reapply formatting

.JuliaFormatter.toml

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

docs/pages.jl

@@ -6,5 +6,5 @@ pages = ["index.md",
         "basics/SampledIntegralProblem.md",
         "basics/solve.md",
         "basics/FAQ.md"],
-    "Solvers" => Any["solvers/IntegralSolvers.md"],
+    "Solvers" => Any["solvers/IntegralSolvers.md"]
 ]

docs/src/basics/FAQ.md

@@ -40,8 +40,9 @@ For badly-behaved integrands, such as (nearly) singular and highly oscillatory
 functions, most algorithms will fail to converge and either throw an error or
 silently return the incorrect result. In some cases Integrals.jl can provide an
 error code when things go wrong, but otherwise you can always check if the error
-estimate for the integral is less than the requested tolerance, e.g. `sol.resid
-< max(abstol, reltol*norm(sol.u))`. Sometimes using a larger tolerance or higher
+estimate for the integral is less than the requested tolerance, e.g.
+`sol.resid < max(abstol, reltol*norm(sol.u))`.
+Sometimes using a larger tolerance or higher
 precision arithmetic may help.
 
 ## How can I integrate arbitrarily-spaced data?
@@ -60,4 +61,4 @@ Otherwise, feel free to open an issue or pull request.
 
 ## Can I take derivatives with respect to the limits of integration?
 
-Currently this is not implemented.
+Currently this is not implemented.

docs/src/basics/IntegralFunction.md

@@ -3,4 +3,4 @@
 ```@docs
 SciMLBase.IntegralFunction
 SciMLBase.BatchIntegralFunction
-```
+```

docs/src/basics/SampledIntegralProblem.md

@@ -12,7 +12,7 @@ Say, by some means we have generated a dataset `x` and `y`:
 ```@example 1
 using Integrals # hide
 f = x -> x^2
-x = range(0, 1, length=20)
+x = range(0, 1, length = 20)
 y = f.(x)
 ```
 
@@ -63,9 +63,9 @@ using Integrals # hide
 f1 = x -> x^2
 f2 = x -> x^3
 f3 = x -> x^4
-x = range(0, 1, length=20)
+x = range(0, 1, length = 20)
 y = [f1.(x) f2.(x) f3.(x)]
-problem = SampledIntegralProblem(y, x; dim=1)
+problem = SampledIntegralProblem(y, x; dim = 1)
 method = SimpsonsRule()
 solve(problem, method)
 ```

docs/src/tutorials/differentiating_integrals.md

@@ -16,7 +16,7 @@ function testf(p)
     sin(solve(prob, CubaCuhre(), reltol = 1e-6, abstol = 1e-6)[1])
 end
 testf(p)
-dp1 = Zygote.gradient(testf,p)
+dp1 = Zygote.gradient(testf, p)
 dp2 = FiniteDiff.finite_difference_gradient(testf, p)
 dp3 = ForwardDiff.gradient(testf, p)
 dp1[1] ≈ dp2 ≈ dp3

ext/IntegralsArblibExt.jl

@@ -3,9 +3,9 @@ module IntegralsArblibExt
 using Arblib
 using Integrals
 
-function Integrals.__solvebp_call(prob::IntegralProblem, alg::ArblibJL, sensealg, domain, p;
-    reltol = 1e-8, abstol = 1e-8, maxiters = nothing)
-
+function Integrals.__solvebp_call(
+        prob::IntegralProblem, alg::ArblibJL, sensealg, domain, p;
+        reltol = 1e-8, abstol = 1e-8, maxiters = nothing)
     lb_, ub_ = domain
     lb, ub = map(first, domain)
     if !isone(length(lb_)) || !isone(length(ub_))
@@ -17,16 +17,18 @@ function Integrals.__solvebp_call(prob::IntegralProblem, alg::ArblibJL, sensealg
         res = Acb(0)
         y_ = similar(prob.f.integrand_prototype, typeof(res))
         f_ = (y, x; kws...) -> (prob.f(y_, x, p; kws...); Arblib.set!(y, only(y_)))
-        val = Arblib.integrate!(f_, res, lb, ub, atol=abstol, rtol=reltol,
-            check_analytic=alg.check_analytic, take_prec=alg.take_prec,
-            warn_on_no_convergence=alg.warn_on_no_convergence, opts=alg.opts)
-        SciMLBase.build_solution(prob, alg, val, get_radius(val), retcode = ReturnCode.Success)
+        val = Arblib.integrate!(f_, res, lb, ub, atol = abstol, rtol = reltol,
+            check_analytic = alg.check_analytic, take_prec = alg.take_prec,
+            warn_on_no_convergence = alg.warn_on_no_convergence, opts = alg.opts)
+        SciMLBase.build_solution(
+            prob, alg, val, get_radius(val), retcode = ReturnCode.Success)
     else
         f_ = (x; kws...) -> only(prob.f(x, p; kws...))
-        val = Arblib.integrate(f_, lb, ub, atol=abstol, rtol=reltol,
-            check_analytic=alg.check_analytic, take_prec=alg.take_prec,
-            warn_on_no_convergence=alg.warn_on_no_convergence, opts=alg.opts)
-        SciMLBase.build_solution(prob, alg, val, get_radius(val), retcode = ReturnCode.Success)
+        val = Arblib.integrate(f_, lb, ub, atol = abstol, rtol = reltol,
+            check_analytic = alg.check_analytic, take_prec = alg.take_prec,
+            warn_on_no_convergence = alg.warn_on_no_convergence, opts = alg.opts)
+        SciMLBase.build_solution(
+            prob, alg, val, get_radius(val), retcode = ReturnCode.Success)
     end
 end
 

