public CreateAWaterfall3DChart()
{
InitializeComponent();
_transform = new FFT2();
Loaded += OnLoaded;
}
public void AnalyzeData()
{
print("AnalyzeData");
total_Acc = new double[raw_data.Count];
textMesh.text = "Calculating total acceleration on " + raw_data.Count.ToString() + " values";
for (int i = 0; i < raw_data.Count; i++)
{
//calculate total acceleration of the 3 axis
total_Acc[i] = Mathf.Sqrt(Mathf.Pow((raw_data.ElementAt(i).x), 2) + Mathf.Pow((raw_data.ElementAt(i).y), 2) + Mathf.Pow((raw_data.ElementAt(i).z), 2));
//print("\t" + total_Acc[i]);
}
FFT2 fft2 = new FFT2();
/**
* Initialize class to perform FFT of specified size.
*
* @param logN Log2 of FFT length. e.g. for 512 pt FFT, logN = 9.
*/
textMesh.text = "starting FFT";
fft2.init((uint)Mathf.Log(total_Acc.Length));
//create array of double for Im part-----> array should be compsed by 0
y_fft = new double[total_Acc.Length];
for (int i = 0; i < total_Acc.Length; i++)
{
y_fft[i] = 0;
}
//run the fft
fft2.run(total_Acc, y_fft);
StartCoroutine(ShowResult());
}
public RealtimeWaterfall3DChart()
{
InitializeComponent();
_random = new Random();
_transform = new FFT2();
Loaded += OnLoaded;
}
public ManipulateSeries3DMvvmViewModel()
{
_random = new FasterRandom();
_transform = new FFT2();
_columnSeries = new ColumnRenderableSeries3DViewModel {
ColumnShape = typeof(CylinderPointMarker3D), DataPointWidthX = 0.5, Opacity = 1.0, DataSeries = GetColumnDataSeries()
};
_impulseSeries = new ImpulseRenderableSeries3DViewModel {
PointMarker = new EllipsePointMarker3D {
Fill = Colors.White, Size = 4, Opacity = 1
}, Opacity = 1.0, DataSeries = GetImpulseDataSeries()
};
_pointLineSeries = new PointLineRenderableSeries3DViewModel {
IsAntialiased = true, PointMarker = new EllipsePointMarker3D {
Fill = Colors.LimeGreen, Size = 2.0f, Opacity = 1
}, DataSeries = GetPointLineDataSeries()
};
_surfaceMeshSeries = new SurfaceMeshRenderableSeries3DViewModel {
StyleKey = "SurfaceMeshStyle", DrawMeshAs = DrawMeshAs.SolidWireFrame, StrokeThickness = 2, DrawSkirt = false, Opacity = 1, DataSeries = GetSurfaceMeshDataSeries()
};
_waterfallSeries = new WaterfallRenderableSeries3DViewModel {
StyleKey = "WaterfallStyle", Stroke = Colors.Blue, Opacity = 0.8, StrokeThickness = 1, SliceThickness = 0, DataSeries = GetWaterfallDataSeries()
};
_scatterSeries = new ScatterRenderableSeries3DViewModel {
PointMarker = new EllipsePointMarker3D {
Fill = Colors.LimeGreen, Size = 2.0f, Opacity = 1
}, DataSeries = GetScatterDataSeries()
};
_mountainSeries = new MountainRenderableSeries3DViewModel {
DataSeries = GetColumnDataSeries()
};
RenderableSeries = new ObservableCollection <IRenderableSeries3DViewModel>();
SeriesTypes = new ObservableCollection <string>
{
"Column Series",
"Impulse Series",
"Mountain Series",
"PointLine Series",
"SurfaceMesh Series",
// "Waterfall Series",
"Scatter Series"
};
RenderableSeries.Add(_waterfallSeries);
}
private void LoadFile(string path, ArrayViewer waveViewer, ArrayViewer fftViewer)
{
FileStream fileStream = new FileStream(path, FileMode.Open);
BinaryReader binaryReader = new BinaryReader(fileStream);
short[] wave = new short[binaryReader.BaseStream.Length / 2];
int i = 0;
while (binaryReader.BaseStream.Position < binaryReader.BaseStream.Length)
{
uint a = binaryReader.ReadByte();
uint b = binaryReader.ReadByte();
wave[i] = (short)((a << 8) | b);
i++;
}
fileStream.Close();
fileStream.Dispose();
waveViewer.DrawWave(wave);
Application.DoEvents();
FFT2 fft2 = new FFT2();
fft2.init((uint)Math.Log((double)wave.Length, 2.0));
double[] re = new double[wave.Length];
double[] img = new double[wave.Length];
for (int j = 0; j < wave.Length; j++)
{
re[j] = wave[j];
img[j] = 0.0F;
}
fft2.run(re, img);
double[] modulo = new double[re.Length / 2];
for (int j = 0; j < modulo.Length; j++)
{
modulo[j] = Math.Sqrt((re[j] * re[j]) + (img[j] * img[j]));
}
int number = (int)(2048.0F*(float) modulo.Length/8000.0F);
fftViewer.DrawWave(modulo.Take(number).ToArray());
}
//public int numPartitions = 10000;
//public float overlapPercent = 0.5f;
//public float threshold = 1 - 0.75f; //larger float values are more strict
//public float beatDetectionOverlapPercent = 0.5f;
/// <summary>
/// Processes raw audio data to find average energy for overlapping partitions.
