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

/*
  Vamp feature extraction plugin for the BeatRoot beat tracker.

  Centre for Digital Music, Queen Mary, University of London.
  This file copyright 2011 Simon Dixon, Chris Cannam and QMUL.
    
  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 "Peaks.h"

int Peaks::pre = 3;
int Peaks::post = 1;


int Peaks::findPeaks(const vector<double> &data, vector<int> peaks, int width) {
    int peakCount = 0;
    int maxp = 0;
    int mid = 0;
    int end = data.size();
    while (mid < end) {
        int i = mid - width;
        if (i < 0)
            i = 0;
        int stop = mid + width + 1;
        if (stop > (int)data.size())
            stop = data.size();
        maxp = i;
        for (i++; i < stop; i++)
            if (data[i] > data[maxp])
                maxp = i;
        if (maxp == mid) {
            int j;
            for (j = peakCount; j > 0; j--) {
                if (data[maxp] <= data[peaks[j-1]])
                    break;
                else if (j < (int)peaks.size())
                    peaks[j] = peaks[j-1];
            }
            if (j != (int)peaks.size())
                peaks[j] = maxp;
            if (peakCount != (int)peaks.size())
                peakCount++;
        }
        mid++;
    }
    return peakCount;
} // findPeaks()

vector<int> Peaks::findPeaks(const vector<double> &data, int width,
                             double threshold, double decayRate, bool isRelative) {
    vector<int> peaks;
    int maxp = 0;
    int mid = 0;
    if (data.empty()) return peaks;
    int end = data.size();
    double av = data[0];
    while (mid < end) {
        av = decayRate * av + (1 - decayRate) * data[mid];
        if (av < data[mid])
            av = data[mid];
        int i = mid - width;
        if (i < 0)
            i = 0;
        int stop = mid + width + 1;
        if (stop > (int)data.size())
            stop = data.size();
        maxp = i;
        for (i++; i < stop; i++)
            if (data[i] > data[maxp])
                maxp = i;
        if (maxp == mid) {
            if (overThreshold(data, maxp, width, threshold, isRelative,av)){
                peaks.push_back(maxp);
            }
        }
        mid++;
    }
    return peaks;
} // findPeaks()

double Peaks::expDecayWithHold(double av, double decayRate,
                               const vector<double> &data, int start, int stop) {
    while (start < stop) {
        av = decayRate * av + (1 - decayRate) * data[start];
        if (av < data[start])
            av = data[start];
        start++;
    }
    return av;
} // expDecayWithHold()

bool Peaks::overThreshold(const vector<double> &data, int index, int width,
                          double threshold, bool isRelative,
                          double av) {
    if (data[index] < av)
        return false;
    if (isRelative) {
        int iStart = index - pre * width;
        if (iStart < 0)
            iStart = 0;
        int iStop = index + post * width;
        if (iStop > (int)data.size())
            iStop = data.size();
        double sum = 0;
        int count = iStop - iStart;
        while (iStart < iStop)
            sum += data[iStart++];
        return (data[index] > sum / count + threshold);
    } else
        return (data[index] > threshold);
} // overThreshold()

void Peaks::normalise(vector<double> &data) {
    double sx = 0;
    double sxx = 0;
    for (int i = 0; i < (int)data.size(); i++) {
        sx += data[i];
        sxx += data[i] * data[i];
    }
    double mean = sx / data.size();
    double sd = sqrt((sxx - sx * mean) / data.size());
    if (sd == 0)
        sd = 1;		// all data[i] == mean  -> 0; avoids div by 0
    for (int i = 0; i < (int)data.size(); i++) {
        data[i] = (data[i] - mean) / sd;
    }
} // normalise()

/** Uses an n-point linear regression to estimate the slope of data.
 *  @param data input data
 *  @param hop spacing of data points
 *  @param n length of linear regression
 *  @param slope output data
 */
void Peaks::getSlope(const vector<double> &data, double hop, int n,
                     vector<double> &slope) {
    int i = 0, j = 0;
    double t;
    double sx = 0, sxx = 0, sy = 0, sxy = 0;
    for ( ; i < n; i++) {
        t = i * hop;
        sx += t;
        sxx += t * t;
        sy += data[i];
        sxy += t * data[i];
    }
    double delta = n * sxx - sx * sx;
    for ( ; j < n / 2; j++)
        slope[j] = (n * sxy - sx * sy) / delta;
    for ( ; j < (int)data.size() - (n + 1) / 2; j++, i++) {
        slope[j] = (n * sxy - sx * sy) / delta;
        sy += data[i] - data[i - n];
        sxy += hop * (n * data[i] - sy);
    }
    for ( ; j < (int)data.size(); j++)
        slope[j] = (n * sxy - sx * sy) / delta;
} // getSlope()

int Peaks::imin(const vector<double> &arr) {
    int i = 0;
    for (int j = 1; j < (int)arr.size(); j++)
        if (arr[j] < arr[i])
            i = j;
    return i;
} // imin()

int Peaks::imax(const vector<double> &arr) {
    int i = 0;
    for (int j = 1; j < (int)arr.size(); j++)
        if (arr[j] > arr[i])
            i = j;
    return i;
} // imax()