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Cleanup
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 18:04:34 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % spikecleaning detects and corrects possible spikes in analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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3 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % DESCRIPTION: SPIKECLEANING detects spikes in the temperature data and
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % replaces them by artificial values depending on the method chosen ('random',
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % 'mean', 'previous').
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % Spikes are defined as singular samples with an (absolute) value
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % higher than kspike times the standard deviation of the high-pass
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % filtered (IIR filter) input AO.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % CALL: b = spikecleaning(a1, a2, ..., an, pl)
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12 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % INPUTS: aN - a list of analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % pl - parameter list
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15 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % OUTPUTS: b - a list of analysis objects with "spike values" removed
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % and corrected
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18 %
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19 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'spikecleaning')">Parameters Description</a>
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20 %
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21 % VERSION: $Id: spikecleaning.m,v 1.17 2011/04/08 08:56:16 hewitson Exp $
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22 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 function varargout=spikecleaning(varargin)
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26
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 %%% Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 if utils.helper.isinfocall(varargin{:})
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29 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 end
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32
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33 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 error('### cat cannot be used as a modifier. Please give an output variable.');
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35 end
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36
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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40
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % Collect all AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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43 pli = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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44
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45 pls = parse(pli, getDefaultPlist());
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46
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47 % initialise output array
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48 bo = [];
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49
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50 % go through each input AO
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51 for i=1:numel(as)
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52 a = as(i);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 d = a.data;
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54
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55 % check this is a time-series object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 if ~isa(d, 'tsdata')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 error(' ### temperature spike detection requires tsdata (time-series) inputs.')
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58 end
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59
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 %--- check input parameters
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61 kspike = find(pls, 'kspike'); % kspike*sigma definition
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 method = find(pls, 'method'); % method of spike-values substitution
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 pls.pset('gain', 1); % gain of the filter
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 pls.pset('type', 'highpass'); % type of the filter
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65 fs = plist();
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66 fs.append('fs', d.fs);
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67 pls.combine(fs); % determination of the sampling frequency of the input AO
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68
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 % high-pass filtering data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 xfiltered = filtfilt(a, miir(pls));
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71
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 % standard deviation of the filtered data is calculated
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 nxfiltered = find(abs(xfiltered) < kspike*std(xfiltered));
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74
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75 xfiltered_2 = xfiltered.data.y(nxfiltered);
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76
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77 std_xfiltered_2 = std(xfiltered_2);
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78
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79 % spikes vector position is determined
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80 nspike = find(abs(xfiltered) > kspike*std_xfiltered_2);
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81
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82 % substitution of spike values starts here
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83 xcleaned = a.data.y;
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84 for j=1:length(nspike)
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85 if nspike(j) <=2 % just in case a spike is detected in the 1st or 2nd sample
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86 xcleaned(nspike(j)) = mean(xcleaned(1:50));
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87 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 if strcmp(method, 'random') % spike is substituted by a random value: N(0,std_xfiltered)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 xcleaned(nspike(j)) = xcleaned(nspike(j)-1) + randn(1)*std_xfiltered_2;
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90 elseif strcmp(method, 'mean') % spike is substituted by the mean if the two previous values
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 xcleaned(nspike(j)) = (xcleaned(nspike(j)-1) + xcleaned(nspike(j)-2))/2;
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92 elseif strcmp(method, 'previous') % spike is substituted by the pervious value
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93 xcleaned(nspike(j)) = xcleaned(nspike(j)-1);
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94 end
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95 end
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96 end
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97
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 % create new output tsdata
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 ts = tsdata(xcleaned, d.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 ts.setYunits(d.yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 ts.setXunits(d.xunits);
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102
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 % % create new output history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 % h = history(ALGONAME, VERSION, pls, a.hist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 % h = set(h, 'invars', invars);
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106
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107 % make output analysis object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 b = ao(ts);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 b.name = sprintf('spikecleaning(%s)', ao_invars{i});
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110 b.addHistory(getInfo('None'), pls, ao_invars(i), as(i).hist);
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111
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112 % add to output array
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113 bo = [bo b];
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114
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115 end
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116
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117 % Set output
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118 if nargout == numel(bo)
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119 % List of outputs
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120 for ii = 1:numel(bo)
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121 varargout{ii} = bo(ii);
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122 end
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123 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124 % Single output
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125 varargout{1} = bo;
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126 end
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127
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128 end
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129
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130 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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131 % Local Functions %
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132 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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133
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134 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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135 %
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136 % FUNCTION: getInfo
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137 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138 % DESCRIPTION: Get Info Object
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139 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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140 % HISTORY: 11-07-07 M Hewitson
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 % Creation.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142 %
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143 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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144
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145 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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146 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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147 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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148 pl = [];
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149 else
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150 sets = {'Default'};
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151 pl = getDefaultPlist();
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152 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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153 % Build info object
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154 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: spikecleaning.m,v 1.17 2011/04/08 08:56:16 hewitson Exp $', sets, pl);
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155 ii.setModifier(false);
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156 end
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157
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158 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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159 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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160 % FUNCTION: getDefaultPlist
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161 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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162 % DESCRIPTION: Get Default Plist
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163 %
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164 % HISTORY: 11-07-07 M Hewitson
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165 % Creation.
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166 %
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167 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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168
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169 function plout = getDefaultPlist()
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170 persistent pl;
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171 if exist('pl', 'var')==0 || isempty(pl)
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172 pl = buildplist();
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173 end
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174 plout = pl;
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175 end
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176
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177 function pl = buildplist()
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178
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179 pl = plist();
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180
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181 % kspike
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182 p = param({'kspike', 'High values imply no correction of relative low amplitude spikes.'}, paramValue.DOUBLE_VALUE(3.3));
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183 pl.append(p);
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184
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185 % fc
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186 p = param({'fc', 'Frequency cut-off of the IIR filter.'}, paramValue.DOUBLE_VALUE(0.025));
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187 pl.append(p);
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188
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189 % Order
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190 p = param({'order', 'The order of the IIR filter.'}, paramValue.DOUBLE_VALUE(2));
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191 pl.append(p);
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192
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193 % Ripple
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194 p = param({'ripple', 'Specify the pass/stop-band ripple for bandpass/bandreject filters'}, ...
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195 paramValue.DOUBLE_VALUE(0.5));
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196 pl.append(p);
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197
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198 % Method
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199 p = param({'method', 'The method used to replace the spike value.'}, {1, {'random', 'mean'}, paramValue.SINGLE});
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200 pl.append(p);
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201
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202 end
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203
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204 % PARAMETERES: 'kspike' - set the kspike value. High values imply
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205 % not correction of relative low amplitude spike
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206 % [default: 3.3]
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207 % 'method' - method used to replace the spike value: 'random,
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208 % 'mean', 'previous' [default:random]
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209 % 'fc' - frequency cut-off of the IIR filter [default: 0.025]
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210 % 'order' - order of the IIR filter [default: 2]
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211 % 'ripple' - specify pass/stop-band ripple for bandpass
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212 % and bandreject filters
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213 % <<default: 0.5>>
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214 %