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Fix. Default password should be [] not an empty string
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
|---|---|
| date | Wed, 07 Dec 2011 17:29:47 +0100 |
| parents | 409a22968d5e |
| children |
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% UTP_AO_LPSD a set of UTPs for the ao/lpsd method % % M Hewitson 06-08-08 % % $Id: utp_ao_lpsd.m,v 1.30 2011/05/22 21:21:30 mauro Exp $ % % <MethodDescription> % % The lpsd method of the ao class computes the spectral density of % time-series AOs on a log-frequency axis. % % </MethodDescription> function results = utp_ao_lpsd(varargin) % Check the inputs if nargin == 0 % Some keywords class = 'ao'; mthd = 'lpsd'; 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); % 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 output of the data results = [results utp_08]; % Test against MATLAB's fft results = [results utp_11(mthd, at1, ple1)]; % Test plotinfo doesn't disappear 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_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 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 ~= 10, atest = false; end % Check key if ~io(3).plists.isparam('kdes'), atest = false; end if ~io(3).plists.isparam('jdes'), atest = false; end if ~io(3).plists.isparam('lmin'), 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('psll'), atest = false; end if ~io(3).plists.isparam('times'), atest = false; end if ~io(3).plists.isparam('split'), atest = false; end % Check default value if ~isequal(io(3).plists.find('kdes'), 100), atest = false; end if ~isequal(io(3).plists.find('jdes'), 1000), atest = false; end if ~isequal(io(3).plists.find('lmin'), 0), 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('psll'), 200), atest = false; end if ~isEmptyDouble(io(3).plists.find('times')), atest = false; end if ~isEmptyDouble(io(3).plists.find('split')), atest = false; end % Check options if ~isequal(io(3).plists.getOptionsForParam('kdes'), {100}), atest = false; end if ~isequal(io(3).plists.getOptionsForParam('jdes'), {1000}), atest = false; end if ~isequal(io(3).plists.getOptionsForParam('lmin'), {0}), 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('psll'), {200}), atest = false; end if ~isequal(io(3).plists.getOptionsForParam('times'), {[]}), atest = false; end if ~isequal(io(3).plists.getOptionsForParam('split'), {[]}), 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 lpsd method works with a vector of AOs as input. % % </TestDescription> function result = utp_02 % <SyntaxDescription> % % Test that the lpsd method works for a vector of AOs as input. % % </SyntaxDescription> % <SyntaxCode> try avec = [at1 at5 at6]; % Vector output out = lpsd(avec); % List output [out1, out2, out3] = lpsd(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), lpsd(avec(kk)), ple1), atest = false; end end % List output if ~eq(out1, lpsd(avec(1)), ple1), atest = false; end if ~eq(out2, lpsd(avec(2)), ple1), atest = false; end if ~eq(out3, lpsd(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 lpsd method works with a matrix of AOs as input. % % </TestDescription> function result = utp_03 % <SyntaxDescription> % % Test that the lpsd method works for a matrix of AOs as input. % % </SyntaxDescription> % <SyntaxCode> try amat = [at1 at5 at6; at5 at6 at1]; % Vector output out = lpsd(amat); % List output [out1, out2, out3, out4, out5, out6] = lpsd(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), lpsd(amat(kk)), ple1), atest = false; end end % List output if ~eq(out1, lpsd(amat(1)), ple1), atest = false; end if ~eq(out2, lpsd(amat(2)), ple1), atest = false; end if ~eq(out3, lpsd(amat(3)), ple1), atest = false; end if ~eq(out4, lpsd(amat(4)), ple1), atest = false; end if ~eq(out5, lpsd(amat(5)), ple1), atest = false; end if ~eq(out6, lpsd(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 lpsd method works with a list of AOs as input. % % </TestDescription> function result = utp_04 % <SyntaxDescription> % % Test that the lpsd method works for a list of AOs as input. % % </SyntaxDescription> % <SyntaxCode> try % Vector output out = lpsd(at1,at5,at6); % List output [out1, out2, out3] = lpsd(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), lpsd(at1), ple1), atest = false; end if ~eq(out(2), lpsd(at5), ple1), atest = false; end if ~eq(out(3), lpsd(at6), ple1), atest = false; end % List output if ~eq(out1, lpsd(at1), ple1), atest = false; end if ~eq(out2, lpsd(at5), ple1), atest = false; end if ~eq(out3, lpsd(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 lpsd method works with a mix of different shaped AOs as % input. % % </TestDescription> function result = utp_05 % <SyntaxDescription> % % Test that the lpsd method works with an input of matrices and vectors % and single AOs. % % </SyntaxDescription> % <SyntaxCode> try % Vector output out = lpsd(at1,[at5 at6],at5,[at5 at1; at6 at1],at6); % List output [out1, out2, out3, out4, out5, out6, out7, out8, out9] = ... lpsd(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), lpsd(at1), ple1), atest = false; end if ~eq(out(2), lpsd(at5), ple1), atest = false; end if ~eq(out(3), lpsd(at6), ple1), atest = false; end if ~eq(out(4), lpsd(at5), ple1), atest = false; end if ~eq(out(5), lpsd(at5), ple1), atest = false; end if ~eq(out(6), lpsd(at6), ple1), atest = false; end if ~eq(out(7), lpsd(at1), ple1), atest = false; end if ~eq(out(8), lpsd(at1), ple1), atest = false; end if ~eq(out(9), lpsd(at6), ple1), atest = false; end % List output if ~eq(out1, lpsd(at1), ple1), atest = false; end if ~eq(out2, lpsd(at5), ple1), atest = false; end if ~eq(out3, lpsd(at6), ple1), atest = false; end if ~eq(out4, lpsd(at5), ple1), atest = false; end if ~eq(out5, lpsd(at5), ple1), atest = false; end if ~eq(out6, lpsd(at6), ple1), atest = false; end if ~eq(out7, lpsd(at1), ple1), atest = false; end if ~eq(out8, lpsd(at1), ple1), atest = false; end if ~eq(out9, lpsd(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 lpsd method properly applies history. % % </TestDescription> function result = utp_06 % <SyntaxDescription> % % Test that the result of applying the lpsd method can be processed back % to an m-file. % % </SyntaxDescription> % <SyntaxCode> try out = lpsd(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 % 'lpsd'. % 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, 'lpsd'), 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> % % Check that the lpsd method pass back the output objects to a list of % output variables or to a single variable. % % </TestDescription> function result = utp_07 % <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] = lpsd(at5, at6); o3 = lpsd(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_07 %% UTP_08 % <TestDescription> % % Tests that the lpsd method agrees with MATLAB's pwelch if the frequency % resolution of the latter is changed to match those used in lpsd % % % % </TestDescription> function result = utp_08 % <SyntaxDescription> % % Test that the applying psd works on a single AO. % % </SyntaxDescription> % <SyntaxCode> try % Construct test AO nsecs = 10; fs = 1000; Nfft = 2*fs; pl = plist('nsecs', nsecs, 'fs', fs, 'tsfcn', 'randn(size(t))'); a1 = ao(pl); % parameter list for lpsd win = specwin('Hanning', Nfft); pl = plist('Kdes', 10, 'Lmin', 2, 'Jdes', 100, 'Win', win.type, 'Olap', 50, 'Order', -1); % compute lpsd a2 = lpsd(a1, pl); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check that the DFT equations (eq. (3) and (4) in [1]) are fulfilled. % 2) Check that we get the same outputs for each frequency bin when % computing the psd using matlab's fft with the frequency resolution % values retrieved by lpsd (and already tested in (1)) % % [1] G. Heinzel, lpsd revisited: ltf, S2-AEI-TN-3052 % % </AlgoDescription> atest = true; if stest % <AlgoCode> % get freq. resolution, bin number and seg. length from procinfo b = find(a2.procinfo,'m'); r = find(a2.procinfo,'r'); L = find(a2.procinfo,'L'); % test eq.(3): f = r*b up to numerical accuracy if all(abs(a2.data.x - r.*b) > max(eps(a2.data.x),eps(r.*b))) atest = false; end % test eq.(4): fs = r*L up to numerical accuracy if all(abs(a2.data.fs - r.*L) > max(eps(a2.data.fs),eps(r.*L))) 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_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) LPSD 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) LPSD of the white noise % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; win_type = 'BH92'; scale = 'PSD'; [a, S, S1] = prepare_analyze_noise(win_type, noise_type, scale, plist); stest = true; catch err disp(err.message) stest = false; end % </SyntaxCode> % <AlgoDescription> % % 1) Check that (calculated LPSD yunits) equals (input units)^2 / Hz % 2) Check that (calculated LPSD 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) LASD 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) LASD of the white noise % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Uniform'; win_type = 'Hamming'; scale = 'ASD'; [a, S, S1] = prepare_analyze_noise(win_type, noise_type, scale, plist); stest = true; catch err disp(err.message) stest = false; end % </SyntaxCode> % <AlgoDescription> % % 1) Check that (calculated LASD yunits) equals (input units) / Hz^(1/2) % 2) Check that (calculated LASD 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) LPS 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) LPS of the white noise % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; win_type = 'Hanning'; scale = 'PS'; [a, S, S1] = prepare_analyze_noise(win_type, noise_type, scale, plist); stest = true; catch err disp(err.message) stest = false; end % </SyntaxCode> % <AlgoDescription> % % 1) Check that (calculated LPS yunits) equals (input units)^2 % 2) Check that (calculated LPS 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) LAS 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) LAS of