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| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 07 Dec 2011 17:29:47 +0100 |
| parents | f0afece42f48 |
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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>Making a time-series AO (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= "ltpda_training_topic_1_2.html"><img src="b_prev.gif" border="0" align= "bottom" alt="Making AOs"></a> <a href= "ltpda_training_topic_1_4.html"><img src="b_next.gif" border="0" align= "bottom" alt="Basic math with AOs"></a></td> </tr> </table> <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Making a time-series AO</h1> <hr> <p> <h2>Exercise 4</h2> <p> Time-series data are stored in a data object of the class <tt>tsdata</tt>. As a user, you don't need to care about this, but it's sometimes nice to know how things work. There are various ways (constructors) to build time-series AOs. For example, you can give a set of values and a sample rate like </p> <div class="fragment"><pre> a = ao([1 2 3 4 5], 2) </pre></div> <p> The first argument is the Y data vector; the second, the sample rate. </p> <p> If you run this command in the MATLAB terminal you should see </p> <div class="fragment"><pre> >> a = ao([1 2 3 4 5], 2) M: constructing from Y values and fs ----------- ao 01: a ----------- name: '' data: (0,1) (0.5,2) (1,3) (1.5,4) (2,5) -------- tsdata 01 ------------ fs: 2 x: [1 5], double y: [1 5], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 2.5 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromXYVals.m,v 1.10 2011/05/07 05:15:26 mauro Exp S-->SId: ao.m,v 1.346 2011/05/07 06:56:17 mauro Exp S description: UUID: 2484d029-4616-4b22-8229-7685c8d3e847 -------------------------------- </pre></div> <p> Now you see that the data type is <tt>tsdata</tt> and the X units are automatically set to seconds ('s'). You can also see that the data series spans 2.5s and that the first sample corresponds to 1970-01-01 00:00:00.000 UTC. You can set further properties of the object, for example </p> <p> <div class="fragment"><pre> a.setT0(<span class="string">'2009-02-03 12:23:44'</span>); a.setDescription(<span class="string">'My lovely time-series'</span>) </pre></div> </p> <br> <p> You can do all of this in one block on the workbench. To do that: <ol> <li>Start the workbench and create a new pipeline</li> <li>Drag an AO constructor block from the library (or use "Quick Block")</li> <li>Select the block and select the "From XY Values" parameter set</li> <li>Click the "Set" button to set the parameters to the block</li> <li>Double-click the value cell for the key "YVALS" and enter some values, e.g., <tt>1:10</tt></li> <li>Double-click the value cell for the key "FS" and enter a sample frequency, e.g., <tt>2</tt>. By setting a set of values for the Y-data and a sample rate, we tell the AO constructor that we want to build a <tt>tsdata</tt> AO.</li> <li>To set the name of the block, double click the block and enter a name in the dialog box. Automatic setting of AO names from the block name only happens for constructor blocks. To the set the name of AOs which are outputs of all other block types, use the <tt>setName</tt> block. </li> <li>You'll notice that the parameter list doesn't contain a <tt>T0</tt> parameter by default, but you can easily add this parameter by clicking on the "plus" button below the parameter list. Enter the key <tt>T0</tt> in the dialog box, and an appropriate value in the next dialog box. (Note: parameter key names are case insensitive.)</li> <li>You can do the same for the description, or any other property of the AO.</li> </ol> </p> <p> The final parameter list in this case might look like: </p> <img src="images/ltpda_training_1/topic1/tsdata_pset.png" alt="Time-series parameter set" border="1"> <br> <br> <!