% UTP_AO_PSD a set of UTPs for the ao/psd method
%
% M Hewitson 06-08-08
%
% $Id: utp_ao_psd.m,v 1.48 2011/07/21 06:46:48 mauro Exp $
%

% <MethodDescription>
%
% The psd method of the ao class computes the spectral density of time-series AOs.
%
% </MethodDescription>

function results = utp_ao_psd(varargin)
  
  % Check the inputs
  if nargin == 0
    
    addpath(fullfile(fileparts(which(mfilename)), 'reference_files'))
    
    % Some keywords
    class   = 'ao';
    mthd    = 'psd';
    
    results = [];
    disp('******************************************************');
    disp(['****  Running UTPs for ' class '/' mthd]);
    disp('******************************************************');
    
    % Test AOs
    [at1,at2,at3,at4,at5,at6,atvec,atmat] = 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);
    
    % Very-long white noise reference time-series
    a_n_long = ao(plist('filename', 'ref_whitenoise_20000s_10Hz.xml'));
    
    % Split the reference data into different segments
    nsecs = a_n_long.data.nsecs;
    Nsegments = 100;
    
    ap = a_n_long.split(plist('split_type', 'chunks', 'chunks', Nsegments));
    
    % Run the tests
    results = [results utp_01];    % getInfo call
    results = [results utp_02];    % Vector input
    results = [results utp_03];    % Matrix input
    results = [results utp_04];    % List input
    results = [results utp_05];    % Test with mixed input
    results = [results utp_06];    % Test history is working
    results = [results utp_07];    % Test against MATLAB's pwelch
    results = [results utp_08];    % Test output of the data
    results = [results utp_09];    % Test against MATLAB's pwelch
    results = [results utp_10];    % Test against MATLAB's pwelch
    
    results = [results utp_11(mthd, at1, ple1)];    % Test plotinfo doesn't disappear
    
    results = [results utp_12];    % Test "conservation of energy":
    %  - white noise from uniform pdf, random parameters
    %  - no detrending, Rectangular window
    results = [results utp_13];    % Test "conservation of energy":
    %  - white noise from normal pdf, fixed parameters
    %  - no detrending, Rectangular window
    results = [results utp_14];    % Test "conservation of energy":
    %  - white noise from uniform pdf, fixed parameters
    %  - no detrending, Rectangular window
    results = [results utp_15];    % Test "conservation of energy":
    %  - white noise from normal pdf, fixed parameters
    %  - no detrending, Rectangular window
    results = [results utp_16];    % Test "conservation of energy":
    %  - white noise from uniform pdf, fixed parameters
    %  - no detrending, Rectangular window
    results = [results utp_17];    % Test units handling: PSD
    results = [results utp_18];    % Test units handling: ASD
    results = [results utp_19];    % Test units handling: PS
    results = [results utp_20];    % Test units handling: AS
    results = [results utp_21];    % Test "conservation of energy": Welch window + no detrending
    results = [results utp_22];    % Test "conservation of energy": Welch window + mean detrending
    results = [results utp_23];    % Test "conservation of energy": Welch window + linear detrending
    results = [results utp_24];    % Test "conservation of energy": Hanning window + no detrending
    results = [results utp_25];    % Test "conservation of energy": Hanning window + mean detrending
    results = [results utp_26];    % Test "conservation of energy": Hanning window + linear detrending
    results = [results utp_27];    % Test "conservation of energy": Hamming window + no detrending
    results = [results utp_28];    % Test "conservation of energy": Hamming window + mean detrending
    results = [results utp_29];    % Test "conservation of energy": Hamming window + linear detrending
    results = [results utp_30];    % Test "conservation of energy": BH92 window + no detrending
    results = [results utp_31];    % Test "conservation of energy": BH92 window + mean detrending
    results = [results utp_32];    % Test "conservation of energy": BH92 window + linear detrending
    results = [results utp_33];    % Test "conservation of energy": Kaiser200 window + no detrending
    results = [results utp_34];    % Test "conservation of energy": Kaiser200 window + mean detrending
    results = [results utp_35];    % Test "conservation of energy": Kaiser200 window + linear detrending
    results = [results utp_38];    % Test detrending
    results = [results utp_39];    % Test detrending
    results = [results utp_40];    % Test detrending
    results = [results utp_41];    % Test different windows: Rectangular
    results = [results utp_42];    % Test different windows: BH92
    results = [results utp_43];    % Test different windows: Hamming
    results = [results utp_44];    % Test different windows: Hanning
    results = [results utp_45];    % Test different windows: Bartlett
    results = [results utp_46];    % Test different windows: Nuttall3
    results = [results utp_47];    % Test different windows: Kaiser psll = [random]
    results = [results utp_48];    % Test different windows: Kaiser psll = [default]
    results = [results utp_49];    % Test different windows: Nuttall4
    results = [results utp_50];    % Test different windows: SFT3F
    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
    results = [results utp_54];    % Test "conservation of energy":
    results = [results utp_60];    % Test 'basic' call: PSD
    results = [results utp_61];    % Test 'basic' call: ASD
    results = [results utp_62];    % Test 'basic' call: PS
    results = [results utp_63];    % Test 'basic' call: AS

    
    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>
    
    % <SyntaxCode>
    try
      % 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
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that getInfo call returned an minfo object in all cases.
    % 2) Check that all plists have the correct parameters.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % 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('scale'), 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('scale'), 'PSD'), 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('scale'), {'PSD', 'ASD', 'PS', 'AS'}), 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
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_01
  
  %% UTP_02
  
  % <TestDescription>
  %
  % Tests that the psd method works with a vector of AOs as input.
  %
  % </TestDescription>
  function result = utp_02
    
