ca_system backup 1.jl

### A Pluto.jl notebook ###
# v0.19.30

#> [[frontmatter.author]]
#> name = "Kishore Shenoy"
#> url = "kichappa.github.io"

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
        local el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
        el
    end
end

# ╔═╡ 8bab643a-9618-4d04-ad1d-0cdd3963a630
# ╠═╡ show_logs = false
begin
	ENV["PYTHON"] = ""
	# Pkg.add(["Conda", "PyCall"])
	# Pkg.build("PyCall")
	using Conda, PyCall
	Conda.add(["numpy"])
end

# ╔═╡ 9083379c-842e-4f7c-936f-1f9e66861af0
begin
	using Plots
	using PlutoPlotly
	using OffsetArrays
	using LaTeXStrings
	using PlutoUI
	using ColorTypes
	using CUDA
	CUDA.allowscalar(false)
	using Random
	using Printf
	md"Just Importing libraries here..."
end

# ╔═╡ 4727903f-a54b-4d73-8998-fa99bb2481aa
md"# CA for Topography and Enemies"

# ╔═╡ c8c9a170-7cc7-4bb3-b9dc-1654f4c2cefd
begin
	# code to display a 2D array as an image
	function show_image(A, color_range=:viridis)
		Plots.heatmap(1:size(A, 1), 1:size(A, 2), A, aspect_ratio=:equal, color=color_range, backend=:gr)
	end
	md"Defining show_image() that can plot the 2D version of our model." 
end

# ╔═╡ d83db108-12df-4094-990d-474accf6e976
md"Max Threads, $(@bind max_threads NumberField(1:32, default=26))"

# ╔═╡ df27f8a4-f258-43b4-acdc-b8ea0f9ffc88
md"## Initial State"

# ╔═╡ e633b8e0-3774-462f-9d6e-1f586a17730a
md"Bush density, $(@bind b_density NumberField(1:100, default=9))"

# ╔═╡ e5c741d7-7c52-4097-8d02-89d76495d53f
function neighbour_sum(A, pos)
	i, j=pos
	neighbours = [[i-1,j],[i-1,j+1],[i,j+1],[i+1,j+1],[i+1,j],[i+1,j-1],[i,j-1],[i-1,j-1]]
	sum=0
	for neighbour in neighbours
		i, j = neighbour
		if(i>0 && i<=size(A, 1) && j>0 && j<=size(A, 2))
			# println("A($i, $j)=")
			# println("$(A[i, j])\n")
			sum+=A[i,j]
		end
	end
	return sum
end

# ╔═╡ 29fb1a62-86bf-4bab-bb7e-dbbfd5024917
function conway(A)
	m, n = size(A)
	B=copy(A)
	# for t in 1:T
		for i in 1:m
			for j in 1:n
				# life_decision = 0
				# for di in -1:1, dj in -1:1
				# 	i_n, j_n = i + di, j + dj
				# 	if 1 <= i_n <= m && 1 <= j_n <= n && !(di==0 && dj===0)
				# 		life_decision += A[i_n, j_n]
				# 	end
				# end
				
				# if life_decision < 2 || life_decision > 3
				# 	B[i, j] = 0
				# elseif life_decision == 3
				# 	B[i, j] = 1
				# else
				# 	B[i, j] = A[i, j]
				# end
				life_decision = neighbour_sum(A, [i,j])
				if(life_decision < 2 || life_decision > 3)
					B[i,j] = 0
				elseif(life_decision === 3)
					B[i,j]=1
				else
					B[i,j]=B[i,j]
				end
			end
		end
	# end
	return B
end	

# ╔═╡ fd3512a7-8d52-4d25-9ad8-0cc80555da7f
function kernel(A, B, m, n)
	i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
	j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
	if i <= m && j <= n
		# B[i,j]=A[i, j]
		life_decision = 0
		for di in -1:1, dj in -1:1
			i_n, j_n = i + di, j + dj
			if 1 <= i_n <= m && 1 <= j_n <= n && !(di==0 && dj===0)
				life_decision += A[i_n, j_n]
			end
		end
		
		if life_decision < 2 || life_decision > 3
			B[i, j] = 0
		elseif life_decision == 3
			B[i, j] = 1
		else
			B[i, j] = A[i, j]
		end
	end
	return
end

# ╔═╡ 2a3753d3-c08c-4e85-907e-9ebb5a67dab3
function conway_gpu(A)
    m, n = size(A)
	A = CuArray(A)
    B = similar(A)  # Create a GPU array of the same size and type as A

	threads_x = min(max_threads, m)  # Limit to max_threads threads in the x dimension
    threads_y = min(max_threads, n)  # Limit to max_threads threads in the y dimension
    blocks_x = ceil(Int, m / threads_x)
    blocks_y = ceil(Int, n / threads_y)
	
 	@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) kernel(A, B, m, n)
    
    return collect(B)
end

# ╔═╡ 8327cfec-51df-4c38-839a-b7212ddb24e7
md"``X_{\max}, Y_{\max}``, L = $(@bind L NumberField(0:100; default=100))"

# ╔═╡ 701891a4-6a87-427e-af9b-487dec1dee4d
md"Time of simulation, ``T_{\text{max}}``"

# ╔═╡ 4ec0a200-78df-4cfd-9efe-105dad6f4ef3
function encode_agent(agent_pos, B)
	B_out = copy(B)
	x, y = agent_pos
	B_out[x, y] = 2
	return B_out
end

# ╔═╡ fffa26a7-ecf6-4be0-ab7c-423665caf7a5
md"## Topography"

# ╔═╡ 72a7cb99-5483-4c82-9554-007c2ba44413
md"Number of height points, $(@bind altPs NumberField(0:100; default=7))"

# ╔═╡ cd4ee775-74d9-417f-9c97-6c8d321d7580
md"Max height, $(@bind max_height NumberField(0:100; default=L/10))"

# ╔═╡ ba6660df-59b7-4c70-b30f-b8548d63b0d2
begin
	function alt_kernel(A, B, m, n, alt_p, k, power)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if i <= m && j <= n
			B[i, j] = 0
			norm = 0
			for ki in 1:k
				d = ((alt_p[ki, 2] - i)^2 + (alt_p[ki, 1] - j)^2)^0.5
				if (d > 0)
					B[i,j] += alt_p[ki, 3]/d^power
					norm += 1/d^power
				else
					B[i,j] = alt_p[ki, 3]
					return
				end
			end
			B[i, j] /= norm
		end
		return
	end
	
