code by jitendra and added more test4.sce

1 files changed, 28 insertions, 75 deletions

diff --git a/help/en_US/medfilt1.xml b/help/en_US/medfilt1.xml
index 2760fe9..ffa7490 100644
--- a/help/en_US/medfilt1.xml
+++ b/help/en_US/medfilt1.xml
@@ -18,107 +18,60 @@
   <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
+Applies a 3rd order 1-dimensional median filter to input x along the
+first non-zero dimension. 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>
+number (even number) of data points.
+y = medfilt1(x,n)
+Applies a nth order 1-dimensional median filter.
+y = medfilt1(x,n,dim)
+Applies the median filter along the n-th dimension
+y = medfilt1(__, nanflag, padding)
+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>
+      <listitem><para> int | double</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>
+      <listitem><para> positive integer scalar</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>
+      <listitem><para> positive integer scalar</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>
-
+      <listitem><para> 'includenan' (default) | 'omitnan'</para></listitem></varlistentry>
+   <varlistentry><term>* includenan:</term>
+      <listitem><para> Filtering such that the median of any segment</para></listitem></varlistentry>
+   <varlistentry><term>* omitnan:</term>
+      <listitem><para> Filtering with NaNs omitted in each segment. If a segment</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>
+      <listitem><para> int | double</para></listitem></varlistentry>
+   <varlistentry><term>Examples :</term>
+      <listitem><para> Noise supression using 10th order (n =10) median filtering</para></listitem></varlistentry>
+   <varlistentry><term>t = 0:</term>
+      <listitem><para>1/fs:1;</para></listitem></varlistentry>
+   <varlistentry><term>Output :</term>
+      <listitem><para> </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>