Fix missed deprecations

README.md

@@ -65,7 +65,7 @@ RobotDynamics.control_dim(::Cartpole) = 1
 
 # Create the model
 model = Cartpole()
-n,m = size(model)
+n,m = RD.dims(model)
 
 # Generate random state and control vector
 x,u = rand(model)

docs/oldsrc/models.md

@@ -69,7 +69,7 @@ dynamics(model, z)
 jacobian!(∇f, model, z)
 ```
 
-We can also use `size(model)` to get `(n,m)`, `rand(model)` to get a tuple of randomly-sampled state and
+We can also use `RD.dims(model)` to get `(n,m,n)`, `rand(model)` to get a tuple of randomly-sampled state and
 control vectors, or `zeros(model)` to get 0-vectors of the state and control.
 
 ### Analytical Jacobians

examples/cartpole.jl

@@ -39,7 +39,7 @@ RobotDynamics.control_dim(::Cartpole) = 1
 
 # Create the model
 model = Cartpole()
-n,m = size(model)
+n,m = RD.dims(model)
 
 # Generate random state and control vector
 x,u = rand(model)

test/cartpole_test.jl

@@ -76,8 +76,8 @@ x0 = rand(model)[1]
 @test RD.state_diff(model, x, x0) ≈ x - x0
 @test RD.errstate_dim(model) == RD.state_dim(model)
 G = zeros(n,n)
-RD.state_diff_jacobian!(model, G, z)
+RD.errstate_jacobian!(model, G, z)
 @test G ≈ I(n)
 G .= 0
-RobotDynamics.state_diff_jacobian!(model, G, x)
+RobotDynamics.errstate_jacobian!(model, G, x)
 @test G ≈ I(n)

test/discretized_dynamics_test.jl

@@ -3,7 +3,7 @@ include("double_integrator.jl")
 ## Test Euler
 @autodiff struct DoubleIntegrator{D} <: DI{D} end
 cmodel = DoubleIntegrator{2}()
-n,m = size(cmodel)
+n,m = RD.dims(cmodel)
 
 # Evaluate Euler step explicitly
 x0,u0 = randn(cmodel)

test/liestate.jl

@@ -77,7 +77,7 @@ s = LieState{R,P}()
 n = length(s)
 n̄ = RD.errstate_dim(s)
 G = zeros(n,n̄)
-RobotDynamics.state_diff_jacobian!(s, G, x)
+RobotDynamics.errstate_jacobian!(s, G, x)
 
 q1 = UnitQuaternion(x[4],x[5],x[6],x[7])
 q2 = UnitQuaternion(x[10],x[11],x[12],x[13])
@@ -92,7 +92,7 @@ dx = @SVector rand(n)
 
 dq1 = SVector(dx[4],dx[5],dx[6],dx[7])
 dq2 = SVector(dx[10],dx[11],dx[12],dx[13])
-RobotDynamics.∇²differential!(s, ∇G, x, dx)
+RobotDynamics.∇errstate_jacobian!(s, ∇G, x, dx)
 ∇G1 = Rotations.∇²differential(q1, dq1)
 ∇G2 = Rotations.∇²differential(q2, dq2)
 ∇G0 = cat(zeros(3,3), ∇G1, zeros(2,2), ∇G2, zeros(3,3), dims=(1,2))
@@ -106,7 +106,7 @@ model = LieBody()
 @test s === LieState(model)
 G .= 0
 ∇G .= 0
-RobotDynamics.state_diff_jacobian!(model, G, x)
-RobotDynamics.∇²differential!(model, ∇G, x, dx)
+RobotDynamics.errstate_jacobian!(model, G, x)
+RobotDynamics.∇errstate_jacobian!(model, ∇G, x, dx)
 @test G0 ≈ G
 @test ∇G0 ≈ ∇G

test/rigid_body_jacobians.jl

@@ -2,7 +2,7 @@ using RobotDynamics: orientation
 
 # Set up model
 model = Body{UnitQuaternion{Float64}}()
-@test size(model) == (13, 6, 13)
+@test RD.dims(model) == (13, 6, 13)
 x, u = rand(model)
 t, dt = 0, 0.1
 z = RD.KnotPoint(x, u, t, dt)
@@ -72,14 +72,14 @@ dx = RobotDynamics.state_diff(model, x, x0)
 G0 = Rotations.∇differential(q)
 G = zeros(RD.state_dim(model), RobotDynamics.errstate_dim(model))
 @test size(G) == (13, 12)
-RD.state_diff_jacobian!(model, G, z)
+RD.errstate_jacobian!(model, G, z)
 @test G ≈ cat(I(3), G0, I(6), dims = (1, 2))
 
 b = @SVector rand(13)
 ∇G0 = Rotations.∇²differential(q, b[SA[4, 5, 6, 7]])
 ∇G = zeros(RD.errstate_dim(model), RD.errstate_dim(model))
 @test size(∇G) == (12, 12)
-RobotDynamics.∇²differential!(model, ∇G, x, b)
+RobotDynamics.∇errstate_jacobian!(model, ∇G, x, b)
 @test ∇G ≈ cat(zeros(3, 3), ∇G0, zeros(6, 6), dims = (1, 2))
 
 # @btime RobotDynamics.state_diff_jacobian!($G, $model, $z)