	function topography_gpu(A, alt_p, power)
	    m, n = size(A)
		k, _ = size(alt_p)
		A_gpu = CuArray(A)
	    B = similar(A_gpu)  # Create a GPU array of the same size and type as A
		if !isnothing(max_threads) 
			if !isnothing(m)
				threads_x = min(max_threads, m)
			else
				threads_x = max_threads
			end
		else
			if !isnothing(m)
				threads_x = min(32, m)
			else
				threads_x = 26
			end
		end
		if !isnothing(max_threads) 
			if !isnothing(n)
				threads_y = min(max_threads, n)
			else
				threads_y = max_threads
			end
		else
			if !isnothing(n)
				threads_y = min(32, n)
			else
				threads_y = 26
			end
		end
		# threads_x = min(max_threads, m)  # Limit to max_threads threads in the x dimension
	 #    threads_y = min(max_threads, n)  # Limit to max_threads threads in the y dimension
	    blocks_x = ceil(Int, m / threads_x)
	    blocks_y = ceil(Int, n / threads_y)
		
	 	@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) alt_kernel(A_gpu, B, m, n, CuArray(alt_p), k, power)
	    
	    return collect(B)
	end
	md"Kernel and Method to generate Topography"
end

# ╔═╡ 82d0e800-deb1-42fe-b1d3-2018d8639ff8
md"neighbourhood radius, `n_radius` $(@bind n_radius NumberField(0:1000; default=3))"

# ╔═╡ 8f0937f0-813b-4256-a8b9-afb22e092a42
md"Topography of the system"

# ╔═╡ 6d4076dc-68c8-42f8-a43e-222e3410bdbf
md"Topography contour"

# ╔═╡ 11f7bf70-4a39-451c-9bdb-9369742dcce0
md"Random Seed, $(@bind seed NumberField(0:1000, default=758))"

# ╔═╡ cb6482b5-c003-4ad2-8d8b-a60f3946b255
md"Power to raise the distance to control point... $(@bind power NumberField(0:1000; default=3))"

# ╔═╡ 9a877efd-b3cc-4d7e-ae9a-89d2e8a53356
md"Topography superposed with vegetation looks like this"

# ╔═╡ 08c8c238-8a24-4743-aed5-0e2649758b61
md"### Slopes"

# ╔═╡ 81653527-a1fb-49ab-99db-5fdda6b669fd
md"""exploration radius, `e_radius = ` $(@bind e_radius NumberField(0:1000; default=3))"""

# ╔═╡ c8171ca3-c2d7-4220-b073-1ec76f559b25
md"""
The taylor series expansion of $f(x+h)$ at $h=0$ is,

$$f(x+h)=\frac{1}{24} h^4 f^{(4)}(x)+\frac{1}{6} h^3 f^{(3)}(x)+\frac{1}{2} h^2 f''(x)+h f'(x)+f(x)+O\left(h^5\right)$$

We can calculate the slope, $f'(x)$, at $x$ in the following manner,

$$\frac{f(x+h)-f(x-h)}{2 h} = f'(x)+\frac{1}{6} h^2 f^{(3)}(x)+O\left(h^4\right)$$

This is accurate with an error term $\propto h^2$. To improve, we use a neighbourhood of radius 2. That is, we use the fact that,

$$\frac{f(x+2h)-f(x-2 h)}{4 h}=f'(x)+\frac{2}{3} h^2 f^{(3)}(x)+O\left(h^4\right)$$

Like so,

$$\frac{1}{3} \left(4\cdot\frac{f(x+h)-f(x-h)}{2 h}-\frac{f(x+2h)-f(x-2 h)}{4 h}\right)=f'(x)-\frac{1}{30} h^4 f^{(5)}(x)+O\left(h^5\right)$$
"""

# ╔═╡ 15f17206-db9f-4896-9e32-93d025501917
begin
	function slope_kernel(A, Bx, By, m, n)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if 3 <= i <= m-2 && 3 <= j <= n-2
			# caluclate second order approximation of differential
			xph = A[i+1,j]
			xmh = A[i-1,j]
			xp2h = A[i+2,j]
			xm2h = A[i-2,j]

			yph = A[i,j+1]
			ymh = A[i,j-1]
			yp2h = A[i,j+2]
			ym2h = A[i,j-2]

			dfbydx = 1/3*(4*(xph-xmh)/2 - (xp2h-xm2h)/4)
			dfbydy = 1/3*(4*(yph-ymh)/2 - (yp2h-ym2h)/4)

			# B[i, j] = atan(dfbydy, dfbydx)
			norm = (dfbydx^2+dfbydy^2)^0.5
			Bx[j, i] = dfbydy/norm
			By[j, i] = dfbydx/norm
		elseif 2 <= i <= m-1 && 2 <= j <= n-1
			xph = A[i+1,j]
			xmh = A[i-1,j]

			yph = A[i,j+1]
			ymh = A[i,j-1]

			dfbydx = (xph-xmh)/2
			dfbydy = (yph-ymh)/2

			# B[j, i] = atan(dfbydy, dfbydx)
			norm = (dfbydx^2+dfbydy^2)^0.5
			Bx[j, i] = dfbydy/norm
			By[j, i] = dfbydx/norm
		elseif 1 <= i <= m && 1 <= j <= n
			Bx[j, i] = 0.0
			By[j, i] = 0.0
		end
		return
	end
	
	function slope_gpu(topo)
	    m, n = size(topo)
		# println("Sizes of m, n = ", m, " ",n)
		topo_gpu = CuArray(topo)
		outp = fill((0.0, 0.0), n, n)
	    output_x = CuArray(fill(0.0, n, n))  
	    output_y = CuArray(fill(0.0, n, n))  
		if !isnothing(max_threads) 
			if !isnothing(m)
				threads_x = min(max_threads, m)
			else
				threads_x = max_threads
			end
		else
			if !isnothing(m)
				threads_x = min(32, m)
			else
				threads_x = 26
			end
		end
		if !isnothing(max_threads) 
			if !isnothing(n)
				threads_y = min(max_threads, n)
			else
				threads_y = max_threads
			end
		else
			if !isnothing(n)
				threads_y = min(32, n)
			else
				threads_y = 26
			end
		end
		# threads_x = min(max_threads, m)  # Limit to max_threads threads in the x dimension
	 #    threads_y = min(max_threads, n)  # Limit to max_threads threads in the y dimension
	    blocks_x = ceil(Int, m / threads_x)
	    blocks_y = ceil(Int, n / threads_y)
		
	 	@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) slope_kernel(topo_gpu, output_x, output_y, m, n)
	    
	    return collect(output_x), collect(output_y)
	end
	md"Kernel and method to generate topography slopes using central differences"
end

# ╔═╡ 73014c35-ab99-47e2-bfcb-9076c0720bdf
md"## Enemies & Hill Climb... racing?"

# ╔═╡ daf19ff1-0012-4b12-b61f-1d9517178bf5
md"Let's first see how we can make our model realistically traverse the topography.

