private void FillDemoPlots()
{
Random rand = new Random();
if (true)
{
var plt = new ScottPlot.Plot(600, 400);
int pointCount = 51;
double[] xs = DataGen.Consecutive(pointCount);
double[] sin = DataGen.Sin(pointCount);
double[] cos = DataGen.Cos(pointCount);
plt.PlotScatter(xs, sin, label: "sin");
plt.PlotScatter(xs, cos, label: "cos");
plt.Legend();
plt.Title("Scatter Plot Quickstart");
plt.YLabel("Vertical Units");
plt.XLabel("Horizontal Units");
var pv1 = new ScottPlot.WpfPlot();
pv1.Height = 200;
plt.Title("Scatter Plot Quickstart");
plt.YLabel("Vertical Units");
plt.XLabel("Horizontal Units");
StackPanelCharts.Children.Add(pv1);
}
if (true)
{
var pv1 = new ScottPlot.WpfPlot();
pv1.Height = 200;
pv1.plt.PlotSignal(DataGen.RandomWalk(rand, 100));
var plt = pv1.plt;
int pointCount = 51;
double[] xs = DataGen.Consecutive(pointCount);
double[] sin = DataGen.Sin(pointCount);
double[] cos = DataGen.Cos(pointCount);
plt.PlotScatter(xs, sin, label: "sin");
plt.PlotScatter(xs, cos, label: "cos");
plt.Legend();
plt.Title("Scatter Plot Quickstart");
plt.YLabel("Vertical Units");
plt.XLabel("Horizontal Units");
StackPanelCharts.Children.Add(pv1);
}
}
public void Test_SaveFig_OutputScaling()
{
var plt = new ScottPlot.Plot();
plt.AddSignal(DataGen.Sin(51), label: "sin");
plt.AddSignal(DataGen.Cos(51), label: "cos");
plt.Title("Scaled Figure Demo");
plt.XLabel("Horizontal Axis");
plt.YLabel("Vertical Axis");
plt.Legend();
System.Drawing.Bitmap bmpA = plt.Render(400, 300);
Assert.AreEqual(400, bmpA.Width);
Assert.AreEqual(300, bmpA.Height);
System.Drawing.Bitmap bmpB = plt.Render(400, 300, scale: .5);
Assert.AreEqual(200, bmpB.Width);
Assert.AreEqual(150, bmpB.Height);
System.Drawing.Bitmap bmpC = plt.Render(400, 300, scale: 2);
Assert.AreEqual(800, bmpC.Width);
Assert.AreEqual(600, bmpC.Height);
System.Drawing.Bitmap bmpD = plt.Render(300, 400, scale: 2);
Assert.AreEqual(600, bmpD.Width);
Assert.AreEqual(800, bmpD.Height);
System.Drawing.Bitmap legendNormal = plt.RenderLegend();
System.Drawing.Bitmap legendBig = plt.RenderLegend(scale: 2);
Assert.Greater(legendBig.Width, legendNormal.Width);
Assert.Greater(legendBig.Height, legendNormal.Height);
}
public void SaveScottPlotTestFromWebsite()
{
string saveFileLocation = "C:\\Temp\\Quickstart_Quickstart_Scatter.png";
var plt = new ScottPlot.Plot(600, 400);
int pointCount = 51;
double[] xs = DataGen.Consecutive(pointCount);
double[] sin = DataGen.Sin(pointCount);
double[] cos = DataGen.Cos(pointCount);
plt.AddScatter(sin, cos, label: "sin");
plt.AddScatter(xs, cos, label: "cos");
plt.Legend();
plt.Title("Scatter Plot Quickstart");
plt.YLabel("Vertical Units");
plt.XLabel("Horizontal Units");
plt.SaveFig(saveFileLocation);
Assert.IsTrue(System.IO.File.Exists(saveFileLocation));
System.IO.File.Delete(saveFileLocation);
}
public void Test_Bollinger_Bands()
{
Random rand = new Random(0);
double[] xs = DataGen.Consecutive(150);
OHLC[] ohlcs = DataGen.RandomStockPrices(rand, xs.Length);
(var sma, var bolL, var bolU) = ScottPlot.Statistics.Finance.Bollinger(ohlcs);
