private void AddToChart(string name, StackPanel legend, ChartControl chart, TimeSeriesDouble ts, Color color)
{
if (ts != null)
{
var e = new LineChartElement {
Color = color
};
for (int i = 0; i < ts.Data.Count; i++)
{
e.Points.Add(new Point(i / ts.SampleRate / 3600.0, ts.Data[i]));
}
chart.ChartElements.Add(e);
var t = new TextBlock();
t.Text = name;
t.FontWeight = FontWeights.Bold;
t.FontSize = 16;
t.Margin = new Thickness(0, 0, 6, 0);
t.Foreground = new SolidColorBrush(color);
legend.Children.Add(t);
}
chart.Canvas.Refresh();
RefreshBorders(chart);
}
public static TimeSeriesDouble GetDerivate(TimeSeriesDouble ts)
{
var d = new double[ts.Data.Count - 1];
for (int i = 0; i < d.Length; i++)
{
d[i] = (ts.Data[i + 1] - ts.Data[i]) * ts.SampleRate;
}
return(new TimeSeriesDouble(ts.SampleRate, ts.StartTime, d));
}
private TimeSeriesDouble[][] Generate(TimeSeriesDouble[][] baseTs, TensorCurve tensorCurve, int window, int step, bool acFilter)
{
var res = new TimeSeriesDouble[tensorCurve.OutputsNumber][];
res[0] = new TimeSeriesDouble[baseTs[0].Length];
res[1] = new TimeSeriesDouble[baseTs[0].Length];
for (int i = 0; i < baseTs[0].Length; i++)
{
var o = Generate(new[] { baseTs[0][i], baseTs[1][i] }, tensorCurve, window, step, acFilter, p => { });
res[0][i] = o[0];
res[1][i] = o[1];
}
return(res);
}
public static Tuple <TimeSeriesDouble, TimeSeriesDouble> Rotate(TimeSeriesDouble x, TimeSeriesDouble y, double radAngle)
{
var xNew = new double[x.Data.Count];
var yNew = new double[y.Data.Count];
var cos = Math.Cos(radAngle);
var sin = Math.Sin(radAngle);
for (int i = 0; i < x.Data.Count; i++)
{
xNew[i] = x.Data[i] * cos + y.Data[i] * sin;
yNew[i] = -x.Data[i] * sin + y.Data[i] * cos;
}
return(new Tuple <TimeSeriesDouble, TimeSeriesDouble>(new TimeSeriesDouble(x.SampleRate, x.StartTime, xNew), new TimeSeriesDouble(y.SampleRate, y.StartTime, yNew)));
}
private TimeSeriesDouble[] Cut(TimeSeriesDouble[] ts, int window, int step)
{
var res = new TimeSeriesDouble[ts.Length];
for (int i = 0; i < ts.Length; i++)
{
var length = ts[i].Data.Count;
var realLength = length - (length - window) % step;
var newAr = new double[realLength];
for (int e = 0; e < realLength; e++)
{
newAr[e] = ts[i].Data[e];
}
res[i] = new TimeSeriesDouble(ts[i].SampleRate, ts[i].StartTime, newAr);
}
return(res);
}
public static PowerSpectra Run(TimeSeriesDouble data, int window, Action <double> progress)
{
while (3 * window > data.Data.Count)
{
window /= 2;
}
var startF = 5;
var endF = window / 2 - 5;
var f = Fft.GetFrequencies(window, data.SampleRate, startF, endF);
var mag = Calculate(data.Data, window, window / 4, startF, endF, progress);
//var c = 1 / Math.Sqrt(data.SampleRate);
//mag = Array.ConvertAll(mag, x => x * c);
progress(100.0);
return(new PowerSpectra(f, mag.Item1, mag.Item2));
}
private TimeSeriesDouble[] Generate(TimeSeriesDouble[] baseTs, TensorCurve tensorCurve, int window, int step, bool acFilter, Action <double> progress)
{
if (tensorCurve.InputsNumber != baseTs.Length)
{
throw new ArgumentException();
}
var sampleRate = baseTs[0].SampleRate;
var sp = new IEnumerable <Complex[]> [baseTs.Length];
for (int i = 0; i < baseTs.Length; i++)
{
sp[i] = FftTapping.Forward(baseTs[i].Data, window, step, progress);
}
var spZip = sp[0].Zip(sp[1], (x, y) => new[] { x, y });
var d1 = new double[baseTs[0].Data.Count];
var m1 = new double[baseTs[0].Data.Count];
var d2 = new double[baseTs[1].Data.Count];
var m2 = new double[baseTs[1].Data.Count];
int counter = 0;
foreach (var s in spZip)
{
var cSp = tensorCurve.Apply(s, window, sampleRate);
FftTapping.BackOneWindow(d1, m1, cSp[0], window, step * counter);
FftTapping.BackOneWindow(d2, m2, cSp[1], window, step * counter);
counter++;
}
FftTapping.NormalizeAfterBackLoop(d1, m1);
FftTapping.NormalizeAfterBackLoop(d2, m2);
// удаление пограничных значений
if (d1.Length > 40)
{
var avg01 = Statistic.Average(d1, 20, 30);
var avg02 = Statistic.Average(d2, 20, 30);
var avgN1 = Statistic.Average(d1, d1.Length - 30, d1.Length - 20);
var avgN2 = Statistic.Average(d2, d2.Length - 30, d2.Length - 20);
for (int i = 0; i < 20; i++)
{
d1[i] = avg01;
d2[i] = avg02;
d1[d1.Length - i - 1] = avgN1;
d2[d2.Length - i - 1] = avgN2;
}
}
if (acFilter)
{
RemoveAc(d1, window);
RemoveAc(d2, window);
}
RemoveDc(d1);
RemoveDc(d2);
var res = new TimeSeriesDouble[tensorCurve.OutputsNumber];
res[0] = new TimeSeriesDouble(sampleRate, baseTs[0].StartTime, d1);
res[1] = new TimeSeriesDouble(sampleRate, baseTs[0].StartTime, d2);
progress(100.0);
return(res);
}
