format and tag

Project.toml

@@ -1,7 +1,7 @@
 name = "OrdinaryDiffEq"
 uuid = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 authors = ["Chris Rackauckas <[email protected]>", "Yingbo Ma <[email protected]>"]
-version = "6.36.0"
+version = "6.36.1"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

src/derivative_wrappers.jl

@@ -2,7 +2,7 @@ const FIRST_AUTODIFF_TGRAD_MESSAGE = """
                                First call to automatic differentiation for time gradient
                                failed. This means that the user `f` function is not compatible
                                with automatic differentiation. Methods to fix this include:
-                               
+
                                1. Turn off automatic differentiation (e.g. Rosenbrock23() becomes
                                   Rosenbrock23(autodiff=false)). More details can befound at
                                   https://docs.sciml.ai/DiffEqDocs/stable/features/performance_overloads/
@@ -31,8 +31,8 @@ const FIRST_AUTODIFF_TGRAD_MESSAGE = """
                                algorithm as well.
                                """
 
-struct FirstAutodiffTgradError <: Exception 
-    e
+struct FirstAutodiffTgradError <: Exception
+    e::Any
 end
 
 function Base.showerror(io::IO, e::FirstAutodiffTgradError)
@@ -44,7 +44,7 @@ const FIRST_AUTODIFF_JAC_MESSAGE = """
                                First call to automatic differentiation for the Jacobian
                                failed. This means that the user `f` function is not compatible
                                with automatic differentiation. Methods to fix this include:
-                               
+
                                1. Turn off automatic differentiation (e.g. Rosenbrock23() becomes
                                   Rosenbrock23(autodiff=false)). More details can befound at
                                   https://docs.sciml.ai/DiffEqDocs/stable/features/performance_overloads/
@@ -66,8 +66,8 @@ const FIRST_AUTODIFF_JAC_MESSAGE = """
                                algorithm as well.
                                """
 
-struct FirstAutodiffJacError <: Exception 
-    e
+struct FirstAutodiffJacError <: Exception
+    e::Any
 end
 
 function Base.showerror(io::IO, e::FirstAutodiffJacError)
@@ -99,7 +99,7 @@ function derivative!(df::AbstractArray{<:Number}, f,
         else
             f(grad_config, xdual)
         end
-        
+
         df .= first.(ForwardDiff.partials.(grad_config))
         integrator.destats.nf += 1
     else

test/interface/autodiff_error_tests.jl

@@ -2,42 +2,42 @@ using OrdinaryDiffEq, Test
 
 const a = Float64[1.0]
 
-function lorenz(u,p,t)
-    du1 = 10.0(u[2]-u[1])
+function lorenz(u, p, t)
+    du1 = 10.0(u[2] - u[1])
     a[1] = u[2]
-    du2 = u[1]*(28.0-u[3]) - u[2]
-    du3 = u[1]*u[2] - (8/3)*u[3]
-    [du1,du2,du3]
-   end
-u0 = [1.0;0.0;0.0]
-tspan = (0.0,1.0)
-prob = ODEProblem(lorenz,u0,tspan)
-@test_throws OrdinaryDiffEq.FirstAutodiffJacError solve(prob,Rosenbrock23())
+    du2 = u[1] * (28.0 - u[3]) - u[2]
+    du3 = u[1] * u[2] - (8 / 3) * u[3]
+    [du1, du2, du3]
+end
+u0 = [1.0; 0.0; 0.0]
+tspan = (0.0, 1.0)
+prob = ODEProblem(lorenz, u0, tspan)
+@test_throws OrdinaryDiffEq.FirstAutodiffJacError solve(prob, Rosenbrock23())
 
-function lorenz(u,p,t)
-    du1 = 10.0(u[2]-u[1])
+function lorenz(u, p, t)
+    du1 = 10.0(u[2] - u[1])
     a[1] = t
-    du2 = u[1]*(28.0-u[3]) - u[2]
-    du3 = u[1]*u[2] - (8/3)*u[3]
-    [du1,du2,du3]
+    du2 = u[1] * (28.0 - u[3]) - u[2]
+    du3 = u[1] * u[2] - (8 / 3) * u[3]
+    [du1, du2, du3]
 end
-@test_throws OrdinaryDiffEq.FirstAutodiffTgradError solve(prob,Rosenbrock23())
+@test_throws OrdinaryDiffEq.FirstAutodiffTgradError solve(prob, Rosenbrock23())
 
-function lorenz!(du,u,p,t)
-    du[1] = 10.0(u[2]-u[1])
+function lorenz!(du, u, p, t)
+    du[1] = 10.0(u[2] - u[1])
     a[1] = u[2]
-    du[2] = u[1]*(28.0-u[3]) - u[2]
-    du[3] = u[1]*u[2] - (8/3)*u[3]
-   end
-u0 = [1.0;0.0;0.0]
-tspan = (0.0,1.0)
-prob = ODEProblem(lorenz!,u0,tspan)
-@test_throws OrdinaryDiffEq.FirstAutodiffJacError solve(prob,Rosenbrock23())
+    du[2] = u[1] * (28.0 - u[3]) - u[2]
+    du[3] = u[1] * u[2] - (8 / 3) * u[3]
+end
+u0 = [1.0; 0.0; 0.0]
+tspan = (0.0, 1.0)
+prob = ODEProblem(lorenz!, u0, tspan)
+@test_throws OrdinaryDiffEq.FirstAutodiffJacError solve(prob, Rosenbrock23())
 
-function lorenz!(du,u,p,t)
-    du[1] = 10.0(u[2]-u[1])
+function lorenz!(du, u, p, t)
+    du[1] = 10.0(u[2] - u[1])
     a[1] = t
-    du[2] = u[1]*(28.0-u[3]) - u[2]
-    du[3] = u[1]*u[2] - (8/3)*u[3]
+    du[2] = u[1] * (28.0 - u[3]) - u[2]
+    du[3] = u[1] * u[2] - (8 / 3) * u[3]
 end
-@test_throws OrdinaryDiffEq.FirstAutodiffTgradError solve(prob,Rosenbrock23())
+@test_throws OrdinaryDiffEq.FirstAutodiffTgradError solve(prob, Rosenbrock23())