xan/awips2
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions
awips2/pythonPackages/scientific/Doc/Reference/Scientific.Signals.Models.AutoRegressiveModel-class.html
mjames-upc ae6bbbc780 python updates: cartopy, cycler, cython, h5py, nose, pil, pyparsing, pygtk, pycairo, pint, python-dateutil, scientific 
Former-commit-id: be884288b7c3019d0df8001a16452a605314623d
2016-06-27 15:10:22 -05:00

556 lines
20 KiB
HTML
Executable file
Raw Blame History

<?xml version="1.0" encoding="ascii"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<title>Scientific.Signals.Models.AutoRegressiveModel</title>
<link rel="stylesheet" href="epydoc.css" type="text/css" />
<script type="text/javascript" src="epydoc.js"></script>
</head>
<body bgcolor="white" text="black" link="blue" vlink="#204080"
alink="#204080">
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Home link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="Scientific-module.html">Home</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Tree link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="module-tree.html">Trees</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Index link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="identifier-index.html">Indices</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Help link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="help.html">Help</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Project homepage -->
<th class="navbar" align="right" width="100%">
<table border="0" cellpadding="0" cellspacing="0">
<tr><th class="navbar" align="center"
><a class="navbar" target="_top" href="http://dirac.cnrs-orleans.fr/ScientificPython/">Scientific Python</a></th>
</tr></table></th>
</tr>
</table>
<table width="100%" cellpadding="0" cellspacing="0">
<tr valign="top">
<td width="100%">
<span class="breadcrumbs">
<a href="Scientific-module.html">Package&nbsp;Scientific</a> ::
<a href="Scientific.Signals-module.html">Package&nbsp;Signals</a> ::
<a href="Scientific.Signals.Models-module.html">Module&nbsp;Models</a> ::
Class&nbsp;AutoRegressiveModel
</span>
</td>
<td>
<table cellpadding="0" cellspacing="0">
<!-- hide/show private -->
<tr><td align="right"><span class="options"
>[<a href="frames.html" target="_top">frames</a
>]&nbsp;|&nbsp;<a href="Scientific.Signals.Models.AutoRegressiveModel-class.html"
target="_top">no&nbsp;frames</a>]</span></td></tr>
</table>
</td>
</tr>
</table>
<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class AutoRegressiveModel</h1><p class="nomargin-top"></p>
<dl><dt>Known Subclasses:</dt>
<dd>
<ul class="subclass-list">
<li><a href="Scientific.Signals.Models.AveragedAutoRegressiveModel-class.html">AveragedAutoRegressiveModel</a></li> </ul>
</dd></dl>
<hr />
<p>Auto-regressive model for stochastic process</p>
<p>This implementation uses the Burg algorithm to obtain the coefficients
of the AR model.</p>
<!-- ==================== INSTANCE METHODS ==================== -->
<a name="section-InstanceMethods"></a>
<table class="summary" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Instance Methods</span></td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type">&nbsp;</span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">order</span>,
<span class="summary-sig-arg">data</span>,
<span class="summary-sig-arg">delta_t</span>=<span class="summary-sig-default">1</span>)</span></td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><a
href="Scientific.Functions.Interpolation.InterpolatingFunction-class.html"
class="link">Scientific.Functions.Interpolation.InterpolatingFunction</a></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#correlation" class="summary-sig-name">correlation</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">nsteps</span>)</span><br />
Returns:
the autocorrelation function of the process as estimated from the AR
model</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type">&nbsp;</span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#frictionConstant" class="summary-sig-name">frictionConstant</a>(<span class="summary-sig-arg">self</span>)</span><br />
Returns:
the friction constant of the process, i.e.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><a
href="Scientific.Functions.Interpolation.InterpolatingFunction-class.html"
class="link">Scientific.Functions.Interpolation.InterpolatingFunction</a></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunction" class="summary-sig-name">memoryFunction</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">nsteps</span>)</span><br />
Returns:
