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comparison m-toolbox/classes/@ssm/cpsdForCorrelatedInputs.m @ 0:f0afece42f48

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % cpsdForCorrelatedInputs computes the output theoretical CPSD shape with given inputs.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % DESCRIPTION: cpsdForCorrelatedInputs computes the output theoretical CPSD
5 % or PSD shape with given inputs.
6 % It returns summed and contributions only and takes
7 % input arrays of objects (instead of vectors)
8 %
9 % CALL: [mat_out] = CPSD(sys, pl)
10 %
11 % INPUTS:
12 % - sys, (array of) ssm object
13 %
14 % OUTPUTS:
15 % _ mat_out contains specified returned aos
16 %
17 % <a href="matlab:utils.helper.displayMethodInfo('ssm', 'cpsdForCorrelatedInputs')">Parameters Description</a>
18 %
19 % VERSION: $Id: cpsdForCorrelatedInputs.m,v 1.2 2011/05/23 14:18:20 adrien Exp $
20 %
21 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
22
23 function varargout = cpsdForCorrelatedInputs(varargin)
24
25 %% starting initial checks
26
27 % use the caller is method flag
28 callerIsMethod = utils.helper.callerIsMethod;
29
30 % Check if this is a call for parameters
31 if utils.helper.isinfocall(varargin{:})
32 varargout{1} = getInfo(varargin{3});
33 return
34 end
35
36 utils.helper.msg(utils.const.msg.MNAME, ['running ', mfilename]);
37
38 % Collect input variable names
39 in_names = cell(size(varargin));
40 for ii = 1:nargin,in_names{ii} = inputname(ii);end
41
42 % Collect all SSMs and plists
43 [sys, ssm_invars, rest] = utils.helper.collect_objects(varargin(:), 'ssm', in_names);
44 [pl, invars2, rest] = utils.helper.collect_objects(rest(:), 'plist');
45 if ~isempty(rest)
46 pl = combine(pl, plist(rest{:}));
47 end
48 pl = combine(pl, getDefaultPlist());
49
50 %%% Internal call: Only one object + don't look for a plist
51 internal = strcmp(varargin{end}, 'internal');
52
53 %% begin function body
54
55 %% retrieve system infos
56
57 if numel(sys)~=1
58 error('noisespectrum needs exactly one ssm as an input')
59 end
60 if ~sys.isnumerical
61 error(['error because system ',sys.name,' is not numerical']);
62 end
63 if ~sys.isStable
64 error('input ssm is not stable!')
65 end
66 if sys.timestep==0
67 timestep = 1;
68 else
69 timestep = sys.timestep;
70 end
71 if ~internal
72 inhist = sys.hist;
73 end
74
75 %% modifying system's ordering
76 if find(pl, 'reorganize')
77 sys = reorganize(sys, pl, 'set', 'for cpsdForCorrelatedInputs', 'internal', 'internal');
78 end
79
80 %% collecting functions i/o data
81 aos_in = find(pl, 'aos');
82 PZ_in = find(pl, 'PZmodels');
83 cov_in = find(pl, 'covariance');
84 cpsd_in = find(pl, 'CPSD');
85 noise_in = blkdiag(cov_in, cpsd_in/(timestep*2));
86 powWhiteNoise = noise_in;
87 [U1,S1,V1] = svd(noise_in.'); % testing hermitian symmetry and definite positiveness
88 if (sum(S1<0)>0)
89 error('covariance/cpsd matrix is not positive')
90 elseif norm(U1-V1')>1e-15*sqrt(numel(U1))
91 error('covariance/cpsd matrix is not hermitian symmetric')
92 end
93
94 %% getting system's i/o sizes
95 inputSizes = sys.inputsizes;