ext/IntegralsCubaExt.jl

@@ -2,8 +2,8 @@ module IntegralsCubaExt
 
 using Integrals, Cuba
 import Integrals: transformation_if_inf,
-    scale_x, scale_x!, CubaVegas, AbstractCubaAlgorithm,
-    CubaSUAVE, CubaDivonne, CubaCuhre
+                  scale_x, scale_x!, CubaVegas, AbstractCubaAlgorithm,
+                  CubaSUAVE, CubaDivonne, CubaCuhre
 
 function Integrals.__solvebp_call(prob::IntegralProblem, alg::AbstractCubaAlgorithm,
         sensealg,
@@ -22,7 +22,8 @@ function Integrals.__solvebp_call(prob::IntegralProblem, alg::AbstractCubaAlgori
         nvec = min(maxiters, prob.f.max_batch)
         # nvec == 1 in Cuba will change vectors to matrices, so we won't support it when
         # batching
-        nvec > 1 || throw(ArgumentError("BatchIntegralFunction must take multiple batch points"))
+        nvec > 1 ||
+            throw(ArgumentError("BatchIntegralFunction must take multiple batch points"))
 
         if mid isa Real
             _x = zeros(typeof(mid), nvec)

ext/IntegralsFastGaussQuadratureExt.jl

@@ -33,7 +33,7 @@ function Integrals.__solvebp_call(prob::IntegralProblem, alg::Integrals.GaussLeg
         sensealg, domain, p;
         reltol = nothing, abstol = nothing,
         maxiters = nothing) where {C}
-    if !all(isone∘length, domain)
+    if !all(isone ∘ length, domain)
         error("GaussLegendre only accepts one-dimensional quadrature problems.")
     end
     @assert prob.f isa IntegralFunction

ext/IntegralsForwardDiffExt.jl

@@ -22,8 +22,8 @@ end
 
 # Manually split for the pushforward
 function Integrals.__solvebp(cache, alg, sensealg, domain,
-        p::Union{D,AbstractArray{<:D}};
-        kwargs...) where {T, V, P, D<:ForwardDiff.Dual{T, V, P}}
+        p::Union{D, AbstractArray{<:D}};
+        kwargs...) where {T, V, P, D <: ForwardDiff.Dual{T, V, P}}
 
     # we need the output type to avoid perturbation confusion while unwrapping nested duals
     # We compute a vector-valued integral of the primal and dual simultaneously

ext/IntegralsMCIntegrationExt.jl

@@ -3,10 +3,10 @@ module IntegralsMCIntegrationExt
 using MCIntegration, Integrals
 
 function Integrals.__solvebp_call(prob::IntegralProblem, alg::VEGASMC, sensealg, domain, p;
-    reltol = nothing, abstol = nothing, maxiters = 10)
+        reltol = nothing, abstol = nothing, maxiters = 10)
     lb, ub = domain
     mid = vec(collect((lb + ub) / 2))
-    var = Continuous(vec([tuple(a,b) for (a,b) in zip(lb, ub)]))
+    var = Continuous(vec([tuple(a, b) for (a, b) in zip(lb, ub)]))
 
