added more docs

scripts/run_simulation.jl

@@ -7,7 +7,7 @@ using .Visualization
 N = 100
 tspan = (0.0, 100.0)
 
-sol = run_simulation(N, tspan)
+sol = AnimalFurFHN.run_simulation(tspan, N)
 
 
 Visualization.step_through_solution(sol, N)

src/solver.jl

@@ -30,7 +30,19 @@ function fhn!(du, u, p::FHNParams, t = 0)
     du .= vcat(vec(fu), vec(fv))
 end
 
-function run_simulation(N::Int, tspan::Tuple{Float64,Float64})
+"""
+    run_simulation(tspan::Tuple{Float64,Float64}, N::Int)
+
+solving the ODE and modelling it after FHN
+
+# Arguments
+- `tspan`: tuple of two Float64's representing start and end times for simulation
+- `N`: size of the N×N grid
+
+# Returns
+- `sol`: solved differential equation (ODE)
+"""
+function run_simulation(tspan::Tuple{Float64,Float64}, N::Int)
     # Turing-spot parameters
     p = FHNParams(N, 1.0, 1e-5, 1e-3, 0.01, 0.1, 0.5)
 

src/visualization.jl

@@ -1,8 +1,19 @@
 module Visualization
 
 using GLMakie
+"""
+    step_through_solution(sol::SolutionType, N::Int)
 
-function step_through_solution(sol, N)
+Function for visualization for the output of run_simulation
+
+# Arguments
+- `sol`: computed differential equation by run_simulation
+- `N`: size of the N×N grid
+
+# Returns
+- ``: Displays created figure
+"""
+function step_through_solution(sol::SolutionType, N::Int)
     fig = Figure(resolution=(600, 600))
     ax = Axis(fig[1, 1])
     slider = Slider(fig[2, 1], range=1:length(sol), startvalue=1)