Move Sampling/Sample to Signals/SignalGenerator to avoid confusion with statistical sampling

src/Examples/Interpolation/AkimaSpline.cs

@@ -30,7 +30,7 @@ namespace Examples.Interpolation
     using MathNet.Numerics.Interpolation;
     using MathNet.Numerics.Interpolation.Algorithms;
     using MathNet.Numerics.Random;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Interpolation example
@@ -69,7 +69,7 @@ namespace Examples.Interpolation
             // 1. Generate 10 samples of the function x*x-2*x on interval [0, 10]
             Console.WriteLine(@"1. Generate 10 samples of the function x*x-2*x on interval [0, 10]");
             double[] points;
-            var values = Sample.EquidistantInterval(TargetFunction, 0, 10, 10, out points);
+            var values = SignalGenerator.EquidistantInterval(TargetFunction, 0, 10, 10, out points);
             Console.WriteLine();
 
             // 2. Create akima spline interpolation 

src/Examples/Interpolation/LinearBetweenPoints.cs

@@ -29,7 +29,7 @@ namespace Examples.Interpolation
     using System;
     using MathNet.Numerics.Interpolation;
     using MathNet.Numerics.Random;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Interpolation example
@@ -68,7 +68,7 @@ namespace Examples.Interpolation
             // 1. Generate 20 samples of the function x*x-2*x on interval [0, 10]
             Console.WriteLine(@"1. Generate 20 samples of the function x*x-2*x on interval [0, 10]");
             double[] points;
-            var values = Sample.EquidistantInterval(TargetFunction, 0, 10, 20, out points);
+            var values = SignalGenerator.EquidistantInterval(TargetFunction, 0, 10, 20, out points);
             Console.WriteLine();
 
             // 2. Create a linear spline interpolation based on arbitrary points 

src/Examples/Interpolation/RationalWithPoles.cs

@@ -29,7 +29,7 @@ namespace Examples.Interpolation
     using System;
     using MathNet.Numerics.Interpolation;
     using MathNet.Numerics.Random;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Interpolation example
@@ -67,7 +67,7 @@ namespace Examples.Interpolation
             // 1. Generate 20 samples of the function f(x) = x on interval [-5, 5]
             Console.WriteLine(@"1. Generate 20 samples of the function f(x) = x on interval [-5, 5]");
             double[] points;
-            var values = Sample.EquidistantInterval(TargetFunction, -5, 5, 20, out points);
+            var values = SignalGenerator.EquidistantInterval(TargetFunction, -5, 5, 20, out points);
             Console.WriteLine();
 
             // 2. Create a burlish stoer rational interpolation based on arbitrary points 

src/Examples/Interpolation/RationalWithoutPoles.cs

@@ -29,7 +29,7 @@ namespace Examples.Interpolation
     using System;
     using MathNet.Numerics.Interpolation;
     using MathNet.Numerics.Random;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Interpolation example
@@ -68,7 +68,7 @@ namespace Examples.Interpolation
             // 1. Generate 10 samples of the function 1/(1+x*x) on interval [-5, 5]
             Console.WriteLine(@"1. Generate 10 samples of the function 1/(1+x*x) on interval [-5, 5]");
             double[] points;
-            var values = Sample.EquidistantInterval(TargetFunction, -5, 5, 10, out points);
+            var values = SignalGenerator.EquidistantInterval(TargetFunction, -5, 5, 10, out points);
             Console.WriteLine();
             
             // 2. Create a floater hormann rational pole-free interpolation based on arbitrary points

src/Examples/Sampling/Chebyshev.cs

@@ -27,7 +27,7 @@
 namespace Examples.Sampling
 {
     using System;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Example of generic function sampling and quantization provider
@@ -62,7 +62,7 @@ namespace Examples.Sampling
         public void Run()
         {
             // 1. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the first kind within interval [0, 10] 
-            var result = Sample.ChebyshevNodesFirstKind(Function, 0, 10, 20);
+            var result = SignalGenerator.ChebyshevNodesFirstKind(Function, 0, 10, 20);
             Console.WriteLine(@"1. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the first kind within interval [0, 10]");
             for (var i = 0; i < result.Length; i++)
             {
@@ -73,7 +73,7 @@ namespace Examples.Sampling
             Console.WriteLine();
 