// replace timestamps with a series of numbers starting at 0
for (int i = 0; i < ohlcs.Length; i++)
{
ohlcs[i].time = i;
}
var plt = new ScottPlot.Plot(600, 400);
plt.PlotCandlestick(ohlcs);
plt.PlotFill(xs, bolL, xs, bolU, fillColor: Color.Blue, fillAlpha: .1);
plt.PlotScatter(xs, bolL, color: Color.Navy, markerSize: 0);
plt.PlotScatter(xs, bolU, color: Color.Navy, markerSize: 0);
plt.PlotScatter(xs, sma, color: Color.Navy, markerSize: 0, lineStyle: LineStyle.Dash);
plt.Title("Bollinger Bands");
plt.YLabel("Price");
plt.XLabel("Days");
plt.Legend();
plt.AxisAutoX(.03);
TestTools.SaveFig(plt);
}
public void Test_Bollinger_Bands()
{
Random rand = new Random(0);
double[] xs = DataGen.Consecutive(150);
OHLC[] ohlcs = DataGen.RandomStockPrices(rand, xs.Length);
// calculate moving average X and Ys
(var sma, var bolL, var bolU) = ScottPlot.Statistics.Finance.Bollinger(ohlcs, 20);
double[] xs2 = xs.Skip(20).ToArray();
// replace timestamps with a series of numbers starting at 0
for (int i = 0; i < ohlcs.Length; i++)
{
ohlcs[i].DateTime = DateTime.FromOADate(i);
}
var plt = new ScottPlot.Plot(600, 400);
plt.AddCandlesticks(ohlcs);
plt.AddFill(xs2, bolL, xs2, bolU, color: Color.FromArgb(10, Color.Blue));
plt.AddScatter(xs2, bolL, color: Color.Navy, markerSize: 0, label: "Bollinger Bands");
plt.AddScatter(xs2, bolU, color: Color.Navy, markerSize: 0);
plt.AddScatter(xs2, sma, color: Color.Navy, markerSize: 0, lineStyle: LineStyle.Dash, label: "SMA 20");
plt.Title("Moving Average and Standard Deviation");
plt.YLabel("Price");
plt.XLabel("Days");
plt.Legend();
plt.AxisAutoX(.03);
TestTools.SaveFig(plt);
}
public void Test_SMA_Candlestick()
{
Random rand = new Random(0);
double[] xs = DataGen.Consecutive(150);
OHLC[] ohlcs = DataGen.RandomStockPrices(rand, xs.Length);
double[] sma20 = ScottPlot.Statistics.Finance.SMA(ohlcs, 20);
double[] sma50 = ScottPlot.Statistics.Finance.SMA(ohlcs, 50);
// replace timestamps with a series of numbers starting at 0
for (int i = 0; i < ohlcs.Length; i++)
{
ohlcs[i].time = i;
}
var plt = new ScottPlot.Plot(600, 400);
plt.PlotCandlestick(ohlcs);
plt.PlotScatter(xs, sma20, label: "20 day SMA",
color: Color.Blue, markerSize: 0, lineWidth: 2);
plt.PlotScatter(xs, sma50, label: "50 day SMA",
color: Color.Maroon, markerSize: 0, lineWidth: 2);
plt.Title("Simple Moving Average (SMA)");
plt.YLabel("Price");
plt.XLabel("Days");
plt.Legend();
plt.AxisAutoX(.03);
TestTools.SaveFig(plt);
}
static public void RunJolka(List <Solver <string> > backtracks, List <Solver <string> > backtrackfws, string heuristicB = "", string heuristicBF = "", string type = "")
{
var plt = new ScottPlot.Plot(1280, 720);
List <double> xlist = new List <double>();
List <double> yfwlist = new List <double>();
List <double> ylist = new List <double>();
foreach (var el in backtrackfws)
{
xlist.Add(el.solvedPuzzle.Puzzle.id);
if (type == "time")
{
yfwlist.Add(el.solvedPuzzle.elapsed.TotalMilliseconds);
}
else
{
yfwlist.Add(el.solvedPuzzle.Puzzle.steps);
}
}
