Format

examples/particle-mcmc/lorenz.jl

@@ -11,45 +11,43 @@ include("particles.jl")
 include("resamplers.jl")
 include("simple-filters.jl")
 
-
 Base.@kwdef struct Parameters{T<:Real}
-	β::T = 8/3
-	ρ::T = 28.
-	σ::T = 10.
-	ν::T = 1. # Obs noise variance
-	dt::T = 0.025 # Time step
+    β::T = 8 / 3
+    ρ::T = 28.0
+    σ::T = 10.0
+    ν::T = 1.0 # Obs noise variance
+    dt::T = 0.025 # Time step
 end
 
-function lorenz!(du, u, p::Parameters, t) 
-	@unpack β, ρ, σ = p
-	du[1] = σ * (u[2] - u[1])
-	du[2] = u[1] * (ρ - u[3]) - u[2]
-	du[3] = u[1] * u[2] - β * u[3]
+function lorenz!(du, u, p::Parameters, t)
+    @unpack β, ρ, σ = p
+    du[1] = σ * (u[2] - u[1])
+    du[2] = u[1] * (ρ - u[3]) - u[2]
+    return du[3] = u[1] * u[2] - β * u[3]
 end
 
-
 struct LatentNoiseProcess{T} <: LatentDynamics{Vector{T}}
-	σ::AbstractPDMat{T}
-	dt::T
-	integrator
+    σ::AbstractPDMat{T}
+    dt::T
+    integrator
 end
 
 struct ObservationNoiseProcess{T} <: ObservationProcess{Vector{T}}
-	σ::AbstractPDMat{T}
+    σ::AbstractPDMat{T}
 end
 
 function SSMProblems.distribution(dyn::LatentNoiseProcess, step::Integer, prev_state, extra)
-	reinit!(dyn.integrator, prev_state)
-	step!(dyn.integrator, dyn.dt, true)
-	return MvNormal(dyn.integrator.u, dyn.σ)
+    reinit!(dyn.integrator, prev_state)
+    step!(dyn.integrator, dyn.dt, true)
+    return MvNormal(dyn.integrator.u, dyn.σ)
 end
 
 function SSMProblems.distribution(dyn::LatentNoiseProcess, extra)
-	return MvNormal([1; 0; 0], dyn.σ)
+    return MvNormal([1; 0; 0], dyn.σ)
 end
 
 function SSMProblems.distribution(obs::ObservationNoiseProcess, step::Integer, state, extra)
-	return MvNormal(state, obs.σ * I)
+    return MvNormal(state, obs.σ * I)
 end
 
 # Simulate some data
@@ -78,7 +76,7 @@ filter = BF(Np; threshold=1.0, resampler=Systematic());
 sparse_ancestry = AncestorCallback(eltype(model.dyn), filter.N, 1.0);
 tree, llbf = sample(rng, model, filter, y; callback=sparse_ancestry);
 lineage = get_ancestry(sparse_ancestry.tree)
- 
+
 # Fancy 3D plot
 # fig = Figure()
 # lines(fig[1, 1], hcat(x0, x...))
@@ -88,8 +86,8 @@ lineage = get_ancestry(sparse_ancestry.tree)
 
 fig = Figure()
 for i in eachindex(first(x))
-	lines(fig[i, 1], hcat(x0, x...)[i, :])
-	for path in lineage
-		lines!(fig[i, 1], hcat(path...)[i, :], color=:black, alpha=0.1)
-	end
-end
+    lines(fig[i, 1], hcat(x0, x...)[i, :])
+    for path in lineage
+        lines!(fig[i, 1], hcat(path...)[i, :]; color=:black, alpha=0.1)
+    end
+end