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annotate m-toolbox/classes/@ao/dropduplicates.m @ 1:2014ba5b353a database-connection-manager

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Remove old code
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Sat, 03 Dec 2011 18:13:55 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % DROPDUPLICATES drops all duplicate samples in time-series AOs.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % DROPDUPLICATES drops all duplicate samples in time-series AOs. Duplicates
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % are identified by having a two consecutive time stamps
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % closer than a set tolerance.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % CALL: bs = dropduplicates(as)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % INPUTS: as - array of analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % pl - parameter list (see below)
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12 %
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13 % OUTPUTS: bs - array of analysis objects, one for each input
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14 %
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15 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'dropduplicates')">Parameters Description</a>
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16 %
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17 % VERSION: $Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $
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18 %
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19 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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20
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21 function varargout = dropduplicates(varargin)
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22
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23 % Check if this is a call for parameters
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24 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 return
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27 end
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28
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29 import utils.const.*
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30 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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31
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32 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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35
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 % Collect all AOs
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37 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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38 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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39
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40 % Decide on a deep copy or a modify
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41 bs = copy(as, nargout);
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42
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43 % Combine plists
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44 pl = parse(pl, getDefaultPlist);
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45
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46 % Get tolerance
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47 tol = find(pl, 'tol');
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48
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49 % Get only tsdata AOs
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50 for j=1:numel(bs)
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51 if isa(bs(j).data, 'tsdata')
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52 d = abs(diff(bs(j).data.getX));
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53 idx = find(d<tol);
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54 utils.helper.msg(msg.PROC1, 'found %d duplicate samples', numel(idx));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 % Wipe out x samples
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56 if ~isempty(bs(j).data.x)
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57 bs(j).data.x(idx) = [];
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58 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 % Wipe out y samples
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60 bs(j).data.y(idx) = [];
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61 % Wipe out error
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62 if numel(bs(j).data.dx) > 1
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63 bs(j).data.dx(idx) = [];
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64 end
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65 if numel(bs(j).data.dy) > 1
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66 bs(j).data.dy(idx) = [];
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67 end
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68 % set name
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69 bs(j).name = sprintf('dropduplicates(%s)', ao_invars{j});
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70 % Add history
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71 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist);
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72 else
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73 warning('!!! Skipping AO %s - it''s not a time-series AO.', ao_invars{j});
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74 bs(j) = [];
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75 end
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76 end
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77
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78 % Set output
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79 if nargout == numel(bs)
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80 % List of outputs
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81 for ii = 1:numel(bs)
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82 varargout{ii} = bs(ii);
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83 end
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84 else
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85 % Single output
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86 varargout{1} = bs;
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87 end
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88 end
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89
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90 %--------------------------------------------------------------------------
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91 % Get Info Object
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92 %--------------------------------------------------------------------------
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93 function ii = getInfo(varargin)
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94 if nargin == 1 && strcmpi(varargin{1}, 'None')
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95 sets = {};
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96 pl = [];
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97 else
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98 sets = {'Default'};
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99 pl = getDefaultPlist;
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100 end
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101 % Build info object
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102 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
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103 end
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104
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105 %--------------------------------------------------------------------------
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106 % Get Default Plist
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107 %--------------------------------------------------------------------------
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108
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109 function plout = getDefaultPlist()
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110 persistent pl;
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111 if exist('pl', 'var')==0 || isempty(pl)
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112 pl = buildplist();
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113 end
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114 plout = pl;
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115 end
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116
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117 function pl = buildplist()
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118 pl = plist();
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119
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120 % tol
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121 p = param({'tol','The time interval tolerance to consider two consecutive samples as duplicates.'}, ...
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122 {1, {5e-3}, paramValue.OPTIONAL});
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123 pl.append(p);
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124
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125 end
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126
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127