foreach (var el in backtracks)
{
if (type == "time")
{
ylist.Add(el.solvedPuzzle.elapsed.TotalMilliseconds);
}
else
{
ylist.Add(el.solvedPuzzle.Puzzle.steps);
}
}
double[] xs = xlist.ToArray();
ylist.Insert(2, (double)1);
double[] dataB = Tools.Log10(ylist.ToArray());
double[] dataBF = Tools.Log10(yfwlist.ToArray());
plt.PlotBar(xs, dataB, label: "Backtrack w/ " + heuristicB, barWidth: .3, xOffset: -0.2, showValues: true);
plt.PlotBar(xs, dataBF, label: "Backtrack with forward w/ " + heuristicBF, barWidth: .3, xOffset: 0.2, showValues: true);
plt.Title(type + " over Jolka id");
plt.Ticks(useExponentialNotation: false, useMultiplierNotation: false, logScaleY: true);
plt.Axis(y1: 0);
plt.XTicks(new string[] { "0", "1", "2", "3", "4" });
if (type == "time")
{
plt.YLabel("Execution time [10 ^ y ms]");
}
else
{
plt.YLabel("Steps [10 ^ y]");
}
plt.XLabel("Jolka ID");
plt.Legend(location: legendLocation.upperRight);
plt.SaveFig(heuristicB + heuristicBF + type + "JolkaBvsBF" + ".png");
}
public void Test_Layout_RotatedTicksWithRoom()
{
var plt = new ScottPlot.Plot(400, 300);
plt.PlotSignal(DataGen.Sin(51), xOffset: 1e6);
plt.PlotSignal(DataGen.Cos(51), xOffset: 1e6);
// WARNING: this resets the layout, so you must call Layout() after this
plt.XLabel("horizontal axis label");
plt.Ticks(xTickRotation: 45);
plt.Layout(xScaleHeight: 50);
TestTools.SaveFig(plt);
}
/// <summary>
/// Method for plotting data to .png
/// </summary>
/// <param name="dataPoints"></param>
public static void Graph(double[,] dataPoints)
{
double[] DataError = new double[dataPoints.GetUpperBound(0) + 1];
double[] DataAccuracy = new double[dataPoints.GetUpperBound(0) + 1];
double[] dataXs = new double[dataPoints.GetUpperBound(0) + 1];
for (int i = 0; i < dataXs.Length; i++)
{
dataXs[i] = i;
DataError[i] = dataPoints[i, 0];
DataAccuracy[i] = dataPoints[i, 1];
}
/// plot the data
///
var plt_acc = new ScottPlot.Plot(800, 600);
var plt_loss = new ScottPlot.Plot(800, 600);
plt_acc.PlotScatter(dataXs, DataAccuracy, label: "Accuracy");
plt_loss.PlotScatter(dataXs, DataError, label: "Error rate");
plt_acc.Grid(xSpacing: 20, ySpacing: .1);
plt_loss.Grid(xSpacing: 5, ySpacing: .1);
plt_acc.Legend(location: legendLocation.upperLeft);
plt_loss.Legend(location: legendLocation.upperLeft);
plt_acc.Title("Accuracy");
plt_loss.Title("Loss");
plt_acc.XLabel("Epoch");
plt_acc.YLabel("Accuracy");
plt_loss.XLabel("Epoch");
plt_loss.YLabel("Loss");
plt_acc.SaveFig("NN_acc.png");
plt_loss.SaveFig("NN_loss.png");
Process.Start(new ProcessStartInfo(@"NN_acc.png")
{
UseShellExecute = true
});
Process.Start(new ProcessStartInfo(@"NN_loss.png")
{
UseShellExecute = true
});
}
/// <summary>
/// Return a new Plot with all the same Plottables (and some of the styles) of this one
/// </summary>
public Plot Copy()
{
Plot plt2 = new Plot(settings.Width, settings.Height);
var settings2 = plt2.GetSettings(false);
settings2.Plottables.AddRange(settings.Plottables);
// TODO: add a Copy() method to the settings module, or perhaps Update(existingSettings).
