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Add unit tests
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Tue, 06 Dec 2011 18:42:11 +0100 |
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% UTP_AO_PSD a set of UTPs for the ao/firwhiten method % % M Hewitson 06-08-08 % % $Id: utp_ao_firwhiten.m,v 1.21 2011/11/16 08:16:08 mauro Exp $ % % <MethodDescription> % % The firwhiten method of the ao class computes the spectral density of time-series AOs. % % </MethodDescription> function results = utp_ao_firwhiten(varargin) % Check the inputs if nargin == 0 % Some keywords class = 'ao'; mthd = 'firwhiten'; 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(); % 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 the modify call works results = [results utp_08]; % Test the data shape results = [results utp_09]; % Test with complex plist results = [results utp_10]; % Test the spectral falttening results = [results utp_11(mthd, at1, ple1)]; % Test plotinfo doesn't disappear results = [results utp_12(mthd, at1, ple1)]; % Test errors are cleared disp('Done.'); disp('******************************************************'); elseif nargin == 1 % Check for UTP functions if strcmp(varargin{1}, 'isutp') results = 1; else results = 0; end else error('### Incorrect inputs') end %% UTP_01 % <TestDescription> % % Tests that the getInfo call works for this method. % % </TestDescription> function result = utp_01 % <SyntaxDescription> % % Test that the getInfo call works for no sets, all sets, and each set % individually. % % </SyntaxDescription> try % <SyntaxCode> % Call for no sets io(1) = eval([class '.getInfo(''' mthd ''', ''None'')']); % Call for all sets io(2) = eval([class '.getInfo(''' mthd ''')']); % Call for each set for kk=1:numel(io(2).sets) io(kk+2) = eval([class '.getInfo(''' mthd ''', ''' io(2).sets{kk} ''')']); end % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check that getInfo call returned an minfo object in all cases. % 2) Check that all plists have the correct parameters. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % check we have minfo objects if isa(io, 'minfo') prefs = getappdata(0, 'LTPDApreferences'); defaultWinType = char(prefs.getMiscPrefs.getDefaultWindow); %%% SET 'None' pn = 1; if ~isempty(io(pn).sets), atest = false; end if ~isempty(io(pn).plists), atest = false; end %%% Check all Sets pn = 2; if ~any(strcmpi(io(pn).sets, 'Default')), atest = false; end if numel(io(pn).plists) ~= numel(io(pn).sets), atest = false; end %%%%%%%%%% SET 'Default' pn = 3; if io(pn).plists.nparams ~= 7, atest = false; end % Check key if ~io(pn).plists.isparam('nfft'), atest = false; end if ~io(pn).plists.isparam('bw'), atest = false; end if ~io(pn).plists.isparam('hc'), atest = false; end if ~io(pn).plists.isparam('win'), atest = false; end if ~io(pn).plists.isparam('order'), atest = false; end if ~io(pn).plists.isparam('firwin'), atest = false; end if ~io(pn).plists.isparam('ntaps'), atest = false; end % Check default value if ~isequal(io(pn).plists.find('nfft'), -1), atest = false; end if ~isequal(io(pn).plists.find('bw'), 20), atest = false; end if ~isequal(io(pn).plists.find('hc'), .8), atest = false; end if ~strcmpi(io(pn).plists.find('win'), defaultWinType), atest = false; end if ~isequal(io(pn).plists.find('order'), -1), atest = false; end if ~strcmpi(io(pn).plists.find('win'), defaultWinType), atest = false; end if ~isequal(io(pn).plists.find('ntaps'), 256), atest = false; end % Check options if ~isequal(io(pn).plists.getOptionsForParam('nfft'), {-1}), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('bw'), {20}), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('hc'), {.8}), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('win'), specwin.getTypes), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('order'), {-1 0 1 2 3 4 5 6 7 8 9}), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('firwin'), specwin.getTypes), atest = false; end if ~isequal(io(pn).plists.getOptionsForParam('ntaps'), {256}), atest = false; end end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_01 %% UTP_02 % <TestDescription> % % Tests that the firwhiten method works with a vector of AOs as input. % % </TestDescription> function result = utp_02 % <SyntaxDescription> % % Test that the firwhiten method works for a vector of AOs as input. % % </SyntaxDescription> try % <SyntaxCode> avec = [at5 at5 at6]; N = 512; fwin = specwin('Hanning', N+1); [out, outf, outxx] = firwhiten(avec, plist('Ntaps', N, 'FIRwin', fwin)); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check the number of elements in 'out' % 2) Check the number of filters (outf) and noise-floor estimates (outxx) % 3) Check that each output AO contains the correct data. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check we have the correct number of outputs if numel(out) ~= numel(avec), atest = false; end if numel(outf) ~= numel(avec), atest = false; end if numel(outxx) ~= numel(avec), atest = false; end % Check the output data prefs = getappdata(0, 'LTPDApreferences'); swin = char(prefs.getMiscPrefs.getDefaultWindow); for kk = 1:numel(avec) pl_psd = plist('Nfft', length(avec(kk).y), 'Win', specwin(swin), 'Order', -1, 'Scale', 'ASD'); axx = psd(avec(kk), pl_psd); nxx = smoother(axx, plist('width', 20, 'hc', 0.8)); w = 1./nxx; ff = mfir(w, plist('Win', fwin, 'N', N)); res = filter(avec(kk), ff); if ~isequal(res.x, out(kk).x), atest = false; end if ~isequal(res.y, out(kk).y), atest = false; end end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_02 %% UTP_03 % <TestDescription> % % Tests that the firwhiten method works with a matrix of AOs as input. % % </TestDescription> function result = utp_03 % <SyntaxDescription> % % Test that the firwhiten method works for a matrix of AOs as input. % % </SyntaxDescription> try % <SyntaxCode> amat = [at1 at5 at6; at5 at6 at1]; N = 512; fwin = specwin('Hanning', N+1); [out, outf, outxx] = firwhiten(amat, plist('Ntaps', N, 'FIRwin', fwin)); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check the number of elements in 'out' % 2) Check the number of filters (outf) and noise-floor estimates (outxx) % 3) Check that each output AO contains the correct data. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check we have the correct number of outputs if numel(out) ~= numel(amat), atest = false; end if numel(outf) ~= numel(amat), atest = false; end if numel(outxx) ~= numel(amat), atest = false; end % Check the output data prefs = getappdata(0, 'LTPDApreferences'); swin = char(prefs.getMiscPrefs.getDefaultWindow); for kk = 1:numel(amat) pl_psd = plist('Nfft', length(amat(kk).y), 'Win', specwin(swin), 'Order', -1, 'Scale', 'ASD'); axx = psd(amat(kk), pl_psd); nxx = smoother(axx, plist('width', 20, 'hc', 0.8)); w = 1./nxx; ff = mfir(w, plist('Win', fwin, 'N', N)); res = filter(amat(kk), ff); if ~isequal(res.x, out(kk).x), atest = false; end if ~isequal(res.y, out(kk).y), atest = false; end end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_03 %% UTP_04 % <TestDescription> % % Tests that the firwhiten method works with a list of AOs as input. % % </TestDescription> function result = utp_04 % <SyntaxDescription> % % Test that the firwhiten method works for a list of AOs as input. % % </SyntaxDescription> try % <SyntaxCode> N = 512; fwin = specwin('Hanning', N+1); [out, outf, outxx] = firwhiten(at1,at5,at6, plist('Ntaps', N, 'FIRwin', fwin)); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check the number of elements in 'out' % 2) Check the number of filters (outf) and noise-floor estimates (outxx) % 3) Check that each output AO contains the correct data. % % </AlgoDescription> atest = true; aoin = [at1, at5, at6]; if stest % <AlgoCode> % Check we have the correct number of outputs if numel(out) ~= 3, atest = false; end if numel(outf) ~= 