annotate m-toolbox/classes/@ao/bin_data.m @ 24:056f8e1e995e database-connection-manager

Properly record history in fromRepository constructors
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 % BIN_DATA rebins aos data, on logarithmic scale, linear scale, or arbitrarly chosen.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 % The rebinning is done taking the mean of the bins included in the range
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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 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % DESCRIPTION: BIN_DATA rebins aos data, on logarithmic scale, linear scale, or arbitrarly chosen.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % The rebinning is done taking the mean of the bins included in the range
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % CALL: bs = bin_data(a1,a2,a3,...,pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % bs = bin_data(as,pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % bs = as.bin_data(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 % INPUTS: aN - input analysis objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % as - input analysis objects array
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % pl - input parameter list
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % OUTPUTS: bs - array of analysis objects, one for each input
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'bin_data')">Parameters Description</a>
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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 % The code is inherited from D Nicolodi, UniTN
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 % VERSION: $Id: bin_data.m,v 1.20 2011/05/10 16:46:48 mauro Exp $
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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
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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 function varargout = bin_data(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 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 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 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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38
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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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 % Collect all AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 [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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45
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 % Decide on a deep copy or a modify
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 bs = copy(as, nargout);
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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 % Apply defaults to plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 usepl = applyDefaults(getDefaultPlist(), varargin{:});
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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 x_scale = find(usepl, 'x_scale', find(usepl, 'xscale'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 x_vals = find(usepl, 'x_vals', find(usepl, 'xvals'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 resolution = find(usepl, 'resolution');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 range = find(usepl, 'range');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 method = lower(find(usepl, 'method'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 inherit_dy = utils.prog.yes2true(find(usepl, 'inherit-dy', find(usepl, 'inherit_dy')));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 % Loop over input AOs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 for jj = 1:numel(bs)
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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 input data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 if isa(bs(jj).data, 'data2D')
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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 w = find(usepl, 'weights');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 if isa(w, 'ao')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 w = w.y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 if isempty(w)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 w = 1./(bs(jj).dy).^2;
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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
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 if isempty(x_vals)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 if isempty(x_scale) || isempty(resolution)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 error('### Please specify a scale and density for binning, OR the list of the values to bin around');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 switch lower(x_scale)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 case {'lin', 'linear'}
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 % Case of linear binning
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 % number of bins in the rebinned data set
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 N = resolution;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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86 % maximum and minimum x
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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87 if ~isempty(range) && isfinite(range(1))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 xmin = range(1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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90 xmin = min(bs(jj).x);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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93 if ~isempty(range) && isfinite(range(2))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 xmax = range(2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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95 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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96 xmax = max(bs(jj).x);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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97 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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98
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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99 dx = (xmax - xmin)/N;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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100
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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101 x_min = bs(jj).x(1) + dx*(0:(N-1))';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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102 x_max = bs(jj).x(1) + dx*(1:N)';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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103
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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104 case {'log', 'logarithmic'}
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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105 % Case of log-based binning
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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>
parents:
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107 % maximum and minimum x
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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108 if ~isempty(range) && isfinite(range(1))
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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109 xmin = range(1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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110 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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111 xmin = min(bs(jj).x(bs(jj).x > 0));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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112 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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113
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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114 if ~isempty(range) && isfinite(range(2))
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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115 xmax = range(2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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116 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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117 xmax = max(bs(jj).x);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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118 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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119
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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120 alph = 10^(1/resolution);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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121
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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122 % number of bins in the rebinned data set
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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123 N = ceil(log10(xmax/xmin) * resolution);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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124
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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125 % maximum and minimum x-value for each bin
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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126 x_min = xmin*alph.^(0:(N-1))';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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127 x_max = xmin*alph.^(1:N)';
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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128 otherwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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129 error(['### Unknown scaling option ' x_scale '. Please choose between ''lin'' and ''log']);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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130 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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131 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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132 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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133 % number of bins in the rebinned data set
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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134 % If the x-scale is an AO, then take the x values
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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135 if isa(x_vals, 'ao')
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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136 if eq(x_vals.xunits, bs(jj).xunits)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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137 x_vals = x_vals.x;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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138 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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139 error('x_vals AO and data AO have different x-units');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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140 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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141 elseif ~isnumeric(x_vals)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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142 error('Unsupported x_vals object');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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143 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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144 N = length(x_vals) - 1;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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145 x_min = x_vals(1:N);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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146 x_max = x_vals(2:N+1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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147 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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148