// copy over only the most relevant styles
plt2.Title(settings.XAxis2.Title.Label);
plt2.XLabel(settings.XAxis.Title.Label);
plt2.YLabel(settings.YAxis.Title.Label);
plt2.AxisAuto();
return(plt2);
}
public void Test_Layout_RotatedTicksWithRoom()
{
var plt = new ScottPlot.Plot(400, 300);
var s1 = plt.AddSignal(DataGen.Sin(51));
var s2 = plt.AddSignal(DataGen.Cos(51));
s1.OffsetX = 1e6;
s2.OffsetX = 1e6;
// WARNING: this resets the layout, so you must call Layout() after this
plt.XLabel("horizontal axis label");
plt.XAxis.TickLabelStyle(rotation: 45);
plt.Layout(bottom: 50);
TestTools.SaveFig(plt);
}
/// <summary>
/// Return a new Plot with all the same Plottables (and some of the styles) of this one
/// </summary>
public Plot Copy()
{
Plot plt2 = new Plot(settings.figureSize.Width, settings.figureSize.Height);
var settings2 = plt2.GetSettings(false);
settings2.plottables.AddRange(settings.plottables);
// TODO: add a Copy() method to the settings module, or perhaps Update(existingSettings).
// copy over only the most relevant styles
plt2.Title(settings.title.text);
plt2.XLabel(settings.xLabel.text);
plt2.YLabel(settings.yLabel.text);
plt2.TightenLayout();
plt2.AxisAuto();
return(plt2);
}
/// <summary>
/// Return a new Plot with all the same Plottables (and some of the styles) of this one.
/// </summary>
public Plot Copy()
{
// This is typically only called when you right-click a plot in a control and hit "open in new window".
// All state from the old plot must be copied to the new plot.
Plot plt2 = new Plot(settings.Width, settings.Height);
var settings2 = plt2.GetSettings(false);
// Copying state of plottables is easy because they contain their own state.
settings2.Plottables.AddRange(settings.Plottables);
// TODO: copy axes, since they now carry their own state too.
plt2.Title(settings.XAxis2.Title.Label);
plt2.XLabel(settings.XAxis.Title.Label);
plt2.YLabel(settings.YAxis.Title.Label);
plt2.AxisAuto();
return(plt2);
}
public void Test_Layout()
{
int pointCount = 50;
double[] dataXs = DataGen.Consecutive(pointCount);
double[] dataSin = DataGen.Sin(pointCount);
double[] dataCos = DataGen.Cos(pointCount);
var plt = new ScottPlot.Plot();
plt.PlotScatter(dataXs, dataSin);
plt.PlotScatter(dataXs, dataCos);
plt.Title("Very Complicated Data");
plt.XLabel("Experiment Duration");
plt.YLabel("Productivity");
TestTools.SaveFig(plt);
}
/// <summary>
/// Return a new Plot with all the same Plottables (and some of the styles) of this one.
/// This is called when you right-click a plot in a control and hit "open in new window".