/// </summary>
/// <returns>The FFT data array.</returns>
/// <param name="clip">Audio clip to process.</param>
/// <param name="numPartitions">Number of pieces to split the song into for analysis</param>
/// <param name="overlapPercent">The percentage which the partitions overlap each other</param>
public double[] ProcessAudio(AudioClip clip, int numPartitions, float overlapPercent)
{
_averages = new double[(int)(numPartitions / overlapPercent) - 1];
int samplesPerPartition = (int)(clip.samples / numPartitions);
int numDivisions = (int)(numPartitions / overlapPercent) - 1;
//Because the partitions overlap, the number of iterations is the number of partitions multiplied by the inverse of the overlap percent
for (int i = 0; i < numDivisions; i++)
{
float[] samples = new float[samplesPerPartition];
int input = i * ((int)(samples.Length * overlapPercent)); //the offset to start getting song data increases by overlapPercent as i is incremented
clip.GetData(samples, input);
//the raw partition data is run through the Blackman-Harris windowing function
for (int n = 0; n < samples.Length; n++)
{
samples [n] *= _a0 - _a1 * Mathf.Cos((2 * Mathf.PI * n) / samples.Length - 1) + _a2 * Mathf.Cos((4 * Mathf.PI * n) / samples.Length - 1) - _a3 * Mathf.Cos((6 * Mathf.PI * n) / samples.Length - 1);
}
FFT2 FFT = new FFT2();
FFT.init((uint)Mathf.Log(samplesPerPartition, 2));
//our array of floats is converted to an array of doubles for use in the FFT function
double[] double_samples = samples.ToList().ConvertAll <double> (new System.Converter <float, double> (F2D)).ToArray();
//runs our sample data through a Fast Fourier Transform to convert it to the frequency domain
FFT.run(double_samples, new double[samples.Length], false);
//gets the average value for this partition and adds it to an array.
//when all of the partitions are completed, averages will contain data for the entire song
double avg = double_samples.Average();
_averages[i] = avg;
}
return(_averages);
}
//public int numPartitions = 10000;
//public float overlapPercent = 0.5f;
//public float threshold = 1 - 0.75f; //larger float values are more strict
//public float beatDetectionOverlapPercent = 0.5f;
/// <summary>
/// Processes raw audio data to find average energy for overlapping partitions.