    % <SyntaxDescription>
    %
    % Test that the psd method works for a vector of AOs as input.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      avec = [at1 at5 at6];
      % Vector output
      out  = psd(avec);
      % List output
      [out1, out2, out3] = psd(avec);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that the number of elements in 'out' is the same as in the input.
    % 2) Check that each output object contains the correct values.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % Check we have the correct number of outputs
      if numel(out) ~= numel(avec), atest = false; end
      % Check we have the correct values in the outputs
      % Vector output
      for kk = 1:numel(out)
        if ~eq(out(kk), psd(avec(kk)), ple1), atest = false; end
      end
      % List output
      if ~eq(out1, psd(avec(1)), ple1), atest = false; end
      if ~eq(out2, psd(avec(2)), ple1), atest = false; end
      if ~eq(out3, psd(avec(3)), ple1), atest = false; end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_02
  
  %% UTP_03
  
  % <TestDescription>
  %
  % Tests that the psd method works with a matrix of AOs as input.
  %
  % </TestDescription>
  function result = utp_03
    
    % <SyntaxDescription>
    %
    % Test that the psd method works for a matrix of AOs as input.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      amat = [at1 at5 at6; at5 at6 at1];
      % Vector output
      out  = psd(amat);
      % List output
      [out1, out2, out3, out4, out5, out6] = psd(amat);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that the number of elements in 'out' is the same as in the input.
    % 2) Check that each output object contains the correct values.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % Check we have the correct number of outputs
      if numel(out) ~= numel(amat), atest = false; end
      % Check we have the correct values in the outputs
      % Vector output
      for kk = 1:numel(out)
        if ~eq(out(kk), psd(amat(kk)), ple1), atest = false; end
      end
      % List output
      if ~eq(out1, psd(amat(1)), ple1), atest = false; end
      if ~eq(out2, psd(amat(2)), ple1), atest = false; end
      if ~eq(out3, psd(amat(3)), ple1), atest = false; end
      if ~eq(out4, psd(amat(4)), ple1), atest = false; end
      if ~eq(out5, psd(amat(5)), ple1), atest = false; end
      if ~eq(out6, psd(amat(6)), ple1), atest = false; end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_03
  
  %% UTP_04
  
  % <TestDescription>
  %
  % Tests that the psd method works with a list of AOs as input.
  %
  % </TestDescription>
  function result = utp_04
    
    % <SyntaxDescription>
    %
    % Test that the psd method works for a list of AOs as input.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Vector output
      out                = psd(at1,at5,at6);
      % List output
      [out1, out2, out3] = psd(at1,at5,at6);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that the number of elements in 'out' is the same as in the input.
    % 2) Check that each output AO contains the correct data.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % Check we have the correct number of outputs
      if numel(out) ~= 3, atest = false; end
      % Check we have the correct values in the outputs
      % Vector output      
      if ~eq(out(1), psd(at1), ple1), atest = false; end
      if ~eq(out(2), psd(at5), ple1), atest = false; end
      if ~eq(out(3), psd(at6), ple1), atest = false; end
      % List output
      if ~eq(out1, psd(at1), ple1), atest = false; end
      if ~eq(out2, psd(at5), ple1), atest = false; end
      if ~eq(out3, psd(at6), ple1), atest = false; end

    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_04
  
  %% UTP_05
  
  % <TestDescription>
  %
  % Tests that the psd method works with a mix of different shaped AOs as
  % input.
  %
  % </TestDescription>
  function result = utp_05
    
    % <SyntaxDescription>
    %
    % Test that the psd method works with an input of matrices and vectors
    % and single AOs.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Vector output
      out = psd(at1,[at5 at6],at5,[at5 at1; at6 at1],at6);
      % List output
      [out1, out2, out3, out4, out5, out6, out7, out8, out9] = ...
        psd(at1,[at5 at6],at5,[at5 at1; at6 at1],at6);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that the number of elements in 'out' is the same as in
    %    input.
    % 2) Check that each output AO contains the correct data.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % Check we have the correct number of outputs
      if numel(out) ~= 9, atest = false; end
      % Check we have the correct values in the outputs
      % Vector output      
      if ~eq(out(1), psd(at1), ple1), atest = false; end
      if ~eq(out(2), psd(at5), ple1), atest = false; end
      if ~eq(out(3), psd(at6), ple1), atest = false; end
      if ~eq(out(4), psd(at5), ple1), atest = false; end
      if ~eq(out(5), psd(at5), ple1), atest = false; end
      if ~eq(out(6), psd(at6), ple1), atest = false; end
      if ~eq(out(7), psd(at1), ple1), atest = false; end
      if ~eq(out(8), psd(at1), ple1), atest = false; end
      if ~eq(out(9), psd(at6), ple1), atest = false; end
      % List output
      if ~eq(out1, psd(at1), ple1), atest = false; end
      if ~eq(out2, psd(at5), ple1), atest = false; end
      if ~eq(out3, psd(at6), ple1), atest = false; end
      if ~eq(out4, psd(at5), ple1), atest = false; end
      if ~eq(out5, psd(at5), ple1), atest = false; end
      if ~eq(out6, psd(at6), ple1), atest = false; end
      if ~eq(out7, psd(at1), ple1), atest = false; end
      if ~eq(out8, psd(at1), ple1), atest = false; end
      if ~eq(out9, psd(at6), ple1), atest = false; end
      
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_05
  
  %% UTP_06
  
  % <TestDescription>
  %
  % Tests that the psd method properly applies history.
  %
  % </TestDescription>
  function result = utp_06
    
    % <SyntaxDescription>
    %
    % Test that the result of applying the psd method can be processed back
    % to an m-file.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      out  = psd(at5);
      mout = rebuild(out);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that the last entry in the history of 'out' corresponds to
    %    'psd'.
    % 2) Check that the re-built object is the same object as 'out'.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    if stest
      % Check the last step in the history of 'out'
      if ~strcmp(out.hist.methodInfo.mname, 'psd'), atest = false; end
      % Check the re-built object
      if ~eq(mout, out, ple2), atest = false; end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_06
  
  %% UTP_07
  
  % <TestDescription>
  %
  % Tests that the psd method agrees with MATLAB's pwelch, within some tolerance,
  % when configured to use the same parameters.
  %
  % </TestDescription>
  function result = utp_07
    
