feat: move to v4

.JuliaFormatter.toml

@@ -1,2 +1,3 @@
 style = "sciml"
-format_markdown = true
+format_markdown = true
+annotate_untyped_fields_with_any = false

.github/workflows/Downgrade.yml

@@ -22,7 +22,6 @@ jobs:
         with:
           version: ${{ matrix.version }}
       - uses: julia-actions/julia-downgrade-compat@v1
-#        if: ${{ matrix.version == '1.6' }}
         with:
           skip: Pkg,TOML
       - uses: julia-actions/julia-buildpkg@v1

Project.toml

@@ -1,57 +1,54 @@
 name = "DiffEqCallbacks"
 uuid = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
-version = "3.9.1"
+version = "4.0.0"
 
 [deps]
+ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
-NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
-Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 
-[weakdeps]
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-OrdinaryDiffEqCore = "bbf590c4-e513-4bbe-9b18-05decba2e5d8"
-Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
-
 [compat]
+ADTypes = "1.9.0"
 Aqua = "0.8"
-DataInterpolations = "4.6"
+ConcreteStructs = "0.2.3"
+DataInterpolations = "4.6, 5, 6"
 DataStructures = "0.18.13"
-DiffEqBase = "6.154"
+DiffEqBase = "6.155.3"
+DifferentiationInterface = "0.6.1"
 ForwardDiff = "0.10.36"
 Functors = "0.4"
 LinearAlgebra = "1.10"
 Markdown = "1.10"
-NonlinearSolve = "3.7.2"
-ODEProblemLibrary = "0.1.5"
+NonlinearSolve = "3.14"
+ODEProblemLibrary = "0.1.8"
 OrdinaryDiffEq = "6.88"
-OrdinaryDiffEqCore = "1"
-Parameters = "0.12"
 QuadGK = "2.9"
 RecipesBase = "1.3.4"
-RecursiveArrayTools = "3.9"
-SciMLBase = "2.26"
-SciMLSensitivity = "7.56"
-StaticArrays = "1.8"
+RecursiveArrayTools = "3.27"
+SciMLBase = "2.54"
+SciMLSensitivity = "7.68"
+StaticArrays = "1.9.7"
 StaticArraysCore = "1.4"
 Sundials = "4.19.2"
-Test = "1"
-Tracker = "0.2.26"
+Test = "1.10"
+Tracker = "0.2.35"
 Zygote = "0.6.69"
 julia = "1.10"
 
 [extras]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 ODEProblemLibrary = "fdc4e326-1af4-4b90-96e7-779fcce2daa5"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
@@ -64,4 +61,4 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "DataInterpolations", "OrdinaryDiffEq", "ODEProblemLibrary", "Test", "QuadGK", "SciMLSensitivity", "StaticArrays", "Tracker", "Zygote", "NonlinearSolve"]
+test = ["ADTypes", "Aqua", "DataInterpolations", "ForwardDiff", "NonlinearSolve", "ODEProblemLibrary", "OrdinaryDiffEq", "QuadGK", "SciMLSensitivity", "StaticArrays", "Test", "Tracker", "Zygote"]

docs/Project.toml

@@ -1,12 +1,14 @@
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 
 [compat]
-DiffEqCallbacks = "3"
+ADTypes = "1.9.0"
+DiffEqCallbacks = "4"
 Documenter = "1"
 OrdinaryDiffEq = "6.88"
 Plots = "1.36"

docs/src/projection.md

@@ -12,7 +12,7 @@ ManifoldProjection
 Here we solve the harmonic oscillator:
 
 ```@example manifold
-using OrdinaryDiffEq, DiffEqCallbacks, Plots
+using OrdinaryDiffEq, DiffEqCallbacks, Plots, ADTypes
 
 u0 = ones(2)
 function f(du, u, p, t)
@@ -28,14 +28,13 @@ to conserve the sum of squares:
 ```@example manifold
 function g(resid, u, p, t)
     resid[1] = u[2]^2 + u[1]^2 - 2
-    resid[2] = 0
 end
 ```
 
 To build the callback, we just call
 
 ```@example manifold
-cb = ManifoldProjection(g)
+cb = ManifoldProjection(g; autodiff = AutoForwardDiff(), resid_prototype = zeros(1))
 ```
 
 Using this callback, the Runge-Kutta method `Vern7` conserves energy. Note that the

src/DiffEqCallbacks.jl

@@ -1,17 +1,22 @@
 module DiffEqCallbacks
 
-using DiffEqBase, RecursiveArrayTools, DataStructures, RecipesBase, LinearAlgebra,
-      StaticArraysCore, NonlinearSolve, ForwardDiff, Functors
-
-import Base.Iterators
-
-using Markdown
-
-using Parameters: @unpack
-
-import SciMLBase
-
-using DiffEqBase: get_tstops, get_tstops_array, get_tstops_max
+using ConcreteStructs: @concrete
+using DataStructures: DataStructures, BinaryMaxHeap, BinaryMinHeap
+using DiffEqBase: DiffEqBase, get_tstops, get_tstops_array, get_tstops_max
+using DifferentiationInterface: DifferentiationInterface, Constant
+using Functors: fmap
+using LinearAlgebra: LinearAlgebra, adjoint, axpy!, copyto!, mul!, ldiv!
+using Markdown: @doc_str
+using RecipesBase: @recipe
+using RecursiveArrayTools: RecursiveArrayTools, DiffEqArray, copyat_or_push!
+using SciMLBase: SciMLBase, CallbackSet, DiscreteCallback, NonlinearFunction,
+                 NonlinearLeastSquaresProblem, NonlinearProblem, RODEProblem,
+                 ReturnCode, SDEProblem, add_tstop!, check_error, get_du,
+                 get_proposed_dt, get_tmp_cache, init, reinit!,
+                 set_proposed_dt!, solve!, terminate!, u_modified!
+using StaticArraysCore: StaticArraysCore
+
+const DI = DifferentiationInterface
 
 include("functor_helpers.jl")
 include("autoabstol.jl")