 9}), atest = false;disp('4'); end
        if ~isequal(io(3).plists.getOptionsForParam('navs'), {-1}), atest = false;disp('5'); end
        if ~isequal(io(3).plists.getOptionsForParam('times'), {[]}), atest = false;disp('6'); end
        if ~isequal(io(3).plists.getOptionsForParam('split'), {[]}), atest = false;disp('6'); end
        if ~isequal(io(3).plists.getOptionsForParam('psll'), {200}), atest = false;disp('7'); 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 cpsd 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 cpsd method works for a vector of AOs as input.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      avec = [at1 at5];
      out  = cpsd(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
    %
    % </AlgoDescription>
    
    atest = true;
    if stest
      % <AlgoCode>
      % Check we have the correct number of outputs
      if numel(out) ~= 1, 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>
  %
  % Tests that the cpsd method doesn't work with a matrix of AOs as input.
  %
  % </TestDescription>
  function result = utp_03
    
    % <SyntaxDescription>
    %
    % Test that the cpsd method doesn't work for a matrix of AOs as input.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      amat = [at1 at5 at6; at5 at6 at1];
      out  = cpsd(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 cpsd method works with a list of AOs as input.
  %
  % </TestDescription>
  function result = utp_04
    
    % <SyntaxDescription>
    %
    % Test that the cpsd method works for a list of AOs as input.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      out = cpsd(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
    %
    % </AlgoDescription>
    
    atest = true;
    if stest
      % <AlgoCode>
      % Check we have the correct number of outputs
      if numel(out) ~= 1, 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>
  %
  % Tests that the cpsd method doesn't work with a mix of different shaped
  % AOs as input.
  %
  % </TestDescription>
  function result = utp_05
    
    % <SyntaxDescription>
    %
    % Test that the cpsd method doesn't work with an input of matrices and
    % vectors and single AOs.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      out = cpsd([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 cpsd method properly applies history.
  %
  % </TestDescription>
  function result = utp_06
    
    % <SyntaxDescription>
    %
    % Test that the result of applying the cpsd method can be processed back
    % to an m-file.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      out  = cpsd(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
    %    'cpsd'.
    % 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, 'cpsd'), 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 cpsd method can not modify the input AO.
  %
  % </TestDescription>
  function result = utp_07
    
    % <SyntaxDescription>
    %
    % Test that the cpsd 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.cpsd(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 cpsd 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 = cpsd(at5, at6);
      out2 = cpsd(at6, at5);
      % </SyntaxCode>
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    
    % <AlgoDescription>
    %
    % 1) Check that the shape 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 cpsd 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 cpsd 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 cpsd method agrees with MATLAB's cpsd when
  % configured to use the same parameters.
  %
  % </TestDescription>
  function result = utp_10
    
    % <SyntaxDescription>
    %
    % Test that applying cpsd 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 cpsd
      Nfft = 2*fs;
      win  = specwin('Hanning', Nfft);
      pl = plist('Nfft', Nfft, 'Win', win.type, 'order', -1);
      out = cpsd(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 cpsd.
    %
    % </AlgoDescription>
    
    atest = true;
    if stest
      % <AlgoCode>
      % Compute cpsd using MATLAB's cpsd
      [cxy, f] = cpsd(a4.data.y, a1.data.y, win.win, Nfft/2, Nfft, a1.data.fs);
      if ~utils.math.isequal(cxy(:), out.data.y(:)) || ~utils.math.isequal(f, out.data.getX), atest = false; end
      % </AlgoCode>
    else
      atest = false;
    end
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_10
  
  
  %% 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) CPSD 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) CPSD of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'BH92';
      
      [a_1, a_2, spec, spec1] = prepare_analyze_noise(win_type, noise_type, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated CPSD yunits) equals
    % input_1 units*input_2 units/Hz
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    u = simplifyYunits(a_1.* a_2, plist('prefixes', false, 'exceptions', 'Hz'));
    if stest
      if ne(spec.Cxy.yunits, u.yunits * unit('Hz^-1')) || ne(spec.Cxy.xunits, unit('Hz'))
        atest = false;
      end
      if ne(spec.Cyx.yunits, u.yunits * unit('Hz^-1')) || ne(spec.Cyx.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) CPSD of the white noise series
  %
  % Comparison with PSD:
  % 4) compares the off-diagonal terms to check they are complex-conjugated
  % 5) compares the diagonal terms with PSD of the individual noise
  %
  
  % </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) CPSD of the white noise
    % 6) PSD of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Uniform';
      win_type = 'BH92';
      
