private void OnAnalyze(object sender, EventArgs e)
{
btnAnalyze.IsEnabled = false;
if (filecontents != null)
{
// Test FFT here first
Spectrum sp = new Spectrum();
N = Int32.Parse(entryFFTSize.Text);
classval = new double[20, N];
int subsampling = 4; // Freq=44100Hz/subsampling
int maxseg = 10;
frames = (int)(filecontents.Length / (subsampling * N));
byte[] tmp = new byte[N];
//double[,] sp_re = new double[frames, N];
//double[,] sp_im = new double[frames, N];
spectrum = new double[frames, N];
double[] tmp_re = new double[N];
double[] tmp_im = new double[N];
//double[,] sumdre = new double[maxseg,N];
//double[,] sumdim = new double[maxseg,N];
double[,] sumd = new double[maxseg, N];
double[,] maxd = new double[maxseg, N];
int[] tmp_sp = new int[N];
int subsampl;
subsampl = Int32.Parse(entrySubsampling.Text);
int trsamples;
trsamples = Int32.Parse(entrySamples.Text);
if (chkTraining.IsChecked == true)
{
// assume it is lower than trsamples .Checked below
curr_train++;
string addtmp = "(" + curr_train.ToString() + ":" + trsamples.ToString() + ")";
l4.Text = tmptrain + addtmp;
}
if (trainval == null)
{
trainval = new double[N];
for (int i = 0; i < N; i++)
{
trainval[i] = 0;
}
}
if (sampleval == null)
{
sampleval = new double[N];
for (int i = 0; i < N; i++)
{
sampleval[i] = 0;
}
}
if (samplemax == null)
{
samplemax = new double[N];
for (int i = 0; i < N; i++)
{
samplemax[i] = 0;
}
}
int frame_no = 0;
int seg_no = 0;
int col, row;
int pos = 0;
double abssum = 0;
string[] newrow = new string[2];
for (int k = 0; k < maxseg; k++)
{
for (int j = 0; j < N; j++)
{
//sumdre[k,j] = sumdim[k,j]=0;
sumd[k, j] = 0;
maxd[k, j] = -9999;
}
}
while (frame_no < frames)
{
newrow[0] = "";
// more frames to FFT!!
for (int i = 0; i < N; i++)
{ // copy N bytes to tmp
tmp[i] = filecontents[pos];
pos += subsampl;
}
sp.DFT_realIn(tmp, N, ref tmp_re, ref tmp_im, ref tmp_sp);
abssum = sp.DFT_check(tmp_re, tmp_im, N);
if (abssum < Math.Pow(10, -20))
{
// almost zero ignore subsequent zero frames and increase seg_no
while (abssum < Math.Pow(10, -20))
{
for (int i = 0; i < N; i++) // copy N bytes to tmp
{
tmp[i] = filecontents[pos++];
}
sp.DFT_realIn(tmp, N, ref tmp_re, ref tmp_im, ref tmp_sp);
abssum = sp.DFT_check(tmp_re, tmp_im, N);
frame_no++;
}
seg_no++;
if (seg_no >= maxseg)
{
break; // up to maxseg frames are checked
}
}
else
if (seg_no == 0)
{
seg_no = 1;
}
double[] pm_out = new double[N];
if ((pickerAnalysis.SelectedIndex == 2) || (pickerAnalysis.SelectedIndex == 3))
{
// PCA
int Seg = Int32.Parse(entryParam1.Text);
int Comps = Int32.Parse(entryParam2.Text);
pca.pca_extract(tmp_sp, N, Seg, Comps, ref pm_out);
}
else if ((pickerAnalysis.SelectedIndex == 4) || (pickerAnalysis.SelectedIndex == 5))
{
// Mov. Average
int win = Int32.Parse(entryParam1.Text);
