[ci-skip] Revert "Apply JuliaFormatter to fix code formatting"

This reverts commit b4097c4.

Reverting JuliaFormatter changes due to formatting issues being addressed in JuliaFormatter.jl PR#933.

Author: ChrisRackauckas

src/callbacks.jl

@@ -50,7 +50,7 @@ has_continuous_callback(cb::DiscreteCallback) = false
 has_continuous_callback(cb::ContinuousCallback) = true
 has_continuous_callback(cb::VectorContinuousCallback) = true
 has_continuous_callback(cb::CallbackSet) = !isempty(cb.continuous_callbacks)
-has_continuous_callback(cb::Nothing) = false #======================================================#
+has_continuous_callback(cb::Nothing) = false#======================================================##======================================================#
 
 # Callback handling
 

src/solve.jl

@@ -719,7 +719,7 @@ function build_null_solution(prob::AbstractDEProblem, args...;
         save_everystep = true,
         save_on = true,
         save_start = save_everystep || isempty(saveat) ||
-                         saveat isa Number || prob.tspan[1] in saveat,
+                     saveat isa Number || prob.tspan[1] in saveat,
         save_end = true,
         kwargs...)
     ts = if saveat === ()
@@ -752,7 +752,7 @@ function build_null_solution(
         save_everystep = true,
         save_on = true,
         save_start = save_everystep || isempty(saveat) ||
-                         saveat isa Number || prob.tspan[1] in saveat,
+                     saveat isa Number || prob.tspan[1] in saveat,
         save_end = true,
         kwargs...)
     prob, success = hack_null_solution_init(prob)

test/downstream/complex_number_ad.jl

@@ -60,7 +60,7 @@ function assert_fun()
     p0 = rand(3)
     isapprox(loss(p0), loss_via_real(p0); rtol = 1e-4)
 end
-@assert all([assert_fun() for _ in 1:(2^6)])
+@assert all([assert_fun() for _ in 1:(2 ^ 6)])
 
 # test ad with ForwardDiff
 function test_ad()
@@ -74,4 +74,4 @@ function test_ad()
     isapprox(grad_complex, grad_real; rtol = 1e-6) ? true : (@show rel_err; false)
 end
 
-@time @test all([test_ad() for _ in 1:(2^6)])
+@time @test all([test_ad() for _ in 1:(2 ^ 6)])

test/downstream/ensemble_ad.jl

@@ -66,7 +66,7 @@ ep = rand(N, 4)
 
 ensemble_prob = EnsembleProblem(prob,
     prob_func = (
-    prob, i, repeat) -> remake(prob,
+        prob, i, repeat) -> remake(prob,
         u0 = eu0[i, :],
         p = ep[i, :],
         saveat = 0.1))
@@ -77,7 +77,7 @@ cache = Ref{Any}()
 function sum_of_e_solution(p)
     ensemble_prob = EnsembleProblem(prob,
         prob_func = (
-        prob, i, repeat) -> remake(prob,
+            prob, i, repeat) -> remake(prob,
             u0 = eu0[i, :],
             p = p[i, :],
             saveat = 0.1))

test/downstream/ensemble_analysis.jl

@@ -49,15 +49,15 @@ m3, m4, c = timepoint_meancov(sim, 0.5, 0.5)
 @test v ≈ c
 m3, m4, c = timepoint_meancor(sim, 0.5, 0.5)
 @test c ≈ one(c)
-m_series = timeseries_point_mean(sim, 0:(1 // 2^(3)):1)
-m_series = timeseries_point_median(sim, 0:(1 // 2^(3)):1)
-m_series = timeseries_point_quantile(sim, 0.5, 0:(1 // 2^(3)):1)
-m_series, v_series = timeseries_point_meanvar(sim, 0:(1 // 2^(3)):1)
-summ = EnsembleSummary(sim, 0:(1 // 2^(3)):1)
+m_series = timeseries_point_mean(sim, 0:(1 // 2 ^ (3)):1)
+m_series = timeseries_point_median(sim, 0:(1 // 2 ^ (3)):1)
+m_series = timeseries_point_quantile(sim, 0.5, 0:(1 // 2 ^ (3)):1)
+m_series, v_series = timeseries_point_meanvar(sim, 0:(1 // 2 ^ (3)):1)
+summ = EnsembleSummary(sim, 0:(1 // 2 ^ (3)):1)
 m5, v5 = m_series.u[5], v_series.u[5]
 @test m ≈ m5
 @test v ≈ v5
-m6, m7, v6 = timeseries_point_meancov(sim, 0:(1 // 2^(3)):1, 0:(1 // 2^(3)):1)[5, 5]
+m6, m7, v6 = timeseries_point_meancov(sim, 0:(1 // 2 ^ (3)):1, 0:(1 // 2 ^ (3)):1)[5, 5]
 @test m ≈ m6
 @test m ≈ m7
 @test v ≈ v6
@@ -107,15 +107,15 @@ m3, m4, c = timepoint_meancov(sim, 0.5, 0.5)
 @test v ≈ c
 m3, m4, c = timepoint_meancor(sim, 0.5, 0.5)
 @test c ≈ ones(size(c)...)
-m_series = timeseries_point_mean(sim, 0:(1 // 2^(3)):1)
-m_series = timeseries_point_median(sim, 0:(1 // 2^(3)):1)
-m_series = timeseries_point_quantile(sim, 0.5, 0:(1 // 2^(3)):1)
-m_series, v_series = timeseries_point_meanvar(sim, 0:(1 // 2^(3)):1)
-summ = EnsembleSummary(sim, 0:(1 // 2^(3)):1)
+m_series = timeseries_point_mean(sim, 0:(1 // 2 ^ (3)):1)
+m_series = timeseries_point_median(sim, 0:(1 // 2 ^ (3)):1)
+m_series = timeseries_point_quantile(sim, 0.5, 0:(1 // 2 ^ (3)):1)
+m_series, v_series = timeseries_point_meanvar(sim, 0:(1 // 2 ^ (3)):1)
+summ = EnsembleSummary(sim, 0:(1 // 2 ^ (3)):1)
 m5, v5 = m_series.u[5], v_series.u[5]
 @test m ≈ m5
 @test v ≈ v5
-m6, m7, v6 = timeseries_point_meancov(sim, 0:(1 // 2^(3)):1, 0:(1 // 2^(3)):1)[5, 5]
+m6, m7, v6 = timeseries_point_meancov(sim, 0:(1 // 2 ^ (3)):1, 0:(1 // 2 ^ (3)):1)[5, 5]
 @test m ≈ m6
 @test m ≈ m7
 @test v ≈ v6