/// </summary>
/// <returns>The FFT data array.</returns>
/// <param name="clip">Audio clip to process.</param>
/// <param name="numPartitions">Number of pieces to split the song into for analysis</param>
/// <param name="overlapPercent">The percentage which the partitions overlap each other</param>
public double[] ProcessAudio(AudioClip clip, int numPartitions, float overlapPercent)
{
_averages = new double[(int)(numPartitions / overlapPercent) - 1];
int samplesPerPartition = (int)(clip.samples / numPartitions);
int numDivisions = (int)(numPartitions / overlapPercent) - 1;
//Because the partitions overlap, the number of iterations is the number of partitions multiplied by the inverse of the overlap percent
for (int i = 0; i < numDivisions; i++) {
float[] samples = new float[samplesPerPartition];
int input = i * ((int) (samples.Length * overlapPercent)); //the offset to start getting song data increases by overlapPercent as i is incremented
clip.GetData(samples, input);
//the raw partition data is run through the Blackman-Harris windowing function
for (int n = 0; n < samples.Length; n++) {
samples [n] *= _a0 - _a1 * Mathf.Cos ((2 * Mathf.PI * n) / samples.Length - 1) + _a2 * Mathf.Cos ((4 * Mathf.PI * n) / samples.Length - 1) - _a3 * Mathf.Cos ((6 * Mathf.PI * n) / samples.Length - 1);
}
FFT2 FFT = new FFT2 ();
FFT.init ((uint)Mathf.Log(samplesPerPartition,2));
//our array of floats is converted to an array of doubles for use in the FFT function
double[] double_samples = samples.ToList ().ConvertAll<double> (new System.Converter<float, double> (F2D)).ToArray ();
//runs our sample data through a Fast Fourier Transform to convert it to the frequency domain
FFT.run (double_samples, new double[samples.Length], false);
//gets the average value for this partition and adds it to an array.
//when all of the partitions are completed, averages will contain data for the entire song
double avg = double_samples.Average ();
_averages[i] = avg;
}
return _averages;
}
private void but_fftimu13_Click(object sender, EventArgs e)
{
Utilities.FFT2 fft = new FFT2();
using (
OpenFileDialog ofd = new OpenFileDialog())
{
ofd.Filter = "*.log;*.bin|*.log;*.bin;*.BIN;*.LOG";
ofd.ShowDialog();
if (!File.Exists(ofd.FileName))
{
return;
}
var file = new DFLogBuffer(File.OpenRead(ofd.FileName));
int bins = (int)NUM_bins.Value;
int N = 1 << bins;
Color[] color = new Color[] { Color.Red, Color.Green, Color.Blue, Color.Black, Color.Violet, Color.Orange };
ZedGraphControl[] ctls = new ZedGraphControl[]
{
zedGraphControl1, zedGraphControl2, zedGraphControl3, zedGraphControl4, zedGraphControl5,
zedGraphControl6
};
// 3 imus * 2 sets of measurements(gyr/acc)
FFT2.datastate[] alldata = new FFT2.datastate[3 * 2];
for (int a = 0; a < alldata.Length; a++)
{
alldata[a] = new FFT2.datastate();
}
foreach (var item in file.GetEnumeratorType(new string[] { "IMU", "IMU2", "IMU3" }))
{
if (item.msgtype == null)
{
continue;
}
if (item.msgtype.StartsWith("IMU"))
{
int sensorno = 0;
if (item.msgtype == "IMU")
{
sensorno = 0;
}
if (item.msgtype == "IMU2")
{
sensorno = 1;
}
if (item.msgtype == "IMU3")
{
sensorno = 2;
}
alldata[sensorno + 3].type = item.msgtype + " ACC";
int offsetAX = file.dflog.FindMessageOffset(item.msgtype, "AccX");
int offsetAY = file.dflog.FindMessageOffset(item.msgtype, "AccY");
int offsetAZ = file.dflog.FindMessageOffset(item.msgtype, "AccZ");
int offsetTime = file.dflog.FindMessageOffset(item.msgtype, "TimeUS");
double time = double.Parse(item.items[offsetTime],
CultureInfo.InvariantCulture) / 1000.0;
if (time != alldata[sensorno + 3].lasttime)
{
alldata[sensorno + 3].timedelta = alldata[sensorno + 3].timedelta * 0.99 +