      [a_1, a_2, spec, spec2] = prepare_analyze_noise(win_type, noise_type, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that CPSD(x,y) equals conj(CPSD(y,x))
    % 2) Check that CPSD(x,x) equals PSD(x)
    % 3) Check that CPSD(y,y) equals PSD(y)
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      if ne(spec.Cxy.y, conj(spec.Cyx.y)), atest = false; end
      if ne(spec.Cxy.x, spec.Cyx.x), atest = false; end
      if ne(spec.Cxx.data, spec.S_1.data), atest = false; end
      if ne(spec.Cyy.data, spec.S_2.data), 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_24
  
  % <TestDescription>
  %
  % Tests that differently sized data sets are treated properly
  %
  % </TestDescription>
  function result = utp_24
    
    % <SyntaxDescription>
    %
    % Test that applying cpsd 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 cpsd
      Nfft = -1;
      win  = 'Hanning';
      pl = plist('Nfft', Nfft, 'Win', win, 'order', -1);
      out = cpsd(a2,a1f,pl);
      % </SyntaxCode>
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    
    % <AlgoDescription>
    %
    % 1) Check that cpsd 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_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) CPSD 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) CPSD 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 cpsd of the time-series data, using Kaiser window
      % Psll and olap are not set
      win = ('Kaiser');
      detrend = 0;
      n_pts = nsecs*fs/10;
      
      C = cpsd(a_1, a_2, plist('Win', win, 'Nfft', n_pts, 'order', detrend));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated CPSD yunits) equals
    %input_1 units*input_2 units/Hz
    
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    u = simplifyYunits(a_1.* a_2, plist('prefixes', false, 'exceptions', 'Hz'));
    if stest
      if ne(C.yunits,  u.yunits * unit('Hz^-1')) || ne(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_51
  
  % <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) cpsd of the noise, without detrending, random window, set number of
  %   averages
  % 3) check the effective number of averages
  %
  
  % </TestDescription>
  function result = utp_51
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) cpsd of the noise, without detrending, random window, set number of
    %   averages
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      
      % Evaluate the cpsd of the white noise time-series data
      [a_1, a_2, C1, C2, navs] = prepare_analyze_noise_navs(noise_type, plist);
      
      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
      if ne(navs, C1.data.navs)
        if ne(find(C1.hist.plistUsed, 'navs'), C1.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_51
  
  %% UTP_52
  
  % <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) cpsd of the noise, without detrending, random window, random navs
  % 3) get the number of averages
  % 4) get the nfft used
  % 5) run cpsd again, with the nfft used
  % 6) compare the calculated objects
  %
  
  % </TestDescription>
  function result = utp_52
    
    % <SyntaxDescription>
    %
    % 1) white noise produced from uniform pdf, with:
    %   a given mean value and sigma (distribution's 1st and 2nd order)
    % 2) cpsd of the noise, without detrending, random window, random navs
    % 3) get the number of averages
    % 4) get the nfft used
    % 5) run cpsd again, with the nfft used
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Uniform';
      
      % Evaluate the cpsd of the white noise time-series data
      [a_1, a_2, C1, C2, navs] = prepare_analyze_noise_navs(noise_type, plist);
      
      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_52
  
  %% UTP_53
  
  % <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) cpsd of the noise, without detrending, random window, random navs
  % 3) get the number of averages
  % 4) get the nfft used
  % 5) run cpsd again, with the nfft used
  % 6) compare navs, nfft, psds
  %
  
  % </TestDescription>
  function result = utp_53
    
    % <SyntaxDescription>
    %
    % 1) white noise produced from normal pdf, with:
    %   a given mean value and sigma (distribution's 1st and 2nd order)
    % 2) cpsd of the noise, without detrending, random window, random navs
    % 3) get the number of averages
    % 4) get the nfft used
    % 5) run cpsd again, with the nfft used
    % 6) run cpsd again, with conflicting parameters, and verify it uses
    %     nfft rather than navs
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Uniform';
      
      % Evaluate the cpsd of the white noise time-series data
      [a_1, a_2, C1, C2, navs] = prepare_analyze_noise_navs(noise_type, plist);
      
      npts_3 = fix(find(C1.hist.plistUsed, 'Nfft')/2);
      
      % Calculates the cpsd asking for the number of points AND the window length
      pl_spec = C1.hist.plistUsed;
      pl_spec.pset('Nfft', npts_3, 'navs', navs);
      C3 = cpsd(a_1, a_2, pl_spec);
      
      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_53
  
  %% Helper function for window call construction
  
  function [a_1, a_2, spec1, spec2] = prepare_analyze_noise(win_type, noise_type, pli)
    % 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_1 = utils.math.randelement(sigma_distr_list, 1);
    sigma_distr_2 = utils.math.randelement(sigma_distr_list, 1);
    trend_0_1 = utils.math.randelement(trend_0_list, 1);
    trend_0_2 = utils.math.randelement(trend_0_list, 1);
    
