Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/delayEstimate.m @ 29:54f14716c721 database-connection-manager

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Update Java code
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % DELAYESTIMATE estimates the delay between two AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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3 %
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4 % DESCRIPTION: DELAYESTIMATE returns an estimate of the delay between the two
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5 % input analysis objects. Different weights in frequency
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6 % domain can be used.
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7 %
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8 % CALL: bs = delayEstimate(a1,a2,pl)
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9 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % INPUTS: a1 - input analysis objects
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11 % a2 - delayed analysis objects
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12 % pl - input parameter list
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13 %
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14 % OUTPUTS: bs - analysis object with the delay (as cdata)
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15 %
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16 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'delayEstimate')">Parameters Description</a>
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17 %
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18 % VERSION: $Id: delayEstimate.m,v 1.6 2011/04/08 08:56:15 hewitson Exp $
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19 %
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20 % HISTORY: 15-10-09 M Nofrarias
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21 % Creation
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22 %
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23 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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24
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25
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26 function varargout = delayEstimate(varargin)
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27
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28 % Check if this is a call for parameters
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29 if utils.helper.isinfocall(varargin{:})
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30 varargout{1} = getInfo(varargin{3});
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31 return
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32 end
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33
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34 import utils.const.*
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35 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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36
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37 % Collect input variable names
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38 in_names = cell(size(varargin));
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39 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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40
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41 % Collect all AOs
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42 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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43 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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44
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45 % Decide on a deep copy or a modify
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46 bs = copy(as, nargout);
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47
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48 % Combine plists
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49 pl = parse(pl, getDefaultPlist);
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50
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51 % get frequency weight
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52 weight = find(pl, 'weight');
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53
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54 N = len(bs(1));
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55 T = bs(1).x(end);
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56 fs = bs(1).fs;
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57 % zeropad to avoid circular convolution in ifft
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58 bs(1).zeropad;
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59 bs(2).zeropad;
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60 % compute spectral density, scale applied after to avoid round-off errors
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61 scale = fs/N;
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62 Gx11 = conj(fft(bs(1).y)).*fft(bs(1).y);
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63 Gx11 = Gx11*scale;
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64 Gx12 = conj(fft(bs(1).y)).*fft(bs(2).y);
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65 Gx12 = Gx12*scale;
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66 Gx22 = conj(fft(bs(2).y)).*fft(bs(2).y);
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67 Gx22 = Gx22*scale;
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68 % frequencies for two-sided spectrum
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69 f = linspace(-fs,fs,2*N);
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70 % select weight
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71 if strcmp(weight,'roth')
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72 weight = Gx11;
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73 elseif strcmp(weight,'scot')
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74 weight = sqrt(Gx11.* Gx22);
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75 elseif strcmp(weight,'scc')
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76 weight = 1;
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77 elseif strcmp(weight,'phat')
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78 weight = abs(Gx12);
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79 elseif strcmp(weight,'eckart')
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80 weight = 1;
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81 elseif strcmp(weight,'ML')
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82 weight = 1;
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83 else
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84 error('### Unknown value for ''weight'' parameter ' )
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85 end
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86 % compute unscaled correlation function
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87 Ru = ifft(Gx12./weight);
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88 % lag= 0:\deltat*(N-1)
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89 % n= 1:N/2-1
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90 r = linspace(0,T-1/fs,length(Ru)/2-1);
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91 % scaling to correct zeropad bias
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92 R = (N./(N-r))'.*Ru(1:length(Ru)/2-1);
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93 % get maximum
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94 [m,ind] = max(R);
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95 del = r(ind);
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96 % plot if required
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97 plt = find(pl, 'plot');
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98 if plt
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99 Rxy = ao(xydata(r,R));
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100 Rxy.setName(sprintf('Correlation(%s,%s)', ao_invars{1},ao_invars{2}))
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101 iplot(Rxy)
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102 end
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103
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104 % create new output cdata
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105 cd = cdata(del);
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106 % add unitss
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107 cd.yunits = 's';
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108 % update AO
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109 c = ao(cd);
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110 % add error
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111 % bs(jj).data.dy = dev;
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112 % Add name
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113 c.name = sprintf('delayEst(%s,%s)', ao_invars{1},ao_invars{2});
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114 % Add history
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115 c.addHistory(getInfo('None'), pl, [ao_invars(:)], [bs(:).hist]);
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116
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117 % set output
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118 varargout{1} = c;
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119
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120 end
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121
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122 %--------------------------------------------------------------------------
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123 % Get Info Object
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124 %--------------------------------------------------------------------------
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125 function ii = getInfo(varargin)
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126 if nargin == 1 && strcmpi(varargin{1}, 'None')
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127 sets = {};
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128 pl = [];
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129 else
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130 sets = {'Default'};
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131 pl = getDefaultPlist;
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132 end
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133 % Build info object
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134 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: delayEstimate.m,v 1.6 2011/04/08 08:56:15 hewitson Exp $', sets, pl);
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135 end
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136
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137 %--------------------------------------------------------------------------
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138 % Get Default Plist
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139 %--------------------------------------------------------------------------
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140
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141 function plout = getDefaultPlist()
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142 persistent pl;
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143 if exist('pl', 'var')==0 || isempty(pl)
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144 pl = buildplist();
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145 end
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146 plout = pl;
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147 end
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148
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149 function pl = buildplist()
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150 pl = plist();
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151 % method
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152 p = param({'weight',['scaling of output. Choose from:<ul>', ...
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153 '<li>scc - </li>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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154 '<li>roth - </li>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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155 '<li>scot - </li>', ...
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156 '<li>phat - </li>', ...
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157 '<li>eckart - </li>', ...
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158 '<li>ML - </li></ul>']}, {1, {'scc','roth', 'scot','phat','eckart','ML'}, paramValue.SINGLE});
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159 pl.append(p);
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160 % Plot
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161 p = param({'Plot', 'Plot the correlation function'}, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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162 {2, {'true', 'false'}, paramValue.SINGLE});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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163 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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164 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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165 % END
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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166