av_win(tmp_sp, N, win, ref pm_out);
}
if ((pickerAnalysis.SelectedIndex == 0) || (pickerAnalysis.SelectedIndex == 1))
{
for (int i = 0; i < N; i++)
{
//sp_re[frame_no, i] = tmp_re[i];
//sp_im[frame_no, i] = tmp_im[i];
//abssum += Math.Abs(tmp_re[i]) + Math.Abs(tmp_im[i]);
spectrum[frame_no, i] = tmp_sp[i];
sumd[seg_no - 1, i] += tmp_sp[i];
if (maxd[seg_no - 1, i] < tmp_sp[i])
{
maxd[seg_no - 1, i] = tmp_sp[i];
}
//sumdim[seg_no-1,i] += tmp_im[i];
//sumdre[seg_no-1,i] += tmp_re[i];
//newrow[0] += tmp_re[i].ToString()+",";
}
}
else if (pickerAnalysis.SelectedIndex >= 2)
{
// PCA or Mov. Average
for (int i = 0; i < N; i++)
{
//sp_re[frame_no, i] = tmp_re[i];
//sp_im[frame_no, i] = tmp_im[i];
//abssum += Math.Abs(tmp_re[i]) + Math.Abs(tmp_im[i]);
spectrum[frame_no, i] = pm_out[i];
sumd[seg_no - 1, i] += pm_out[i];
if (maxd[seg_no - 1, i] < pm_out[i])
{
maxd[seg_no - 1, i] = pm_out[i];
}
//sumdim[seg_no-1,i] += tmp_im[i];
//sumdre[seg_no-1,i] += tmp_re[i];
//newrow[0] += tmp_re[i].ToString()+",";
}
}
// NPETDEBUG
//File.AppendAllLines("C:\\users\\nikos\\source\\repos\\Coronario21\\Coronario2\\Coronario2.UWP\\bin\\x86\\Debug\\AppX\\FFT_magn.csv", newrow);
frame_no++;
}
// Here either use tmp_sp as the original output or apply PCA or moving average
newrow[0] = "";
newrow[1] = "";
for (int k = 0; k < seg_no; k++)
{
double sum = 0;
for (int j = 0; j < N; j++)
{
//newrow[0] += sumdre[k, j].ToString() + ",";
sampleval[j] = sumd[k, j] / frame_no;
samplemax[j] = maxd[k, j];
sum += sampleval[j];
//newrow[0] += sampleval[j].ToString() + ",";
//newrow[1] += maxd[k, j].ToString() + ",";
//newrow[1] += sumdim[k, j].ToString() + ",";
if ((pickerAnalysis.SelectedIndex == 1) ||
(pickerAnalysis.SelectedIndex == 3) || (pickerAnalysis.SelectedIndex == 5))
{
newrow[0] += samplemax[j].ToString() + ",";
if (chkTraining.IsChecked == true)
{
trainval[j] += samplemax[j];
}
}
else
{
newrow[0] += sampleval[j].ToString() + ",";
if (chkTraining.IsChecked == true)
{
trainval[j] += sampleval[j];
}
}
}
;
cur_mean = sum / N;
tottrain++;
newrow[0] += "$$$,";
//newrow[1] += "$$$,";
}
if (chkTraining.IsChecked == true)
{
if (curr_train + 1 > trsamples)
{
// stop training. Show training results. Deactivate checkbox.k
chkTraining.IsChecked = false;
for (int j = 0; j < N; j++)
{
//newrow[0] += sumdre[k, j].ToString() + ",";
newrow[1] += (trainval[j] / tottrain).ToString() + ",";
}
editorResults.IsVisible = false;
editorResults.IsEnabled = false;
editorResults.IsEnabled = true;
editorResults.IsVisible = true;
editorResults.Text = newrow[1];
}
}
else
{
editorResults.IsVisible = false;
editorResults.IsEnabled = false;
editorResults.IsEnabled = true;
editorResults.IsVisible = true;
editorResults.Text = newrow[0];
}
btnAnalyze.IsEnabled = true;
}
}