/// </summary>
public Plot Copy()
{
Settings oldSettings = settings;
Plot oldPlot = this;
Plot newPlot = new Plot(oldSettings.Width, oldSettings.Height);
foreach (IPlottable oldPlottable in oldPlot.GetPlottables())
{
newPlot.Add(oldPlottable);
}
newPlot.AxisAuto();
newPlot.XLabel(oldSettings.XAxis.Label());
newPlot.YLabel(oldSettings.YAxis.Label());
newPlot.Title(oldSettings.XAxis2.Label());
return(newPlot);
}
public void Test_SMA_DoubleArray()
{
Random rand = new Random(0);
double[] xs = DataGen.Consecutive(150);
double[] prices = DataGen.RandomWalk(rand, xs.Length, offset: 50);
double[] sma20 = ScottPlot.Statistics.Finance.SMA(prices, 20);
double[] sma50 = ScottPlot.Statistics.Finance.SMA(prices, 50);
var plt = new ScottPlot.Plot(600, 400);
plt.PlotScatter(xs, prices, lineWidth: 0, label: "Price");
plt.PlotScatter(xs, sma20, label: "20 day SMA", markerSize: 0, lineWidth: 2);
plt.PlotScatter(xs, sma50, label: "50 day SMA", markerSize: 0, lineWidth: 2);
plt.YLabel("Price");
plt.XLabel("Days");
plt.Legend();
TestTools.SaveFig(plt);
}
public void Test_Layout_LabelsWithLineBreaks()
{
int pointCount = 50;
double[] dataXs = DataGen.Consecutive(pointCount);
double[] dataSin = DataGen.Sin(pointCount);
double[] dataCos = DataGen.Cos(pointCount);
var plt = new ScottPlot.Plot();
plt.PlotScatter(dataXs, dataSin);
plt.PlotScatter(dataXs, dataCos);
string labelWithLineBreak = "Line One\nLine Two";
plt.Title(labelWithLineBreak, fontSize: 30);
plt.XLabel(labelWithLineBreak);
plt.YLabel(labelWithLineBreak);
TestTools.SaveFig(plt);
}
static public void RunSudoku(double[] difficulty, double[] dataB, double[] dataBF, string heuristicB = "", string heuristicBF = "", string type = "")
{
var plt = new ScottPlot.Plot(1280, 720);
int pointCount = dataB.Length;
plt.PlotBar(difficulty, dataB, label: "Backtrack w/ " + heuristicB, barWidth: .3, xOffset: -0.2);
plt.PlotBar(difficulty, dataBF, label: "Backtrack with forward w/ " + heuristicBF, barWidth: .3, xOffset: 0.2);
plt.Title("Average " + type + " over sudoku difficulty");
plt.Axis(y1: 0);
if (type == "time")
{
plt.YLabel("Execution time [ms]");
}
else
{
plt.YLabel("Steps");
}
plt.XLabel("Difficulty");
plt.Legend(location: legendLocation.upperRight);
plt.Ticks(useExponentialNotation: false, useMultiplierNotation: false);
plt.SaveFig(heuristicB + heuristicBF + type + "BvsBF" + ".png");
}
public void Test_scatter_10kPoints()
{
double[] pointCounts = { 10, 100, 1000, 10000 };
const int REPS = 10;
double[] speeds = new double[pointCounts.Length];
for (int i = 0; i < pointCounts.Length; i++)
{
int pointCount = (int)pointCounts[i];
var plt = new ScottPlot.Plot();
Random rand = new Random(0);
double[] xs = DataGen.Random(rand, pointCount);
double[] ys = DataGen.RandomWalk(rand, pointCount);
plt.AddScatter(xs, ys);
plt.Render(lowQuality: true);
List <double> times = new List <double>();
for (int j = 0; j < REPS; j++)
{
plt.Render(lowQuality: true);
times.Add(plt.GetSettings(false).BenchmarkMessage.MSec);
}
var stats = new ScottPlot.Statistics.Population(times.ToArray());
speeds[i] = stats.mean;
Console.WriteLine($"Rendered {pointCount} points in {stats.mean} ms");
}
var plt2 = new ScottPlot.Plot(400, 300);
plt2.Title("Scatter Plot Benchmark");
plt2.YLabel("Time (ms)");
plt2.XLabel("Number of Points");
plt2.AddScatter(pointCounts, speeds);
TestTools.SaveFig(plt2);
}
private void MakeAPlot(double[,] data, double[,] dataEmp)
{
var plt = new ScottPlot.Plot(1000, 800);
double[] x, y1, y2, y3, y4, empX, empY1, empY2, empY3, empY4;
int linewidth = 2, markersize = 0;
