remove the unused argument df

docs/src/beetle_example.md

@@ -108,8 +108,8 @@ plot!(xyt[:,1],xyt[:,2], c=:red, lab="measurement")
 ```
 as well as the angle state variable (we subsample the particles to not get sluggish plots)
 ```@example beetle
-fig2 = scatter(to1series(ϕ.(x)'[:,1:5:end])..., m=(:black, 0.03, 2), lab="", size=(500,300), format=:png)
-plot!(identity.(xh[:,4]), lab="Filtered angle", legend=:topleft, ylims=(-30, 70))
+fig2 = scatter(to1series(ϕ.(x)'[:,1:5:end])..., m=(:black, 0.03, 2), lab="", size=(500,300), format=:png, dpi=100)
+plot!(identity.(xh[:,4]), lab="Filtered angle", legend=:topleft, ylims=(-30, 70), format=:png)
 ```
 The particle plot above indicate that the posterior is multimodal. This phenomenon arises due to the simple model that uses an angle that is allowed to leave the interval ``0-2\pi` rad. In this example, we are not interested in the angle, but rather when the beetle switches mode. The filtering distribution above gives a hint at when this happens, but we will not plot the mode trajectory until we have explored smoothing as well.
 

docs/src/index.md

@@ -289,7 +289,7 @@ nothing # hide
 We now create the [`AdvancedParticleFilter`](@ref) and use it in the same way as the other filters:
 
 ```@example lingauss
-apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, df, d0)
+apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, d0)
 sol = forward_trajectory(apf, u, y, p)
 ```
 

src/PFtypes.jl

@@ -164,7 +164,7 @@ end
     dynamics::FT
     measurement::GT
     measurement_likelihood::GLT
-    dynamics_density::FDT = Normal()
+    dynamics_density::FDT = MissingDistribution()
     initial_density::IDT
     resample_threshold::Float64 = 0.5
     resampling_strategy::RST = ResampleSystematic
@@ -175,7 +175,7 @@ end
 
 
 """
-    AdvancedParticleFilter(N::Integer, dynamics::Function, measurement::Function, measurement_likelihood, dynamics_density, initial_density; p = SciMLBase.NullParameters(), threads = false, kwargs...)
+    AdvancedParticleFilter(N::Integer, dynamics::Function, measurement::Function, measurement_likelihood, initial_density; p = SciMLBase.NullParameters(), threads = false, kwargs...)
 
 This type represents a standard particle filter but affords extra flexibility compared to the [`ParticleFilter`](@ref) type, e.g., non-additive noise in the dynamics and measurement functions.
 
@@ -186,7 +186,6 @@ See the docs for more information: https://baggepinnen.github.io/LowLevelParticl
 - `dynamics`: A discrete-time dynamics function `(x, u, p, t, noise=false) -> x⁺`. It's important that the `noise` argument defaults to `false`.
 - `measurement`: A measurement function `(x, u, p, t, noise=false) -> y`. It's important that the `noise` argument defaults to `false`.
 - `measurement_likelihood`: A function `(x, u, y, p, t)->logl` to evaluate the log-likelihood of a measurement.
-- `dynamics_density`: This field is not used by the advanced filter and can be set to `nothing`.
 - `initial_density`: The distribution of the initial state.
 - `threads`: use threads to propagate particles in parallel. Only activate this if your dynamics is thread-safe. `SeeToDee.SimpleColloc` is not thread-safe by default due to the use of internal caches, but `SeeToDee.Rk4` is.
 """
@@ -202,6 +201,9 @@ function AdvancedParticleFilter(N::Integer, dynamics::Function, measurement::Fun
     initial_density, kwargs...)
 end
 
+# Constructor without dynamics_density which is not used except for in smoothing
+AdvancedParticleFilter(N::Integer, dynamics::Function, measurement::Function, measurement_likelihood, initial_density; kwargs...) = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, MissingDistribution(), initial_density; kwargs...)
+
 
 Base.@propagate_inbounds function measurement_equation!(pf::AbstractParticleFilter, u, y, p, t, w = weights(pf))
     g = measurement_likelihood(pf)

src/smoothing.jl

@@ -43,6 +43,7 @@ function smooth(pf::AbstractParticleFilter, xf, wf, wef, ll, M, u, y, p=paramete
     N = num_particles(pf)
     f = dynamics(pf)
     df = dynamics_density(pf)
+    df isa MissingDistribution && error("In order to perform smoothing, you must provide a noise density for the dynamics. Use the constructor `AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, dynamics_density, initial_density; kwargs...)` to do this")
     @assert M <= N "Must extend cache size of bins and j to allow this"
     xb = Array{particletype(pf)}(undef,M,T)
     j = resample(ResampleSystematic, wef[:,T], M)

src/utils.jl

@@ -115,6 +115,8 @@ Mixed value support indicates that the distribution is a mix of continuous and d
 """
 struct Mixed <: ValueSupport end
 
+struct MissingDistribution <: MultivariateDistribution{Mixed} end
+
 """
     TupleProduct(v::NTuple{N,UnivariateDistribution})
 

test/runtests.jl

@@ -292,7 +292,7 @@ end
     T     = 200 # Number of time steps
     N     = 500
     Random.seed!(0)
-    apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, df, d0, threads=false)
+    apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, d0, threads=false)
     x,u,y = LowLevelParticleFilters.simulate(apf,T,du) # Simuate trajectory using the model in the filter
     x,u,y = tosvec.((x,u,y))
     @time resapf,ll = mean_trajectory(apf, u, y)
@@ -302,7 +302,7 @@ end
 
     # With threads
     Random.seed!(0)
-    apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, df, d0, threads=true)
+    apf = AdvancedParticleFilter(N, dynamics, measurement, measurement_likelihood, d0, threads=true)
     x,u,y = LowLevelParticleFilters.simulate(apf,T,du) # Simuate trajectory using the model in the filter
     x,u,y = tosvec.((x,u,y))
     @time resapf,ll = mean_trajectory(apf, u, y)