Function Registration and Tracing

Direct Tracing

Because Symbolics expressions respect Julia semantics, one way to generate symbolic expressions is to simply place Symbolics variables as inputs into existing Julia code. For example, the following uses the standard Julia function for the Lorenz equations to generate the symbolic expression for the Lorenz equations:

function lorenz(du,u,p,t)
 du[1] = 10.0(u[2]-u[1])
 du[2] = u[1]*(28.0-u[3]) - u[2]
 du[3] = u[1]*u[2] - (8/3)*u[3]
end
@variables t p[1:3] u[1:3](t) du[1:3](t)
lorenz(du,u,p,t)
du

3-element Array{Num,1}:
                 10.0 * (u₂(t) - u₁(t))
         u₁(t) * (28.0 - u₃(t)) - u₂(t)
u₁(t) * u₂(t) - 2.6666666666666665 * u₃(t)

Or similarly:

@variables t x(t) y(t) z(t) dx(t) dy(t) dz(t) σ ρ β
du = [dx,dy,dz]
u = [x,y,z]
p = [σ,ρ,β]
lorenz(du,u,p,t)
du

3-element Array{Num,1}:
                10.0 * (y(t) - x(t))
         x(t) * (28.0 - z(t)) - y(t)
x(t) * y(t) - 2.6666666666666665 * z(t)

Registering Functions

The Symbolics graph only allows registered Julia functions within its type. All other functions are automatically traced down to registered functions. By default, Symbolics.jl pre-registers the common functions utilized in SymbolicUtils.jl and pre-defines their derivatives. However, the user can utilize the @register macro to add their function to allowed functions of the computation graph.

@register(expr, define_promotion, Ts = [Num, Symbolic, Real])

@register foo(x, y)
@register goo(x, y::Int) # `y` is not overloaded to take symbolic objects
@register hoo(x, y)::Int # `hoo` returns `Int`