Refactor visualizers package to improve separation between package specific visualizers and reusable visualizers

Bonsai.ML.sln

@@ -1,4 +1,4 @@
-
+
 Microsoft Visual Studio Solution File, Format Version 12.00
 # Visual Studio Version 17
 VisualStudioVersion = 17.0.31903.59
@@ -26,6 +26,10 @@ Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Bonsai.ML.Python", "src\Bon
 EndProject
 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Bonsai.ML.Data", "src\Bonsai.ML.Data\Bonsai.ML.Data.csproj", "{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13}"
 EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Bonsai.ML.Visualizers.LinearDynamicalSystems", "src\Bonsai.ML.Visualizers.LinearDynamicalSystems\Bonsai.ML.Visualizers.LinearDynamicalSystems.csproj", "{17DF50BE-F481-4904-A4C8-5DF9725B2CA1}"
+EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Bonsai.ML.Visualizers.HiddenMarkovModels", "src\Bonsai.ML.Visualizers.HiddenMarkovModels\Bonsai.ML.Visualizers.HiddenMarkovModels.csproj", "{FC395DDC-62A4-4E14-A198-272AB05B33C7}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -56,22 +60,18 @@ Global
 		{39A4414F-52B1-42D7-82FA-E65DAD885264}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{39A4414F-52B1-42D7-82FA-E65DAD885264}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{39A4414F-52B1-42D7-82FA-E65DAD885264}.Release|Any CPU.Build.0 = Release|Any CPU
-		{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE
 	EndGlobalSection
 	GlobalSection(NestedProjects) = preSolution
+		{FC395DDC-62A4-4E14-A198-272AB05B33C7} = {12312384-8828-4786-AE19-EFCEDF968290}
 		{AA6BE73F-1E15-49A5-AC3C-CD069035C940} = {12312384-8828-4786-AE19-EFCEDF968290}
 		{17AABD18-E275-4409-9E33-3D755B809FF6} = {12312384-8828-4786-AE19-EFCEDF968290}
 		{196AA5C7-AE8A-477B-B01A-B94676EC60EE} = {12312384-8828-4786-AE19-EFCEDF968290}
 		{81DB65B3-EA65-4947-8CF1-0E777324C082} = {461FE3E2-21C4-47F9-8405-DF72326AAB2B}
 		{BAD0A733-8EFB-4EAF-9648-9851656AF7FF} = {12312384-8828-4786-AE19-EFCEDF968290}
 		{39A4414F-52B1-42D7-82FA-E65DAD885264} = {12312384-8828-4786-AE19-EFCEDF968290}
-		{A135C7DB-EA50-4FC6-A6CB-6A5A5CC5FA13} = {12312384-8828-4786-AE19-EFCEDF968290}
 	EndGlobalSection
 	GlobalSection(ExtensibilityGlobals) = postSolution
 		SolutionGuid = {B6468F13-97CD-45E0-9E1E-C122D7F1E09F}

README.md

@@ -36,6 +36,12 @@ Facilitates inference using Hidden Markov Models (HMMs). It interfaces with the
 ### Bonsai.ML.Visualizers
 Graphing and plotting library for visualizing data.
 
+- **Bonsai.ML.Visualizers.LinearDynamicalSystems**
+    Visualizers specific to the LinearDynamicalSystems package.
+
+- **Bonsai.ML.Visualizers.HiddenMarkovModels**
+    Visualizers specific to the HiddenMarkovModels package.
+
 > [!NOTE]
 > Bonsai.ML packages can be installed through Bonsai's integrated package manager and are generally ready for immediate use. However, some packages may require additional installation steps. Refer to the specific package section for detailed installation guides and documentation.
 

src/Bonsai.ML.Visualizers.HiddenMarkovModels/Bonsai.ML.Visualizers.HiddenMarkovModels.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <Title>Bonsai.ML.Visualizers.HiddenMarkovModels</Title>
+    <Description>A package for visualizing data from the Bonsai.ML.HiddenMarkovModels package.</Description>
+    <PackageTags>Bonsai Rx ML Machine Learning Visualizers Hidden Markov Models</PackageTags>
+    <TargetFramework>net472</TargetFramework>
+    <UseWindowsForms>true</UseWindowsForms>
+  </PropertyGroup>
+  <ItemGroup>
+    <PackageReference Include="Bonsai.Core" Version="2.8.2" />
+  </ItemGroup>
+  <ItemGroup>
+    <ProjectReference Include="..\Bonsai.ML.Visualizers\Bonsai.ML.Visualizers.csproj" />
+    <ProjectReference Include="..\Bonsai.ML.HiddenMarkovModels\Bonsai.ML.HiddenMarkovModels.csproj" />
+  </ItemGroup>
+</Project>

src/Bonsai.ML.Visualizers/GaussianObservationsStatisticsClustersVisualizer.cs → src/Bonsai.ML.Visualizers.HiddenMarkovModels/GaussianObservationsStatisticsClustersVisualizer.cs

