Make tests more robust

Author: YingboMa

test/direct.jl

@@ -40,11 +40,15 @@ simpexpr = [
  :($(+)($(*)(x, y), $(*)(-1, z, β)))
 ]
 
+σ, β, ρ = 2//3, 3//4, 4//5
+x, y, z = 6//7, 7//8, 8//9
 for i in 1:3
-   @test ModelingToolkit.toexpr.(eqs)[i] == simpexpr[i]
-   @test ModelingToolkit.toexpr.(eqs)[i] == simpexpr[i]
+    @test eval(ModelingToolkit.toexpr.(eqs)[i]) == eval(simpexpr[i])
+    @test eval(ModelingToolkit.toexpr.(eqs)[i]) == eval(simpexpr[i])
 end
 
+@parameters t σ ρ β
+@variables x y z
 ∂ = ModelingToolkit.jacobian(eqs,[x,y,z])
 for i in 1:3
     ∇ = ModelingToolkit.gradient(eqs[i],[x,y,z])

test/reduction.jl

@@ -212,7 +212,7 @@ nlprob = NonlinearProblem(reducedsys, u0, pp)
 reducedsol = solve(nlprob, NewtonRaphson())
 residual = fill(100.0, length(states(reducedsys)))
 nlprob.f(residual, reducedsol.u, pp)
-@test hypot(nlprob.f.observed(u2, reducedsol.u, pp), nlprob.f.observed(u1, reducedsol.u, pp)) * pp ≈ reducedsol.u atol=1e-9
+@test hypot(nlprob.f.observed(u2, reducedsol.u, pp), nlprob.f.observed(u1, reducedsol.u, pp)) * pp[1] ≈ nlprob.f.observed(u3, reducedsol.u, pp) atol=1e-9
 
 @test all(x->abs(x) < 1e-5, residual)