Removed ambiguous covar of matrix.

Author: olof3

src/ControlSystems.jl

@@ -155,6 +155,11 @@ include("plotting.jl")
 @deprecate den denvec
 @deprecate norminf hinfnorm
 
+function covar(D::Union{AbstractMatrix,UniformScaling}, R)
+    error("This call is ambiguous, use `covar(ss(D), R)` or `covar(ss(D, Ts), R)`")
+end
+
+
 # The path has to be evaluated upon initial import
 const __CONTROLSYSTEMS_SOURCE_DIR__ = dirname(Base.source_path())
 

src/matrix_comps.jl

@@ -186,12 +186,6 @@ end
 
 covar(sys::TransferFunction, W) = covar(ss(sys), W)
 
-"""
-    covar(C,W)
-If `C` is a matrix, return CWC'
-"""
-covar(C::Union{AbstractMatrix,UniformScaling}, R) = C*R*C'
-
 
 # Note: the H∞ norm computation is probably not as accurate as with SLICOT,
 # but this seems to be still reasonably ok as a first step

test/test_matrix_comps.jl

@@ -37,6 +37,12 @@ D2 = [1 0; 0 1]
 # Discrete system can have direct term
 @test covar(ss(A,B,C,D2,0.1),W) ≈ [1.00011010837831 -1.0098377309782909e-5; -1.0098377309782909e-5 1.00011010837831]
 
+# Static systems
+R = fill(1.0,1,1)
+@test covar(ss(2.0), R) == fill(Inf, 1, 1)
+@test covar(ss(2.0, 0.2), R) == fill(4.0, 1, 1)
+
+
 # TODO test in Julia 0.7 to see if supported
 # # Test special matrices
 As = sparse(A)