x = GetColumn(data, 0);
y1 = GetColumn(data, 1);
y2 = GetColumn(data, 2);
y3 = GetColumn(data, 3);
y4 = GetColumn(data, 4);
empX = GetRow(dataEmp, 0);
empY1 = GetRow(dataEmp, 1);
empY2 = GetRow(dataEmp, 2);
empY3 = GetRow(dataEmp, 3);
empY4 = GetRow(dataEmp, 4);
plt.PlotScatter(x, y1, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Green, label: "Susceptible", lineStyle: LineStyle.Dot);
plt.PlotScatter(x, y2, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Red, label: "Infected", lineStyle: LineStyle.Dot);
plt.PlotScatter(x, y3, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Gray, label: "Vaccinated", lineStyle: LineStyle.Dot);
plt.PlotScatter(x, y4, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Blue, label: "Recovered", lineStyle: LineStyle.Dot);
plt.PlotScatter(empX, empY1, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Green, label: "Susceptible emp");
plt.PlotScatter(empX, empY2, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Red, label: "Infected emp");
plt.PlotScatter(empX, empY3, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Gray, label: "Vaccinated emp");
plt.PlotScatter(empX, empY4, markerSize: markersize, lineWidth: linewidth, color: System.Drawing.Color.Blue, label: "Recovered emp");
//plt.PlotAnnotation("Population: " + N + "\nβ = " + beta + "\nγ = " + gamma, 10, 10);
plt.Legend();
plt.XLabel("Time");
plt.YLabel("Population");
image.Source = CreateBitmapSourceFromGdiBitmap(plt.GetBitmap());
}
static void Main(string[] args)
{
//Read data
var USDataReader = new USYieldDataXMLReader();
USDataReader.filepath = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Data\\USDYieldCurveDailyData2020.xml"));
var Data = USDataReader.GetFullTimeSeriesData();
var yields = Data["2020-02-11"];
var tau = new double[12] {
(double)1 / 12, (double)2 / 12, (double)3 / 12, (double)6 / 12, 1, 2, 3, 5, 7, 10, 20, 30
};
//// Test--------------------------------------- Static NS 3 factors model------------------------------------------------//
//var NS3factorCalibration = new StaticNS3FactorModelCalibration();
//NS3factorCalibration.yields = yields;
//NS3factorCalibration.maturities = tau;
//var optimziedpara = NS3factorCalibration.Calibration();
//var modeloutput = NS3factorCalibration.CalculateModelOutput(tau, optimziedpara);
//Console.WriteLine("NS 3 Factor Model Is Calibrated.");
////Plot
//var plt = new ScottPlot.Plot(600, 400);
//plt.PlotSignalXY(tau, yields, color: Color.Red, label: "Market Data");
//plt.PlotSignalXY(tau, modeloutput, color: Color.Blue, label: "Model Output");
//plt.Legend();
//plt.XLabel("Time to maturity");
//plt.YLabel("Annualized yields (%)");
//var savepath = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\NS3FactorModelEmpiricalResult.png"));
//plt.SaveFig(savepath);
//Process.Start(savepath);
//// Test--------------------------------------- Static NS 4 factors model------------------------------------------------//
//var NS4factorCalibration = new StaticNS4FactorModelCalibration();
//NS4factorCalibration.yields = yields;
//NS4factorCalibration.maturities = tau;
//var optimziedpara2 = NS4factorCalibration.Calibration();
//var modeloutput2 = NS4factorCalibration.CalculateModelOutput(tau, optimziedpara2);
//Console.WriteLine("NS 4 Factor Model Is Calibrated.");
////Plot
//var plt2 = new ScottPlot.Plot(600, 400);
//plt2.PlotSignalXY(tau, yields, color: Color.Red, label: "Market Data");
//plt2.PlotSignalXY(tau, modeloutput2, color: Color.Blue, label: "Model Output");
//plt2.Legend();
//plt2.XLabel("Time to maturity");
//plt2.YLabel("Annualized yields (%)");
