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annotate m-toolbox/m/sigproc/frequency_domain/ltpda_spsd.m @ 22:b11e88004fca database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % ltpda_spsd smooths a spectrum.
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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3 %
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4 % DESCRIPTION: ltpda_spsd smooths a spectrum.
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5 %
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6 % CALL: [freqsOut, pow, nFreqs, sigmaP, sumMat] = ltpda_spsd(freqs, pow, linCoef, logCoef, sumMat,nFreqs)
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7 %
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8 % INPUTS: freqsOut - frequency vector, can be left empty
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9 % pow - power spectrum (density)
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10 % nFreqs - frequency intervals used to derive averages
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11 % linCoef, logCoef
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12 % - values to use to scale the smoothing
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13 % averaging filter. It will be in linCoef.NBins^logCoef.
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14 % logCoef should be 2/3 for PSD and 4/5 for PSD data
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15 % which is later plotted in ASD scale.
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16 % mode - 'keepFrequencies' convoles using a filter, leaving all
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17 % frequencies but they are correlated
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18 % 'removeFrequencies' convoles using a filter, leaving
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19 % only data at some uncorrelated frequencies
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20 % 'addUp' like above, but sums up instead of takin a
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21 % mean value.
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22 %
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23 % OUTPUTS: freqs, pow : output frequency and power vector.
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24 %
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25 % <a href="matlab:web(ao.getInfo('spsd').tohtml, '-helpbrowser')">Parameter Sets</a>
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26 %
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27 % VERSION: $Id: ltpda_spsd.m,v 1.4 2011/02/21 16:55:21 adrien Exp $
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28 %
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29 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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30 function varargout = ltpda_spsd(varargin)
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31 %% collecting inputs
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32 freqs = varargin{1};
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33 pow = varargin{2};
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34 L = length(pow);
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35 pow = reshape(pow, [L, 1]);
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36 freqs = reshape(freqs, [numel(freqs), 1]);
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37 linCoef = varargin{3};
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38 logCoef = varargin{4};
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39 if nargin >4
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40 % optional inputs include the averaging matrix
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41 sumMat = varargin{5};
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42 nFreqs = varargin{6};
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43 else
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44 % averaging matrix must be computed from input data
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45 [nFreqs, sumMat] = getMatrix(L, linCoef, logCoef);
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46 end
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47 %% evaluating output
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48 computeVar = nargout>=4;
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49 computeFreqs = ~isempty(freqs);
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50 sumPow = sumMat * pow; % power sums
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51 powOut = sumPow ./ nFreqs; % power average
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52 if computeFreqs
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53 freqsOut = (sumMat * freqs) ./ nFreqs; % correponding mean frequency
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54 else
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55 freqsOut = [];
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56 end
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57 if computeVar
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58 nBinsEff = sumPow.^2 ./ (sumMat * pow.^2) ; % number of Chi2 variables for STD
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59 else
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60 nBinsEff = [];
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61 end
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62
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63 %% allocating output
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64 varargout = {freqsOut, powOut, nFreqs, nBinsEff, sumMat};
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65
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66 end
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67
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68
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69 function [nFreqs, sumMat] = getMatrix(L, linCoef, logCoef)
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70 %% initializing loop
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71 iAvg = 1;
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72 iMin = 1;
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73 idxAvg = zeros(1,L); % index of corresponding average
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74 widths = ceil(linCoef):ceil(linCoef * L^logCoef + 2); % proposed width of averagin filter
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75 iMaxForWidths = min(L, floor( (widths./linCoef).^(1/logCoef) + widths-1 ) ); % last sample to be processed with a given filter width
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76 lastWidth = find(iMaxForWidths==L,1,'first'); % width when the end fo te freq. series is reached
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77
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78 %% 1st loop on filter width
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79 for iiWidth = 1:lastWidth
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80 NAverages = ceil( (iMaxForWidths(iiWidth)-iMin+1) / widths(iiWidth) ); % number of times the width avgWidth is going to be used
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81 for kk=1:NAverages
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82 %% second loop on filter iteration
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83 iMax = min(L, iMin + widths(iiWidth)-1); % last sample for current average
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84 idxAvg(iMin:iMax) = ones(1,iMax-iMin+1) * iAvg; % index of corresponding average
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85 iMin = iMax + 1;
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86 iAvg = iAvg + 1;
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87 end
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88 end
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89
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90 %% creating output data
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91 sumMat = sparse(idxAvg, 1:L, ones(1,L));
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92 nFreqs = sumMat*ones(L,1);
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93
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94 end