Since it's unlikely that a troop can climb any slope, we will try to make them move in the direction with the max feasible slope.

Let's deal with the following question: Should they do a random walk or should there be an \"ulterior\" motive? Time to explore!

What will a random walk look like?"

# ╔═╡ 5b8de4a5-f6d7-407a-8709-4e0d392e21b9
md"Set climbable slope to... $(@bind max_slope NumberField(1:10, default=7))%"

# ╔═╡ e9055da6-3c24-4fe9-919c-1040916c79c3
md"Let there be... $(@bind n_enem NumberField(1:10, default=5)) enemy clusters"

# ╔═╡ 477ae165-07d6-4a64-8ce4-8c4b4c25011e
begin
	function neighbourhoods(radius, inc=0)
		n = []
		for r in 0:radius
			for i in 0:r
				if (inc!==0 || r !== 0)
					if(i !== 0) 
						push!(n, [-i, abs(r-i)], [i, abs(r-i)])
						if (r-i !== 0) 
							push!(n, [-i, -abs(r-i)], [i, -abs(r-i)])
						end
					else
						push!(n, [i, abs(r-i)])					
						if (r-i !== 0) 
							push!(n, [i, -abs(r-i)])
						end
					end
				end
			end					
		end
		return n
	end
	md"Definition of neighbourhood function that returns a von neumann neighbourhood set"
end

# ╔═╡ 1add5389-3a8b-40b7-b999-8df22bb45900
begin
	function topo_bush_kernel(topo, bushes, out, m, n)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if 1 <= i <= m && 1 <= j <= n
			out[i, j] = topo[i,j] + bushes[i,j]*1
		end
		return
	end
	
	function topo_bush_gpu(topo, bushes, enemies=nothing, preceedEnemies=false)
	    m, n = size(topo)
		# println(topo)
		# println("Sizes of m, n = ", m, " ",n)
		# println(min(max_threads, m))
		topo_gpu = CuArray(topo)
		bushes_gpu = CuArray(bushes)
		output_gpu = similar(topo_gpu)
		if !isnothing(max_threads) 
			if !isnothing(m)
				threads_x = min(max_threads, m)
			else
				threads_x = max_threads
			end
		else
			if !isnothing(m)
				threads_x = min(32, m)
			else
				threads_x = 26
			end
		end
		if !isnothing(max_threads) 
			if !isnothing(n)
				threads_y = min(max_threads, n)
			else
				threads_y = max_threads
			end
		else
			if !isnothing(n)
				threads_y = min(32, n)
			else
				threads_y = 26
			end
		end
	    blocks_x = ceil(Int, m / threads_x)
	    blocks_y = ceil(Int, n / threads_y)
		
	 	@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) topo_bush_kernel(topo_gpu, bushes_gpu, output_gpu, m, n)

		output = collect(output_gpu)

		if !isnothing(enemies) && preceedEnemies
			enemies_m, _ = size(enemies)
			for e in 1:enemies_m
				for nh in neighbourhoods(enemies[e, 3] * Int(n/L), 1)
					Y, X = enemies[e, 1] * Int(n/L) + nh[1], enemies[e, 2] * Int(n/L) + nh[2]
					output[X, Y] = topo[X, Y]
				end
			end
		end
	    
	    return output
	end
	md"Kernel and GPU handler for superposing bushes onto the topography"
end

# ╔═╡ 86078a29-e2a6-470b-8757-b2efe2bf9eb8
md"Let's attempt to plot the enemies just like how we plotted bushes"

# ╔═╡ c0bc8f94-9636-461a-9b34-fe0ccfefcb69
md"That doesn't look so great now, does it?

Let's plot the agents along with the bushes in a more beautiful manner. Green represents bushes and white for enemies."

# ╔═╡ 924c9d77-af8c-44b7-9053-b48aae4ad475
ENV["JULIA_CUDA_DEBUG"] = "2"

# ╔═╡ 9f30ffe2-6546-480b-a89d-0f557469e82d
begin
	function color_kernel(colors_A_gpu, alt_p_gpu, A_gpu, enemiesInA_gpu, m, n, max_height, power)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if 1 <= i <= m && 1 <= j <= n
			colors_A_gpu[i, j]=0.0
			alt_ps_m, _ = size(alt_p_gpu)
			norm = 0
	
			if (enemiesInA_gpu[j, i]!=0)
				colors_A_gpu[i, j] = max_height+10
			elseif(A_gpu[i, j]!=0)
				colors_A_gpu[i, j] = -10
			else
				flag = 1
				for k in 1:alt_ps_m
					d = ((alt_p_gpu[k, 2] - i)^2 + (alt_p_gpu[k, 1] - j)^2)^0.5
					if (d > 0 && flag==1)
						colors_A_gpu[i, j] += alt_p_gpu[k, 3]/d^power
						norm += 1/d^power
					else
						colors_A_gpu[i, j] = alt_p_gpu[k, 3]
						flag = 0
					end
				end
				if(flag==1)
					colors_A_gpu[i, j] /= norm
				end
			end
		end
		return
	end
				
	function color_gpu(alt_p, A, enemiesInA, max_height, power)
		m, n = size(A)
		alt_p_gpu = CuArray(alt_p)
		A_gpu = CuArray(A)
		colors_A_gpu = similar(A_gpu)
		enemiesInA_gpu = CuArray(enemiesInA)  
		
		threads_x = min(max_threads, m)  # Limit to max_threads threads in the x dimension
		threads_y = min(max_threads, n)  # Limit to max_threads threads in the y dimension
		blocks_x = ceil(Int, m / threads_x)
		blocks_y = ceil(Int, n / threads_y)
		
		@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) color_kernel(colors_A_gpu, alt_p_gpu, A_gpu, enemiesInA_gpu, m, n, max_height, power)
		
		return collect(colors_A_gpu)
	end
end

# ╔═╡ a077d240-36e0-41cd-a4ff-f7e0ca62ca4e
md"Let's follow a \"gradient ascend\" method where the clusters just follow the direction with maximum ascend in hopes of reaching the peak."

