made docs foldable

scripts/run_simulation.jl

@@ -5,7 +5,7 @@ include("../src/visualization.jl")
 using .Visualization
 
 N = 100
-tspan = (0.0, 100.0)
+tspan = (0.0, 1000.0)
 
 sol = AnimalFurFHN.run_simulation(tspan, N)
 

src/laplacian.jl

@@ -1,16 +1,16 @@
 """
     laplacian(U::Matrix{Float64}, N::Int, h::Float64)
 
-Computes the discrete 2D Laplacian of a matrix `U` using a 5-point stencil
-and circular boundary conditions.
+    Computes the discrete 2D Laplacian of a matrix `U` using a 5-point stencil
+    and circular boundary conditions.
 
-# Arguments
-- `U::Matrix{Float64}`: The input 2D matrix representing the field or image.
-- `N::Int`: Integer
-- `h::Float64`: The spatial step size or grid spacing between points in the discretization.
+    # Arguments
+    - `U::Matrix{Float64}`: The input 2D matrix representing the field or image.
+    - `N::Int`: Integer
+    - `h::Float64`: The spatial step size or grid spacing between points in the discretization.
 
-# Returns
-- `Vector{Float64}`: A flattened (vectorized) representation of the approximated Laplacian values for each element in `U`. The boundary conditions are handled circularly.
+    # Returns
+    - `Vector{Float64}`: A flattened (vectorized) representation of the approximated Laplacian values for each element in `U`. The boundary conditions are handled circularly.
 """
 function laplacian(U::Matrix{Float64}, N::Int, h::Float64)
     # shifts matrices and sums them up

src/solver.jl

@@ -5,17 +5,17 @@ using .Constants
 """
     fhn(du, u, p:FHNParams, t:)
 
-Implements the spatial dynamics of FitzHugh-Nagumo (fhn). Designed to be 
-within a larger numerical solver of partial differential equations.
+    Implements the spatial dynamics of FitzHugh-Nagumo (fhn). Designed to be 
+    within a larger numerical solver of partial differential equations.
 
-# Arguments
-- `du`: output argument which stores the calculated derivatives 
-- `u`: input vector containing the current state of the system at time t
-- `p`: holds all the fixed parameters of the FHN model
-- `t`: current time 
+    # Arguments
+    - `du`: output argument which stores the calculated derivatives 
+    - `u`: input vector containing the current state of the system at time t
+    - `p`: holds all the fixed parameters of the FHN model
+    - `t`: current time 
 
-# Returns
-- `du`: calculated derivatives put back into the du array
+    # Returns
+    - `du`: calculated derivatives put back into the du array
 """
 function fhn!(du, u, p::FHNParams, t = 0)
     u_mat = reshape(u[1:p.N^2], p.N, p.N) # activation variable
@@ -33,14 +33,14 @@ end
 """
     run_simulation(tspan::Tuple{Float64,Float64}, N::Int)
 
-solving the ODE and modelling it after FHN
+    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
+    # 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)
+    # Returns
+    - `sol`: solved differential equation (ODE)
 """
 function run_simulation(tspan::Tuple{Float64,Float64}, N::Int)
     # Turing-spot parameters

src/visualization.jl

@@ -4,14 +4,14 @@ using GLMakie
 """
     step_through_solution(sol::SolutionType, N::Int)
 
-Function for visualization for the output of run_simulation
+    Function for visualization for the output of run_simulation
 
-# Arguments
-- `sol`: computed differential equation by run_simulation
-- `N`: size of the N×N grid
+    # Arguments
+    - `sol`: computed differential equation by run_simulation
+    - `N`: size of the N×N grid
 
-# Returns
-- ``: Displays created figure
+    # Returns
+    - ``: Displays created figure
 """
 function step_through_solution(sol, N::Int)
     fig = Figure(resolution=(600, 600))