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| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 07 Dec 2011 17:29:47 +0100 |
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd"> <html lang="en"> <head> <meta name="generator" content= "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org"> <meta http-equiv="Content-Type" content= "text/html; charset=us-ascii"> <title>Creating Analysis Objects (LTPDA Toolbox)</title> <link rel="stylesheet" href="docstyle.css" type="text/css"> <meta name="generator" content="DocBook XSL Stylesheets V1.52.2"> <meta name="description" content= "Presents an overview of the features, system requirements, and starting the toolbox."> </head> <body> <a name="top_of_page" id="top_of_page"></a> <p style="font-size:1px;"> </p> <table class="nav" summary="Navigation aid" border="0" width= "100%" cellpadding="0" cellspacing="0"> <tr> <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td> <td valign="baseline" align="right"><a href= "ao_intro.html"><img src="b_prev.gif" border="0" align= "bottom" alt="Analysis Objects"></a> <a href= "ao_save.html"><img src="b_next.gif" border="0" align= "bottom" alt="Saving Analysis Objects"></a></td> </tr> </table> <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Creating Analysis Objects</h1> <hr> <p> <p>Analysis objects can be created in MATLAB in many ways. Apart from being created by the many algorithms in the LTPDA Toolbox, AOs can also be created from initial data or descriptions of data. The various <i>constructors</i> are listed in the function help: <a href="matlab:doc('ao')">ao help</a>.</p> <h3>Examples of creating AOs</h3> <p>The following examples show some ways to create Analysis Objects.</p> <ul> <li><a href="ao_create.html#text">Creating AOs from text files</a></li> <li><a href="ao_create.html#xml">Creating AOs from XML or MAT files</a></li> <li><a href="ao_create.html#fcn">Creating AOs from MATLAB functions</a></li> <li><a href="ao_create.html#tsfcn">Creating AOs from functions of time</a></li> <li><a href="ao_create.html#window">Creating AOs from window functions</a></li> <li><a href="ao_create.html#waveform">Creating AOs from waveform descriptions</a></li> <li><a href="ao_create.html#pzmodel">Creating AOs from pole zero models</a></li> </ul> <hr> <h4><a name="text"></a>Creating AOs from text files.</h4> <p>Analysis Objects can be created from text files containing two columns of ASCII numbers. Files ending in '.txt' or '.dat' will be handled as ASCII file inputs. The first column is taken to be the time instances; the second column is taken to be the amplitude samples. The created AO is of type <tt>tsdata</tt> with the sample rate set by the difference between the time-stamps of the first two samples in the file. The name of the resulting AO is set to the filename (without the file extension). The filename is also stored as a parameter in the history parameter list. The following code shows this in action:</p> <div class="fragment"><pre> >> a = ao(<span class="string">'data.txt'</span>) ----------- ao 01: data.txt_01_02 ----------- name: data.txt_01_02 data: (0,-1.06421341288933) (0.1,1.60345729812004) (0.2,1.23467914689078) ... -------- tsdata 01 ------------ fs: 10 x: [100 1], double y: [100 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 10 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromDatafile ... $-->$Id: ao ... S mdlfile: empty description: UUID: e6ccfcb6-da49-4f4c-8c2c-3054fe5d2762 --------------------------------------------- </pre></div> <p>As with most constructor calls, an equivalent action can be achieved using an input <a href="plist_intro.html">Parameter List</a>.</p> <div class="fragment"><pre> >> a = ao(plist(<span class="string">'filename'</span>, <span class="string">'data.txt'</span>)) </pre></div> <hr> <h4><a name="xml"></a>Creating AOs from XML or .mat files</h4> <p>AOs can be saved as both XML and .MAT files. As such, they can also be created from these files.