Pin JuliaFormatter to v1.0.62

docs/examples/Basics.jl

@@ -82,7 +82,7 @@ raster
 # We can bin the data in small time windows to get a sense of the event rate over time. From the assumptions above, we expect the distribution of
 # counts to be roughly Poisson distributed with rate λ * Δt in each bin of width Δt.
 bin_width = 1.0
-bins = collect(h.tmin:bin_width:h.tmax)           # bin edges
+bins = collect((h.tmin):bin_width:(h.tmax))           # bin edges
 counts = fit(Histogram, h.times, bins).weights;    # counts per bin
 
 # Plot counts over time (counts/bin_width is a crude rate estimate)

docs/examples/Hawkes.jl

@@ -42,7 +42,7 @@ dt = DateTime("12/22 3:09 pm", fmt)
 
 function parse_timestamp_min(s::AbstractString; year=2024)
     dt = DateTime(s, dateformat"m/d I:M p")
-    DateTime(year, month(dt), day(dt), hour(dt), minute(dt))
+    return DateTime(year, month(dt), day(dt), hour(dt), minute(dt))
 end
 
 data.TimestampDT = parse_timestamp_min.(String.(data.Timestamp))
@@ -66,7 +66,7 @@ function eventplot(
     xlabel="Time (minutes)",
     ylabel="Events",
 )
-    scatter(
+    return scatter(
         event_times,
         ones(length(event_times));
         markershape=:vline,
@@ -145,8 +145,9 @@ println("Branching ratio (n = α/ω): ", hawkes_model.α / hawkes_model.ω) #hid
 ts = sort(data.t)
 
 function λ_hawkes(t::Real)
-    hawkes_model.μ +
-    sum((hawkes_model.α * exp(-hawkes_model.ω * (t - ti)) for ti in ts if ti < t); init=0.0)
+    return hawkes_model.μ + sum(
+        (hawkes_model.α * exp(-hawkes_model.ω * (t - ti)) for ti in ts if ti < t); init=0.0
+    )
 end
 
 u = range(0.0, maximum(ts) + 1.0; length=2000)

docs/examples/Inhomogeneous.jl

@@ -252,7 +252,7 @@ models = [
 ]
 
 println("\nModel Comparison (Negative Log-Likelihood):") # hide
-println("-" ^ 50) # hide
+println("-"^50) # hide
 for (name, model) in models # hide
     nll = compute_nll(model, h) # hide
     println("  $name: ", round(nll; digits=2)) # hide 

src/HypothesisTests/PPTests/bootstrap_test.jl

@@ -94,7 +94,7 @@ function BootstrapTest(
                 # Simulates a process and uses the process estimated from
                 # the simulation to calculate the test statistic
                 sim = simulate(local_rng, pp_est, h.tmin, h.tmax)
-                sim_est = fit(PP, sim, rng=local_rng)
+                sim_est = fit(PP, sim; rng=local_rng)
                 chunk[i] = statistic(S, sim_est, sim)
             end
         end

src/HypothesisTests/point_process_tests.jl

@@ -19,7 +19,7 @@ Calculate the p-value of a goodness-of-fit test on a process.
 function StatsAPI.pvalue(::PointProcessTest) end
 
 function Base.show(io::IO, t::PointProcessTest)
-    print(io, "$(typeof(t)) - pvalue = $(pvalue(t))")
+    return print(io, "$(typeof(t)) - pvalue = $(pvalue(t))")
 end
 
 #=

src/bounded_point_process.jl

@@ -34,8 +34,8 @@ mark_distribution(bpp::BoundedPointProcess, args...) = mark_distribution(bpp.pp,
 intensity(bpp::BoundedPointProcess, args...) = intensity(bpp.pp, args...)
 log_intensity(bpp::BoundedPointProcess, args...) = log_intensity(bpp.pp, args...)
 function ground_intensity_bound(bpp::BoundedPointProcess, args...)
-    ground_intensity_bound(bpp.pp, args...)
+    return ground_intensity_bound(bpp.pp, args...)
 end
 function integrated_ground_intensity(bpp::BoundedPointProcess, args...)
-    integrated_ground_intensity(bpp.pp, args...)
+    return integrated_ground_intensity(bpp.pp, args...)
 end

src/history.jl

@@ -177,7 +177,7 @@ event_times(h::History) = h.times
 Return the sorted vector of event times for `h` in dimension `d`.
 """
 function event_times(h::History, d::Int)
-    h.N == 1 && d == 1 ? h.times : (@view h.times[h.dims .== d])
+    return h.N == 1 && d == 1 ? h.times : (@view h.times[h.dims .== d])
 end
 
 """
@@ -216,7 +216,7 @@ event_marks(h::History) = h.marks
 Return the vector of event marks in dimension `d` of `h`, sorted according to their event times.
 """
 function event_marks(h::History, d::Int)
-    h.N == 1 && d == 1 ? h.marks : (@view h.marks[h.dims .== d])
+    return h.N == 1 && d == 1 ? h.marks : (@view h.marks[h.dims .== d])
 end
 