the memory function of the process as estimated from the AR model</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><a href="Scientific.Functions.Rational.RationalFunction-class.html"
class="link">Scientific.Function.Rational.RationalFunction</a></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunctionZ" class="summary-sig-name">memoryFunctionZ</a>(<span class="summary-sig-arg">self</span>)</span><br />
Returns:
the <i class="math">z</i>-transform of the process' memory function</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><a href="Scientific.Functions.Rational.RationalFunction-class.html"
class="link">Scientific.Function.Rational.RationalFunction</a></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#memoryFunctionZapprox" class="summary-sig-name">memoryFunctionZapprox</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">den_order</span>)</span><br />
Returns:
an approximation to the <i class="math">z</i>-transform of the
process' memory function that correponds to an expansion of the
denominator up to order den_order</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><code>Numeric.array</code> of <code>complex</code></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#poles" class="summary-sig-name">poles</a>(<span class="summary-sig-arg">self</span>)</span><br />
Returns:
the poles of the model in the complex <i class="math">z</i>-plane</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><code>float</code> or <code>complex</code></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#predictStep" class="summary-sig-name">predictStep</a>(<span class="summary-sig-arg">self</span>)</span><br />
Calculates the linear prediction of the next step in the series.</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
<tr>
<td width="15%" align="right" valign="top" class="summary">
<span class="summary-type"><code>Numeric.array</code> of <code>float</code></span>
</td><td class="summary">
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr>
<td><span class="summary-sig"><a href="Scientific.Signals.Models.AutoRegressiveModel-class.html#spectrum" class="summary-sig-name">spectrum</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">omega</span>)</span><br />
Returns:
the frequency spectrum of the process</td>
<td align="right" valign="top">
</td>
</tr>
</table>
</td>
</tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
<a name="section-MethodDetails"></a>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
<td align="left" colspan="2" class="table-header">
<span class="table-header">Method Details</span></td>
</tr>
</table>
<a name="__init__"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">order</span>,
<span class="sig-arg">data</span>,
<span class="sig-arg">delta_t</span>=<span class="sig-default">1</span>)</span>
<br /><em class="fname">(Constructor)</em>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>order</code></strong> (<code>int</code>) - the order of the model</li>
<li><strong class="pname"><code>data</code></strong> (sequence of <code>float</code> or <code>complex</code>) - the time series</li>
<li><strong class="pname"><code>delta_t</code></strong> (<code>float</code>) - the sampling interval for the time series</li>
</ul></dd>
</dl>
</td></tr></table>
</div>
<a name="correlation"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">correlation</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">nsteps</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>nsteps</code></strong> (<code>int</code>) - the number of time steps for which the autocorrelation function
is to be evaluated</li>
</ul></dd>
<dt>Returns: <a
href="Scientific.Functions.Interpolation.InterpolatingFunction-class.html"
class="link">Scientific.Functions.Interpolation.InterpolatingFunction</a></dt>
<dd>the autocorrelation function of the process as estimated from the
AR model</dd>
</dl>
</td></tr></table>
</div>
<a name="frictionConstant"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">frictionConstant</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Returns:</dt>
<dd>the friction constant of the process, i.e. the integral over the
memory function</dd>
</dl>
</td></tr></table>
</div>
<a name="memoryFunction"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">memoryFunction</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">nsteps</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>nsteps</code></strong> (<code>int</code>) - the number of time steps for which the memory function is to be
evaluated</li>
</ul></dd>
<dt>Returns: <a
href="Scientific.Functions.Interpolation.InterpolatingFunction-class.html"
