format

Author: ChrisRackauckas

README.md

@@ -20,4 +20,3 @@ For information on using the package,
 [see the stable documentation](https://docs.sciml.ai/SciMLSensitivity/stable/). Use the
 [in-development documentation](https://docs.sciml.ai/SciMLSensitivity/dev/) for the version of
 the documentation, which contains the unreleased features.
-

src/concrete_solve.jl

@@ -1301,7 +1301,7 @@ function DiffEqBase._concrete_solve_adjoint(
                             SciMLBase.FullSpecialize
                         }(_f,
                             _g),
-                            g = _g,
+                        g = _g,
                         u0 = _u0, p = SciMLStructures.replace(Tunable(), p, _p),
                         tspan = _tspan, callback = nothing)
                 else

src/forward_sensitivity.jl

@@ -678,11 +678,11 @@ function SciMLBase.remake(
 
     if typeof(_f) <: ODEForwardSensitivityFunction
         _u0 = u0 === nothing ? state_values(prob, 1:(_f.numindvar)) :
-            u0[1:(_f.numindvar)]
+              u0[1:(_f.numindvar)]
     else
         _u0 = u0 === nothing ? state_values(prob) : u0
     end
-    
+
     _tspan = tspan === nothing ? prob.tspan : tspan
     ODEForwardSensitivityProblem(_f.f, _u0,
         _tspan, _p; sensealg = prob.problem_type.sensealg,

src/steadystate_adjoint.jl

@@ -99,14 +99,16 @@ end
             nlprob, vec(y), (λ); autodiff = get_autodiff_from_vjp(sensealg.autojacvec))
         soperator = StatefulJacobianOperator(operator, vec(λ), p)
         linear_problem = LinearProblem(soperator, vec(dgdu_val); u0 = vec(λ))
-        solve(linear_problem, linsolve; alias = LinearAliasSpecifier(alias_A = true), sensealg.linsolve_kwargs...)
+        solve(linear_problem, linsolve; alias = LinearAliasSpecifier(alias_A = true),
+            sensealg.linsolve_kwargs...)
     else
         if linsolve === nothing && isempty(sensealg.linsolve_kwargs)
             # For the default case use `\` to avoid any form of unnecessary cache allocation
             vec(λ) .= diffcache.J' \ vec(dgdu_val)
         else
             linear_problem = LinearProblem(diffcache.J', vec(dgdu_val'); u0 = vec(λ))
-            solve(linear_problem, linsolve; alias = LinearAliasSpecifier(alias_A = true), sensealg.linsolve_kwargs...) # u is vec(λ)
+            solve(linear_problem, linsolve; alias = LinearAliasSpecifier(alias_A = true),
+                sensealg.linsolve_kwargs...) # u is vec(λ)
         end
     end
 

test/alternative_ad_frontend.jl

@@ -247,5 +247,5 @@ grad_fd = ForwardDiff.gradient(loss2, p)
 grad_zg = Zygote.gradient(loss2, p)[1]
 grad_rd = ReverseDiff.gradient(loss2, p)
 @test grad_fd≈grad_fi atol=1e-2
-@test grad_fd ≈ grad_zg atol=1e-4
-@test grad_fd ≈ grad_rd atol=1e-4
+@test grad_fd≈grad_zg atol=1e-4
+@test grad_fd≈grad_rd atol=1e-4

test/forward.jl

@@ -270,4 +270,4 @@ solve(prob, Tsit5(), abstol = 1e-14, reltol = 1e-14)
 f = prob.f
 @assert f isa ODEForwardSensitivityFunction
 @test hasproperty(f, :observed)
-@test f.observed == SciMLBase.DEFAULT_OBSERVED
+@test f.observed == SciMLBase.DEFAULT_OBSERVED

test/forward_remake.jl

@@ -44,12 +44,13 @@ end
 
 p = [1.5, 1.0, 3.0]
 ts = (0, 10)
-prob = ODEForwardSensitivityProblem(ff3, [1.0; 1.0], ts, p, sensealg=ForwardDiffSensitivity())
+prob = ODEForwardSensitivityProblem(
+    ff3, [1.0; 1.0], ts, p, sensealg = ForwardDiffSensitivity())
 sol = solve(prob, Tsit5())
 
 # https://github.com/SciML/SciMLSensitivity.jl/issues/1143
 
-prob1 = ODEForwardSensitivityProblem(ff3, [1.0, 1.0], (0.0,10.0), p,
-                                     sensealg = ForwardSensitivity())
+prob1 = ODEForwardSensitivityProblem(ff3, [1.0, 1.0], (0.0, 10.0), p,
+    sensealg = ForwardSensitivity())
 prob2 = remake(prob1, tspan = (0.0, 10.0))
-@test length(prob1.u0) == length(prob2.u0) == 8
+@test length(prob1.u0) == length(prob2.u0) == 8