Fast sympy vectorization on julia Arrays

I have a python library I’ve built on sympy and specifically sympy.vector which does vector calculus computations (divergence, gradient, curl, laplacians, etc) for calculations related to PDEs. Sympy provides methods that build vectorized functions that can evaluate symbolic expressions on np.ndarray objects (for example, evaluating the exact divergence over a 3d array with shape (domain, node, coordinate)).

I see that Sympy.jl allows interaction with Sympy through julia, but is there an analogy to sympy.vectorize which would allow me to evaluate symbolic expressions on Julia arrays?

The setup in SymPy uses:

pytype_mapping(sympy_matrices.Matrix, Array{Sym})

which instructs PyCall to convert (mutable) matrices into arrays of symbolic variables. However, immutable matrices are not converted this way and remain as refrences to the underlying sympy object. So possibly two things to try: use ImmutableMatrix or add pytype_mappingdirective forndarray`