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annotate m-toolbox/classes/@ao/lpsd.m @ 36:5eb86f6881ef database-connection-manager

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Remove commented-out code
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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0
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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1 % LPSD implements the LPSD algorithm for analysis objects.
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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: LPSD implements the LPSD algorithm for analysis objects.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % CALL: bs = lpsd(a1,a2,a3,...,pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % bs = lpsd(as,pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % bs = as.lpsd(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % INPUTS: aN - input analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % as - input analysis objects array
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % pl - input parameter list
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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 % OUTPUTS: bs - array of analysis objects, one for each input
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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 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'lpsd')">Parameters Description</a>
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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 % VERSION: $Id: lpsd.m,v 1.55 2011/05/22 21:22:09 mauro Exp $
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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 % References: "Improved spectrum estimation from digitized time series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % on a logarithmic frequency axis", Michael Troebs, Gerhard Heinzel,
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 % Measurement 39 (2006) 120-129.
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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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 function varargout = lpsd(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 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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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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Daniele Nicolodi <nicolodi@science.unitn.it>
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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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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 % Apply defaults to plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 pl = applyDefaults(getDefaultPlist, varargin{:});
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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 inhists = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 % Loop over input AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 for jj = 1 : numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 % gather the input history objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 inhists = [inhists bs(jj).hist];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 % check this is a time-series object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 if ~isa(bs(jj).data, 'tsdata')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 warning('!!! lpsd requires tsdata (time-series) inputs. Skipping AO %s', ao_invars{jj});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 % Check the time range.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 time_range = mfind(pl, 'split', 'times');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 if ~isempty(time_range)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 switch class(time_range)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 case 'double'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 bs(jj) = split(bs(jj), plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 'times', time_range));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 case 'timespan'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 bs(jj) = split(bs(jj), plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 'timespan', time_range));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 case 'time'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 bs(jj) = split(bs(jj), plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 'start_time', time_range(1), ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 'end_time', time_range(2)));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 case 'cell'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 bs(jj) = split(bs(jj), plist(...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 'start_time', time_range{1}, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 'end_time', time_range{2}));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 otherwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 end
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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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 % Check the length of the object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 if bs(jj).len <= 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 error('### The object is empty! Please revise your settings ...');
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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 pl = utils.helper.process_spectral_options(pl, 'log');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 % Desired number of averages
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 Kdes = find(pl, 'Kdes');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 % num desired spectral frequencies
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 Jdes = find(pl, 'Jdes');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 % Minimum segment length
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 Lmin = find(pl, 'Lmin');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 % Window function
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 Win = find(pl, 'Win');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 % Overlap
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 Nolap = find(pl, 'Olap')/100;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 % Order of detrending
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 Order = find(pl, 'Order');
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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 % Get frequency vector
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 [f, r, m, L, K] = ao.ltf_plan(length(bs(jj).data.y), bs(jj).data.fs, Nolap, 1, Lmin, Jdes, Kdes);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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106
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 % compute LPSD
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 try
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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109 if find(pl, 'M-FILE ONLY')
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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110 % Using pure m-file version
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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111 [P, Pxx, ENBW] = ao.mlpsd_m(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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112 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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113 [P, Pxx, dev, devxx, ENBW] = ao.mlpsd_mex(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap*100, Lmin);
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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 catch ME
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Daniele Nicolodi <nicolodi@science.unitn.it>
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116 warning('!!! mex file dft failed. Using m-file version of lpsd.');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 % Using pure m-file version
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 [P, Pxx, ENBW] = ao.mlpsd_m(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 % Keep the data shape of the input AO
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 if size(bs(jj).data.y,1) == 1
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 P = P.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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124 Pxx = Pxx.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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125 dev = dev.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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126 devxx = devxx.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 f = f.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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128 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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130 % create new output fsdata
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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131 scale = find(pl, 'Scale');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132 switch lower(scale)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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133 case 'as'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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134 fsd = fsdata(f, sqrt(P), bs(jj).data.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 fsd.setYunits(bs(jj).data.yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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136 std = sqrt(dev);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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137 case 'asd'
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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138 fsd = fsdata(f, sqrt(Pxx), bs(jj).data.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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139 fsd.setYunits(bs(jj).data.yunits / unit('Hz^0.5'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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140 std = sqrt(devxx);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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141 case 'ps'
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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142 fsd = fsdata(f, P, bs(jj).data.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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143 fsd.setYunits(bs(jj).data.yunits.^2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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144 std = dev;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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145 case 'psd'
