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| author | ttt | 2018-12-06 13:42:14 +0530 |
|---|---|---|
| committer | ttt | 2018-12-06 13:42:14 +0530 |
| commit | d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795 (patch) | |
| tree | fdbe9d1a10e7c256e86d7efae276fa75615cd0ba | |
| parent | 3ffa5ac619587eadfdb4ffd3e2fee57fee385e21 (diff) | |
| download | FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.tar.gz FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.tar.bz2 FOSSEE-Signal-Processing-Toolbox-d6e8cfd86be242d0a1a09a1ef7d8b7f3d12af795.zip | |
code by jitendra and added more test4.sce
145 files changed, 1151 insertions, 5909 deletions
diff --git a/builder.sce b/builder.sce index 00d6a5b..57c6155 100644 --- a/builder.sce +++ b/builder.sce @@ -29,6 +29,7 @@ TOOLBOX_TITLE = "FOSSEE Signal Processing Toolbox"; toolbox_dir = get_absolute_file_path("builder.sce"); +tbx_builder_help(toolbox_dir); tbx_builder_macros(toolbox_dir); tbx_build_loader(TOOLBOX_NAME, toolbox_dir); tbx_build_cleaner(TOOLBOX_NAME, toolbox_dir); diff --git a/help/en_US/ac2poly.xml b/help/en_US/ac2poly.xml index 33cbd96..e90759d 100644 --- a/help/en_US/ac2poly.xml +++ b/help/en_US/ac2poly.xml @@ -57,6 +57,6 @@ Parthe Pandit <refsection> <title>Bibliography</title> - <para>kartik Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</para> + <para>Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</para> </refsection> </refentry> diff --git a/help/en_US/arburg.xml b/help/en_US/arburg.xml index 775d9c1..4cea095 100644 --- a/help/en_US/arburg.xml +++ b/help/en_US/arburg.xml @@ -17,55 +17,7 @@ <refnamediv> <refname>arburg</refname> - <refpurpose>This function calculates coefficients of an autoregressive (AR) model of complex data.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - a = arburg(x, poles) - a = arburg(x, poles, criterion) - [a, v] = arburg(...) - [a, v, k] = arburg(...) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> vector of real or complex numbers, of length > 2</para></listitem></varlistentry> - <varlistentry><term>poles:</term> - <listitem><para> positive integer value < length(x) - 2</para></listitem></varlistentry> - <varlistentry><term>criterion:</term> - <listitem><para> string value, takes in "AKICc", "KIC", "AICc", "AIC" and "FPE", default it not using a model-selection criterion</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> list of autoregression coefficients.</para></listitem></varlistentry> - <varlistentry><term>v:</term> - <listitem><para> mean square of residual noise from the whitening operation of the Burg lattice filter</para></listitem></varlistentry> - <varlistentry><term>k:</term> - <listitem><para> reflection coefficients defining the lattice-filter embodiment of the model</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function calculates coefficients of an autoregressive (AR) model of complex data x using the whitening lattice-filter method of Burg. - </para> - <para> -The first argument is the data sampled. The second argument is the number of poles in the model (or limit in case a criterion is supplied). -The third parameter takes in the criterion to limit the number of poles. The acceptable values are "AIC", "AKICc", "KIC", "AICc" which are based on information theory. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -x = [1,2,3,4,5] ; -poles = 2 ; -arburg(x,poles) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/arch_rnd.xml b/help/en_US/arch_rnd.xml index fe24c5c..d7441d2 100644 --- a/help/en_US/arch_rnd.xml +++ b/help/en_US/arch_rnd.xml @@ -17,62 +17,7 @@ <refnamediv> <refname>arch_rnd</refname> - <refpurpose>Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a.</refpurpose> + <refpurpose>funcprot(0);</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - arch_rnd (a, b, t) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para> CH coefficients</para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> AR coefficients</para></listitem></varlistentry> - <varlistentry><term>t:</term> - <listitem><para> Length of ARCH sequence</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It Simulates an ARCH sequence of length t with AR coefficients b and CH coefficients a. -The result y(t) follows the model - </para> - <para> - <latex> - \begin{eqnarray} -y(t) = b(1) + b(2) * y(t-1) + … + b(lb) * y(t-lb+1) + e(t) - \end{eqnarray} - </latex> -<para> -where e(t), given y up to time t-1, is N(0, h(t)), with -</para> - </para> - <para> - <latex> - \begin{eqnarray} -h(t) = a(1) + a(2) * e(t-1)^2 + … + a(la) * e(t-la+1)^2 - \end{eqnarray} - </latex> -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -a = [1 2 3 4 5]; -b = [7 8 9 10]; -t = 5 ; -arch_rnd (a, b, t) - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/arma_rnd.xml b/help/en_US/arma_rnd.xml index 922bfb3..14b658b 100644 --- a/help/en_US/arma_rnd.xml +++ b/help/en_US/arma_rnd.xml @@ -17,64 +17,7 @@ <refnamediv> <refname>arma_rnd</refname> - <refpurpose>Return a simulation of the ARMA model.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - arma_rnd (a, b, v, t, n) - arma_rnd (a, b, v, t) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para> vector</para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> vector</para></listitem></varlistentry> - <varlistentry><term>v:</term> - <listitem><para> Variance</para></listitem></varlistentry> - <varlistentry><term>t:</term> - <listitem><para> Length of output vector</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> Number of dummy x(i) used for initialization</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -The ARMA model is defined by - </para> - <para> - <latex> - \begin{eqnarray} -x(n) = a(1) * x(n-1) + … + a(k) * x(n-k) + e(n) + b(1) * e(n-1) + … + b(l) * e(n-l) - \end{eqnarray} - </latex> -</para> -<para> -in which k is the length of vector a, l is the length of vector b and e is Gaussian white noise with variance v. The function returns a vector of length t. - </para> - <para> -The optional parameter n gives the number of dummy x(i) used for initialization, i.e., a sequence of length t+n is generated and x(n+1:t+n) is returned. If n is omitted, n = 100 is used. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -a = [1 2 3 4 5]; -b = [7; 8; 9; 10; 11]; -v = 10; -t = 5; -n = 100; -arma_rnd (a, b, v, t, n) - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/armcov.xml b/help/en_US/armcov.xml index 79ed57c..62e33b7 100644 --- a/help/en_US/armcov.xml +++ b/help/en_US/armcov.xml @@ -17,46 +17,7 @@ <refnamediv> <refname>armcov</refname> - <refpurpose>This function uses the modified covariance method to fit a pth-order autoregressive (AR) model to the input signal x </refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - a=armcov(x,p) - [a,e] = armcov(x,p) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> input signal</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> order</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> output of an AR system driven by white noise</para></listitem></varlistentry> - <varlistentry><term>e:</term> - <listitem><para> variance estimate</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function uses the modified covariance method to fit a pth-order autoregressive (AR) model to the input signal x. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -A = [1 -2.7607 3.8106 -2.6535 0.9238]; -y = filter(1,A,0.2*rand(1024,1,"normal")); -arcoeffs = armcov(y,4) - - ]]></programlisting> - -</refsection> </refentry> diff --git a/help/en_US/aryule.xml b/help/en_US/aryule.xml index 6ad7b46..3d2e956 100644 --- a/help/en_US/aryule.xml +++ b/help/en_US/aryule.xml @@ -17,49 +17,7 @@ <refnamediv> <refname>aryule</refname> - <refpurpose>This function fits an AR (p)-model with Yule-Walker estimates.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - a = aryule (x, p) - [a, v] = aryule (x, p) - [a, v, k] = aryule (x, p) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> vector of real or complex numbers, length > 2</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> positive integer value < length(x) - 1</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> gives the AR coefficients </para></listitem></varlistentry> - <varlistentry><term>v:</term> - <listitem><para> gives the variance of the white noise, </para></listitem></varlistentry> - <varlistentry><term>k:</term> - <listitem><para> gives the reflection coefficients to be used in the lattice filter</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function fits an AR (p)-model with Yule-Walker estimates. -The first argument is the data vector which is to be estimated. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -x = [1,2,3,4,5] ; -poles = 2 ; -arburg(x,poles) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/autoreg_matrix.xml b/help/en_US/autoreg_matrix.xml index 217a151..9751861 100644 --- a/help/en_US/autoreg_matrix.xml +++ b/help/en_US/autoreg_matrix.xml @@ -17,48 +17,7 @@ <refnamediv> <refname>autoreg_matrix</refname> - <refpurpose>Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns.</refpurpose> + <refpurpose>funcprot(0);</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - autoreg_matrix(Y, K) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Y:</term> - <listitem><para> Vector</para></listitem></varlistentry> - <varlistentry><term>K:</term> - <listitem><para> Scalar or Vector</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns. - </para> - <para> -In other words, for T > K, '[1, Y(T-1), ..., Y(T-K)]' is the t-th row of the result. - </para> - <para> -The resulting matrix may be used as a regressor matrix in autoregressions. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - -Y = [1,2,3] ; -K = 2 ; -autoreg_matrix(Y,K) - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/barthannwin.xml b/help/en_US/barthannwin.xml index ad1139a..56bfcb7 100644 --- a/help/en_US/barthannwin.xml +++ b/help/en_US/barthannwin.xml @@ -17,44 +17,7 @@ <refnamediv> <refname>barthannwin</refname> - <refpurpose>This function returns the filter coefficients of a modified Bartlett-Hann window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = barthannwin(m) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a modified Bartlett Hann window of length m supplied as input, to the output vector y. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -barthannwin(4) -ans = -0. -0.73 -0.73 -0. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/besselap.xml b/help/en_US/besselap.xml index 1704b68..2bb67c3 100644 --- a/help/en_US/besselap.xml +++ b/help/en_US/besselap.xml @@ -20,49 +20,4 @@ <refpurpose>Bessel analog filter prototype.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [zero, pole, gain] = besselap (n) - zero = besselap (n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>zero:</term> - <listitem><para> Zeros of filter</para></listitem></varlistentry> - <varlistentry><term>pole:</term> - <listitem><para> Poles of filter</para></listitem></varlistentry> - <varlistentry><term>gain:</term> - <listitem><para> Gain of filter</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It Return Bessel analog filter prototype of nth order. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 5; -[zero, pole, gain] = besselap (n) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/besself.xml b/help/en_US/besself.xml index f7456d9..c4c814b 100644 --- a/help/en_US/besself.xml +++ b/help/en_US/besself.xml @@ -20,47 +20,4 @@ <refpurpose>Bessel filter design.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -[b, a] = besself(n, Wc) -[b, a] = besself (n, Wc, "high") -[b, a] = besself (n, [Wl, Wh]) -[b, a] = besself (n, [Wl, Wh], "stop") -[z, p, g] = besself (…) -[…] = besself (…, "z") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> positive integer value (order of filter)</para></listitem></varlistentry> - <varlistentry><term>W:</term> - <listitem><para> positive real value</para><para> 1).Analog 3dB cutoff frequency/frequencies for analog filter, in the range [0, Inf] {rad/sec}</para><para> 2).Normalised digital 3dB cutoff frequency/frequencies for digital filter, in the range [0, 1] {dimensionless}</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function generates a Bessel filter. The default is a Laplace space (s) or analog filter.</para> -<para>If second argument is scalar the third parameter takes in high or low, the default value being low. The cutoff is Wc rad/sec.</para> -<para>If second argument is vector of length 2 ie [Wl Wh] then third parameter may be pass or stop default is pass for bandpass and band reject filter respectively</para> -<para>[z,p,g] = besself(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.</para> -<para>[...] = besself(...,’z’) returns a discrete space (Z) filter. Wc must be less than 1 {dimensionless}. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 2; -wc = 0.3; -[b, a]=besself(n, wc, "high", "z") - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/bilinear.xml b/help/en_US/bilinear.xml index e6a2e46..488dfa9 100644 --- a/help/en_US/bilinear.xml +++ b/help/en_US/bilinear.xml @@ -17,82 +17,7 @@ <refnamediv> <refname>bilinear</refname> - <refpurpose>Transforms a s-plane filter (Analog) into a z-plane filter (Digital) using Bilinear transformation</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [Zb, Za] = bilinear(Sb, Sa, T) - [Zb, Zb] = bilinear(Sz, Sp, Sg, T) - [Zz, Zp, Zg] = bilinear(...) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Sb:</term> - <listitem><para> Numerator coefficient vector in s-domain. </para></listitem></varlistentry> - <varlistentry><term>Sa:</term> - <listitem><para> Denumerator coefficient vector s-domain.</para></listitem></varlistentry> - <varlistentry><term>Sz:</term> - <listitem><para> zeros in s-plane.</para></listitem></varlistentry> - <varlistentry><term>Sp:</term> - <listitem><para> poles in s-plane.</para></listitem></varlistentry> - <varlistentry><term>Sg:</term> - <listitem><para> gain in s-domain.</para></listitem></varlistentry> - <varlistentry><term>T:</term> - <listitem><para> Sampling period (double).</para></listitem></varlistentry> - <varlistentry><term>Zb:</term> - <listitem><para> Numerator coefficient vector in z-domain.</para></listitem></varlistentry> - <varlistentry><term>Za:</term> - <listitem><para> Denumerator coefficient vector z-domain.</para></listitem></varlistentry> - <varlistentry><term>Zz:</term> - <listitem><para> zeros in z-plane.</para></listitem></varlistentry> - <varlistentry><term>Zp:</term> - <listitem><para> poles in z-plane.</para></listitem></varlistentry> - <varlistentry><term>Zg:</term> - <listitem><para> gain in z-domain.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -a filter design can be transformed from the s-plane to the z-plane while maintaining the band edges by means of the bilinear transform. This maps the left hand side of the s-plane into the interior of the unit circle in z-plane. The mapping is highly non-linear, so you must design your filter with band edges in the s-plane positioned at 2/T tan(w*T/2) so that they will be positioned at w after the bilinear transform is complete. - </para> - <para> -It does following transformation from s-plane to z-plane - </para> - - <para> - <latex> - \begin{eqnarray} - s --> \frac{2} {T} \frac {z - 1} {z + 1} - \end{eqnarray} - </latex> - </para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -Sz = [1 2 3] -Sp = [4 5 6] -Sg = 1; -T = 1; -[Zb,Za] = bilinear (Sz, Sp, Sg, T) - - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/bitrevorder.xml b/help/en_US/bitrevorder.xml index 35ca8f4..d35978f 100644 --- a/help/en_US/bitrevorder.xml +++ b/help/en_US/bitrevorder.xml @@ -17,45 +17,7 @@ <refnamediv> <refname>bitrevorder</refname> - <refpurpose> Returns input data in bit-reversed order </refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [y,i] = bitrevorder(x) - y = bitrevorder(x) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> Vector of real or complex values</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> input vector in bit reverse order</para></listitem></varlistentry> - <varlistentry><term>i:</term> - <listitem><para> indices after bit reversing </para></listitem></varlistentry> - <para> </para> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function returns the input data after reversing the bits of the indices and reordering the elements of the input array. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -x = [%i,1,3,6*%i] ; -[y i]=bitrevorder(x) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/blackmanharris.xml b/help/en_US/blackmanharris.xml index 25e09e2..98cafff 100644 --- a/help/en_US/blackmanharris.xml +++ b/help/en_US/blackmanharris.xml @@ -17,49 +17,7 @@ <refnamediv> <refname>blackmanharris</refname> - <refpurpose>This function returns the filter coefficients of a Blackman-Harris window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = blackmanharris (m) - w = blackmanharris (m, opt) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>opt:</term> - <listitem><para> string value, takes "periodic" or "symmetric"</para></listitem></varlistentry> - <varlistentry><term>w:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a Blackman-Harris window of length m supplied as input, to the output vector w. -The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -blackmanharris(5,"periodic") -ans = -0.00006 -0.1030115 -0.7938335 -0.7938335 -0.1030115 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/bohmanwin.xml b/help/en_US/bohmanwin.xml index 082342d..77151da 100644 --- a/help/en_US/bohmanwin.xml +++ b/help/en_US/bohmanwin.xml @@ -17,44 +17,7 @@ <refnamediv> <refname>bohmanwin</refname> - <refpurpose>This function returns the filter coefficients of a Bohman window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = bohmanwin (m) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a Bohman window of length m supplied as input, to the output vector y. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -bohmanwin(4) -ans = -0. -0.6089978 -0.6089978 -0. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/boxcar.xml b/help/en_US/boxcar.xml index ad5da0a..f38bb7e 100644 --- a/help/en_US/boxcar.xml +++ b/help/en_US/boxcar.xml @@ -17,46 +17,7 @@ <refnamediv> <refname>boxcar</refname> - <refpurpose>This function returns the filter coefficients of a rectangular window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = boxcar (m) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a rectangular window of length m supplied as input, to the output vector y. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -boxcar(6) -ans = -1. -1. -1. -1. -1. -1. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/buffer.xml b/help/en_US/buffer.xml index 9a8db7b..eb33141 100644 --- a/help/en_US/buffer.xml +++ b/help/en_US/buffer.xml @@ -17,20 +17,10 @@ <refnamediv> <refname>buffer</refname> - <refpurpose>This function buffers the given data into a matrix of signal frames</refpurpose> + <refpurpose>Calling Sequence</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [y] = buffer (x, n) - [y] = buffer (x, n, p) - [y] = buffer (x, n, p) - [y, z, opt] = buffer (...) - </synopsis> -</refsynopsisdiv> - <refsection> <title>Parameters</title> <variablelist> @@ -56,11 +46,10 @@ This function buffers the given data into a matrix of signal frames <title>Examples</title> <programlisting role="example"><![CDATA[ buffer(1,3,2) -//ans = -//0 0 -//0 1 -//1 0 -//This function is being called from Octave +ans = +0 0 +0 1 +1 0 ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/buttap.xml b/help/en_US/buttap.xml index 9a7a948..0bd172b 100644 --- a/help/en_US/buttap.xml +++ b/help/en_US/buttap.xml @@ -17,51 +17,7 @@ <refnamediv> <refname>buttap</refname> - <refpurpose>Produces a lowpass analog prototype Butterworth filter.</refpurpose> + <refpurpose>Produces analog prototype Butterworth filter</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [z, p, g] = buttap (n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para> Gain</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It gives a lowpass analog prototype Butterworth filter of nth order. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 5 ; -[z, p, g] = buttap (n) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/butter.xml b/help/en_US/butter.xml index 8137c23..1122f95 100644 --- a/help/en_US/butter.xml +++ b/help/en_US/butter.xml @@ -17,59 +17,7 @@ <refnamediv> <refname>butter</refname> - <refpurpose>Butterworth filter design..</refpurpose> + <refpurpose>Butterworth filter design.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b, a] = butter (n, wc) - [b, a] = butter (n, wc, "high") - [b, a] = butter (n, [wl, wh]) - [b, a] = butter (n, [wl, wh], "stop") - [z, p, g] = butter (…) - […] = butter (…, "s") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> positive integer value (order of filter).</para></listitem></varlistentry> - <varlistentry><term>wc:</term> - <listitem><para> positive real value,</para><para> 1).Normalised digital 3dB cutoff frequency/frequencies for digital filter, in the range [0, 1] {dimensionless}</para><para> 2).Analog 3dB cutoff frequency/frequencies for analog filter, in the range [0, Inf] {rad/sec}</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function generates a Butterworth filter. Default is a discrete space (z) or digital filter using Bilinear transformation from s to z plane.</para> - <para>If second argument is scalar the third parameter takes in low or high, default value is low. The cutoff is pi*wc radians.</para> - <para>[b,a] = butter(n, [wl, wh]) indicates a band pass filter with cutoffs pi*Wl and pi*wh radians. </para> - <para>[b,a] = butter(n, [wl, wh], ’stop’) indicates a band reject filter with cutoffs pi*wl and pi*wh radians.</para> - <para>[z,p,g] = butter(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.</para> - <para>[...] = butter(...,’s’) returns a Laplace space filter,here cutoff(s) wc can be larger than 1 (rad/sec). -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 4; -wc = 0.3; -[b a] = butter(n, wc, "high") - - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/buttord.xml b/help/en_US/buttord.xml index 32b7f21..4e314b0 100644 --- a/help/en_US/buttord.xml +++ b/help/en_US/buttord.xml @@ -17,63 +17,7 @@ <refnamediv> <refname>buttord</refname> - <refpurpose>Minimum filter order and 3dB cutoff frequency of a digital Butterworth filter with the desired response characteristics. </refpurpose> + <refpurpose>Minimum filter order and 3dB cutoff frequency of a digital Butterworth filter with the desired response characteristics</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - n = buttord(Wp, Ws, Rp, Rs) - [n, Wc] = buttord(Wp, Ws, Rp, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Wp:</term> - <listitem><para> scalar or vector of length 2 (passband edge(s)), elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Ws:</term> - <listitem><para> scalar or vector of length 2 (stopband edge(s)), elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> passband ripple in dB.</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> stopband attenuation in dB.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> Minimum filter order satisfying specs.</para></listitem></varlistentry> - <varlistentry><term>Wc:</term> - <listitem><para> 3dB cutoff frequency/frequencies.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function computes the minimum filter order of a Butterworth filter with the desired response characteristics. </para> -<para>The filter frequency band edges are specified by the passband frequency wp and stopband frequency ws.</para> -<para>Frequencies are normalized to the Nyquist frequency in the range [0,1]. </para> -<para>Rp is measured in decibels and is the allowable passband ripple, and Rs is also in decibels and is the minimum attenuation in the stop band.</para> -<para>If ws>wp, the filter is a low pass filter. If wp>ws, the filter is a high pass filter.</para> -<para>If wp and ws are vectors of length 2, then the passband interval is defined by wp the stopband interval is defined by ws. </para> -<para>If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp the filter is a band-stop or band-reject filter. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - Wp = 40/500 ; - Ws = 150/500 ; - [n, Wc] = buttord(Wp, Ws, 3, 60) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/cceps.xml b/help/en_US/cceps.xml index f6610c9..b23e426 100644 --- a/help/en_US/cceps.xml +++ b/help/en_US/cceps.xml @@ -35,7 +35,7 @@ <varlistentry><term>x:</term> <listitem><para> vector.</para></listitem></varlistentry> <varlistentry><term>correct:</term> - <listitem><para> if 1, a correction method is applied.</para></listitem></varlistentry> + <listitem><para> if 1, a correction method is applied.</para></listitem></varlistentry> </variablelist> </refsection> @@ -50,10 +50,9 @@ This function return the complex cepstrum of the vector x. If the optional argum <title>Examples</title> <programlisting role="example"><![CDATA[ cceps([1,2,3],1) -ans = -1.92565 -0.96346 --1.09735 +ans = 1.9256506 +0.9634573 +- 1.0973484 ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/cconv.xml b/help/en_US/cconv.xml index 3cb0ea8..3b7e061 100644 --- a/help/en_US/cconv.xml +++ b/help/en_US/cconv.xml @@ -17,56 +17,7 @@ <refnamediv> <refname>cconv</refname> - <refpurpose> circularly convolves vectors a and b. n is the length of the resulting vector. </refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - o = cconv(a,b) - o = cconv(a,b,n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para> a real or complex vector.</para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> a real or complex vector.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> length of circular convolution</para></listitem></varlistentry> - <varlistentry><term>o:</term> - <listitem><para> convolution sequence</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function buffers the given data into a matrix of signal frames -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -a=[1 2 3] -b=[4 5 6] -o=cconv(a,b,3) - -//Output : o= 31 31 28 - ]]></programlisting> - - <programlisting role="example"><![CDATA[ -a=[1 2+%i 4] -b=[2 3*%i 5] -o=cconv(a,b) -o=clean(o) - -//Output: o= 2. 4. + 5.i 10. + 6.i 10. + 17.i 20. - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/cheb.xml b/help/en_US/cheb.xml index 7e39e30..31a71b7 100644 --- a/help/en_US/cheb.xml +++ b/help/en_US/cheb.xml @@ -17,49 +17,7 @@ <refnamediv> <refname>cheb</refname> - <refpurpose>Calculates the nth-order Chebyshev polynomial at the point x.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - cheb(n, x) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter order</para></listitem></varlistentry> - <varlistentry><term>x:</term> - <listitem><para> Point at which the Chebyshev polynomial is calculater.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -Equation for Chebyshev polynomial is -/ cos(n acos(x), |x| <= 1 -Tn(x) = | -\ cosh(n acosh(x), |x| > 1 - </para> - <para> -x can also be a vector. In that case the output will also be a vector of same size as x. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -x = [1 2 3 4] -cheb(10, x) -ans = - -1.0000e+00 2.6209e+05 2.2620e+07 4.5747e+08 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/cheb1ap.xml b/help/en_US/cheb1ap.xml index beae9a2..f0688d5 100644 --- a/help/en_US/cheb1ap.xml +++ b/help/en_US/cheb1ap.xml @@ -17,55 +17,7 @@ <refnamediv> <refname>cheb1ap</refname> - <refpurpose>Produces a lowpass analog Chebyshev type I prototype filter.</refpurpose> + <refpurpose>This function designs a lowpass analog Chebyshev type I filter.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [z, p, g] = cheb1ap (n, Rp) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> Peak-to-peak passband ripple in dB</para></listitem></varlistentry> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para> Gain</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It gives a lowpass analog Chebyshev type I prototype filter of nth order and with a Peak-to-peak passband ripple of Rp. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 10 ; -Rp = 3 ; -[z, p, g] = cheb1ap (n, Rp) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai</member> - <member>FOSSEE, IIT Bombay</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/cheb1ord.xml b/help/en_US/cheb1ord.xml index 843753c..0fa51b7 100644 --- a/help/en_US/cheb1ord.xml +++ b/help/en_US/cheb1ord.xml @@ -20,61 +20,4 @@ <refpurpose>Minimum filter order of a digital Chebyshev type I filter with the desired response characteristics.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - n = cheb1ord(Wp, Ws, Rp, Rs) - [n, Wp] = cheb1ord(Wp, Ws, Rp, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Wp:</term> - <listitem><para> scalar or vector of length 2 (passband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Ws:</term> - <listitem><para> scalar or vector of length 2 (stopband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> passband ripple in dB.</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> stopband attenuation in dB.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> Minimum filter order satisfying specs.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics. </para> -<para>Stopband frequency ws and passband frequency wp specify the the filter frequency band edges. </para> -<para>Frequencies are normalized to the Nyquist frequency in the range [0,1]. </para> -<para>Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.</para> -<para>If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.</para> -<para>If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws. </para> -<para>If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -wp = [0.25 0.3]; -ws = [0.24 0.31]; -Rp = 3; -Rs = 10; -[n, wp]=cheb1ord(wp, ws, Rp, Rs) - - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/cheb2ap.xml b/help/en_US/cheb2ap.xml index 7035925..7319295 100644 --- a/help/en_US/cheb2ap.xml +++ b/help/en_US/cheb2ap.xml @@ -17,55 +17,7 @@ <refnamediv> <refname>cheb2ap</refname> - <refpurpose>Produces a lowpass analog Chebyshev type II prototype filter.</refpurpose> + <refpurpose>This function produces a lowpass analog Chebyshev type II prototype filter.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [z, p, g] = cheb2ap (n, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> Stopband attenuation in dB</para></listitem></varlistentry> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para> Gain</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function gives a lowpass analog Chebyshev type II prototpye filter of nth order and with a stopband attenuation of Rs. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 4; -Rs = 10; -[z, p, g] = cheb2ap (n, Rs) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai</member> - <member>FOSSEE, IIT Bombay</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/cheb2ord.xml b/help/en_US/cheb2ord.xml index 279e258..140ba89 100644 --- a/help/en_US/cheb2ord.xml +++ b/help/en_US/cheb2ord.xml @@ -17,63 +17,7 @@ <refnamediv> <refname>cheb2ord</refname> - <refpurpose>Minimum filter order of a digital Chebyshev type II filter with the desired response characteristics. </refpurpose> + <refpurpose>Minimum filter order of a digital Chebyshev type II filter with the desired response characteristics.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - n = cheb2ord(Wp, Ws, Rp, Rs) - [n, Ws] = cheb2ord(Wp, Ws, Rp, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Wp:</term> - <listitem><para> scalar or vector of length 2 (passband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Ws:</term> - <listitem><para> scalar or vector of length 2 (stopband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> passband ripple in dB.</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> stopband attenuation in dB.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> Minimum order of filter satisfying given specs.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics.</para> -<para>Stopband frequency ws and passband frequency wp specify the the filter frequency band edges.</para> -<para>Frequencies are normalized to the Nyquist frequency in the range [0,1].</para> -<para>Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.</para> -<para>If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.</para> -<para>If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws.</para> -<para>If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - Wp = 40/500; - Ws = 150/500; - Rp = 3; - Rs = 60; - [n,Ws] = cheb2ord(Wp,Ws,Rp,Rs) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/chebwin.xml b/help/en_US/chebwin.xml index e901bef..e8e2353 100644 --- a/help/en_US/chebwin.xml +++ b/help/en_US/chebwin.xml @@ -17,51 +17,7 @@ <refnamediv> <refname>chebwin</refname> - <refpurpose>This function returns the filter coefficients of a Dolph-Chebyshev window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = chebwin (m) - w = chebwin (m, at) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>at:</term> - <listitem><para> real scalar value</para></listitem></varlistentry> - <varlistentry><term>w:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a Dolph-Chebyshev window of length m supplied as input, to the output vector w. -The second parameter is the stop band attenuation of the Fourier transform in dB. The default value is 100 dB. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -chebwin(7) -ans = -0.0565041 -0.3166085 -0.7601208 -1. -0.7601208 -0.3166085 -0.0565041 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/cheby1.xml b/help/en_US/cheby1.xml index e33a506..bd22992 100644 --- a/help/en_US/cheby1.xml +++ b/help/en_US/cheby1.xml @@ -20,58 +20,4 @@ <refpurpose>Chebyshev type I filter design with rp dB of passband ripple.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b, a] = cheby1 (n, rp, wp) - [b, a] = cheby1 (n, rp, wp, "high") - [b, a] = cheby1 (n, rp, [wl, wh]) - [b, a] = cheby1 (n, rp, [wl, wh], "stop") - [z, p, g] = cheby1 (…) - […] = cheby1 (…, "s") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> positive integer value (order of filter)</para></listitem></varlistentry> - <varlistentry><term>rp:</term> - <listitem><para> non negative scalar value (passband ripple)</para></listitem></varlistentry> - <varlistentry><term>wp:</term> - <listitem><para> vector, all elements must be in the range [0,1]</para><para> 1).Normalised digital passband edge(s) for digital filter, in the range [0, 1] {dimensionless}</para><para> 2).Analog passband edge(s) for analog filter, in the range [0, Inf] {rad/sec}</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function generates a Chebyshev type I filter with rp dB of passband ripple.</para> -<para>if third parameter is scalar the fourth parameter takes in high or low, default value is low. The cutoff is pi*Wc radians.</para> -<para>[b, a] = cheby1(n, Rp, [Wl, Wh]) indicates a band pass filter with edges pi*Wl and pi*Wh radians.</para> -<para>[b, a] = cheby1(n, Rp, [Wl, Wh], ’stop’) indicates a band reject filter with edges pi*Wl and pi*Wh radians.</para> -<para>[z, p, g] = cheby1(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.</para> -<para>[...] = cheby1(...,’s’) returns a Laplace space filter, w can be larger than 1 rad/sec. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 2; -rp = 6; -wp = 0.7; -[z, p, k]=cheby1(n, rp, wp, "high") - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/cheby2.xml b/help/en_US/cheby2.xml index 7eb4fea..76751b3 100644 --- a/help/en_US/cheby2.xml +++ b/help/en_US/cheby2.xml @@ -20,59 +20,4 @@ <refpurpose>Chebyshev type II filter design with rs dB of stopband attenuation.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b, a] = cheby2 (n, rs, ws) - [b, a] = cheby2 (n, rs, ws, "high") - [b, a] = cheby2 (n, rs, [wl, wh]) - [b, a] = cheby2 (n, rs, [wl, wh], "stop") - [z, p, g] = cheby2 (…) - […] = cheby2 (…, "s") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> positive integer value (order of filter)</para></listitem></varlistentry> - <varlistentry><term>rs:</term> - <listitem><para> non negative scalar value (stopband attenuation in dB)</para></listitem></varlistentry> - <varlistentry><term>ws:</term> - <listitem><para> vector, all elements must be in the range [0,1]</para><para> 1).Normalised digital stopband edge(s) for digital filter, in the range [0, 1] {dimensionless}</para><para> 2).Analog stopband edge(s) for analog filter, in the range [0, Inf] {rad/sec}</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function generates a Chebyshev type II filter with rs dB of stopband attenuation.</para> -<para>If third parameter is scalar then,the fourth parameter takes in high or low, default value is low. The cutoff is pi*Wc radians.</para> -<para>[b, a] = cheby2(n, Rp, [Wl, Wh]) indicates a band pass filter with edges pi*Wl and pi*Wh radians.</para> -<para>[b, a] = cheby2(n, Rp, [Wl, Wh], ’stop’) indicates a band reject filter with edges pi*Wl and pi*Wh radians.</para> -<para>[z, p, g] = cheby2(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.</para> -<para>[...] = cheby2(...,’s’) returns a Laplace space filter, wp can be larger than 1 rad/sec. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 2; -rs = 5; -ws = 0.7; -[z, p, g]=cheby2(n, rs, ws, "high") - - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/check.xml b/help/en_US/check.xml index f8956dc..a96a670 100644 --- a/help/en_US/check.xml +++ b/help/en_US/check.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>check</refname> - <refpurpose>funcprot(0);</refpurpose> + <refpurpose>It checks whether the input string is equal to "AKICc". If it is equal, then it results T(True) else it returns F(False)</refpurpose> </refnamediv> </refentry> diff --git a/help/en_US/chirp.xml b/help/en_US/chirp.xml index 60fc279..4aa93a1 100644 --- a/help/en_US/chirp.xml +++ b/help/en_US/chirp.xml @@ -17,67 +17,7 @@ <refnamediv> <refname>chirp</refname> - <refpurpose>Chirp signal at time t.</refpurpose> + <refpurpose>This function evaluates a chirp signal at time t.</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = chirp(t) - y = chirp(t, f0) - y = chirp(t, f0, t1) - y = chirp(t, f0, t1, f1) - y = chirp(t, f0, t1, f1, form) - y = chirp(t, f0, t1, f1, form, phase) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>t:</term> - <listitem><para> a vector of times to evaluate the chirp signal.</para></listitem></varlistentry> - <varlistentry><term>f0:</term> - <listitem><para> the frequency at t=0. [default value = 0 Hz]</para></listitem></varlistentry> - <varlistentry><term>t1:</term> - <listitem><para> some intermediate time. [default value = 1 sec] </para></listitem></varlistentry> - <varlistentry><term>f1:</term> - <listitem><para> frequency at t=t1. [default value = 100 Hz]</para></listitem></varlistentry> - <varlistentry><term>form:</term> - <listitem><para> string value, takes in "linear", "quadratic", "logarithmic". [default value = "linear"]</para></listitem></varlistentry> - <varlistentry><term>phase:</term> - <listitem><para> phase shift at t=0. [default value = 0]</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> chirp signal value corresponding to t. </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function evaluates a chirp signal at time t. A chirp signal is a frequency swept cosine wave.</para> -<para>The first argument is a vector of times to evaluate the chirp signal, second argument is the frequency at t=0, third argument is time t1 and fourth argument is frequency at t1. -The fifth argument is the form which takes in values "linear", "quadratic" and "logarithmic", the sixth argument gives the phase shift at t=0. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - t = [4,3,2,1]; - f0 = 4; - t1 = 5; - f1 = 0.9; - form = "quadratic"; - y = chirp(t, f0, t1, f1, form) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/circshift.xml b/help/en_US/circshift.xml index d457205..aa54d4c 100644 --- a/help/en_US/circshift.xml +++ b/help/en_US/circshift.xml @@ -17,42 +17,7 @@ <refnamediv> <refname>circshift</refname> - <refpurpose>Shifts array/matrix circularly</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -R = circshift(M, d) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>M , R :</term> - <listitem><para> vector or matrix of any data type</para></listitem></varlistentry> - <varlistentry><term>d :</term> - <listitem><para> vector of integers. d(i) is the shift to be applied to the M's components along its ith dimension.</para>for example d = [0 n] will shift element n position along column<para></para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> -circularly shifts by d(i) positions components of M along its ith dimensions -</para> - <para> </para> -</refsection> - -<refsection> - <title>Example : 1</title> - <programlisting role="example"><![CDATA[ - M = [1 2 3 4]; - circshift(M, [0 1]) - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/cmorwavf.xml b/help/en_US/cmorwavf.xml index aa03ba8..19eb018 100644 --- a/help/en_US/cmorwavf.xml +++ b/help/en_US/cmorwavf.xml @@ -17,7 +17,46 @@ <refnamediv> <refname>cmorwavf</refname> - <refpurpose>funcprot(0);</refpurpose> + <refpurpose></refpurpose> </refnamediv> + +<refsynopsisdiv> + <title>Calling Sequence</title> + <synopsis> + [psi,x]=cmorwavf(lb,ub,n,fb,fc) + </synopsis> +</refsynopsisdiv> + +<refsection> + <title>Parameters</title> + <variablelist> + <varlistentry><term>lb:</term> + <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> + <varlistentry><term>ub:</term> + <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> + <varlistentry><term>n:</term> + <listitem><para> Real scalar strictly positive integer</para></listitem></varlistentry> + <varlistentry><term>fb:</term> + <listitem><para> Real or complex scalar value</para></listitem></varlistentry> + <varlistentry><term>fc:</term> + <listitem><para> Real or complex scalar value</para></listitem></varlistentry> + </variablelist> +</refsection> + +<refsection> + <title>Description</title> + <para> +This function returns the value of the Complex Morlet Waveform defined by a positive bandwidth parameter FB, a wavelet center frequency FC on an N point regular grid for the interval [LB,UB]. +</para> +</refsection> + +<refsection> + <title>Examples</title> + <programlisting role="example"><![CDATA[ +[a,b]=cmorwavf(1,2,1,3,4) +b=2 +a=0.0858628 -1.682D-16i + ]]></programlisting> +</refsection> </refentry> diff --git a/help/en_US/convmtx.xml b/help/en_US/convmtx.xml index 80ff071..582e9ca 100644 --- a/help/en_US/convmtx.xml +++ b/help/en_US/convmtx.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>convmtx</refname> - <refpurpose>n=double(n);</refpurpose> + <refpurpose>Calling sequence:</refpurpose> </refnamediv> </refentry> diff --git a/help/en_US/cummax.xml b/help/en_US/cummax.xml index e385ac0..5d81b71 100644 --- a/help/en_US/cummax.xml +++ b/help/en_US/cummax.xml @@ -25,42 +25,32 @@ <title>Calling Sequence</title> <synopsis> M = cummax(A) + returns the cumulative maximum of the arguments of A. The dimension + of M is same as the dimension of A. If A is a 2D matrix, the operation + is performed along the columns. For a hypermatrix, the operation is + performed along the first non-zero dimension M = cummax(A,dim) + The operation is performed along the dimension specified by dim M = cummax(_,direction) + direction specifies as the direction of operation + </synopsis> </refsynopsisdiv> <refsection> <title>Parameters</title> <variablelist> - - <varlistentry><term>A:</term> - <listitem><para> positive integer value<para></para>For complex elements, cummax compares the magnitude of elements. If the magnitude are same, phase angles are compared.</para></listitem></varlistentry> - <varlistentry><term>dim:</term> - <listitem><para> Dimension to operate along<para></para>If no dimension is specified, then the default value is the first array dimension whose value is greater than 1 </para></listitem></varlistentry> - <varlistentry><term>direction:</term> - <listitem><para>Direction of cumulation<para></para>If the direction is forward, cummax works from 1 to end of the active dimension. Otherwise, it works in the opposite sense</para></listitem></varlistentry> - </variablelist> </refsection> <refsection> - <title>Description</title> - <para> -The function returns the cumulative maximum elements of the input vector/array. - - -</para> -</refsection> - -<refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -Cumulative maximum values in a vector +1) Cumulative maximum values in a vector v = [8 9 1 10 6 1 3 6 10 10] M = cummax(v) -//Expected output: [8 9 9 10 10 10 10 10 10 10] +Expected output: [8 9 9 10 10 10 10 10 10 10] ]]></programlisting> </refsection> @@ -72,5 +62,9 @@ M = cummax(v) </simplelist> </refsection> - +<refsection> + <title>See also</title> + <simplelist type="inline"> + </simplelist> +</refsection> </refentry> diff --git a/help/en_US/dctmtx.xml b/help/en_US/dctmtx.xml index 2594b48..81eb9cb 100644 --- a/help/en_US/dctmtx.xml +++ b/help/en_US/dctmtx.xml @@ -20,42 +20,4 @@ <refpurpose>Return the DCT transformation matrix of size N-by-N.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - T = dctmtx(n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Real scalar integer greater than or equal to 1</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -dctmtx(n) returns a Discrete cosine transform matrix of order n-by-n. It is useful for jpeg image compression. D*A is the DCT of the columns of A and D'*A is the inverse DCT of the columns of A (when A is n-by-n). -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - n= 3; - T = dctmtx(n) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/decimate.xml b/help/en_US/decimate.xml index 30440dd..4779f15 100644 --- a/help/en_US/decimate.xml +++ b/help/en_US/decimate.xml @@ -20,60 +20,4 @@ <refpurpose>Decimation — decrease sample rate by integer factor</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = decimate(x,q) - y = decimate(x,q,n) - y = decimate (…, "fir") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> input sequence</para></listitem></varlistentry> - <varlistentry><term>q:</term> - <listitem><para> reduction factor </para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> filter order</para></listitem></varlistentry> - <varlistentry><term>ftype:</term> - <listitem><para> filter type: iir or fir</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -y = decimate(x,q) reduces the sample rate of x, the input signal, by a factor of q. -By default, an order n Chebyshev type I filter is used. If n is not specified, the default is 8. -If the optional argument "fir" is given, an order n FIR filter is used, with a default order of 30 if n is not given. -Note that q must be an integer for this rate change method. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -t = 0:.00025:1; -x = sin(2*%pi*30*t) + sin(2*%pi*60*t); -y = decimate(x,4); -subplot(211) -plot2d3((0:120),x(1:121)) -subplot(212) -plot2d3((0:30),y(1:31)) - ]]></programlisting> - -</refsection> - - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Ayush Baid</member> - </simplelist> -</refsection> - - </refentry> diff --git a/help/en_US/deconv.xml b/help/en_US/deconv.xml index c056db2..d8602df 100644 --- a/help/en_US/deconv.xml +++ b/help/en_US/deconv.xml @@ -17,52 +17,7 @@ <refnamediv> <refname>deconv</refname> - <refpurpose>Deconvolve two vectors.</refpurpose> + <refpurpose>calling sequence:</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b,r]= deconv (y, a) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>y:</term> - <listitem><para>Input vector. The convolution of a and b.</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para>The input vector.</para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> The quotient polynomial coeffecients vector.</para></listitem></varlistentry> - <varlistentry><term>r:</term> - <listitem><para>The remainder polynomial coefficients vector. </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -[b, r] = deconv (y, a) solves for b and r such that - y = conv (a, b) + r.</para> -<para> -If y and a are polynomial coefficient vectors, b will - contain the coefficients of the polynomial quotient and r will be - a remainder polynomial of lowest order. - -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -[b, r] = deconv ([3, 6, 9, 9], [1, 2, 3]) -//Output: -//b=[3, 0] -//r=[0, 0, 0, 9] - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/dftmtx.xml b/help/en_US/dftmtx.xml index 4197d98..c455f6c 100644 --- a/help/en_US/dftmtx.xml +++ b/help/en_US/dftmtx.xml @@ -17,44 +17,7 @@ <refnamediv> <refname>dftmtx</refname> - <refpurpose>Computes n-by-n Discrete Fourier transformation matrix. </refpurpose> + <refpurpose>Computes n-by-n Discrete Fourier transformation matrix</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - d = dftmtx(n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Real positive scalar number</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This fuction gives a complex matrix of values whose product with a vector produces the discrete Fourier transform. This can also be achieved by directly using the fft function i.e. y=fft(x) is same as y=A*x where A=dftmtx(n). -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -d = dftmtx(4) - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/digitrevorder.xml b/help/en_US/digitrevorder.xml index 988f372..0bb8577 100644 --- a/help/en_US/digitrevorder.xml +++ b/help/en_US/digitrevorder.xml @@ -20,44 +20,4 @@ <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [y,i] = digitrevorder(x,r) - y = digitrevorder(x,r) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> Vector of real or complex values</para></listitem></varlistentry> - <varlistentry><term>r:</term> - <listitem><para> radix / base </para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para> input vector in bit reverse order</para></listitem></varlistentry> - <varlistentry><term>i:</term> - <listitem><para> indices after bit reversing </para></listitem></varlistentry> - <para> </para> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function returns the input data after reversing the digits of the indices and reordering the elements of the input array. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -x = [%i,1,3,6*%i] ; -r = 2 ; -[y i]=digitrevorder(x, r) - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/diric.xml b/help/en_US/diric.xml index fdbc3fa..9b7098c 100644 --- a/help/en_US/diric.xml +++ b/help/en_US/diric.xml @@ -41,18 +41,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function -y=diric(x,n) returns the dirichlet function values of parameter x. </para> </refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -1. diric([1 2 3],3) -ans= 0.6935349 0.0559021 -0.3266617 -2. diric(1,2) -ans= 0.8775826 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/downsample.xml b/help/en_US/downsample.xml index bbdfcd0..3e25d21 100644 --- a/help/en_US/downsample.xml +++ b/help/en_US/downsample.xml @@ -17,45 +17,7 @@ <refnamediv> <refname>downsample</refname> - <refpurpose>This function downsamples the signal by selecting every nth element.</refpurpose> + <refpurpose>[nargout,nargin]=argn();</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = downsample (x, n) - y = downsample (x, n, phase) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> scalar, vector or matrix of real or complex numbers</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> real number or vector</para></listitem></varlistentry> - <varlistentry><term>phase:</term> - <listitem><para> integer value, 0 <= phase <= (n - 1), default value 0, or logical</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function downsamples the signal by selecting every nth element supplied as parameter 2. If x is a matrix, the function downsamples every column. -If the phase is specified, every nth element is selected starting from the sample phase. The default phase is 0. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -downsample([1,2,4],2) -ans = -1. 4. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/ellip.xml b/help/en_US/ellip.xml index 66ec6fc..29a521b 100644 --- a/help/en_US/ellip.xml +++ b/help/en_US/ellip.xml @@ -17,56 +17,7 @@ <refnamediv> <refname>ellip</refname> - <refpurpose>Elliptic/Cauer filter design with rp dB of passband ripple and rs dB of stopband attenuation.</refpurpose> + <refpurpose>Elliptic or Cauer filter design with rp dB of passband ripple and rs dB of stopband attenuation.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b, a] = ellip (n, rp, rs, wp) - [b, a] = ellip (n, rp, rs, wp, "high") - [b, a] = ellip (n, rp, rs, [wl, wh]) - [b, a] = ellip (n, rp, rs, [wl, wh], "stop") - [z, p, g] = ellip (…) - […] = ellip (…, "s") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> positive integer value (order of filter)</para></listitem></varlistentry> - <varlistentry><term>rp:</term> - <listitem><para> non negative scalar value (passband ripple)</para></listitem></varlistentry> - <varlistentry><term>rs:</term> - <listitem><para> non negative scalar value (stopband attenuation)</para></listitem></varlistentry> - <varlistentry><term>ws:</term> - <listitem><para> scalar or vector of length 2, all elements should be in the range [0,1]</para><para> 1).Normalised digital passband edge(s) for digital filter, in the range [0, 1] {dimensionless}</para><para> 2).Analog passband edge(s) for analog filter, in the range [0, Inf] {rad/sec}</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation.</para> -<para>[b, a] = ellip(n, Rp, Rs, Wp) indicates low pass filter with order n, Rp decibels of ripple in the passband and a stopband Rs decibels down and cutoff of pi*Wp radians. If the fifth argument is high, then the filter is a high pass filter.</para> -<para>[b, a] = ellip(n, Rp, Rs, [Wl, Wh]) indictaes band pass filter with band pass edges pi*Wl and pi*Wh. If the fifth argument is stop, the filter is a band reject filter.</para> -<para>[b,a] = ellip(n, Rp, Rs, [Wl, Wh], 'stop') band reject filter with edges pi*Wl and pi*Wh</para> -<para>[z, p, g] = ellip(...) returns filter as zero-pole-gain.</para> -<para>[...] = ellip(...,’s’) returns a Laplace space filter, wp can be larger than 1 rad/sec. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 2; -rp = 3; -rs = 40; -wp = [0.3 0.4]; -[b, a]=ellip(n, rp, rs, wp) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/ellipap.xml b/help/en_US/ellipap.xml index b0cc2fb..84220a6 100644 --- a/help/en_US/ellipap.xml +++ b/help/en_US/ellipap.xml @@ -17,58 +17,7 @@ <refnamediv> <refname>ellipap</refname> - <refpurpose>Produces a lowpass analog elliptic prototype filter.</refpurpose> + <refpurpose>Produces a lowpass analog prototype elliptic filter.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [z, p, g] = ellipap (n, Rp, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> Peak-to-peak passband ripple in dB</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> Stopband attenuation in dB</para></listitem></varlistentry> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para> Gain</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It gives a lowpass analog elliptic prototype filter of nth order, with a Peak-to-peak passband ripple of Rp dB and a stopband attenuation of Rs dB. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 4; -Rp = 3; -Rs = 10; -[z, p, g] = ellipap (n, Rp, Rs) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai</member> - <member>FOSSEE, IIT Bombay</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/ellipord.xml b/help/en_US/ellipord.xml index 2a3dbe2..2b42f22 100644 --- a/help/en_US/ellipord.xml +++ b/help/en_US/ellipord.xml @@ -17,55 +17,7 @@ <refnamediv> <refname>ellipord</refname> - <refpurpose>Minimum filter order of a digital elliptic/Cauer filter with the desired response characteristics. </refpurpose> + <refpurpose>Minimum filter order of a digital elliptic or Cauer filter with the desired response characteristics.