annotate m-toolbox/classes/@ao/linSubtract.m @ 38:3aef676a1b20 database-connection-manager

Keep backtrace on error
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 % LINSUBTRACT subtracts a linear contribution from an input ao.
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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: LINSUBTRACT subtracts a linear contribution from an input ao.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % The methods assumes the input data to be synchronous. The
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % user selects a filter to be applied to the data before
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % fitting and a time segment where the fit is performed.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % CALL: c = linSubtract(a,b1,b2,b3,...,bN, pl)
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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 % INPUTS: a - AO from where subtract linear contributions
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % b - AOs with noise contributions
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % pl - parameter list (see below)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % OUTPUTs: c - output AO with contributions subtracted (tsdata)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'linSubtract')">Parameters Description</a>
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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 % EXAMPLES:
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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 % 1) Given the data (d):
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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 % d = a + c1*b1 + c2*(b2+b3)^2
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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 % where (bs) are noisy contributions added to a signal (a). To recover (a)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 % in the [1 1e3] segment applying a [5e-2 0.1] 2nd order bandpass
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 % filter to the data, the call to the function would be
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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 % pl = plist('type','bandpass',...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 % 'fc',[5e-2 0.1],...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 % 'order',2,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 % 'times',[1 1e3],...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 % 'coupling',{{'n(1)'},{'(n(2) + n(3)).^2'}});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 % a = linSubtract(d,b1,b2,b3, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 % VERSION: $Id: linSubtract.m,v 1.13 2011/04/08 08:56:15 hewitson Exp $
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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 % TODO: option for parallel and serial subtraction
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % handling errors
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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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 function varargout = linSubtract(varargin)
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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 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 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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55
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 % Method can not be used as a modifier
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 error('### tfe cannot be used as a modifier. Please give an output variable.');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 end
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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 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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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 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 [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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67 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 % Decide on a deep copy or a modify
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 bs = copy(as, nargout);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 % Combine plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 pl = parse(pl, getDefaultPlist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 % get parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 fc = find(pl,'fc');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 if isempty(fc)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 error('### Please define a cut-off frequency ''fc''');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 times = find(pl,'times');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 cp = find(pl,'coupling');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 if isempty(cp)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 error('### Please define a coupling model ''coupling''')
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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 order = find(pl,'order');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 type = find(pl,'type');
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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 % split in time
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 if ~isempty(times)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 cs = split(bs,plist('times',times));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 cs = bs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 s = cs(1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 for i = 2:length(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 n(i-1) = cs(i);
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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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 subt = ao();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 % Loop noise sources
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 for i=1:length(cp)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 % coupling
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 nterm = ao();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 if numel(cp{i}) == 1
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 nterm = eval([char(cp{i}) ';']);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 nn = numel(cp{i});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 for j =1:nn
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 nterm(j) = eval([char(cp{i}{j}) ';']);
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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 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 % bandpass filter
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 fbp = miir(plist('type',type,'fc',fc,'order',order,'fs',s.fs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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116 sbp = filtfilt(s,fbp);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 nterm_bp = filtfilt(nterm,fbp);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 % linear fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 c = lscov(nterm_bp,sbp);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 sn = lincom(nterm,c);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 % subtract
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 s = s - sn;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 % new tsdata
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126 fsd = tsdata(s.x,s.y,s.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 % make output analysis object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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128 cs = ao(fsd);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 % set name
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 cs.name = sprintf('linSubtract(%s)', ao_invars{1});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131 % t0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132 if ~isempty(times)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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133 cs.setT0(times(1));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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134 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 cs.setT0(bs(1).t0);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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136 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 % Propagate 'plotinfo'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138 plotinfo = [as(:).plotinfo];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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139 if ~isempty(plotinfo)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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140 cs.plotinfo = combine(plotinfo);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142 % Add history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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143 cs.addHistory(getInfo('None'), pl, [ao_invars(:)], [bs(:).hist]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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144 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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145 varargout{1} = cs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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146
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Daniele Nicolodi <nicolodi@science.unitn.it>
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147
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Daniele Nicolodi <nicolodi@science.unitn.it>
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148 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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149
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Daniele Nicolodi <nicolodi@science.unitn.it>
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150
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Daniele Nicolodi <nicolodi@science.unitn.it>
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151 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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152 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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153 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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154 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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155 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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156 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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157 pl = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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158 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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159 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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160 pl = getDefaultPlist;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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161 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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162 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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163 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: linSubtract.m,v 1.13 2011/04/08 08:56:15 hewitson Exp $', sets, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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164 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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165
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166
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167 %--------------------------------------------------------------------------
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168 % Get Default Plist
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169 %--------------------------------------------------------------------------
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170 function plout = getDefaultPlist()
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171 persistent pl;
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172 if exist('pl', 'var')==0 || isempty(pl)
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173 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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174 end
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175 plout = pl;
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176 end
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177
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178 function pl = buildplist()
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179
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180 pl = plist();
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181
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182 % Type
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183 p = param({'type', 'Sets the type of filter used to fit the data.'}, {1, {'bandpass', 'bandreject', 'highpass', 'lowpass'}, paramValue.SINGLE});
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184 pl.append(p);
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185
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186 % fc
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187 p = param({'fc', 'Frequency cut-off of the filter.'}, paramValue.EMPTY_DOUBLE);
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188 pl.append(p);
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189
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190 % Order
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191 p = param({'order', 'Order of the filter.'}, {1, {2}, paramValue.OPTIONAL});
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192 pl.append(p);
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193
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194 % Times
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195 p = param({'times', 'A set of times where the fit+subtraction is applied.'}, paramValue.EMPTY_DOUBLE);
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196 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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197
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198 % Coupling
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Daniele Nicolodi <nicolodi@science.unitn.it>
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199 p = param({'coupling', ['A cell-array defining the model of the noise<br>'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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200 'terms to be subtracted. In the cell expression<br>'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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201 '''s'' stands for the input ao and ''n(i)'' for the N<br>' ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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202 'N noise contributions.']}, {1, {'{}'}, paramValue.OPTIONAL});
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203 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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204
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205 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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206 % PARAMETERS:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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207 % 'type' - Sets the type of filter used to fit the data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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208 % (help miir).
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Daniele Nicolodi <nicolodi@science.unitn.it>
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209 % 'fc' - Frequency cut-off of the filter (help miir)'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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210 % 'order' - Order of the filter (help miir).
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Daniele Nicolodi <nicolodi@science.unitn.it>
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211 % 'times' - Sets split times where the subtraction applies
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Daniele Nicolodi <nicolodi@science.unitn.it>
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212 % (help split).
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Daniele Nicolodi <nicolodi@science.unitn.it>
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213 % 'coupling' - A cell-array defining the model of the noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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214 % terms to be subtracted. In the cell expression
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Daniele Nicolodi <nicolodi@science.unitn.it>
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215 % 's' stands for the input ao and 'n(i)' for the N
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Daniele Nicolodi <nicolodi@science.unitn.it>
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216 % N noise contributions.