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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149 x = bs(jj).x;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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150 y = bs(jj).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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151 dy = bs(jj).dy;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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152
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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153 % preallocate output vectors
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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154 xr = zeros(N, 1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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155 yr = zeros(N, size(y, 2));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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156 if strcmpi(method, 'mean') || strcmpi(method, 'wmean')
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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157 dyr = zeros(N, size(y, 2));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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158 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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159 dyr = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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160 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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161 nr = zeros(N, 1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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162
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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163 % compute the averages
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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164 for kk = 1:N
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
165 in = x >= x_min(kk) & x < x_max(kk);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
166 if any(in)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
167 nr(kk) = sum(in); % number of points averaged in this bin
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
168
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
169 switch method
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
170 case {'mean', 'median', 'max', 'min', 'rms'}
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
171 xr(kk) = feval(method, x(in)); % rebinned x bins;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
172 yr(kk) = feval(method, y(in)); % rebinned y bins;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
173 if strcmpi(method, 'mean')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
174 dyr(kk) = std(y(in), 0)/sqrt(nr(kk));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
175 % check for zeros in the uncertainty and replace it with the individual point uncertainty
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
176 if dyr(kk) == 0
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
177 if inherit_dy && ~isempty(dy)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
178 dyr(kk) = mean(dy(in));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
179 else
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
180 dyr(kk) = Inf;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
181 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
182 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
183 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
184 case {'wmean'}
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
185 xr(kk) = mean(x(in)); % rebinned x bins;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
186 yr(kk) = sum(y(in).*w(in))./sum(w(in)); % rebinned y bins;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
187 dyr(kk) = 1./sqrt(sum(w(in))); % rebinned dy bins;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
188 otherwise
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
189 error(['### Unsupported method ' method]);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
190 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
191 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
192 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
193
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
194 % remove bins where we do not have nothing to average
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
195 in = nr ~= 0;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
196 nr = nr(in);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
197 xr = xr(in);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
198 yr = yr(in,:);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
199 if strcmpi(method, 'mean') || strcmpi(method, 'wmean')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
200 dyr = dyr(in,:);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
201 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
202
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
203 % set the new object data
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
204 bs(jj).setXY(xr, yr);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
205 bs(jj).setDy(dyr);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
206
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
207 % nr goes into the procinfo
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
208 bs(jj).procinfo = plist('navs', nr);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
209
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
210 % set name
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
211 bs(jj).name = sprintf('bin_data(%s)', ao_invars{jj});
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
212 % Add history
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
213 bs(jj).addHistory(getInfo('None'), usepl, ao_invars(jj), bs(jj).hist);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
214 else
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
215 warning('### Ignoring input AO number %d (%s); it is not a 2D data object.', jj, bs(jj).name)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
216 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
217 end % loop over analysis objects
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
218
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
219 % Set output
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
220 varargout = utils.helper.setoutputs(nargout, bs);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
221 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
222
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
223 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
224 % Get Info Object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
225 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
226 function ii = getInfo(varargin)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
227 if nargin == 1 && strcmpi(varargin{1}, 'None')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
228 sets = {};
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
229 pl = [];
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
230 else
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
231 sets = {'Default'};
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
232 pl = getDefaultPlist();
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
233 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
234 % Build info object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
235 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: bin_data.m,v 1.20 2011/05/10 16:46:48 mauro Exp $', sets, pl);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
236 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
237
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
238 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
239 % Get Default Plist
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
240 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
241
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
242 function plout = getDefaultPlist()
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
243 persistent pl;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
244 if ~exist('pl', 'var') || isempty(pl)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
245 pl = buildplist();
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
246 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
247 plout = pl;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
248 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
249
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
250 function pl = buildplist()
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
251
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
252 pl = plist();
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
253
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
254 % method
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
255 p = param({'method',['method for binning. Choose from:<ul>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
256 '<li>mean</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
257 '<li>median</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
258 '<li>max</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
259 '<li>min</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
260 '<li>rms</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
261 '<li>weighted mean (weights can be input or are taken from data dy)</li></ul>']}, ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
262 {1, {'MEAN', 'MEDIAN', 'MAX', 'MIN', 'RMS', 'WMEAN'}, paramValue.SINGLE});
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
263 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
264
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
265 % x-scale
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
266 p = param({'xscale',['scaling of binning. Choose from:<ul>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
267 '<li>log - logaritmic</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
268 '<li>lin - linear</li></ul>']}, {1, {'LOG', 'LIN'}, paramValue.SINGLE});
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
269 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
270
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
271 % resolution
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
272 p = param({'resolution',['When setting logaritmic x scale, it sets the number of points per decade.<br>' ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
273 'When setting linear x scale, it sets the number of points.']}, paramValue.DOUBLE_VALUE(10));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
274 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
275
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
276 % x_vals
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
277 p = param({'xvals',['List of x values to evaluate the binning between.<br>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
278 'It may be a vector or an ao, in which case it will take the x field']}, paramValue.DOUBLE_VALUE([]));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
279 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
280
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
281 % weights
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
282 p = param({'weights', ['List of weights for the case of weighted mean.<br>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
283 'If empty, weights will be taken from object(s) dy field as w = 1/dy^2']}, paramValue.DOUBLE_VALUE([]));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
284 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
285
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
286 % range
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
287 p = param({'range', ['Range of x where to operate.<br>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
288 'If empty, the whole data set will be used']}, paramValue.DOUBLE_VALUE([]));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
289 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
290
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
291 % inherit_dy
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
292 p = param({'inherit_dy', ['Choose what to do in the case of mean, and bins with only one point. Choose from:<ul>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
293 '<li>''yes'' - take the uncertainty from the original data, if defined</li>', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
294 '<li>''no'' - set it to Inf so it weighs 0 in averaged means</li></ul>' ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
295 ]}, paramValue.YES_NO);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
296 pl.append(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
297 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
298 % END