     if prob.f isa BatchIntegralFunction
         error("VEGASMC doesn't support batching. See https://github.com/numericalEFT/MCIntegration.jl/issues/29")
@@ -16,7 +16,7 @@ function Integrals.__solvebp_call(prob::IntegralProblem, alg::VEGASMC, sensealg,
             f_ = (x, f, c) -> begin
                 n = 0
                 for v in x
-                    mid[n+=1] = first(v)
+                    mid[n += 1] = first(v)
                 end
                 prob.f(f0, mid, p)
                 f .= vec(f0)
@@ -26,21 +26,22 @@ function Integrals.__solvebp_call(prob::IntegralProblem, alg::VEGASMC, sensealg,
             f_ = (x, c) -> begin
                 n = 0
                 for v in x
-                    mid[n+=1] = first(v)
+                    mid[n += 1] = first(v)
                 end
                 fx = prob.f(mid, p)
                 fx isa AbstractArray ? vec(fx) : fx
             end
         end
         dof = ones(Int, length(f0)) # each composite Continuous var gets 1 dof
-        res = integrate(f_; var, dof, inplace=isinplace(prob), type=eltype(f0),
-            solver=:vegasmc, niter=maxiters, verbose=-2,  print=-2, alg.kws...)
+        res = integrate(f_; var, dof, inplace = isinplace(prob), type = eltype(f0),
+            solver = :vegasmc, niter = maxiters, verbose = -2, print = -2, alg.kws...)
         out, err, chi = if f0 isa Number
             map(only, (res.mean, res.stdev, res.chi2))
         else
             map(a -> reshape(a, size(f0)), (res.mean, res.stdev, res.chi2))
         end
-        SciMLBase.build_solution(prob, alg, out, err, chi=chi, retcode = ReturnCode.Success)
+        SciMLBase.build_solution(
+            prob, alg, out, err, chi = chi, retcode = ReturnCode.Success)
     end
 end
 

ext/IntegralsZygoteExt.jl

@@ -28,7 +28,8 @@ function ChainRulesCore.rrule(::typeof(Integrals.__solvebp), cache, alg, senseal
             # zygote doesn't support mutation, so we build an oop pullback
             if sensealg.vjp isa Integrals.ZygoteVJP
                 if cache.f isa BatchIntegralFunction
-                    dx = similar(cache.f.integrand_prototype, size(cache.f.integrand_prototype)[begin:end-1]..., 1)
+                    dx = similar(cache.f.integrand_prototype,
+                        size(cache.f.integrand_prototype)[begin:(end - 1)]..., 1)
                     _f = x -> (cache.f(dx, x, p); dx)
                     # TODO: let the user pass a batched jacobian so we can return a BatchIntegralFunction
                     dfdp_ = function (x, p)
@@ -104,7 +105,7 @@ function ChainRulesCore.rrule(::typeof(Integrals.__solvebp), cache, alg, senseal
             # TODO replace evaluation at endpoint (which anyone can do without Integrals.jl)
             # with integration of dfdx uing the same quadrature
             dlb = cache.f isa BatchIntegralFunction ? -batch_unwrap(_f([lb])) : -_f(lb)
-            dub = cache.f isa BatchIntegralFunction ?  batch_unwrap(_f([ub])) : _f(ub)
+            dub = cache.f isa BatchIntegralFunction ? batch_unwrap(_f([ub])) : _f(ub)
             return (NoTangent(),
                 NoTangent(),
                 NoTangent(),
@@ -128,7 +129,7 @@ function ChainRulesCore.rrule(::typeof(Integrals.__solvebp), cache, alg, senseal
     out, quadrature_adjoint
 end
 
-batch_unwrap(x::AbstractArray) = dropdims(x; dims=ndims(x))
+batch_unwrap(x::AbstractArray) = dropdims(x; dims = ndims(x))
 
 Zygote.@adjoint function Zygote.literal_getproperty(sol::SciMLBase.IntegralSolution,
         ::Val{:u})

src/Integrals.jl

@@ -66,7 +66,6 @@ end
 # Give a layer to intercept with AD
 __solvebp(args...; kwargs...) = __solvebp_call(args...; kwargs...)
 
