Merge pull request #404 from SciML/sb/ps

refactor: pass in defaults in `transform_pde_system!`

src/system_parsing/pde_system_transformation.jl

@@ -31,7 +31,7 @@ function PDEBase.transform_pde_system!(
         end
     end
 
-    sys = PDESystem(eqs, bcs, sys.domain, sys.ivs, Num.(v.ū), sys.ps, name = sys.name)
+    sys = PDESystem(eqs, bcs, sys.domain, sys.ivs, Num.(v.ū), sys.ps, name = sys.name, defaults = sys.defaults)
     return sys
 end
 

test/components/ODEFunction_test.jl

@@ -26,8 +26,8 @@ bcs = [u(0, x) ~ x,
 domains = [t ∈ Interval(t_min, t_max),
     x ∈ Interval(x_min, x_max)]
 
-@named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [a => 1.0],
-    analytic_func = [u(t, x) => analytic_u])
+@named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [a],
+    analytic_func = [u(t, x) => analytic_u], defaults = Dict(a => 1.0))
 
 disc = MOLFiniteDifference([x => 30], t, advection_scheme = WENOScheme())
 

test/pde_systems/MOL_1D_Linear_Diffusion.jl

@@ -78,7 +78,7 @@ end
         x ∈ Interval(0.0, 1.0)]
 
     # PDE system
-    @named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [D => 10.0])
+    @named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [D]; defaults = Dict(D => 10.0))
 
     # Method of lines discretization
     dx = 1 / (5pi)
@@ -596,7 +596,7 @@ end
         x ∈ Interval(0.0, 1.0)]
 
     @named pdesys = PDESystem(
-        eqs, bcs, domains, [t, x], [u(t, x), v(t, x)], [Dn => 0.5, Dp => 2])
+        eqs, bcs, domains, [t, x], [u(t, x), v(t, x)], [Dn, Dp]; defaults = Dict(Dn => 0.5, Dp => 2))
     discretization = MOLFiniteDifference([x => 0.1], t)
     prob = discretize(pdesys, discretization)
     # Make sure it can be solved

test/pde_systems/MOL_1D_Linear_Diffusion_NonUniform.jl

@@ -86,7 +86,7 @@ end
         x ∈ Interval(0.0, 1.0)]
 
     # PDE system
-    @named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [D => 10.0])
+    @named pdesys = PDESystem(eq, bcs, domains, [t, x], [u(t, x)], [D]; defaults = Dict(D => 10.0))
 
     # Method of lines discretization
     dx = 0.0:0.1:1.0
@@ -624,7 +624,7 @@ end
                        rand(StableRNG(0), [0.001, -0.001], length(dx[2:(end - 1)]))
 
     @named pdesys = PDESystem(
-        eqs, bcs, domains, [t, x], [u(t, x), v(t, x)], [Dn => 0.5, Dp => 2])
+        eqs, bcs, domains, [t, x], [u(t, x), v(t, x)], [Dn, Dp]; defaults = Dict(Dn => 0.5, Dp => 2))
     discretization = MOLFiniteDifference([x => dx], t)
     prob = discretize(pdesys, discretization)
     # Make sure it can be solved

test/pde_systems/MOLtest2.jl

@@ -373,7 +373,7 @@ end
     Dt = Differential(t)
     Dx = Differential(x)
     Dxx = Differential(x)^2
-    params = Symbolics.scalarize(reduce(vcat, [p .=> [1.5, 2.0], q .=> [1.2, 1.8]]))
+    params = [p, q]
     # 1D PDE and boundary conditions
 
     eqs = [Dt(u(t, x)[i]) ~ p[i] * Dxx(u(t, x)[i]) for i in 1:n_comp]
@@ -391,7 +391,7 @@ end
     # PDE system
 
     @named pdesys = PDESystem(eqs, bcs_collected, domains, [t, x],
-        [u(t, x)[i] for i in 1:n_comp], Symbolics.scalarize(params))
+        [u(t, x)[i] for i in 1:n_comp], params; defaults = Dict(p => [1.5, 2.0], q => [1.2, 1.8]))
 
     # Method of lines discretization
     dx = 0.1