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - n = ellipord(Wp, Ws, Rp, Rs) - [n, Wp] = ellipord(Wp, Ws, Rp, Rs) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Wp:</term> - <listitem><para> scalar or vector of length 2 (passband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Ws:</term> - <listitem><para> scalar or vector of length 2 (stopband edge(s)), all elements must be in the range [0,1]</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> passband ripple in dB.</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> stopband attenuation in dB.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> Minimum order of filter satisfying given specs.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This function computes the minimum filter order of an elliptic filter with the desired response characteristics.</para> -<para>Stopband frequency ws and passband frequency wp specify the the filter frequency band edges.</para> -<para>Frequencies are normalized to the Nyquist frequency in the range [0,1].</para> -<para>Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.</para> -<para>If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.</para> -<para>If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws.</para> -<para>If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - Wp = [60 200]/500; - Ws = [50 250]/500; - Rp = 3; - Rs = 40; - [n,Wp] = ellipord(Wp,Ws,Rp,Rs) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/filtfilt.xml b/help/en_US/filtfilt.xml index e8d9c99..fd97e93 100644 --- a/help/en_US/filtfilt.xml +++ b/help/en_US/filtfilt.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>filtfilt</refname> - <refpurpose> Zero phase digital filtering </refpurpose> + <refpurpose></refpurpose> </refnamediv> @@ -51,9 +51,8 @@ In theory, it forwards and reverse filters the signal and corrects phase distort <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -1. [a]=filtfilt (1,2i,[i -4 0]) -//Expected output -//a = [-0.25i 1 0] +1. y=filtfilt (1,2*%i,[%i -4 0]) // Number of Output argument should be equal to 1 +y = [-0.25i 1 0] ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/filtic.xml b/help/en_US/filtic.xml index f27f53e..161cca0 100644 --- a/help/en_US/filtic.xml +++ b/help/en_US/filtic.xml @@ -55,12 +55,10 @@ The vectors b and a represent the numerator and denominator coefficients of the <title>Examples</title> <programlisting role="example"><![CDATA[ filtic([%i,1,-%i,5], [1,2,3*%i], [0.8*%i,7,9]) -//Expected Output: -//ans = -//-22.6i -// 2.4 -// 0 - +ans = +0.00000 - 22.60000i +2.40000 + 0.00000i +0.00000 + 0.00000i ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/fir1.xml b/help/en_US/fir1.xml index c5650ba..3ec9403 100644 --- a/help/en_US/fir1.xml +++ b/help/en_US/fir1.xml @@ -17,52 +17,7 @@ <refnamediv> <refname>fir1</refname> - <refpurpose>Produce an order N FIR filter with the given frequency cutoff, returning the N+1 filter coefficients in B.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - B = fir1(N, W) - B = fir1(N, W, TYPE) - B = fir1(N, W, TYPE, WINDOW) - B = fir1(N, W, TYPE, WINDOW, NOSCALE) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>N:</term> - <listitem><para> Integer</para></listitem></varlistentry> - <varlistentry><term>W:</term> - <listitem><para> Integer or Vector</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Produce an order N FIR filter with the given frequency cutoff W, returning the N+1 filter coefficients in B. If W is a scalar, it specifies the frequency cutoff for a lowpass or highpass filter. If W is a two-element vector, the two values specify the edges of a bandpass or bandstop filter. If W is an N-element vector, each value specifies a band edge of a multiband pass/stop filter. - </para> - <para> -The filter TYPE can be specified with one of the following strings: "low", "high", "stop", "pass", "bandpass", "DC-0", or "DC-1". The default is "low" is W is a scalar, "pass" if W is a pair, or "DC-0" if W is a vector with more than 2 elements. - </para> - <para> -An optional shaping WINDOW can be given as a vector with length N+1. If not specified, a Hamming window of length N+1 is used. - </para> - <para> -With the option "noscale", the filter coefficients are not normalized. The default is to normalize the filter such that the magnitude response of the center of the first passband is 1. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -fir1 (5, 0.4) -ans = -9.2762e-05 9.5482e-02 4.0443e-01 4.0443e-01 9.5482e-02 9.2762e-05 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/fir2.xml b/help/en_US/fir2.xml index 932c474..14a4ecf 100644 --- a/help/en_US/fir2.xml +++ b/help/en_US/fir2.xml @@ -17,55 +17,7 @@ <refnamediv> <refname>fir2</refname> - <refpurpose>Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - B = fir2(N, F, M) - B = fir2(N, F, M, GRID_N) - B = fir1(N, F, M, GRID_N, RAMP_N) - B = fir1(N, F, M, GRID_N, RAMP_N, WINDOW) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>N:</term> - <listitem><para> Integer</para></listitem></varlistentry> - <varlistentry><term>F, M:</term> - <listitem><para> Vector</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B. The vector F specifies the frequency band edges of the filter response and M specifies the magnitude response at each frequency. - </para> - <para> -The vector F must be nondecreasing over the range [0,1], and the first and last elements must be 0 and 1, respectively. A discontinuous jump in the frequency response can be specified by duplicating a band edge in F with different values in M. - </para> - <para> -The resolution over which the frequency response is evaluated can be controlled with the GRID_N argument. The default is 512 or the next larger power of 2 greater than the filter length. - </para> - <para> -The band transition width for discontinuities can be controlled with the RAMP_N argument. The default is GRID_N/25. Larger values will result in wider band transitions but better stopband rejection. - </para> - <para> -An optional shaping WINDOW can be given as a vector with length N+1. If not specified, a Hamming window of length N+1 is used. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -fir2 (10, [0, 0.5, 1], [1, 2, 3]) -ans = --0.00130 0.00000 -0.01792 0.00000 -0.36968 2.00000 -0.36968 0.00000 -0.01792 0.00000 -0.00130 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/firtype.xml b/help/en_US/firtype.xml index c4bc7e5..577a6c6 100644 --- a/help/en_US/firtype.xml +++ b/help/en_US/firtype.xml @@ -17,47 +17,7 @@ <refnamediv> <refname>firtype</refname> - <refpurpose> This function identifies Type of linear phase FIR filter -</refpurpose> + <refpurpose>This function identifies Type of linear phase FIR filter</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - t=firtype(b) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>t:</term> - <listitem><para> type of an FIR filter </para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> Filter coefficients</para></listitem></varlistentry> - - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -"t = firtype(b)" determines the type, t, of an FIR filter with coefficients b. </para><para>"t" can be 1, 2, 3, or 4. </para><para> -The filter must be real and have linear phase. - -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -b=[9.2762e-05 9.5482e-02 4.0443e-01 4.0443e-01 9.5482e-02 9.2762e-05] -firtype(b) -//Expected Output -//2 - ]]></programlisting> -</refsection> - - - </refentry> diff --git a/help/en_US/flattopwin.xml b/help/en_US/flattopwin.xml index d990123..10faa71 100644 --- a/help/en_US/flattopwin.xml +++ b/help/en_US/flattopwin.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>flattopwin</refname> - <refpurpose>This function returns the filter coefficients of a Flat Top window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> diff --git a/help/en_US/fractdiff.xml b/help/en_US/fractdiff.xml index c26508f..0929067 100644 --- a/help/en_US/fractdiff.xml +++ b/help/en_US/fractdiff.xml @@ -31,7 +31,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1. </para> </refsection> diff --git a/help/en_US/fwhmjlt.xml b/help/en_US/fwhmjlt.xml index c093385..a6ec500 100644 --- a/help/en_US/fwhmjlt.xml +++ b/help/en_US/fwhmjlt.xml @@ -20,37 +20,4 @@ <refpurpose>This function Computes peak full-width at half maximum</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - f = fwhm (y) - f = fwhm (x, y) - f = fwhm (…, "zero") - f = fwhm (…, "min") - f = fwhm (…, "alevel", level) - f = fwhm (…, "rlevel", level) - </synopsis> -</refsynopsisdiv> - - - -<refsection> - <title>Description</title> - <para> -Compute peak full-width at half maximum (FWHM) or at another level of peak maximum for vector or matrix data y, optionally sampled as y(x). If y is a matrix, return FWHM for each column as a row vector.<para></para>The default option "zero" computes fwhm at half maximum, i.e. 0.5*max(y). The option "min" computes fwhm at the middle curve, i.e. 0.5*(min(y)+max(y)). <para></para>The option "rlevel" computes full-width at the given relative level of peak profile<para></para>The option "alevel" computes full-width at the given absolute level of y. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -t=-50:0.01:50; -y=(1/(2*sqrt(2*%pi)))*exp(-(t.^2)/8); -z=fwhmjlt(y) -//Expected Output -//470.96442 - ]]></programlisting> -</refsection> - - </refentry> diff --git a/help/en_US/fwht.xml b/help/en_US/fwht.xml index 4e0d8a6..4140453 100644 --- a/help/en_US/fwht.xml +++ b/help/en_US/fwht.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>fwht</refname> - <refpurpose>Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm..</refpurpose> + <refpurpose>Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm</refpurpose> </refnamediv> diff --git a/help/en_US/gauspuls.xml b/help/en_US/gauspuls.xml index e24d9e4..7583e87 100644 --- a/help/en_US/gauspuls.xml +++ b/help/en_US/gauspuls.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>gauspuls</refname> - <refpurpose></refpurpose> + <refpurpose>Generates Gaussian-modulated sinusoidal pulses</refpurpose> </refnamediv> @@ -45,7 +45,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function This function returns a Gaussian RF pulse of unity amplitude at the times indicated in array t. </para> </refsection> diff --git a/help/en_US/gausswin.xml b/help/en_US/gausswin.xml index a128a08..8eac2b2 100644 --- a/help/en_US/gausswin.xml +++ b/help/en_US/gausswin.xml @@ -17,47 +17,7 @@ <refnamediv> <refname>gausswin</refname> - <refpurpose>This function returns the filter coefficients of a Gaussian window.</refpurpose> + <refpurpose>This function is used to generate the gaussian window.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = gausswin (m) - w = gausswin (m, a) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> </para></listitem></varlistentry> - <varlistentry><term>w:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> - -This function returns the filter coefficients of a Gaussian window of length m supplied as input, to the output vector w. -The second parameter is the width measured in sample rate/number of samples and should be f for time domain and 1/f for frequency domain. The width is inversely proportional to a. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -gausswin(3) -//ans = -//0.2493522 -//1. -//0.2493522 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/gmonopuls.xml b/help/en_US/gmonopuls.xml index f514aa4..198de7a 100644 --- a/help/en_US/gmonopuls.xml +++ b/help/en_US/gmonopuls.xml @@ -20,41 +20,4 @@ <refpurpose></refpurpose> </refnamediv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>t:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - <varlistentry><term>fc:</term> - <listitem><para> Real non-negative value or complex value or a vector or matrix with not all real values negative.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> - -This function returns samples of the Gaussian monopulse of amplitude unity. -</para> -</refsection> - -<refsection> - <title>Example 1.</title> - <programlisting role="example"><![CDATA[ - gmonopuls([1 2 3],0.1) -//ans= 0.85036 0.94070 0.52591 - - ]]></programlisting> -</refsection> - -<refsection> - <title>Example 2.</title> - <programlisting role="example"><![CDATA[ - - gmonopuls([1 2 3],[]) -//ans= 0 0 0 - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/hamming.xml b/help/en_US/hamming.xml index 9a072e8..f6803e1 100644 --- a/help/en_US/hamming.xml +++ b/help/en_US/hamming.xml @@ -17,32 +17,7 @@ <refnamediv> <refname>hamming</refname> - <refpurpose>Return the filter coefficients of a Hamming window of length M</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - hamming (M) - hamming (M, "periodic") - hamming (M, "symmetric") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>M:</term> - <listitem><para> real scalar, which will be the length of hamming window</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Return the filter coefficients of a Hamming window of length M. -If the optional argument "periodic" is given, the periodic form of the window is returned. This is equivalent to the window of length M+1 with the last coefficient removed. The optional argument "symmetric" is equivalent to not specifying a second argument. -</para> -</refsection> </refentry> diff --git a/help/en_US/hanning.xml b/help/en_US/hanning.xml index 5350656..dc1e475 100644 --- a/help/en_US/hanning.xml +++ b/help/en_US/hanning.xml @@ -17,32 +17,7 @@ <refnamediv> <refname>hanning</refname> - <refpurpose>Return the filter coefficients of a Hanning window of length M</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - hanning (M) - hanning (M, "periodic") - hanning (M, "symmetric") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>M:</term> - <listitem><para> real scalar, which will be the length of hanning window</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Return the filter coefficients of a Hanning window of length M. -If the optional argument "periodic" is given, the periodic form of the window is returned. This is equivalent to the window of length M+1 with the last coefficient removed. The optional argument "symmetric" is equivalent to not specifying a second argument. -</para> -</refsection> </refentry> diff --git a/help/en_US/helperHarmonicDistortion.xml b/help/en_US/helperHarmonicDistortion.xml index b49a559..4e087e2 100644 --- a/help/en_US/helperHarmonicDistortion.xml +++ b/help/en_US/helperHarmonicDistortion.xml @@ -20,62 +20,4 @@ <refpurpose>helperHarmonicDistortionADC Helper function for HarmonicDistortionExample.m</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - outputVoltage=helperHarmonicDistortion(inputVoltage) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>inputVmax:</term> - <listitem><para> Maximum operating voltage times the sinusoidal signal.</para></listitem></varlistentry> - - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Analizing the harmonic distortion of a weakly non-linear system in the presence of noise.... -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -VmaxPk = 2; -Fi = 2000; -Fs = 44.1e3; -Tstop = 50e-3; -t = 0:1/Fs:Tstop; -inputVmax = VmaxPk*sin(2*%pi*Fi*t); -outputVmax = helperHarmonicDistortion(inputVmax); -plot(t, outputVmax) -replot([0,-2.5,0.005,2.5]) -xlabel('Time') -ylabel('Output Voltage') -title('Amplifier output') - ]]></programlisting> - <scilab:image> - VmaxPk = 2; - Fi = 2000; - Fs = 44.1e3; - Tstop = 50e-3; - t = 0:1/Fs:Tstop; - inputVmax = VmaxPk*sin(2*%pi*Fi*t); - outputVmax = helperHarmonicDistortion(inputVmax); - plot(t, outputVmax) - replot([0,-2.5,0.005,2.5]) - xlabel('Time') - ylabel('Output Voltage') - title('Amplifier output') - </scilab:image> - -</refsection> - - </refentry> diff --git a/help/en_US/icceps.xml b/help/en_US/icceps.xml index 0cae986..bd10adc 100644 --- a/help/en_US/icceps.xml +++ b/help/en_US/icceps.xml @@ -20,43 +20,4 @@ <refpurpose>ICCEPS computes the inverse cepstrum of a real-valued input. This spectrum</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - inv_ceps = icceps(xhat,remv_samp) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>remv_samp:</term> - <listitem><para> Number of samples of delay to be removed </para></listitem></varlistentry> - <varlistentry><term>xhat:</term> - <listitem><para> real valued input </para></listitem></varlistentry> - <varlistentry><term>inv_ceps:</term> - <listitem><para> Inverse cepstrum output </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -inv_ceps = icceps(xhat,remv_samp) returns the inverse complex cepstrum of the real data sequence "xhat", removing "remv_samp" samples of delay. If "xhat" was obtained with cceps(x), then the amount of delay that was added to x was the element of round(unwrap(angle(fft(x)))/pi) corresponding to π radians. - -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -xhat=[ 2.2428 -0.0420 -0.0210 0.0045 0.0366 0.0788 0.1386 0.2327 0.4114 0.9249] -icc = icceps(xhat,2); -round(icc) -//EXPECTED OUTPUT: -// 2 3 4 5 6 7 8 9 10 1 - - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/impz.xml b/help/en_US/impz.xml index 197c5c8..35d1065 100644 --- a/help/en_US/impz.xml +++ b/help/en_US/impz.xml @@ -17,67 +17,7 @@ <refnamediv> <refname>impz</refname> - <refpurpose> It gives Impulse response of digital filter -</refpurpose> + <refpurpose>It gives Impulse response of digital filter</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - x_r = impz(b) - x_r = impz(b, a) - x_r = impz(b, a, n) - x_r = impz(b, a, n, fs) - [x_r, t_r] = impz(b, a, n, fs) - </synopsis> -</refsynopsisdiv> - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x_r:</term> - <listitem><para> impz chooses the number of samples and returns the response in the column vector, x_r.</para></listitem></varlistentry> - <varlistentry><term>t_r:</term> - <listitem><para> impz returns the sample times in the column vector, t_r </para></listitem></varlistentry> - <varlistentry><term>b:</term> - <listitem><para> numerator coefficients of the filter</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para>denominator coefficients of the filter</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> samples of the impulse response t(by default ,n = length(t) and is computed automatically.</para></listitem></varlistentry> - <varlistentry><term>fs:</term> - <listitem><para>sampling frequency</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -[x_r,t_r] = impz(b,a) returns the impulse response of the filter with numerator coefficients, b, and denominator coefficients, a. impz chooses the number of samples and returns the response in the column vector, x_r, and the sample times in the column vector, t_r. t_r = [0:n-1]' and n = length(t) is computed automatically. -</para> -</refsection> - -<refsection> - <title>Example 1</title> - <programlisting role="example"><![CDATA[ -[x_r,t_r]=impz([0 1 1],[1 -3 3 -1],10) -//EXPECTED OUTPUT : -// t_r = 0. 1. 2. 3. 4. 5. 6. 7. 8. 9 -// x_r= 0. 1. 4. 9. 16. 25. 36. 49 64 81 - - ]]></programlisting> -</refsection> - -<refsection> - <title>Example 2</title> - <programlisting role="example"><![CDATA[ -[x_r,t_r]=impz(1,[1 1],5) -//EXPECTED OUTPUT -// t_r = 0. 1. 2. 3. 4 -//x_r = 1. - 1. 1. - 1. 1. - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/impzlength.xml b/help/en_US/impzlength.xml index 6ed57fc..1828927 100644 --- a/help/en_US/impzlength.xml +++ b/help/en_US/impzlength.xml @@ -24,34 +24,10 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - len=impzlength(b,a) - len=impzlength(sos) - len=impzlength(b,a,tol) - - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para>Numerator coefficients. real|complex - vector|scalar</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> Denominator coefficients. real|complex - vector|scalar</para></listitem></varlistentry> - <varlistentry><term>sos:</term> - <listitem><para> Second order estimates. real|complex - matrix (K-by-6)</para></listitem></varlistentry> - <varlistentry><term>tol:</term> - <listitem><para> Tolerance for estimating the effective length of an IIR filter impulse response. scalar</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -len = impzlength(b, a, tol) + len = impzlength(b, a, tol) returns the impulse response length for the causal discrete-time filter with the transfer function coefficients for numerator and denominator in - a and b respectively. For stable IIR filters, len is the effective length + b and a respectively. For stable IIR filters, len is the effective length impulse response length, i.e. the length after which the response is essentially zero len = impzlength(sos) @@ -63,34 +39,32 @@ len = impzlength(b, a, tol) specifies a tolerance for estimating the effective impulse response length in case of an IIR filter. By default, tol is 5e-5. Increasing tol leads to shorter len and vice-versa -</para> + + </synopsis> +</refsynopsisdiv> + +<refsection> + <title>Parameters</title> + <variablelist> + </variablelist> </refsection> <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -//Low pass IIR filter with pole at 0.9 +1) Low pass IIR filter with pole at 0.9 b = 1; a = [1 -0.9]; -len = impzlength(b,a); -//Expected Output : -//len=93 +len = impzlength(b,a) +OUTPUT : +len=93 +2) High pass IIR filter with pole at -0.5 +b = 1; +a = [1 0.5]; +len = impzlength(b,a) +OUTPUT : +len=14 ]]></programlisting> </refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="impz"></link></member><para></para> - <member><link linkend="zp2sos"></link></member><para></para> - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Ayush Baid</member> - </simplelist> -</refsection> </refentry> diff --git a/help/en_US/intfilt.xml b/help/en_US/intfilt.xml index 29c93f6..a674813 100644 --- a/help/en_US/intfilt.xml +++ b/help/en_US/intfilt.xml @@ -17,63 +17,7 @@ <refnamediv> <refname>intfilt</refname> - <refpurpose>The "intfilt" function estimate Interpolated FIR Filter Design</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - h=intfilt(R,L,freqmult) - [h a]=intfilt(R,L,freqmult) - - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>R:</term> - <listitem><para> Samples. It should be numeric</para></listitem></varlistentry> - <varlistentry><term>L:</term> - <listitem><para> bandlimited interpolation samples. It must be nonzero.</para></listitem></varlistentry> - <varlistentry><term>freqmult:</term> - <listitem><para> bandlimitedness of ALPHA times the Nyquist frequency. It can be numeric or character ('B' or 'L', B is length (N+1)*L-1 for N odd and (N+1)*L for N even) </para></listitem></varlistentry> - - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -"h=intfilt(R,L,freqmult)" designs a linear phase FIR filter. This function does ideal bandlimited interpolation using the nearest 2*L nonzero samples, when used on a sequence inserted with l-1 consecutive zeros between every l samples. It assumes an original bandlimitedness of "freqmult" times the Nyquist frequency. -</para> -</refsection> - -<refsection> - <title>Example 1</title> - <programlisting role="example"><![CDATA[ -h1=intfilt(2,3,'l'); -//EXPECTED OUTPUT : -// -0.0625 0. 0.5625 1. 0.5625 0. -0.0625 - - ]]></programlisting> -</refsection> - -<refsection> - <title>Example 2</title> - <programlisting role="example"><![CDATA[ -h1=intfilt(4,1,1); -//EXPECTED OUTPUT : -// 0.3001054 0.6366198 0.9003163 1. 0.9003163 0.6366198 0.3001054 - - ]]></programlisting> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Jitendra Singh</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/is2rc.xml b/help/en_US/is2rc.xml index 0da550b..9836d50 100644 --- a/help/en_US/is2rc.xml +++ b/help/en_US/is2rc.xml @@ -25,7 +25,7 @@ <title>Calling Sequence</title> <synopsis> K = is2rc(isin) - + </synopsis> </refsynopsisdiv> @@ -42,45 +42,19 @@ <refsection> <title>Description</title> <para> -This function returns a vector of reflection coefficients from a vector of inverse sine parameters.<para></para>output array has k(i) = sin(pi/2*isin(i)) - +This function returns a vector of reflection coefficients from a vector of inverse sine parameters +output array has k(i) = sin(pi/2*isin(i)) </para> <para> +Example +k = [0.3090 0.9801 0.0031 0.0082 -0.0082]; +isin = rc2is(k) //Gives inverse sine parameters +k_dash = is2rc(isin) + </para> + <para> +OUTPUT : +isin = [0.1999886 0.8727832 0.0019735 0.0052203.....- 0.0052203 ] +k_dash =[0.309 0.9801 0.0031 0.0082 - 0.0082] </para> </refsection> - -<refsection> - <title>Example 1</title> - <programlisting role="example"><![CDATA[ - k = [0.3090 0.9801 0.0031 0.0082 -0.0082]; - isin = rc2is(k) //Gives inverse sine parameters - k_dash = is2rc(isin) -// EXPECTED OUTPUT : -//isin = [0.1999886 0.8727832 0.0019735 0.0052203 -0.0052203 ] -//k_dash =[0.309 0.9801 0.0031 0.0082 - 0.0082] - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="rc2is">rc2is</link></member> - <member><link linkend="rc2poly">rc2poly</link></member> - <member><link linkend="rc2ac">rc2ac</link></member> - <member><link linkend="rc2lar">rc2lar</link></member> - - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Parthe Pandit</member> - </simplelist> -</refsection> - -<refsection> - <title>Bibliography</title> - <para>J.R. Deller, J.G. Proakis, J.H.L. Hansen, "Discrete-Time Processing of Speech Signals", Prentice Hall, Section 7.4.5</para> -</refsection> </refentry> diff --git a/help/en_US/isallpass.xml b/help/en_US/isallpass.xml index 4dbf9ab..944cc48 100644 --- a/help/en_US/isallpass.xml +++ b/help/en_US/isallpass.xml @@ -17,77 +17,7 @@ <refnamediv> <refname>isallpass</refname> - <refpurpose>Determines whether the filter is allpass or not</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - flag = isallpass(b,a) - flag = isallpass(sos) - flag = isallpass(...,tol) - - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para>vector containing zero coefficients of the transfer function</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> vector containing pole coefficients of the transfer functtion</para></listitem></varlistentry> - <varlistentry><term>sos:</term> - <listitem><para> Second order estimates. real|complex - matrix (K-by-6)</para></listitem></varlistentry> - <varlistentry><term>tol:</term> - <listitem><para> tolerance is used to determine when two numbers are close enough to be considered equal. scalar</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -flag = isallpass(b,a) returns a logical output that is either 1 or 0 depending on output parameter "flag" by the filter specified by numerator coefficients, b, and denominator coefficients, a, is an allpass filter or not. If the filter is not an allpass filter, flag is equal to false.<para></para>flag = isallpass(sos) returns true if the filter specified by second order sections matrix ie sos, is an allpass filter.