96 outputSizes = sys.outputsizes; %#ok<NASGU>
97
98 Naos_in = inputSizes(1);
99 NPZmodels = inputSizes(3);
100
101 %% retrieving frequency vector
102 if isempty(Naos_in)==0
103 f1 = find(pl,'f1');
104 f2 = find(pl,'f2');
105 NFreqs = find(pl,'nf');
106 if isempty(f1) || isempty(f2)|| isempty(NFreqs)
107 error('### Please specify frequency vector a start and stop frequency .');
108 else
109 freqs = 10.^linspace(log10(f1), log10(f2), NFreqs);
110 end
111 else
112 freqs = aos_in(1).x;
113 end
114
115 %% checking frequency vector
116 for i=2:numel(aos_in)
117 if ~isequal(freqs,aos_in(i).x)
118 error('there exist different frequency vectors');
119 end
120 end
121
122 %% reshape pzmodels and aos for input cross-spectra
123 if size(PZ_in,1)==NPZmodels
124 PZdata = zeros(Npzmodels,Npzmodels,NFreqs);
125 for i=1:NPZmodels
126 for j=1:Npzmodels
127 a = resp(PZ_in(i,j), freqs);
128 PZdata(i,j,:) = reshape(a.y,[1,NFreqs]) ;
129 end
130 end
131 else
132 error('Wrong size for field PZ_in')
133 end
134
135 if size(aos_in,1)==Naos_in && size(aos_in,2)==Naos_in
136 AOdata = zeros(Naos_in,Naos_in,NFreqs);
137 for i=1:Naos_in
138 for j=1:Naos_in
139 AOdata(i,j,:) = reshape(aos_in(i,j).y,[1,NFreqs]) ;
140 end
141 end
142 else
143 error('Wrong size for field aos_in')
144 end
145
146 %% SSM Transfer function
147 [a, b, c, d, Ts, InputName, StateName, OutputName,...
148 inputvarunits, ssvarunits, outputvarunits] = double(sys); %#ok<ASGLU>
149 resps = ssm.doBode(a, b, c, d, 2*pi*freqs, Ts);
150 Noutputs = numel(OutputName);
151
152 %% power for each frequency with SVD computation
153 diagOnly = pl.find('DIAGONAL ONLY');
154 if diagOnly
155 Result = zeros(Noutputs,NFreqs);
156 else
157 Result = zeros(Noutputs,Noutputs,NFreqs);
158 end
159
160 for i_freq=1:NFreqs
161 %% contribution from aos, testing positiveness
162 powAO = squeeze(AOdata(:,:,i_freq));
163 [U1,S1,V1] = svd(powAO.'); % testing hermitian symmetry and definite positiveness
164 if (sum(S1<0)>0)
165 error('AO covariance matrix is not positive')
166 elseif norm(U1-V1')>1e-15*sqrt(numel(U1))
167 error('AO covariance matrix is not hermitian symmetric')
168 end
169 %% contribution from PZmodels, testing positiveness
170 tfPZ = squeeze(PZdata(:,:,i_freq));
171 powPZ = tfPZ * tfPZ';
172 %% summing all three contributions sources, computing CPSD
173 pow = blkdiag(powAO, powWhiteNoise, powPZ);
174 RespLoc = squeeze(resps(:,:,i_freq));
175 noise = RespLoc * pow * RespLoc' * (2*timestep) ;
176 if diagOnly
177 Result(:,i_freq) = real(diag(noise)) ;
178 else
179 Result(:,:,i_freq) = noise ;
180 end
181 end
182
183 %% saving in aos
184 if diagOnly % making a psd only
185 ao_out = ao.initObjectWithSize(Noutputs, 1);
186 for io=1:Noutputs
187 ao_out(io).setData(fsdata(freqs, squeeze(Result(io,:)) ));
188 ao_out(io).setName( ['PSD of ' , OutputName{io}]);
189 ao_out(io).setXunits('Hz');
190 ao_out(io).setYunits(outputvarunits(io)*outputvarunits(io)/unit('Hz'));
191 ao_out(io).setDescription( ['PSD of ' , OutputName{io}]);
192 end
193 else % making a cpsd matrix
194 ao_out = ao.initObjectWithSize(Noutputs, Noutputs);
195 for io=1:Noutputs
196 for jo=1:Noutputs
