using CairoMakie using LaTeXStrings: @L_str using Dates: Dates using Random: Random using StaticArrays: SVector using JLD2: JLD2 using CellListMap: CellListMap using ProgressMeter: ProgressMeter using ReCo: ReCo includet("../src/Visualization/common_CairoMakie.jl") # IMPORTANT: Disable the periodic boundary conditions # The arguments types have to match for the function to be overwritten! ReCo.push_to_verlet_list!(::Any, ::Any, ::Any) = nothing ReCo.update_verlet_lists!(::Any, ::CellListMap.CellList) = nothing ReCo.update_verlet_lists!(::Any, ::Nothing) = nothing ReCo.gen_cell_list(::Vector{SVector{2,Float64}}, ::CellListMap.Box) = nothing ReCo.gen_cell_list(::Vector{SVector{2,Float64}}, ::Nothing) = nothing ReCo.gen_cell_list_box(::Float64, ::Float64) = nothing ReCo.restrict_coordinate(value::Float64, ::Float64) = value ReCo.restrict_coordinates(v::SVector{2,Float64}, ::Float64) = v ReCo.restrict_coordinates!(::ReCo.Particle, ::Float64) = nothing const δt = 1e-4 const Dₜ = ReCo.DEFAULT_Dₜ function max_possible_displacement(T::Float64, v₀::Float64, δt::Float64=δt, Dₜ::Float64=Dₜ) return T * v₀ + T / δt * sqrt(2 * Dₜ * δt) end function msd_simulation( v₀::Float64, half_box_len::Float64, T::Float64, snapshot_at::Float64, parent_dir::String; comment::String="", seed::Int64=42, ) Random.seed!(seed) dir = ReCo.init_sim(; n_particles=1, v₀=v₀, parent_dir=parent_dir, comment=comment, half_box_len=half_box_len, ) ReCo.run_sim( dir; duration=T, seed=rand(1:typemax(Int64)), snapshot_at=snapshot_at, n_bundle_snapshots=1000, ) return dir end function mean_squared_displacement(; n_simulations::Int64, v₀s::NTuple{N,Float64}, T::Float64 ) where {N} n_v₀s = length(v₀s) main_parent_dir = "mean_squared_displacement_$(Dates.now())" sim_dirs = Matrix{String}(undef, (n_simulations, n_v₀s)) progress = ProgressMeter.Progress(n_v₀s * n_simulations; dt=3, desc="MSD: ") for (v₀_ind, v₀) in enumerate(v₀s) half_box_len = max_possible_displacement(T, v₀) parent_dir = main_parent_dir * "/$v₀" Threads.@threads for sim_ind in 1:n_simulations dir = msd_simulation(v₀, half_box_len, T, 0.5, parent_dir; comment="$sim_ind") sim_dirs[sim_ind, v₀_ind] = dir ProgressMeter.next!(progress; showvalues=[(:v₀, v₀)]) end end ts = Float64[] ReCo.append_bundle_properties!( (ts,), (:t,), sim_dirs[1, 1]; particle_slice=1, snapshot_slice=:, first_bundle=2 ) # Skip the first bundle to avoid t = 0 mean_sq_displacements = zeros((length(ts), n_v₀s)) @simd for v₀_ind in 1:n_v₀s for sim_ind in 1:n_simulations sim_dir = sim_dirs[sim_ind, v₀_ind] bundle_paths = ReCo.sorted_bundle_paths(sim_dir) snapshot_ind = 1 for i in 2:length(bundle_paths) # Skip the first bundle to avoid t = 0 bundle::ReCo.Bundle = JLD2.load_object(bundle_paths[i]) for c in bundle.c sq_displacement = ReCo.sq_norm2d(c) mean_sq_displacements[snapshot_ind, v₀_ind] += sq_displacement snapshot_ind += 1 end end end end mean_sq_displacements ./= n_simulations return (ts, mean_sq_displacements) end function expected_mean_squared_displacement(t::Float64, v₀::Float64) Dₜ = ReCo.DEFAULT_Dₜ particle_radius = ReCo.DEFAULT_PARTICLE_RADIUS Dᵣ = 3 * Dₜ / ((2 * particle_radius)^2) return (4 * Dₜ + 2 * v₀^2 / Dᵣ) * t + 2 * v₀^2 * (exp(-Dᵣ * t) - 1) / (Dᵣ^2) end function plot_mean_sq_displacement_with_expectation( ts::Vector{Float64}, mean_sq_displacements::Matrix{Float64}, v₀s::NTuple{N,Float64} ) where {N} init_cairomakie!() fig = gen_figure() ax = Axis(fig[1, 1]; xlabel=L"t", ylabel=L"\mathbf{MSD}", xscale=log10, yscale=log10) t_linrange = LinRange(ts[1], ts[end], 1000) v₀_scatter_plots = [] for (v₀_ind, v₀) in enumerate(v₀s) scatter_plot = scatter!( ax, ts, view(mean_sq_displacements, :, v₀_ind); markersize=4 ) push!(v₀_scatter_plots, scatter_plot) expected_mean_sq_displacements = expected_mean_squared_displacement.(t_linrange, v₀) lines!(ax, t_linrange, expected_mean_sq_displacements) end Legend(fig[1, 2], v₀_scatter_plots, [L"v_0 = %$v₀" for v₀ in v₀s]) set_gaps!(fig) save_fig("mean_squared_displacement.pdf", fig) return nothing end function run_msd_analysis() v₀s = (0.0, 20.0, 40.0, 60.0, 80.0) ts, mean_sq_displacements = mean_squared_displacement(; n_simulations=200 * Threads.nthreads(), v₀s=v₀s, T=100.0 ) plot_mean_sq_displacement_with_expectation(ts, mean_sq_displacements, v₀s) return nothing end function plot_random_walk(; T::Float64, v₀::Float64, seed::Int64) half_box_len = max_possible_displacement(T, v₀) dir = msd_simulation(v₀, half_box_len, T, 8.0, "random_walk_$(Dates.now())"; seed=seed) ts = Float64[] cs = SVector{2,Float64}[] ReCo.append_bundle_properties!( (ts, cs), (:t, :c), dir; particle_slice=1, snapshot_slice=: ) init_cairomakie!() fig = gen_figure() max_displacement = maximum(ReCo.norm2d.(cs)) expected_mean_displacement = sqrt(expected_mean_squared_displacement(ts[end], v₀)) limit = max(max_displacement, expected_mean_displacement) limit *= 1.04 ax = Axis( fig[1, 1]; xlabel=L"x", ylabel=L"y", aspect=AxisAspect(1), limits=(-limit, limit, -limit, limit), ) path_plot = lines!(ax, cs; linewidth=0.5) marker_size = 4 start_point_plot = scatter!(ax, cs[1]; markersize=marker_size, color=:lime) end_point_plot = scatter!(ax, cs[end]; markersize=marker_size, color=:red) expected_mean_squared_displacement_plot = arc!( ax, Point2(0.0, 0.0), expected_mean_displacement, 0, 2 * π; color=:orange, linewidth=1, ) Legend( fig[1, 2], [ start_point_plot, end_point_plot, path_plot, expected_mean_squared_displacement_plot, ], ["Start point", "End point", "Path", "Expected mean displacement"], ) set_gaps!(fig) save_fig("random_walk.pdf", fig) return nothing end function run_random_walk() plot_random_walk(; T=100_000.0, v₀=0.0, seed=12345) return nothing end