             // 2. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the second kind within interval [0, 10]
-            result = Sample.ChebyshevNodesSecondKind(Function, 0, 10, 20);
+            result = SignalGenerator.ChebyshevNodesSecondKind(Function, 0, 10, 20);
             Console.WriteLine(@"2. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the second kind within interval [0, 10]");
             for (var i = 0; i < result.Length; i++)
             {

src/Examples/Sampling/Equidistant.cs

@@ -27,7 +27,7 @@
 namespace Examples.Sampling
 {
     using System;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Example of generic function sampling and quantization provider
@@ -62,7 +62,7 @@ namespace Examples.Sampling
         public void Run()
         {
             // 1. Get 11 samples of f(x) = (x * x) / 2 equidistant within interval [-5, 5] 
-            var result = Sample.EquidistantInterval(Function, -5, 5, 11);
+            var result = SignalGenerator.EquidistantInterval(Function, -5, 5, 11);
             Console.WriteLine(@"1. Get 11 samples of f(x) = (x * x) / 2 equidistant within interval [-5, 5]");
             for (var i = 0; i < result.Length; i++)
             {
@@ -74,7 +74,7 @@ namespace Examples.Sampling
 
             // 2. Get 10 samples of f(x) = (x * x) / 2 equidistant starting at x=1 with step = 0.5 and retrieve sample points
             double[] samplePoints;
-            result = Sample.EquidistantStartingAt(Function, 1, 0.5, 10, out samplePoints);
+            result = SignalGenerator.EquidistantStartingAt(Function, 1, 0.5, 10, out samplePoints);
             Console.WriteLine(@"2. Get 10 samples of f(x) = (x * x) / 2 equidistant starting at x=1 with step = 0.5 and retrieve sample points");
             Console.Write(@"Points: ");
             for (var i = 0; i < samplePoints.Length; i++)
@@ -93,7 +93,7 @@ namespace Examples.Sampling
             Console.WriteLine();
 
             // 3. Get 10 samples of f(x) = (x * x) / 2 equidistant within period = 10 and period offset = 5
-            result = Sample.EquidistantPeriodic(Function, 10, 5, 10);
+            result = SignalGenerator.EquidistantPeriodic(Function, 10, 5, 10);
             Console.WriteLine(@"3. Get 10 samples of f(x) = (x * x) / 2 equidistant within period = 10 and period offset = 5");
             for (var i = 0; i < result.Length; i++)
             {
@@ -104,7 +104,7 @@ namespace Examples.Sampling
             Console.WriteLine();
 
             // 4. Sample f(x) = (x * x) / 2 equidistant  to an integer-domain function starting at x = 0 and step = 2
-            var equidistant = Sample.EquidistantToFunction(Function, 0, 2);
+            var equidistant = SignalGenerator.EquidistantToFunction(Function, 0, 2);
             Console.WriteLine(@" 4. Sample f(x) = (x * x) / 2 equidistant  to an integer-domain function starting at x = 0 and step = 2");
             for (var i = 0; i < 10; i++)
             {