# ╔═╡ 2fe91b37-1c3f-49ce-bfa2-702a180b78a0
begin
	md"``X``, ``Y`` subdivisions, ``n`` = $(@bind n NumberField(0:100; default=100))"
end

# ╔═╡ 4167489e-715b-4e62-8e56-3f2cd1317ccd
begin
	Random.seed!(seed)
	# sample code for a 2D array
	A_L = rand(Float64, L, L) .< (b_density/100)
	A = zeros(n, n)

	for i in 1:n
		for j in 1:n
			A[i, j] = A_L[Int(ceil(i/(n/L))), Int(ceil(j/(n/L)))]
		end
	end
	
	
	# use show_image to display the array A
	show_image(A)
	# show_image(A, [(0,0,0), (1,1,1)])
end

# ╔═╡ 0f344406-4816-4cd6-ae8e-83a8b918fa11
function next_pos(current_pos, B, seed)
	i, j=current_pos

	# # println("Seed = $seed")
	# # Random.seed!(seed)
	# d = rand(-1:1, (2,1))
	
	# println("d=$d")
	# println("x0, y0= $(current_pos)")
	# x, y=current_pos+d
	# println("Old x, y= $([x, y])")

	neighbors = [[-1,0],[0-1,0+1],[0,0+1],[0+1,0+1],[0+1,0],[0+1,0-1],[0,0-1],[0-1,0-1]]
	direction = [0, 0]
	for neighbor in neighbors
		i_n, j_n = neighbor
		direction += B[i_n+i, j_n+j]*[i_n, j_n]
	end
	# println("Direction=$direction")
	direction[1] = sign(direction[1])*ceil(abs(direction[1])/8)
	direction[2] = sign(direction[2])*ceil(abs(direction[2])/8)
	direction = 1 * direction
	# println("NormDirection=$direction")

	if direction[1]==0 && direction[2]==0
		direction = rand(-1:1, (2,1))
	end
	
	x = i + direction[1]
	y = j + direction[2]
	
	x=min(max(x, 1), n)
	y=min(max(y, 1), n)
	# println("New x, y= $([x, y])")
	return [x, y]
end

# ╔═╡ 0f0779fa-d610-429f-acd3-ac82b7842b14
begin
	Random.seed!(seed)
	alt_pos = rand(1:n, (altPs,2));
	alt_h = rand(Float64, (altPs,1))*max_height;
	hcat(alt_pos, alt_h)
	alt_p = hcat(alt_pos, alt_h);
	md"Generating random control points..."
end

# ╔═╡ b1538261-175d-4892-ab3d-2963f239b8df
alt_p

# ╔═╡ 8532f267-7e5f-45bb-8d82-6f86cfff7cc4
begin
	topo = zeros(Float64, n, n);
	topo = topography_gpu(topo, alt_p, power)
	md"Let's define the topography using the control points"
	# plotly()
	# show_image(topo, :grays)
end

# ╔═╡ be20aaf3-473e-4be5-adcc-3db9eb3de213
begin
	Random.seed!(seed)
	enem_pos = rand(1:L, (n_enem,2));
	enem_z = [topo[row[1], row[2]] for row in eachrow(enem_pos)]
	# enem_T = fill(1.0, (n_enem,1)) # temperature
	enem_r = rand(1:3, (n_enem,1));
	enemies = hcat(enem_pos, enem_r);
	md"Generating random enemy clusters. The look like so..."
end

# ╔═╡ cb0bb5cd-a02b-457d-b47a-be623e8d50ed
enemies

# ╔═╡ 1036ebbb-a16e-4674-b786-9aa9325b0090
enemies

# ╔═╡ 12351738-ddd3-4051-8880-504ecff343af
begin
	plotly()
	Plots.plot(1:n, 1:n,topo, st=:surface, ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n],xlabel="X", ylabel="Y", zlabel="Z")
end

# ╔═╡ 3750d105-df07-4af7-9143-82b065fbb041
begin
	plotly()
	Plots.contour(1:n, 1:n,topo, levels=60, xlim=[0,n], ylim=[0,n], ratio=1, fill=true)
end

# ╔═╡ 230af3ed-9267-497c-a697-e422bcf04665
begin
	dx, dy = slope_gpu(topo);
	
	slope = [(dx[i, j], dy[i, j]) for i in 1:n, j in 1:n];
	md"Calculating the slope with a double central difference method"
end

# ╔═╡ c2a9fa1f-a405-4767-aec2-42196a70cc61
begin
	using DelimitedFiles;
	writedlm("slope.txt", slope);
	md"Let's write the slopes into a txt file for debugging"
end

# ╔═╡ 8a586d49-86c9-4f7f-b438-15ba8181ed2c
begin
	x_coordinates = [el[1] for el in slope];
	y_coordinates = [el[2] for el in slope];
	quiver(transpose(repeat(reshape(1:n, 1, n), n, 1)),repeat(reshape(1:n, 1, n), n, 1), quiver=( x_coordinates, y_coordinates), arrow_size=1,ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n])
end

# ╔═╡ a22d6084-18ed-4f71-886d-2ffc40ce599f
begin
	function gen_e_in_A(enemies, n, L)
		enemiesInA = zeros(n, n)
		r = Int(n/L)
		for e in 1:size(enemies)[1]
			for nh in neighbourhoods(enemies[e, 3] * r, 1)
				enemiesInA[
					enemies[e, 1] * r + nh[1], 
					enemies[e, 2] * r + nh[2]						
				] = 1
			end
		end
		return enemiesInA
	end
	
	function gen_a_in_A(agents, n, L)
		agentsInA = zeros(n, n)
		r = Int(n/L)
		# for a in 1:size(agents)[1]
		# 	for ii in 1: r
		# 		for jj in 1:r
		# 			if(1<= (agents[a, 2]-1)*r+ii <= n) && (1<= (agents[a, 3]-1)*r+jj <= n)
		# 				agentsInA[(agents[a, 2]-1)*r+ii, (agents[a, 3]-1)*r+jj] = 1
		# 			end
		# 		end
		# 	end
		# end
		for a in 1:size(agents)[1]
			for nh in neighbourhoods(1 * r, 1)
				if (1<=(agents[a, 2] * r + nh[1])<= n)&&(1<=(agents[a, 3] * r + nh[2])<= n)
					agentsInA[
						agents[a, 2] * r + nh[1], 
						agents[a, 3] * r + nh[2]						
					] = 1
				end
			end
		end
		return agentsInA
	end
	enemiesInA = gen_e_in_A(enemies, n, L)
	