//var savepath2 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\NS4FactorModelEmpiricalResult.png"));
//plt2.SaveFig(savepath2);
//Process.Start(savepath2);
//// Test--------------------------------------- Static Vasicek Two factors model------------------------------------------------//
//var V2FactorCal = new StaticVasicekTwoFactorModelCalibration();
//V2FactorCal.yields = yields;
//V2FactorCal.maturities = tau;
//var V2FactorOptPara = V2FactorCal.Calibration();
//var V2Factor = new StaticVasicekTwoFactorModel();
//V2Factor.maturities = tau;
//var modeloutputV2 = V2FactorCal.CalcualteModelOutput(V2FactorOptPara);
//Console.WriteLine("Vasicek 2 Factor Model Is Calibrated.");
////Plot
//var pltv2 = new ScottPlot.Plot(600, 400);
//pltv2.PlotSignalXY(tau, yields, color: Color.Red, label: "Market Data");
//pltv2.PlotSignalXY(tau, modeloutputV2, color: Color.Blue, label: "Model Output");
//pltv2.Legend();
//pltv2.XLabel("Time to maturity");
//pltv2.YLabel("Annualized yields (%)");
//var savepathV2 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\VasicekTwoFactorModelEmpiricalResult.png"));
//pltv2.SaveFig(savepathV2);
//Process.Start(savepathV2);
// Test--------------------------------------- Static Longstaff Schwartz Two factors model------------------------------------------------//
var LS2FactorCal = new StaticTwoFactorLongstaffSchwartzModelCalibration();
LS2FactorCal.yields = yields;
LS2FactorCal.maturities = tau;
var LS2FactorOptPara = LS2FactorCal.Calibration();
var LS2Factor = new StaticTwoFactorLongstaffSchwartzModel();
LS2Factor.maturities = tau;
var modeloutputLS2 = LS2FactorCal.CalcualteModelOutput(LS2FactorOptPara);
Console.WriteLine("Longstaff Schwartz 2 Factor Model Is Calibrated.");
//Plot
var pltls2 = new ScottPlot.Plot(600, 400);
pltls2.PlotSignalXY(tau, yields, color: Color.Red, label: "Market Data");
pltls2.PlotSignalXY(tau, modeloutputLS2, color: Color.Blue, label: "Model Output");
pltls2.Legend();
pltls2.XLabel("Time to maturity");
pltls2.YLabel("Annualized yields (%)");
var savepathls2 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\LongstaffSchwartzTwoFactorModelEmpiricalResult.png"));
pltls2.SaveFig(savepathls2);
Process.Start(savepathls2);
////Test dynamic NS3 factor model
//var DynaimcNS3factor = new DynamicNS3FactorModelCalibration();
//DynaimcNS3factor.yields = Data;
//DynaimcNS3factor.maturities = tau;
//var optimziedpara=DynaimcNS3factor.Optimize();
//(var tvbeta1, var tvbeta2, var tvbeta3) = DynaimcNS3factor.GetDynamicBetas(optimziedpara);
////Plot
//// Beta1
//var pltbeta1 = new ScottPlot.Plot(600, 400);
//pltbeta1.PlotSignal(tvbeta1, color: Color.Red, label: "Time-varying Beta1");
//pltbeta1.Legend();
//pltbeta1.XLabel("History Time");
//pltbeta1.YLabel("Beta1");
//var savepathbeta1 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta1.png"));
//pltbeta1.SaveFig(savepathbeta1);
//Process.Start(savepathbeta1);
//// Beta2
//var pltbeta2 = new ScottPlot.Plot(600, 400);
//pltbeta2.PlotSignal(tvbeta2, color: Color.Red, label: "Time-varying Beta2");
//pltbeta2.Legend();
//pltbeta2.XLabel("History Time");
//pltbeta2.YLabel("Beta2");
//var savepathbeta2 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta2.png"));
//pltbeta2.SaveFig(savepathbeta2);
//Process.Start(savepathbeta2);
//// Beta3
//var pltbeta3 = new ScottPlot.Plot(600, 400);
//pltbeta3.PlotSignal(tvbeta3, color: Color.Red, label: "Time-varying Beta3");
//pltbeta3.Legend();
//pltbeta3.XLabel("History Time");
//pltbeta3.YLabel("Beta3");