    % <SyntaxDescription>
    %
    % Test that applying psd works on a single AO.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Load reference ao
      a1 = ao('ref_whitenoise_10s_1000Hz.xml');
      fs = a1.fs;
      % Compute PSD
      Nfft = 2*fs;
      win  = specwin('Hanning', Nfft);
      pl = plist('Nfft', Nfft, 'Win', win.type, 'order', -1);
      S1 = a1.psd(pl);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that output agrees with the output of MATLAB's pwelch within tolerance.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1.8e-15;
    
    if stest
      % Load reference psd
      ref = load('ref_psd_10s_1000Hz.txt');
      if ~isequal(ref(:,1), S1.x) || any((abs(S1.y - ref(:,2)) ./ S1.y) >= TOL)
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_07
  
  %% UTP_08
  
  % <TestDescription>
  %
  % Check that the psd method pass back the output objects to a list of
  % output variables or to a single variable.
  %
  % </TestDescription>
  function result = utp_08
    
    % <SyntaxDescription>
    %
    % Call the method with a list of output variables and with a single output
    % variable. Additionaly check that the rebuild method works on the output.
    %
    % </SyntaxDescription>
    
    try
      % <SyntaxCode>
      [o1, o2] = psd(at5, at6);
      o3  = psd(at5, at6);
      mout1 = rebuild(o1);
      mout2 = rebuild(o2);
      mout3 = rebuild(o3);
      % </SyntaxCode>
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    
    % <AlgoDescription>
    %
    % 1) Check that the output contains the right number of objects
    % 2) Check that the 'rebuild' method produces the same object as 'out'.
    %
    % </AlgoDescription>
    
    atest = true;
    if stest
      % <AlgoCode>
      % Check the number of outputs
      if numel(o1) ~=1, atest = false; end
      if numel(o2) ~=1, atest = false; end
      if numel(o3) ~=2, atest = false; end
      % Check the rebuilding of the object
      if ~eq(o1, mout1, ple2), atest = false; end
      if ~eq(o2, mout2, ple2), atest = false; end
      if ~eq(o3, mout3, ple2), 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>
  %
  % Tests that the psd method agrees with MATLAB's pwelch when
  % configured to use the same parameters.
  %
  % </TestDescription>
  function result = utp_09
    
    % <SyntaxDescription>
    %
    % Test that the applying psd works on a single AO.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Load reference ao
      a1 = ao('ref_whitenoise_3600s_1Hz.xml');
      % Compute PSD
      Nfft = 1000;
      win  = specwin('BH92', Nfft);
      pl = plist('Nfft', Nfft, 'Win', win.type, 'order', -1);
      S1 = a1.psd(pl);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that output agrees with the output of MATLAB's pwelch.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-15;
    
    if stest
      % Load reference psd
      ref = load('ref_psd_3600s_1Hz.txt');
      if ~isequal(ref(:,1), S1.x) || any((abs(S1.y - ref(:,2)) ./ S1.y) >= TOL)
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_09
  
  %% UTP_10
  
  % <TestDescription>
  %
  % Tests that the psd method agrees with MATLAB's pwelch when
  % configured to use the same parameters.
  %
  % </TestDescription>
  function result = utp_10
    
    % <SyntaxDescription>
    %
    % Test that the applying psd works on a single AO.
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Load reference ao
      a1 = ao('ref_whitenoise_100000s_100mHz.xml');
      % Compute PSD
      Nfft = a1.fs * a1.data.nsecs;
      psll = 100;
      win  = specwin('Kaiser', Nfft, psll);
      pl = plist('Nfft', Nfft, 'Win', win.type, 'psll', psll, 'order', -1);
      S1 = a1.psd(pl);
      stest = true;
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that output agrees with the output of MATLAB's pwelch.
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-13;
    
    if stest
      % Load reference psd
      ref = load('ref_psd_100000s_100mHz.txt');
      if ~isequal(ref(:,1), S1.x) || any((abs(S1.y - ref(:,2)) ./ S1.y) >= TOL)
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_10
  
  
  
  %% UTP_12
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from uniform pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_12
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      % Parameters are crucial for the estimate to be correct!
      
      noise_type = 'Uniform';
      win_type = 'Rectangular';
      olap = 0;
      detrend_order = 0;
      
      scale = 'PSD';
      
      [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, ...
        plist('detrend_order', detrend_order, 'olap', olap));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL
        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 "conservation of energy" with fixed parameters:
  % 1) white noise produced from normal pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_13
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      % Parameters are crucial for the estimate to be correct!
      
      noise_type = 'Normal';
      win_type = 'Rectangular';
      olap = 0;
      detrend_order = 0;
      
      scale = 'PSD';
      
      % Build with fixed parameters
      fs = 10;
      nsecs = 3600;
      sigma_distr = 1;
      mu_distr = 0;
      [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, ...
        plist('detrend_order', detrend_order, 'olap', olap, ...
        'fs', fs, 'nsecs', nsecs, 'sigma_distr', sigma_distr, 'mu_distr', mu_distr));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL
        atest = false;
      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 "conservation of energy" with fixed parameters:
  % 1) white noise produced from uniform pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_14
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      % Parameters are crucial for the estimate to be correct!
      
      noise_type = 'Uniform';
      win_type = 'Rectangular';
      olap = 0;
      detrend_order = 0;
      
      scale = 'PSD';
      
      % Build with fixed parameters
      fs = 1;
      nsecs = 86400;
      sigma_distr = 1e-9;
      mu_distr = 3e-7;
      [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, ...
        plist('detrend_order', detrend_order, 'olap', olap, ...
        'fs', fs, 'nsecs', nsecs, 'sigma_distr', sigma_distr, 'mu_distr', mu_distr));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= 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 "conservation of energy" with fixed parameters:
  % 1) white noise produced from normal pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_15
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      % Parameters are crucial for the estimate to be correct!
      
      noise_type = 'Normal';
      win_type = 'Rectangular';
      olap = 0;
      detrend_order = 0;
      
      scale = 'PSD';
      