</p> <div class="fragment"><pre> >> a = ao(<span class="string">'a.xml'</span>) ----------- ao 01: a ----------- name: None data: (0,-0.493009815316451) (0.1,-0.180739356415037) (0.2,0.045841105713705) ... -------- tsdata 01 ------------ fs: 10 x: [100 1], double y: [100 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 10 t0: 1970-01-01 00:00:01.000 ------------------------------- hist: ao / ao / SId: fromVals ... $-->$Id: ao ... S mdlfile: empty description: UUID: 2fed6155-6468-4533-88f6-e4b27bc6e1aa -------------------------------- </pre></div> <hr> <h4><a name="fcn"></a>Creating AOs from MATLAB functions</h4> <p>AOs can be created from any valid MATLAB function which returns a vector or matrix of values. For such calls, a parameter list is used as input. For example, the following code creates an AO containing 1000 random numbers:</p> <div class="fragment"><pre> >> a = ao(plist(<span class="string">'fcn'</span>, <span class="string">'randn(1000,1)'</span>)) ----------- ao 01: a ----------- name: None data: -1.28325610460477 -2.32895451628334 0.901931466951714 -1.83563868373519 0.06675 ... -------- cdata 01 ------------ y: [1000x1], double dy: [0x0], double yunits: [] ------------------------------ hist: ao / ao / SId: fromFcn ... $-->$Id ... $ mdlfile: empty description: UUID: 0072f8d0-f804-472b-a4aa-e9ec6a8de803 -------------------------------- </pre></div> <p>Here you can see that the AO is a <tt>cdata</tt> type and the name is set to be the function that was input.</p> <hr> <h4><a name="tsfcn"></a>Creating AOs from functions of time</h4> <p>AOs can be created from any valid MATLAB function which is a function of the variable <tt>t</tt>. For such calls, a parameter list is used as input. For example, the following code creates an AO containing sinusoidal signal at 1Hz with some additional Gaussian noise:</p> <div class="fragment"><pre> pl = plist(); pl = append(pl, <span class="string">'nsecs'</span>, 100); pl = append(pl, <span class="string">'fs'</span>, 10); pl = append(pl, <span class="string">'tsfcn'</span>, <span class="string">'sin(2*pi*1*t)+randn(size(t))'</span>); a = ao(pl) ----------- ao 01: a ----------- name: None data: (0,1.37694916561229) (0.1,-0.820427237640771) (0.2,1.09228819960292) ... -------- tsdata 01 ------------ fs: 10 x: [1000 1], double y: [1000 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 100 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromTSfcn ... $-->$Id: ao ... S mdlfile: empty description: UUID: c0f481cf-4bdd-4a91-bc78-6d34f8222313 -------------------------------- </pre></div> <p>Here you can see that the AO is a <tt>tsdata</tt> type, as you would expect. Also note that you need to specify the sample rate (<tt>fs</tt>) and the number of seconds of data you would like to have (<tt>nsecs</tt>).</p> <hr> <h4><a name="window"></a>Creating AOs from window functions</h4> <p>The LTPDA Toolbox contains a class for designing spectral windows (see <a href="specwin.html">Spectral Windows</a>). A spectral window object can also be used to create an Analysis Object as follows:</p> <div class="fragment"><pre> >> w = specwin(<span class="string">'Hanning'</span>, 1000) ------ specwin/1 ------- type: Hanning alpha: 0 psll: 31.5 rov: 50 nenbw: 1.5 w3db: 1.4382 flatness: -1.4236 ws: 500 ws2: 375.000000000001 win: [0 9.86957193144233e-06 3.94778980919441e-05 8.88238095955174e-05 0.0001579 ... version: SId: specwin.m,v 1.67 2009/09/01 09:25:24 ingo Exp S ------------------------ >> a = ao(w) ----------- ao 01: ao(Hanning) ----------- name: ao(Hanning) data: 0 9.86957193144233e-06 3.94778980919441e-05 8.88238095955174e-05 0.0001579 ... -------- cdata 01 ------------ y: [1x1000], double dy: [0x0], double yunits: [] ------------------------------ hist: ao / ao / SId: fromSpecWin ... $-->$Id: ao ... S mdlfile: empty description: UUID: ea1a9036-b9f5-4bdb-b3a3-211e9d697060 ------------------------------------------ </pre></div> <p> It is also possible to pass the information about the window as a plist to the ao constructor. </p> <div class="fragment"><pre> >> ao(plist(<span class="string">'win'</span>, <span class="string">'Hanning'</span>, <span class="string">'length'</span>, 1000)) ----------- ao 01: ao(Hanning) ----------- name: ao(Hanning) data: 0 9.86957193144233e-06 3.94778980919441e-05 8.88238095955174e-05 0.0001579 ... -------- cdata 01 ------------ y: [1x1000], double dy: [0x0], double yunits: [] ------------------------------ hist: ao / ao / SId: fromSpecWin ... -->$Id: ao ... S mdlfile: empty description: UUID: 5b81f67a-45b9-43f8-a74a-bc5161fd718f ------------------------------------------ </pre></div> <p> The example code above creates a Hanning window object with 1000 points. The call to the AO constructor then creates a <tt>cdata</tt> type AO with 1000 points. This AO can then be multiplied against other AOs in order to window the data. </p> <hr> <h4><a name="waveform"></a>Creating AOs from waveform descriptions</h4> <p> MATLAB contains various functions for creating different waveforms, for example, <tt>square</tt>, <tt>sawtooth</tt>. Some of these functions can be called upon to create Analysis Objects. The following code creates an AO with a sawtooth waveform: </p> <div class="fragment"><pre> pl = plist(); pl = append(pl, <span class="string">'fs'</span>, 100); pl = append(pl, <span class="string">'nsecs'</span>, 5); pl = append(pl, <span class="string">'waveform'</span>, 'Sawtooth'); pl = append(pl, <span class="string">'f'</span>, 1); pl = append(pl, <span class="string">'width'</span>, 0.5); asaw = ao(pl) ----------- ao 01: Sawtooth ----------- name: Sawtooth data: (0,-1) (0.01,-0.96) (0.02,-0.92) (0.03,-0.88) (0.04,-0.84) ... -------- tsdata 01 ------------ fs: 100 x: [500 1], double y: [500 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 5 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromWaveform ... $-->$Id: ao ... S mdlfile: empty description: UUID: cb76c866-ee3f-47e6-bb29-290074666e43 --------------------------------------- </pre></div> <p> You can call the <tt>iplot</tt> function to view the resulting waveform: </p> <div class="fragment"><pre> iplot(asaw); </pre></div> <img src="images/ao_create_sawtooth.png" alt="Sawtooth waveform" border="3" width="600px"> <hr> <h4><a name="pzmodel"></a>Creating AOs from pole zero models</h4> <p> When generating an AO from a pole zero model, the noise generator function is called. This a method to generate arbitrarily long time series with a prescribed spectral density. The algorithm is based on the following paper: </p> <p>Franklin, Joel N.: <i> Numerical simulation of stationary and non-stationary gaussian random processes </i>, SIAM review, Volume {<b> 7</b>}, Issue 1, page 68--80, 1965. </p> <p> The Document <i> Generation of Random time series with prescribed spectra </i> by Gerhard Heinzel (S2-AEI-TN-3034) <br> corrects a mistake in the aforesaid paper and describes the practical implementation. The following code creates an AO with a time series having a prescribed spectral density, defined by the input pole zero model: </p> <div class="fragment"><pre> f1 = 5; f2 = 10; f3 = 1; gain = 1; fs = 10; <span class="comment">%sampling frequancy</span> nsecs = 100; <span class="comment">%number of seconds to be generated</span> p = [pz(f1) pz(f2)]; z = [pz(f3)]; pzm = pzmodel(gain, p, z); a = ao(pzm, nsecs, fs) ----------- ao 01: noisegen(None) ----------- name: noisegen(None) data: (0,9.20287001568168) (0.1,-3.88425345108961) (0.2,6.31042718242658) ... -------- tsdata 01 ------------ fs: 10 x: [1000 1], double y: [1000 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 100 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromPzmodel ... $-->$Id: ao ... S mdlfile: empty description: UUID: 4a89d910-8672-475f-91cd-4fcc4b52a6b4 --------------------------------------------- </pre></div> <p> You can call the <tt>iplot</tt> function to view the resulting noise. </p> <div class="fragment"><pre> iplot(a); </pre></div> <img src="images/ao_create_niose.png" alt="Random time series" border="3" width="600px"> </p> <br> <br> <table class="nav" summary="Navigation aid" border="0" width= "100%" cellpadding="0" cellspacing="0"> <tr valign="top"> <td align="left" width="20"><a href="ao_intro.html"><img src= "b_prev.gif" border="0" align="bottom" alt= "Analysis Objects"></a> </td> <td align="left">Analysis Objects</td> <td> </td> <td align="right">Saving Analysis Objects</td> <td align="right" width="20"><a href= "ao_save.html"><img src="b_next.gif" border="0" align= "bottom" alt="Saving Analysis Objects"></a></td> </tr> </table><br> <p class="copy">©LTP Team</p> </body> </html>