added: print Jacobian for ExtendedKalmanFilter

Author: franckgaga

src/estimator/kalman.jl

@@ -1177,6 +1177,10 @@ function update_estimate!(estim::ExtendedKalmanFilter{NT}, y0m, d0, u0) where NT
     return predict_estimate_kf!(estim, u0, d0, F̂)
 end
 
+function print_details(io::IO, estim::ExtendedKalmanFilter)
+    println(io, "├ jacobian: $(backend_str(estim.jacobian))")
+end
+
 "Set `estim.cov.P̂` to `estim.cov.P̂_0` for the time-varying Kalman Filters."
 function init_estimate_cov!(
     estim::Union{KalmanFilter, UnscentedKalmanFilter, ExtendedKalmanFilter}, _ , _ , _

src/estimator/mhe.jl

@@ -1,26 +1,20 @@
 include("mhe/construct.jl")
 include("mhe/execute.jl")
 
-function Base.show(io::IO, estim::MovingHorizonEstimator)
-    model = estim.model
-    nu, nd = model.nu, model.nd
-    nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
-    n = maximum(ndigits.((nu, nx̂, nym, nyu, nd))) + 1
-    println(io, "$(nameof(typeof(estim))) estimator with a sample time Ts = $(model.Ts) s:")
-    println(io, "├ model: $(nameof(typeof(model)))")
+"Print optimizer and other information for `MovingHorizonEstimator`."
+function print_details(io::IO, estim::MovingHorizonEstimator)
     println(io, "├ optimizer: $(JuMP.solver_name(estim.optim)) ")
-    print_backends(io, estim, model)
-    println(io, "└ dimensions:")
-    print_estim_dim(io, estim, n)
+    print_backends(io, estim, estim.model)
 end
 
+"Print the differentiation backends for `SimModel`."
 function print_backends(io::IO, estim::MovingHorizonEstimator, ::SimModel)
     println(io, "├ gradient: $(backend_str(estim.gradient))")
     println(io, "├ jacobian: $(backend_str(estim.jacobian))")
 end
+"No differentiation backends to print for `LinModel`."
 print_backends(::IO, ::MovingHorizonEstimator, ::LinModel) = nothing
 
-
 "Print the overall dimensions of the MHE `estim` with left padding `n`."
 function print_estim_dim(io::IO, estim::MovingHorizonEstimator, n)
     nu, nd = estim.model.nu, estim.model.nd

src/model/nonlinmodel.jl

@@ -315,9 +315,9 @@ include("solver.jl")
 
 function print_details(io::IO, model::NonLinModel{<:Real, <:RungeKutta{N}}) where N
     println(io, "├ solver: $(nameof(typeof(model.solver)))($N)")
-    println(io, "├ linearization: $(backend_str(model.jacobian))")
+    println(io, "├ jacobian: $(backend_str(model.jacobian))")
 end
 function print_details(io::IO, model::NonLinModel) 
     println(io, "├ solver: $(nameof(typeof(model.solver)))")
-    println(io, "├ linearization: $(backend_str(model.jacobian))")
+    println(io, "├ jacobian: $(backend_str(model.jacobian))")
 end

src/state_estim.jl

@@ -35,10 +35,14 @@ function Base.show(io::IO, estim::StateEstimator)
     n = maximum(ndigits.((nu, nx̂, nym, nyu, nd))) + 1
     println(io, "$(nameof(typeof(estim))) estimator with a sample time Ts = $(model.Ts) s:")
     println(io, "├ model: $(nameof(typeof(estim.model)))")
+    print_details(io, estim)
     println(io, "└ dimensions:")
     print_estim_dim(io, estim, n)
 end
 
+"Print additional details of `estim` if any (no details by default)."
+print_details(::IO, ::StateEstimator) = nothing
+
 "Print the overall dimensions of the state estimator `estim` with left padding `n`."
 function print_estim_dim(io::IO, estim::StateEstimator, n)
     nu, nd = estim.model.nu, estim.model.nd