changed: DimensionMismatch except. for all kalman Filters

Author: franckgaga

src/estimator/construct.jl

@@ -122,14 +122,16 @@ Also validate initial estimate covariance `P̂_0`, if provided.
 function validate_kfcov(model, i_ym, nint_u, nint_ym, Q̂, R̂, P̂_0=nothing)
     nym = length(i_ym)
     nx̂  = model.nx + sum(nint_u) + sum(nint_ym)
-    size(Q̂)  ≠ (nx̂, nx̂)     && error("Q̂ size $(size(Q̂)) ≠ nx̂, nx̂ $((nx̂, nx̂))")
+    (any(<(0), nint_u) || any(<(0), nint_ym)) && return nothing # a clearer error is thrown later 
+    size(Q̂)  ≠ (nx̂, nx̂)     && throw(DimensionMismatch("Q̂ size $(size(Q̂)) ≠ nx̂, nx̂ $((nx̂, nx̂))"))
     !ishermitian(Q̂)         && error("Q̂ is not Hermitian")
-    size(R̂)  ≠ (nym, nym)   && error("R̂ size $(size(R̂)) ≠ nym, nym $((nym, nym))")
+    size(R̂)  ≠ (nym, nym)   && throw(DimensionMismatch("R̂ size $(size(R̂)) ≠ nym, nym $((nym, nym))"))
     !ishermitian(R̂)         && error("R̂ is not Hermitian")
     if ~isnothing(P̂_0)
-        size(P̂_0) ≠ (nx̂, nx̂) && error("P̂_0 size $(size(P̂_0)) ≠ nx̂, nx̂ $((nx̂, nx̂))")
+        size(P̂_0) ≠ (nx̂, nx̂) && throw(DimensionMismatch("P̂_0 size $(size(P̂_0)) ≠ nx̂, nx̂ $((nx̂, nx̂))"))
         !ishermitian(P̂_0)    && error("P̂_0 is not Hermitian")
     end
+    return nothing
 end
 
 @doc raw"""
@@ -210,9 +212,10 @@ function init_integrators(nint::IntVectorOrInt, ny, varname::String)
         nint = fill(0, ny)
     end
     if length(nint) ≠ ny
-        error("nint_$(varname) length ($(length(nint))) ≠ n$(varname) ($ny)")
+        msg = "nint_$(varname) length ($(length(nint))) ≠ n$(varname) ($ny)" 
+        throw(DimensionMismatch(msg))
     end
-    any(nint .< 0) && error("nint_$(varname) values should be ≥ 0")
+    any(nint .< 0) && throw(ArgumentError("nint_$(varname) values should be ≥ 0"))
     nx = sum(nint)
     A, C = zeros(nx, nx), zeros(ny, nx)
     if nx ≠ 0

test/2_test_state_estim.jl

@@ -49,10 +49,10 @@
     @test skalmanfilter9.cov.Q̂ ≈ I(4)
     @test skalmanfilter9.cov.R̂ ≈ I(2)
 
-    @test_throws ErrorException SteadyKalmanFilter(linmodel, nint_ym=[1,1,1])
-    @test_throws ErrorException SteadyKalmanFilter(linmodel, nint_ym=[-1,0])
-    @test_throws ErrorException SteadyKalmanFilter(linmodel, nint_ym=0, σQ=[1])
-    @test_throws ErrorException SteadyKalmanFilter(linmodel, nint_ym=0, σR=[1,1,1])
+    @test_throws DimensionMismatch SteadyKalmanFilter(linmodel, nint_ym=[1,1,1])
+    @test_throws ArgumentError SteadyKalmanFilter(linmodel, nint_ym=[-1,0])
+    @test_throws DimensionMismatch SteadyKalmanFilter(linmodel, nint_ym=0, σQ=[1])
+    @test_throws DimensionMismatch SteadyKalmanFilter(linmodel, nint_ym=0, σR=[1,1,1])
     @test_throws ErrorException SteadyKalmanFilter(linmodel3, nint_ym=[1, 0, 0])
     model_unobs = LinModel([1 0;0 1.5], [1; 0], [1 0], zeros(2,0), zeros(1,0), 1.0)
     @test_throws ErrorException SteadyKalmanFilter(model_unobs, nint_ym=[1])
@@ -188,7 +188,7 @@ end
     kalmanfilter8 = KalmanFilter(linmodel2)
     @test isa(kalmanfilter8, KalmanFilter{Float32})
 
-    @test_throws ErrorException KalmanFilter(linmodel, nint_ym=0, σP_0=[1])
+    @test_throws DimensionMismatch KalmanFilter(linmodel, nint_ym=0, σP_0=[1])
 end
 
 @testitem "KalmanFilter estimator methods" setup=[SetupMPCtests] begin
@@ -302,8 +302,8 @@ end
     lo6 = Luenberger(linmodel2)
     @test isa(lo6, Luenberger{Float32})
 
-    @test_throws ErrorException Luenberger(linmodel, nint_ym=[1,1,1])
-    @test_throws ErrorException Luenberger(linmodel, nint_ym=[-1,0])
+    @test_throws DimensionMismatch Luenberger(linmodel, nint_ym=[1,1,1])
+    @test_throws ArgumentError Luenberger(linmodel, nint_ym=[-1,0])
     @test_throws ErrorException Luenberger(linmodel, poles=[0.5])
     @test_throws ErrorException Luenberger(linmodel, poles=fill(1.5, lo1.nx̂))
     @test_throws ErrorException Luenberger(LinModel(tf(1,[1, 0]),0.1), poles=[0.5,0.6])
@@ -1603,8 +1603,8 @@ end
 @testitem "ManualEstimator construction" setup=[SetupMPCtests] begin
     using .SetupMPCtests, ControlSystemsBase, LinearAlgebra
     linmodel = LinModel(sys,Ts,i_u=[1,2])
-    f(x,u,d,model) = model.A*x + model.Bu*u + model.Bd*d
-    h(x,d,model)   = model.C*x + model.Du*d
+    f = (x,u,d,model) -> model.A*x + model.Bu*u + model.Bd*d
+    h = (x,d,model)   -> model.C*x + model.Du*d
     nonlinmodel = NonLinModel(f, h, Ts, 2, 4, 2, 1, solver=nothing, p=linmodel)
 
     manual1 = ManualEstimator(linmodel)
@@ -1651,8 +1651,8 @@ end
 @testitem "ManualEstimator estimator methods" setup=[SetupMPCtests] begin
     using .SetupMPCtests, ControlSystemsBase, LinearAlgebra
     linmodel = LinModel(sys,Ts,i_u=[1,2])
-    f(x,u,d,model) = model.A*x + model.Bu*u + model.Bd*d
-    h(x,d,model)   = model.C*x + model.Du*d
+    f = (x,u,d,model) -> model.A*x + model.Bu*u + model.Bd*d
+    h = (x,d,model)   -> model.C*x + model.Du*d
     nonlinmodel = NonLinModel(f, h, Ts, 2, 2, 2, 0, solver=nothing, p=linmodel)
 
     manual1 = ManualEstimator(linmodel)