      % Build with fixed parameters
      fs = 100;
      nsecs = 100;
      sigma_distr = 1e-12;
      mu_distr = -5e-12;
      [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, ...
        plist('detrend_order', detrend_order, 'olap', olap, ...
        'fs', fs, 'nsecs', nsecs, 'sigma_distr', sigma_distr, 'mu_distr', mu_distr));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL
        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 "conservation of energy" with fixed parameters:
  % 1) white noise produced from uniform pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_16
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      % Parameters are crucial for the estimate to be correct!
      
      noise_type = 'Uniform';
      win_type = 'Rectangular';
      olap = 0;
      detrend_order = 0;
      
      scale = 'PSD';
      
      % Build with fixed parameters
      fs = 0.01;
      nsecs = 3*86400;
      sigma_distr = 1e-15;
      mu_distr = -7.72e-13;
      [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, ...
        plist('detrend_order', detrend_order, 'olap', olap, ...
        'fs', fs, 'nsecs', nsecs, 'sigma_distr', sigma_distr, 'mu_distr', mu_distr));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= 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) PSD of the white noise
  % 3) 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) PSD of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      noise_type = 'Normal';
      win_type = 'BH92';
      reduce_pts = 10;
      scale = 'PSD';
      olap = specwin(win_type).rov;
      
      [a, S, S1, S2] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('olap', olap, 'reduce_pts', reduce_pts));
      % S1 and S2 are empty in this case
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated PSD yunits) equals (input units)^2 / Hz
    % 2) Check that (calculated PSD xunits) equals Hz
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      if ne(S.yunits, (a.yunits).^2 * unit('Hz^-1')) || ne(S.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 uniform pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd orders)
  % 2) ASD of the white noise
  % 3) compares the units of the input and output
  %
  
  % </TestDescription>
  function result = utp_18
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Assign a random unit
    % 3) ASD of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      noise_type = 'Uniform';
      win_type = 'Hamming';
      reduce_pts = 4;
      scale = 'ASD';
      olap = specwin(win_type).rov;
      
      [a, S, S1, S2] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('olap', olap, 'reduce_pts', reduce_pts));
      % S1 and S2 are empty in this case
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated ASD yunits) equals (input units) / Hz^(1/2)
    % 2) Check that (calculated ASD xunits) equals Hz
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      if ne(S.yunits, (a.yunits) * unit('Hz^-1/2')) || ne(S.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 handling of units:
  % 1) white noise produced from normal pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd orders)
  % 2) PS of the white noise
  % 3) compares the units of the input and output
  %
  
  % </TestDescription>
  function result = utp_19
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Assign a random unit
    % 3) PS of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      noise_type = 'Normal';
      win_type = 'Hanning';
      reduce_pts = 5;
      scale = 'PS';
      olap = specwin(win_type).rov;
      
      [a, S, S1, S2] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('olap', olap, 'reduce_pts', reduce_pts));
      % S1 and S2 are empty in this case
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated PS yunits) equals (input units)^2
    % 2) Check that (calculated PS xunits) equals Hz
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      if ne(S.yunits, (a.yunits).^2)  || ne(S.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_19
  
  %% UTP_20
  
  % <TestDescription>
  %
  % Tests handling of units:
  % 1) white noise produced from uniform distribution, with a given mean value and
  % sigma (distribution's 1st and 2nd orders)
  % 2) AS of the white noise
  % 3) compares the units of the input and output
  %
  
  % </TestDescription>
  function result = utp_20
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Assign a random unit
    % 3) AS of the white noise
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % White noise
      noise_type = 'Uniform';
      win_type = 'Rectangular';
      reduce_pts = 10;
      scale = 'AS';
      olap = specwin(win_type).rov;
      
      [a, S, S1, S2] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('olap', olap, 'reduce_pts', reduce_pts));
      % S1 and S2 are empty in this case
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that (calculated AS yunits) equals (input units)
    % 2) Check that (calculated AS xunits) equals Hz
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      if ne(S.yunits, a.yunits)  || ne(S.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_20
  
  %% UTP_21
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Welch window and no detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_21
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Welch window
      % - no detrend
      win_type = 'Welch';
      detrend_order = -1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    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 "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Welch window and mean detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_22
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Welch window
      % - mean detrend
      win_type = 'Welch';
      detrend_order = 0;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    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 "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Welch window and linear detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_23
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Welch window
      % - linear detrend
      win_type = 'Welch';
      detrend_order = 1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    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 "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hanning window and no detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_24
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hanning window
      % - no detrend
      win_type = 'Hanning';
      detrend_order = -1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_24
  
  
  %% UTP_25
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hanning window and mean detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_25
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hanning window
      % - mean detrend
      win_type = 'Hanning';
      detrend_order = 0;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    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 "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hanning window and linear detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_26
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hanning window
      % - linear detrend
      win_type = 'Hanning';
      detrend_order = 1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_26
  
  %% UTP_27
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hamming window and no detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_27
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hamming window
      % - no detrend
      win_type = 'Hamming';
      detrend_order = -1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_27
  
  
  %% UTP_28
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hamming window and mean detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_28
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hamming window
      % - mean detrend
      win_type = 'Hanning';
      detrend_order = 0;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_28
  
  %% UTP_29
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Hamming window and linear detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_29
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Hamming window
      % - linear detrend
      win_type = 'Hamming';
      detrend_order = 1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_29
  
  
  %% UTP_30
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   BH92 window and no detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_30
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - BH92 window
      % - no detrend
      win_type = 'BH92';
      detrend_order = -1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_30
  
  
  %% UTP_31
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   BH92 window and mean detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_31
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - BH92 window
      % - mean detrend
      win_type = 'BH92';
      detrend_order = 0;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_31
  
  %% UTP_32
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   BH92 window and linear detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_32
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - BH92 window
      % - linear detrend
      win_type = 'BH92';
      detrend_order = 1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_32
  
  %% UTP_33
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Kaiser200 window and no detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_33
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Kaiser200 window
      % - no detrend
      win_type = 'Kaiser';
      psll = 200;
      detrend_order = -1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist('psll', psll));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_33
  
