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/*!
* \file
* \brief Definitions of signal processing functions
* \author Tony Ottosson, Thomas Eriksson, Pal Frenger, and Tobias Ringstrom
*
* -------------------------------------------------------------------------
*
* IT++ - C++ library of mathematical, signal processing, speech processing,
* and communications classes and functions
*
* Copyright (C) 1995-2008 (see AUTHORS file for a list of contributors)
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
*
* -------------------------------------------------------------------------
*/
#ifndef SIGFUN_H
#define SIGFUN_H
#include <itpp/base/vec.h>
namespace itpp {
/*!
* \addtogroup sigproc
* @{
*/
/*!
\brief Cross-correlation calculation
\c z=xcorr(x,y,max_lag), where \a x and \a y are length \a M vectors \a (M>1), returns the length \c 2*max_lag+1
cross-correlation sequence \a z. (lags: \c -max_lag,...,0,...,max_lag)
For \c max_lag=-1 the cross-correlation sequence is of length \c 2*M-1, i.e., the cross-correlation for all possible lags.
Scaling options \a scaleopt:
\arg \a none \c => No scaling of the cross-correlation vector
\arg \a biased \c => Scales the cross-correlation vector by \c 1/M
\arg \a unbiased \c => Scales the cross-correlation vector by \c 1/(M-abs(lag))
\arg \a coeff \c => Normalises the cross-correlation to 1 for zero lag.
\note \c max_lag \c <= \c M-1
\note If \c x and \c y are of different length, the shortest one is zero-padded
\param x (Input) Vector of samples
\param y (Input) Vector of samples
\param out (Output) The cross correlation between \a x and \a y.
\param max_lag (Input) Maximum lag for which the cross-correlation is calculated. The output vector is of size \c 2*maxlag+1.
Default value: \c max_lag=-1 calculates the cross-correlations for all possible lags
\param scaleopt (Input) Indicates how the cross-correlation function should be scaled. Default value: \c "none" indicates that no scaling is done
@{
*/
void xcorr_old(const vec &x, const vec &y, vec &out, const int max_lag=-1, const std::string scaleopt="none");
void xcorr(const vec &x, const vec &y, vec &out, const int max_lag=-1, const std::string scaleopt="none");
/*! @} */
/*!
\brief Cross-correlation calculation
\c z=xcorr(x,y,max_lag), where \a x and \a y are length \a M vectors \a (M>1), returns the length \c 2*max_lag+1
cross-correlation sequence \a z. (lags: \c -max_lag,...,0,...,max_lag)
For \c max_lag=-1 the cross-correlation sequence is of length \c 2*M-1, i.e., the cross-correlation for all possible lags.
Scaling options \a scaleopt:
\arg \a none \c => No scaling of the cross-correlation vector
\arg \a biased \c => Scales the cross-correlation vector by \c 1/M
\arg \a unbiased \c => Scales the cross-correlation vector by \c 1/(M-abs(lag))
\arg \a coeff \c => Normalises the cross-correlation to 1 for zero lag.
\note \c max_lag \c <= \c M-1
\note If \c x and \c y are of different length, the shortest one is zero-padded
\param x (Input) Vector of samples
\param y (Input) Vector of samples
\param max_lag (Input) Maximum lag for which the cross-correlation is calculated. The output vector is of size \c 2*maxlag+1.
Default value: \c max_lag=-1 calculates the cross-correlations for all possible lags
\param scaleopt (Input) Indicates how the cross-correlation function should be scaled. Default
value: \c "none" indicates that no scaling is done
\returns The cross correlation between \a x and \a y.
@{
*/
vec xcorr_old(const vec &x, const vec &y, const int max_lag=-1, const std::string scaleopt="none");
vec xcorr(const vec &x, const vec &y, const int max_lag=-1, const std::string scaleopt="none");
/*! @} */
/*!
\brief Cross Correlation
\code r = xcorr(x,y) \endcode returns the cross-correlation vector \b r.
*/
cvec xcorr(const cvec &x, const cvec &y,const int max_lag=-1,const std::string scaleopt="none");
/*!
\brief Auto-correlation calculation
\c z=xcorr(x,max_lag), where \a x and is a length \a M vector \a (M>1), returns the length \c 2*max_lag+1 auto-correlation
sequence \a z. (lags: \c -max_lag,...,0,...,max_lag)
For \c max_lag=-1 the auto-correlation sequence is of length \c 2*M-1, i.e., the cross correlation for all possible lags.
Scaling options \a scaleopt:
\arg \a none \c => No scaling of the auto-correlation vector
\arg \a biased \c => Scales the auto-correlation vector by \c 1/M
\arg \a unbiased \c => Scales the auto-correlation vector by \c 1/(M-abs(lag))
\arg \a coeff \c => Normalises the auto-correlation so that \c acf(x)=1 for zero lag.
\note \c max_lag \c <= \c M-1
\param x (Input) Vector of samples
\param max_lag (Input) Maximum lag for which the auto-correlation is calculated. The output vector is of size \c 2*maxlag+1.
Default value \c max_lag=-1 calculates the auto-correlations for all possible lags.
\param scaleopt (Input) Indicates how the auto-correlation function should be scaled.
Default value: \c "none" indicates that no scaling is done.
\returns The auto-correlation of \a x.
@{
*/
vec xcorr_old(const vec &x, const int max_lag=-1, const std::string scaleopt="none");
vec xcorr(const vec &x, const int max_lag=-1, const std::string scaleopt="none");
/*! @} */
/*!
\brief Cross Correlation
\code r = xcorr(x) \endcode returns the auto-correlation vecotr \b r.
*/
cvec xcorr(const cvec &x, const int max_lag=-1,const std::string scaleopt="none");
/*!
\brief Cross Correlation
\code xcorr(x,y,out) \endcode Computes the cross-correlatin and returns in vector \b out
*/
void xcorr(const cvec &x, const cvec &y, cvec &out, const int max_lag=-1,const std::string scaleopt="none",
bool autoflag=true);
/*!
\brief Covariance matrix calculation
Calculates the covariance matrix of the observations in the matrix \f$X\f$. Each
row is an observation and each column represents a variable.
The covariance is normalized with the number of observations \f$N\f$.
The mean value is removed before calculation.
Set is_zero_mean if X already has zero mean.
*/
mat cov(const mat &X, bool is_zero_mean=false);
//vec cov(const vec &x, short order);
/*!
\brief Power spectrum calculation
Calculates the power spectrum using the Welch method and a Hanning window.
*/
vec spectrum(const vec &v, int nfft=256, int noverlap=0);
/*!
\brief Power spectrum calculation
Calculates the power spectrum using using the Welch method and the supplied window w.
*/
vec spectrum(const vec &v, const vec &w, int noverlap=0);
/*!
\brief Power spectrum calculation of a filter
Calculates the power spectrum of a filter with transfer function a(z)
*/
vec filter_spectrum(const vec &a, int nfft=256);
/*!
\brief Power spectrum calculation of a filter
Calculates the power spectrum of a filter with transfer function a(z)/b(z)
*/
vec filter_spectrum(const vec &a, const vec &b, int nfft=256);
/*! @} */
} // namespace itpp
#endif // #ifndef SIGFUN_H
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