src/Examples/Sampling/Random.cs

@@ -28,7 +28,7 @@ namespace Examples.Sampling
 {
     using System;
     using MathNet.Numerics.Distributions;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Example of generic function sampling and quantization provider
@@ -64,7 +64,7 @@ namespace Examples.Sampling
         {
             // 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]
             var uniform = new ContinuousUniform(-10, 10);
-            var result = Sample.Random(Function, uniform, 10);
+            var result = SignalGenerator.Random(Function, uniform, 10);
             Console.WriteLine(@" 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]");
             for (var i = 0; i < result.Length; i++)
             {
@@ -77,7 +77,7 @@ namespace Examples.Sampling
             // 2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points
             var exponential = new Exponential(1);
             double[] samplePoints;
-            result = Sample.Random(Function, exponential, 10, out samplePoints);
+            result = SignalGenerator.Random(Function, exponential, 10, out samplePoints);
             Console.WriteLine(@"2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points");
             Console.Write(@"Points: ");
             for (var i = 0; i < samplePoints.Length; i++)
@@ -97,7 +97,7 @@ namespace Examples.Sampling
 
             // 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution
             var chiSquare = new ChiSquare(10);
-            result = Sample.Random(TwoDomainFunction, chiSquare, 10);
+            result = SignalGenerator.Random(TwoDomainFunction, chiSquare, 10);
             Console.WriteLine(@" 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution");
             for (var i = 0; i < result.Length; i++)
             {

src/Examples/SpecialFunctions/ErrorFunction.cs

@@ -27,7 +27,7 @@ namespace Examples.SpecialFunctions
 {
     using System;
     using MathNet.Numerics;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
 
     /// <summary>
     /// Special Functions: error functions
@@ -69,7 +69,7 @@ namespace Examples.SpecialFunctions
 
             // 2. Sample 10 values of the error function in [-1.0; 1.0]
             Console.WriteLine(@"2. Sample 10 values of the error function in [-1.0; 1.0]");
-            var data = Sample.EquidistantInterval(SpecialFunctions.Erf, -1.0, 1.0, 10);
+            var data = SignalGenerator.EquidistantInterval(SpecialFunctions.Erf, -1.0, 1.0, 10);
             for (var i = 0; i < data.Length; i++)
             {
                 Console.Write(data[i].ToString("N") + @" ");
@@ -85,7 +85,7 @@ namespace Examples.SpecialFunctions
 
             // 4. Sample 10 values of the complementary error function in [-1.0; 1.0]
             Console.WriteLine(@"4. Sample 10 values of the complementary error function in [-1.0; 1.0]");
-            data = Sample.EquidistantInterval(SpecialFunctions.Erfc, -1.0, 1.0, 10);
+            data = SignalGenerator.EquidistantInterval(SpecialFunctions.Erfc, -1.0, 1.0, 10);
             for (var i = 0; i < data.Length; i++)
             {
                 Console.Write(data[i].ToString("N") + @" ");
@@ -101,7 +101,7 @@ namespace Examples.SpecialFunctions
 
             // 6. Sample 10 values of the inverse error function in [-1.0; 1.0]
             Console.WriteLine(@"6. Sample 10 values of the inverse error function in [-1.0; 1.0]");
-            data = Sample.EquidistantInterval(SpecialFunctions.ErfInv, -1.0, 1.0, 10);
+            data = SignalGenerator.EquidistantInterval(SpecialFunctions.ErfInv, -1.0, 1.0, 10);
             for (var i = 0; i < data.Length; i++)
             {
                 Console.Write(data[i].ToString("N") + @" ");
@@ -117,7 +117,7 @@ namespace Examples.SpecialFunctions
 
             // 8. Sample 10 values of the complementary inverse error function in [-1.0; 1.0]
             Console.WriteLine(@"8. Sample 10 values of the complementary inverse error function in [-1.0; 1.0]");
-            data = Sample.EquidistantInterval(SpecialFunctions.ErfcInv, -1.0, 1.0, 10);
+            data = SignalGenerator.EquidistantInterval(SpecialFunctions.ErfcInv, -1.0, 1.0, 10);
             for (var i = 0; i < data.Length; i++)
             {
                 Console.Write(data[i].ToString("N") + @" ");

src/Examples/Statistics.cs

@@ -28,7 +28,7 @@ namespace Examples
 {
     using System;
     using MathNet.Numerics.Distributions;
-    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Signals;
     using MathNet.Numerics.Statistics;
 