-- Plists --> <h2>Digression: Introducing parameter lists</h2> <br> <p> The time has come to go back to that <tt>plist</tt> command we saw earlier when plotting the AO history via the graphviz renderer. </p> <p> The following two commands are equivalent: </p> <div class="fragment"><pre> a = ao([1 2 3 4 5], 2); a = ao(plist(<span class="string">'yvals'</span>, [1 2 3 4 5], <span class="string">'fs'</span>, 2)) </pre></div> <p> Here we introduce the idea of parameter lists (<tt>plist</tt>). A <tt>plist</tt> is a list of parameters, each parameter being defined by a key/value pair. The key of a <tt>plist</tt> is always a string and is always case insensitive. The value can be anything: a number, a string, another LTPDA object, a cell-array, a structure, etc. For more information about parameter lists, see the <a href="plist_intro.html">appropriate section</a> of the LTPDA user manual. </p> <p> Going on with time-series objects: The following is almost equivalent: </p> <div class="fragment"><pre> a = ao(plist(<span class="string">'xvals'</span>, [0 0.5 1 1.5 2], <span class="string">'yvals'</span>, [1 2 3 4 5])) </pre></div> <p> The difference is, if you run this command, you will see that the resulting AO has data of type <tt>xydata</tt>. To make this a time-series object, we need to tell the constructor some more information. Either you need to specify the sample-rate, or you can explicitly set the data type: </p> <div class="fragment"><pre> a = ao(plist(<span class="string">'xvals'</span>, [0 0.5 1 1.5 2], <span class="string">'yvals'</span>, [1 2 3 4 5], <span class="string">'fs'</span>, 2)) a = ao(plist(<span class="string">'xvals'</span>, [0 0.5 1 1.5 2], <span class="string">'yvals'</span>, [1 2 3 4 5], <span class="string">'type'</span>,... <span class="string">'tsdata'</span>)) </pre></div> <p> <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1"> <tr width="90%"> <td> The elipsis (<tt>...</tt>) in MATLAB means join the two lines. </td> </tr> </table> </p> <p> If you specify the samples rate with the key 'fs', then the 'xvals' are just ignored. If you tell the data type with the key 'type', then the sample rate is computed from the 'xvals'. </p> <p> You can add additional parameters to these constructor lines. For example, </p> <div class="fragment"><pre> a = ao(plist(<span class="string">'xvals'</span>, [0 0.5 1 1.5 2], <span class="string">'yvals'</span>, [1 2 3 4 5], ... <span class="string">'type'</span>, <span class="string">'tsdata'</span>, ... <span class="string">'name'</span>, <span class="string">'Bob'</span>, ... <span class="string">'t0'</span>, <span class="string">'2008-09-01'</span>)) </pre></div> <p> There are other constructors which make constructing time-series AOs from simulated data more convenient. Two of these are discussed below. </p> <br> <!-- Time-series f(t) --> <h2>Times-series AO as a function of <tt>t</tt></h2> <br> <p> If you want to specify your time-series as a function of the variable <tt>t</tt>, then you can use the following constructor: </p> <div class="fragment"><pre> a = ao(plist(<span class="string">'tsfcn'</span>, <span class="string">'t.^2 + t'</span>, ... <span class="string">'fs'</span>, 10, <span class="string">'nsecs'</span>, 1000)) </pre></div> <p> You specify the function of t with the key 'tsfcn', then give the sample rate and the number of seconds. If you run this command you should see the output: </p> <div class="fragment"><pre> >> a = ao(plist('tsfcn', 't.^2 + t', 'fs', 10, 'nsecs', 1000)) M: constructing from plist ----------- ao 01: a ----------- name: '' data: (0,0) (0.1,0.11) (0.2,0.24) (0.3,0.39) (0.4,0.56) ... -------- tsdata 01 ------------ fs: 10 x: [10000 1], double y: [10000 1], double dx: [0 0], double dy: [0 0], double xunits: [s] yunits: [] nsecs: 1000 t0: 1970-01-01 00:00:00.000 ------------------------------- hist: ao / ao / SId: fromTSfcn.m,v 1.22 2010/07/28 16:31:01 ingo Exp S-->SId: ao.m,v 1.346 2011/05/07 06:56:17 mauro Exp S description: UUID: d01615d6-82ad-4736-8a0e-4096dc023149 -------------------------------- </pre></div> <p> You can write any valid MATLAB expression as a function of <tt>t</tt>. </p> <p> Plists can be reused, of course. Suppose we define a recipe for an AO as </p> <div class="fragment"><pre> pl = plist(<span class="string">'tsfcn'</span>, <span class="string">'randn(size(t))'</span>, <span class="string">'fs'</span>, 10, <span class="string">'nsecs'</span>, 1000) </pre></div> <p> then we can make repeated AOs from this recipe: </p> <div class="fragment"><pre> a1 = ao(pl) a2 = ao(pl) <span class="comment">% Or