add test

Author: baggepinnen

docs/src/tutorials/noise.md

@@ -151,13 +151,13 @@ The default option `midrise = true` includes both end points as possible output
 
 
 ## Sampling with AD effects
-The block [`SampleWithADEffects`](@ref) combines a [`Sampler`](@ref), a [`NormalNoise](@ref) and a [`Quantization`](@ref) block to simulate the effects of practical sampling, noise and quantization in an AD converter. The block has the connectors `input` and `output`, where the input is the continuous-time signal to be sampled, and the output is the quantized, noisy signal. Example
+The block [`SampleWithADEffects`](@ref) combines an ideal [`Sampler`](@ref), a [`NormalNoise](@ref) and a [`Quantization`](@ref) block to simulate the undesirable but practically occurring effects of sampling, noise and quantization in an AD converter. The block has the connectors `input` and `output`, where the input is the continuous-time signal to be sampled, and the output is the quantized, noisy signal. Example:
 
 ```@example QUANT
 @mtkmodel PracticalSamplerModel begin
     @components begin
         input = Sine(amplitude=1.2, frequency=1, smooth=false)
-        sampling = SampleWithADEffects(; dt=0.03, bits=3, y_min = -1, y_max = 1, sigma = 0.05, noisy = false, quantized=false, midrise=true)
+        sampling = SampleWithADEffects(; dt=0.03, bits=3, y_min = -1, y_max = 1, sigma = 0.1, noisy = true, quantized=true, midrise=true)
     end
     @variables begin
         x(t) = 0 # Dummy variable to work around a bug for models without continuous-time state
@@ -170,10 +170,16 @@ end
 @named m = PracticalSamplerModel()
 m = complete(m)
 ssys = structural_simplify(IRSystem(m))
-# prob = ODEProblem(ssys, [m.sampling.noise.y(z-1) => 0], (0.0, 2.0))
-prob = ODEProblem(ssys, [], (0.0, 2.0))
+prob = ODEProblem(ssys, [m.sampling.noise.y(z-1) => 0], (0.0, 2.0))
 sol = solve(prob, Tsit5())
 plot(sol, idxs=m.input.output.u)
 plot!(sol, idxs=m.sampling.y, label="AD converted output")
-# plot!(sol, idxs=m.sampling.sampler.y, label="AD converted output")
-```
+```
+
+Both quantization and noise addition are optional and turned off by default. In the example above, we turn them on with keywords `noisy = true` and `quantized = true`. The noise is Gaussian white noise with standard deviation `sigma`, and the quantization is a 3-bit midrise quantizer (8 output levels) with limits `y_min` and `y_max`. Limits have to be provided when quantization is used. The `dt` parameter is the sampling time, if left unspecified, it will be inferred from context.
+
+Things to notice in the plot:
+- The sampled signal is saturated at the quantization limits ±1.
+- The noise is added to the signal before quantization, which means that the sampled signal has ``2^\text{bits}`` distinct output levels only.
+- 0 is not a possible output value. In situations where 0 is an important value (such as in the presence of integration of a quantized value that is expected to be close to 0), the mid-tread quantizer should be used instead by passing `midrise = false`.
+

src/discrete_blocks.jl

@@ -910,7 +910,7 @@ This block is not differentiable, the derivative is zero everywhere exect for at
     @parameters begin
         y_max = 1, [description = "Upper limit of output"]
         y_min = -1, [description = "Lower limit of output"]
-        bits::Int = 8, [description = "Number of bits of quantization"]
+        bits = 8, [description = "Number of bits of quantization"]
         quantized::Bool = true, [description = "If quantization effects shall be computed."]
     end
     @equations begin
@@ -1057,7 +1057,7 @@ The operations occur in the order
         else
             noise = Gain(; k = 1)
         end
-        quantization = Quantization(; y_min, y_max, midrise, quantized)
+        quantization = Quantization(; bits, y_min, y_max, midrise, quantized)
     end
     @equations begin
         connect(input, sampler.input)

test/test_discrete_blocks.jl

@@ -514,4 +514,31 @@ end
     @test sol(0.999, idxs=m.filter.y) == 0
     @test sol(1.1, idxs=m.filter.y) > 0
 
+end
+
+@testset "sampling with AD effects" begin
+    @info "Testing sampling with AD effects"
+
+    @mtkmodel PracticalSamplerModel begin
+        @components begin
+            input = Sine(amplitude=1.2, frequency=1, smooth=false)
+            sampling = SampleWithADEffects(; dt=0.03, bits=3, y_min = -1, y_max = 1, sigma = 0.1, noisy = true, quantized=true, midrise=true)
+        end
+        @variables begin
+            x(t) = 0 # Dummy variable to work around a bug for models without continuous-time state
+        end
+        @equations begin
+            connect(input.output, sampling.input)
+            D(x) ~ Hold(sampling.y) # Dummy equation
+        end
+    end
+    @named m = PracticalSamplerModel()
+    m = complete(m)
+    ssys = structural_simplify(IRSystem(m))
+    prob = ODEProblem(ssys, [m.sampling.noise.y(z-1) => 0], (0.0, 2.0))
+    sol = solve(prob, Tsit5())
+    
+    @test length(unique(sol[m.sampling.y])) == 8
+    @test maximum(abs, sol(0:0.03:1, idxs=m.sampling.y) - sol(0:0.03:1, idxs=m.sampling.u)) < 0.3
+
 end