 """

src/multivariate/independent_multivariate.jl

@@ -47,7 +47,7 @@ function intensity(pp::IndependentMultivariateProcess, m, t, h)
 end
 
 function log_intensity(pp::IndependentMultivariateProcess, m, t, h, d)
-    log(intensity(pp, m, t, h, d))
+    return log(intensity(pp, m, t, h, d))
 end
 log_intensity(pp::IndependentMultivariateProcess, m, t, h) = log.(intensity(pp, m, t, h))
 

src/univariate/hawkes/hawkes_process.jl

@@ -34,7 +34,7 @@ struct HawkesProcess{T<:Real} <: AbstractUnivariateProcess
             throw(DomainError((α, ω), "Parameter ω must be strictly smaller than α"))
         T = promote_type(T1, T2, T3)
         (μ_T, α_T, ω_T) = convert.(T, (μ, α, ω))
-        new{T}(μ_T, α_T, ω_T)
+        return new{T}(μ_T, α_T, ω_T)
     end
 end
 

src/univariate/poisson/inhomogeneous/fit.jl

@@ -66,11 +66,13 @@ function StatsAPI.fit(
 )
 
     # Define objective function
-    objective(params) = negative_loglikelihood_ipp(
-        h,
-        from_params(F, params; intensity_kwargs...);
-        integration_config=integration_config,
-    )
+    function objective(params)
+        return negative_loglikelihood_ipp(
+            h,
+            from_params(F, params; intensity_kwargs...);
+            integration_config=integration_config,
+        )
+    end
 
     result = optimize(objective, init_params, optimizer; autodiff=autodiff)
 

src/univariate/poisson/inhomogeneous/integration_config.jl

@@ -37,7 +37,7 @@ struct IntegrationConfig
     function IntegrationConfig(;
         solver=QuadGKJL(), abstol::Float64=1e-8, reltol::Float64=1e-8, maxiters::Int=1000
     )
-        new(solver, abstol, reltol, maxiters)
+        return new(solver, abstol, reltol, maxiters)
     end
 end
 

src/univariate/poisson/inhomogeneous/intensity_methods.jl

@@ -56,7 +56,7 @@ function intensity_bound(
     f::PolynomialIntensity{R},
     t::T,
     h::History;
-    lookahead_factor::Real=1/100,
+    lookahead_factor::Real=1 / 100,
     n_samples::Int=10 * length(f.coefficients),
 ) where {R,T}
     dur = duration(h)             # or max_time(h) - min_time(h)
@@ -101,7 +101,7 @@ function intensity_bound(f::ExponentialIntensity{R}, t::T; lookahead::T=one(T))
 end
 
 function intensity_bound(
-    f::ExponentialIntensity{R}, t::T, h::History; lookahead_factor::Real=1/100
+    f::ExponentialIntensity{R}, t::T, h::History; lookahead_factor::Real=1 / 100
 ) where {R,T}
     dur = duration(h)
     lookahead = T(lookahead_factor * dur)
@@ -222,7 +222,7 @@ function intensity_bound(
     f::LinearCovariateIntensity{R},
     t::T,
     h::History;
-    lookahead_factor::Real=1/100,
+    lookahead_factor::Real=1 / 100,
     n_samples::Int=100,
 ) where {R,T}
     dur = duration(h)
@@ -255,7 +255,7 @@ function intensity_bound(f::F, t::T; lookahead::T=one(T), n_samples::Int=100) wh
 end
 
 function intensity_bound(
-    f::F, t::T, h::History; lookahead_factor::Real=1/100, n_samples::Int=100
+    f::F, t::T, h::History; lookahead_factor::Real=1 / 100, n_samples::Int=100
 ) where {F,T}
     dur = duration(h)
     lookahead = T(lookahead_factor * dur)

test/Project.toml

@@ -4,10 +4,14 @@ DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+JuliaFormatter = "1.0.62"

test/history.jl

@@ -1,5 +1,5 @@
 @testset "Univariate History" begin
-    h = History([0.2, 0.8, 1.1], 0.0, 2.0, ["a", "b", "c"]);
+    h = History([0.2, 0.8, 1.1], 0.0, 2.0, ["a", "b", "c"])
 
     @test duration(h) == 2.0
     @test nb_events(h) == 3

test/hypothesis_tests.jl

@@ -19,15 +19,15 @@ h2 = History(collect(0:999), 0, 1000)
     bt1 = BootstrapTest(KSDistance{Uniform}, PP, h2; n_sims=10000)
 
     @test bt1.n_sims == 10000
-    @test isapprox(bt1.stat, 1/1000, rtol=0.01);
+    @test isapprox(bt1.stat, 1 / 1000, rtol=0.01)
     @test pvalue(bt1) isa Float64
     @test pvalue(bt1) > 0.99
     @test string(bt1) == "BootstrapTest - pvalue = 1.0"
 
     bt2 = BootstrapTest(KSDistance{Exponential}, PP, h2)
 
