Mercurial > hg > ltpda

annotate m-toolbox/classes/@ssm/parameterDiff.m @ 28:01b86b780ba7 database-connection-manager

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Remove LTPDARepositoryManager implementation. 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 % PARAMETERDIFF Makes a ssm that produces the output and state derivatives.
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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3 %
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4 % DESCRIPTION: PARAMETERDIFF Makes a ssm that produces the output
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5 % and state derivative in regard with specified parameters, for a specificed variation.
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6 %
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7 % CALL: obj = obj.parameterDiff({'key1', ...}, [val1, ...]);
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8 % obj = obj.parameterDiff(plist);
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9 % obj = obj.parameterDiff('key', val);
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10 %
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11 % <a href="matlab:utils.helper.displayMethodInfo('ssm', 'parameterDiff')">Parameters Description</a>
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12 %
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13 % VERSION: $Id: parameterDiff.m,v 1.9 2011/04/08 08:56:22 hewitson Exp $
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14 %
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15 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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16
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17 function varargout = parameterDiff(varargin)
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18
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19 % Check if this is a call for parameters
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20 if utils.helper.isinfocall(varargin{:})
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21 varargout{1} = getInfo(varargin{3});
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22 return
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23 end
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24
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25 %% starting initial checks
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26 utils.helper.msg(utils.const.msg.MNAME, ['running ', mfilename]);
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27
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28 % Collect input variable names
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29 in_names = cell(size(varargin));
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30 for ii = 1:nargin, in_names{ii} = inputname(ii); end
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31
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32 % Collect all SSMs and options
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33 [sys, ssm_invars, rest] = utils.helper.collect_objects(varargin(:), 'ssm', in_names);
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34 [pl, invars2, rest] = utils.helper.collect_objects(rest(:), 'plist');
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35 if ~isempty(rest)
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36 pl = combine(pl, plist(rest{:}));
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37 end
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38 pl = combine(pl, getDefaultPlist());
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39
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40 %%% Internal call: Only one object + don't look for a plist
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41 internal = strcmp(varargin{end}, 'internal');
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42
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43 %% processing input
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44 names = pl.find('names');
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45 if ischar(names)
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46 names = {names};
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47 elseif ~iscellstr(names)
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48 error('### Parameter names must be a cell-array of strings')
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49 end
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50
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51 values = pl.find('values');
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52 if ~isa(values, 'double')
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53 error('### param values should be a double')
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54 end
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55
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56 Nsys = numel(sys);
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57 sys_out = ssm.initObjectWithSize(Nsys,1);
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58
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59 %% checking data
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60 Ndiff = length(names);
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61 if ~(Ndiff== length(values))
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62 error(['### The number of parameter names is ' num2str(Ndiff) ' and the number of parameter values is ' num2str(length(values))]);
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63 end
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64 if ~isa(values, 'double')
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65 error(['### Parameter ''values'' is not a double array but of class ' class(values)]);
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66 end
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67
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68 for i_sys = 1:Nsys
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69 %% getting matrix sizes
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70 Nss = sys(i_sys).Nstates;
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71 Ninputs = sys(i_sys).Ninputs;
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72 Noutputs = sys(i_sys).Noutputs;
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73 sssizes = sys(i_sys).statesizes;
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74 inputsizes = sys(i_sys).inputsizes;
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75 outputsizes = sys(i_sys).outputsizes;
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76
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77 %% setting matrix sizes
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78 amats = cell(Nss*(Ndiff+1), Nss*(Ndiff+1));
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79 bmats = cell(Nss*(Ndiff+1), Ninputs);
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80 cmats = cell(Noutputs*(Ndiff+1), Nss*(Ndiff+1));
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81 dmats = cell(Noutputs*(Ndiff+1), Ninputs);
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82 sys_num = sys(i_sys).keepParameters;
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83
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84 %% assigning system matrices for nominal values
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85 amats(1:Nss,1:Nss) = sys_num.amats;
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86 bmats(1:Nss,1:Ninputs) = sys_num.bmats;
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87 cmats(1:Noutputs,1:Nss) = sys_num.cmats;
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88 dmats(1:Noutputs,1:Ninputs) = sys_num.dmats;
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89
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90 outputs = sys(i_sys).outputs;
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91 states = sys(i_sys).states;
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92
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93 %% loop over parameters
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94 for i_p = 1:Ndiff
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95 % computing ssm derivative
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96 sys_loc = copy(sys(i_sys), true);
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97 value_loc = sys(i_sys).params.find(names{i_p}) + values(i_p);
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98 sys_loc.doSetParameters(names(i_p), value_loc);