     /// <summary>
@@ -124,8 +124,8 @@ namespace Examples
             Console.WriteLine();
 
             // 6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x
-            data = Sample.EquidistantInterval(x => x * 2, 0, 100, 1000);
-            dataB = Sample.EquidistantInterval(x => x * x, 0, 100, 1000);
+            data = SignalGenerator.EquidistantInterval(x => x * 2, 0, 100, 1000);
+            dataB = SignalGenerator.EquidistantInterval(x => x * x, 0, 100, 1000);
             Console.WriteLine(@"6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x is {0}", Correlation.Pearson(data, dataB).ToString("N04"));
             Console.WriteLine();
         }

src/Numerics/Numerics.csproj

@@ -451,9 +451,9 @@
     <Compile Include="Random\WH1982.cs" />
     <Compile Include="Random\WH2006.cs" />
     <Compile Include="Random\Xorshift.cs" />
-    <Compile Include="Sampling\Sample.Random.cs" />
-    <Compile Include="Sampling\Sample.Chebyshev.cs" />
-    <Compile Include="Sampling\Sample.Equidistant.cs" />
+    <Compile Include="Signals\SignalGenerator.Random.cs" />
+    <Compile Include="Signals\SignalGenerator.Chebyshev.cs" />
+    <Compile Include="Signals\SignalGenerator.Equidistant.cs" />
     <Compile Include="Sorting.cs" />
     <Compile Include="SpecialFunctions.cs" />
     <Compile Include="SpecialFunctions\Erf.cs" />

src/Numerics/Sampling/Sample.Chebyshev.cs → src/Numerics/Signals/SignalGenerator.Chebyshev.cs

@@ -1,4 +1,4 @@
-// <copyright file="Sample.Chebyshev.cs" company="Math.NET">
+// <copyright file="SignalGenerator.Chebyshev.cs" company="Math.NET">
 // Math.NET Numerics, part of the Math.NET Project
 // http://numerics.mathdotnet.com
 // http://github.com/mathnet/mathnet-numerics
@@ -28,14 +28,14 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
-namespace MathNet.Numerics.Sampling
+namespace MathNet.Numerics.Signals
 {
     using System;
 
     /// <summary>
     /// Generic Function Sampling and Quantization Provider
     /// </summary>
-    public static partial class Sample
+    public static partial class SignalGenerator
     {
         /// <summary>
         /// Samples a function at the roots of the Chebyshev polynomial of the first kind.

src/Numerics/Sampling/Sample.Equidistant.cs → src/Numerics/Signals/SignalGenerator.Equidistant.cs

@@ -1,4 +1,4 @@
-// <copyright file="Sample.Equidistant.cs" company="Math.NET">
+// <copyright file="SignalGenerator.Equidistant.cs" company="Math.NET">
 // Math.NET Numerics, part of the Math.NET Project
 // http://numerics.mathdotnet.com
 // http://github.com/mathnet/mathnet-numerics
@@ -28,7 +28,7 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
-namespace MathNet.Numerics.Sampling
+namespace MathNet.Numerics.Signals
 {
     using System;
     using System.Collections.Generic;
@@ -36,7 +36,7 @@ namespace MathNet.Numerics.Sampling
     /// <summary>
     /// Generic Function Sampling and Quantization Provider
     /// </summary>
-    public static partial class Sample
+    public static partial class SignalGenerator
     {
         /// <summary>
         /// Samples a function equidistant within the provided interval.

src/Numerics/Sampling/Sample.Random.cs → src/Numerics/Signals/SignalGenerator.Random.cs