-
 function quadgk_prob_types(f, lb::T, ub::T, p, nrm) where {T}
     DT = float(T)   # we need to be careful to infer the same result as `evalrule`
     RT = Base.promote_op(*, DT, Base.promote_op(f, DT, typeof(p)))    # kernel
@@ -106,10 +105,10 @@ function __solvebp_call(cache::IntegralCache, alg::QuadGKJL, sensealg, domain, p
                 BatchIntegrand((y, x) -> prob.f(y, x, p), similar(bu))
             else
                 fsize = size(bu)[begin:(end - 1)]
-                BatchIntegrand{Array{eltype(bu),ndims(bu)-1}}() do y, x
+                BatchIntegrand{Array{eltype(bu), ndims(bu) - 1}}() do y, x
                     y_ = similar(bu, fsize..., length(y))
                     prob.f(y_, x, p)
-                    map!(collect, y, eachslice(y_; dims=ndims(bu)))
+                    map!(collect, y, eachslice(y_; dims = ndims(bu)))
                     return nothing
                 end
             end
@@ -121,8 +120,8 @@ function __solvebp_call(cache::IntegralCache, alg::QuadGKJL, sensealg, domain, p
             f = if u isa AbstractVector
                 BatchIntegrand((y, x) -> y .= prob.f(x, p), u)
             else
-                BatchIntegrand{Array{eltype(u),ndims(u)-1}}() do y, x
-                    map!(collect, y, eachslice(prob.f(x, p); dims=ndims(u)))
+                BatchIntegrand{Array{eltype(u), ndims(u) - 1}}() do y, x
+                    map!(collect, y, eachslice(prob.f(x, p); dims = ndims(u)))
                     return nothing
                 end
             end
@@ -134,7 +133,8 @@ function __solvebp_call(cache::IntegralCache, alg::QuadGKJL, sensealg, domain, p
         if isinplace(prob)
             result = prob.f.integrand_prototype * mid   # result may have different units than prototype
             f = (y, x) -> prob.f(y, x, p)
-            val, err = quadgk!(f, result, lb, ub, segbuf = cache.cacheval, maxevals = maxiters,
+            val, err = quadgk!(
+                f, result, lb, ub, segbuf = cache.cacheval, maxevals = maxiters,
                 rtol = reltol, atol = abstol, order = alg.order, norm = alg.norm)
             SciMLBase.build_solution(prob, alg, val, err, retcode = ReturnCode.Success)
         else
@@ -189,8 +189,9 @@ function __solvebp_call(prob::IntegralProblem, alg::VEGAS, sensealg, domain, p;
             # This is an ugly hack that is compatible with both
             f = x -> (prob.f(y, eltype(x) <: SubArray ? parent(first(x)) : x', p); vec(y))
         else
-            y = prob.f(mid isa Number ? typeof(mid)[] :
-                       Matrix{eltype(mid)}(undef, length(mid), 0),
+            y = prob.f(
+                mid isa Number ? typeof(mid)[] :
+                Matrix{eltype(mid)}(undef, length(mid), 0),
                 p)
             f = x -> prob.f(eltype(x) <: SubArray ? parent(first(x)) : x', p)
         end
@@ -221,7 +222,8 @@ function __solvebp_call(prob::IntegralProblem, alg::VEGAS, sensealg, domain, p;
     SciMLBase.build_solution(prob, alg, val, err, chi = chi, retcode = ReturnCode.Success)
 end
 
-export QuadGKJL, HCubatureJL, VEGAS, VEGASMC, GaussLegendre, QuadratureRule, TrapezoidalRule, SimpsonsRule
+export QuadGKJL, HCubatureJL, VEGAS, VEGASMC, GaussLegendre, QuadratureRule,
+       TrapezoidalRule, SimpsonsRule
 export CubaVegas, CubaSUAVE, CubaDivonne, CubaCuhre
 export CubatureJLh, CubatureJLp
 export ArblibJL

src/algorithms.jl

@@ -127,9 +127,8 @@ function GaussLegendre(; n = 250, subintervals = 1, nodes = nothing, weights = n
     return GaussLegendre(nodes, weights, subintervals)
 end
 
-
 """
-  QuadratureRule(q; n=250)
+QuadratureRule(q; n=250)
 
 Algorithm to construct and evaluate a quadrature rule `q` of `n` points computed from the
 inputs as `x, w = q(n)`. It assumes the nodes and weights are for the standard interval

src/algorithms_extension.jl

@@ -123,13 +123,15 @@ end
 
 function CubaVegas(; flags = 0, seed = 0, minevals = 0, nstart = 1000, nincrease = 500,
         gridno = 0)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) && error("CubaVegas requires `using Cuba`")
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) &&
+        error("CubaVegas requires `using Cuba`")
     return CubaVegas(flags, seed, minevals, nstart, nincrease, gridno)
 end
 
 function CubaSUAVE(; flags = 0, seed = 0, minevals = 0, nnew = 1000, nmin = 2,
         flatness = 25.0)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) && error("CubaSUAVE requires `using Cuba`")
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) &&
+        error("CubaSUAVE requires `using Cuba`")
     return CubaSUAVE(flags, seed, minevals, nnew, nmin, flatness)
 end
 
