public void PerfectFilter(PointPairList h)
{
for (var i = 0; i < Data.Count; i++)
{
Data[i] = Analyser.PerfectFilter(Data[i], h);
}
}
public void PerfectFilterNoise(PointPairList h, double alpha)
{
for (var i = 0; i < Data.Count; i++)
{
Data[i] = Analyser.PerfectFilterNoise(Data[i], h, alpha);
}
}
public Complex[,] ReverseSpectreComplexes(Complex[,] Spectre)
{
var reverse_spec = new Complex[Spectre.GetLength(0), Spectre.GetLength(1)];
for (var i = 0; i < Spectre.GetLength(0); ++i)
{
var row_spec = new List <Complex>();
for (var j = 0; j < Spectre.GetLength(1); ++j)
{
row_spec.Add(Spectre[i, j]);
}
var column_reverse_spec = Analyser.ReverseSpectre(row_spec);
for (var k = 0; k < Spectre.GetLength(1); ++k)
{
reverse_spec[i, k] = column_reverse_spec[k];
}
}
for (var i = 0; i < Spectre.GetLength(1); ++i)
{
var column_spec = new List <Complex>();
for (var j = 0; j < Spectre.GetLength(0); ++j)
{
column_spec.Add(reverse_spec[j, i]);
}
var columnSpectre = Analyser.ReverseSpectre(column_spec);
for (var k = 0; k < columnSpectre.Count; ++k)
{
reverse_spec[k, i] = columnSpectre[k];
}
}
return(reverse_spec);
}
public Complex[,] Spectre2D()
{
var row_spec = new Complex[Data.Count, Data[0].Count];
for (var i = 0; i < Data.Count; ++i)
{
var lineSpectre = Analyser.GetSpectre(Analyser.ListToComplex(Data[i]));
for (var j = 0; j < lineSpectre.Count; ++j)
{
row_spec[i, j] = lineSpectre[j];
}
}
for (var i = 0; i < Data[0].Count; ++i)
{
var column_spec = new List <Complex>();
for (var j = 0; j < Data.Count; ++j)
{
column_spec.Add(row_spec[j, i]);
}
var columnSpectre = Analyser.GetSpectre(column_spec);
for (var k = 0; k < columnSpectre.Count; ++k)
{
row_spec[k, i] = columnSpectre[k];
}
}
return(row_spec);
}
public static void AntiShift(CurveItem ci)
{
var list = GraphConverter.GetPointPairListFromCurve(ci);
var average = Analyser.GetAverageOnRange(list);
Shift(ci, -average);
}
public void PottersFilters(PointPairList ppl)
{
foreach (var line in Data)
{
var newLine = Analyser.GetConvolution(line, ppl);
for (var i = 0; i < line.Count; i++)
{
line[i] = newLine[i + 64];
}
}
}
public static bool CheckStationary(PointPairList list, double percent = 2.5, int n = 10)
{
var min = list.Min(x => x.Y);
var max = list.Max(x => x.Y);
var part = list.Count / n;
var averages = new List <double>(n);
var sigmas = new List <double>(n);
var meanSquares = new List <double>(n);
var ans = new StringBuilder();
var isFirst = true;
for (var i = 0; i < n; i++)
{
averages.Add(Analyser.GetAverageOnRange(list, i * part, Math.Min((i + 1) * part - 1, list.Count - 1)));;
sigmas.Add(Analyser.GetSigmaOnRange(list, i * part, Math.Min((i + 1) * part - 1, list.Count - 1)));
meanSquares.Add(Analyser.GetMeanSquare(list, i * part, Math.Min((i + 1) * part - 1, list.Count - 1)));
ans.Append($"Range [{i + 1}/{n}]\n");
ans.Append($"Average: {averages[i] }\n");
ans.Append($"Dispersion: {sigmas[i] }\n");
ans.Append($"Mean Square: {meanSquares[i] }\n");
if (!isFirst)
{
ans.Append($"Average diff: {Analyser.GetPercentDiff(averages[i], averages[i - 1], min, max)}%\n");
ans.Append($"Dispersion diff: {Analyser.GetPercentDiff(sigmas[i], sigmas[i - 1], min, max)}%\n");
ans.Append($"Mean squares diff: {Analyser.GetPercentDiff(meanSquares[i], meanSquares[i - 1], min, max)}%\n");
}
isFirst = false;
}
var adiff = Analyser.GetPercentDiff(averages.Min(), averages.Max(), min, max);
var ddiff = Analyser.GetPercentDiff(sigmas.Min(), sigmas.Max(), min, max);
var meandiff = Analyser.GetPercentDiff(meanSquares.Min(), meanSquares.Max(), min, max);
ans.Append($"Globally:\nMax average diff: {adiff}%\nMax dispersion diff: {ddiff}%\nMax mean square diff: {meandiff}%\n");
return(adiff < percent * 2 && ddiff < percent * 2);
}
public static void Trend(CurveItem ci, int SizeWindow)
{
var input_dots = GraphConverter.GetPointPairListFromCurve(ci);
for (var i = 1; i < SizeWindow; i += 2)
{
var average = Analyser.GetAverageOnRange(input_dots, 0, i);
ci.Points[i / 2].Y = average;
}
for (var i = 0; i < ci.Points.Count - SizeWindow; i++)
{
var average = Analyser.GetAverageOnRange(input_dots, i, i + SizeWindow);
ci.Points[i + SizeWindow / 2].Y = average;
}
for (var i = SizeWindow; i >= 1; i -= 2)
{
var average = Analyser.GetAverageOnRange(input_dots, input_dots.Count - i - 1, input_dots.Count - 1);
ci.Points[ci.Points.Count - 1 - i / 2].Y = average;
}
}
// x - частоты; y - амплитуды
public void DeleteLinesRejectFilter()
{
var line_d = Analyser.Derivative(Data[0]);
var sum = 0.0;
var counter = 0;
for (var i = 10; i < Data.Count; i += 10)
{
var lineDnext = Analyser.Derivative(Data[i]);
lineDnext = Analyser.GetRxy(line_d, lineDnext);
lineDnext = Analyser.GetSpectre(lineDnext);
var lineMax = lineDnext.Count / 2;
for (var j = lineMax; j < lineDnext.Count; ++j)
{
if (lineDnext[j].Y > lineDnext[lineMax].Y)
{
lineMax = j;
}
}
sum += lineDnext[lineMax].X;
counter++;
}
sum /= counter;
var filter = Analyser.GetHarryPotter_BS(sum - 0.05, sum + 0.05, 32, 1);
var newData = new List <PointPairList>();
foreach (var ppl in Data)
{
var newppl = Analyser.GetConvolution(ppl, filter);
newppl.RemoveRange(0, 32);
newppl.RemoveRange(ppl.Count, newppl.Count - ppl.Count);
newData.Add(newppl);
}
Data = newData;
}