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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146 fsd = fsdata(f, Pxx, bs(jj).data.fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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147 fsd.setYunits(bs(jj).data.yunits.^2/unit('Hz'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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148 std = devxx;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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149 otherwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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150 error(['### Unknown scaling:' scale]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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151 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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152 fsd.setXunits('Hz');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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153 fsd.setEnbw(ENBW);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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154 fsd.setT0(bs(jj).data.t0);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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155 % make output analysis object
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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156 bs(jj).data = fsd;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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157 % set name
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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158 bs(jj).name = sprintf('L%s(%s)', upper(scale), ao_invars{jj});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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159 % Add processing info
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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160 bs(jj).procinfo = plist('r', r, 'm', m, 'l', L, 'k', K);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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161 % Add standard deviation
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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162 bs(jj).data.dy = std;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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163 % Add history
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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164 bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), inhists(jj));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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165
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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166 end % End tsdata if/else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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167 end % loop over analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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168
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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169 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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170 varargout = utils.helper.setoutputs(nargout, bs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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171 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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172
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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173 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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174 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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175 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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176 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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177 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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178 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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179 pl = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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180 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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181 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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182 pl = getDefaultPlist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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183 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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184 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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185 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: lpsd.m,v 1.55 2011/05/22 21:22:09 mauro Exp $', sets, pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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186 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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187
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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188 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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189 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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190 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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191 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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192 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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193 if ~exist('pl', 'var') || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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194 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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195 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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196 plout = pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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197 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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198
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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199 function pl = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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200
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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201 % General plist for Welch-based, log-scale spaced spectral estimators
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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202 pl = plist.LPSD_PLIST;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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203
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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204 % Scale
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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205 p = param({'Scale',['The scaling of output. Choose from:<ul>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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206 '<li>PSD - Power Spectral Density</li>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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207 '<li>ASD - Amplitude (linear) Spectral Density</li>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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208 '<li>PS - Power Spectrum</li>', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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209 '<li>AS - Amplitude (linear) Spectrum</li></ul>']}, {1, {'PSD', 'ASD', 'PS', 'AS'}, paramValue.SINGLE});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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210 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
211
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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212 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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213
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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214 % PARAMETERS:
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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215 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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216 % 'Kdes' - desired number of averages to perform [default: 100]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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217 % 'Jdes' - number of spectral frequencies to compute [default: 1000]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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218 % 'Lmin' - minimum segment length [default: 0]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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219 % 'Win' - the window to be applied to the data to remove the
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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220 % discontinuities at edges of segments. [default: taken from
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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221 % user prefs]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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222 % Only the design parameters of the window object are
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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223 % used. Enter either:
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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224 % - a specwin window object OR
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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225 % - a string value containing the window name
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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226 % e.g., plist('Win', 'Kaiser', 'psll', 200)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
227 % 'Olap' - segment percent overlap [default: -1, (taken from window function)]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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228 % 'Scale' - scaling of output. Choose from:
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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229 % PSD - Power Spectral Density [default]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
230 % ASD - Amplitude (linear) Spectral Density
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
231 % PS - Power Spectrum
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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232 % AS - Amplitude (linear) Spectrum
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
233 % 'Order' - order of segment detrending
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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234 % -1 - no detrending
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
235 % 0 - subtract mean [default]
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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236 % 1 - subtract linear fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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237 % N - subtract fit of polynomial, order N