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<?xml version="1.0" encoding="UTF-8"?>
<!--
*
* This help file was generated from medfilt1.sci using help_from_sci().
*
-->
<refentry version="5.0-subset Scilab" xml:id="medfilt1" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">
<refnamediv>
<refname>medfilt1</refname>
<refpurpose>1D median filtering</refpurpose>
<para> </para>
</refnamediv>
<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
y = medfilt1(x)
y = medfilt1(x, n)
y = medfilt1(x, n, dim)
y = medfitl1(__, nanflag, padding)
</synopsis>
<para> </para>
</refsynopsisdiv>
<refsection>
<title>Description</title>
<para>
y = medfilt1(x)
</para>
<para>Applies a 3rd order 1-dimensional median filter to input x along the
first non-zero dimension.</para>
<para>The function appropriately pads the signal
with zeros at the endings. For a segment, a median is calculated as
the middle value (average of two middle values) for odd number
number (even number) of data points.</para>
<para> </para>
<para>y = medfilt1(x,n)</para>
<para>Applies a nth order 1-dimensional median filter.</para>
<para> </para>
<para>y = medfilt1(x,n,dim)</para>
<para>Applies the median filter along the n-th dimension</para>
<para> </para>
<para>y = medfilt1(__, nanflag, padding)</para>
<para>nanflag specifies how NaN values are treated. padding specifies the
type of filtering to be performed at the signal edges.</para>
<para> </para>
</refsection>
<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>x:</term>
<listitem><para> int | double</para> <para>Input signal.</para></listitem></varlistentry>
<varlistentry><term>n:</term>
<listitem><para> positive integer, scalar</para><para>
Filter order. </para>
<para> Defaults to 3.The order of the median filter. Must be less than
(length of the signal) where signals are 1D vectors along the
dimension of x to be filtered </para></listitem></varlistentry>
<varlistentry><term>dim:</term>
<listitem><para> positive integer scalar</para><para>
Dimension to filter along. </para>
<para> Defaults to first non-singleton dimension of x</para></listitem></varlistentry>
<varlistentry><term>nanflag:</term>
<listitem><para> 'includenan' (default) | 'omitnan'</para> <para>
NaN condition.</para>
<para> * includenan: Filtering such that the median of any segment containing a NaN is also a NaN. </para>
<para>* omitnan: Filtering with NaNs omitted in each segment. If a segment contains all NaNs, the result is NaN</para>
</listitem></varlistentry>
<varlistentry><term>y:</term>
<listitem><para> int | double</para><para>
The filtered signal.</para>
<para>y has the same size as x</para></listitem></varlistentry>
</variablelist>
<para> </para>
</refsection>
<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
//Generate a sinusoidal signal sampled for 1 second at 100 Hz. Add a higher-frequency sinusoid to simulate noise.
fs = 100;
t = 0:1/fs:1;
x = sin(2*%pi*t*3)+0.25*sin(2*%pi*t*40);
//Use a 10th-order median filter to smooth the signal. Plot the result.
y = medfilt1(x,10);
plot(t,x,t,y)
legend('Original','Filtered');
y = round(y*10000)/10000;
y = y'
]]></programlisting>
<scilab:image>
//Generate a sinusoidal signal sampled for 1 second at 100 Hz. Add a higher-frequency sinusoid to simulate noise.
fs = 100;
t = 0:1/fs:1;
x = sin(2*%pi*t*3)+0.25*sin(2*%pi*t*40);
//Use a 10th-order median filter to smooth the signal. Plot the result.
y = medfilt1(x,10);
plot(t,x,t,y)
legend('Original','Filtered');
y = round(y*10000)/10000;
y = y'
</scilab:image>
</refsection>
<refsection>
<title>See also</title>
<simplelist type="inline">
<member><link linkend="filter">| hampel | median | sgolayfilt</link></member>
</simplelist>
</refsection>
<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Ayush Baid</member>
</simplelist>
</refsection>
</refentry>
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