Remove Symbolics from test dependencies

Removes Symbolics from the main test target to avoid dependency
conflicts with DataStructures v0.19 and the symbolic stack.

The only test using Symbolics was nonfulldiagonal_sparse.jl,
which used Symbolics.jacobian_sparsity to compute a sparsity pattern.
This has been replaced with a hardcoded sparsity pattern that was
pre-computed using the same function, preserving identical test behavior.

This allows packages depending on newer versions of DataStructures
and other dependencies to work without conflicts from the symbolic
stack (Symbolics/DynamicPolynomials/MultivariatePolynomials).

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: claude

Project.toml

@@ -206,4 +206,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [targets]
-test = ["ComponentArrays", "Symbolics", "AlgebraicMultigrid", "IncompleteLU", "DiffEqCallbacks", "DifferentiationInterface", "DiffEqDevTools", "ODEProblemLibrary", "ElasticArrays", "JLArrays", "Random", "SafeTestsets", "StructArrays", "Test", "Unitful", "Pkg", "NLsolve", "RecursiveFactorization", "SparseConnectivityTracer", "SparseMatrixColorings", "Statistics"]
+test = ["ComponentArrays", "AlgebraicMultigrid", "IncompleteLU", "DiffEqCallbacks", "DifferentiationInterface", "DiffEqDevTools", "ODEProblemLibrary", "ElasticArrays", "JLArrays", "Random", "SafeTestsets", "StructArrays", "Test", "Unitful", "Pkg", "NLsolve", "RecursiveFactorization", "SparseConnectivityTracer", "SparseMatrixColorings", "Statistics"]

test/interface/nonfulldiagonal_sparse.jl

@@ -1,7 +1,6 @@
 using OrdinaryDiffEq, SparseArrays, LinearSolve, LinearAlgebra
 using SimpleUnPack
 using ComponentArrays
-using Symbolics
 
 function enclosethetimedifferential(parameters::NamedTuple)::Function
     @info "Enclosing the time differential"
@@ -124,9 +123,12 @@ odeprob = ODEProblem(dudt,
     (0, 2.1),
     parameters.prior);
 du0 = copy(odeprob.u0);
-jac_sparsity = Symbolics.jacobian_sparsity((du, u) -> dudt(du, u, parameters.prior, 0.0),
-    du0,
-    odeprob.u0);
+# Hardcoded sparsity pattern for 15 spatial points + 3 state variables (18x18 matrix)
+# Previously computed using: Symbolics.jacobian_sparsity((du, u) -> dudt(du, u, parameters.prior, 0.0), du0, odeprob.u0)
+# This avoids the dependency on Symbolics in tests
+I = [1, 2, 16, 18, 1, 2, 3, 18, 2, 3, 4, 18, 3, 4, 5, 18, 4, 5, 6, 18, 5, 6, 7, 18, 6, 7, 8, 18, 7, 8, 9, 18, 8, 9, 10, 18, 9, 10, 11, 18, 10, 11, 12, 18, 11, 12, 13, 18, 12, 13, 14, 18, 13, 14, 15, 18, 14, 15, 17, 18, 1, 16, 17, 15, 17, 18, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18]
+J = [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18]
+jac_sparsity = sparse(I, J, ones(Bool, length(I)), 18, 18);
 f = ODEFunction(dudt;
     jac_prototype = float.(jac_sparsity));
 sparseodeprob = ODEProblem(f,