//var savepathbeta3 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta3.png"));
//pltbeta3.SaveFig(savepathbeta3);
//Process.Start(savepathbeta3);
////Test dynamic NS4 factor model
//var DynaimcNS4factor = new DynamicNS4FactorModelCalibration();
//DynaimcNS4factor.yields = Data;
//DynaimcNS4factor.maturities = tau;
//var optimziedpara = DynaimcNS4factor.Optimize();
//(var tvbeta1, var tvbeta2, var tvbeta3, var tvbeta4) = DynaimcNS4factor.GetDynamicBetas(optimziedpara);
////Plot
//// Beta1
//var pltbeta1 = new ScottPlot.Plot(600, 400);
//pltbeta1.PlotSignal(tvbeta1, color: Color.Red, label: "Time-varying Beta1");
//pltbeta1.Legend();
//pltbeta1.XLabel("History Time");
//pltbeta1.YLabel("Beta1");
//var savepathbeta1 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta1.png"));
//pltbeta1.SaveFig(savepathbeta1);
//Process.Start(savepathbeta1);
//// Beta2
//var pltbeta2 = new ScottPlot.Plot(600, 400);
//pltbeta2.PlotSignal(tvbeta2, color: Color.Red, label: "Time-varying Beta2");
//pltbeta2.Legend();
//pltbeta2.XLabel("History Time");
//pltbeta2.YLabel("Beta2");
//var savepathbeta2 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta2.png"));
//pltbeta2.SaveFig(savepathbeta2);
//Process.Start(savepathbeta2);
//// Beta3
//var pltbeta3 = new ScottPlot.Plot(600, 400);
//pltbeta3.PlotSignal(tvbeta3, color: Color.Red, label: "Time-varying Beta3");
//pltbeta3.Legend();
//pltbeta3.XLabel("History Time");
//pltbeta3.YLabel("Beta3");
//var savepathbeta3 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta3.png"));
//pltbeta3.SaveFig(savepathbeta3);
//Process.Start(savepathbeta3);
//// Beta3
//var pltbeta4 = new ScottPlot.Plot(600, 400);
//pltbeta4.PlotSignal(tvbeta4, color: Color.Red, label: "Time-varying Beta4");
//pltbeta4.Legend();
//pltbeta4.XLabel("History Time");
//pltbeta4.YLabel("Beta4");
//var savepathbeta4 = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), @"..\..\", "Pictures\\DynamicNS3FactorModelBeta4.png"));
//pltbeta4.SaveFig(savepathbeta4);
//Process.Start(savepathbeta4);
Console.WriteLine("Press any key to exit");
Console.ReadKey();
}
private void btnSubmitHilos_Click(object sender, EventArgs e)
{
txtResFact.Clear();
lblResponse.Text = "";
BigInteger numero = 0;
int iteraciones = 0;
try
{
numero = int.Parse(numeroFact.Text);
iteraciones = int.Parse(numItera.Text);
}
catch (Exception)
{
numero = 0;
iteraciones = 0;
}
if (numero > 0 && iteraciones > 0)
{
txtResFact.Visible = false;
Dictionary <double[], string> response = new Dictionary <double[], string>();
lblError.Visible = false;
string formated = string.Format("{0}", numero);
//OBTENER Numero Hilos, Tiempo en ms , y el string con info completa
response = Principal.ObtenerFactoriales(numero, iteraciones);
//PARA GRAFICA
var plt = new ScottPlot.Plot(600, 400);
double[] tiempos = new double[response.Count];
double[] hilos = new double[response.Count];
int i = 0;
foreach (double[] val in response.Keys)
{
hilos[i] = val[0];
tiempos[i] = val[1];
i++;
}
plt.PlotScatter(hilos, tiempos);
plt.Legend();
plt.Title("Hilos vs Tiempo de Obtener Factorial");
plt.YLabel("Tiempo(ms)");
plt.XLabel("Hilos");
string nombreImagen = string.Format("../../{0}_{1}_Grafica.png", numero, iteraciones);
try
{
plt.SaveFig(nombreImagen);
lblConfirmed.Text = "Se ha guardado la gráfica en png";
}
catch (Exception)
{
lblConfirmed.Text = "No se ha podido guardar la información";
}
lblConfirmed.Visible = true;
//IMPRIMIR EN FORMS
foreach (string val in response.Values)
{
lblResponse.Text += string.Format($"{val} \n");
Console.WriteLine(val);
}
}
else
{
lblError.Visible = true;
}
}