@@ -1,4 +1,4 @@
-// <copyright file="Sample.Random.cs" company="Math.NET">
+// <copyright file="SignalGenerator.Random.cs" company="Math.NET">
 // Math.NET Numerics, part of the Math.NET Project
 // http://numerics.mathdotnet.com
 // http://github.com/mathnet/mathnet-numerics
@@ -28,7 +28,7 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
-namespace MathNet.Numerics.Sampling
+namespace MathNet.Numerics.Signals
 {
     using System;
     using Distributions;
@@ -36,7 +36,7 @@ namespace MathNet.Numerics.Sampling
     /// <summary>
     /// Generic Function Sampling and Quantization Provider
     /// </summary>
-    public static partial class Sample
+    public static partial class SignalGenerator
     {
         /// <summary>
         /// Samples a function randomly with the provided distribution.

src/Silverlight/Silverlight.csproj

@@ -933,14 +933,14 @@
     <Compile Include="..\Numerics\Random\Xorshift.cs">
       <Link>Random\Xorshift.cs</Link>
     </Compile>
-    <Compile Include="..\Numerics\Sampling\Sample.Chebyshev.cs">
-      <Link>Sampling\Sample.Chebyshev.cs</Link>
+    <Compile Include="..\Numerics\Signals\SignalGenerator.Chebyshev.cs">
+      <Link>Signals\SignalGenerator.Chebyshev.cs</Link>
     </Compile>
-    <Compile Include="..\Numerics\Sampling\Sample.Equidistant.cs">
-      <Link>Sampling\Sample.Equidistant.cs</Link>
+    <Compile Include="..\Numerics\Signals\SignalGenerator.Equidistant.cs">
+      <Link>Signals\SignalGenerator.Equidistant.cs</Link>
     </Compile>
-    <Compile Include="..\Numerics\Sampling\Sample.Random.cs">
-      <Link>Sampling\Sample.Random.cs</Link>
+    <Compile Include="..\Numerics\Signals\SignalGenerator.Random.cs">
+      <Link>Signals\SignalGenerator.Random.cs</Link>
     </Compile>
     <Compile Include="..\Numerics\Sorting.cs">
       <Link>Sorting.cs</Link>

src/UnitTests/IntegralTransformsTests/FourierTest.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
     using IntegralTransforms;
     using IntegralTransforms.Algorithms;
     using NUnit.Framework;
-    using Sampling;
+    using Signals;
 
     /// <summary>
     /// Fourier test.
@@ -51,7 +51,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void NaiveTransformsRealSineCorrectly()
         {
-            var samples = Sample.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 16);
+            var samples = SignalGenerator.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 16);
 
             // real-odd transforms to imaginary odd
             var dft = new DiscreteFourierTransform();
@@ -87,7 +87,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void Radix2ThrowsWhenNotPowerOfTwo()
         {
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, 0x7F);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, 0x7F);
 
             var dft = new DiscreteFourierTransform();
 

src/UnitTests/IntegralTransformsTests/HartleyTest.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
     using IntegralTransforms;
     using IntegralTransforms.Algorithms;
     using NUnit.Framework;
-    using Sampling;
+    using Signals;
 
     /// <summary>
     /// Hartley tests.
@@ -78,7 +78,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void NaiveMatchesDft([Values(HartleyOptions.Default, HartleyOptions.AsymmetricScaling, HartleyOptions.NoScaling)] HartleyOptions hartleyOptions, [Values(FourierOptions.Default, FourierOptions.AsymmetricScaling, FourierOptions.NoScaling)] FourierOptions fourierOptions)
         {
             var dht = new DiscreteHartleyTransform();
-            var samples = Sample.Random(x => x, _uniform, 0x80);
+            var samples = SignalGenerator.Random(x => x, _uniform, 0x80);
 
             VerifyMatchesDft(
                 samples, 

src/UnitTests/IntegralTransformsTests/InverseTransformTest.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
     using IntegralTransforms;
     using IntegralTransforms.Algorithms;
     using NUnit.Framework;
-    using Sampling;
+    using Signals;
 