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99 sys_loc.keepParameters;
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100
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101 % computing derivatives of matrices
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102 dAmats = ssm.blockMatRecut( ( ssm.blockMatFusion(sys_loc.amats, sssizes, sssizes) - ssm.blockMatFusion(sys_num.amats, sssizes, sssizes) )/ values(i_p) , sssizes, sssizes);
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103 dBmats = ssm.blockMatRecut( ( ssm.blockMatFusion(sys_loc.bmats, sssizes, inputsizes) - ssm.blockMatFusion(sys_num.bmats, sssizes, inputsizes) )/ values(i_p) , sssizes, inputsizes);
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104 dCmats = ssm.blockMatRecut( ( ssm.blockMatFusion(sys_loc.cmats, outputsizes, sssizes) - ssm.blockMatFusion(sys_num.cmats, outputsizes, sssizes) )/ values(i_p) , outputsizes, sssizes);
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105 dDmats = ssm.blockMatRecut( ( ssm.blockMatFusion(sys_loc.dmats, outputsizes, inputsizes) - ssm.blockMatFusion(sys_num.dmats, outputsizes, inputsizes) )/ values(i_p) , outputsizes, inputsizes);
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106
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107 % assigning matrices for derivatives
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108 amats( (1+i_p*Nss):((i_p+1)*Nss), (1+i_p*Nss):((i_p+1)*Nss) ) = sys_num.amats;
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109 amats( (1+i_p*Nss):((i_p+1)*Nss), 1:Nss ) = dAmats;
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110 bmats( (1+i_p*Nss):((i_p+1)*Nss), 1:Ninputs ) = dBmats;
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111 cmats( (1+i_p*Noutputs):((i_p+1)*Noutputs), (1+i_p*Nss):((i_p+1)*Nss) ) = sys_num.cmats;
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112 dmats( (1+i_p*Noutputs):((i_p+1)*Noutputs), 1:Ninputs ) = dDmats;
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113 cmats( (1+i_p*Noutputs):((i_p+1)*Noutputs), 1:Nss ) = dCmats;
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114
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115 % assigning outputs
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116 outputs((1+i_p*Noutputs):((i_p+1)*Noutputs)) = sys_loc.outputs ;
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117 % renaming outputs
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118 for i=(1+i_p*Noutputs):((i_p+1)*Noutputs)
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119 outputs(i).setBlockNames( [outputs(i).name '_DIFF_' names{i_p}] );
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120 end
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121
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122 % assigning states
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123 states((1+i_p*Nss):((i_p+1)*Nss)) = sys_loc.states ;
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124 % renaming states
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 for i=(1+i_p*Nss):((i_p+1)*Nss)
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126 states(i).setBlockNames( [states(i).name '_DIFF_' names{i_p}] );
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127 end
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128
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129 clear sys_loc
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130 end
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131
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132 %% proceeding parameters update
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133 sys_out(i_sys).amats = amats;
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134 sys_out(i_sys).bmats = bmats;
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135 sys_out(i_sys).cmats = cmats;
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136 sys_out(i_sys).dmats = dmats;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 sys_out(i_sys).timestep = sys(i_sys).timestep;
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138 sys_out(i_sys).name = sys(i_sys).name;
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139 sys_out(i_sys).description = sys(i_sys).description;
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140 sys_out(i_sys).params = plist;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 sys_out(i_sys).outputs = outputs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142 sys_out(i_sys).inputs = sys(i_sys).inputs;
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143 sys_out(i_sys).states = states;
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144
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145 sys_out(i_sys).validate;
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146
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147 %% history and output arguments
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148 if ~internal
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149 sys_out(i_sys).addHistory(ssm.getInfo(mfilename), pl , {''}, sys(i_sys).hist );
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150 end
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151 end
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152
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153 if nargout == numel(sys_out)
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154 for ii = 1:numel(sys_out)
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155 varargout{ii} = sys_out(ii);
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156 end
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157 else
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158 varargout{1} = sys_out;
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159 end
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160
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161 end
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162
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163
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164
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165 %--------------------------------------------------------------------------
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166 % Get Info Object
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167 %--------------------------------------------------------------------------
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168 function ii = getInfo(varargin)
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169
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170 if nargin == 1 && strcmpi(varargin{1}, 'None')
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171 sets = {};
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172 pl = [];
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173 else
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174 sets = {'Default'};
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175 pl = getDefaultPlist;
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176 end
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177 % Build info object
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178 ii = minfo(mfilename, 'ssm', 'ltpda', utils.const.categories.helper, '$Id: parameterDiff.m,v 1.9 2011/04/08 08:56:22 hewitson Exp $', sets, pl);
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179 end
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180
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181 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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182 % Get Default Plist
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183 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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184 function pl = getDefaultPlist()
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185 pl = plist();
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186
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187 p = param({'names', 'A cell-array of parameter names for numerical differentiations.'}, {});
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188 pl.append(p);
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189
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190 p = param({'values', 'An array of parameter values for numerical step size.'}, []);
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191 pl.append(p);
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192 end