  
  %% UTP_34
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Kaiser200 window and mean detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_34
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Kaiser200 window
      % - mean detrend
      win_type = 'Kaiser';
      psll = 200;
      detrend_order = 0;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist('psll', psll));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_34
  
  %% UTP_35
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) Calculate the expected level of noise from sample statistics
  % 3) Calculates the PSD of the individual parts, with:
  %   Kaiser200 window and linear detrend
  % 4) Evaluate the mean and standard deviation of the Checks on individual PSDs:
  % 5) Checks on individual PSDs: mean and standard deviation of the PSD points
  % 6) Evaluate the expected value, estimated from the std of the
  %   individual segments
  % 7) Compares with the mean performed on the individual segments
  % 8) Checks on averaged PSDs: mean and standard deviation of all the PSD points
  % 9) Compares the grand-mean with the estimated white noise level
  
  % </TestDescription>
  function result = utp_35
    
    % <SyntaxDescription>
    %
    % 1) Split the reference data into different segments
    % 2) Calculates the PSD of the individual parts
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      % Calculate the expected level of noise from sample statistics
      
      % Evaluate the PSDs on the parts, employing:
      % - Kaiser200 window
      % - linear detrend
      win_type = 'Kaiser';
      psll = 200;
      detrend_order = 1;
      
      [S_ap, S_sigma_total, S_expected_total] = ...
        test_psd_energy_prepare_data(win_type, detrend_order, plist('psll', psll));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Evaluate the mean and standard deviation of the Checks on individual PSDs:
    % 2) Checks on individual PSDs: mean and standard deviation of the PSD points
    % 3) Evaluate the expected value, estimated from the std of the
    % individual segments
    % 4) Compares with the mean performed on the individual segments
    % 5) Checks on averaged PSDs: mean and standard deviation of all the PSD points
    % 6) Compares the grand-mean with the estimated white noise level
    
    % </AlgoDescription>
    
    % <AlgoCode>
    
    if stest
      atest = algo_test_psd_energy(ap, S_ap, S_expected_total, plist);
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_35
  
  %% UTP_38
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) add a given trend of order n
  % 4) psd of the noise, with proper detrending
  % 5) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_38
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Add a trend
    % 4) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      fs = 10;
      nsecs = 3600;
      sigma_distr = 1;
      trend_0 = 0;
      trend_1 = 2e-6;
      trend_2 = 5e-10;
      
      % White noise
      type = 'Normal';
      a_n = ao(plist('waveform', 'noise', ...
        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
      
      % Drift signal
      a_d = ao(plist('tsfcn', [num2str(trend_0) '*t.^0 + ' num2str(trend_1) ' *t + ' num2str(trend_2) ' *t.^2'], ...
        'fs', fs, 'nsecs', nsecs));
      
      % Total signal
      a = a_n + a_d;
      
      % Estimate the mean value and the sigma of the white noise time-series data
      sigma_sample = std(a_n.y, 1);
      
      % Evaluate the psd of the white noise time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 0;
      n_pts = -1;
      scale = 'PSD';
      
      S_n = a_n.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      
      % Evaluate the psd of the "drifting" time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 2;
      n_pts = -1;
      scale = 'PSD';
      
      S = a.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-3;
    
    if stest
      % Integrals of the spectra
      sigma_psd = sqrt(sum(S.y * S.x(2)));
      sigma_psd_n = sqrt(sum(S_n.y * S_n.x(2)));
      
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL || ...
          abs(sigma_psd_n - sigma_sample) / mean([sigma_psd_n sigma_sample]) >= TOL
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_38
  
  
  %% UTP_39
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from uniform pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) add a given trend of order n
  % 4) psd of the noise, with proper detrending
  % 5) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_39
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from uniform distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Add a trend
    % 4) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Build time-series test data
      fs = 1;
      nsecs = 86400;
      sigma_distr = 1e-9;
      trend_0 = 3e-7;
      trend_1 = 1e-7;
      trend_2 = 2e-14;
      
      % White noise
      type = 'Uniform';
      a_n = ao(plist('waveform', 'noise', ...
        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
      
      % Drift signal
      a_d = ao(plist('tsfcn', [num2str(trend_0) '*t.^0 + ' num2str(trend_1) ' *t + ' num2str(trend_2) ' *t.^2'], ...
        'fs', fs, 'nsecs', nsecs));
      
      % Total signal
      a = a_n + a_d;
      
      % Estimate the mean value and the sigma of the white noise time-series data
      sigma_sample = std(a_n.y, 1);
      
      % Evaluate the psd of the white noise time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 0;
      n_pts = -1;
      scale = 'PSD';
      
      S_n = a_n.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      
      % Evaluate the psd of the "drifting" time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 2;
      n_pts = -1;
      scale = 'PSD';
      
      S = a.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-3;
    
    if stest
      % Integrals of the spectra
      sigma_psd = sqrt(sum(S.y * S.x(2)));
      sigma_psd_n = sqrt(sum(S_n.y * S_n.x(2)));
      
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL || ...
          abs(sigma_psd_n - sigma_sample) / mean([sigma_psd_n sigma_sample]) >= TOL
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_39
  
  %% UTP_40
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) add a given trend of order n
  % 4) psd of the noise, with proper detrending
  % 5) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_40
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Add a trend
    % 4) Estimate the psd
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      % Build time-series test data
      
      fs = 100;
      nsecs = 100;
      sigma_distr = 1e-12;
      trend_0 = -5e-12;
      trend_1 = 4e-13;
      trend_2 = 1.17e-14;
      
      % White noise
      type = 'Normal';
      a_n = ao(plist('waveform', 'noise', ...
        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
      
      % Drift signal
      a_d = ao(plist('tsfcn', [num2str(trend_0) '*t.^0 + ' num2str(trend_1) ' *t + ' num2str(trend_2) ' *t.^2'], ...
        'fs', fs, 'nsecs', nsecs));
      
      % Total signal
      a = a_n + a_d;
      
      % Estimate the mean value and the sigma of the white noise time-series data
      sigma_sample = std(a_n.y, 1);
      