(time - alldata[sensorno + 3].lasttime) * 0.01;
}
alldata[sensorno + 3].lasttime = time;
alldata[sensorno + 3].datax.Add(double.Parse(item.items[offsetAX],
CultureInfo.InvariantCulture));
alldata[sensorno + 3].datay.Add(double.Parse(item.items[offsetAY],
CultureInfo.InvariantCulture));
alldata[sensorno + 3].dataz.Add(double.Parse(item.items[offsetAZ],
CultureInfo.InvariantCulture));
//gyro
alldata[sensorno].type = item.msgtype + " GYR";
int offsetGX = file.dflog.FindMessageOffset(item.msgtype, "GyrX");
int offsetGY = file.dflog.FindMessageOffset(item.msgtype, "GyrY");
int offsetGZ = file.dflog.FindMessageOffset(item.msgtype, "GyrZ");
if (time != alldata[sensorno].lasttime)
{
alldata[sensorno].timedelta = alldata[sensorno].timedelta * 0.99 +
(time - alldata[sensorno].lasttime) * 0.01;
}
alldata[sensorno].lasttime = time;
alldata[sensorno].datax.Add(double.Parse(item.items[offsetGX],
CultureInfo.InvariantCulture));
alldata[sensorno].datay.Add(double.Parse(item.items[offsetGY],
CultureInfo.InvariantCulture));
alldata[sensorno].dataz.Add(double.Parse(item.items[offsetGZ],
CultureInfo.InvariantCulture));
}
}
int controlindex = 0;
foreach (var sensordata in alldata)
{
if (sensordata.datax.Count <= N)
{
continue;
}
double samplerate = 0;
samplerate = Math.Round(1000 / sensordata.timedelta, 1);
double[] freqt = fft.FreqTable(N, (int)samplerate);
double[] avgx = new double[N / 2];
double[] avgy = new double[N / 2];
double[] avgz = new double[N / 2];
int totalsamples = sensordata.datax.Count;
int count = totalsamples / N;
int done = 0;
while (count > 1) // skip last part
{
var fftanswerx = fft.rin(sensordata.datax.AsSpan().Slice(N * done, N), (uint)bins, indB);
var fftanswery = fft.rin(sensordata.datay.AsSpan().Slice(N * done, N), (uint)bins, indB);
var fftanswerz = fft.rin(sensordata.dataz.AsSpan().Slice(N * done, N), (uint)bins, indB);
for (int b = 0; b < N / 2; b++)
{
if (freqt[b] < (double)NUM_startfreq.Value)
{
continue;
}
avgx[b] += fftanswerx[b] / (done + count);
avgy[b] += fftanswery[b] / (done + count);
avgz[b] += fftanswerz[b] / (done + count);
}
count--;
done++;
}
ZedGraph.PointPairList pplx = new ZedGraph.PointPairList(freqt, avgx);
ZedGraph.PointPairList pply = new ZedGraph.PointPairList(freqt, avgy);
ZedGraph.PointPairList pplz = new ZedGraph.PointPairList(freqt, avgz);
var curvex = new LineItem(sensordata.type + " x", pplx, color[0], SymbolType.None);
var curvey = new LineItem(sensordata.type + " y", pply, color[1], SymbolType.None);
var curvez = new LineItem(sensordata.type + " z", pplz, color[2], SymbolType.None);
ctls[controlindex].GraphPane.Legend.IsVisible = true;
ctls[controlindex].GraphPane.XAxis.Title.Text = "Freq Hz";
ctls[controlindex].GraphPane.YAxis.Title.Text = "Amplitude";
ctls[controlindex].GraphPane.Title.Text = "FFT " + sensordata.type + " - " +
Path.GetFileName(ofd.FileName) + " - " + samplerate +
"hz input";
ctls[controlindex].GraphPane.CurveList.Clear();
ctls[controlindex].GraphPane.CurveList.Add(curvex);
ctls[controlindex].GraphPane.CurveList.Add(curvey);
ctls[controlindex].GraphPane.CurveList.Add(curvez);
ctls[controlindex].Invalidate();
ctls[controlindex].AxisChange();
ctls[controlindex].GraphPane.XAxis.Scale.Max = samplerate / 2;
ctls[controlindex].Refresh();
controlindex++;
}
SetScale(ctls);
}
}
double[] DoFFT(double[] values) {
double[] real = new double[values.Length];
double[] imaginary = new double[values.Length];
for (int i = 0; i < values.Length; i++) {
imaginary[i] = 0f;
real[i] = values[i];
}
FFT2 fft = new FFT2();
fft.init((uint)Mathf.Log ((float)values.Length));
fft.run (real, imaginary, false);
return real;
}