	function color(i, j, alt_ps, A, enemiesInA)
		z=0.0
		m, _ = size(alt_ps)
		norm = 0
		
		if (enemiesInA[i, j]!=0)
			return max_height+10
		elseif(A[j, i]!=0)
			return -10
		end
		for k in 1:m
			d = ((alt_ps[k, 1] - i)^2 + (alt_ps[k, 2] - j)^2)^0.5
			if (d > 0)
				z += alt_ps[k, 3]/d^power
				norm += 1/d^power
			else
				z = alt_ps[k, 3]
				return z
			end
		end
		z /= norm
		# println(typeof(z))
		return z
	end

	min_v = 10/(max_height+20)
	max_v = (max_height+10)/(max_height+20)
	custom_colorscale = [
	    (0.00, "#3bff00"),  # Green
	    (min_v - 0.000000001, "#3bff00"),  # Green
	    (min_v, "#222224"),  # Blue
	    (min_v + 1*(max_v-min_v)/5, "#3E2163"),  # Blue
		(min_v + 2*(max_v-min_v)/5, "#88236A"),# Yellow
		(min_v + 3*(max_v-min_v)/5, "#D04544"),# Yellow
		(min_v + 4*(max_v-min_v)/5, "#F78D1E"),# Yellow
		(max_v - 0.000000001, "#F1E760"),# Yellow
	    (max_v, "#ffffff"),  # Blue
	    (1.00, "#ffffff"),  # Blue
	]

	function colors_alias(x, y)
		return color(x, y, alt_p, A, enemiesInA)
	end
	
	x = 1:n
	y = 1:n
	
	# Plots.surface(x = x, y = y, topo_bush_gpu(topo, A, enemies, true), colorscale=custom_colorscale, surfacecolor = colors_alias.(x', y), ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n], xlabel="X", ylabel="Y", zlabel="Z", showscale=false)
end

# ╔═╡ 84bc9a37-dce3-40cf-85ae-b9107339aabe
	Plots.contour(1:n, 1:n,topo, levels=60, ratio=1, xlim=[0,n], ylim=[0,n], fill=true, showscale=false)

# ╔═╡ 6f603c0b-b852-473f-9099-b6292ad395b9
enemies

# ╔═╡ 076eb88e-fa80-40a0-9873-74329bf9b5a5
md"Clock $(@bind t2 Clock())"

# ╔═╡ 18b40b26-9338-4616-8deb-1a5c9a6a7ae8
md"## Python for Agents"

# ╔═╡ 1cd0c84c-2cca-4251-b718-822477ab0b31
md"Let's import python things"

# ╔═╡ 5d68976b-cacd-4ac5-88e2-b669e2a29490
begin
	py"""
import numpy as np

class Agent:
    def __init__(self, unique_id, x, y, view_sight, gather_sight, env_len, type_='agent'):
        self.unique_id = unique_id
        self.x = x                        # new position
        self.y = y                        # new position
        self.prevx = -1                   # one step previous position
        self.prevy = -1
        #self.pattern_index = 0
        # self.type_ = type_
        self.view = view_sight
        self.gather = gather_sight
        self.env = None
        self.env_len = env_len            # length of environment 
        self.new_enemy = list()    # for newly discovered enemies
        self.enemies_seen = list()        # for already analized enemies
        self.bushes = list()
        self.target = 0      # flag is agent is following an enemy
        self.target_id = None  # id of the enemy follwing
        self.target_dist = -1 
        self.target_x = ""
        self.target_y = ""
        self.move_x = ""
        self.move_y = ""
        self.enemy_end_1 = None
        self.enemy_end_2 = None

    def deterministic_search(self): # "deterministic" movement of the agents 
        # eight possible moves are there
        possible_moves = [
            (self.x + 1, self.y), # movement to the right
            (self.x + 1, self.y + 1),
            (self.x + 1, self.y - 1),
            (self.x - 1, self.y), # movement to the left
            (self.x - 1, self.y + 1),
            (self.x - 1, self.y - 1),
            (self.x, self.y + 1), # movement in y-direction only
            (self.x, self.y - 1)
        ]

        # Filter out moves that go outside the environment boundaries
        valid_moves = [
            (x, y) for x, y in possible_moves[self.pattern_index] if 0 <= x < self.env_len and 0 <= y < self.env_len
        ]

        # Filter out moves that correspond to bushes (since bushes are not in enemy camps)
        valid_moves = [move for move in valid_moves if move not in self.bushes]

        # Update the pattern index for the next move
        self.pattern_index = (self.pattern_index + 1) % len(possible_moves)

        # Return the first valid move if any, otherwise stay in the current position
        return valid_moves[0] if valid_moves else (self.x, self.y)

    def random_search(self, seed): # "stochastic" movement of the agents 
        # eight possible moves are there
        np.random.seed(seed=seed)
        possible_moves = [
            (self.x + 1, self.y), # movement to the right
            (self.x + 1, self.y + 1),
            (self.x + 1, self.y - 1),
            (self.x - 1, self.y), # movement to the left
            (self.x - 1, self.y + 1),
            (self.x - 1, self.y - 1),
            (self.x, self.y + 1), # movement in y-direction only
            (self.x, self.y - 1)
        ]

        # Filter out moves that go outside the environment boundaries
        valid_moves = [(x, y) for x, y in possible_moves if 0 <= x < self.env_len and 0 <= y < self.env_len]