use the random factory:</span> <span class="comment">% a = ao.randn(nsecs, fs)</span> a3 = ao.randn(1000, 10) </pre></div> <p> Here we have made three AOs with different random white-noise data vectors. </p> <br> <h2>Digression: plotting the data</h2> <br> <p> To plot the data in the AO, you can use the intelligent plotting method, <tt>iplot</tt>. For example, type in the MATLAB terminal: </p> <div class="fragment"><pre> a1.iplot </pre></div> <p> and you should see a plot like the one below. </p> <img src="images/ltpda_training_1/topic1/iplot1.png" alt="iplot example 1" border="1"> <p> We can make a more interesting plot if we first specify some of the properties of the AOs. For example, type the following commands to set the names and Y units of the two AOs we made earlier: </p> <div class="fragment"><pre> a1.setName a2.setName setYunits(a1,a2,<span class="string">'N'</span>) </pre></div> <p> Now plot both time-series together with: </p> <div class="fragment"><pre> iplot(a1,a2) </pre></div> <p> and you shoud see a plot like the following: </p> <img src="images/ltpda_training_1/topic1/iplot2.png" alt="iplot example 2" border="1"> <p> <table cellspacing="0" class="note" summary="Note" cellpadding="5" border="1"> <tr width="90%"> <td> Calling the <tt>setName</tt> method with no input argument causes the AO to be named with the variable name. </td> </tr> </table> </p> <p> <tt>iplot</tt> has many configurable parameters which are (mostly) documented in the help. </p> <br> <!-- Time-series waveform --> <h2>Times-series AO from built in waveforms</h2> <br> <p> MATLAB has various functions for creating standard waveforms, for example, sine waves, square waves, and saw-tooth signals. These are available as convenient AO constructors. For example suppose we want to create a square-wave pulse train with a 30% duty cycle at 2Hz sampled at 100Hz lasting for 5s, then we can do </p> <div class="fragment"><pre> sw = ao(plist(<span class="string">'waveform'</span>, <span class="string">'square wave'</span>, <span class="string">'f'</span>, 2, <span class="string">'duty'</span>, 30, ... <span class="string">'fs'</span>, 100, <span class="string">'nsecs'</span>, 5)) </pre></div> <p> If you run that command and plot the result, you should see the square wave you were expecting: </p> <img src="images/ltpda_training_1/topic1/iplot_squarewave.png" alt="iplot squarewave" border="1"> <p> You can construct various different waveforms, but each has different parameters to set. The help of the AO method details the possibilities (<tt>help ao -> click on "Parameters Description" -> select "From Window"</tt>); here is the relevant extract: </p> <div class="fragment"><pre> 'waveform' - a waveform description (see options below). You can also specify additional parameters: 'fs' - sampling frequency [default: 10 Hz] 'nsecs' - length in seconds [default: 10 s] 't0' - time-stamp of the first data sample [default time(0)] and, for the following waveform types: 'sine wave' - 'A', 'f', 'phi', 'nsecs', 'toff' (can be vectors for sum of sine waves) 'A' - Amplitude of the wave 'f' - Frequency of the wave 'phi' - Phase of the eave 'nsecs' - Number of seconds (in seconds) 'toff' - Offset of the wave (in seconds) 'gaps' - Instead of defining an offset it is possible to define a gap (in seconds) before the sine wave. 'noise' - 'type' (can be 'Normal' or 'Uniform') 'sigma' specify the standard deviation 'chirp' - 'f0', 'f1', 't1' (help chirp) 'gaussian pulse' - 'f0', 'bw' (help gauspuls) 'square wave' - 'f', 'duty' (help square) 'sawtooth' - 'f', 'width' (help sawtooth) You can also specify the initial time (t0) associated with the time-series by passing a parameter 't0' with a value that is a time object. </pre></div> </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="ltpda_training_topic_1_2.html"><img src= "b_prev.gif" border="0" align="bottom" alt= "Making AOs"></a> </td> <td align="left">Making AOs</td> <td> </td> <td align="right">Basic math with AOs</td> <td align="right" width="20"><a href= "ltpda_training_topic_1_4.html"><img src="b_next.gif" border="0" align= "bottom" alt="Basic math with AOs"></a></td> </tr> </table><br> <p class="copy">©LTP Team</p> </body> </html>