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annotate m-toolbox/classes/@tsdata/fitfs.m @ 52:daf4eab1a51e database-connection-manager tip

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Fix. Default password should be [] not an empty string
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 07 Dec 2011 17:29:47 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % FITFS estimates the sample rate of the input data.
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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: Estimates the sample rate of the input data and detects if the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % data is evenly sampled or not. Data where any of the fluctuations in the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % time difference between subsequent samples is greater than the one due to
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % finite numerical precision is detected as unevenly sampled.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % CALL: fs = fitfs(x)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % [fs, t0] = fitfs(x)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % [fs, t0, unevenly] = fitfs(x)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % INPUTS: x - sampling times vector
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % OUTPUTS: fs - estimated samplig frequency
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % t0 - estimates start time
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % unevenly - signals whether the data is regularly sampled or not
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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 % VERSION: $Id: fitfs.m,v 1.12 2010/05/04 11:23:59 nicolodi Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 function [fs, t0, unevenly] = fitfs(varargin)
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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 import utils.const.*
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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 % Get input vertices
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 xi = varargin{1};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 % Reshape x to match with linspace output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 ss = size(xi);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 if ss(1) > ss(2)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 xi = xi.';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 d = diff(xi);
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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 % Special case of just a single number time-series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 if isempty(d)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 fs = 1;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 t0 = 0;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 unevenly = false;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 return
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42 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 % Initial estimate
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 dt = mean(d);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 fs = 1.0 / dt;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 t0 = xi(1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 unevenly = false;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 % Strict check for unevenly sampled data. This detects as unevenly
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 % sampled all data where any of the fluctuations in the time
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 % difference between subsequent samples is greater than the one due
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 % to finite numerical precision
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 if any(abs(d - dt) > 2*eps(xi(2:end)))
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 utils.helper.msg(msg.PROC1, 'unevenly sampled data detected');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 unevenly = true;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 % The median is much less sensible to outliers than the mean. It is
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 % therefore a better estimate of the sampling frequency in case of
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 % unevenly sampled data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 dt = median(d);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 fs = 1.0 / dt;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 t0 = 0;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65
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66 end
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67