minor changes

test/BPINN_Tests.jl

@@ -2,9 +2,7 @@
 using DifferentialEquations, MCMCChains, ForwardDiff, Distributions
 using NeuralPDE, Flux, OptimizationOptimisers, AdvancedHMC, Lux
 using StatProfilerHTML, Profile, Statistics, Random, Functors, ComponentArrays
-using BenchmarkTools, Plots, StatsPlots, Test
-plotly()
-Profile.init()
+using BenchmarkTools, Test
 
 # for sampled params->lux ComponentArray
 function vector_to_parameters(ps_new::AbstractVector, ps::NamedTuple)
@@ -155,12 +153,11 @@ linear_analytic = (u0, p, t) -> exp(-t / 5) * (u0 + sin(t))
 sol = solve(prob, Tsit5(); saveat = 0.05)
 u = sol.u[1:100]
 time = sol.t[1:100]
-plot(sol1.t, sol1.u)
 
 # dataset and BPINN create
 x̂ = collect(Float64, Array(u) + 0.05 * randn(size(u)))
 dataset = [x̂, time]
-plot!(time, x̂)
+
 chainflux1 = Flux.Chain(Flux.Dense(1, 5, tanh), Flux.Dense(5, 5, tanh), Flux.Dense(5, 1))
 chainlux1 = Lux.Chain(Lux.Dense(1, 5, tanh), Lux.Dense(5, 5, tanh), Lux.Dense(5, 1))
 
@@ -212,7 +209,6 @@ init1, re1 = destructure(chainflux1)
 t = sol.t
 p = prob.p
 physsol1 = [linear_analytic(prob.u0, p, t[i]) for i in eachindex(t)]
-plot!(t, physsol1)
 
 # Mean of last 500 sampled parameter's curves(flux chains)[Ensemble predictions]
 out = re1.([fhsamplesflux1[i][1:22] for i in 500:1000])
@@ -277,7 +273,6 @@ prob = ODEProblem(lotka_volterra, u0, tspan, p)
 solution = solve(prob, Tsit5(); saveat = 0.05)
 
 # Plot simulation.
-plot(solution)
 time = solution.t
 u = hcat(solution.u...)
 # BPINN AND TRAINING DATASET CREATION, NN create, Reconstruct
@@ -378,7 +373,6 @@ function getensemble(yu, num_models)
 end
 fluxmean = getensemble(yu, length(out))
 meanscurve1_1 = prob.u0 .+ (t' .- prob.tspan[1]) .* fluxmean
-plot!(t, meanscurve1_1')
 mean(abs.(u .- meanscurve1_1))
 
 @test mean(abs2.(x̂ .- meanscurve1_1)) < 2e-2
@@ -390,7 +384,6 @@ out = re1.([fhsamplesflux2[i][1:68] for i in 500:1000])
 yu = collect(out[i](t') for i in eachindex(out))
 fluxmean = getensemble(yu, length(out))
 meanscurve1_2 = prob.u0 .+ (t' .- prob.tspan[1]) .* fluxmean
-plot!(t, meanscurve1_2')
 mean(abs.(u .- meanscurve1_2))
 
 @test mean(abs2.(x̂ .- meanscurve1)) < 2e-2