      % Evaluate the psd of the white noise time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 0;
      n_pts = -1;
      scale = 'PSD';
      
      S_n = a_n.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      
      % Evaluate the psd of the "drifting" time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 2;
      n_pts = -1;
      scale = 'PSD';
      
      S = a.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-3;
    
    if stest
      % Integrals of the spectra
      sigma_psd = sqrt(sum(S.y * S.x(2)));
      sigma_psd_n = sqrt(sum(S_n.y * S_n.x(2)));
      
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL || ...
          abs(sigma_psd_n - sigma_sample) / mean([sigma_psd_n sigma_sample]) >= TOL
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_40
  
  
  
  %% UTP_41
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Rectangular window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_41
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Rectangular window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Rectangular';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_41
  
  %% UTP_42
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, BH92 window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_42
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, BH92 window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'BH92';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_42
  
  
  %% UTP_43
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Hamming window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_43
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Hamming window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Hamming';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_43
  
  %% UTP_44
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Hanning window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_44
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Hanning window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    msg = '';
    try
      
      noise_type = 'Normal';
      win_type = 'Hanning';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      msg = err.message;
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      [atest, msg] = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename, msg);
  end % END UTP_44
  
  %% UTP_45
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Bartlett window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_45
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Bartlett window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Bartlett';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_45
  
  %% UTP_46
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Nuttall3 window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_46
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Nuttall3 window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    msg = '';
    try
      
      noise_type = 'Normal';
      win_type = 'Nuttall3';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      msg = err.message;
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      [atest, msg] = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename, msg);
  end % END UTP_46
  
  %% UTP_47
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Kaiser psll = random window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_47
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Kaiser psll = random window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Kaiser';
      psll = utils.math.randelement([10:10:200],1);
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_47
  
  %% UTP_48
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Kaiser psll = default window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_48
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Kaiser psll = default window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Kaiser';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_48
  
  
  %% UTP_49
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, Nuttall4 window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_49
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, Nuttall4 window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'Nuttall4';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_49
  
  %% UTP_50
  
  % <TestDescription>
  %
  % Tests the effect of windowing:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, SFT3F window
  % 3) Apply the detrending and the window manually
  % 4) psd of the noise, without detrending, Rectangular window
  % 5) compares the to psds
  %
  
  % </TestDescription>
  function result = utp_50
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd without detrending, SFT3F window
    % 4) Manually apply window to the data
    % 5) Estimate the psd without detrending, Rectangular window
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = 'SFT3F';
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', true));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psds are identical within a part in 10^12
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    
    TOL = 1e-12;
    
    if stest
      atest = algo_test_psd_win(S_name, S_obj, S_man, plist('TOL', TOL));
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_50
  
  %% 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) psd 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) psd of the noise, without detrending, random window, set number of
    %   averages
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      noise_type = 'Normal';
      
      % Evaluate the psd of the white noise time-series data
      [a, S1, S2, 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
      % Compare the navs written in the output object with the requested one
      if ne(navs, S1.data.navs)
        if ne(find(S1.hist.plistUsed, 'navs'), S1.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 normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, without detrending, random window, random navs
  % 3) get the number of averages
  % 4) get the nfft used
  % 5) run psd again, with the nfft used
  % 6) compare the calculated objects
  %
  
  % </TestDescription>
  function result = utp_52
    
    % <SyntaxDescription>
    %
    % 1) white noise produced from normal pdf, with:
    %   a given mean value and sigma (distribution's 1st and 2nd order)
    % 2) psd of the noise, without detrending, random window, random navs
    % 3) get the number of averages
    % 4) get the nfft used
    % 5) run psd again, with the nfft used
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      noise_type = 'Normal';
      
      % Evaluate the psd of the white noise time-series data
      [a, S1, S2, 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 S1 and S2 are identical
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      % Compare the output objects
      if ne(S1, S2, 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) psd of the noise, without detrending, random window, random navs
  % 3) get the number of averages
  % 4) get the nfft used
  % 5) run psd 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) psd of the noise, without detrending, random window, random navs
    % 3) get the number of averages
    % 4) get the nfft used
    % 5) run psd again, with the nfft used
    % 6) run psd again, with conflicting parameters, and verify it uses
    %     nfft rather than navs
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      noise_type = 'Uniform';
      
      % Evaluate the psd of the white noise time-series data
      [a, S1, S2, navs] = prepare_analyze_noise_navs(noise_type, plist);
      
      npts_3 = fix(find(S1.hist.plistUsed, 'Nfft')/2);
      
      % Calculates the psd asking for the number of points AND the window length
      pl_spec = S1.hist.plistUsed;
      pl_spec.pset('Nfft', npts_3, 'navs', navs);
      S3 = a.psd(pl_spec);
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated objects S1 and S2 are identical
    % 2) Check that S3 used different values
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    
    if stest
      % Compare the navs written in the output object with the requested one
      if  ne(S1, S2, ple3) || ...
          ne(find(S3.hist.plistUsed, 'Nfft'), npts_3) || eq(S3.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
  
  %% UTP_54
  
  % <TestDescription>
  %
  % Tests "conservation of energy":
  % 1) white noise produced from normal pdf, with a given mean value and
  % sigma (distribution's 1st and 2nd order)
  % 2) evaluate the sample mean value m and standard deviation s
  % 3) psd of the white noise
  % 4) compares the sqrt(sum(S * df)) with the standard deviation s
  %
  
  % </TestDescription>
  function result = utp_54
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd
    %
    % </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);
      
      % White noise
      type = 'Normal';
      a_n = ao(plist('waveform', 'noise', ...
        'type', type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
      a_const = ao(mu_distr);
      a = a_n + a_const;
      
      % Estimate the mean value and the sigma of the time-series data
      sigma_sample = std(a.y, 1);
      mu_sample = mean(a.y);
      
      % Evaluate the psd of the time-series data
      % Parameters are crucial for the estimate to be correct!
      win = 'Rectangular';
      olap = 0;
      detrend = 0;
      n_pts = -1;
      scale = 'PSD';
      
