using CairoMakie using LaTeXStrings: @L_str using Dates: Dates using Random: Random using StaticArrays: SVector using JLD2: JLD2 using CellListMap: CellListMap using ReCo: ReCo # IMPORTANT: Disable the periodic boundary conditions # The arguments types have to match for the function to be overwritten! ReCo.update_verlet_lists!(::Any, ::CellListMap.CellList) = nothing ReCo.gen_cell_list(::Vector{SVector{2,Float64}}, ::CellListMap.Box) = nothing ReCo.gen_cell_list_box(::Float64, ::Float64) = nothing ReCo.push_to_verlet_list!(::Any, ::Any, ::Any) = nothing ReCo.restrict_coordinate(value::Float64, ::Float64) = value ReCo.restrict_coordinates(v::SVector{2,Float64}, ::Float64) = v ReCo.restrict_coordinates!(::Particle, ::Float64) = nothing ReCo.minimum_image_coordinate(value::Float64, ::Float64) = value ReCo.minimum_image(v::SVector{2,Float64}, ::Float64) = v function mean_squared_displacement(; n_simulations::Int64, v₀s::AbstractVector{Float64}, T::Float64 ) Random.seed!(42) n_v₀s = length(v₀s) δt = ReCo.DEFAULT_δt Dₜ = ReCo.DEFAULT_Dₜ main_parent_dir = "mean_squared_displacement_$(Dates.now())" mkpath(main_parent_dir) sim_dirs = Matrix{String}(undef, (n_simulations, n_v₀s)) for (v₀_ind, v₀) in enumerate(v₀s) max_possible_displacement = T * v₀ + T / δt * sqrt(2 * Dₜ * δt) parent_dir = main_parent_dir * "/$v₀" Threads.@threads for sim_ind in 1:n_simulations dir = ReCo.init_sim(; n_particles=1, v₀=v₀, parent_dir=parent_dir, comment="$sim_ind", half_box_len=max_possible_displacement, ) sim_dirs[sim_ind, v₀_ind] = dir ReCo.run_sim( dir; duration=T, seed=rand(1:typemax(Int64)), snapshot_at=0.01, n_bundle_snapshots=200, ) end end ts = Float64[] bundle_paths = ReCo.sorted_bundle_paths(sim_dirs[1, 1]) for i in 2:length(bundle_paths) # Skip the first bundle to avoid t = 0 bundle::ReCo.Bundle = JLD2.load_object(bundle_paths[i]) append!(ts, bundle.t) end mean_sq_displacements = zeros((length(ts), n_v₀s)) @simd for v₀_ind in 1:n_v₀s for sim_ind in 1:n_simulations sim_dir = sim_dirs[sim_ind, v₀_ind] bundle_paths = ReCo.sorted_bundle_paths(sim_dir) snapshot_ind = 1 for i in 2:length(bundle_paths) bundle::ReCo.Bundle = JLD2.load_object(bundle_paths[i]) for c in bundle.c sq_displacement = ReCo.sq_norm2d(c) mean_sq_displacements[snapshot_ind, v₀_ind] += sq_displacement snapshot_ind += 1 end end end end mean_sq_displacements ./= n_simulations return (ts, mean_sq_displacements) end function expected_mean_squared_displacement(t::Float64, v₀::Float64) Dₜ = ReCo.DEFAULT_Dₜ particle_radius = ReCo.DEFAULT_PARTICLE_RADIUS Dᵣ = 3 * Dₜ / ((2 * particle_radius)^2) return (4 * Dₜ + 2 * v₀^2 / Dᵣ) * t + 2 * v₀^2 * (exp(-Dᵣ * t) - 1) / (Dᵣ^2) end function plot_mean_sq_displacement_with_expectation( ts::Vector{Float64}, mean_sq_displacements::Matrix{Float64}, v₀s::AbstractVector{Float64}, ) CairoMakie.activate!() set_theme!() text_width_in_pt = 405 fig = Figure(; resolution=(text_width_in_pt, 0.55 * text_width_in_pt), fontsize=10, figure_padding=1, ) ax = Axis(fig[1, 1]; xlabel=L"t", ylabel=L"\mathbf{MSD}", xscale=log10, yscale=log10) t_linrange = LinRange(ts[1], ts[end], 1000) v₀_scatter_plots = [] for (v₀_ind, v₀) in enumerate(v₀s) scatter_plot = scatter!( ax, ts, view(mean_sq_displacements, :, v₀_ind); markersize=4 ) push!(v₀_scatter_plots, scatter_plot) expected_mean_sq_displacements = expected_mean_squared_displacement.(t_linrange, v₀) lines!(ax, t_linrange, expected_mean_sq_displacements) end Legend(fig[1, 2], v₀_scatter_plots, [L"v₀ = %$v₀" for v₀ in v₀s]) colgap!(fig.layout, 5) rowgap!(fig.layout, 5) parent_dir = "exports/graphics/" mkpath(parent_dir) save("$parent_dir/mean_squared_displacement.pdf", fig; pt_per_unit=1) return nothing end function run_analysis() v₀s = SVector(0.0, 20.0, 40.0, 60.0, 80.0) ts, mean_sq_displacements = mean_squared_displacement(; n_simulations=3 * Threads.nthreads(), v₀s=v₀s, T=10.0 ) plot_mean_sq_displacement_with_expectation(ts, mean_sq_displacements, v₀s) return nothing end # run_analysis()