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annotate m-toolbox/classes/@ao/linfit.m @ 6:2b57573b11c7 database-connection-manager

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Add utils.mysql.execute
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 % LINFIT is a linear fitting tool
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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: LINFIT is a linear fitting tool based on MATLAB's
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % lscov function. It solves an equation in the form
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % Y = P(1) + X * P(2)
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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 % for the fit parameters P.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % The output is a pest object where the fields are containing:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % Quantity % Field
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % Fit parameters y
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % Uncertainties on the fit parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % (given as standard deviations) dy
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % The reduced CHI2 of the fit chi2
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % The covariance matrix cov
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % The degrees of freedom of the fit dof
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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 % CALL: P = linfit(X, Y, PL)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % P = linfit(A, PL)
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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 % INPUTS: Y - dependent variable
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 % X - input variable
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 % A - data ao whose x and y fields are used in the fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % PL - parameter list
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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 % OUTPUT: P - a pest object with the fitting coefficients
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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 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 % PARAMETERS:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 % 'dy' - uncertainty on the dependent variable
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 % 'dx' - uncertainties on the input variable
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 % 'p0' - initial guess on the fit parameters used ONLY to propagate
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 % uncertainities in the input variable X to the dependent variable Y
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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 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'linfit')">Parameters Description</a>
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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: linfit.m,v 1.23 2011/05/15 22:52:57 mauro 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 % EXAMPLES:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 % %% Make fake AO from polyval
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 % nsecs = 100;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 % fs = 10;
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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 % u1 = unit('fm s^-2');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 % u2 = unit('nT');
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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 % pl1 = plist('nsecs', nsecs, 'fs', fs, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 % 'tsfcn', 'polyval([10 1], t) + randn(size(t))', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 % 'xunits', 's', 'yunits', u1);
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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 % pl2 = plist('nsecs', nsecs, 'fs', fs, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 % 'tsfcn', 'polyval([-5 0.2], t) + randn(size(t))', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 % 'xunits', 's', 'yunits', u2);
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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 % a1 = ao(pl1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 % a2 = ao(pl2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 % %% 1) Determine dependance from time of a time-series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 % %% Fit a stright line the a1 dependance from time
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 % p1 = linfit(a1, plist());
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 % p2 = linfit(a1, plist('dx', 0.1*ones(size(a1.x)), 'dy', 0.1*ones(size(a1.y)), 'P0', ao([0 0])));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 % p3 = linfit(a1, plist('dx', ao(0.1, plist('yunits', a1.xunits)), 'dy', ao(0.1, plist('yunits', a1.yunits)), 'P0', p1));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 % %% Compute fit: evaluating pest
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 % b1 = p1.eval(plist('type', 'tsdata', 'XData', a1, 'xfield', 'x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 % b2 = p2.eval(plist('type', 'tsdata', 'XData', a1.x));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 % b3 = p3.eval(plist('type', 'tsdata', 'XData', a1.x));
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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 % %% Plot fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 % iplot(a1, b1, b2, b3, plist('LineStyles', {'', '--', ':', '-.'}));
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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 % %% Remove linear trend
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 % c = a1 - b1;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 % iplot(c)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 % %% 2) Determine dependance of a time-series from another time-series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 % %% Fit with a straight line the a1 dependance from a2
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 % p1 = linfit(a1, a2, plist());
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 % p2 = linfit(a1, a2, plist('dx', 0.1*ones(size(a1.x)), 'dy', 0.1*ones(size(a1.x)), 'P0', ao([0 0])));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 % p3 = linfit(a1, a2, plist('dx', ao(0.1, plist('yunits', a1.yunits)), 'dy', ao(0.1, plist('yunits', a2.yunits)), 'P0', p1));
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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>
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86 % %% Compute fit: evaluating pest
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 % b1 = p1.eval(plist('type', 'xydata', 'XData', a1.y, 'xunits', a1.yunits));
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89 % b2 = p2.eval(plist('type', 'xydata', 'XData', a1));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 % b3 = p3.eval(plist('type', 'xydata', 'XData', a1.y, 'xunits', a1.yunits));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 % %% Build reference object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 % a12 = ao(plist('xvals', a1.y, 'yvals', a2.y, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 % 'xunits', a1.yunits, 'yunits', a2.yunits));
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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 % %% Plot fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 % iplot(a12, b1, b2, b3, plist('LineStyles', {'', '--', ':', '-.'}));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 % %% Remove linear trend
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 % c = a12 - b3;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 % iplot(c)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 function varargout = linfit(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 % check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113 % tell the system we are runing
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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116
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 % collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 for ii = 1:nargin,in_names{ii} = inputname(ii);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 % collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 [aos, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 pli = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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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 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126 error('### linfit can not be used as a modifier method. Please give at least one output');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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128
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 % combine plists, making sure the user input is not empty
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 pli = combine(pli, plist());
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131 pl = parse(pli, getDefaultPlist());
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132
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Daniele Nicolodi <nicolodi@science.unitn.it>
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133 % extract arguments
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Daniele Nicolodi <nicolodi@science.unitn.it>
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134 if (length(aos) == 1)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 % we are using x and y fields of the single ao we have
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Daniele Nicolodi <nicolodi@science.unitn.it>
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136 x = aos(1).x;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 dx = aos(1).dx;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138 y = aos(1).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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139 dy = aos(1).dy;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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140 xunits = aos(1).xunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 yunits = aos(1).yunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142 argsname = aos(1).name;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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143 elseif (length(aos) == 2)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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144 % we are using y fields of the two aos we have
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Daniele Nicolodi <nicolodi@science.unitn.it>