@@ -3,17 +3,16 @@
 using System.Collections.Generic;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.HiddenMarkovModels;
 using Bonsai.ML.HiddenMarkovModels.Observations;
 using OxyPlot;
 using OxyPlot.Series;
 using OxyPlot.Axes;
 using OxyPlot.WindowsForms;
-using MathNet.Numerics.LinearAlgebra;
 
 [assembly: TypeVisualizer(typeof(GaussianObservationsStatisticsClustersVisualizer), Target = typeof(GaussianObservationsStatistics))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.HiddenMarkovModels
 {
     /// <summary>
     /// Provides a type visualizer of <see cref="GaussianObservationsStatistics"/> to display how the observations 
@@ -245,40 +244,20 @@ public override void Show(object value)
                     var yVar = gaussianObservationsStatistics.CovarianceMatrices[i, dimension2SelectedIndex, dimension2SelectedIndex];
                     var xyCov = gaussianObservationsStatistics.CovarianceMatrices[i, dimension2SelectedIndex, dimension1SelectedIndex];
 
-                    var covariance = Matrix<double>.Build.DenseOfArray(new double[,] {
-                        {
-                            xVar,
-                            xyCov
-                        },
-                        {
-                            xyCov,
-                            yVar
-                        },
-                    });
-
-                    var evd = covariance.Evd();
-                    var evals = evd.EigenValues.Real();
-                    evals = evals.PointwiseAbsoluteMaximum(0);
-                    var evecs = evd.EigenVectors;
-
-                    double angle = Math.Atan2(evecs[1, 0], evecs[0, 0]);
+                    var ellipseParameters = EllipseHelper.GetEllipseParameters(xVar, yVar, xyCov);
 
                     for (int j = 1; j < 4; j++)
                     {
-
-                        var majorAxis = j * Math.Sqrt(evals[0]);
-                        var minorAxis = j * Math.Sqrt(evals[1]);
-
                         var points = new List<DataPoint>();
                         int numPoints = 100;
                         for (int k = 0; k < numPoints + 1; k++)
                         {
                             double theta = 2 * Math.PI * k / numPoints;
-                            double x = majorAxis * Math.Cos(theta);
-                            double y = minorAxis * Math.Sin(theta);
+                            double x = j * ellipseParameters.MajorAxis * Math.Cos(theta);
+                            double y = j * ellipseParameters.MinorAxis * Math.Sin(theta);
 
-                            double xRot = x * Math.Cos(angle) - y * Math.Sin(angle);
-                            double yRot = x * Math.Sin(angle) + y * Math.Cos(angle);
+                            double xRot = x * Math.Cos(ellipseParameters.Angle) - y * Math.Sin(ellipseParameters.Angle);
+                            double yRot = x * Math.Sin(ellipseParameters.Angle) + y * Math.Cos(ellipseParameters.Angle);
 
                             points.Add(new DataPoint(xMean + xRot, yMean + yRot));
                         }

src/Bonsai.ML.Visualizers/GaussianObservationsStatisticsVisualizer.cs → src/Bonsai.ML.Visualizers.HiddenMarkovModels/GaussianObservationsStatisticsVisualizer.cs

@@ -3,14 +3,14 @@
 using System.Collections.Generic;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.HiddenMarkovModels;
 using Bonsai.ML.HiddenMarkovModels.Observations;
 using OxyPlot;
 using OxyPlot.Series;
 
 [assembly: TypeVisualizer(typeof(GaussianObservationStatisticsVisualizer), Target = typeof(GaussianObservationsStatistics))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.HiddenMarkovModels
 {
     /// <summary>
     /// Provides a type visualizer of <see cref="GaussianObservationsStatistics"/> to display the means and standard 

src/Bonsai.ML.Visualizers.HiddenMarkovModels/Properties/launchSettings.json

@@ -0,0 +1,10 @@
+{
+  "profiles": {
+    "Bonsai": {
+      "commandName": "Executable",
+      "executablePath": "$(registry:HKEY_CURRENT_USER\\Software\\Bonsai Foundation\\Bonsai@InstallDir)Bonsai.exe",
+      "commandLineArgs": "--lib:\"$(TargetDir).\"",
+      "nativeDebugging": true
+    }
+  }
+}

src/Bonsai.ML.Visualizers/StateProbabilityVisualizer.cs → src/Bonsai.ML.Visualizers.HiddenMarkovModels/StateProbabilityVisualizer.cs

