Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/dsmean.m @ 0:f0afece42f48

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Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % DSMEAN performs a simple downsampling by taking the mean of every N samples.
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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 % DESCRIPTION: DSMEAN performs a simple downsampling by taking the mean of
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % every N samples. The downsample factor (N) is taken as
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % round(fs/fsout). The original vector is then truncated to a
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % integer number of segments of length N. It is the reshaped
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % to N x length(y)/N. Then the mean is taken.
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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 % CALL: b = dsmean(a, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'dsmean')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % VERSION: $Id: dsmean.m,v 1.23 2011/05/11 08:42:19 mauro Exp $
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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 % HISTORY: 20-04-08 M Hewitson
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % Creation
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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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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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 function varargout = dsmean(varargin)
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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 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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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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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 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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31
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Daniele Nicolodi <nicolodi@science.unitn.it>
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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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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 all AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 [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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38 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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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 % Decide on a deep copy or a modify
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 bs = copy(as, nargout);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 % Combine plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 pl = parse(pl, getDefaultPlist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 % Extract necessary parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 fsout = find(pl, 'fsout');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 % Loop over input AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 for jj = 1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 if ~isa(bs(jj).data, 'tsdata')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 warning('!!! Can only downsample time-series (tsdata) objects. Skipping AO %s', ao_invars{j});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 % downsample factor
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 dsf = round(bs(jj).data.fs/fsout);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 if dsf < 1
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 error('### I can''t downsample - the sample rate is already lower than the requested.');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 elseif dsf>1
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 % Do Y data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 n = floor(length(bs(jj).data.y) / dsf);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 y = bs(jj).data.y(1:n*dsf);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 % reshape and take mean
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 bs(jj).data.setY(mean(reshape(y, dsf, length(y)/dsf)));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 % If we have an x we should resample it
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 if ~isempty(bs(jj).data.x)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 x = bs(jj).data.x(1:n*dsf);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 % reshape and take mean
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 bs(jj).data.setX(mean(reshape(x, dsf, length(x)/dsf)));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 % otherwise we need to adjust t0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 bs(jj).data.setT0(bs(jj).data.t0 + dsf/(2*bs(jj).data.fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 end
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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 % Build output AO
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 bs(jj).data.setFs(fsout);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 bs(jj).name = sprintf('dsmean(%s)', ao_invars{jj});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 % Add history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), bs(jj).hist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 % Clear the errors since they don't make sense anymore
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 clearErrors(bs(jj));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 varargout = utils.helper.setoutputs(nargout, bs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 pl = [];
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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>
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97 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 pl = getDefaultPlist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: dsmean.m,v 1.23 2011/05/11 08:42:19 mauro Exp $', sets, pl);
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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>
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104 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 if ~exist('pl', 'var') || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113 plout = pl;
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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>
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116 function pl = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 pl = plist({'fsout', 'The output sample rate.'}, {1, {10}, paramValue.OPTIONAL});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120