        # Filter out moves that correspond to bushes (since bushes are not in enemy-camps)
        valid_moves = [move for move in valid_moves if move not in self.bushes]

        lmoves = len(valid_moves)
        if lmoves == 0:
            return (self.x, self.y)
        
        rng = np.random.randint(0, lmoves)
        return valid_moves[rng]
    
    def get_enemy_cells(self):
        id = self.target_id
        cell_list = []
        topy = max(0, self.y-self.view)
        boty = min(self.env_len, self.y+self.view+1)
        leftx = max(0, self.x-self.view)
        rightx = min(self.env_len, self.x+self.view+1)
        for i in range(topy, boty):
            for j in range(leftx, rightx):
                if (self.env[i][j][0] == 2): # change with enemy label in environment
                    if self.env[i][j][1] == id:
                        cell_list.append((i, j))
        return cell_list
    
    def get_enemy_distance(self, cell_list):
        min_dist = 4*self.view
        min_eu_dist = 4*self.view
        ei, ej = -1, -1
        for loc in cell_list:
            (i, j) = loc
            min_eu_dist = min(min_eu_dist, abs(self.x-i)+abs(self.y-j))
            if min_dist > max(abs(self.x-i), abs(self.y-j) ):
                min_dist =  max(abs(self.x-i), abs(self.y-j) )
                ei, ej = i,j
        return [min_dist, min_eu_dist, ei,ej]
    
    def is_in_limit(self, pos, radius):
        (pi, pj) = pos
        if(pi<self.x-radius or pi>self.x+radius or pj<self.y-radius or pj>self.y+radius):
            return False
        return True

    def approach_direction(self, pos, move_x, move_y):
        (pi, pj) = pos
        # should also add another condition to check if the bush is adjacent to an enemy
        # better to avoid such bush

        if (move_x=="" and move_y=="up"):
            if (pj>self.y):
                return True
            else:
                return False
        if (move_x=="" and move_y=="down"):
            if (pj<self.y):
                return True
            else:
                return False
        if (move_x=="right" and move_y==""):
            if (pi>self.x):
                return True
            else:
                return False
        if (move_x=="left" and move_y==""):
            if (pi<self.x):
                return True
            else:
                return False
        if (move_x=="right" and move_y=="up"):
            if (pi>=self.x and pj>=self.y):
                return True
            else:
                return False
        if (move_x=="right" and move_y=="down"):
            if (pi>=self.x and pj<=self.y):
                return True
            else:
                return False
        if (move_x=="left" and move_y=="up"):
            if (pi<=self.x and pj>=self.y):
                return True
            else:
                return False
        if (move_x=="left" and move_y=="down"):
            if (pi<=self.x and pj<=self.y):
                return True
            else:
                return False
        return False
    
    def set_corner(self, ci, cj, label):
        if(label==1):
            if(self.enemy_end_1 is not None):
                if(abs(self.y-cj)<abs(self.y-self.enemy_end_1[1])):
                    self.enemy_end_1 = (ci, cj)
            else:
                self.enemy_end_1 = (ci, cj)
        else:
            if(self.enemy_end_2 is not None):
                if(abs(self.x-ci)<abs(self.x-self.enemy_end_1[0])):
                    self.enemy_end_2 = (ci, cj)
            else:
                self.enemy_end_2 = (ci, cj)
   
    def check_corner(self):
        for i in range(max(0,self.x-self.gather), min(self.env_len, self.x+self.gather+1)):
            for j in range(max(0,self.y-self.gather), min(self.env_len, self.y+self.gather+1)):
                if self.env[i][j][0] == 2 and self.env[i][j][1] == self.target_id:
                    if(i==0 or i==self.env_len-1):
                        self.set_corner(i,j,label=2)
                    if(j==0 or j==self.env_len-1):
                        self.set_corner(i,j,label=1)
                    if(self.env[i-1][j][0] != 2 and self.env[i+1][j][0] != 2 and self.env[i][j-1][0] != 2):
                        self.set_corner(i,j,label=1)
                    if(self.env[i-1][j][0] != 2 and self.env[i+1][j][0] != 2 and self.env[i][j+1][0] != 2):
                        self.set_corner(i,j,label=1)
                    if(self.env[i-1][j][0] != 2 and self.env[i][j-1][0] != 2 and self.env[i][j+1][0] != 2):
                        self.set_corner(i,j,label=2)
                    if(self.env[i+1][j][0] != 2 and self.env[i][j+1][0] != 2 and self.env[i][j-1][0] != 2):
                        self.set_corner(i,j,label=2)

    def strategic_search(self, seed):
        # Implement a search function when the enemy is detected
        # Will implement the spliting of the searching agent team
        # may need to define a new class
        np.random.seed(seed=seed)
        cell_list = self.get_enemy_cells()
        [dist, eu_dist, ei, ej] = self.get_enemy_distance(cell_list)
        if(dist > self.gather):
            if(ei >= self.x):
                self.target_x = "right"
                if(ei > self.x+self.gather):
                    self.move_x = "right"
                else:
                    self.move_x = ""
            else:
                self.target_x = "left"
                if(ei < self.x-self.gather):
                    self.move_x = "left"
                else:
                    self.move_x = ""
            if(ej >= self.y):
                self.target_y = "up"
                if(ej > self.y+self.gather):
                    self.move_y = "up"
                else:
                    self.move_y = ""
            else:
                self.target_y = "down"
                if(ej < self.y-self.gather):
                    self.move_y = "down"
                else:
                    self.move_y = ""
            bush_around = [bush for bush in self.bushes if self.is_in_limit(bush, self.gather)]
            eff_bushes = [bush for bush in bush_around if self.approach_direction(bush, self.move_x, self.move_y)]
            self.target_dist = -1

            lbush = len(eff_bushes)
            if lbush == 0:
                if len(bush_around) == 0:
                    return (self.x, self.y)
                else:
                    laround = len(bush_around)
                    rng = np.random.randint(0, laround)
                    return bush_around[rng]
            rng = np.random.randint(0, lbush)
            return eff_bushes[rng]
        
        else:
            self.check_corner()    
            if(self.enemy_end_1 is not None and self.enemy_end_2 is not None):
                return (self.x, self.y)
            if(self.enemy_end_1 is not None):
                self.move_x = self.invert(self.target_x)
                self.move_y = self.target_y
                if(self.move_x == "left"):
                    x_pseudo = self.x + eu_dist - self.gather
                else:
                    x_pseudo = self.x - eu_dist + self.gather
                reg = self.get_region(self.x, self.y, self.move_x, self.move_y, label = 1) # to be implemented
                bush_around = [bush for bush in self.bushes if self.is_in_limit(bush, self.gather)]
                eff_bushes = [bush for bush in bush_around if self.approach_direction(bush, self.target_x, self.target_y)]
                opt_bushes = [bush for bush in bush_around if self.opt_region(bush, x_pseudo, self.y, self.move_x, self.move_y, label = 1)]
                lbush = len(opt_bushes)
                if lbush == 0:
                    if len(eff_bushes) == 0:
                        return (self.x, self.y)
                    else:
                        laround = len(eff_bushes)
                        rng = np.random.randint(0, laround)
                        return eff_bushes[rng]
                rng = np.random.randint(0, lbush)
                return opt_bushes[rng]
    
            else:
                self.move_x = self.target_x
                self.move_y = self.invert(self.target_y)
             