    % Pick 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;
    
    % White noise
    a_n = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_1));
    
    % Constant signal
    a_c = ao(trend_0_1);
    
    % Total signal
    a_1 = a_n + a_c;
    
    % White noise
    a_n = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr_2));
    % Constant signal
    a_c = ao(trend_0_2);
    
    % Total signal
    a_2 = a_n + a_c;
    
    % Set units
    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 cpsd of the white noise time-series data
    olap = 0;
    detrend_order = 0;
    
    switch lower(win_type)
      case 'kaiser'
        psll = find(pli, 'psll');
        if isempty(psll)
          psll = find(ao.getInfo('psd').plists, 'psll');
        end
        pl_spec = plist('Win', win_type, 'psll', psll, 'olap', olap, 'order', detrend_order);
        
      case 'levelledhanning'
        levelCoef = find(pli, 'levelCoef');
        if isempty(levelCoef)
          levelCoef = 1;
        end
        pl_spec = plist('Win', win_type, 'levelCoef', levelCoef, 'olap', olap, 'order', detrend_order);
        
      otherwise
        pl_spec = plist('Win', win_type, 'olap', olap, 'order', detrend_order);
        
    end
    
    if find(pli, 'win_obj')
      % Calls the cpsd applying the detrend and window internally
      % (passig window object)
      spec2.pl = pl_spec;
      spec2.Cxy = cpsd(a_1, a_2, spec2.pl);
      spec2.Cyx = cpsd(a_2, a_1, spec2.pl);
      spec2.Cxx = cpsd(a_1, a_1, spec2.pl);
      spec2.Cyy = cpsd(a_2, a_2, spec2.pl);
      spec2.S_1 = simplifyYunits(psd(a_1, spec2.pl), ...
        plist('prefixes', false, 'exceptions','Hz'));
      spec2.S_2 = simplifyYunits(psd(a_2, spec2.pl), ...
        plist('prefixes', false, 'exceptions','Hz'));
    else
      spec2 = struct;
    end
    % Calls the cpsd applying the detrend and window internally
    % (passig window name)
    spec1.pl = pl_spec.pset('Win', win_type);
    spec1.Cxy = cpsd(a_1, a_2, spec1.pl);
    spec1.Cyx = cpsd(a_2, a_1, spec1.pl);
    spec1.Cxx = cpsd(a_1, a_1, spec1.pl);
    spec1.Cyy = cpsd(a_2, a_2, spec1.pl);
    spec1.S_1 = simplifyYunits(psd(a_1, spec1.pl), ...
      plist('prefixes', false, 'exceptions','Hz'));
    spec1.S_2 = simplifyYunits(psd(a_2, spec1.pl), ...
      plist('prefixes', false, 'exceptions','Hz'));
    
  end
  
  %% Helper function for window call construction, navs option
  
  function [a_1, a_2, C1, C2, navs] = prepare_analyze_noise_navs(noise_type, pli)
    % 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);
    
    % Pick 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;
    
    % White noise
    a_n1 = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
    a_n2 = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
    
    % Constant signal
    a_c = ao(trend_0);
    
    % Total signals
    a_1 = a_n1 + a_c;
    a_2 = a_n2 + a_c;
    
    % Set units
    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 cpsd of the white noise time-series data
    olap = 0;
    detrend_order = 0;
    n_pts = -1;
    
    navs = fix(utils.math.randelement(logspace(0,log10(max(0,a_1.len/10)),50),1));
    
    % Evaluate the cpsd of the white noise time-series data
    % Window
    win_list = specwin.getTypes;
    win_type = utils.math.randelement(win_list(~strcmpi(win_list, 'levelledhanning')), 1);
    win_type = win_type{1};
    
    switch lower(win_type)
      case 'kaiser'
        psll =  utils.math.randelement([0:10:200],1);
        if psll == 0
          psll = find(ao.getInfo('psd').plists, 'psll');
        end
        pl_spec = plist('Win', win_type, 'psll', psll, 'olap', olap, 'order', detrend_order);
      otherwise
        pl_spec = plist('Win', win_type, 'olap', olap, 'order', detrend_order);
    end
    
    % Calls cpsd asking for the number of averages
    pl_spec.pset('Nfft', n_pts, 'navs', navs);
    C1 = cpsd(a_1, a_2, pl_spec);
    
    % Calls cpsd asking for the number of points just evaluated
    pl_spec.pset('Nfft', find(C1.hist.plistUsed, 'Nfft'));
    pl_spec.remove('navs');
    C2 = cpsd(a_1, a_2, pl_spec);
    
  end
  
end