      S = a.psd(plist('Win', win, 'olap', olap, ...
        'Nfft', n_pts, 'order', detrend, 'scale', scale));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Check that calculated psd energy equals the rms content of the tsdata, estimated by the std of the sample
    %
    % </AlgoDescription>
    
    % <AlgoCode>
    atest = true;
    TOL = 1e-12;
    
    if stest
      % Integral of the spectrum
      sigma_psd = sqrt(sum(S.y * S.x(2)));
      if abs(sigma_psd - sigma_sample) / mean([sigma_psd sigma_sample]) >= TOL
        atest = false;
      end
    else
      atest = false;
    end
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_54
  
  %% UTP_60
  
  % <TestDescription>
  %
  % Tests the 'basic' call:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, defualt detrending, default window, no averaging
  %
  
  % </TestDescription>
  function result = utp_60
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd with default detrending, default window, no averaging
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = char(prefs.getMiscPrefs.getDefaultWindow);
      scale = 'PSD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', false, 'reduce_pts', false));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Nothing to check
    %
    % </AlgoDescription>
    
    % <AlgoCode>        
    
    if stest
      atest = true;
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_60
  
  %% UTP_61
  
  % <TestDescription>
  %
  % Tests the 'basic' call:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, defualt detrending, default window, no averaging
  %
  
  % </TestDescription>
  function result = utp_61
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd with default detrending, default window, no averaging
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = char(prefs.getMiscPrefs.getDefaultWindow);
      scale = 'ASD';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', false, 'reduce_pts', false));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Nothing to check
    %
    % </AlgoDescription>
    
    % <AlgoCode>        
    
    if stest
      atest = true;
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_61
  
  %% UTP_62
  
  % <TestDescription>
  %
  % Tests the 'basic' call:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, defualt detrending, default window, no averaging
  %
  
  % </TestDescription>
  function result = utp_62
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd with default detrending, default window, no averaging
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = char(prefs.getMiscPrefs.getDefaultWindow);
      scale = 'PS';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', false, 'reduce_pts', false));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Nothing to check
    %
    % </AlgoDescription>
    
    % <AlgoCode>        
    
    if stest
      atest = true;
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_62
  
  %% UTP_63
  
  % <TestDescription>
  %
  % Tests the 'basic' call:
  % 1) white noise produced from normal pdf, with:
  %   a given mean value and sigma (distribution's 1st and 2nd order)
  % 2) psd of the noise, defualt detrending, default window, no averaging
  %
  
  % </TestDescription>
  function result = utp_63
    
    % <SyntaxDescription>
    %
    % 1) Prepare the test tsdata:
    %   white noise from normal distribution + offset
    % 2) Calculate the statistical parameters
    % 3) Estimate the psd with default detrending, default window, no averaging
    %
    % </SyntaxDescription>
    
    % <SyntaxCode>
    try
      
      noise_type = 'Normal';
      win_type = char(prefs.getMiscPrefs.getDefaultWindow);
      scale = 'AS';
      
      [a, S_name, S_obj, S_man] = prepare_analyze_noise_win(win_type, noise_type, scale, ...
        plist('win_test', false, 'reduce_pts', false));
      
      stest = true;
      
    catch err
      disp(err.message)
      stest = false;
    end
    % </SyntaxCode>
    
    % <AlgoDescription>
    %
    % 1) Nothing to check
    %
    % </AlgoDescription>
    
    % <AlgoCode>        
    
    if stest
      atest = true;
    else
      atest = false;
    end
    
    % </AlgoCode>
    
    % Return a result structure
    result = utp_prepare_result(atest, stest, dbstack, mfilename);
  end % END UTP_63
  
  %% Helper function for window call construction
  
  function [a, S_name, S_obj, S_w] = prepare_analyze_noise_win(win_type, noise_type, scale, 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 = 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_n = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
    
    % Constant signal
    a_c = ao(trend_0);
    
    % Total signal
    a = a_n + a_c;
    
    % Set units
    a.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
    
    % Evaluate the psd of the white noise time-series data
    n_pts_reduction_factor = find(pli, 'reduce_pts');
    if isempty(n_pts_reduction_factor) || ~n_pts_reduction_factor
      n_pts_reduction_factor = 1; % Taking single windows, no reduction in number of points
    end
    n_pts = a.nsecs * a.fs/ n_pts_reduction_factor;
    
    olap = find(pli, 'olap');
    if isempty(olap)
      olap = 0; %% Should we take the rov instead?
    end
    if n_pts_reduction_factor == 1
      olap = 0; % This does not matter in the case of single window
    end
    
    detrend_order = 0;
    
    switch lower(win_type)
      case 'kaiser'
        psll = find(pli, 'psll');
        if isempty(psll)
          psll = find(ao.getInfo('psd').plists, 'psll');
        end
        win = specwin(win_type, fs*nsecs, psll);
        pl_spec = plist('Win', win_type, 'psll', psll, 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
      otherwise
        win = specwin(win_type, fs*nsecs);
        pl_spec = plist('Win', win_type, 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
    end
    
    if find(pli, 'win_test')
      % Makes a copy of the data, and apply the window manually
      % Apply the detrending
      a_w = a.detrend(plist('N', detrend_order));
      % Apply the window, taking into account the correct normalization
      a_w.setY(a_w.y .* win.win'./sqrt(win.ws2 / win.len));
      
      % Evaluate the psd of the windowed data
      S_w = a_w.psd(plist('Win', 'Rectangular', 'olap', olap, ...
        'Nfft', n_pts, 'order', -1, 'scale', scale));
      
      % Calls psd applying the detrend and window internally
      % (passig window object)
      S_obj = a.psd(pl_spec);
      
    else
      S_obj = ao;
      S_w = ao;
    end
    
    % Calls psd applying the detrend and window internally
    % (passig window name)
    pl_spec.pset('Win', win_type);
    S_name = a.psd(pl_spec);
    
  end
  
  
  %% Helper function for window call construction
  
  function [sigma_sample, sigma_psd] = prepare_analyze_noise_energy(win_type, noise_type, scale, pli)
    
