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annotate m-toolbox/test/test_ao_lscov.m @ 45:a59cdb8aaf31 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Tue, 06 Dec 2011 19:07:22 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % TEST_AO_LSCOV tests the lscov method of the AO class.
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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 % M Hueller 19-03-10
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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 % $Id: test_ao_lscov.m,v 1.7 2010/03/19 11:25:36 mauro Exp $
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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 % function test_ao_lscov()
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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 %% 1) Determine the coefficients of a linear combination of noises:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % Make some data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 nsecs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 B1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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15 B2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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16 B3 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 n = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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18 c = [ao(1,plist('yunits','m/T')) ao(2,plist('yunits','m/T')) ao(3,plist('yunits','m T^-1'))];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 y = c(1)*B1 + c(2)*B2 + c(3)*B3 + n;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 y.simplifyYunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % Get a fit for c
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 p_s = lscov(B1, B2, B3, y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 % do linear combination: using lincom
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 yfit1 = lincom(B1, B2, B3, p_s);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 yfit1.simplifyYunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 % do linear combination: using eval
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 yfit2 = p_s.eval(B1, B2, B3);
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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 % Plot (compare data with fit)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 iplot(y, yfit1, yfit2, plist('Linestyles', {'-','--'}))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 %% 2) Determine the coefficients of a linear combination of noises:
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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 % Make some data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 nsecs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 x1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 x2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 x3 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'C'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 n = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 c = [ao(1,plist('yunits','m/T')) ao(2,plist('yunits','m/m')) ao(3,plist('yunits','m C^-1'))];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 y = c(1)*x1 + c(2)*x2 + c(3)*x3 + n;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 y.simplifyYunits;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 % Get a fit for c
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 p_m = lscov(x1, x2, x3, y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 % do linear combination: using lincom
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 yfit1 = lincom(x1, x2, x3, p_m);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 % do linear combination: using eval
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 pl_split = plist('times', [1 5]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 yfit2 = p_m.eval(plist('Xdata', {split(x1, pl_split), split(x2, pl_split), split(x3, pl_split)}));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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51 % Plot (compare data with fit)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 iplot(y, yfit1, yfit2, plist('Linestyles', {'-','--'}))