<para></para> sos is a K-by-6 matrix, where the number of sections, K, must be greater than or equal to 2. Each row of sos corresponds to the coefficients of a second order (biquad) filter. The ith row of the sos matrix corresponds to [bi(1) bi(2) bi(3) ai(1) ai(2) ai(3)]. -</para> -</refsection> - -<refsection> - <title>Example 1</title> - <programlisting role="example"><![CDATA[ -k = [1/2 1/3 1/4 1/5]; -[b,a] = latc2tf(k,'allpass'); -flag_isallpass = isallpass(b,a) -//Expected Output -//flag_isallpass =1 - ]]></programlisting> -</refsection> - -<refsection> - <title>Example 2</title> - <programlisting role="example"><![CDATA[ -b = [1/3 1/4 1/5 1]; -a=b($:-1:1); -flag = isallpass(b,a) -//Expected Output -//flag = 1 - ]]></programlisting> -</refsection> - -<refsection> - <title>Example 3</title> - <programlisting role="example"><![CDATA[ - -flag = isallpass(1,[1 1]) -//Expected Output -//flag = 0 - ]]></programlisting> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Parthasarathi Panda</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/isfir.xml b/help/en_US/isfir.xml index 5974df7..be874f4 100644 --- a/help/en_US/isfir.xml +++ b/help/en_US/isfir.xml @@ -17,60 +17,7 @@ <refnamediv> <refname>isfir</refname> - <refpurpose> Determines whether given system function is FIR or not</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - fir = isfir(b,a) - fir = isfir(sos) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b</term> - <listitem><para> Coefficients of numerator polynomial of system function </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of denumerator polynomial of system function</para></listitem></varlistentry> - <varlistentry><term>sos</term> - <listitem><para> Second order section matrix of order Kx6 </para></listitem></varlistentry> - <varlistentry><term>fir</term> - <listitem><para> This function returns logical 1 if system is FIR (fir = 1) </para> <para> Otherwise it returns logical 0 (fir = 0)</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> This function takes input a System function in terms of numerator and denumerator coefficients or in second order section (sos) matrix and checks for system having Finite Impulse Response (FIR) or not </para> -<para> </para> -</refsection> - -<refsection> - <title>Examples</title> - <para>Checking given system function is FIR or not</para> - <para> - <latex> - \begin{eqnarray} - H(z) = 1 - z^{-1} + z^{-2} - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ -b = [1 -1 1] ; -a = 1 ; -f = isfir(b,a) //This is an example of FIR system function, hence output will be f = 1 -]]></programlisting> -</refsection> - - - </refentry> diff --git a/help/en_US/islinphase.xml b/help/en_US/islinphase.xml index 47e4407..54ba2d7 100644 --- a/help/en_US/islinphase.xml +++ b/help/en_US/islinphase.xml @@ -17,62 +17,7 @@ <refnamediv> <refname>islinphase</refname> - <refpurpose>Checks given system function is linearphase or not</refpurpose> - <para> </para> + <refpurpose>[nargout,nargin]=argn();</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - flag = islinphase(b,a) - flag = islinphase(sos) - flag = islinphase(...,tol) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b</term> - <listitem><para> Coefficients of numerator polynomial of system function </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of denumerator polynomial of system function</para></listitem></varlistentry> - <varlistentry><term>sos</term> - <listitem><para> Second order section matrix of order Kx6 </para></listitem></varlistentry> - <varlistentry><term>tol</term> - <listitem><para> Tolerence in similarities between two numbers in phase response </para></listitem></varlistentry> - <varlistentry><term>flag</term> - <listitem><para> This function returns logical 1 if system is linear phase (flag = 1) </para> <para> Otherwise it returns logical 0 (flag = 0)</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> This function takes input a System function in terms of numerator and denumerator coefficients or in second order section (sos) matrix and checks for system having Linear Phase Response or not </para> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - - <para>Checking given system function is linear-phase or not</para> - <para> - <latex> - \begin{eqnarray} - H(z) = z^{-1} + 2 z^{-2} + 2 z^{-3} + z^{-4} - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ -b = [0 1 2 2 1 0] ; //Numerator polynomial coefficients -a = 1 ; //Denumerator polynomial coefficients -flag = islinphase(b,a) //This is an example of Symmetric FIR Filter which is Linear Phase, hence it will give output : flag = 1 -]]></programlisting> -</refsection> - - </refentry> diff --git a/help/en_US/ismaxphase.xml b/help/en_US/ismaxphase.xml index f3b3d65..863459c 100644 --- a/help/en_US/ismaxphase.xml +++ b/help/en_US/ismaxphase.xml @@ -17,61 +17,7 @@ <refnamediv> <refname>ismaxphase</refname> - <refpurpose>Checks given system function is Maximum-Phase system or not</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -flag = ismaxphase(b,a) -flag = ismaxphase(sos) -flag = ismaxphase(...,tol) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b</term> - <listitem><para> Coefficients of numerator polynomial of system function </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of denumerator polynomial of system function</para></listitem></varlistentry> - <varlistentry><term>sos</term> - <listitem><para> Second order section matrix of order Kx6 </para></listitem></varlistentry> - <varlistentry><term>tol</term> - <listitem><para> Tolerence in similarities between two numbers in phase response </para></listitem></varlistentry> - <varlistentry><term>flag</term> - <listitem><para> This function returns logical 1 if system is maximum-phase system (flag = 1) </para> <para> Otherwise it returns logical 0 (flag = 0)</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> This function determines whether the given system function is maximum-phase system or not . Maximum-phase system means all zeros of transfer function will be outside the unit circle in z-plane, also poles must be within unit circle for stability and causality </para> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - - <para>Checking given system function is maximum phase system or not</para> - <para> - <latex> - \begin{eqnarray} - H(z) = 1 -5z^{-1} + 6 z^{-2} - \end{eqnarray} - </latex> - </para> - <para> </para> - <programlisting role="example"><![CDATA[ -b = [1 -5 6] ; //Numerator polynomial coefficients -a = 1 ; //Denumerator polynomial coefficients -flag = ismaxphase(b,a) //This is an example of Maximum-Phase system, hence it will give output as : flag = 1 -]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/isminphase.xml b/help/en_US/isminphase.xml index 40eef06..9829deb 100644 --- a/help/en_US/isminphase.xml +++ b/help/en_US/isminphase.xml @@ -17,61 +17,7 @@ <refnamediv> <refname>isminphase</refname> - <refpurpose>Checks given system function is Maximum-Phase system or not</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -flag = ismaxphase(b,a) -flag = ismaxphase(sos) -flag = ismaxphase(...,tol) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b</term> - <listitem><para> Coefficients of numerator polynomial of system function </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of denumerator polynomial of system function</para></listitem></varlistentry> - <varlistentry><term>sos</term> - <listitem><para> Second order section matrix of order Kx6 </para></listitem></varlistentry> - <varlistentry><term>tol</term> - <listitem><para> Tolerence in similarities between two numbers in phase response </para></listitem></varlistentry> - <varlistentry><term>flag</term> - <listitem><para> This function returns logical 1 if system is minimum-phase system (flag = 1) </para> <para> Otherwise it returns logical 0 (flag = 0)</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> This function determines whether the given system function is minimum-phase system or not . Minimum-phase system means all zeros of transfer function will be inside the unit circle in z-plane,also poles must be within unit circle for stability and causality </para> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - - <para>Checking given system function is minimum phase system or not</para> - <para> - <latex> - \begin{eqnarray} - H(z) = 1 -0.3z^{-1} + 0.02 z^{-2} - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ -b = [1 -0.3 0.02] ; //Numerator polynomial coefficients -a = 1 ; //Denumerator polynomial coefficients -flag = isminphase(b,a) //This is an example of Minimum-Phase system, hence it will give output as : flag = 1 -]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/isstable.xml b/help/en_US/isstable.xml index 9b53add..bfe50f0 100644 --- a/help/en_US/isstable.xml +++ b/help/en_US/isstable.xml @@ -17,75 +17,7 @@ <refnamediv> <refname>isstable</refname> - <refpurpose>Checks stability of given Discrete time system function</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -flag = ismaxphase(b,a) -flag = ismaxphase(sos) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b</term> - <listitem><para> Coefficients of numerator polynomial of system function </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of denumerator polynomial of system function</para></listitem></varlistentry> - <varlistentry><term>sos</term> - <listitem><para> Second order section matrix of order Kx6 </para></listitem></varlistentry> - <varlistentry><term>flag</term> - <listitem><para> This function returns logical 1 if system is Stable (flag = 1) </para> <para> Otherwise it returns logical 0 (flag = 0)</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> This function determines whether the given system function is Stable system function or not. Stable system means all poles of transfer function will be inside the unit circle in z-plane </para> - <para> </para> -</refsection> - -<refsection> - <title>Example : 1 <para>Example of stable system function </para> </title> - <para> - <latex> - \begin{eqnarray} - H(z) = \frac{1 +2z^{-1} } {1 -0.7z^{-1} + 0.1 z^{-2} } - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ -b = [1 2] ; //Numerator polynomial coefficients -a = [1 -0.7 0.1] ; //Denumerator polynomial coefficients -flag = isstable(b,a) //This is an example of Stable system, hence it will give output as : flag = 1 -]]></programlisting> -</refsection> - -<refsection> -<title> Example : 2 <para>Example of unstable system function</para> </title> - - <para> - <latex> - \begin{eqnarray} - H(z) = \frac{1 +2z^{-1} } {1 +5z^{-1} + 6z^{-2} } - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ -b = [1 2] ; //Numerator polynomial coefficients -a = [1 5 6] ; //Denumerator polynomial coefficients -flag = isstable(b,a) //This is an example of Unstable system, hence it will give output as : flag = 0 -]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/kaiser.xml b/help/en_US/kaiser.xml index 32afa90..8758d78 100644 --- a/help/en_US/kaiser.xml +++ b/help/en_US/kaiser.xml @@ -20,42 +20,4 @@ <refpurpose>This function returns the filter coefficients of a Kaiser window.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = kaiser (m) - w = kaiser (m, beta) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>beta:</term> - <listitem><para> real scalar value</para></listitem></varlistentry> - <varlistentry><term>w:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a Kaiser window of length m supplied as input, to the output vector w. -The second parameter gives the stop band attenuation of the Fourier transform of the window on derivation. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -L = 6 ; -beta = 0.2 ; -kaiser(L, beta) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/kaiserord.xml b/help/en_US/kaiserord.xml index 8a76cf7..a6d15be 100644 --- a/help/en_US/kaiserord.xml +++ b/help/en_US/kaiserord.xml @@ -47,7 +47,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. The vector f contains pairs of frequency band edges in the range [0,1]. The vector m specifies the magnitude response for each band. The values of m must be zero for all stop bands and must have the same magnitude for all pass bands. The deviation of the filter dev can be specified as a scalar or a vector of the same length as m. The optional sampling rate fs can be used to indicate that f is in Hz in the range [0,fs/2]. @@ -75,8 +74,8 @@ n = (A-8)/(2.285 dw) <title>Examples</title> <programlisting role="example"><![CDATA[ [n, w, beta, ftype] = kaiserord ([1000, 1200], [1, 0], [0.05, 0.05], 11025) -n = 1 -w = 1100 +n = 70 +w = 0.199 beta = 1.5099 ftype = low ]]></programlisting> diff --git a/help/en_US/lar2rc.xml b/help/en_US/lar2rc.xml index 4a40678..9348113 100644 --- a/help/en_US/lar2rc.xml +++ b/help/en_US/lar2rc.xml @@ -17,8 +17,7 @@ <refnamediv> <refname>lar2rc</refname> - <refpurpose> Log Area Ratio to Reflection Coefficient</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> @@ -27,7 +26,6 @@ <synopsis> k = lar2rc(g) </synopsis> - <para> </para> </refsynopsisdiv> <refsection> @@ -38,31 +36,5 @@ <varlistentry><term>k:</term> <listitem><para> returns the reflection coefficients.</para></listitem></varlistentry> </variablelist> - <para> </para> </refsection> - -<refsection> - <title>Description</title> - <para> This function takes input as a vector of log area ratios and returns vector of reflection coefficients corresponding to log area ratio vector </para> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - <para> </para> - <programlisting role="example"><![CDATA[ -g = [0.6389 4.5989 0.0063 0.0163 -0.0163]; //log area ratio inputs -k = lar2rc(g) // returns reflection coefficients corresponding to log area ratios - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="Author">Author</link></member> - <member><link linkend="Jitendra">Singh</link></member> - <member><link linkend="Modified">to match MATLAB o/p when i/p is of type char and is a string by Debdeep Dey</link></member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/levinson.xml b/help/en_US/levinson.xml index ac340a6..056795f 100644 --- a/help/en_US/levinson.xml +++ b/help/en_US/levinson.xml @@ -17,81 +17,7 @@ <refnamediv> <refname>levinson</refname> - <refpurpose>Levinson-Durbin Recurssion Algorithm</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -a = levinson(r) -a = levinson(r,n) -[a,e] = levinson(r,n) -[a,e,k] = levinson(r,n) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>r</term> - <listitem><para> Real or complex deterministic autocorrelation sequence input </para></listitem></varlistentry> - <varlistentry><term>a</term> - <listitem><para> Coefficients of length(r)-1 order Autoregressive linear process </para></listitem></varlistentry> - <varlistentry><term>n</term> - <listitem><para> Order of autoregressive model (default value is length(r)-1 , if n is not given)</para></listitem></varlistentry> - <varlistentry><term>e</term> - <listitem><para> Prediction error of order n</para></listitem></varlistentry> - <varlistentry><term>k</term> - <listitem><para> Reflection coefficient vector of length n</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> The Levinson-Durbin recursion algorithm is used for finding an all-pole IIR filter with a given deterministic autocorrelation sequence (r) </para> - - <para> - <latex> - \begin{eqnarray} - H(z) = \frac{1}{1+a(2)z^{-1}+a(3)z^{-2} + ... +a(n+1)z^{-n}} - \end{eqnarray} - </latex> - </para> - - <para> </para> -</refsection> - -<refsection> - <title>Examples </title> - <para> Estimate the coefficients of an autoregressive process given by equation </para> - - <para> - <latex> - \begin{eqnarray} - x(n) = 0.1x(n-1) - 0.8x(n-2) + w(n) - \end{eqnarray} - </latex> - </para> - <para> </para> - - <programlisting role="example"><![CDATA[ - -a = [1 0.1 -0.8]; - -v = 0.4; //variance, v=0.4 -w = sqrt(v)*rand(15000,1,"normal"); -x = filter(1,a,w); - -[r,lg] = xcorr(x,'biased'); -r(lg<0) = []; - -ar = levinson(r,length(a)-1) //coefficients of autoregressive process - -]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/lpc.xml b/help/en_US/lpc.xml index 079923e..1f5bcd4 100644 --- a/help/en_US/lpc.xml +++ b/help/en_US/lpc.xml @@ -18,7 +18,6 @@ <refnamediv> <refname>lpc</refname> <refpurpose>Linear prediction filter coefficients</refpurpose> - <para> </para> </refnamediv> @@ -27,8 +26,9 @@ <synopsis> [a,g] = lpc(x) [a,g] = lpc(x,p) + + </synopsis> - <para> </para> </refsynopsisdiv> <refsection> @@ -36,37 +36,31 @@ <para> [a,g] = lpc(x,p) Determines the coefficients of a pth order forward linear predictor -filter by minimizing the squared error. <para>If p is unspecified, a -default value of length(x)-1 is used.</para> +filter by minimizing the squared error. If p is unspecified, a +default value of length(x)-1 is used. </para> - <para> </para> + <para> +</para> </refsection> <refsection> <title>Parameters</title> <variablelist> <varlistentry><term>x:</term> - <listitem><para> double</para><para>input signal, if it is a matrix each column is computed independently</para></listitem></varlistentry> + <listitem><para> double</para></listitem></varlistentry> <varlistentry><term>p:</term> - <listitem><para> int, natural number, scalar</para><para>order of linear predictor filter, value must be scalar, positive and must be less than or equal to length of input signal </para></listitem></varlistentry> + <listitem><para> int, natural number, scalar</para></listitem></varlistentry> <varlistentry><term>a:</term> - <listitem><para> double</para><para>coefficient of forward linear predictor, coefficient for each signal input is returned as a row vector</para></listitem></varlistentry> + <listitem><para> double</para></listitem></varlistentry> <varlistentry><term>g:</term> - <listitem><para> double</para><para>Column vector of averaged square prediction error</para></listitem></varlistentry> + <listitem><para> double</para></listitem></varlistentry> </variablelist> </refsection> <refsection> - <title>Description</title> - <para> This function determines coefficients of a forward linear predictor by minimizing prediction error in least squares sense. It is used in Digital Filter Design </para> - <para> </para> -</refsection> - -<refsection> <title>Examples</title> - <programlisting role="example"><![CDATA[ -noise = rand(50000,1,"normal"); //Gaussian White Noise +noise = rand(50000,1,"normal"); x = filter(1,[1 1/2 1/3 1/4],noise); x = x(45904:50000); [a,g]= lpc(x,3) @@ -80,38 +74,5 @@ title 'Original Signal vs. LPC Estimate'; xlabel 'Sample number', ylabel 'Amplitude'; legend('Original signal','LPC estimate'); ]]></programlisting> - - <scilab:image> -noise = rand(50000,1,"normal"); //Gaussian White Noise -x = filter(1,[1 1/2 1/3 1/4],noise); -x = x(45904:50000); -[a,g]= lpc(x,3) -est_x = filter([0 -a(2:$)],1,x); -e = x-est_x; -[acs,lags] = xcorr(e,'coeff'); -plot(1:97,x(4001:4097),1:97,est_x(4001:4097),'--'); -a = gca(); -a.grid = [1,1]; -title 'Original Signal vs. LPC Estimate'; -xlabel 'Sample number', ylabel 'Amplitude'; -legend('Original signal','LPC estimate'); - </scilab:image> - <para> </para> - -</refsection> - - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="aryule">| levinson | prony | pyulear | stmcb</link></member> - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Ayush Baid</member> - </simplelist> </refsection> </refentry> 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> diff --git a/help/en_US/mexihat.xml b/help/en_US/mexihat.xml index 81f77a1..391588e 100644 --- a/help/en_US/mexihat.xml +++ b/help/en_US/mexihat.xml @@ -52,11 +52,11 @@ This function returns values of the Mexican hat wavelet in the specified interva <title>Examples</title> <programlisting role="example"><![CDATA[ 1. [a,b]= mexihat(1,2,3) -//a = [0.00000 -0.35197 -0.35214] -//b = [1.0000 1.5000 2.0000] +a = [0.00000 -0.35197 -0.35214] +b = [1.0000 1.5000 2.0000] 2. [a,b]= mexihat([1 2 3],1,1) -//a = [0;0;0] -//b = [1;1;1] +a = [0;0;0] +b = [1;1;1] ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/meyeraux.xml b/help/en_US/meyeraux.xml index 897a8fa..bc5d9f1 100644 --- a/help/en_US/meyeraux.xml +++ b/help/en_US/meyeraux.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>meyeraux</refname> - <refpurpose></refpurpose> + <refpurpose>Returns value of Meyer Wavelet Auxiliary function</refpurpose> </refnamediv> @@ -39,7 +39,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. This function returns values of the auxiliary function used for Meyer wavelet generation. </para> </refsection> diff --git a/help/en_US/movingrms.xml b/help/en_US/movingrms.xml index 5498eb9..eab4d87 100644 --- a/help/en_US/movingrms.xml +++ b/help/en_US/movingrms.xml @@ -17,54 +17,7 @@ <refnamediv> <refname>movingrms</refname> - <refpurpose>Find moving RMS value of signal in x</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [rmsx,w]=movingrms(x,width,risetime) - [rmsx,w]=movingrms(x,width,risetime,Fs) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> (Real or complex valued vector or matrix) Input signal samples</para></listitem></varlistentry> - <varlistentry><term>width:</term> - <listitem><para> Real or complex scalar value</para></listitem></varlistentry> - <varlistentry><term>risetime:</term> - <listitem><para> Real or complex scalar value</para></listitem></varlistentry> - <varlistentry><term>Fs:</term> - <listitem><para> (Real or complex scalar value) Smapling frequency</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Description</title> - <para> -In this function signal is convoluted against a sigmoid window of width w and risetime rc with the units of these parameters relative to the value of the sampling frequency given in Fs (Default value=1). -</para> - <para> </para> -</refsection> - -<refsection> - <title>Example : 1</title> - <programlisting role="example"><![CDATA[ -[a,b]=movingrms ([4.4 94 1;-2 5*%i 5],1,-2) // moving rms with default value of Fs = 1 - ]]></programlisting> - -<title>Example : 2</title> - <programlisting role="example"><![CDATA[ -[a,b]=movingrms ([4.4 94 1;-2 5*%i 5],1,-2,2)// moving rms with value of Fs = 2 - - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/mpoles.xml b/help/en_US/mpoles.xml index 99ff1d2..7246219 100644 --- a/help/en_US/mpoles.xml +++ b/help/en_US/mpoles.xml @@ -17,62 +17,7 @@ <refnamediv> <refname>mpoles</refname> - <refpurpose>Identifies unique poles in p and their associated multiplicity.</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [multp, idxp] = mpoles (p) - [multp, idxp] = mpoles (p, tol) - [multp, idxp] = mpoles (p, tol, reorder) - </synopsis> -</refsynopsisdiv> - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>p:</term> - <listitem><para> Real vector whose poles has to be calculated</para></listitem></varlistentry> - <varlistentry><term>tol:</term> - <listitem><para> tolerence,by default, tol=0.001</para></listitem></varlistentry> - <varlistentry><term>reorder:</term> - <listitem><para> the optional parameter, if reorder is zero, poles are not sorted.</para></listitem></varlistentry> - <varlistentry><term>multp:</term> - <listitem><para> is a vector specifying the multiplicity of the poles.</para></listitem></varlistentry> -<varlistentry><term>indxp:</term> - <listitem><para> indices of the poles</para></listitem></varlistentry> - -</variablelist> -</refsection> -<refsection> - <title>Description</title> - <para> -The output is ordered from largest to the smallest poles. If the relative difference between the poles is less than the tolerance(tol), then those poles are considered as multiples. By default, the tolerance value is 0.001. -</para> - -<para> -If the optional parameter, reorder is 0, then the poles are not sorted. -</para> -</refsection> -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - p = [2 3 1 1 2]; - [m, n] = mpoles (p) -// n = -// 2. -// 5. -// 1. -// 4. -// 3. -// m = -// 1. -// 1. -// 2. -// 1. -// 2. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/ncauer.xml b/help/en_US/ncauer.xml index 3260e0f..b13344c 100644 --- a/help/en_US/ncauer.xml +++ b/help/en_US/ncauer.xml @@ -17,62 +17,7 @@ <refnamediv> <refname>ncauer</refname> - <refpurpose>Analog prototype for Cauer filter.</refpurpose> + <refpurpose>Analog prototype for Cauer filter (Cauer filter and elliptic filters are same).</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [Zz, Zp, Zg] = ncauer(Rp, Rs, n) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>n:</term> - <listitem><para> Filter Order</para></listitem></varlistentry> - <varlistentry><term>Rp:</term> - <listitem><para> Peak-to-peak passband ripple in dB</para></listitem></varlistentry> - <varlistentry><term>Rs:</term> - <listitem><para> Stopband attenuation in dB</para></listitem></varlistentry> - <varlistentry><term>Zz:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>Zp:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>Zg:</term> - <listitem><para> Gain</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It gives an analog prototype for Cauer filter (elliptic filter) of nth order, with a Peak-to-peak passband ripple of Rp and a stopband attenuation of Rs. -</para> -<para> -This function is same as ellipap() only order of input argument is different,</para> - <para> This function is only for octave compatibility only </para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n = 4; -Rp = 3; -Rs = 10; -[Zz, Zp, Zg] = ncauer(Rp, Rs, n) - - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai</member> - <member>FOSSEE, IIT Bombay</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/nuttallwin.xml b/help/en_US/nuttallwin.xml index 5732027..b385b9f 100644 --- a/help/en_US/nuttallwin.xml +++ b/help/en_US/nuttallwin.xml @@ -17,46 +17,7 @@ <refnamediv> <refname>nuttallwin</refname> - <refpurpose>This function returns the filter coefficients of a Blackman-Harris window.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = nuttallwin (m) - w = nuttallwin (m, opt) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>opt:</term> - <listitem><para> string value, takes in "periodic" or "symmetric"</para></listitem></varlistentry> - <varlistentry><term>w:</term> - <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the filter coefficients of a Blackman-Harris window defined by Nuttall of length m supplied as input, to the output vector w. -The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -nuttallwin(2, "periodic") -ans = -- 2.429D-17 -1. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/parzenwin.xml b/help/en_US/parzenwin.xml index e3bbbee..504c8b4 100644 --- a/help/en_US/parzenwin.xml +++ b/help/en_US/parzenwin.xml @@ -24,7 +24,7 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - y = parzenwin (m) + w = parzenwin (m) </synopsis> </refsynopsisdiv> @@ -33,7 +33,7 @@ <variablelist> <varlistentry><term>m:</term> <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>y:</term> + <varlistentry><term>w:</term> <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> </variablelist> </refsection> diff --git a/help/en_US/pchip.xml b/help/en_US/pchip.xml index 059f02e..c9aaa34 100644 --- a/help/en_US/pchip.xml +++ b/help/en_US/pchip.xml @@ -17,65 +17,7 @@ <refnamediv> <refname>pchip</refname> - <refpurpose>This function returns piecewise cubic hermite interpolating polynomial.</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - d=pchip(x,y) - d= pchip(x,y,xx) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> points at which the function is defined</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>y:</term> - <listitem><para> function of variable x</para><para>Note that the lengths of x and y should be the same.</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>xx:</term> - <listitem><para> points of interpolation</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>d:</term> - <listitem><para> vector of interpolantant at xx -</para></listitem></varlistentry> - </variablelist> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - x=[0 1 2 3 4 5] - y=[1 0 1 0 1 0] - xx=linspace(0,5,800) - v=pchip(x, y) - v=pchip(x,y,xx) - plot(x,y,xx,v,'o') - ]]></programlisting> - -<scilab:image> - - x=[0 1 2 3 4 5] - y=[1 0 1 0 1 0] - xx=linspace(0,5,800) - v=pchip(x, y) - v=pchip(x,y,xx) - plot(x,y,xx,v,'o') -</scilab:image> - -</refsection> - - - - - - </refentry> diff --git a/help/en_US/pchips.xml b/help/en_US/pchips.xml index 8f356f5..887fd00 100644 --- a/help/en_US/pchips.xml +++ b/help/en_US/pchips.xml @@ -20,4 +20,14 @@ <refpurpose></refpurpose> </refnamediv> + +<refsection> + <title>Parameters</title> + <variablelist> + <varlistentry><term>x:</term> + <listitem><para> a vector</para></listitem></varlistentry> + <varlistentry><term>y:</term> + <listitem><para> is Y is vector then it must have the same length as x and Y is matrix then the last dimension of Y must equal</para></listitem></varlistentry> + </variablelist> +</refsection> </refentry> diff --git a/help/en_US/peak2rms.xml b/help/en_US/peak2rms.xml index 4e1e4e0..2dfe34c 100644 --- a/help/en_US/peak2rms.xml +++ b/help/en_US/peak2rms.xml @@ -73,13 +73,11 @@ If the elements of matrix are complex the absolute values are considered in the <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -//To calculate peak2rms of a vector: +To calculate peak2rms of a vector: IN=[6 19 10 25] OUT=peak2rms(IN) -//The output is OUT= -//1.4638501 -//1.3887301 -//1.119186 +The output is OUT= +1.4927036 ]]></programlisting> </refsection> @@ -87,54 +85,13 @@ OUT=peak2rms(IN) <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -To calculate peak2rms of rows of matrix: +//To calculate peak2rms of rows of matrix: IN=[1 3 5;2 4 6;7 8 9] OUT=peak2rms(IN,2) -//The output is -//OUT= 1.3719887 - +The output is +OUT= 1.4638501 +1.3887301 +1.119186 ]]></programlisting> </refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -To calculate peak magnitude to RMS value of sinusoid: - -t=0:0.6:9 -IN=cos(6*%pi*t); -OUT= peak2rms(IN) -//The output is -//OUT= 1.3719887 - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="abs">abs</link></member> - <member><link linkend="mean">mean</link></member> - <member><link linkend="max">max</link></member> - <member><link linkend="sqrt">sqrt</link></member> - <member><link linkend="isempty">isempty</link></member> - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Indira Askaukar</member> - </simplelist> -</refsection> - -<refsection> - <title>Bibliography</title> - <para>Matlab help document.