Add __auto

src/CTBase.jl

@@ -15,7 +15,7 @@ module CTBase
 import Base
 using DocStringExtensions
 using DifferentiationInterface: AutoForwardDiff, derivative, gradient, jacobian, prepare_derivative, prepare_gradient, prepare_jacobian
-using ForwardDiff: ForwardDiff # automatic differentiation
+import ForwardDiff
 using Interpolations: linear_interpolation, Line, Interpolations # for default interpolation
 using MLStyle # pattern matching
 using Parameters # @with_kw: to have default values in struct
@@ -95,6 +95,8 @@ Type alias for a tangent vector to the costate space.
 """
 const DCostate   = ctVector
 
+__auto() = AutoForwardDiff()
+
 #
 include("exception.jl")
 include("description.jl")

src/utils.jl

@@ -74,7 +74,7 @@ $(TYPEDSIGNATURES)
 Return the gradient of `f` at `x`.
 """
 function ctgradient(f::Function, x::ctNumber)
-    backend = AutoForwardDiff()
+    backend = __auto()
     extras = prepare_derivative(f, backend, x)
     return derivative(f, backend, x, extras)
 end
@@ -85,7 +85,7 @@ $(TYPEDSIGNATURES)
 Return the gradient of `f` at `x`.
 """
 function ctgradient(f::Function, x)
-    backend = AutoForwardDiff()
+    backend = __auto()
     extras = prepare_gradient(f, backend, x)
     return gradient(f, backend, x, extras)
 end
@@ -104,7 +104,7 @@ Return the Jacobian of `f` at `x`.
 """
 function ctjacobian(f::Function, x::ctNumber)
     f_number_to_number = only ∘ f ∘ only
-    backend = AutoForwardDiff()
+    backend = __auto()
     extras = prepare_derivative(f_number_to_number, backend, x)
     der = derivative(f_number_to_number, backend, x, extras)
     return [der;;]
@@ -116,7 +116,7 @@ $(TYPEDSIGNATURES)
 Return the Jacobian of `f` at `x`.
 """
 function ctjacobian(f::Function, x)
-    backend = AutoForwardDiff()
+    backend = __auto()
     extras = prepare_jacobian(f, backend, x)
     return jacobian(f, backend, x, extras)
 end
@@ -207,4 +207,4 @@ function matrix2vec(x::Matrix{<:ctNumber}, dim::Integer=__matrix_dimension_stock
         end
     end
     return y
-end
+end