                if(self.move_x == "left"):
                    x_pseudo = self.x + eu_dist - self.gather
                else:
                    x_pseudo = self.x - eu_dist + self.gather
                reg = self.get_region(self.x, self.y, self.move_x, self.move_y, label = 2)
                bush_around = [bush for bush in self.bushes if self.is_in_limit(bush, self.gather)]
                eff_bushes = [bush for bush in bush_around if self.approach_direction(bush, self.move_x, self.move_y)]
                opt_bushes = [bush for bush in bush_around if (bush in reg)]
                lbush = len(opt_bushes)
                if lbush == 0:
                    if len(eff_bushes) == 0:
                        return (self.x, self.y)
                    else:
                        laround = len(eff_bushes)
                        rng = np.random.randint(0, laround)
                        return eff_bushes[rng]
                rng = np.random.randint(0, lbush)
                return opt_bushes[rng]

    def get_region(self, x, y, move_x, move_y, label):
        if (label==2):
            if(move_x=="left"):
                x_new = x - self.gather
            else:
                x_new = x + self.gather
            if(move_y=="up"):
                y_new = y + self.gather
            else:
                y_new = y - self.gather
            return self.get_region(x_new, y_new, self.invert(move_x), self.invert(move_y), 1)
        else:
            pass
    
    def opt_region(self, pos, x, y, move_x, move_y, label):
        if (label==2):
            if(move_x=="left"):
                x_new = x - self.gather
            else:
                x_new = x + self.gather
            if(move_y=="up"):
                y_new = y + self.gather
            else:
                y_new = y - self.gather
            return self.opt_region(pos, x_new, y_new, self.invert(move_x), self.invert(move_y), 1)
        else:
            (pi, pj) = pos
            if(move_y == "up" and (pj<y or pj>y+self.gather)):
                return False
            if(move_y == "down" and (pj>y or pj<y-self.gather)):
                return False
            if(move_x == "left" and (pi < x - abs(y-pj))):
                return False
            if(move_x == "right" and (pi > x + abs(y-pj))):
                return False
            return True

    def invert(self, direction):
        if direction == "up":
            return "down"
        if direction == "down":
            return "up"
        if direction == "left":
            return "right"
        if direction == "right":
            return "left"

    def find_bushes(self):
        # Can be used to make a list of bushes using the information from self.surr_field
        # assume self.surr_field is 2D grid slice of environment surrounding the agent
        bushes = list()
        topy = max(0, self.y-self.view)
        boty = min(self.env_len, self.y+self.view+1)
        leftx = max(0, self.x-self.view)
        rightx = min(self.env_len, self.x+self.view+1)
        for i in range(topy, boty):
            for j in range(leftx, rightx):
                if self.env[i][j][0] == 1: # change with bush label in environment
                    bushes.append((j, i))
        self.bushes = bushes

    def find_enemy(self, seen_enemies = []):
        # Can be used to make a list of nearby enemy cell
        # identify enemy ids also distinguish between new enemy and already analyzed enemy
        # return a list of nested tuples or empty list if there are no enemies
        # update enemies_seen list to keep track of them
        # if new enemy is found update new_enemy
        topy = max(0, self.y-self.view)
        boty = min(self.env_len, self.y+self.view+1)
        leftx = max(0, self.x-self.view)
        rightx = min(self.env_len, self.x+self.view+1)
        for i in range(topy, boty):
            for j in range(leftx, rightx):
                if (self.env[i][j][0] == 2): # change with enemy label in environment
                    if self.env[i][j][1] in seen_enemies:
                        self.enemies_seen.append((i, j)) # mark as seen
                    else:    
                        self.new_enemy.append((i, j)) # set new discovered enemy
        
        if(self.new_enemy):
            self.target = 1
            (ei, ej) = self.new_enemy[0]
            self.target_id = self.env[ei][ej][1]
            if(ei >= self.x):
                self.target_x = "right"
                self.move_x = "right"
            else:
                self.target_x = "left"
                self.move_x = "left"
            if(ej >= self.y):
                self.target_y = "up"
                self.move_y = "up"
            else:
                self.target_y = "down"
                self.move_y = "down"

    def scan_surrounding(self, env):
        # Implement function to get surrounging information from the Environemt Class for bushes and enemies present
        self.env = env
        # need to set self.surr_field as a slice of the total environment
        # centered around current cell
   
    def check_camp(self):
        if (self.enemy_end_1 is None) or (self.enemy_end_2 is None):
            return [-1, -1]
      
        if abs(self.enemy_end_1[0] - self.enemy_end_2[0]) == abs(self.enemy_end_1[1]-self.enemy_end_2[1]):
            #return the center of the rhombus
            if (self.enemy_end_1[0]-self.prevx)**2 +(self.enemy_end_2[1]-self.prevy)**2 >  (self.enemy_end_2[0]-self.prevx)**2 +(self.enemy_end_1[1]-self.prevy)**2:
                center_pos = (self.enemy_end_1[0],self.enemy_end_2[1])# or (self.enemy_end_2[0],self.enemy_end_1[1])
            else:
                center_pos = (self.enemy_end_2[0],self.enemy_end_1[1])
            enemy_size = abs(self.enemy_end_1[0] - center_pos[0]) + abs(self.enemy_end_1[1] - center_pos[1])
            return [center_pos, enemy_size]
        else: 
            if (self.enemy_end_1[0]-self.x)**2 +(self.enemy_end_1[1]-self.y)**2 < (self.enemy_end_2[0]-self.x)**2 +(self.enemy_end_2[1]-self.y)**2:
                self.enemy_end_2 = None
            else:
                self.enemy_end_1 = None
            return [-1, -1]  
    
    def print_pos(self):
      return "Agent {} is at ({}, {})".format(self.unique_id, self.x, self.y)

    def XY(self):
      return self.unique_id, self.x, self.y
"""
	Agent = py"Agent"
end

