Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/interpmissing.m @ 30:317b5f447f3e database-connection-manager

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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>
parents:
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1 % INTERPMISSING interpolate missing samples in a time-series.
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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>
parents:
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4 % INTERPMISSING interpolate missing samples in a time-series. Missing samples
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % are identified as being those where the time-span between one
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % sample and the next is larger than d/fs where d is a
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % tolerance value. Missing data is then placed in the gap in
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % steps of 1/fs. Obviously this is only really correct for
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % evenly sampled time-series.
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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: bs = interpmissing(as)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % INPUTS: as - array of analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % pl - parameter list (see below)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % OUTPUTS: bs - array of analysis objects, one for each input
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'interpmissing')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % VERSION: $Id: interpmissing.m,v 1.30 2011/04/08 08:56:16 hewitson Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 function varargout = interpmissing(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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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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Daniele Nicolodi <nicolodi@science.unitn.it>
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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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Daniele Nicolodi <nicolodi@science.unitn.it>
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32
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 % Decide on a deep copy or a modify
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 bs = copy(as, nargout);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 % Combine plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 pl = parse(pl, getDefaultPlist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 % Get tolerance
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 dtol = find(pl, 'd');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 % Get only tsdata AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 for j=1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 if isa(bs(j).data, 'tsdata')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 % capture input history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 ih = bs(j).hist;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 % find missing samples
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 t = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 d = diff(bs(j).data.getX);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 idxs = find(d>dtol/bs(j).data.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 utils.helper.msg(msg.PROC1, 'found %d data gaps', numel(idxs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 % create new time grid
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 count = 0;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 fs = bs(j).data.fs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 for k=1:numel(idxs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 idx = idxs(k);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 if isempty(t)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 t = bs(j).data.getX(1:idxs(1));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 % now add samples at 1/fs until we are within 1/fs of the next sample
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 gap = bs(j).data.getX(idx+1) - bs(j).data.getX(idx) - 1/fs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 tfill = [[1/fs:1/fs:gap] + bs(j).data.getX(idx)].';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 count = count + numel(tfill);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 if k==numel(idxs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 t = [t; tfill; bs(j).data.getX(idx+1:end)];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 t = [t; tfill; bs(j).data.getX(idx+1:idxs(k+1))];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 utils.helper.msg(msg.PROC1, 'filled with %d samples', count);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 % now interpolate onto this new time-grid
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 if ~isempty(t)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 bs(j).interp(plist('vertices', t, 'method', find(pl, 'method')));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 bs(j).name = sprintf('interpmissing(%s)', ao_invars{j});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 % Add history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), ih);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 % clear errors
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 bs(j).clearErrors;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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97 utils.helper.msg(msg.PROC1, 'no missing samples found in %s - no action performed.', ao_invars{j});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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100 utils.helper.msg(msg.PROC1, 'skipping AO %s - it''s not a time-series AO.', ao_invars{j});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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104 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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105 if nargout == numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 % List of outputs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 for ii = 1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 varargout{ii} = bs(ii);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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109 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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110 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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111 % Single output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 varargout{1} = bs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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113 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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116 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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117 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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118 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 pl = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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124 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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125 pl = getDefaultPlist;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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126 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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127 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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128 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: interpmissing.m,v 1.30 2011/04/08 08:56:16 hewitson Exp $', sets, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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130
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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131 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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132 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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133 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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134 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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135 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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136 if exist('pl', 'var')==0 || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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137 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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138 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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139 plout = pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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140 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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141
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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142 function pl = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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143 pl = plist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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144
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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145 % d
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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146 p = param({'d','The time interval tolerance for finding missing samples.'}, {1, {1.5}, paramValue.OPTIONAL});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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147 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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148
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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149 % Interpolation method
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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150 pli = ao.getInfo('interp').plists;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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151 p = pli.params(pli.getIndexForKey('method'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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152 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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153
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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154 end