     /// <summary>
     /// Inverse Transform test.
@@ -58,7 +58,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
             Func<Complex[], Complex[]> forward, 
             Func<Complex[], Complex[]> inverse)
         {
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, count);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, count);
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -84,7 +84,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
             Func<double[], double[]> forward, 
             Func<double[], double[]> inverse)
         {
-            var samples = Sample.Random(x => x, _uniform, count);
+            var samples = SignalGenerator.Random(x => x, _uniform, count);
             var work = new double[samples.Length];
             samples.CopyTo(work, 0);
 
@@ -183,7 +183,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void FourierDefaultTransformIsReversible()
         {
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, 0x7FFF);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, 0x7FFF);
             var work = new Complex[samples.Length];
             samples.CopyTo(work, 0);
 

src/UnitTests/IntegralTransformsTests/MatchingNaiveTransformTest.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
     using IntegralTransforms;
     using IntegralTransforms.Algorithms;
     using NUnit.Framework;
-    using Sampling;
+    using Signals;
 
     /// <summary>
     /// Matching Naive transform tests.
@@ -75,7 +75,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierRadix2MatchesNaiveOnRealSine([Values(FourierOptions.Default, FourierOptions.Matlab, FourierOptions.NumericalRecipes)] FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = Sample.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 16);
+            var samples = SignalGenerator.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 16);
 
             VerifyMatchesNaiveComplex(
                 samples, 
@@ -98,7 +98,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierRadix2MatchesNaiveOnRandom([Values(FourierOptions.Default, FourierOptions.Matlab, FourierOptions.NumericalRecipes)] FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, 0x80);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, 0x80);
 
             VerifyMatchesNaiveComplex(
                 samples, 
@@ -121,7 +121,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierBluesteinMatchesNaiveOnRealSineNonPowerOfTwo([Values(FourierOptions.Default, FourierOptions.Matlab, FourierOptions.NumericalRecipes)] FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = Sample.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 14);
+            var samples = SignalGenerator.EquidistantPeriodic(w => new Complex(Math.Sin(w), 0), Constants.Pi2, 0, 14);
 
             VerifyMatchesNaiveComplex(
                 samples, 
@@ -144,7 +144,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierBluesteinMatchesNaiveOnRandomPowerOfTwo([Values(FourierOptions.Default, FourierOptions.Matlab, FourierOptions.NumericalRecipes)] FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, 0x80);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, 0x80);
 
             VerifyMatchesNaiveComplex(
                 samples, 
@@ -167,7 +167,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         public void FourierBluesteinMatchesNaiveOnRandomNonPowerOfTwo([Values(FourierOptions.Default, FourierOptions.Matlab, FourierOptions.NumericalRecipes)] FourierOptions options)
         {
             var dft = new DiscreteFourierTransform();
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, 0x7F);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, 0x7F);
 
             VerifyMatchesNaiveComplex(
                 samples, 

src/UnitTests/IntegralTransformsTests/ParsevalTheoremTest.cs

@@ -32,7 +32,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
     using IntegralTransforms;
     using IntegralTransforms.Algorithms;
     using NUnit.Framework;
-    using Sampling;
+    using Signals;
     using Statistics;
 
     /// <summary>
@@ -53,7 +53,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void FourierDefaultTransformSatisfiesParsevalsTheorem([Values(0x1000, 0x7FF)] int count)
         {
-            var samples = Sample.Random((u, v) => new Complex(u, v), _uniform, count);
+            var samples = SignalGenerator.Random((u, v) => new Complex(u, v), _uniform, count);
 
             var timeSpaceEnergy = (from s in samples select s.MagnitudeSquared()).Mean();
 
@@ -75,7 +75,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         [Test]
         public void HartleyDefaultNaiveSatisfiesParsevalsTheorem([Values(0x40, 0x1F)] int count)
         {
-            var samples = Sample.Random(x => x, _uniform, count);
+            var samples = SignalGenerator.Random(x => x, _uniform, count);
 
             var timeSpaceEnergy = (from s in samples select s * s).Mean();