Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/smoother.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 % SMOOTHER smooths a given series of data points using the specified method.
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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3 %
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4 % DESCRIPTION: SMOOTHER smooths a given series of data points using
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5 % the specified method.
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6 %
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7 % CALL: b = smoother(a, pl)
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8 %
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9 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'smoother')">Parameters Description</a>
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10 %
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11 % VERSION: $Id: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $
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12 %
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13 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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14
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15 function varargout = smoother(varargin)
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16
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17 callerIsMethod = utils.helper.callerIsMethod;
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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 import utils.const.*
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26 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), 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 AOs and plists
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33 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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34 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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35
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36 % Decide on a deep copy or a modify
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37 bs = copy(as, nargout);
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38
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39 % combine plists
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40 pl = parse(pl, getDefaultPlist());
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41
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42 % Get parameters from plist
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43 bw = find(pl, 'width');
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44 hc = find(pl, 'hc');
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45 method = find(pl, 'method');
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46
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47 % check the method
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48 if ~strcmp(method, 'median') && ...
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49 ~strcmp(method, 'mean') && ...
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50 ~strcmp(method, 'min') && ...
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51 ~strcmp(method, 'max') && ...
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52 ~strcmp(method, 'mode')
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53 help(mfilename)
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54 error('### Unknown smoothing method');
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55 end
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56
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57 % Loop over input AOs
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58 for j=1:numel(bs)
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59 utils.helper.msg(msg.PROC1, 'smoothing %s', bs(j).name);
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60 switch lower(method)
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61 case {'median', 'mean', 'min', 'max'}
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62 bs(j).data.setY(ltpda_smoother(bs(j).data.getY, bw, hc, method));
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63 otherwise
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64 bs(j).data.setY(smooth(bs(j).data.getY, bw, hc, method));
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65 end
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66 % set name
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67 bs(j).name = sprintf('smoother(%s)', ao_invars{j});
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68 % Add history
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69 if ~callerIsMethod
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70 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist);
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71 end
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72 end
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73
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74 % clear errors
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75 bs.clearErrors;
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76
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77 % Set output
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78 if nargout == numel(bs)
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79 % List of outputs
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80 for ii = 1:numel(bs)
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81 varargout{ii} = bs(ii);
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82 end
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83 else
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84 % Single output
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85 varargout{1} = bs;
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86 end
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87 end
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88
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89 %--------------------------------------------------------------------------
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90 % Get Info Object
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91 %--------------------------------------------------------------------------
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92 function ii = getInfo(varargin)
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93 if nargin == 1 && strcmpi(varargin{1}, 'None')
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94 sets = {};
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95 pl = [];
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96 else
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97 sets = {'Default'};
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98 pl = getDefaultPlist;
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99 end
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100 % Build info object
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101 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $', sets, pl);
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102 end
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103
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104
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105 %--------------------------------------------------------------------------
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106 % smooth data
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107 function ys = smooth(y, bw, hc, method)
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108 N = length(y);
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109 ys = zeros(size(y));
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110
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111 % function to smooth with
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112 mfcn = eval(['@(x) ' method '(x)' ]);
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113
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114 for kk=1:N
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115 if mod(kk, 1000)==0
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116 utils.helper.msg(utils.const.msg.PROC1, 'smoothed %06d samples', kk);
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117 end
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118 % Determine the interval we are looking in
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119 interval = kk-bw/2:kk+bw/2;
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120 interval(interval<=0)=1;
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121 interval(interval>N)=N;
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122 % calculate method(values) of interval
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123 % after throwing away outliers
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124 trial = sort(y(interval));
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125 b = round(hc*length(trial));
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126 ys(kk) = mfcn(trial(1:b));
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127 end
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128 end
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129
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130 %--------------------------------------------------------------------------
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131 % Get Default Plist
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132 %--------------------------------------------------------------------------
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133 function plout = getDefaultPlist()
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134 persistent pl;
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135 if exist('pl', 'var')==0 || isempty(pl)
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136 pl = buildplist();
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137 end
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138 plout = pl;
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139 end
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140
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141 function pl = buildplist()
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142 pl = plist();
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143
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144 % width
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145 p = param({'width', 'The width of the smoothing filter.'}, paramValue.DOUBLE_VALUE(20));
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146 pl.append(p);
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147
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148 % hc
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149 p = param({'hc', 'A cutoff to throw away outliers (0-1).'}, paramValue.DOUBLE_VALUE(0.8));
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150 pl.append(p);
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151
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152 % Method
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153 p = param({'method', 'The smoothing method.'}, {1, {'median', 'mean', 'max', 'mode'}, paramValue.SINGLE});
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154 pl.append(p);
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155
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156 end
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157 % END
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158
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159 % PARAMETERS: width - the width of the smoothing filter [default: 20 samples]
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160 % hc - a cutoff to throw away outliers (0-1) [default: 0.8]
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Daniele Nicolodi <nicolodi@science.unitn.it>
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161 % method - the smoothing method:
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162 % 'median' [default]
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Daniele Nicolodi <nicolodi@science.unitn.it>
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163 % 'mean', 'min', 'max', 'mode'
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164 %