</para> - <para>Modified to accept char i/p</para> - <para>MOdified function to match MATLAB input arguments</para> - <para>Now for calculating the values of ratio of peak to RMS of columns of matrix use peak2rms(in,1)</para> - <para>And for calculates the values of ratio of peak to RMS of rows of matrix. use peak2rms(in,2)</para> - <para>Updated help comments accordingly</para> - <para>MOdifications done by by Debdeep Dey</para> -</refsection> </refentry> diff --git a/help/en_US/peig.xml b/help/en_US/peig.xml index b19c370..9ea911c 100644 --- a/help/en_US/peig.xml +++ b/help/en_US/peig.xml @@ -33,138 +33,61 @@ [S,f] = peig(x,p,nfft,fs,nwin,noverlap) [...] = peig(...,freqrange) [...,v,e] = peig(...) - - - - - - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> int|double - vector|matrix</para> -<para>Input signal. In case of a matrix, each row of x represents a + + Parameters: + x - int|double - vector|matrix + Input signal. In case of a matrix, each row of x represents a seperate observation of the signal. If 'corr' flag is specified, then x is the correlation matrix. If w is not specified in the input, it is determined by the algorithm. If x is real valued, then range of w is [0, pi]. - Otherwise, the range of w is [0, 2pi)</para> -</listitem></varlistentry> - </variablelist> - -<variablelist> - <varlistentry><term>p:</term> - <listitem><para> int|double - scalar|vector</para> -<para>p(1) is the dimension of the signal subspace + Otherwise, the range of w is [0, 2pi) + p - int|double - scalar|vector + p(1) is the dimension of the signal subspace p(2), if specified, represents a threshold that is multiplied by - the smallest estimated eigenvalue of the signal's correlation matrix.</para> - -</listitem></varlistentry> - </variablelist> - -<variablelist> -<varlistentry><term>w:</term> - <listitem><para> int|double - vector</para> -<para>w is the vector of normalized frequencies over which the - pseuspectrogram is to be computed.</para> - -</listitem></varlistentry> - -</variablelist> - - -<variablelist> -<varlistentry><term>nfft:</term> - <listitem><para> int - scalar (Default = 256)</para> -<para>Length of the fft used to compute pseudospectrum. The length of S + the smallest estimated eigenvalue of the signal's correlation matrix. + w - int|double - vector + w is the vector of normalized frequencies over which the + pseuspectrogram is to be computed. + nfft - int - scalar (Default = 256) + Length of the fft used to compute pseudospectrum. The length of S (and hence w/f) depends on the type of values in x and nfft. If x is real, length of s is (nfft/2 + 1) {Range of w = [0, pi]} if nfft is even and (nfft+1)/2 {Range of w = [0, pi)} otherwise. - If x is complex, length of s is nfft.</para> - -</listitem></varlistentry> - -</variablelist> - -<variablelist> -<varlistentry><term>fs:</term> - <listitem><para> int|double - scalar (Default = 1)</para> -<para>Sampling rate. Used to convert the normalized frequencies (w) to - actual values (f) and vice-versa.</para> - -</listitem></varlistentry> - -</variablelist> - -<variablelist> -<varlistentry><term>nwin:</term> - <listitem><para> int|double - scalar (int only)|vector (Default = 2*p(1))</para> -<para> If nwin is scalar, it is the length of the rectangular window. + If x is complex, length of s is nfft. + fs - int|double - scalar (Default = 1) + Sampling rate. Used to convert the normalized frequencies (w) to + actual values (f) and vice-versa. + nwin - int|double - scalar (int only)|vector (Default = 2*p(1)) + If nwin is scalar, it is the length of the rectangular window. Otherwise, the vector input is considered as the window coefficients. - Not used if 'corr' flag present.</para> - -</listitem></varlistentry> - -</variablelist> -<variablelist> -<varlistentry><term>noverlap:</term> - <listitem><para> int - scalar (Default = nwin-1)</para> -<para> number of points by which successive windows overlap. noverlap not - used if x is a matrix</para> - -</listitem></varlistentry> - -</variablelist> -<variablelist> -<varlistentry><term>freqrange:</term> - <listitem><para> string</para> -<para>The range of frequencies over which the pseudospetrogram is + Not used if 'corr' flag present. + If x is a vector, windowing not done in nwin in scalar. If x is a + matrix, + noverlap - int - scalar (Default = nwin-1) + number of points by which successive windows overlap. noverlap not + used if x is a matrix + freqrange - string + The range of frequencies over which the pseudospetrogram is computed. Three possible values - 'onesided', 'twosided', 'centered' 'corr' flag Presence indicates that the primary input x is actually a correlation matrix - </para> - -</listitem></varlistentry> - -</variablelist> -</refsection> - - - - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - - fs = 100; - t = 0:1/fs:1-1/fs; - s = 2*sin(2*%pi*25*t)+sin(2*%pi*35*t)+rand(1,100,"normal"); - [S,w]=peig(s,2,512,fs,'half'); - plot(w,S); - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="rooteig">| pmusic | pmtm | pcov | pmcov | pburg | pyulear | pwelch | corrmtx</link></member> - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Ayush Baid</member> - <member>References</member> - <member>[1] Petre Stoica and Randolph Moses, Introduction To Spectral</member> - <member>Analysis, Prentice-Hall, 1997, pg. 15</member> - <member>[2] S. J. Orfanidis, Optimum Signal Processing. An Introduction.</member> - <member>2nd Ed., Macmillan, 1988.</member> - </simplelist> -</refsection> + + Examples: + 1. + fs = 100; + t = 0:1/fs:1-1/fs; + s = 2*sin(2*%pi*25*t)+sin(2*%pi*35*t)+rand(1,100,"normal"); + [S,w]=peig(s,2,512,fs,'half'); + plot(w,S); + OUTPUT: gives the plot of power vs normalised frequencies + //2. + fs = 100; + t = 0:1/fs:1-1/fs; + s = 2*sin(2*%pi*25*t)+sin(2*%pi*35*t); + [S,w]=peig(s,2,512,fs,'half'); + plot(w,S); + </synopsis> +</refsynopsisdiv> </refentry> diff --git a/help/en_US/periodogram.xml b/help/en_US/periodogram.xml index 5566e39..a214632 100644 --- a/help/en_US/periodogram.xml +++ b/help/en_US/periodogram.xml @@ -17,67 +17,7 @@ <refnamediv> <refname>periodogram</refname> - <refpurpose>Return the periodogram (Power Spectral Density) of X</refpurpose> + <refpurpose>Calling Sequence:</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - - [PXX, W] = periodogram (X, WIN, NFFT, FS) - [PXX, W] = periodogram (..., "RANGE") - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>X:</term> - <listitem><para>data vector. If X is real-valued a one-sided spectrum is estimated. If X is complex-valued, or "RANGE" specifies "twosided", the full spectrum is estimated.</para></listitem></varlistentry> - <varlistentry><term>WIN:</term> - <listitem><para> window weight data. If window is empty or unspecified a default rectangular window is used. Otherwise, the window is applied to the signal ('X .* WIN') before computing th periodogram. The window data must be a vector of the same length as X.</para></listitem></varlistentry> - <varlistentry><term>NFFT:</term> - <listitem><para>number of frequency bins. The default is 256 or the next higher power of 2 greater than the length of X ('max (256,2.^nextpow2 (length (x)))'). If NFFT is greater than the length of the input then X will be zero-padded to the length of NFFT.</para></listitem></varlistentry> - <varlistentry><term>FS:</term> - <listitem><para>sampling rate. The default is 1.</para></listitem></varlistentry> - <varlistentry><term>RANGE:</term> - <listitem><para>range of spectrum. "onesided" computes spectrum from [0..nfft/2+1]."twosided" computes spectrum from [0..nfft-1].</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -The optional second output W are the normalized angular frequencies. For a one-sided calculation W is in the range [0, pi]. If NFFT is even and [0, pi) if NFFT is odd. Similarly, for a two-sided calculation W is in the range [0, 2*pi] or [0, 2*pi)depending on NFFT. - </para> - <para> -If a sampling frequency is specified, FS, then the output frequencies F will be in the range [0, FS/2] or [0, FS/2) for one-sided calculations. For two-sided calculations the range will be [0, FS). - </para> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n=0:319; -x=cos(%pi/4*n)+rand(size(n,"r"),"normal"); -[pxx,w]=periodogram(x,ones(1,320),256,2000,"onesided"); -plot2d(w,10*log10(pxx)) -xtitle('periodogram','frequency','magnitude(db)') -xgrid() - ]]></programlisting> -<scilab:image> -n=0:319; -x=cos(%pi/4*n)+rand(size(n,"r"),"normal"); -[pxx,w]=periodogram(x,ones(1,320),256,2000,"onesided"); -plot2d(w,10*log10(pxx)) -xtitle('periodogram','frequency','magnitude(db)') -xgrid() -</scilab:image> - - -</refsection> - - </refentry> diff --git a/help/en_US/pmusic.xml b/help/en_US/pmusic.xml index b445409..107a1d2 100644 --- a/help/en_US/pmusic.xml +++ b/help/en_US/pmusic.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>pmusic</refname> - <refpurpose>Computes Psuedospectrum using MUSIC algorithm</refpurpose> + <refpurpose>Psuedospectrum using MUSIC algorithm</refpurpose> </refnamediv> @@ -33,144 +33,54 @@ [S,f] = pmusic(x,p,nfft,fs,nwin,noverlap) [...] = pmusic(...,freqrange) [...,v,e] = pmusic(...) - - - - - - </synopsis> -</refsynopsisdiv> - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> int|double - vector|matrix</para> -<para>Input signal. In case of a matrix, each row of x represents a + + Parameters: + x - int|double - vector|matrix + Input signal. In case of a matrix, each row of x represents a seperate observation of the signal. If 'corr' flag is specified, then x is the correlation matrix. If w is not specified in the input, it is determined by the algorithm. If x is real valued, then range of w is [0, pi]. - Otherwise, the range of w is [0, 2pi)</para> -</listitem></varlistentry> - </variablelist> - -<variablelist> - <varlistentry><term>p:</term> - <listitem><para> int|double - scalar|vector</para> -<para>p(1) is the dimension of the signal subspace + Otherwise, the range of w is [0, 2pi) + p - int|double - scalar|vector + p(1) is the dimension of the signal subspace p(2), if specified, represents a threshold that is multiplied by - the smallest estimated eigenvalue of the signal's correlation matrix.</para> - -</listitem></varlistentry> - </variablelist> - -<variablelist> -<varlistentry><term>w:</term> - <listitem><para> int|double - vector</para> -<para>w is the vector of normalized frequencies over which the - pseuspectrogram is to be computed.</para> - -</listitem></varlistentry> - -</variablelist> - - -<variablelist> -<varlistentry><term>nfft:</term> - <listitem><para> int - scalar (Default = 256)</para> -<para>Length of the fft used to compute pseudospectrum. The length of S + the smallest estimated eigenvalue of the signal's correlation matrix. + w - int|double - vector + w is the vector of normalized frequencies over which the + pseuspectrogram is to be computed. + nfft - int - scalar (Default = 256) + Length of the fft used to compute pseudospectrum. The length of S (and hence w/f) depends on the type of values in x and nfft. If x is real, length of s is (nfft/2 + 1) {Range of w = [0, pi]} if nfft is even and (nfft+1)/2 {Range of w = [0, pi)} otherwise. - If x is complex, length of s is nfft.</para> - -</listitem></varlistentry> - -</variablelist> - -<variablelist> -<varlistentry><term>fs:</term> - <listitem><para> int|double - scalar (Default = 1)</para> -<para>Sampling rate. Used to convert the normalized frequencies (w) to - actual values (f) and vice-versa.</para> - -</listitem></varlistentry> - -</variablelist> - -<variablelist> -<varlistentry><term>nwin:</term> - <listitem><para> int|double - scalar (int only)|vector (Default = 2*p(1))</para> -<para> If nwin is scalar, it is the length of the rectangular window. + If x is complex, length of s is nfft. + fs - int|double - scalar (Default = 1) + Sampling rate. Used to convert the normalized frequencies (w) to + actual values (f) and vice-versa. + nwin - int|double - scalar (int only)|vector (Default = 2*p(1)) + If nwin is scalar, it is the length of the rectangular window. Otherwise, the vector input is considered as the window coefficients. - Not used if 'corr' flag present.</para> - -</listitem></varlistentry> - -</variablelist> -<variablelist> -<varlistentry><term>noverlap:</term> - <listitem><para> int - scalar (Default = nwin-1)</para> -<para> number of points by which successive windows overlap. noverlap not - used if x is a matrix</para> - -</listitem></varlistentry> - -</variablelist> -<variablelist> -<varlistentry><term>freqrange:</term> - <listitem><para> string</para> -<para>The range of frequencies over which the pseudospetrogram is + Not used if 'corr' flag present. + If x is a vector, windowing not done in nwin in scalar. If x is a + matrix, + noverlap - int - scalar (Default = nwin-1) + number of points by which successive windows overlap. noverlap not + used if x is a matrix + freqrange - string + The range of frequencies over which the pseudospetrogram is computed. Three possible values - 'onesided', 'twosided', 'centered' 'corr' flag Presence indicates that the primary input x is actually a correlation matrix - </para> - -</listitem></varlistentry> - -</variablelist> -</refsection> - -<refsection> - <title>Description</title> -<para>[S,w] = pmusic(x,p) implements the MUSIC (Multiple Signal Classification) algorithm and returns S, the pseudospectrum estimate of the input signal x, and a vector w of normalized frequencies (in rad/sample) at which the pseudospectrum is evaluated. The pseudospectrum is calculated using estimates of the eigenvectors of a correlation matrix associated with the input data x, where x is specified as either:</para> - - <para> A row or column vector representing one observation of the signal - - </para> -<para>A rectangular array for which each row of x represents a separate observation of the signal (for example, each row is one output of an array of sensors, as in array processing), such that x'*x is an estimate of the correlation matrix</para> -</refsection> - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - n = 0:199; - x = cos(0.257*%pi*n) + sin(0.2*%pi*n) ; - [S,w]=pmusic(x,2,16,1) - ]]></programlisting> -</refsection> - - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="pburg">pburg| peig | periodogram | pmtm | prony | pwelch | rooteig | rootmusic</link></member> - </simplelist> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Ayush Baid</member> - <member>References</member> - <member>[1] Petre Stoica and Randolph Moses, Introduction To Spectral</member> - <member>Analysis, Prentice-Hall, 1997, pg. 15</member> - <member>[2] S. J. Orfanidis, Optimum Signal Processing. An Introduction.</member> - <member>2nd Ed., Macmillan, 1988.</member> - </simplelist> -</refsection> + + Examples: + + Ex1: + n = 0:199; + x = cos(0.257*%pi*n) + sin(0.2*%pi*n) + 0.01*rand(size(n,"r"),"normal"); + [S,w]=pmusic(x,[%inf,1.1],[],8000,2) ;//where x: [1x200 constant] p:-infinite signal space and threshold value is 1.1 window length:-7 Fs:-8000Hz........fftlength:-256 + plot(w,S);.........to see the plot of psuedospectrum estimate of x vs frequencies w + </synopsis> +</refsynopsisdiv> </refentry> diff --git a/help/en_US/poly2lsf.xml b/help/en_US/poly2lsf.xml index e3cc732..27ca6a2 100644 --- a/help/en_US/poly2lsf.xml +++ b/help/en_US/poly2lsf.xml @@ -17,45 +17,7 @@ <refnamediv> <refname>poly2lsf</refname> - <refpurpose>Calculates the line spectral frequencies for the given prediction polynomial</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - lsf = poly2lsf(a) -poly2lsf maps Prediction polynomial coefficients to line spectral frequencies. - - </synopsis> -</refsynopsisdiv> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para> Coefficients of prediction polynomial(the roots of the polynomial has to be inside the unity circle)</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>lsf:</term> - <listitem><para> Line spectral frequencies</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Example</title> - <programlisting role="example"><![CDATA[ -X=[1 0.6149 0.9899 0 0.0031 -0.0082] -lsf = poly2lsf(X) -//EXPECTED OUTPUT: -//lsf =0.7841731 1.5605415 1.8776459 1.8984313 2.3592523 - ]]></programlisting> -</refsection> - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Jitnder Singh</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/polyreduce.xml b/help/en_US/polyreduce.xml index 13e759a..2438c27 100644 --- a/help/en_US/polyreduce.xml +++ b/help/en_US/polyreduce.xml @@ -17,42 +17,7 @@ <refnamediv> <refname>polyreduce</refname> - <refpurpose></refpurpose> + <refpurpose>Calling sequence</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - p= polyreduce (c) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>c:</term> - <listitem><para>polynomial coefficients vector.</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para>Reduced polynomial coefficients.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Reduce a polynomial coefficient vector to a minimum number of terms by -stripping off any leading zeros. This function is used in residue(). - -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -polyreduce ([0, 0, 1, 2, 3]) -//Output: [1, 2, 3] - ]]></programlisting> -</refsection> - - </refentry> diff --git a/help/en_US/polyscale.xml b/help/en_US/polyscale.xml index cc62663..f689e36 100644 --- a/help/en_US/polyscale.xml +++ b/help/en_US/polyscale.xml @@ -17,77 +17,7 @@ <refnamediv> <refname>polyscale</refname> - <refpurpose> scales the roots of a polynomial in the z plane</refpurpose> + <refpurpose>errcheck1</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - b = polyscale(a,alpha) - - </synopsis> -</refsynopsisdiv> - -<refsection> -<title>Description</title> -<para> -b = polyscale(a,alpha) scales the roots of a polynomial in the z-plane, where a is a vector containing the polynomial coefficients and alpha is the scaling factor. -</para> -<para> -If alpha is a real value in the range [0 1], then the roots of a are radially scaled toward the origin in the z-plane. Complex values for alpha allow arbitrary changes to the root locations. -</para> - - -</refsection> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para> vector containing the polynomial coefficients</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>alpha:</term> - <listitem><para> the scaling vector</para></listitem></varlistentry> - </variablelist> -<variablelist> -<varlistentry><term>b:</term> - <listitem><para>vector of scaled roots. </para></listitem></varlistentry> - </variablelist> -</refsection> - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -p=[1 0 0 0 0 0 0 -1] -b=polyscale(p,0.95) -//EXPECTED OUTPUT:b=1. 0. 0. 0. 0. 0. 0. - 0.6983373 - - - ]]></programlisting> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -t -p=[1 0 0 0 0 0 0 -1] -b=polyscale(p,0.85) -//EXPECTED OUTPUT:b=1. 0. 0. 0. 0. 0. 0. - 0.3205771 - - - ]]></programlisting> -</refsection> - -<refsection> -<title>Author</title> -<simplelist type="vert"> - <member>Debdeep Dey</member> - </simplelist> -</refsection> - - - </refentry> diff --git a/help/en_US/polyval.xml b/help/en_US/polyval.xml index 8150805..08b5974 100644 --- a/help/en_US/polyval.xml +++ b/help/en_US/polyval.xml @@ -17,54 +17,7 @@ <refnamediv> <refname>polyval</refname> - <refpurpose>returns the value of a polynomial of degree n evaluated at x. y = polyval(p,x,[],mu) or [y,delta] = polyval(p,x,S,mu) use x^=(x−μ1)/μ2 in place of x. -In this equation, μ1=mean(x)and μ2=std(x). The centering and scaling parameters mu = [μ1,μ2] is optional </refpurpose> + <refpurpose>y = polyval(p,x) returns the value of a polynomial of degree n evaluated at x. The input argument p is a vector of length n+1 whose elements are the coefficients in descending powers of the polynomial to be evaluated.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y= polyval(p,x) - [y,delta] = polyval(p,x,S) - </synopsis> -</refsynopsisdiv> - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>p:</term> - <listitem><para> vector of length n+1 whose elements are the coefficients in descending powers of the polynomial to be evaluated.</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>x:</term> - <listitem><para> -can be a matrix or a vector. In either case, polyval evaluates p at each element of x.</para></listitem></varlistentry> - </variablelist> -<variablelist> -<varlistentry><term>delta:</term> - <listitem><para>-is an estimate of the standard deviation of the error in predicting a future observation at x by p(x). </para></listitem></varlistentry> - </variablelist> -</refsection> - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -p = [3 2 1]; -y=polyval(p,[5 7 9]) -//EXPECTED OUTPUT: -//y= 86 162 262 - - - ]]></programlisting> -</refsection> - - - - - - - - </refentry> diff --git a/help/en_US/postpad.xml b/help/en_US/postpad.xml index 8c96bae..e5e0979 100644 --- a/help/en_US/postpad.xml +++ b/help/en_US/postpad.xml @@ -20,55 +20,4 @@ <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - Y = postpad (X, L) - Y = postpad (X, L, C) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>X:</term> - <listitem><para>Input vector or matrix of real numbers.</para></listitem></varlistentry> - <varlistentry><term>L:</term> - <listitem><para> The length of the final postpadded sequence.</para></listitem></varlistentry> - <varlistentry><term>C:</term> - <listitem><para>number used for postpadding.</para></listitem></varlistentry> - <varlistentry><term>Y:</term> - <listitem><para>Output</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> - Append the scalar value C to the vector X until it is of length L. - </para> - <para> - If C is not given, a value of 0 is used. - </para> - <para> - If 'length (X) > L', elements from the end of X are removed until a vector of length L is obtained. - </para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - x = [1 2 3]; - L = 6; - y = postpad(x, L) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/prepad.xml b/help/en_US/prepad.xml index 81797e0..f79ba1d 100644 --- a/help/en_US/prepad.xml +++ b/help/en_US/prepad.xml @@ -17,57 +17,7 @@ <refnamediv> <refname>prepad</refname> - <refpurpose></refpurpose> + <refpurpose>Calling sequence:</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y= prepad (x, l) - y= prepad (x, l, c) - y= prepad (x, l, c, dim) - </synopsis> -</refsynopsisdiv> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para>Input vector or matrix of real numbers.</para></listitem></varlistentry> - <varlistentry><term>l:</term> - <listitem><para> The length of the final prepadded sequence.</para></listitem></varlistentry> - <varlistentry><term>c:</term> - <listitem><para>number used for prepadding.</para></listitem></varlistentry> - <varlistentry><term>dim:</term> - <listitem><para>The dimension along which the prepadding should happen.</para></listitem></varlistentry> - <varlistentry><term>y:</term> - <listitem><para>Output</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Prepend the scalar value c to the vector x until it is of length - l. If c is not given, a value of 0 is used. - - If length (x) > l, elements from the beginning of x - are removed until a vector of length l is obtained. - - If x is a matrix, elements are prepended or removed from each row. - - If the optional argument dim is given, operate along this dimension. - - If dim is larger than the dimensions of x, the result will have - dim dimensions. - -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -prepad ([1,2], 4,0,2) -//Output: [0,0,1,2] - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/prony.xml b/help/en_US/prony.xml index 42ca98a..25734a1 100644 --- a/help/en_US/prony.xml +++ b/help/en_US/prony.xml @@ -17,67 +17,7 @@ <refnamediv> <refname>prony</refname> - <refpurpose>Prony's method for time-domain design of IIR Filters</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [b,a] = prony(h,nb,na) - - </synopsis> -</refsynopsisdiv> - -<refsection> -<title>Description</title> -<para> -The system function has numerator order,nb and denominator order,na. The lengths of Num and Den are num_ord+1 and denom_ord+1. If the length of impulse_resp is less than the largest order (num_ord or denom_ord), impulse_resp is padded with zeros. -</para> - - - -</refsection> - - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para> coefficients of the numerator of the TF</para></listitem></varlistentry> - </variablelist> -<variablelist> - <varlistentry><term>a:</term> - <listitem><para> coefficients of the denominator of the TF</para></listitem></varlistentry> - </variablelist> -<variablelist> -<varlistentry><term>h:</term> - <listitem><para>impulse response of the digital filter </para></listitem></varlistentry> - </variablelist> -<variablelist> -<varlistentry><term>nb:</term> - <listitem><para>number of zeros(order of numerator) </para></listitem></varlistentry> - </variablelist> - -<variablelist> -<varlistentry><term>na:</term> - <listitem><para>number of poles(order of denominator) </para></listitem></varlistentry> - </variablelist> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -V=filter([1,1],[1,1,2],[1 zeros(1,31)]); -[b,a]=prony(V,1,2) - -//OUTPUT:a = 1. 1. 2.//denominator coefficients - //b = 1. 1. //numerator coefficients - - ]]></programlisting> -</refsection> - - - </refentry> diff --git a/help/en_US/pulseperiod.xml b/help/en_US/pulseperiod.xml index c28e26e..5cff5f8 100644 --- a/help/en_US/pulseperiod.xml +++ b/help/en_US/pulseperiod.xml @@ -17,86 +17,7 @@ <refnamediv> <refname>pulseperiod</refname> - <refpurpose>This function estimate pulse period of real vector x.</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - p=pulseperiod(x) - p=pulseperiod(x, Fs) - p=pulseperiod(x, t) - p=pulseperiod (x, t, 'Polarity', pol) - p=pulseperiod(x, t, 'MidPercentReferenceLevel', N ) - p=pulseperiod(x, t, 'Tolerance', M) - p=pulseperiod(x, t,'StateLevels', O) - [p initialcross finalcross nextcross midreference]=pulseperiod(x) - [p initialcross finalcross nextcross midreference]=pulseperiod(x, t) - [p initialcross finalcross nextcross midreference]=pulseperiod(x, t) - [p initialcross finalcross nextcross midreference]=pulseperiod(x, t, 'Polarity', pol) - [p initialcross finalcross nextcross midreference]=pulseperiod(x, t, 'MidPercentReferenceLevel', N ) - [p initialcross finalcross nextcross midreference]= pulseperiod(x, t, 'Tolerance', M) - [p initialcross finalcross nextcross midreference]= pulseperiod(x, t,'StateLevels', O) - [p initialcross finalcross nextcross midreference]= pulseperiod(x, t,'StateLevels', O, 'fig', f) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x</term> - <listitem><para> : real input vector </para></listitem></varlistentry> - <varlistentry><term>Fs</term> - <listitem><para> : specifies the sample rate, Fs, as a positive scalar, where the first sample instant corresponds to a time of zero. </para></listitem></varlistentry> - <varlistentry><term>t</term> - <listitem><para> : defiene instant sample time t as vector with same length of x, or specifies the sample rate, t, as a positive scalar.