    % Build time-series test data
    if find(pli, 'fs')
      fs = find(pli, 'fs');
      nsecs = find(pli, 'nsecs');
      sigma_distr = find(pli, 'sigma_distr');
      mu_distr = find(pli, 'mu_distr');
    else
      % 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]';
      
      % 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);
    end
    
    % 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));
    
    % Constant signal
    a_c = ao(mu_distr);
    
    % Total signal
    a = a_n + a_c;
    
    % Set units
    a.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
    
    % Evaluate the psd of the white noise time-series data
    olap = find(pli, 'olap');
    if isempty(olap)
      olap = 0; %% Should we take the rov instead?
    end
    
    detrend_order = find(pli, 'detrend_order');
    if isempty(detrend_order)
      detrend_order = 0;
    end
    
    n_pts = -1;
    
    switch lower(win_type)
      case 'kaiser'
        psll = find(pli, 'psll');
        if isempty(psll)
          psll = find(ao.getInfo('psd').plists, 'psll');
        end
        win = specwin(win_type, fs*nsecs, psll);
        pl_spec = plist('Win', win_type, 'psll', psll, 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
      otherwise
        win = specwin(win_type, fs*nsecs);
        pl_spec = plist('Win', win_type, 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
    end
    
    % Calls psd applying the detrend and window internally
    % (passig window name)
    pl_spec.pset('Win', win_type);
    S = a.psd(pl_spec);
    
    % Evaluate the statistics of the time-series and spectra
    
    % Estimate the mean value and the sigma of the time-series data
    sigma_sample = std(a.y, 1);
    mu_sample = mean(a.y);
    
    % Integral of the spectrum
    sigma_psd = sqrt(sum(S.y * S.x(2)));
    
  end
  
  %% Helper function for window call construction, navs option
  
  function [a, S, S1, 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_n = ao(plist('waveform', 'noise', ...
      'type', noise_type, 'fs', fs, 'nsecs', nsecs, 'sigma', sigma_distr));
    
    % Constant signal
    a_c = ao(trend_0);
    
    % Total signals
    a = a_n + a_c;
    
    % Set units
    a.setYunits(unit([cell2mat(utils.math.randelement(prefix_list,1)) cell2mat(utils.math.randelement(unit_list,1))]));
    
    % Evaluate the psd 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.len/10)),50),1));
    
    % Evaluate the psd of the white noise time-series data
    % Window
    win_list = specwin.getTypes;
    win_type = utils.math.randelement(win_list,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);
      case 'levelledhanning'
        pl_spec = plist('Win', 'BH92', 'olap', olap, 'order', detrend_order);
      otherwise
        pl_spec = plist('Win', win_type, 'olap', olap, 'order', detrend_order);
    end
    
    % Calls psd asking for the number of averages
    pl_spec.pset('Nfft', n_pts, 'navs', navs);
    S = a.psd(pl_spec);
    
    % Calls psd asking for the number of points just evaluated
    pl_spec.pset('Nfft', find(S.hist.plistUsed, 'Nfft'));
    pl_spec.remove('navs');
    S1 = a.psd(pl_spec);
    
  end
  
  %% Helper function for window call construction, navs option
  function [S_ap, S_sigma_total, S_expected_total] = test_psd_energy_prepare_data(win_type, detrend_order, pli)
    
    % Calculate the expected level of noise from sample statistics
    fs = a_n_long.fs;
    S_sigma_total = std(a_n_long.y, 1);
    S_expected_total = S_sigma_total^2 / fs * 2;
    
    olap = 0;
    n_pts = -1;
    scale = 'PSD';
    
    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, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
      case 'levelledhanning'
        pl_spec = plist('Win', 'BH92', 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
      otherwise
        pl_spec = plist('Win', win_type, 'olap', olap, ...
          'Nfft', n_pts, 'order', detrend_order, 'scale', scale);
    end
    
    % Calls psd applying the detrend and window internally
    % (passig window name)
    S_ap = ap.psd(pl_spec);
    
  end
  
  %% Algorithm test for window comparison
  
  function varargout = algo_test_psd_win(S_name, S_obj, S_man, pli)
    atest = true;
    msg = '';
    pl_def = plist('TOL', 1e-12);
    TOL = find(parse(pli, pl_def), 'TOL');
    
    % Compare the psd evaluated with the window name / object
    if ~eq(S_name, S_obj, ple1)
      atest = false;
      msg = ['Two PSD objects are not equal: ' lastwarn];
    end
    % Compare the psd evaluated with the two methods
    res = abs(S_obj - S_man) ./ S_obj;
    if any(res.y >= TOL)
      atest = false;
      msg = 'Two PSDs are not equal';
    end
    
    if nargout == 1
      varargout{1} = atest;
    elseif nargout == 2
      varargout{1} = atest;
      varargout{2} = msg;
    else
      error('Incorrect outputs');
    end
    
  end
  
  
  %% Algorithm test for energy conservation tests
  
  function atest = algo_test_psd_energy(ap, S_ap, S_expected_total, pli)
    
    % Checks on individual PSDs: mean and standard deviation of the
    % PSD points
    fs = ap.fs;
    Npoints = length(S_ap(1).y);
    S_mean = mean(S_ap.y);
    S_err = std(S_ap.y, 1) / sqrt(Npoints);
    
    % Expected value, estimated from the std of the individual segments
    S_sigma = std(ap.y, 1);
    S_expected = S_sigma.^2 / fs * 2;
    
    % Comparison with the mean performed on the individual segments
    test_level = abs(S_mean - S_expected) < S_err;
    sum(test_level) / Nsegments;   % TODO: implement hypothesis test here
    
    % Checks on averaged PSDs: mean and standard deviation of all the
    % PSD points
    S_mean_total = mean(S_mean);
    S_err_total = std(S_mean) / sqrt(Nsegments);
    
    % Compares the grand-mean with the estimated white noise level
    if sum(abs(S_mean_total - S_expected_total) < S_err_total)
      atest = true;
    else
      atest = false;
    end
    
  end
  
  
end