3, atest = false; end if numel(outxx) ~= 3, atest = false; end % Check the output data prefs = getappdata(0, 'LTPDApreferences'); swin = char(prefs.getMiscPrefs.getDefaultWindow); for kk = 1:numel(aoin) pl_psd = plist('Nfft', length(aoin(kk).y), 'Win', specwin(swin), 'Order', -1, 'Scale', 'ASD'); axx = psd(aoin(kk), pl_psd); nxx = smoother(axx, plist('width', 20, 'hc', 0.8)); w = 1./nxx; ff = mfir(w, plist('Win', fwin, 'N', N)); res = filter(aoin(kk), ff); if ~isequal(res.x, out(kk).x), atest = false; end if ~isequal(res.y, out(kk).y), atest = false; end end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_04 %% UTP_05 % <TestDescription> % % Tests that the firwhiten method works with a mix of different shaped AOs as % input. % % </TestDescription> function result = utp_05 % <SyntaxDescription> % % Test that the firwhiten method works with an input of matrices and vectors % and single AOs. % % </SyntaxDescription> try % <SyntaxCode> N = 512; fwin = specwin('Hanning', N+1); pl = plist('Ntaps', N, 'FIRwin', fwin); [out, outf, outxx] = firwhiten(at1,[at5 at6],at5,[at5 at1; at6 at1],at6, pl); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check the number of elements in 'out' % 2) Check the number of filters (outf) and noise-floor estimates (outxx) % 3) Check that each output AO contains the correct data. % % </AlgoDescription> atest = true; aoin = [at1, reshape([at5 at6], 1, []), at5, reshape([at5 at1; at6 at1], 1, []), at6]; if stest % <AlgoCode> % Check we have the correct number of outputs if numel(out) ~= 9, atest = false; end if numel(outf) ~= 9, atest = false; end if numel(outxx) ~= 9, atest = false; end % Check the output data prefs = getappdata(0, 'LTPDApreferences'); swin = char(prefs.getMiscPrefs.getDefaultWindow); for kk = 1:numel(aoin) pl_psd = plist('Nfft', length(aoin(kk).y), 'Win', specwin(swin), 'Order', -1, 'Scale', 'ASD'); axx = psd(aoin(kk), pl_psd); nxx = smoother(axx, plist('width', 20, 'hc', 0.8)); w = 1./nxx; ff = mfir(w, plist('Win', fwin, 'N', N)); res = filter(aoin(kk), ff); if ~isequal(res.x, out(kk).x), atest = false; end if ~isequal(res.y, out(kk).y), atest = false; end end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_05 %% UTP_06 % <TestDescription> % % Tests that the firwhiten method properly applies history. % % </TestDescription> function result = utp_06 % <SyntaxDescription> % % Test that the result of applying the firwhiten method can be processed back % to an m-file. % % </SyntaxDescription> try % <SyntaxCode> N = 512; fwin = specwin('Hanning', N+1); pl = plist('Ntaps', N, 'FIRwin', fwin); out = firwhiten(at5, pl); mout = rebuild(out); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check that the last entry in the history of 'out' corresponds to % 'firwhiten'. % 2) Check that the re-built object is the same object as 'out'. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check the last step in the history of 'out' if ~strcmp(out.hist.methodInfo.mname, 'firwhiten'), atest = false; end % Check the re-built object if ~eq(mout, out, ple4), atest = false; end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_06 %% UTP_07 % <TestDescription> % % Tests that the firwhiten method can modify the input AO. % % </TestDescription> function result = utp_07 % <SyntaxDescription> % % Test that the firwhiten method can modify the input AO by calling % with no output. % % </SyntaxDescription> try % <SyntaxCode> N = 512; fwin = specwin('Hanning', N+1); pl = plist('Ntaps', N, 'FIRwin', fwin); % copy at1 to work with ain = ao(at1); % modify ain aout = ain.firwhiten(pl); ain.firwhiten(pl); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check that 'at1' and 'ain' are now different. % 2) Check that 'ain' is firwhiten(at1). % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check that firwhiten modified the input by comparing to the copy if eq(ao(at1), ain, ple1), atest = false; end % Check