@@ -4,15 +4,15 @@
 using System.Linq;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.HiddenMarkovModels;
 using Bonsai.ML.HiddenMarkovModels;
 using OxyPlot;
 using OxyPlot.Series;
 using OxyPlot.Axes;
 
 [assembly: TypeVisualizer(typeof(StateProbabilityVisualizer), Target = typeof(StateProbability))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.HiddenMarkovModels
 {
     /// <summary>
     /// Provides a type visualizer of <see cref="StateProbability"/> to display the probabilities 
@@ -67,7 +67,7 @@ public override void Show(object value)
                 var paddingPercentage = 0.05;
 
                 var nStates = stateProbability.Probabilities.Length;
-                CategoryAxis categoryAxis = (CategoryAxis)Plot.xAxis;
+                CategoryAxis categoryAxis = (CategoryAxis)Plot.XAxis;
                 categoryAxis.ItemsSource = Enumerable.Range(0, nStates);
 
                 for (int i = 0; i < nStates; i++)

src/Bonsai.ML.Visualizers.LinearDynamicalSystems/Bonsai.ML.Visualizers.LinearDynamicalSystems.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <Title>Bonsai.ML.Visualizers.LinearDynamicalSystems</Title>
+    <Description>A package for the Bonsai visual programming language.</Description>
+    <PackageTags>Bonsai Rx ML Machine Learning Visualizers LinearDynamicalSystems</PackageTags>
+    <TargetFramework>net472</TargetFramework>
+    <UseWindowsForms>true</UseWindowsForms>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\Bonsai.ML.Visualizers\Bonsai.ML.Visualizers.csproj" />
+    <ProjectReference Include="..\Bonsai.ML.LinearDynamicalSystems\Bonsai.ML.LinearDynamicalSystems.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Bonsai.Vision.Design" Version="2.8.1" />
+  </ItemGroup>
+
+</Project>

src/Bonsai.ML.Visualizers/ForecastImageOverlay.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/ForecastImageOverlay.cs

@@ -1,7 +1,7 @@
 using Bonsai.Design;
 using Bonsai.Vision.Design;
 using Bonsai;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems.Kinematics;
 using System;
 using System.Collections.Generic;
@@ -11,7 +11,7 @@
 
 [assembly: TypeVisualizer(typeof(ForecastImageOverlay), Target = typeof(MashupSource<ImageMashupVisualizer, ForecastVisualizer>))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
     /// <summary>
     /// Provides a mashup visualizer to display the forecast of a Kalman Filter kinematics model overtime of an ImageMashupVisualizer.
@@ -50,26 +50,17 @@ public override void Show(object value)
                 double yVar = kinematicState.Position.Y.Variance;
                 double xyCov = kinematicState.Position.Covariance;
 
-                var covariance = Matrix<double>.Build.DenseOfArray(new double[,] {
-                    { xVar, xyCov },
-                    { xyCov, yVar }
-                });
-
-                var evd = covariance.Evd();
-                var evals = evd.EigenValues.Real();
-                var evecs = evd.EigenVectors;
-
-                double angle = Math.Atan2(evecs[1, 0], evecs[0, 0]) * 180 / Math.PI;
+                EllipseParameters ellipseParameters = EllipseHelper.GetEllipseParameters(xVar, yVar, xyCov);
 
                 Size axes = new Size
                 {
-                    Width = (int)(2 * Math.Sqrt(evals[0])),
-                    Height = (int)(2 * Math.Sqrt(evals[1]))
+                    Width = (int)(2 * ellipseParameters.MajorAxis),
+                    Height = (int)(2 * ellipseParameters.MinorAxis)
                 };
 
                 OxyColor color = OxyColors.Yellow;
 
-                CV.Ellipse(overlay, center, axes, angle, 0, 360, new Scalar(color.B, color.G, color.R, color.A), -1);
+                CV.Ellipse(overlay, center, axes, ellipseParameters.Angle, 0, 360, new Scalar(color.B, color.G, color.R, color.A), -1);
             }
 
             CV.AddWeighted(image, 1 - alpha, overlay, alpha, 1, image);

src/Bonsai.ML.Visualizers/ForecastPlotOverlay.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/ForecastPlotOverlay.cs