</para></listitem></varlistentry> - <varlistentry><term> 'Polarity', pol</term> - <listitem><para> : pol specify the polarity of the pulse as either 'positive' or 'negative', where the default value is 'positive'</para></listitem></varlistentry> - <varlistentry><term> 'MidPercentReferenceLevel', N </term> - <listitem><para> : specify the mid percent reference leves as a percentage, default value of N is 50.</para></listitem></varlistentry> - <varlistentry><term> 'Tolerance', M </term> - <listitem><para> : define the tolerance value as real scaler value, where default value of M is 2.0.</para></listitem></varlistentry> - <varlistentry><term>'StateLevels', O</term> - <listitem><para> : O define the lower and upper state levels as two element real vector.</para></listitem></varlistentry> - <varlistentry><term> 'fig', f</term> - <listitem><para> : f specify the logical input value to display figure as one of 'on' or 'off', where the default input in 'off'.</para></listitem></varlistentry> - - <varlistentry><term> p </term> - <listitem><para> : returns a vector of difference between the mid-crossings of the initial transition of each positive-polarity pulse and the next positive-going transition </para></listitem></varlistentry> - <varlistentry><term> initialcross </term> - <listitem><para> : returns a vector of initial cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> finalcross </term> - <listitem><para> : returns a vector of final cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> nextcross </term> - <listitem><para> : returns a vector of next cross values of bilevel waveform transitions x. </para></listitem></varlistentry> - <varlistentry><term> midreference </term> - <listitem><para> : return mid reference value corrosponding to mid percent reference value. </para></listitem></varlistentry> -</variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Examples </title> - - <programlisting role="example"><![CDATA[ - - x = fscanfMat("macros/pulsedata_x.txt"); //Importing samples of pulse - t = fscanfMat("macros/pulsedata_t.txt"); - clf //clearing plot history - p = pulseperiod(x,t,'fig','ON') // Detremining pulse period and ploting it - -]]></programlisting> - -<scilab:image> - x = [0. 0.0108 0.0367 -0.0452 0.0172 0.0064 -0.0262 -0.0087 0.0069 0.0716 0.0554 -0.027 0.0607 5.0145 4.9987 5.0143 4.9959 4.9975 5.0298 0.0282 0.0283 0.0134 -0.0241 0.0143 0.0326 0.0098 0.0207 0.0145 -0.0061 0.0059 -0.0157 0.0178 -0.0229 4.9786]; - t = [0. 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4 0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 ]; - clf - p = pulseperiod(x,t,'fig','ON') -</scilab:image> - -</refsection> - </refentry> diff --git a/help/en_US/pulsesep.xml b/help/en_US/pulsesep.xml index 32a8915..6ccfa78 100644 --- a/help/en_US/pulsesep.xml +++ b/help/en_US/pulsesep.xml @@ -17,86 +17,7 @@ <refnamediv> <refname>pulsesep</refname> - <refpurpose>This function estimate pulse separation between bilevel waveform pulses.</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - s=pulsesep(x) - s=pulsesep(x, Fs) - s=pulsesep(x, t) - s=pulsesep (x, t, 'Polarity', pol) - s=pulsesep(x, t, 'MidPercentReferenceLevel', N ) - s=pulsesep(x, t, 'Tolerance', M) - s=pulsesep(x, t,'StateLevels', O) - [s initialcross finalcross nextcross midreference]=pulsesep(x) - [s initialcross finalcross nextcross midreference]=pulsesep(x, t) - [s initialcross finalcross nextcross midreference]=pulsesep(x, t) - [s initialcross finalcross nextcross midreference]=pulsesep(x, t, 'Polarity', pol) - [s initialcross finalcross nextcross midreference]=pulsesep(x, t, 'MidPercentReferenceLevel', N ) - [s initialcross finalcross nextcross midreference]= pulsesep(x, t, 'Tolerance', M) - [s initialcross finalcross nextcross midreference]= pulsesep(x, t,'StateLevels', O) - [s initialcross finalcross nextcross midreference]= pulsesep(x, t,'StateLevels', O, 'fig', f) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x</term> - <listitem><para> : real input vector </para></listitem></varlistentry> - <varlistentry><term>Fs</term> - <listitem><para> : specifies the sample rate, Fs, as a positive scalar, where the first sample instant corresponds to a time of zero. </para></listitem></varlistentry> - <varlistentry><term>t</term> - <listitem><para> : defiene instant sample time t as vector with same length of x, or specifies the sample rate, t, as a positive scalar.</para></listitem></varlistentry> - <varlistentry><term> 'Polarity', pol</term> - <listitem><para> : pol specify the polarity of the pulse as either 'positive' or 'negative', where the default value is 'positive'</para></listitem></varlistentry> - <varlistentry><term> 'MidPercentReferenceLevel', N </term> - <listitem><para> : specify the mid percent reference leves as a percentage, default value of N is 50.</para></listitem></varlistentry> - <varlistentry><term> 'Tolerance', M </term> - <listitem><para> : define the tolerance value as real scaler value, where default value of M is 2.0.</para></listitem></varlistentry> - <varlistentry><term>'StateLevels', O</term> - <listitem><para> : O define the lower and upper state levels as two element real vector.</para></listitem></varlistentry> - <varlistentry><term> 'fig', f</term> - <listitem><para> : f specify the logical input value to display figure as one of 'on' or 'off', where the default input in 'off'.</para></listitem></varlistentry> - - <varlistentry><term> s </term> - <listitem><para> : returns a vector of differences between the mid-crossings of each final negative-going transition of every positive-polarity pulse and the next positive-going transition. </para></listitem></varlistentry> - <varlistentry><term> initialcross </term> - <listitem><para> : returns a vector of initial cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> finalcross </term> - <listitem><para> : returns a vector of final cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> nextcross </term> - <listitem><para> : returns a vector of next cross values of bilevel waveform transitions x. </para></listitem></varlistentry> - <varlistentry><term> midreference </term> - <listitem><para> : return mid reference value corrosponding to mid percent reference value. </para></listitem></varlistentry> -</variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Examples </title> - - <programlisting role="example"><![CDATA[ - - x = fscanfMat("macros/pulsedata_x.txt"); //Importing samples of pulse - t = fscanfMat("macros/pulsedata_t.txt"); - clf //clearing plot history - p = pulsesep(x,t,'fig','ON') // Detremining pulse separation and ploting it - -]]></programlisting> - -<scilab:image> - x = [0. 0.0108 0.0367 -0.0452 0.0172 0.0064 -0.0262 -0.0087 0.0069 0.0716 0.0554 -0.027 0.0607 5.0145 4.9987 5.0143 4.9959 4.9975 5.0298 0.0282 0.0283 0.0134 -0.0241 0.0143 0.0326 0.0098 0.0207 0.0145 -0.0061 0.0059 -0.0157 0.0178 -0.0229 4.9786]; - t = [0. 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4 0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 ]; - clf - p = pulsesep(x,t,'fig','ON') -</scilab:image> - -</refsection> - </refentry> diff --git a/help/en_US/pulsewidth.xml b/help/en_US/pulsewidth.xml index 9964429..0b5e4d7 100644 --- a/help/en_US/pulsewidth.xml +++ b/help/en_US/pulsewidth.xml @@ -17,85 +17,7 @@ <refnamediv> <refname>pulsewidth</refname> - <refpurpose>This function estimate pulse width of real vector x.</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - p=pulsewidth(x) - p=pulsewidth(x, Fs) - p=pulsewidth(x, t) - p=pulsewidth (x, t, 'Polarity', pol) - p=pulsewidth(x, t, 'MidPercentReferenceLevel', N ) - p=pulsewidth(x, t, 'Tolerance', M) - p=pulsewidth(x, t,'StateLevels', O) - [p initialcross finalcross nextcross midreference]=pulsewidth(x) - [p initialcross finalcross nextcross midreference]=pulsewidth(x, t) - [p initialcross finalcross nextcross midreference]=pulsewidth(x, t) - [p initialcross finalcross nextcross midreference]=pulsewidth(x, t, 'Polarity', pol) - [p initialcross finalcross nextcross midreference]=pulsewidth(x, t, 'MidPercentReferenceLevel', N ) - [p initialcross finalcross nextcross midreference]= pulsewidth(x, t, 'Tolerance', M) - [p initialcross finalcross nextcross midreference]= pulsewidth(x, t,'StateLevels', O) - [p initialcross finalcross nextcross midreference]= pulsewidth(x, t,'StateLevels', O, 'fig', f) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x</term> - <listitem><para> : real input vector </para></listitem></varlistentry> - <varlistentry><term>Fs</term> - <listitem><para> : specifies the sample rate, Fs, as a positive scalar, where the first sample instant corresponds to a time of zero. </para></listitem></varlistentry> - <varlistentry><term>t</term> - <listitem><para> : defiene instant sample time t as vector with same length of x, or specifies the sample rate, t, as a positive scalar.</para></listitem></varlistentry> - <varlistentry><term> 'Polarity', pol</term> - <listitem><para> : pol specify the polarity of the pulse as either 'positive' or 'negative', where the default value is 'positive'</para></listitem></varlistentry> - <varlistentry><term> 'MidPercentReferenceLevel', N </term> - <listitem><para> : specify the mid percent reference leves as a percentage, default value of N is 50.</para></listitem></varlistentry> - <varlistentry><term> 'Tolerance', M </term> - <listitem><para> : define the tolerance value as real scaler value, where default value of M is 2.0.</para></listitem></varlistentry> - <varlistentry><term>'StateLevels', O</term> - <listitem><para> : O define the lower and upper state levels as two element real vector.</para></listitem></varlistentry> - <varlistentry><term> 'fig', f</term> - <listitem><para> : f specify the logical input value to display figure as one of 'on' or 'off', where the default input in 'off'.</para></listitem></varlistentry> - - <varlistentry><term> p </term> - <listitem><para> : returns a vector of difference between the mid-crossings of the initial transition of each positive-polarity pulse and the next positive-going transition </para></listitem></varlistentry> - <varlistentry><term> initialcross </term> - <listitem><para> : returns a vector of initial cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> finalcross </term> - <listitem><para> : returns a vector of final cross values of bilevel waveform transitions x.</para></listitem></varlistentry> - <varlistentry><term> nextcross </term> - <listitem><para> : returns a vector of next cross values of bilevel waveform transitions x. </para></listitem></varlistentry> - <varlistentry><term> midreference </term> - <listitem><para> : return mid reference value corrosponding to mid percent reference value. </para></listitem></varlistentry> -</variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Examples </title> - - <programlisting role="example"><![CDATA[ - - x = fscanfMat("macros/pulsedata_x.txt"); //Importing samples of pulse - t = fscanfMat("macros/pulsedata_t.txt"); - clf //clearing plot history - p = pulsewidth(x,t,'fig','ON') // Detremining pulse width and ploting it - -]]></programlisting> - -<scilab:image> - x = [0. 0.0108 0.0367 -0.0452 0.0172 0.0064 -0.0262 -0.0087 0.0069 0.0716 0.0554 -0.027 0.0607 5.0145 4.9987 5.0143 4.9959 4.9975 5.0298 0.0282 0.0283 0.0134 -0.0241 0.0143 0.0326 0.0098 0.0207 0.0145 -0.0061 0.0059 -0.0157 0.0178 -0.0229 4.9786]; - t = [0. 0.025 0.05 0.075 0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325 0.35 0.375 0.4 0.425 0.45 0.475 0.5 0.525 0.55 0.575 0.6 0.625 0.65 0.675 0.7 0.725 0.75 0.775 0.8 0.825 ]; - clf - p = pulsewidth(x,t,'fig','ON') -</scilab:image> - -</refsection> - </refentry> diff --git a/help/en_US/rc2ac.xml b/help/en_US/rc2ac.xml index 1462720..acfe121 100644 --- a/help/en_US/rc2ac.xml +++ b/help/en_US/rc2ac.xml @@ -27,45 +27,4 @@ a = rc2ac(k, R0) </synopsis> </refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>k:</term> - <listitem><para>Lattice form reflection coefficients.</para></listitem></varlistentry> - <varlistentry><term>r0:</term> - <listitem><para> Initial zero lag autocorrelation.</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para>Autocorrelation coefficients.</para></listitem></varlistentry> - - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Uses rlevinson to function convert reflection coefficients to autocorrelation sequence. -</para> -<para> -r = rc2ac(k,r0) finds the autocorrelation coefficients, r, of the output of the discrete-time prediction error filter from the lattice-form reflection coefficients k and initial zero-lag autocorrelation r0 -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -k = [0.3090 0.9800 0.0031 0.0082 -0.0082]; -r0 = 0.1; -R=rc2ac(k,r0) -//Output: -//R = -// 0.1 -// - 0.0309 -// - 0.0790948 -// 0.0786627 -// 0.0293629 -// - 0.0950000 - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/rc2poly.xml b/help/en_US/rc2poly.xml index a2f2993..f8c66a4 100644 --- a/help/en_US/rc2poly.xml +++ b/help/en_US/rc2poly.xml @@ -3,11 +3,7 @@ <!-- * * This help file was generated from rc2poly.sci using help_from_sci(). - *kr: Refelection coefficient. -// R0: the zero lag autocorrelation, R0. -// a: Return the prediction polynomial. -// efinal: Return the final prediction error. - + * --> <refentry version="5.0-subset Scilab" xml:id="rc2poly" xml:lang="en" @@ -21,7 +17,7 @@ <refnamediv> <refname>rc2poly</refname> - <refpurpose>Convert reflection coefficients to prediction filter polynomial</refpurpose> + <refpurpose></refpurpose> </refnamediv> @@ -32,42 +28,4 @@ [a, efinal] = rc2poly(kr,R0) </synopsis> </refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>kr:</term> - <listitem><para>Reflection coefficients.</para></listitem></varlistentry> - <varlistentry><term>R0:</term> - <listitem><para>The zero-lag autocorrelation.</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para>Prediciton polynomial.</para></listitem></varlistentry> - -<varlistentry><term>efinal:</term> - <listitem><para>the final prediction error.</para></listitem></varlistentry> -</variablelist> -</refsection> - -<refsection> -<title>Description</title> -<para>a = rc2poly(k) converts the reflection coefficients k corresponding to the lattice structure to the prediction filter polynomial a, with a(1) = 1.</para> -<para>The output a is row vector of length length(k) + 1.</para> - -<para>[a,efinal] = rc2poly(k,r0) returns the final prediction error efinal based on the zero-lag autocorrelation, r0.</para> -</refsection> - - -<refsection> -<title>Example</title> - <programlisting role="example"><![CDATA[ -k = [0.3090 0.9800 0.0031 0.0082 -0.0082]; -a = rc2poly(k) -//EXPECTED OUTPUT:a= 1. 0.6148162 0.9898814 0.0000243 0.0031580 - 0.0082 - ]]></programlisting> - - -</refsection> - - - </refentry> diff --git a/help/en_US/rcosdesign.xml b/help/en_US/rcosdesign.xml index 397be44..e586d0d 100644 --- a/help/en_US/rcosdesign.xml +++ b/help/en_US/rcosdesign.xml @@ -17,66 +17,7 @@ <refnamediv> <refname>rcosdesign</refname> - <refpurpose><para>RCOSDESIGN computes the raised cosine FIR filter.</para> -<para>The output result for the input parameter of shape 'normal' is not equivalent to the matlab output because of the use of sinc function in the computation. Matlab and scilab sinc functions seem to not be equivalent. -</para> -</refpurpose> + <refpurpose>RCOSDESIGN computes the raised cosine FIR filter</refpurpose> </refnamediv> - - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - h = rcosdesign(rollof_factor, num_of_symb, samp_per_symb, varargin) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>rollof_factor:</term> - <listitem><para> roll-off factor of the designed filter</para></listitem></varlistentry> - </variablelist> - - - <variablelist> - <varlistentry><term>num_of_symb:</term> - <listitem><para>filter truncated to these many number of symbols</para></listitem></varlistentry> - </variablelist> - - - <variablelist> - <varlistentry><term>samp_of_symb:</term> - <listitem><para> each symbol represented by these many samples</para></listitem></varlistentry> - </variablelist> - - - - <variablelist> - <varlistentry><term>shape:</term> - <listitem><para> returns a normal raised-cosine FIR filter when set to 'normal'.</para> -<para>returns a square-root raised cosing filter when set to 'sqrt'.</para></listitem></varlistentry> - </variablelist> - - - <variablelist> - <varlistentry><term>h:</term> - <listitem><para> returned filter coefficients</para></listitem></varlistentry> - </variablelist> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -rolloff = 0.25; -span = 3; -sample per symbol=sps=2; -b=rcosdesign(rolloff,span,sps); -//OUTPUT: -//b=- 0.1210006 - 0.0456421 0.4418023 0.7590604 0.4418023 - 0.0456421 - 0.1210006 - ]]></programlisting> -</refsection> - </refentry> diff --git a/help/en_US/rectpuls.xml b/help/en_US/rectpuls.xml index 261d203..3cd54c0 100644 --- a/help/en_US/rectpuls.xml +++ b/help/en_US/rectpuls.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>rectpuls</refname> - <refpurpose></refpurpose> + <refpurpose>Generates a Rectangular pulse based on the width and sampling times.</refpurpose> </refnamediv> @@ -42,7 +42,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function y = rectpuls(t) returns a continuous, aperiodic, unity-height rectangular pulse depending upon input t, centered about t=0 and having default width of 1. y = rectpuls(t,w) generates a rectangle of width w. </para> diff --git a/help/en_US/resample.xml b/help/en_US/resample.xml index 809b4ff..b6e0591 100644 --- a/help/en_US/resample.xml +++ b/help/en_US/resample.xml @@ -20,44 +20,4 @@ <refpurpose>This function resamples in the input sequence x supplied by a factor of p/q.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = resample(x, p, q) - y = resample(x, p, q, h) - [y, h] = resample(...) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> scalar, vector or matrix of real or complex numbers</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>q:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>h:</term> - <listitem><para> scalar, vector or matrix of real or complex numbers</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function resamples in the input sequence x supplied by a factor of p/q. If x is a matrix, then every column is resampled changing the sample rate of x by a factor of p/q. -This is performed using a polyphase algorithm. The impulse response h, given as parameter 4, of the antialiasing filter is either specified or designed with a Kaiser-windowed sinecard. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -resample(1,2,3) -ans = 0.66667 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/residue.xml b/help/en_US/residue.xml index 0d969a8..299efcc 100644 --- a/help/en_US/residue.xml +++ b/help/en_US/residue.xml @@ -19,53 +19,5 @@ <refname>residue</refname> <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [r, p, k, e] = residue (b, a) - [b, a] = residue (r, p, k) - [b, a] = residue (r, p, k, e) - </synopsis> -</refsynopsisdiv> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -The first calling form computes the partial fraction expansion for the quotient of the polynomials, b and a. -This function works for transfer functions which are proper fractions. -NOTE that the polynomials 'b' and 'a' should have real coefficients(because of the function 'filter' used in polyval) -</para> -<para>The second and the third calling forms perform the inverse operation of the first calling form.</para> -<para> -<latex> -\begin{eqnarray} -\frac{B(s)}{A(s)}=\sum_{m=1}^{M}\frac{r(m)}{(s-p(m))^{e(m)}}+\sum_{i=1}^{N}k(i)*{s^{N-i}} -\end{eqnarray} -</latex> -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -1. -b = [1, 1, 1]; -a = [1, -5, 8, -4]; - [r, p, k, e] = residue (b, a) -// result r = [-2; 7; 3] -// result p = [2; 2; 1] -// result k = [](0x0) -// result e = [1; 2; 1] - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/residued.xml b/help/en_US/residued.xml index 1aa9f3c..257c2bf 100644 --- a/help/en_US/residued.xml +++ b/help/en_US/residued.xml @@ -17,59 +17,7 @@ <refnamediv> <refname>residued</refname> - <refpurpose></refpurpose> + <refpurpose>IIR part (poles p and residues r) is driven in parallel</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [r,p,f,m]=residued(b,a) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Similar to the "residuez" function. The difference being in the function "residuez", the IIR part (poles p and residues r) is driven in parallel with the FIR part(f) whereas in the function "residued", the IIR part is driven by the output of the FIR part. In signal modeling applications, this structure can be more accurate.NOTE that the polynomials 'b' and 'a' should have real coefficients(because of the function 'filter' used in polyval). This function works for transfer functions which are proper fractions. -</para> -<para> -<latex> -\begin{eqnarray} -\frac{B(s)}{A(s)}=\sum_{m=1}^{M}\frac{r(m)}{(s-p(m))^{e(m)}}+\sum_{i=1}^{N}k(i)*{s^{N-i}} -\end{eqnarray} -</latex> -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -1. -B=[1 1 ]; A=[1 -2 1]; - [r,p,f,m] = residued(B,A) -//OUTPUT -//r = -// -1 -// 2 -//p = -// 1 -// 1 -// -//f = [](0x0) -//e = -// 1 -// 2 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/residuez.xml b/help/en_US/residuez.xml index b004fea..c18bb61 100644 --- a/help/en_US/residuez.xml +++ b/help/en_US/residuez.xml @@ -20,49 +20,4 @@ <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [r,p,f,m]=residuez(b,a) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - <varlistentry><term>a:</term> - <listitem><para> Real or complex valued vector or matrix</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It computes the partial fraction expansion of filter H(z)= B(z)/A(z) where inputs b and a are vectors specifying the digital filter.NOTE that the polynomials 'b' and 'a' should have real coefficients(because of the function 'filter' used in polyval). This function works for transfer functions which are proper fractions. -</para> -<para> -<latex> -\begin{eqnarray} -H(z)=\frac{r(1)}{[1-\frac{p(1)}{z}]^{m(1)}}+..........+\frac{r(N)}{[1-\frac{p(N)}{z}]^{m(N)}} -\end{eqnarray} -</latex> -</para> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -1.B=[1 1 1]; A=[1 -2 1]; - [r,p,f,m] = residuez(B,A) - //OUTPUT: - //r=[0;3] - //p=[1;1] - //f=1 - //e=[1;2] - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/rlevinson.xml b/help/en_US/rlevinson.xml index 5017af8..799fcb1 100644 --- a/help/en_US/rlevinson.xml +++ b/help/en_US/rlevinson.xml @@ -17,116 +17,17 @@ <refnamediv> <refname>rlevinson</refname> - <refpurpose>This function computes the autocorrelation coefficients using prediction polynomial method.</refpurpose> + <refpurpose></refpurpose> </refnamediv> <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - R = rlevinson(a, efinal) - [R, U] = rlevinson(a, efinal) - [R, U, kr] = rlevinson(a, efinal) - [R, U, kr, e] = rlevinson(a, efinal) + a = rlevinson(a, efinal) + [a, U] = rlevinson(a, efinal) + [a, U, kr] = rlevinson(a, efinal) + [a, U, kr, e] = rlevinson(a, efinal) </synopsis> </refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>a:</term> - <listitem><para>Input argument ,prediction polynomial coefficients.</para></listitem></varlistentry> - <varlistentry><term>efinal:</term> - <listitem><para>Input argument, final prediction error.</para></listitem></varlistentry> - <varlistentry><term>R:</term> - <listitem><para>Returns the auto-correlation coefficients.</para></listitem></varlistentry> - -<varlistentry><term>U:</term> - <listitem><para>Returns upper triangular matrix of order length(a)*length(a).</para></listitem></varlistentry> - <varlistentry><term>kr:</term> - <listitem><para>Reflection coefficients.</para></listitem></varlistentry> - -<varlistentry><term>e:</term> - <listitem><para>Prediction error.</para></listitem></varlistentry> - </variablelist> - -</refsection> - -<refsection> - <title>Description - </title> -<para>The reverse Levinson-Durbin recursion implements the step-down algorithm for solving the following symmetric Toeplitz system of linear equations for r, where r = [r(1) … r(p + 1)] and r(i)* denotes the complex conjugate of r(i).</para> -<para> -<latex> -\begin{eqnarray} -\begin{bmatrix} - r(1) r(2)* - - r(p)* \\ - r(2) r(1) - - r(p-1)* \\ - - - - - - \\ - - - - - - \\ - - - - - - \\ - r(p) r(p-1) - r(2) r(1) -\end{bmatrix}\begin{bmatrix} -a(2)\\ -a(3)\\ -.\\ -.\\ -.\\ -a(p+1) -\end{bmatrix}=\begin{bmatrix} --r(2)\\ --r(3)\\ -.\\ -.\\ -.\\ --r(p+1) -\end{bmatrix} -\end{eqnarray} - -</latex> -</para> - -<para> -Input vector a represents the polynomial coefficients of this prediction error filter in descending powers of z. -<para> -<latex> -\begin{eqnarray} -A(z)=1+a(2)^{z-1}+.............+a(n+1)^{z-p} -\end{eqnarray} -</latex></para> -</para> -<para>[r,u] = rlevinson(a,efinal) returns upper triangular matrix U from the UDU* decomposition - -</para> -<latex> -\begin{eqnarray} -R^{-1}=UE^{-1}U* -\end{eqnarray} -</latex> -<para> -<latex> - -</latex> -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -X=[1 6/7 5/7 8/7 3/7 6/7]; -[R U kr e] = rlevinson(X, 0.3) - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="levinson">abs</link></member> - <member><link linkend="levdown">mean</link></member> - - </simplelist> -</refsection> - - - </refentry> diff --git a/help/en_US/rooteig.xml b/help/en_US/rooteig.xml index b8ad409..13f9f7d 100644 --- a/help/en_US/rooteig.xml +++ b/help/en_US/rooteig.xml @@ -24,99 +24,17 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> + w = rooteig(x,p) [w,pow] = rooteig(x,p) - [w,pow] = rooteig(...,fs) + [f,pow] = rooteig(...,fs) [w,pow] = rooteig(...,'corr') - + </synopsis> </refsynopsisdiv> <refsection> <title>Parameters</title> <variablelist> - <varlistentry><term>x:</term> - <listitem><para> int|double - vector|matrix</para> -<para>Input signal. In case of a matrix, each row of x represents a - seperate observation of the signal. If 'corr' flag is specified, - then x is the correlation matrix. - If w is not specified in the input, it is determined by the - algorithm. If x is real valued, then range of w is [0, pi]. - Otherwise, the range of w is [0, 2pi)</para> -</listitem></varlistentry> - </variablelist> - -<variablelist> - <varlistentry><term>p:</term> - <listitem><para> int|double - scalar|vector</para> -<para>p(1) is the dimension of the signal subspace - p(2), if specified, represents a threshold that is multiplied by - the smallest estimated eigenvalue of the signal's correlation matrix.</para> - -</listitem></varlistentry> </variablelist> - -<variablelist> -<varlistentry><term>w:</term> - <listitem><para> int|double - vector</para> -<para>w is the vector of normalized frequencies over which the - pseuspectrogram is to be computed.</para> - -</listitem></varlistentry> - -</variablelist> - - - - -<variablelist> -<varlistentry><term>fs:</term> - <listitem><para> int|double - scalar (Default = 1)</para> -<para>Sampling rate. Used to convert the normalized frequencies (w) to - actual values (f) and vice-versa.