the white noise % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Uniform'; win_type = 'Rectangular'; scale = 'AS'; [a, S, S1] = prepare_analyze_noise(win_type, noise_type, scale, plist); stest = true; catch err disp(err.message) stest = false; end % </SyntaxCode> % <AlgoDescription> % % 1) Check that (calculated LAS yunits) equals (input units) % 2) Check that (calculated LAS 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_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) lpsd passing the window name (Rectangular) % 3) lpsd passing the window object (Rectangular type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Rectangular window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Rectangular'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (BH92) % 3) lpsd passing the window object (BH92 type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, BH92 window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'BH92'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (Hamming) % 3) lpsd passing the window object (Hamming type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Hamming window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Hamming'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (Hanning) % 3) lpsd passing the window object (Hanning type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Hanning window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Hanning'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end else atest = false; end % </AlgoCode> % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_44 %% 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) lpsd passing the window name (Bartlett) % 3) lpsd passing the window object (Bartlett type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Bartlett window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Bartlett'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (Nuttall3) % 3) lpsd passing the window object (Nuttall3 type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Nuttall3 window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Nuttall3'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end else atest = false; end % </AlgoCode> % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); 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) lpsd passing the window name (Kaiser, psll = 150) % 3) lpsd passing the window object (Kaiser type, psll = 150) % 4) compares the two 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 window (name) % 4) Estimate the psd without detrending, Kaiser window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Kaiser'; psll = utils.math.randelement([10:10:200],1); [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (Kaiser, psll = default) % 3) lpsd passing the window object (Kaiser type, psll = default) % 4) compares the two 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 window (name) % 4) Estimate the psd without detrending, Kaiser window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Kaiser'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (Nuttall4) % 3) lpsd passing the window object (Nuttall4 type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, Nuttall4 window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'Nuttall4'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end 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) lpsd passing the window name (SFT3F) % 3) lpsd passing the window object (SFT3F type) % 4) compares the two 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 (name) % 4) Estimate the psd without detrending, SFT3F window (object) % 5) Compare results % % </SyntaxDescription> % <SyntaxCode> try noise_type = 'Normal'; scale = 'PSD'; win_type = 'SFT3F'; [a, S, S1] = prepare_analyze_noise(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 % % </AlgoDescription> % <AlgoCode> atest = true; if stest % Compare the psd evaluated with the window name / object if ~eq(S, S1, ple1) atest = false; end else atest = false; end % </AlgoCode> % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_50 %% Helper function for window call construction function [a, S_name, S_obj] = prepare_analyze_noise(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 lpsd 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 = 0; switch lower(win_type) case 'kaiser' psll = find(pli, 'psll'); if isempty(psll) psll = find(ao.getInfo('lpsd').plists, 'psll'); end pl_spec = plist('Win', win_type, 'psll', psll, 'olap', olap, ... 'order', detrend_order, 'scale', scale); otherwise pl_spec = plist('Win', win_type, 'olap', olap, ... 'order', detrend_order, 'scale', scale); end if find(pli, 'win_test') % Calls the lpsd applying the detrend and window internally % (passig window object) S_obj = a.lpsd(pl_spec); else S_obj = ao; end % Calls the lpsd applying the detrend and window internally % (passig window name) pl_spec.pset('Win', win_type); S_name = a.lpsd(pl_spec); end end