@@ -138,13 +140,15 @@ function CubaDivonne(; flags = 0, seed = 0, minevals = 0,
         maxchisq = 10.0, mindeviation = 0.25,
         xgiven = zeros(Cdouble, 0, 0),
         nextra = 0, peakfinder = C_NULL)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) && error("CubaDivonne requires `using Cuba`")
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) &&
+        error("CubaDivonne requires `using Cuba`")
     return CubaDivonne(flags, seed, minevals, key1, key2, key3, maxpass, border, maxchisq,
         mindeviation, xgiven, nextra, peakfinder)
 end
 
 function CubaCuhre(; flags = 0, minevals = 0, key = 0)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) && error("CubaCuhre requires `using Cuba`")
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubaExt)) &&
+        error("CubaCuhre requires `using Cuba`")
     return CubaCuhre(flags, minevals, key)
 end
 
@@ -174,8 +178,9 @@ publisher={Elsevier}
 struct CubatureJLh <: AbstractCubatureJLAlgorithm
     error_norm::Int32
 end
-function CubatureJLh(; error_norm=0)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubatureExt)) && error("CubatureJLh requires `using Cubature`")
+function CubatureJLh(; error_norm = 0)
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubatureExt)) &&
+        error("CubatureJLh requires `using Cubature`")
     return CubatureJLh(error_norm)
 end
 
@@ -193,13 +198,12 @@ Defaults to `Cubature.INDIVIDUAL`, other options are
 struct CubatureJLp <: AbstractCubatureJLAlgorithm
     error_norm::Int32
 end
-function CubatureJLp(; error_norm=0)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubatureExt)) && error("CubatureJLp requires `using Cubature`")
+function CubatureJLp(; error_norm = 0)
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsCubatureExt)) &&
+        error("CubatureJLp requires `using Cubature`")
     return CubatureJLp(error_norm)
 end
 
-
-
 """
     ArblibJL(; check_analytic=false, take_prec=false, warn_on_no_convergence=false, opts=C_NULL)
 
@@ -221,8 +225,10 @@ struct ArblibJL{O} <: AbstractIntegralExtensionAlgorithm
     warn_on_no_convergence::Bool
     opts::O
 end
-function ArblibJL(; check_analytic=false, take_prec=false, warn_on_no_convergence=false, opts=C_NULL)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsArblibExt)) && error("ArblibJL requires `using Arblib`")
+function ArblibJL(; check_analytic = false, take_prec = false,
+        warn_on_no_convergence = false, opts = C_NULL)
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsArblibExt)) &&
+        error("ArblibJL requires `using Arblib`")
     return ArblibJL(check_analytic, take_prec, warn_on_no_convergence, opts)
 end
 
@@ -232,14 +238,15 @@ end
 Markov-chain based Vegas algorithm from MCIntegration.jl
 
 Refer to
-[`MCIntegration.integrate`](https://numericaleft.github.io/MCIntegration.jl/dev/lib/montecarlo/#MCIntegration.integrate-Tuple{Function})
+[`MCIntegration.integrate`](https://numericaleft.github.io/MCIntegration.jl/dev/lib/montecarlo/#MCIntegration.integrate-Tuple%7BFunction%7D)
 for documentation on the keywords, which are passed directly to the solver with a set of
 defaults that works for conforming integrands.
 """
-struct VEGASMC{K<:NamedTuple} <: AbstractIntegralExtensionAlgorithm
+struct VEGASMC{K <: NamedTuple} <: AbstractIntegralExtensionAlgorithm
     kws::K
 end
 function VEGASMC(; kws...)
-    isnothing(Base.get_extension(@__MODULE__, :IntegralsMCIntegrationExt)) && error("VEGASMC requires `using MCIntegration`")
+    isnothing(Base.get_extension(@__MODULE__, :IntegralsMCIntegrationExt)) &&
+        error("VEGASMC requires `using MCIntegration`")
     return VEGASMC(NamedTuple(kws))
 end