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145 x = aos(1).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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146 dx = aos(1).dy;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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147 y = aos(2).y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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148 dy = aos(2).dy;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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149 xunits = aos(1).yunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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150 yunits = aos(2).yunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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151 argsname = [aos(1).name ',' aos(2).name];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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152 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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153 error('### linfit needs one or two input AOs');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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154 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
155
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
156 % extract plist parameters. For dx and dy we check the user input plist before
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
157 dx = find(pli, 'dx', dx);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
158 dy = find(pli, 'dy', dy);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
159 p0 = find(pl, 'p0');
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
160
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
161 % vectors length
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
162 len = length(y);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
163
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
164 % uncertainty on Y
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
165 if isempty(dy)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
166 dy = 1;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
167 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
168 if isa(dy, 'ao')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
169 % check units
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
170 if yunits ~= dy.yunits
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
171 error('### Y and DY units are not compatible - %s %s', char(yunits), char(dy.yunits));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
172 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
173 % extract values from AO
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
174 dy = dy.y;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
175 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
176 if isscalar(dy)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
177 % given a single value construct a vector
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
178 dy = ones(len, 1) * dy;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
179 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
180
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
181 % weights
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
182 sigma2 = dy.^2;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
183
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
184 % extract values for initial guess
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
185 if (isa(p0, 'ao') || isa(p0, 'pest'))
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
186 p0 = p0.y;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
187 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
188
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
189 % uncertainty on X
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
190 if ~isempty(dx)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
191 if length(p0) ~= 2
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
192 error('### initial parameters guess p0 is mandatory for proper handling of X uncertainties');
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
193 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
194
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
195 if isa(dx, 'ao')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
196 % check units
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
197 if xunits ~= dx.yunits
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
198 error('### X and DX units are not compatible - %s %s', char(xunits), char(dx.yunits));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
199 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
200 % extract values from AO
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
201 dx = dx.y;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
202 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
203 if isscalar(dx)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
204 % given a single value construct a vector
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
205 dx = ones(len, 1) * dx;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
206 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
207
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
208 % add contribution to weights
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
209 sigma2 = sigma2 + p0(2)^2 .* dx.^2;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
210 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
211
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
212 % construct matrix
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
213 m = [ ones(len, 1) x ];
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
214
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
215 % solve
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
216 [p, stdp, mse, s] = lscov(m, y, 1./sigma2);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
217
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
218 % scale errors and covariance matrix
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
219 stdp = stdp ./ sqrt(mse);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
220 s = s ./ mse;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
221
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
222 % compute chi2
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
223 dof = len - 2;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
224 chi2 = sum((y - p(1)-p(2)*x).^2 ./ sigma2) / dof;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
225
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
226 % prepare model, units, names
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
227 names = {'P1', 'P2'};
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
228 model = 'P1 + P2*X';
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
229 model = smodel(plist('expression', model, ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
230 'params', names, ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
231 'values', p, ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
232 'xvar', 'X', ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
233 'xunits', xunits, ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
234 'yunits', yunits ...
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
235 ));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
236 units = [yunits simplify(yunits/xunits)];
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
237
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
238 % Build the output pest object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
239 out = pest;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
240 out.setY(p);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
241 out.setDy(stdp);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
242 out.setCov(s);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
243 out.setChi2(chi2);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
244 out.setDof(dof);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
245 out.setNames(names{:});
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
246 out.setYunits(units);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
247 out.setModels(model);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
248 out.name = sprintf('linfit(%s)', argsname);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
249 out.addHistory(getInfo('None'), pl, ao_invars, [aos(:).hist]);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
250 % Set procinfo object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
251 out.procinfo = plist('MSE', mse);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
252
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
253 % set outputs
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
254 varargout{1} = out;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
255
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
256 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
257
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
258 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
259 % Get Info Object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
260 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
261 function ii = getInfo(varargin)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
262
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
263 if nargin == 1 && strcmpi(varargin{1}, 'None')
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
264 sets = {};
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
265 pl = [];
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
266 else
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
267 sets = {'Default'};
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
268 pl = getDefaultPlist();
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
269 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
270 % build info object
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
271 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.op, '$Id: linfit.m,v 1.23 2011/05/15 22:52:57 mauro Exp $', sets, pl);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
272 ii.setModifier(false);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
273 ii.setArgsmin(1);
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
274 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
275
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
276 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
277 % Get Default Plist
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
278 %--------------------------------------------------------------------------
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
279
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
280 function plout = getDefaultPlist()
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
281 persistent pl;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
282 if ~exist('pl', 'var') || isempty(pl)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
283 pl = buildplist();
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
284 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
285 plout = pl;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
286 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
287
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
288 function pl = buildplist()
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
289
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
290 % default plist for linear fitting
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
291 pl = plist.LINEAR_FIT_PLIST;
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
292
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
293 end
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
294