that firwhiten doesn't modified the input for the function notation if ~eq(aout, ain, ple1), atest = false; end % Check that the modified input is the firwhiten of the copy % Check the output data of ain prefs = getappdata(0, 'LTPDApreferences'); swin = char(prefs.getMiscPrefs.getDefaultWindow); pl_psd = plist('Nfft', length(at1.y), 'Win', specwin(swin), 'Order', -1, 'Scale', 'ASD'); axx = psd(at1, pl_psd); nxx = smoother(axx, plist('width', 20, 'hc', 0.8)); w = 1./nxx; ff = mfir(w, plist('Win', fwin, 'N', N)); res = filter(at1, ff); if ~isequal(res.x, ain.x), atest = false; end if ~isequal(res.y, ain.y), 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 firwhiten method keeps the data shape of the input object. % % </TestDescription> function result = utp_08 % <SyntaxDescription> % % Test that the firwhiten method keeps the data shape of the input object. The % input AO must be an AO with row data and an AO with column data. % % </SyntaxDescription> try % <SyntaxCode> N = 512; fwin = specwin('Hanning', N+1); pl = plist('Ntaps', N, 'FIRwin', fwin); out1 = firwhiten(at5, pl); out2 = firwhiten(at6, pl); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check that the shpe of the data doesn't change. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check the shape of the output data if size(out1.data.y,2) ~= 1, atest = false; end if size(out2.data.y,1) ~= 1, atest = false; end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_08 %% UTP_09 % <TestDescription> % % Tests that the firwhiten method with a complex plist. % % </TestDescription> function result = utp_09 % <SyntaxDescription> % % Test that the result of applying the firwhiten method with a complex plist % can be processed back to a m-file. % % </SyntaxDescription> try % <SyntaxCode> N = 512; Nfft = 100; order = 1; fwin = specwin('Hanning', N+1); swin = specwin('Kaiser', 10, 100); pl = plist('Ntaps', N, 'FIRwin', fwin, 'win', swin, 'Nfft', Nfft, 'order', order); [out, outf, outxx] = firwhiten(at5, pl); mout = rebuild(out); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Check the output data % 2) Check the output filter % 3) Check the noise-floor estimates % 4) Check that the re-built object is the same object as 'out'. % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check the last step in the history of 'out' if ~strcmp(out.hist.methodInfo.mname, 'firwhiten'), atest = false; end % Check the re-built object if ~eq(mout, out, ple2), atest = false; end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_09 %% UTP_10 % <TestDescription> % % Test the spectral falttening capability of firwhiten method. % % </TestDescription> function result = utp_10 % <SyntaxDescription> % % Test that the application of the firwhiten method enhances the % spectral flatness of input data. % % </SyntaxDescription> try % <SyntaxCode> % Making test data fs = at5.fs; pzm = pzmodel(2, [pz(0.1, 2) pz(0.5, 1)] , [pz(1) pz(2.55, 2)]); ft = miir(pzm,plist('fs',fs)); af = filter(at5, ft); % Colored noise % Whitening N = 512; Nfft = 100; order = 1; fwin = specwin('Hanning', N+1); swin = specwin('Kaiser', 10, 100); pl = plist('Ntaps', N, 'FIRwin', fwin, 'win', swin, 'Nfft', Nfft, 'order', order); out = firwhiten(af, pl); % </SyntaxCode> stest = true; catch err disp(err.message) stest = false; end % <AlgoDescription> % % 1) Calculate PSD of input and whitened data % 2) Compare relative spectral flatness coefficients % % </AlgoDescription> atest = true; if stest % <AlgoCode> % Check the output data % pl_psd = plist('Nfft', Nfft, 'Win', swin, 'Order', order, 'Scale', 'PSD'); axx = af.psd; awxx = out.psd; % Claculating flatness sf1 = utils.math.spflat(axx.data.y); sf2 = utils.math.spflat(awxx.data.y); % Checking flatness if sf1>sf2, atest = false; end % </AlgoCode> else atest = false; end % Return a result structure result = utp_prepare_result(atest, stest, dbstack, mfilename); end % END UTP_10 end