attempt #2

src/linear_algebra.jl

@@ -93,14 +93,18 @@ julia> Symbolics.solve_for([x + y ~ 0, x - y ~ 2], [x, y])
  -1.0
 ```
 """
-function solve_for(eq::AbstractArray, var::AbstractArray; simplify=false, check=true) # scalar case
+function solve_for(eq, var; simplify=false, check=true) # scalar case
     # simplify defaults for `false` as canonicalization should handle most of
     # the cases.
     a, b, islinear = linear_expansion(eq, var)
     check && @assert islinear
 
-    for eqᵢ in eq
-        islinear &= Symbolics.isaffine(eqᵢ.lhs-eqᵢ.rhs, var)
+    if eq isa AbstractArray
+        for eqᵢ in eq
+            islinear &= Symbolics.isaffine(eqᵢ.lhs-eqᵢ.rhs, var)
+        end
+    else
+        islinear &= Symbolics.isaffine(eq.lhs-eq.rhs, [var])
     end
 
     if !islinear 
@@ -123,7 +127,6 @@ function solve_for(eq::AbstractArray, var::AbstractArray; simplify=false, check=
 end
 solve_for(eq::Equation, var::T; x...) where {T<:AbstractArray} = solve_for([eq],var, x...)
 solve_for(eq::T, var::Num; x...) where {T<:AbstractArray} = first(solve_for(eq,[var], x...))
-solve_for(eq::Equation, var::Num; x...) = (solve_for([eq],[var], x...))
 
 function _solve(A::AbstractMatrix, b::AbstractArray, do_simplify)
     A = Num.(SymbolicUtils.quick_cancel.(A))