annotate maths/MathUtilities.cpp @ 96:88f3cfcff55f

A threshold (delta) is added in the peak picking parameters structure (PPickParams). It is used as an offset when computing the smoothed detection function. A constructor for the structure PPickParams is also added to set the parameters to 0 when a structure instance is created. Hence programmes using the peak picking parameter structure and which do not set the delta parameter (e.g. QM Vamp note onset detector) won't be affected by the modifications.

Functions modified:
- dsp/onsets/PeakPicking.cpp
- dsp/onsets/PeakPicking.h
- dsp/signalconditioning/DFProcess.cpp
- dsp/signalconditioning/DFProcess.h

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    QM DSP Library

    Centre for Digital Music, Queen Mary, University of London.
    This file 2005-2006 Christian Landone.

    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.  See the file
    COPYING included with this distribution for more information.
*/

#include "MathUtilities.h"

#include <iostream>
#include <cmath>


double MathUtilities::mod(double x, double y)
{
    double a = floor( x / y );

    double b = x - ( y * a );
    return b;
}

double MathUtilities::princarg(double ang)
{
    double ValOut;

    ValOut = mod( ang + M_PI, -2 * M_PI ) + M_PI;

    return ValOut;
}

void MathUtilities::getAlphaNorm(const double *data, unsigned int len, unsigned int alpha, double* ANorm)
{
    unsigned int i;
    double temp = 0.0;
    double a=0.0;
	
    for( i = 0; i < len; i++)
    {
	temp = data[ i ];
		
	a  += ::pow( fabs(temp), double(alpha) );
    }
    a /= ( double )len;
    a = ::pow( a, ( 1.0 / (double) alpha ) );

    *ANorm = a;
}

double MathUtilities::getAlphaNorm( const std::vector <double> &data, unsigned int alpha )
{
    unsigned int i;
    unsigned int len = data.size();
    double temp = 0.0;
    double a=0.0;
	
    for( i = 0; i < len; i++)
    {
	temp = data[ i ];
		
	a  += ::pow( fabs(temp), double(alpha) );
    }
    a /= ( double )len;
    a = ::pow( a, ( 1.0 / (double) alpha ) );

    return a;
}

double MathUtilities::round(double x)
{
    double val = (double)floor(x + 0.5);
  
    return val;
}

double MathUtilities::median(const double *src, unsigned int len)
{
    unsigned int i, j;
    double tmp = 0.0;
    double tempMedian;
    double medianVal;
 
    double* scratch = new double[ len ];//Vector < double > sortedX = Vector < double > ( size );

    for ( i = 0; i < len; i++ )
    {
	scratch[i] = src[i];
    }

    for ( i = 0; i < len - 1; i++ )
    {
	for ( j = 0; j < len - 1 - i; j++ )
	{
	    if ( scratch[j + 1] < scratch[j] )
	    {
		// compare the two neighbors
		tmp = scratch[j]; // swap a[j] and a[j+1]
		scratch[j] = scratch[j + 1];
		scratch[j + 1] = tmp;
	    }
	}
    }
    int middle;
    if ( len % 2 == 0 )
    {
	middle = len / 2;
	tempMedian = ( scratch[middle] + scratch[middle - 1] ) / 2;
    }
    else
    {
	middle = ( int )floor( len / 2.0 );
	tempMedian = scratch[middle];
    }

    medianVal = tempMedian;

    delete [] scratch;
    return medianVal;
}

double MathUtilities::sum(const double *src, unsigned int len)
{
    unsigned int i ;
    double retVal =0.0;

    for(  i = 0; i < len; i++)
    {
	retVal += src[ i ];
    }

    return retVal;
}

double MathUtilities::mean(const double *src, unsigned int len)
{
    double retVal =0.0;

    double s = sum( src, len );
	
    retVal =  s  / (double)len;

    return retVal;
}

double MathUtilities::mean(const std::vector<double> &src,
                           unsigned int start,
                           unsigned int count)
{
    double sum = 0.;
	
    for (int i = 0; i < count; ++i)
    {
        sum += src[start + i];
    }

    return sum / count;
}

void MathUtilities::getFrameMinMax(const double *data, unsigned int len, double *min, double *max)
{
    unsigned int i;
    double temp = 0.0;
    double a=0.0;

    if (len == 0) {
        *min = *max = 0;
        return;
    }
	
    *min = data[0];
    *max = data[0];

    for( i = 0; i < len; i++)
    {
	temp = data[ i ];

	if( temp < *min )
	{
	    *min =  temp ;
	}
	if( temp > *max )
	{
	    *max =  temp ;
	}
		