@@ -1,6 +1,6 @@
 using Bonsai.Design;
 using Bonsai;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems.Kinematics;
 using System;
@@ -10,10 +10,10 @@
 
 [assembly: TypeVisualizer(typeof(ForecastPlotOverlay), Target = typeof(MashupSource<KinematicStateVisualizer, ForecastVisualizer>))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
     /// <summary>
-    /// Provides a mashup visualizer to display the forecast of a Kalman Filter kinematics model overtime of a KinematicStateVisualizer.
+    /// Provides a mashup visualizer to display the forecast of a Kalman Filter kinematics model alongside the kinematic state.
     /// </summary>
     public class ForecastPlotOverlay : DialogTypeVisualizer
     {

src/Bonsai.ML.Visualizers/ForecastVisualizer.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/ForecastVisualizer.cs

@@ -3,7 +3,7 @@
 using System.Collections.Generic;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems.Kinematics;
 using OxyPlot;
 using System.Reactive;
@@ -12,7 +12,7 @@
 
 [assembly: TypeVisualizer(typeof(ForecastVisualizer), Target = typeof(Forecast))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
     /// <summary>
     /// Provides a type visualizer to display the forecast of a Kalman Filter kinematics model.

src/Bonsai.ML.Visualizers/KinematicStateVisualizer.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/KinematicStateVisualizer.cs

@@ -1,7 +1,7 @@
 using Bonsai.Design;
 using Bonsai;
 using Bonsai.ML.LinearDynamicalSystems.Kinematics;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using System.Collections.Generic;
 using System;
 using System.Windows.Forms;
@@ -11,7 +11,7 @@
 
 [assembly: TypeVisualizer(typeof(KinematicStateVisualizer), Target = typeof(KinematicState))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
 
     /// <summary>
@@ -20,7 +20,7 @@ namespace Bonsai.ML.Visualizers
     public class KinematicStateVisualizer : MashupVisualizer
     {
         private TableLayoutPanel container;
-        private int updateFrequency = 1000 / 50;
+        private int updateFrequency = 20;
         private bool resetAxes = true;
         private int rowCount = 3;
         private int columnCount = 2;

src/Bonsai.ML.Visualizers/MultivariatePDFVisualizer.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/MultivariatePDFVisualizer.cs

@@ -1,23 +1,17 @@
 using System;
 using System.Windows.Forms;
-using System.Collections.Generic;
-using System.Reflection;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
-using Bonsai.ML.LinearDynamicalSystems;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems.LinearRegression;
-using System.Drawing;
-using System.Reactive;
-using Bonsai.Reactive;
 
 [assembly: TypeVisualizer(typeof(MultivariatePDFVisualizer), Target = typeof(MultivariatePDF))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
 
     /// <summary>
-    /// Provides a type visualizer to display the state components of a Kalman Filter kinematics model.
+    /// Provides a type visualizer to display a multivariate probability distribution as a heatmap.
     /// </summary>
     public class MultivariatePDFVisualizer : DialogTypeVisualizer
     {

src/Bonsai.ML.Visualizers.LinearDynamicalSystems/Properties/launchSettings.json

@@ -0,0 +1,10 @@
+{
+  "profiles": {
+    "Bonsai": {
+      "commandName": "Executable",
+      "executablePath": "$(registry:HKEY_CURRENT_USER\\Software\\Bonsai Foundation\\Bonsai@InstallDir)Bonsai.exe",
+      "commandLineArgs": "--lib:\"$(TargetDir).\"",
+      "nativeDebugging": true
+    }
+  }
+}

src/Bonsai.ML.Visualizers/StateComponentVisualizer.cs → src/Bonsai.ML.Visualizers.LinearDynamicalSystems/StateComponentVisualizer.cs

@@ -3,7 +3,7 @@
 using System.Collections.Generic;
 using Bonsai;
 using Bonsai.Design;
-using Bonsai.ML.Visualizers;
+using Bonsai.ML.Visualizers.LinearDynamicalSystems;
 using Bonsai.ML.LinearDynamicalSystems;
 using OxyPlot;
 using System.Reactive;
@@ -12,7 +12,7 @@
 
 [assembly: TypeVisualizer(typeof(StateComponentVisualizer), Target = typeof(StateComponent))]
 
-namespace Bonsai.ML.Visualizers
+namespace Bonsai.ML.Visualizers.LinearDynamicalSystems
 {
     /// <summary>
     /// Provides a type visualizer to display the state components of a Kalman Filter kinematics model.