update export

Author: agerlach

lib/IntegralsCubature/src/IntegralsCubature.jl

@@ -3,15 +3,16 @@ module IntegralsCubature
 using Integrals, Cubature
 
 import Integrals: transformation_if_inf, scale_x, scale_x!
+import Cubature: INDIVIDUAL, PAIRED, L1, L2, LINF
 
 abstract type AbstractCubatureJLAlgorithm <: SciMLBase.AbstractIntegralAlgorithm end
 """
     CubatureJLh()
 
 Multidimensional h-adaptive integration from Cubature.jl.
 `error_norm` specifies the convergence criterion  for vector valued integrands.
-Defaults to `Cubature.INDIVIDUAL`, other options are
-`Cubature.PAIRED`, `Cubature.L1`, `Cubature.L2`, or `Cubature.LINF`.
+Defaults to `IntegralsCubature.INDIVIDUAL`, other options are
+`IntegralsCubature.PAIRED`, `IntegralsCubature.L1`, `IntegralsCubature.L2`, or `IntegralsCubature.LINF`.
 
 ## References
 
@@ -39,8 +40,8 @@ This method is based on repeatedly doubling the degree of the cubature rules,
 until convergence is achieved.
 The used cubature rule is a tensor product of Clenshaw–Curtis quadrature rules.
 `error_norm` specifies the convergence criterion  for vector valued integrands.
-Defaults to `Cubature.INDIVIDUAL`, other options are
-`Cubature.PAIRED`, `Cubature.L1`, `Cubature.L2`, or `Cubature.LINF`.
+Defaults to `IntegralsCubature.INDIVIDUAL`, other options are
+`IntegralsCubature.PAIRED`, `IntegralsCubature.L1`, `IntegralsCubature.L2`, or `IntegralsCubature.LINF`.
 """
 struct CubatureJLp <: AbstractCubatureJLAlgorithm
     error_norm::Int32
@@ -183,9 +184,6 @@ function Integrals.__solvebp_call(prob::IntegralProblem,
     SciMLBase.build_solution(prob, alg, val, err, retcode = ReturnCode.Success)
 end
 
-
-
 export CubatureJLh, CubatureJLp
-export Cubature.INDIVIDUAL, Cubature.PAIRED, Cubature.L1, Cubature.L2, Cubature.LINF
 
 end