annotate base/LogRange.cpp @ 1520:954d0cf29ca7 import-audio-data

Switch the normalisation option in WritableWaveFileModel from normalising on read to normalising on write, so that the saved file is already normalised and therefore can be read again without having to remember to normalise it

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

/*
    Sonic Visualiser
    An audio file viewer and annotation editor.
    Centre for Digital Music, Queen Mary, University of London.
    This file copyright 2006 Chris Cannam.
    
    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 "LogRange.h"
#include "system/System.h"

#include <algorithm>
#include <iostream>
#include <cmath>

void
LogRange::mapRange(double &min, double &max, double logthresh)
{
    static double eps = 1e-10;
    
    // ensure that max > min:
    if (min > max) std::swap(min, max);
    if (max == min) max = min + 1;

    if (min >= 0.0) {

        // and max > min, so we know min >= 0 and max > 0
        
        max = log10(max);

        if (min == 0.0) min = std::min(logthresh, max);
        else min = log10(min);

    } else if (max <= 0.0) {

        // and max > min, so we know min < 0 and max <= 0
        
        min = log10(-min);

        if (max == 0.0) max = std::min(logthresh, min);
        else max = log10(-max);
        
        std::swap(min, max);

    } else {
        
        // min < 0 and max > 0
        
        max = log10(std::max(max, -min));
        min = std::min(logthresh, max);
    }

    if (fabs(max - min) < eps) min = max - 1;
}        

double
LogRange::map(double value, double thresh)
{
    if (value == 0.0) return thresh;
    return log10(fabs(value));
}

double
LogRange::unmap(double value)
{
    return pow(10.0, value);
}

static double
sd(const std::vector<double> &values, int start, int n)
{
    double sum = 0.0, mean = 0.0, variance = 0.0;
    for (int i = 0; i < n; ++i) {
        sum += values[start + i];
    }
    mean = sum / n;
    for (int i = 0; i < n; ++i) {
        double diff = values[start + i] - mean;
        variance += diff * diff;
    }
    variance = variance / n;
    return sqrt(variance);
}

bool
LogRange::shouldUseLogScale(std::vector<double> values)
{
    // Principle: Partition the data into two sets around the median;
    // calculate the standard deviation of each set; if the two SDs
    // are very different, it's likely that a log scale would be good.

    int n = int(values.size());
    if (n < 4) return false;
    std::sort(values.begin(), values.end());
    int mi = n / 2;

    double sd0 = sd(values, 0, mi);
    double sd1 = sd(values, mi, n - mi);

    SVDEBUG << "LogRange::useLogScale: sd0 = "
              << sd0 << ", sd1 = " << sd1 << endl;

    if (sd0 == 0 || sd1 == 0) return false;

    // I wonder what method of determining "one sd much bigger than
    // the other" would be appropriate here...
    if (std::max(sd0, sd1) / std::min(sd0, sd1) > 10.) return true;
    else return false;
}