     @test bt2.n_sims == 1000
-    @test bt2.stat ≈ 1 - exp(-1);
+    @test bt2.stat ≈ 1 - exp(-1)
     @test pvalue(bt2) isa Float64
     @test pvalue(bt2) < 0.01
 
@@ -45,8 +45,8 @@ end
 
     @test nbt1.n_sims == 1000
     @test nbt2.n_sims == 10000
-    @test isapprox(nbt1.stat, 1/1000, rtol=0.01);
-    @test isapprox(nbt1.stat, nbt2.stat, rtol=0.01);
+    @test isapprox(nbt1.stat, 1 / 1000, rtol=0.01)
+    @test isapprox(nbt1.stat, nbt2.stat, rtol=0.01)
     @test pvalue(nbt1) isa Float64
     @test pvalue(nbt1) > 0.99
     @test pvalue(nbt2) > 0.99
@@ -57,8 +57,8 @@ end
 
     @test nbt3.n_sims == 1000
     @test nbt4.n_sims == 1000
-    @test nbt3.stat ≈ 1 - exp(-1);
-    @test nbt3.stat ≈ nbt4.stat;
+    @test nbt3.stat ≈ 1 - exp(-1)
+    @test nbt3.stat ≈ nbt4.stat
     @test pvalue(nbt3) isa Float64
     @test pvalue(nbt3) < 0.01
     @test pvalue(nbt4) < 0.01

test/inhomogeneous_poisson_process.jl

@@ -509,9 +509,9 @@ end
         # λ(t) = exp(η(t))
         @test intensity_cov(0.0) ≈ exp(1.0) rtol = 1e-6
 
-        @test intensity_cov(1.0) ≈ exp(1.0 + 0.5*1.0) rtol = 1e-6
+        @test intensity_cov(1.0) ≈ exp(1.0 + 0.5 * 1.0) rtol = 1e-6
 
-        @test intensity_cov(0.25) ≈ exp(1.0 + 0.5*0.25 + 0.3*1.0) rtol = 1e-6
+        @test intensity_cov(0.25) ≈ exp(1.0 + 0.5 * 0.25 + 0.3 * 1.0) rtol = 1e-6
     end
 
     @testset "Simulation" begin
@@ -1122,7 +1122,7 @@ end
 
     @testset "InhomogeneousPoissonProcess with custom integration config" begin
         intensity = ExponentialIntensity(2.0, 0.1)
-        custom_config = IntegrationConfig(abstol=1e-10, reltol=1e-10, maxiters=5000)
+        custom_config = IntegrationConfig(; abstol=1e-10, reltol=1e-10, maxiters=5000)
         pp = InhomogeneousPoissonProcess(intensity; integration_config=custom_config)
 
         @test pp isa InhomogeneousPoissonProcess
@@ -1147,7 +1147,7 @@ end
     end
 
     @testset "Custom config" begin
-        config = IntegrationConfig(abstol=1e-12, reltol=1e-10, maxiters=10000)
+        config = IntegrationConfig(; abstol=1e-12, reltol=1e-10, maxiters=10000)
         config_str = string(config)
         @test occursin("IntegrationConfig", config_str)
         @test occursin("abstol=1.0e-12", config_str)
@@ -1404,7 +1404,7 @@ end
         h = simulate(rng, pp_true, 0.0, 20.0)
 
         # Fit with custom integration config
-        custom_config = IntegrationConfig(abstol=1e-10, reltol=1e-10, maxiters=5000)
+        custom_config = IntegrationConfig(; abstol=1e-10, reltol=1e-10, maxiters=5000)
         pp_est = fit(
             InhomogeneousPoissonProcess{PolynomialIntensity{Float64},Dirac{Nothing}},
             h,

test/runtests.jl

@@ -3,6 +3,7 @@ using DensityInterface
 using Documenter
 using Distributions
 using ForwardDiff
+using JuliaFormatter
 using LinearAlgebra
 using Pkg
 using PointProcesses
@@ -16,13 +17,18 @@ Random.seed!(63)
 DocMeta.setdocmeta!(PointProcesses, :DocTestSetup, :(using PointProcesses); recursive=true)
 
 @testset verbose = true "PointProcesses.jl" begin
+    @testset verbose = false "Code Formatting" begin
+        if VERSION >= v"1.10"
+            @test JuliaFormatter.format(PointProcesses; verbose=false, overwrite=false)
+        end
+    end
     @testset verbose = false "Code quality (Aqua.jl)" begin
         Aqua.test_all(PointProcesses; ambiguities=false, deps_compat=(; check_extras=false))
     end
     @testset verbose = false "Code Linting" begin
         # Skip JET on Julia pre-releases (where JET typically hasn't caught up
         # yet and there is no compatible JET version to resolve against). JET is
-        # not listed in test/Project.toml's [deps] for the same reaso, having
+        # not listed in test/Project.toml's [deps] for the same reason, having
         # it there would make `Pkg.test` fail at the resolution step on
         # prereleases, before this guard ever runs. Install on demand only when
         # we're on a stable Julia.