</para> - -</listitem></varlistentry> - -</variablelist> - - -</refsection> - - - - - - - -<refsection> -<title>Description</title> -[w,pow] = rooteig(x,p) estimates the frequency content in the time samples of a signal x, and returns w, a vector of frequencies in rad/sample, and the corresponding signal power in the vector pow in units of power, such as volts^2. The input signal x is specified either as: - <para> A row or column vector representing one observation of the signal - - </para> -<para>A rectangular array for which each row of x represents a separate observation of the signal (for example, each row is one output of an array of sensors, as in array processing), such that x'*x is an estimate of the correlation matrix</para> -</refsection> -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - n=0:99; - s=exp(1*%i*%pi/2*n)+2*exp(1*%i*%pi/4*n)+exp(1*%i*%pi/3*n)+rand(1,100,"normal"); - X = corrmtx(s,12,'mod'); - [W,P] = rooteig(X,3); - ]]></programlisting> -</refsection> -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="corrmtx">| peig | pmusic | rootmusic</link></member> - <member><link linkend="References">References</link></member> - <member><link linkend="1)">Stoica, P. and R. Moses, INTRODUCTION TO SPECTRAL ANALYSIS,</link></member> - <member><link linkend="Prentice-Hall">Prentice-Hall</link></member> - <member><link linkend="Output">arguments</link></member> - <member><link linkend="w">- double - vector</link></member> - <member><link linkend="Estimated">frequencies of the complex sinusoids</link></member> - <member><link linkend="pow">- double - vector</link></member> - <member><link linkend="estimated">absolute value squared amplitudes of the sinusoids at</link></member> - <member><link linkend="the">frequencies w</link></member> - </simplelist> </refsection> </refentry> diff --git a/help/en_US/rootmusic.xml b/help/en_US/rootmusic.xml index 19aa883..760a993 100644 --- a/help/en_US/rootmusic.xml +++ b/help/en_US/rootmusic.xml @@ -28,109 +28,19 @@ [w,pow] = rootmusic(x,p) [f,pow] = rootmusc(...,fs) [w,pow] = rootmusic(...,'corr') - + </synopsis> </refsynopsisdiv> <refsection> <title>Parameters</title> <variablelist> - <varlistentry><term>x:</term> - <listitem><para> int|double - vector|matrix</para> -<para>Input signal. In case of a matrix, each row of x represents a - seperate observation of the signal. If 'corr' flag is specified, - then x is the correlation matrix. - If w is not specified in the input, it is determined by the - algorithm. If x is real valued, then range of w is [0, pi]. - Otherwise, the range of w is [0, 2pi)</para> -</listitem></varlistentry> - </variablelist> - -<variablelist> - <varlistentry><term>p:</term> - <listitem><para> int|double - scalar|vector</para> -<para>p(1) is the dimension of the signal subspace - p(2), if specified, represents a threshold that is multiplied by - the smallest estimated eigenvalue of the signal's correlation matrix.</para> - -</listitem></varlistentry> + <varlistentry><term>Examples:</term> + <listitem><para> </para></listitem></varlistentry> + <varlistentry><term>1) 3 complex exponentials:</term> + <listitem><para> </para></listitem></varlistentry> + <varlistentry><term>n=0:</term> + <listitem><para>99;</para></listitem></varlistentry> </variablelist> - -<variablelist> -<varlistentry><term>w:</term> - <listitem><para> int|double - vector</para> -<para>w is the vector of normalized frequencies over which the - pseuspectrogram is to be computed.</para> - -</listitem></varlistentry> - -</variablelist> - - -<variablelist> -<varlistentry><term>pow:</term> - <listitem><para> the estimated absolute value squared amplitudes of the sinusoids at the frequencies w.</para> - -</listitem></varlistentry> - -</variablelist> - -<variablelist> -<varlistentry><term>fs:</term> - <listitem><para> int|double - scalar (Default = 1)</para> -<para>Sampling rate. Used to convert the normalized frequencies (w) to - actual values (f) and vice-versa.</para> - -</listitem></varlistentry> - -</variablelist> - - -<variablelist> -<varlistentry><term>corr:</term> - <listitem><para> If specified, x is interpreted as a correlation matrix rather than a matrix of the signal data. For x to be a correlation matrix, x must be a square matrix and all its eigenvalues must be nonnegative.</para> - -</listitem></varlistentry> - -</variablelist> -</refsection> - -<refsection> - <title>Description</title> -<para>W = rootmusic(X,P) returns the frequencies in radians/sample for the P complex exponentials (sinusoids) that make up the signal X. - -The input X is specified either as:</para> - - <para> A row or column vector representing one observation of the signal - - </para> -<para>A rectangular array for which each row of x represents a separate observation of the signal (for example, each row is one output of an array of sensors, as in array processing), such that x'*x is an estimate of the correlation matrix</para> -</refsection> - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -n=0:99; -s=exp(1*%i*%pi/2*n)+2*exp(1*%i*%pi/4*n)+exp(1*%i*%pi/3*n)+rand(1,100,"normal"); -[A,R]=corrmtx(s,12,'mod'); -[W,P] = rootmusic(R,3,'corr'); - ]]></programlisting> -</refsection> - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="corrmtx">| peig | pmusic | rooteig</link></member> - <member><link linkend="References">References</link></member> - <member><link linkend="1)">Monson H. Hayes, Statistical Digital Signal Processing And Modeling,</link></member> - <member><link linkend="Wiley">& Sons, Inc, [Section 8.6.3]</link></member> - <member><link linkend="Output">arguments</link></member> - <member><link linkend="w">- double - vector</link></member> - <member><link linkend="Estimated">frequencies of the complex sinusoids</link></member> - <member><link linkend="pow">- double - vector</link></member> - <member><link linkend="estimated">absolute value squared amplitudes of the sinusoids at</link></member> - <member><link linkend="the">frequencies w</link></member> - </simplelist> </refsection> </refentry> diff --git a/help/en_US/roundn.xml b/help/en_US/roundn.xml new file mode 100644 index 0000000..fc90fba --- /dev/null +++ b/help/en_US/roundn.xml @@ -0,0 +1,23 @@ +<?xml version="1.0" encoding="UTF-8"?> + +<!-- + * + * This help file was generated from roundn.sci using help_from_sci(). + * + --> + +<refentry version="5.0-subset Scilab" xml:id="roundn" 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>roundn</refname> + <refpurpose>r=(round(x*10^n))/(10^n);</refpurpose> + </refnamediv> + +</refentry> diff --git a/help/en_US/sampled2continuous.xml b/help/en_US/sampled2continuous.xml index 8d3d2df..89e1dc0 100644 --- a/help/en_US/sampled2continuous.xml +++ b/help/en_US/sampled2continuous.xml @@ -17,42 +17,7 @@ <refnamediv> <refname>sampled2continuous</refname> - <refpurpose>This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time.</refpurpose> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - x = sampled2continuous (n, s, t) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>xn:</term> - <listitem><para> sampled signal</para></listitem></varlistentry> - <varlistentry><term>s:</term> - <listitem><para> sampling rate</para></listitem></varlistentry> - <varlistentry><term>t:</term> - <listitem><para>all instants of time t at which we need x(t) from x[n] </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para>This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time. -The third parameter t is all the instants where output x is needed from intput n and this time is relative to x(0). -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -sampled2continuous([1,2,3],5,6) -//ans = -//2.4166806 - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/sawtooth.xml b/help/en_US/sawtooth.xml index aee4088..9a0b0a8 100644 --- a/help/en_US/sawtooth.xml +++ b/help/en_US/sawtooth.xml @@ -42,7 +42,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function This function returns a sawtooth wave with period 2*pi with +1/-1 as the maximum and minimum values for elements of t. If width is specified, it determines where the maximum is in the interval [0,2*pi]. </para> </refsection> diff --git a/help/en_US/schurrc.xml b/help/en_US/schurrc.xml index 481430a..b269840 100644 --- a/help/en_US/schurrc.xml +++ b/help/en_US/schurrc.xml @@ -17,52 +17,7 @@ <refnamediv> <refname>schurrc</refname> - <refpurpose>Computes reflection coefficients from auto-correlation sequence using Schrur algorithm.</refpurpose> + <refpurpose>narginchk(1,1,argn(2));</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - k = schurrc(r) -[k,e] = schurrc(r) - </synopsis> -</refsynopsisdiv> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>k:</term> - <listitem><para> reflection coefficients or lattice parameters of prediction filter</para></listitem></varlistentry> - <varlistentry><term>e:</term> - <listitem><para> prediction error variance</para></listitem></varlistentry> - <varlistentry><term>r:</term> - <listitem><para>auto correltion sequence </para></listitem></varlistentry> - </variablelist> -</refsection> - - -<refsection> - <title>Description</title> - <para>k = schurrc(r) uses the Schur algorithm to compute a vector k of reflection coefficients from a vector r representing an autocorrelation sequence. k and r are the same size. The reflection coefficients represent the lattice parameters of a prediction filter for a signal with the given autocorrelation sequence, r. When r is a matrix, schurrc treats each column of r as an independent autocorrelation sequence, and produces a matrix k, the same size as r. Each column of k represents the reflection coefficients for the lattice filter for predicting the process with the corresponding autocorrelation sequence r.</para> - -<para>[k,e] = schurrc(r) also computes the scalar e, the prediction error variance. When r is a matrix, e is a column vector. The number of rows of e is the same as the number of columns of r. - -</para> -</refsection> - - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -m=linspace(1,100); -r = xcorr(m(1:5),'unbiased'); -[k,e] = schurrc(r(5:$)) - -//EXPECTED OUTPUT -//e =1.6212406 - //k = - 0.9090909 0.2222222 0.2244898 0.2434211 - - - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/sftrans.xml b/help/en_US/sftrans.xml index 3a2e0c5..16bfd81 100644 --- a/help/en_US/sftrans.xml +++ b/help/en_US/sftrans.xml @@ -20,61 +20,4 @@ <refpurpose>Transform band edges of a prototype filter (cutoff at W=1) represented in s-plane zero-pole-gain form (Frequency Transformation in Analog domain).</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [Sz, Sp, Sg] = sftrans (Sz, Sp, Sg, W, stop) - [Sz, Sp] = sftrans (Sz, Sp, Sg, W, stop) - [Sz] = sftrans (Sz, Sp, Sg, W, stop) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>Sz:</term> - <listitem><para> Zeros.</para></listitem></varlistentry> - <varlistentry><term>Sp:</term> - <listitem><para> Poles.</para></listitem></varlistentry> - <varlistentry><term>Sg:</term> - <listitem><para> Gain.</para></listitem></varlistentry> - <varlistentry><term>W:</term> - <listitem><para> Edge of target filter.</para></listitem></varlistentry> - <varlistentry><term>stop:</term> - <listitem><para> True(%T or 1) for high pass and band stop filters or false (%F or 0) for low pass and band pass filters.</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -Given a low pass filter represented by poles and zeros in the splane, can be converted into a </para> -<para> low pass (having diffrent cutoff frequency),</para> -<para> high pass,</para> -<para> band pass,</para> -<para> band stop.</para> -<para> by transforming each of the poles and zeros individually.</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -Sz = [1 2 3]; -Sp = [4 5 6]; -Sg = 15; -W = 20; -stop = %T; -[Sz, Sp, Sg] = sftrans (Sz, Sp, Sg, W, stop) - - ]]></programlisting> -</refsection> - -<refsection> -<title>Modified by :</title> -<simplelist type="vert"> -<member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> -</refsection> - </refentry> diff --git a/help/en_US/sgolay.xml b/help/en_US/sgolay.xml index 676b931..02ac23e 100644 --- a/help/en_US/sgolay.xml +++ b/help/en_US/sgolay.xml @@ -47,7 +47,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. This function computes the filter coefficients for all Savitzsky-Golay smoothing filters of order p for length n (odd). m can be used in order to get directly the mth derivative; ts is a scaling factor. </para> diff --git a/help/en_US/sigmoid_train.xml b/help/en_US/sigmoid_train.xml index 3e23d09..fd204d0 100644 --- a/help/en_US/sigmoid_train.xml +++ b/help/en_US/sigmoid_train.xml @@ -17,41 +17,7 @@ <refnamediv> <refname>sigmoid_train</refname> - <refpurpose>Evaluate a train of sigmoid functions at t.</refpurpose> - <para> </para> + <refpurpose>Evaluate a train of sigmoid functions at T.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = sigmoid_train(t, ranges, rc) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>t:</term> - <listitem><para> integer</para></listitem></varlistentry> - <varlistentry><term>ranges:</term> - <listitem><para> matrix</para></listitem></varlistentry> - <varlistentry><term>rc:</term> - <listitem><para> time constant (scalar)</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -The number and duration of each sigmoid is determined from RANGES. Each row of RANGES represents a real interval, e.g. if sigmoid 'i' starts at 't=0.1' and ends at 't=0.5', then 'RANGES(i,:) = [0.1 0.5]'. The input RC is an array that defines the rising and falling time constants of each sigmoid. Its size must equal the size of RANGES. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -sigmoid_train(0.1,[1:3],4) - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/slewrate.xml b/help/en_US/slewrate.xml index c2a9002..97afbfb 100644 --- a/help/en_US/slewrate.xml +++ b/help/en_US/slewrate.xml @@ -17,52 +17,7 @@ <refnamediv> <refname>slewrate</refname> - <refpurpose> returns the slew rate for all transitions found in the bilevel waveform</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - s=slewrate(x) - s=slewrate(x, t) - s=slewrate(x, Fs) - s=slewrate(x, t, 'PercentReferenceLevels', N ) - s=slewrate(x, t, 'Tolerance', M) - s=slewrate(x, t,'StateLevels', O) - - [s lowercrossvalue uppercrossvalue lowerreference upperreference]=slewrate(x) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]=slewrate(x, t) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]=slewrate(x, Fs) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]=slewrate(x, t, 'PercentReferenceLevels', N ) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]= slewrate(x, t, 'Tolerance', M) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]= slewrate(x, t,'StateLevels', O) - [s lowercrossvalue uppercrossvalue lowerreference upperreference]= slewrate(x, t,'StateLevels', O, 'fig', on or off) - - </synopsis> -</refsynopsisdiv> - - - - -<refsection> - <title>Description</title> - returns the slew rate for all transitions found in the bilevel waveform, X. The slew rate is the slope of the line connecting the 10% and 90% reference levels. The sample instants of X are the indices of the vector. To determine the transitions, slewrate estimates the state levels of the input waveform by a histogram method. slewrate identifies all regions that cross the upper-state boundary of the low state and the lower-state boundary of the high state. The low-state and high-state boundaries are expressed as the state level plus or minus a multiple of the difference between the state levels. -</refsection> - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - x=[ones(1,25) -ones(1,25) ones(1,25) ] - t=1:length(x) - s=slewrate(x,t) - //EXPECTED OUTPUT: - //s = - 2 2 - - ]]></programlisting> -</refsection> - - - - </refentry> diff --git a/help/en_US/sos2ss.xml b/help/en_US/sos2ss.xml index d69c4c7..99a5812 100644 --- a/help/en_US/sos2ss.xml +++ b/help/en_US/sos2ss.xml @@ -17,86 +17,7 @@ <refnamediv> <refname>sos2ss</refname> - <refpurpose>Converts Digital Filter Second order section parameters to steady state form. </refpurpose> + <refpurpose>[nargout,nargin]=argn();</refpurpose> </refnamediv> - -<refsection> -<title>Calling Sequence</title> - -[A,B,C,D] = sos2ss(sos) -[A,B,C,D] = sos2ss(sos,g) - -</refsection> -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>[A,B,C,D]:</term> - <listitem><para>Steady state parameters.</para></listitem></varlistentry> - <varlistentry><term>sos:</term> - <listitem><para> Second order sectional matrix.</para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para>gain of the system.</para></listitem></varlistentry> - </variablelist> -</refsection> - - -<refsection> -<title>Description</title> -<para>sos2ss converts a second-order section representation of a digital filter to an equivalent state-space representation.</para> -<para> -[A,B,C,D] = sos2ss(sos) converts sos, a system expressed in second-order section form, to a single-input, single-output state-space representation: - -x(n+1)=Ax(n)+Bu(n), -y(n)=Cx(n)+Du(n).</para> - -<para> - - <latex> - \begin{eqnarray} - f(x,a,r) = \frac{1}{r^{-a}\Gamma(a)} \int_0^x t^{a-1} \exp\left(-rt\right) dt - \end{eqnarray} - </latex> - -</para> - - - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -sos = [1 1 1 1 0 -1 ; - -2 3 1 1 10 1]; -[A,B,C,D] = sos2ss(sos,2); -//EXPECTED OUTPUT: -//D =- 4. -//C =42. 4. - 32. - 2. -//B =[1. 0. 0. 0. 0.]' -//A =[- 10. 0. 10. 1. ; 1. 0. 0. 0. ; 0. 1. 0. 0. ; 0. 0. 1. 0. ] - ]]></programlisting> -</refsection> - - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Parthasrathi Panda</member> - </simplelist> -</refsection> - - - -<refsection> - <title>See also</title> - <simplelist type="inline"> - <member><link linkend="sos2tf">abs</link></member> - <member><link linkend="sosbreak">mean</link></member> - <member><link linkend="sos2ss">max</link></member> - - </simplelist> -</refsection> - - </refentry> diff --git a/help/en_US/sos2tf.xml b/help/en_US/sos2tf.xml index 972ba79..ef7fe71 100644 --- a/help/en_US/sos2tf.xml +++ b/help/en_US/sos2tf.xml @@ -34,22 +34,15 @@ <title>Parameters</title> <variablelist> <varlistentry><term>sos:</term> - <listitem><para> matrix of real or complex numbers</para><para>Second Order Sectional form of the system.</para></listitem></varlistentry> + <listitem><para> matrix of real or complex numbers</para></listitem></varlistentry> <varlistentry><term>g:</term> - <listitem><para> real or complex value, default value is 1</para><para>Gain of the system.</para></listitem></varlistentry> - -<varlistentry><term>B:</term> - <listitem><para>Vector of numerator coefficients.</para><para>Gain of the system.</para></listitem></varlistentry> - -<varlistentry><term>A:</term> - <listitem><para>Vector of denominator coefficients.</para><para>Gain of the system.</para></listitem></varlistentry> + <listitem><para> real or complex value, default value is 1</para></listitem></varlistentry> </variablelist> </refsection> <refsection> <title>Description</title> <para> -This is an Octave function. This function converts series second-order sections to direct H(z) = B(z)/A(z) form. The input is the sos matrix and the second parameter is the overall gain, default value of which is 1. The output is a vector. @@ -59,11 +52,12 @@ The output is a vector. <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -[a,b]=sos2tf([1,2,3,4,5,6]) -//a = -//1 2 3 -//b = -//4 5 6 +sos = [1 1 1 1 0 -1; -2 3 1 1 10 1]; +//[b,a] = sos2tf(sos) +a = +-2 1 2 4 1 +b = +1 10 0 -10 -1 ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/sos2zp.xml b/help/en_US/sos2zp.xml index efabf07..cccdd11 100644 --- a/help/en_US/sos2zp.xml +++ b/help/en_US/sos2zp.xml @@ -48,7 +48,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. This function converts series second-order sections to zeros, poles, and gains (pole residues). The input is the sos matrix and the second parameter is the overall gain, default value of which is 1. The outputs are z, p, k. z and p are column vectors containing zeros and poles respectively, and k is the overall gain. diff --git a/help/en_US/sosbreak.xml b/help/en_US/sosbreak.xml index 383f449..08050b8 100644 --- a/help/en_US/sosbreak.xml +++ b/help/en_US/sosbreak.xml @@ -17,56 +17,7 @@ <refnamediv> <refname>sosbreak</refname> - <refpurpose>function for breaking a polynomial in second order polynomials (and an extra linear)</refpurpose> + <refpurpose>function for breaking a polynomial in second order polynomials and an extra linear term (g)</refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [zerosort,g]=sosbreak(p) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>p:</term> - <listitem><para>-the input polynomial.</para></listitem></varlistentry> - <varlistentry><term>zerosort:</term> - <listitem><para>-the array of the broken polynomials </para></listitem></varlistentry> - <varlistentry><term>g:</term> - <listitem><para>the interger multiple obtained after breaking the polynomial</para></listitem></varlistentry> - - - -</variablelist> - -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -v=[1+4*%s+6*%s^2+4*%s^3+%s^4]; -[zerosort,g]=sosbreak(v); -//OUTPUT: -// g =1. - //zerosort = - // zerosort(1) -// 2 -// 1 + 2s + s -// zerosort(2) -// 2 -//1.0000000 + 2s + s - - ]]></programlisting> -</refsection> - - -<refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Parthasarathi Panda</member> -</simplelist> -</refsection> - </refentry> diff --git a/help/en_US/specgram.xml b/help/en_US/specgram.xml index d603043..370c6b9 100644 --- a/help/en_US/specgram.xml +++ b/help/en_US/specgram.xml @@ -17,83 +17,7 @@ <refnamediv> <refname>specgram</refname> - <refpurpose>Generate a spectrogram for the signal x.</refpurpose> - </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [S, f, t] = specgram (x,n,fs,win,overlap) - [S, f, t] =specgram(x) - </synopsis> -</refsynopsisdiv> - - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>S:</term> - <listitem><para>is the complex output of the FFT, one row per slice.</para></listitem></varlistentry> - <varlistentry><term>f:</term> - <listitem><para>is the frequency indices corresponding to the rows of S.</para></listitem></varlistentry> - <varlistentry><term>t:</term> - <listitem><para>is the time indices corresponding to the columns of S.</para></listitem></varlistentry> - -<varlistentry><term>x:</term> - <listitem><para>the input signal.</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para>length of the overlapping segments(default,n=256).</para></listitem></varlistentry> - -<varlistentry><term>fs:</term> - <listitem><para>sampling rate of input signal (if specified).</para></listitem></varlistentry> -<varlistentry><term>win:</term> - <listitem><para>window vector,alternate window to apply rather than the default of hanning (n).</para></listitem></varlistentry> -<varlistentry><term>overlap:</term> - <listitem><para>specifies the number of samples overlap between successive segments of the input signal. The default overlap is length (window)/2.</para></listitem></varlistentry> - - -</variablelist> - -</refsection> - - -<refsection> - <title>Description - </title> -<para>Generates a spectrogram for the signal x. The signal is chopped into overlapping segments of length n, and each segment is windowed and transformed into the frequency domain using the FFT. The default segment size is 256. If fs is given, it specifies the sampling rate of the input signal. The argument window specifies an alternate window to apply rather than the default of hanning (n). The argument overlap specifies the number of samples overlap between successive segments of the input signal. The default overlap is length (window)/2. -</para> - -</refsection> - - - - - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -N=1024; -n=0:N-1; -w=2*%pi/5; -x=sin(w*n)+10*sin(2*w*n); -[s,f,t]=specgram(x); -//grayplot(f,t,s) -//xlabel("frequency") -//ylabel("time") - ]]></programlisting> -<scilab:image> -N=1024; -n=0:N-1; -w=2*%pi/5; -x=sin(w*n)+10*sin(2*w*n); -[s,f,t]=specgram(x); -grayplot(f,t,s) -xlabel("frequency") -ylabel("time") - -</scilab:image> - - -</refsection> - + <refpurpose>Generate a spectrogram for the signal x. The signal is chopped into overlapping segments of length n, and each segment is windowed and transformed into the frequency domain using the FFT. The default segment size is 256. If fs is given, it specifies the sampling rate of the input signal. The argument window specifies an alternate window to apply rather than the default of hanning (n). The argument overlap specifies the number of samples overlap between successive segments of the input signal. The default overlap is length (window)/2.</refpurpose> + </refnamediv> </refentry> diff --git a/help/en_US/stmcb.xml b/help/en_US/stmcb.xml index 7972926..ca81a0c 100644 --- a/help/en_US/stmcb.xml +++ b/help/en_US/stmcb.xml @@ -17,128 +17,7 @@ <refnamediv> <refname>stmcb</refname> - <refpurpose>Compute linear model using Steiglitz-McBride iteration</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -[b,a] = stmcb(h,nb,na) -[b,a] = stmcb(y,x,nb,na) -[b,a] = stmcb(h,nb,na,niter) -[b,a] = stmcb(y,x,nb,na,niter) -[b,a] = stmcb(h,nb,na,niter,ai) -[b,a] = stmcb(y,x,nb,na,niter,ai) - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>b,a </term> - <listitem><para> : coefficients of the system function,nb is number of zeros and na is number of poles</para></listitem></varlistentry> - <varlistentry><term>h</term> - <listitem><para> : impulse response of the system</para></listitem></varlistentry> - <varlistentry><term>x, y</term> - <listitem><para> : input and output of same length given to the system</para></listitem></varlistentry> - <varlistentry><term>nitren</term> - <listitem><para> : number of iterations</para></listitem></varlistentry> - <varlistentry><term>ai</term> - <listitem><para> : initial estimate of the denominator coefficients</para></listitem></varlistentry> - <para> </para> - <para>Accepts only real i/ps, complex i/ps are not accepted due to limitations of the 'filter' function in Scilab</para> -</variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -h = fscanfMat("macros/stmcb_h_data.txt"); //required impulse response samples -plot(h); //plotting required impulse response -title "Required impulse respose (h) plot " -xlabel "samples (n)" -ylabel "magnitude {h(n)} " -nb = 4 ; // four zeros -na = 4 ; //four poles -stmcb(h,nb,na) - ]]></programlisting> - -<scilab:image> -h = [0.0003 -0.0033 -0.0149 -0.0432 -0.0911 -0.1493 -0.1985 -0.2190 -0.2007 -0.1479 -0.0764 -0.0070 --0.0429 --0.0649 --0.0601 --0.0375 --0.0092 -0.0142 -0.0265 -0.0267 -0.0180 -0.0057 --0.0052 --0.0115 --0.0123 --0.0088 --0.0033 -0.0018 -0.0050 -0.0057 -0.0043 -0.0018 --0.0006 --0.0021 --0.0026 --0.0021 --0.0010 -0.0001 -0.0009 -0.0012 -0.0010 -0.0005 -0.0000 --0.0004 --0.0005 --0.0005 --0.0003 --0.0000 -0.0002 -0.0002 -0.0002 -0.0001 -0.0000 --0.0001 --0.0001 --0.0001 --0.0001 --0.0000 -0.0000 -0.0000 -0.0000 -0.0000 -0.0000 --0.0000 ] - -plot(h); //plotting required impulse response -title "Required impulse respose (h) plot " -xlabel "samples (n)" -ylabel "magnitude {h(n)} " - -</scilab:image> -</refsection> - </refentry> diff --git a/help/en_US/subspaceMethodsInputPars.xml b/help/en_US/subspaceMethodsInputPars.xml index 908ead3..999f1ff 100644 --- a/help/en_US/subspaceMethodsInputPars.xml +++ b/help/en_US/subspaceMethodsInputPars.xml @@ -2,11 +2,11 @@ <!-- * - * This help file was generated from subspaceMethodsInputParser.sci using help_from_sci(). + * This help file was generated from subspaceMethodsInputPars.sci using help_from_sci(). * --> -<refentry version="5.0-subset Scilab" xml:id="subspaceMethodsInputParser" xml:lang="en" +<refentry version="5.0-subset Scilab" xml:id="subspaceMethodsInputPars" 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" @@ -16,7 +16,7 @@ xmlns:db="http://docbook.org/ns/docbook"> <refnamediv> - <refname>subspaceMethodsInputParser</refname> + <refname>subspaceMethodsInputPars</refname> <refpurpose>Input parser to be used by pmusic and peig</refpurpose> </refnamediv> diff --git a/help/en_US/tf2zp.xml b/help/en_US/tf2zp.xml index d8ae69b..dfc5e3e 100644 --- a/help/en_US/tf2zp.xml +++ b/help/en_US/tf2zp.xml @@ -17,56 +17,7 @@ <refnamediv> <refname>tf2zp</refname> - <refpurpose>Transfer function to zero pole conversion</refpurpose> + <refpurpose></refpurpose> </refnamediv> -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [z p k] = tf2zp(num, den) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>k:</term> - <listitem><para> Leading coefficient (Gain)</para></listitem></varlistentry> - <varlistentry><term>Num:</term> - <listitem><para> Numerator coefficients of the transfer function</para></listitem></varlistentry> - <varlistentry><term>den:</term> - <listitem><para> Denomenator coefficients of the transfer function</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It converts transfer function representation to zeros / poles representation. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -b = [1 2 3] -a = [4 5 6] -[z, p, k] = zp2tf(b, a) - - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Debdeep Dey (FOSSEE, IIT Bombay)</member> - <member>MODIFIED BY: Sonu Sharma (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> - </refsection> - - </refentry> diff --git a/help/en_US/triang.xml b/help/en_US/triang.xml index f351107..65a6353 100644 --- a/help/en_US/triang.xml +++ b/help/en_US/triang.xml @@ -24,7 +24,7 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - y = triang (m) + w = triang (m) </synopsis> </refsynopsisdiv> @@ -33,7 +33,7 @@ <variablelist> <varlistentry><term>m:</term> <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>y:</term> + <varlistentry><term>w:</term> <listitem><para> output variable, vector of real numbers</para></listitem></varlistentry> </variablelist> </refsection> diff --git a/help/en_US/ultrwin.xml b/help/en_US/ultrwin.xml index 16c6400..fce80c5 100644 --- a/help/en_US/ultrwin.xml +++ b/help/en_US/ultrwin.xml @@ -20,52 +20,4 @@ <refpurpose>This function returns the coefficients of an Ultraspherical window.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - w = ultrwin (m, mu, par) - w = ultrwin (m, mu, par, key) - w = ultrwin (m, mu, par, key, norm) - [w, xmu] = ultrwin (...) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>m:</term> - <listitem><para> positive integer value</para></listitem></varlistentry> - <varlistentry><term>mu:</term> - <listitem><para> </para></listitem></varlistentry> - <varlistentry><term>par:</term> - <listitem><para> </para></listitem></varlistentry> - <varlistentry><term>key:</term> - <listitem><para> </para></listitem></varlistentry> - <varlistentry><term>norm:</term> - <listitem><para> </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function returns the coefficients of an Ultraspherical window of length m supplied as input, to the output vector w. -The second parameter controls the ratio between side lobe to side lobe of the window's Fourier transform. -The third parameter controls the ratio between main lobe width to side lobe. The default value is beta. -The value of xmu is also returned for given beta, att or latt. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -ultrwin(3,-0.4,0.5) -ans = -- 1. -1. -- 1. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/upfirdn.xml b/help/en_US/upfirdn.xml index 98da4ec..f8826a6 100644 --- a/help/en_US/upfirdn.xml +++ b/help/en_US/upfirdn.xml @@ -17,46 +17,7 @@ <refnamediv> <refname>upfirdn</refname> - <refpurpose>This function upsamples the input data, applies the FIR filter and then downsamples it.</refpurpose> + <refpurpose>Esentially it is a cascade of three processes:</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = upfirdn (x, h, p, q) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> The input signal.</para></listitem></varlistentry> - <varlistentry><term>h:</term> - <listitem><para> The impulse response of the FIR filter.</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para>The upsampling factor(default=1) </para></listitem></varlistentry> - <varlistentry><term>q:</term> - <listitem><para>The downsampling factor(default=1) </para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> - -This function upsamples the input data in the matrix by a factor of n. Then the upsampled data is FIR filtered. After this, the resultant is downsampled. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -upfirdn([1,2,3],2,3,5) -//ans = - -//2. 0. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/upsample.xml b/help/en_US/upsample.xml index 3d73473..4200c51 100644 --- a/help/en_US/upsample.xml +++ b/help/en_US/upsample.xml @@ -17,45 +17,7 @@ <refnamediv> <refname>upsample</refname> - <refpurpose>This function upsamples the signal, inserting n-1 zeros between every element.</refpurpose> + <refpurpose>[nargout,nargin]=argn()</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = upsample (x, n) - y = upsample (x, n, phase) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> scalar, vector or matrix of real or complex numbers</para></listitem></varlistentry> - <varlistentry><term>n:</term> - <listitem><para> real number or vector</para></listitem></varlistentry> - <varlistentry><term>phase:</term> - <listitem><para> integer value, 0 <= phase <= (n - 1 ), default value 0, or logical</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function upsamples the signal, inserting n-1 zeros between every element. If x is a matrix, every column is upsampled. -The phase determines the position of the inserted sample in the block of zeros. The default value is 0. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -upsample(4,5,2) -ans = -0. 0. 4. 0. 0. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/upsamplefill.xml b/help/en_US/upsamplefill.xml index 8a6e352..1d42bd2 100644 --- a/help/en_US/upsamplefill.xml +++ b/help/en_US/upsamplefill.xml @@ -24,7 +24,7 @@ <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - y = upsamplefill (x, w) + y = upsamplefill (x, w, cpy) </synopsis> </refsynopsisdiv> @@ -54,9 +54,6 @@ The third argument, if true, means that w should be scalar and that each value i <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ -upsamplefill([0.4,0.5],7) -ans = -0.4 7. 0.5 7. ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/validate_filter_bands.xml b/help/en_US/validate_filter_bands.xml new file mode 100644 index 0000000..ad46491 --- /dev/null +++ b/help/en_US/validate_filter_bands.xml @@ -0,0 +1,23 @@ +<?xml version="1.0" encoding="UTF-8"?> + +<!-- + * + * This help file was generated from validate_filter_bands.sci using help_from_sci(). + * + --> + +<refentry version="5.0-subset Scilab" xml:id="validate_filter_bands" 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>validate_filter_bands</refname> + <refpurpose>funcprot();</refpurpose> + </refnamediv> + +</refentry> diff --git a/help/en_US/wconv.xml b/help/en_US/wconv.xml index 4fe678b..50d243a 100644 --- a/help/en_US/wconv.xml +++ b/help/en_US/wconv.xml @@ -20,57 +20,4 @@ <refpurpose>Performs 1D or 2D convolution.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - y = wconv (type, x, f) - y = wconv (type, x, f, shape) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>type:</term> - <listitem><para> convolution type.</para> - <para> * 1 or "1" for 1D convolution.</para> - <para> * 2 or "2" for 2D convolution.</para> - </listitem></varlistentry> - <varlistentry><term>x:</term> - <listitem><para> Signal vector or matrix.</para></listitem></varlistentry> - <varlistentry><term>f:</term> - <listitem><para> FIR filter coefficients.</para></listitem></varlistentry> - <varlistentry><term>shape:</term> - <listitem> - <para> * "full", computes the full one/two-dimensional convolution. It is the default value.</para> - <para> * "same", computes the central part of the convolution of the same size as x.</para> - <para> * "valid", computes the convolution parts without the zero-padding of x.</para> - </listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It performs 1D or 2D convolution between the signal x and the filter coefficients f. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -a = [1 2 3 4 5]; -b = [7 8 9 10]; -wconv(1, a, b) - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> - </refsection> - </refentry> diff --git a/help/en_US/wind.xml b/help/en_US/wind.xml index 19d4312..6402b7c 100644 --- a/help/en_US/wind.xml +++ b/help/en_US/wind.xml @@ -2,11 +2,11 @@ <!-- * - * This help file was generated from window.sci using help_from_sci(). + * This help file was generated from wind.sci using help_from_sci(). * --> -<refentry version="5.0-subset Scilab" xml:id="window" xml:lang="en" +<refentry version="5.0-subset Scilab" xml:id="wind" 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" @@ -16,7 +16,7 @@ xmlns:db="http://docbook.org/ns/docbook"> <refnamediv> - <refname>window</refname> + <refname>wind</refname> <refpurpose>This function creates an m-point window from the function f given as input.</refpurpose> </refnamediv> @@ -33,7 +33,7 @@ <title>Parameters</title> <variablelist> <varlistentry><term>f:</term> - <listitem><para> string value</para></listitem></varlistentry> + <listitem><para> string value/window name</para></listitem></varlistentry> <varlistentry><term>m:</term> <listitem><para> positive integer value</para></listitem></varlistentry> <varlistentry><term>opts:</term> @@ -46,22 +46,6 @@ <refsection> <title>Description</title> <para> -This is an Octave function. -This function creates an m-point window from the function f given as input, in the output vector w. -f can take any valid function as a string, for example "blackmanharris". </para> </refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -window("hanning",5) -ans = -0. -0.5 -1. -0.5 -0. - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/wkeep.xml b/help/en_US/wkeep.xml index 85c0692..a96a08e 100644 --- a/help/en_US/wkeep.xml +++ b/help/en_US/wkeep.xml @@ -17,7 +17,7 @@ <refnamediv> <refname>wkeep</refname> - <refpurpose></refpurpose> + <refpurpose>Extracts a vector from the given vector of length l</refpurpose> </refnamediv> @@ -44,7 +44,7 @@ <refsection> <title>Description</title> <para> -This function extracts a vector from the given vector of length l. +This is an Octave function [y]=wkeep(x,l) extracts a vector of length l from the centre of input vector x. [y]=wkeep(x,l,opt) extracts vector based on opt which could be 'l','r' or 'c' (left, right or central). </para> @@ -54,9 +54,9 @@ This function extracts a vector from the given vector of length l. <title>Examples</title> <programlisting role="example"><![CDATA[ 1. [y]=wkeep([1 2 3;4 5 6],[2 2]) -//y= 1 2 +y= 1 2 2. [y]=wkeep([1 2 3 4 5 6],3,'r') -//y= 4 5 6 +y= 4 5 6 ]]></programlisting> </refsection> </refentry> diff --git a/help/en_US/wrev.xml b/help/en_US/wrev.xml index 2068027..4c94b1d 100644 --- a/help/en_US/wrev.xml +++ b/help/en_US/wrev.xml @@ -20,37 +20,4 @@ <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [y]=wrev(x) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>x:</term> - <listitem><para> Input vector of string, real or complex values</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -This is an Octave function. -This function reverses the order of elements of the input vector x. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -1. wrev([1 2 3]) -ans= 3 2 1 -2. wrev(['a','b','c']) -ans= cba - ]]></programlisting> -</refsection> </refentry> diff --git a/help/en_US/xcorr2.xml b/help/en_US/xcorr2.xml index 3768635..a1b51b6 100644 --- a/help/en_US/xcorr2.xml +++ b/help/en_US/xcorr2.xml @@ -29,4 +29,23 @@ c = xcorr2 (a, b, biasflag) </synopsis> </refsynopsisdiv> + +<refsection> + <title>Parameters</title> + <variablelist> + <varlistentry><term>a:</term> + <listitem><para> </para></listitem></varlistentry> + <varlistentry><term>b:</term> + <listitem><para> </para></listitem></varlistentry> + <varlistentry><term>biasflag:</term> + <listitem><para> </para></listitem></varlistentry> + </variablelist> +</refsection> + +<refsection> + <title>Description</title> + <para> +This is an Octave function. +</para> +</refsection> </refentry> diff --git a/help/en_US/yulewalker.xml b/help/en_US/yulewalker.xml index d4393fb..5fdac3c 100644 --- a/help/en_US/yulewalker.xml +++ b/help/en_US/yulewalker.xml @@ -44,19 +44,4 @@ Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovarianc Returns the AR coefficients, A, and the variance of white noise, V. </para> </refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ - [A,V]=yulewalker([1 2 3]) -// V = -// - 2.6666667 -// A = -// 1.3333333 -// 0.3333333 - - ]]></programlisting> -</refsection> - - </refentry> diff --git a/help/en_US/zerocrossing.xml b/help/en_US/zerocrossing.xml index 4d7da0f..71c89ec 100644 --- a/help/en_US/zerocrossing.xml +++ b/help/en_US/zerocrossing.xml @@ -17,57 +17,45 @@ <refnamediv> <refname>zerocrossing</refname> - <refpurpose>This function estimates the points at which a given waveform y=y(x) crosses the x-axis using linear interpolation. </refpurpose> + <refpurpose></refpurpose> </refnamediv> <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> - w = zerocrossing (x, y) + r = zerocrossing (w, y) </synopsis> </refsynopsisdiv> <refsection> <title>Parameters</title> <variablelist> - <varlistentry><term>w:</term> - <listitem><para>vector of points at which the function y(x) crosses x-axis. </para></listitem></varlistentry> + <varlistentry><term>r:</term> + <listitem><para> zero crossing points</para></listitem></varlistentry> <varlistentry><term>y:</term> - <listitem><para> The dependant variable,y(x)</para></listitem></varlistentry> + <listitem><para>function y=y(x)...the dependant variable</para></listitem></varlistentry> <varlistentry><term>x:</term> - <listitem><para> The independant variable</para></listitem></varlistentry> + <listitem><para>the independant variable</para></listitem></varlistentry> </variablelist> </refsection> <refsection> <title>Description</title> <para> -This function estimates the points at which a given waveform y = y(x) crosses the x-axis. It uses linear interpolation. +This function estimates the points at which a given waveform y = y(w) crosses the x-axis. It uses linear interpolation. </para> </refsection> <refsection> <title>Examples</title> <programlisting role="example"><![CDATA[ +//1. x = linspace(0,1,100); -y = 2*sin(2*%pi*x); +y = rand(1,100)-0.5; x0= zerocrossing(x,y); -//ans: -// x0 = 0. 0.5 1.0 1.5 y0=interp1(x,y,x0) plot(x,y,x0,y0,'x') - ]]></programlisting> - -<scilab:image> -x = linspace(0,2,200); -y = 2*sin(2*%pi*x); -x0= zerocrossing(x,y); -y0=interp1(x,y,x0) -plot(x,y,x0,y0,'x') - -</scilab:image> - </refsection> </refentry> diff --git a/help/en_US/zp2tf.xml b/help/en_US/zp2tf.xml index 7d8852b..ad9d451 100644 --- a/help/en_US/zp2tf.xml +++ b/help/en_US/zp2tf.xml @@ -20,55 +20,4 @@ <refpurpose>Converts zeros / poles to a transfer function.</refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> - [num, den] = zp2tf (z, p, k) - num = zp2tf (z, p, k) - </synopsis> -</refsynopsisdiv> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>z:</term> - <listitem><para> Zeros</para></listitem></varlistentry> - <varlistentry><term>p:</term> - <listitem><para> Poles</para></listitem></varlistentry> - <varlistentry><term>k:</term> - <listitem><para> Leading coefficient (Gain)</para></listitem></varlistentry> - <varlistentry><term>Num:</term> - <listitem><para> Numerator coefficients of the transfer function</para></listitem></varlistentry> - <varlistentry><term>den:</term> - <listitem><para> Denomenator coefficients of the transfer function</para></listitem></varlistentry> - </variablelist> -</refsection> - -<refsection> - <title>Description</title> - <para> -It converts zeros / poles representation to transfer function representation. -</para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -z = [1 2 3] -p = [4 5 6] -k = 5 -[num, den] = zp2tf (z, p, k) - - ]]></programlisting> -</refsection> - - <refsection> - <title>Authors</title> - <simplelist type="vert"> - <member>Sonu Sharma, RGIT Mumbai (fellow at FOSSEE, IIT Bombay)</member> - </simplelist> - </refsection> - - </refentry> diff --git a/help/en_US/zplane.xml b/help/en_US/zplane.xml index 57b9d40..46ec674 100644 --- a/help/en_US/zplane.xml +++ b/help/en_US/zplane.xml @@ -17,53 +17,7 @@ <refnamediv> <refname>zplane</refname> - <refpurpose>Pole-Zero plot for Discrete time systems</refpurpose> - <para> </para> + <refpurpose></refpurpose> </refnamediv> - -<refsynopsisdiv> - <title>Calling Sequence</title> - <synopsis> -zplane(z); -zplane(z,p); - </synopsis> - <para> </para> -</refsynopsisdiv> - -<refsection> - <title>Description</title> - <para> -This function gives pole zero plote of discrete time systems - </para> - <para> </para> -</refsection> - -<refsection> - <title>Parameters</title> - <variablelist> - <varlistentry><term>z</term> - <listitem><para> : vector containing numerator coefficients</para></listitem></varlistentry> - <varlistentry><term>p</term> - <listitem><para> : vector containing denumerator coefficients</para></listitem></varlistentry> - </variablelist> - <para> </para> -</refsection> - -<refsection> - <title>Examples</title> - <programlisting role="example"><![CDATA[ -z = [1 2 3]; -p = [4 5 6]; -zplane(z,p); - ]]></programlisting> - -<scilab:image> -z = [1 2 3]; -p = [4 5 6]; -zplane(z,p); -</scilab:image> - -</refsection> - </refentry> @@ -4,6 +4,7 @@ exec builder.sce exec('test1.sce',-1) exec('test2.sce',-1) exec('test3.sce',-1) +exec('test4.sce',-1) if test1==1 exit(1) @@ -11,6 +12,8 @@ elseif test2==1 exit(1) elseif test3==1 exit(1) +elseif test4==1 + exit(1) else disp("ALL OK") exit() diff --git a/test4.sce b/test4.sce new file mode 100644 index 0000000..28ca209 --- /dev/null +++ b/test4.sce @@ -0,0 +1,702 @@ +test_pass=[] +res=[] + + +test4=0 + + +//<----------------test case for cceps------------------> +//x=[1 2 3]; +//correct=1; + +//vp=cceps(x, correct); +//vi=[ 1.92565 +// 0.96346 +// -1.09735]; +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('cceps test failed.'); +//end + + + +//<----------------test case for clustersegment------------------> +// x=[0,1,0,0,1,1] +//vp = clustersegment(x) +// +//vi=[2 5;2 6] +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('clustersegment test failed.'); +//end +// +//<----------------test case for cmorwavf------------------> +lb=1; ub=2; n=1; fb=3; +fc=4; + +[a,b]=cmorwavf(lb,ub,n,fb,fc) + +a1=0.0858628; +b1=2; + +a1=round(a1*100)/100; +a=round(a*100)/100; +b1=round(b1*100)/100; +b=round(b*100)/100; + + +if(a==a1) then + if (b==b1) then + test_pass=[test_pass,1]; + end +else + test_pass=[test_pass, 0]; + disp('cmorwavf test failed.'); +end + +////<----------------test case for czt------------------> +// x=[4 5 6 3 2]; fs = 1000; f1 = 100; f2 = 150; +// m = 8; +// w = exp(-%i*2*%pi*(f2-f1)/(m*fs)); +//a = exp(%i*2*%pi*f1/fs); +// +// vp = czt(x, m, w, a); +//// +//vi=[7.3541-12.6740*%i 6.2892-12.5620*%i 5.2710-12.3493*%i 4.3097-12.04432*%i 3.4142-11.6568*%i 2.5919-11.1975*%i 1.84859-10.6776*%i 1.18835-10.1088*%i +//] +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +//// +//// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('czt test failed.'); +//end +// + + +//<----------------test case for diric------------------> + x=[1 2 3]; + n=3; + + vp=diric(x,n); + +vi=[0.6935349 0.0559021 -0.3266617] +vi=round(vi*100)/100; +vp=round(vp*100)/100; + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('diric test failed.'); +end + +//<----------------test case for dst1 ------------------> +// x=[3 5 2 7]; +// n=3; +// +// vp=dst1(x,n); +// +//vi=[ 8.5355339 +// 1. +// - 1.4644661 ] +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('dst1 test failed.'); +//end + + + +//<----------------test case for fft1 ------------------> +//x = [1 2 3; 4 5 6; 7 8 9] +//n = 3 +//dim = 2 +//vp=fft1 (x, n, dim) +// +//vi=[6.0 -1.5000000000000009+0.8660254037844375*%i -1.4999999999999984-0.8660254037844406*%i +//15.0 -1.5000000000000018+0.8660254037844357*%i -1.4999999999999973-0.8660254037844428*%i +//24.0 -1.5000000000000018+0.8660254037844348*%i -1.4999999999999964-0.8660254037844455*%i] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('fft1 test failed.'); +//end +// + + + + +//<----------------test case for fftconv ------------------> +//x=[1, 2, 3]; +//y=[3,4,5] +//vp=fftconv(x, y) +// +//vi=[3; 10; 22; 22; 15] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('fftconv test failed.'); +//end + +//<----------------test case for fftn ------------------> +//x=[2 3 4]; +//siz=[1 5] +// +//vp=fftn(x,siz); +// +// +//vi=[9.0000+0.0000*%i; -0.3090-5.2043*%i ; 0.8090+2.0409*%i; 0.8090-2.0409*%i; -0.3090+5.2043*%i] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('fftn test failed.'); +//end +// + +//<----------------test case for fht ------------------> +//x=1:4; +//vp=fht(x) +// +// +//vi=[10; -4; -2; 0] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('fht test failed.'); +//end + + +//<----------------test case for filter1 ------------------> +//b=[1,2,3]; +//a= [3,4,5]; +//x= [5,6,7]; +//[a b]=filter1(b, a, x) +// +// +//a1=[1.6666667 3.1111111 4.4074074]; +//b1=[- 0.3950617; - 0.3456790]; +// +//a=round(a*100)/100; +//a1=round(a1*100)/100; +//b=round(b*100)/100; +//b1=round(b1*100)/100; +// +// +//if(a==a1) then +// if (b==b1) then +// test_pass=[test_pass,1]; +// end +//else +// test_pass=[test_pass, 0]; +// disp('filter1 test failed.'); +//end + +//<----------------test case for filtic ------------------> +b=[%i,1,-%i,5]; +a= [1,2,3*%i]; +y= [0.8*%i,7,9]; + +vp=filtic(b,a,y) + + + +vi=[- 22.6*%i; 2.4; 0 ] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('filtic test failed.'); +end + +//<----------------test case for fractdiff ------------------> +x=[4 5 6]; +d=3; + +vp=fractdiff(x,d) + +vi=[] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('fractdiff test failed.'); +end + + +//<----------------test case for gauspuls ------------------> +t=[1 2 3]; +fc=1; bw=1; +vp=gauspuls(t,fc,bw) + + +vi=[0.0281016 0.0000006 1.093D-14] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('gauspuls test failed.'); +end + + +//<----------------test case for gaussian ------------------> +//m=5;a=6; +//vp = gaussian (m, a) +// +//vi=[ 5.380D-32; 1.523D-08; 1; 1.523D-08; 5.380D-32] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('gaussian test failed.'); +//end +// +////<----------------test case for idct1 ------------------> +//x=[1 3 6];n=6; +//vp=idct1(x,n) +// +//vi=[ 5.0812809 +// 1.6329932 +// - 2.143464 +// - 3.0400394 +// - 0.8164966 +// 1.7352157] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('idct1 test failed.'); +//end +// + +//<----------------test case for idst1 ------------------> +//x=[1 3 6];n=6; +//vp=idst1(x,n) +// +//vi=[ 2.4654143 +// 1.8028286 +// - 0.6898286 +// - 1.4336286 +// 0.1315286 +// 1.1251286 ] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('idst1 test failed.'); +//end +// + +//<----------------test case for ifft1 ------------------> +//x = [1 2 3; 4 5 6; 7 8 9]; +//n = 3; +//dim = 2 +//vp=ifft1 (x, n, dim) +// +//vi=[ 2 -0.5+0.2886751*%i -0.5-0.2886751*%i +// 5 -0.5+0.2886751*%i -0.5-0.2886751*%i +// 8 -0.5+0.2886751*%i -0.5-0.2886751*%i]; +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('ifft1 test failed.'); +//end +// + +//<----------------test case for ifht ------------------> +//x = [1 2 3 4]; +//n = 2; +//dim = 2; +// +//vp=ifht(x,n, dim) +// +//vi=[1.5 -0.5]; +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('ifht test failed.'); +//end +// +//<----------------test case for kaiserord ------------------> + +f=[1000, 1200]; m=[1, 0]; dev= [0.05, 0.05]; fs= 11025; + +[a b c d] = kaiserord (f, m, dev, fs) + +a1=70; b1=0.19955; c1=1.5099; d1='low' + + +a=round(a*100)/100; +a1=round(a1*100)/100; +b=round(b*100)/100; +b1=round(b1*100)/100; +c=round(c*100)/100; +c1=round(c1*100)/100; + + +if(a==a1) + if (b==b1) + if (c==c1) + if (d==d1) + test_pass=[test_pass,1]; +end +end +end +else + test_pass=[test_pass, 0]; + disp('kaiserord test failed.'); +end + + +//<----------------test case for meyeraux ------------------> +x = [1 2 3]; + +vp=meyeraux(x); + +vi=[1 -208 -10287]; + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('meyeraux test failed.'); +end + +//<----------------test case for morlet ------------------> +//lb = [1 2 3]; +//ub=[1 2 3]; +//n=1; +// +//[a,b]=morlet(lb, ub, n); +// +//a1=[0.1720498 +// -0.1135560 +// -0.0084394]; +// +// b1=[1;2;3] +// +//a=round(a*100)/100; +//a1=round(a1*100)/100; +//b=round(b*100)/100; +//b1=round(b1*100)/100; +// +// +//if(a==a1) +// if (b==b1) +// test_pass=[test_pass,1]; +// end +//else +// test_pass=[test_pass, 0]; +// disp('morlet test failed.'); +//end +// +//<----------------test case for rceps ------------------> + +//x =[1 4.0315 8.1095 11.9561]; +// +//[a b]=rceps(x); +// +//a1=[2.4703812 +// 0.3236025 +// 0.1051662 +// 0.3236025]; +// +// b1=[12.240472 +// 7.7643989 +// 3.7471281 +// 1.3451011] +// +//a=round(a*100)/100; +//a1=round(a1*100)/100; +//b=round(b*100)/100; +//b1=round(b1*100)/100; +// +// +//if(a==a1) +// if (b==b1) +// test_pass=[test_pass,1]; +// end +//else +// test_pass=[test_pass, 0]; +// disp('rceps test failed.'); +//end +// + + + +//<----------------test case for rectpuls------------------> +x = [1 2 3]; + +vp=meyeraux(x); + +vi=[1 -208 -10287]; + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('rectpuls test failed.'); +end + + +//<----------------test case for sawtooth------------------> +t = [1 2 3 4 5]; +width=0.5; +vp=sawtooth(t,width); +vi=[-0.36338 0.27324 0.90986 0.45352 -0.18310] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('sawtooth test failed.'); +end + + + +//<----------------test case for sgolay------------------> +p=1; n=3; m=0; +vp=sgolay (p, n, m) + +vi=[0.83333 0.33333 -0.16667 + 0.33333 0.33333 0.33333 + -0.16667 0.33333 0.83333] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('sgolay test failed.'); +end + +//<----------------test case for sgolayfilt------------------> +//x=[1;2;4;7]; p=0.3; n= 3; m=0; ts=0; +//vp=sgolayfilt (x, p, n, m, ts) +// +//vi=[ 2.3333333 +// 2.3333333 +// 4.3333333 +// 4.3333333] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('sgolayfilt test failed.'); +//end +// +//<----------------test case for shanwavf------------------> +//lb=2; ub=8; n=3; fb=1; fc=6 +//[a b]=shanwavf(lb,ub,n,fb,fc) +// +//a1 = [-3.8982e-17 + 1.1457e-31*%i 3.8982e-17 - 8.4040e-31*%i -3.8982e-17 + 4.5829e-31*%i] +// b1 = [2 5 8] +//a=round(a*100)/100; +//a1=round(a1*100)/100; +//b=round(b*100)/100; +//b1=round(b1*100)/100; +// +//if(a==a1) +// if (b==b1) +// test_pass=[test_pass,1]; +// end +//else +// test_pass=[test_pass, 0]; +// disp('shanwavf test failed.'); +//end +// +//<----------------test case for sinewave------------------> +//x=8; n= 4; +//vp=sinewave(x, n) +// +//vi=[0 1 1.225D-16 -1 -2.449D-16 1 3.674D-16 -1] +// +//vi=round(vi*100)/100; +//vp=round(vp*100)/100; +// +// +//if(vp==vi) +// test_pass=[test_pass,1]; +//else +// test_pass=[test_pass, 0]; +// disp('sinewave test failed.'); +//end +// +//<----------------test case for sos2tf------------------> +sos = [1 1 1 1 0 -1; -2 3 1 1 10 1]; +[a b]=sos2tf(sos) + +a1 =[ -2 1 2 4 1]; +b1 =[ 1 10 0 -10 -1]; + +a=round(a*100)/100; +b=round(b*100)/100; +a1=round(a1*100)/100; +b1=round(b1*100)/100; + + +if(a==a1) + if (b==b1) + test_pass=[test_pass,1]; + end +else + test_pass=[test_pass, 0]; + disp('sos2tf test failed.'); +end + +//<----------------test case for sos2zp------------------> +sos = [1,2,3,4,5,6]; +[a b c ] = sos2zp (sos) + +a1 =[-1.0000 + 1.4142*%i +-1.0000 - 1.4142*%i]; +b1 =[-0.6250 + 1.0533*%i + -0.6250 - 1.0533*%i]; +c1 = 1; + +a=round(a*100)/100; +b=round(b*100)/100; +a1=round(a1*100)/100; +b1=round(b1*100)/100; + +c1=round(c1*100)/100; +c=round(c*100)/100; + + +if(a==a1) + if (b==b1) + if (c==c1) + test_pass=[test_pass,1]; +end +end +else + test_pass=[test_pass, 0]; + disp('sos2zp test failed.'); +end + +//<----------------test case for welchwin------------------> +x=4; opt="symmetric"; +vp=welchwin(x, opt) + +vi=[0. + 0.8888889 + 0.8888889 + 0.] + +vi=round(vi*100)/100; +vp=round(vp*100)/100; + + +if(vp==vi) + test_pass=[test_pass,1]; +else + test_pass=[test_pass, 0]; + disp('welchwin test failed.'); +end + +res=find(test_pass==0) + +if(res~=[]) + disp("One or more tests failed in test1") + test4=1; +end + |