class="link">Scientific.Functions.Interpolation.InterpolatingFunction</a></dt>
<dd>the memory function of the process as estimated from the AR model</dd>
</dl>
</td></tr></table>
</div>
<a name="memoryFunctionZ"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">memoryFunctionZ</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Returns: <a href="Scientific.Functions.Rational.RationalFunction-class.html"
class="link">Scientific.Function.Rational.RationalFunction</a></dt>
<dd>the <i class="math">z</i>-transform of the process' memory
function</dd>
</dl>
</td></tr></table>
</div>
<a name="memoryFunctionZapprox"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">memoryFunctionZapprox</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">den_order</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>den_order</code></strong> (<code>int</code>)</li>
</ul></dd>
<dt>Returns: <a href="Scientific.Functions.Rational.RationalFunction-class.html"
class="link">Scientific.Function.Rational.RationalFunction</a></dt>
<dd>an approximation to the <i class="math">z</i>-transform of the
process' memory function that correponds to an expansion of the
denominator up to order den_order</dd>
</dl>
</td></tr></table>
</div>
<a name="poles"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">poles</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Returns: <code>Numeric.array</code> of <code>complex</code></dt>
<dd>the poles of the model in the complex <i class="math">z</i>-plane</dd>
</dl>
</td></tr></table>
</div>
<a name="predictStep"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">predictStep</span>(<span class="sig-arg">self</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<p>Calculates the linear prediction of the next step in the series. This
step is appended internally to the current trajectory, making it
possible to call this method repeatedly in order to obtain a sequence of
predicted steps.</p>
<dl class="fields">
<dt>Returns: <code>float</code> or <code>complex</code></dt>
<dd>the predicted step</dd>
</dl>
</td></tr></table>
</div>
<a name="spectrum"></a>
<div>
<table class="details" border="1" cellpadding="3"
cellspacing="0" width="100%" bgcolor="white">
<tr><td>
<table width="100%" cellpadding="0" cellspacing="0" border="0">
<tr valign="top"><td>
<h3 class="epydoc"><span class="sig"><span class="sig-name">spectrum</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">omega</span>)</span>
</h3>
</td><td align="right" valign="top"
>&nbsp;
</td>
</tr></table>
<dl class="fields">
<dt>Parameters:</dt>
<dd><ul class="nomargin-top">
<li><strong class="pname"><code>omega</code></strong> (<code>Numeric.array</code> of <code>float</code>) - the angular frequencies at which the spectrum is to be evaluated</li>
</ul></dd>
<dt>Returns: <code>Numeric.array</code> of <code>float</code></dt>
<dd>the frequency spectrum of the process</dd>
</dl>
</td></tr></table>
</div>
<br />
<!-- ==================== NAVIGATION BAR ==================== -->
<table class="navbar" border="0" width="100%" cellpadding="0"
bgcolor="#a0c0ff" cellspacing="0">
<tr valign="middle">
<!-- Home link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="Scientific-module.html">Home</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Tree link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="module-tree.html">Trees</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Index link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="identifier-index.html">Indices</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Help link -->
<th>&nbsp;&nbsp;&nbsp;<a
href="help.html">Help</a>&nbsp;&nbsp;&nbsp;</th>
<!-- Project homepage -->
<th class="navbar" align="right" width="100%">
<table border="0" cellpadding="0" cellspacing="0">
<tr><th class="navbar" align="center"
><a class="navbar" target="_top" href="http://dirac.cnrs-orleans.fr/ScientificPython/">Scientific Python</a></th>
</tr></table></th>
</tr>
</table>
<table border="0" cellpadding="0" cellspacing="0" width="100%%">
<tr>
<td align="left" class="footer">
Generated by Epydoc 3.0 on Tue Oct 28 14:16:07 2008
</td>
<td align="right" class="footer">
<a target="mainFrame" href="http://epydoc.sourceforge.net"
>http://epydoc.sourceforge.net</a>
</td>
</tr>
</table>
<script type="text/javascript">
<!--
// Private objects are initially displayed (because if
// javascript is turned off then we want them to be
// visible); but by default, we want to hide them. So hide
// them unless we have a cookie that says to show them.
checkCookie();
// -->
</script>
</body>
</html>
Reference in a new issue View git blame Copy permalink