197 ao_out(io,jo).setData(fsdata(freqs, squeeze(Result(jo,io,:)) ));
198 ao_out(io,jo).setXunits('Hz');
199 ao_out(io,jo).setYunits(outputvarunits(io)*outputvarunits(jo)/unit('Hz'));
200 if io~=jo
201 ao_out(io,jo).setName( ['Cross PSD of ', OutputName{jo}, ' and ', OutputName{io}]);
202 ao_out(io,jo).setDescription( ['Cross PSD of ', OutputName{jo}, ' and ', OutputName{io}]);
203 else
204 ao_out(io,jo).setName( ['PSD of ' , OutputName{jo}]);
205 ao_out(io,jo).setDescription( ['PSD of ' , OutputName{jo}]);
206 end
207 end
208 end
209 end
210
211 %% construct output matrix object
212 out = matrix(ao_out);
213 if callerIsMethod
214 % do nothing
215 else
216 myinfo = getInfo('None');
217 out.addHistory(myinfo, pl , ssm_invars(1), inhist );
218 end
219
220 %% Set output depending on nargout
221 if nargout == 1;
222 varargout = {out};
223 elseif nargout == 0;
224 iplot(ao_out);
225 else
226 error('Wrong number of outputs')
227 end
228 end
229
230
231 %--------------------------------------------------------------------------
232 % Get Info Object
233 %--------------------------------------------------------------------------
234 function ii = getInfo(varargin)
235
236 if nargin == 1 && strcmpi(varargin{1}, 'None')
237 sets = {};
238 pl = [];
239 else
240 sets = {'Default'};
241 pl = getDefaultPlist;
242 end
243 % Build info object
244 ii = minfo(mfilename, 'ssm', 'ltpda', utils.const.categories.op, '$Id: cpsdForCorrelatedInputs.m,v 1.2 2011/05/23 14:18:20 adrien Exp $', sets, pl);
245
246 end
247
248 %--------------------------------------------------------------------------
249 % Get Default Plist
250 %--------------------------------------------------------------------------
251 function pl = getDefaultPlist()
252 pl = ssm.getInfo('reorganize', 'for cpsdForCorrelatedInputs').plists;
253 pl.remove('set');
254
255 p = param({'covariance', 'The covariance matrix of this noise between input ports for the <i>time-discrete</i> noise model.'}, []);
256 pl.append(p);
257
258 p = param({'CPSD', 'The one sided cpsd matrix of the white noise between input ports.'}, []);
259 pl.append(p);
260
261 p = param({'aos', 'An array of input AOs, provides the cpsd of the input noise.'}, ao.initObjectWithSize(1,0));
262 pl.append(p);
263
264 p = param({'PZmodels', 'An array of input pzmodels, used to filter the input noise.'}, paramValue.DOUBLE_VALUE(zeros(0,1)));
265 pl.append(p);
266
267 p = param({'reorganize', 'When set to 0, this means the ssm does not need be modified to match the requested i/o. Faster but dangerous!'}, paramValue.TRUE_FALSE);
268 pl.append(p);
269
270 p = param({'f2', 'The maximum frequency. Default is Nyquist or 1Hz.'}, paramValue.EMPTY_DOUBLE);
271 pl.append(p);
272
273 p = param({'f1', 'The minimum frequency. Default is f2*1e-5.'}, paramValue.EMPTY_DOUBLE);
274 pl.append(p);
275
276 p = param({'nf', 'The number of frequency bins. Frequencies are scale logarithmically'}, paramValue.DOUBLE_VALUE(200));
277 pl.append(p);
278
279 p = param({'diagonal only', 'Set to true if you want the PSD instead of the CPSD'}, paramValue.TRUE_FALSE);
280 pl.append(p);
281
282 end
283