    }
}

int MathUtilities::getMax( double* pData, unsigned int Length, double* pMax )
{
	unsigned int index = 0;
	unsigned int i;
	double temp = 0.0;
	
	double max = pData[0];

	for( i = 0; i < Length; i++)
	{
		temp = pData[ i ];

		if( temp > max )
		{
			max =  temp ;
			index = i;
		}
		
   	}

	if (pMax) *pMax = max;


	return index;
}

int MathUtilities::getMax( const std::vector<double> & data, double* pMax )
{
	unsigned int index = 0;
	unsigned int i;
	double temp = 0.0;
	
	double max = data[0];

	for( i = 0; i < data.size(); i++)
	{
		temp = data[ i ];

		if( temp > max )
		{
			max =  temp ;
			index = i;
		}
		
   	}

	if (pMax) *pMax = max;


	return index;
}

void MathUtilities::circShift( double* pData, int length, int shift)
{
	shift = shift % length;
	double temp;
	int i,n;

	for( i = 0; i < shift; i++)
	{
		temp=*(pData + length - 1);

		for( n = length-2; n >= 0; n--)
		{
			*(pData+n+1)=*(pData+n);
		}

        *pData = temp;
    }
}

int MathUtilities::compareInt (const void * a, const void * b)
{
  return ( *(int*)a - *(int*)b );
}

void MathUtilities::normalise(double *data, int length, NormaliseType type)
{
    switch (type) {

    case NormaliseNone: return;

    case NormaliseUnitSum:
    {
        double sum = 0.0;
        for (int i = 0; i < length; ++i) {
            sum += data[i];
        }
        if (sum != 0.0) {
            for (int i = 0; i < length; ++i) {
                data[i] /= sum;
            }
        }
    }
    break;

    case NormaliseUnitMax:
    {
        double max = 0.0;
        for (int i = 0; i < length; ++i) {
            if (fabs(data[i]) > max) {
                max = fabs(data[i]);
            }
        }
        if (max != 0.0) {
            for (int i = 0; i < length; ++i) {
                data[i] /= max;
            }
        }
    }
    break;

    }
}

void MathUtilities::normalise(std::vector<double> &data, NormaliseType type)
{
    switch (type) {

    case NormaliseNone: return;

    case NormaliseUnitSum:
    {
        double sum = 0.0;
        for (int i = 0; i < data.size(); ++i) sum += data[i];
        if (sum != 0.0) {
            for (int i = 0; i < data.size(); ++i) data[i] /= sum;
        }
    }
    break;

    case NormaliseUnitMax:
    {
        double max = 0.0;
        for (int i = 0; i < data.size(); ++i) {
            if (fabs(data[i]) > max) max = fabs(data[i]);
        }
        if (max != 0.0) {
            for (int i = 0; i < data.size(); ++i) data[i] /= max;
        }
    }
    break;

    }
}

void MathUtilities::adaptiveThreshold(std::vector<double> &data)
{
    int sz = int(data.size());
    if (sz == 0) return;

    std::vector<double> smoothed(sz);
	
    int p_pre = 8;
    int p_post = 7;

    for (int i = 0; i < sz; ++i) {

        int first = std::max(0,      i - p_pre);
        int last  = std::min(sz - 1, i + p_post);

        smoothed[i] = mean(data, first, last - first + 1);
    }

    for (int i = 0; i < sz; i++) {
        data[i] -= smoothed[i];
        if (data[i] < 0.0) data[i] = 0.0;
    }
}

bool
MathUtilities::isPowerOfTwo(int x)
{
    if (x < 2) return false;
    if (x & (x-1)) return false;
    return true;
}

int
MathUtilities::nextPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n = 1;
    while (x) { x >>= 1; n <<= 1; }
    return n;
}

int
MathUtilities::previousPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n = 1;
    x >>= 1;
    while (x) { x >>= 1; n <<= 1; }
    return n;
}

int
MathUtilities::nearestPowerOfTwo(int x)
{
    if (isPowerOfTwo(x)) return x;
    int n0 = previousPowerOfTwo(x), n1 = nearestPowerOfTwo(x);
    if (x - n0 < n1 - x) return n0;
    else return n1;
}