# ╔═╡ b2c0fadb-1471-4fdc-a56c-e1313cbd3b58
md"Step function that controls the agents"

# ╔═╡ 56209a97-52d3-4d15-8e12-ea9fff19a1b1
begin
	py"""
import numpy as np
def step(agents, grid, seen_enemies, seed):
    new_agents_positions = [] 
    for agent in agents:
        # If you have an Environment class, uncomment the line below
        # surrounding_info = self.environment.get_surrounding_info(agent.x, agent.y, agent)

        agent.scan_surrounding(grid)

        bushes = agent.find_bushes()# list of bush coordinates in field of vision
        agent.find_enemy(seen_enemies=seen_enemies)
        enemies = agent.new_enemy # list of enemy coordinates in current field of vision

        if len(enemies):
            # new_position = agent.random_search(seed)
            # new_position = [agent.x + np.random.randint(-1, 2), agent.y + np.random.randint(-1, 2)]
            if(agent.target != 1):
                enemy_pos = [enemies[0][0], enemies[0][1]]
                print('New enemy detected at ({},{}). Performing Strategic Search'.format(enemy_pos[0],enemy_pos[1]))
                agent.target = 1
                agent.target_id = grid[enemies[0][0]][enemies[0][0]][1]  # to be changes according to environment code
            elif(agent.target_id not in seen_enemies):
                new_position = agent.strategic_search(seed)
                [enemy_center_pos, enemy_size] = agent.check_camp()
                if enemy_center_pos==-1:
                    new_position = agent.strategic_search(seed)
                else:
                    seen_enemies.append(enemy_center_pos)  
                    print("Enemy discovered with centre at ({}, {}) and of the size of {}".format(enemy_center_pos[0], enemy_center_pos[1], enemy_size))
        else:
            new_position = agent.random_search(seed)
            # new_position = [agent.x + np.random.randint(-1, 2), agent.y + np.random.randint(-1, 2)]

        # new_position = [agent.x + np.random.randint(-1, 2), agent.y + np.random.randint(-1, 2)]
        # new_position = [agent.x, agent.y]
        
        agent.prevx, agent.prevy = agent.x, agent.y 
        agent.x, agent.y = new_position[0], new_position[1]
        new_agents_positions.append([agent.unique_id, agent.x, agent.y])
        #  self.env.move_agent(agent, new_position[0], new_position[1])
    return new_agents_positions, seen_enemies
"""
	step_agents = py"step"
end

# ╔═╡ 572f1062-de7d-4bf1-a506-cd7c644390c0
begin
	function topo_bush_python_kernel(topo, bushes, out, m, n, Lx, Ly, k)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if 1 <= i <= Lx && 1 <= j <= Ly
			if(bushes[i * k, j * k]==1)
				out[i, j] = (1, 0)
			else
				out[i, j] = (0, 0)
			end
		end
		return
	end
	
	function topo_bush_python_gpu(topo, bushes, enemies)
	    m, n = size(topo)
		# println(topo)
		# println("Sizes of m, n = ", m, " ",n)
		# println(min(max_threads, m))
		topo_gpu = CuArray(topo)
		bushes_gpu = CuArray(bushes)
		output_gpu = CuArray(fill((0, 0), (L, L)))
		if !isnothing(max_threads) 
			if !isnothing(m)
				threads_x = min(max_threads, m)
			else
				threads_x = max_threads
			end
		else
			if !isnothing(m)
				threads_x = min(32, m)
			else
				threads_x = 26
			end
		end
		if !isnothing(max_threads) 
			if !isnothing(n)
				threads_y = min(max_threads, n)
			else
				threads_y = max_threads
			end
		else
			if !isnothing(n)
				threads_y = min(32, n)
			else
				threads_y = 26
			end
		end
	    blocks_x = ceil(Int, m / threads_x)
	    blocks_y = ceil(Int, n / threads_y)
		
	 	@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) topo_bush_python_kernel(topo_gpu, bushes_gpu, output_gpu, m, n, L, L, Int(n/L))

		output = collect(output_gpu)

		enemies_m, _ = size(enemies)
		for e in 1:enemies_m
			for nh in neighbourhoods(enemies[e, 3] * Int(n/L), 1)
				X, Y = enemies[e, 1] + nh[1], enemies[e, 2] + nh[2]
				output[X, Y] = (2, e)
			end
		end
		
	    
	    return output
	end
	md"Kernel and GPU handler for creating grid that the agents.py functions take.
	Scheme:
		(0, 0) 		--> Nothing 
		(1, 0) 		--> Bush
		(2, enemy#) --> enemy (superseeds bushes)
	"
end

# ╔═╡ d9e8c8a8-9d42-471e-af65-c7a95aa43e24
begin
	function convert_to_nested_array(arr)
	    return [arr[i, :] for i in 1:size(arr, 1)]
	end
	
	# Create the nested array
	nestedArr = convert_to_nested_array(topo_bush_python_gpu(topo, A, enemies))
end

# ╔═╡ 4f42ab19-8862-4d74-a0a8-53baab8d0b42
topo_bush_python_gpu(topo, A, enemies);

# ╔═╡ 45261482-7c2a-4213-a62b-bbd70de2c704
md"## Agents"

# ╔═╡ 0b2788f8-d225-4cb9-bdfb-00616c09ab8e
md"Let there be... $(@bind n_agent NumberField(1:10, default=9)) agents"

# ╔═╡ 5b1f8188-8d08-4486-adf7-7624fdc7202e
begin
	function color_kernel2(colors_A_gpu, alt_p_gpu, A_gpu, enemiesInA_gpu, agentsInA_gpu, m, n, max_height, power)
		i = threadIdx().x + (blockIdx().x - 1) * blockDim().x
		j = threadIdx().y + (blockIdx().y - 1) * blockDim().y
		if 1 <= i <= m && 1 <= j <= n
			colors_A_gpu[i, j]=0.0
			alt_ps_m, _ = size(alt_p_gpu)
			norm = 0
	
			if (enemiesInA_gpu[j, i]!=0) # enemy
				colors_A_gpu[i, j] = max_height+6
			elseif(agentsInA_gpu[j, i]!=0) # agent
				colors_A_gpu[i, j] = max_height+20
			elseif(A_gpu[i, j]!=0) # bush
				colors_A_gpu[i, j] = -10
			else
				flag = 1
				for k in 1:alt_ps_m
					d = ((alt_p_gpu[k, 2] - i)^2 + (alt_p_gpu[k, 1] - j)^2)^0.5
					if (d > 0 && flag==1)
						colors_A_gpu[i, j] += alt_p_gpu[k, 3]/d^power
						norm += 1/d^power
					else
						colors_A_gpu[i, j] = alt_p_gpu[k, 3]
						flag = 0
					end
				end
				if(flag==1)
					colors_A_gpu[i, j] /= norm
				end
			end
		end
		return
	end
				
	function color_gpu2(alt_p, A, enemiesInA, agentsInA, max_height, power)
		m, n = size(A)
		alt_p_gpu = CuArray(alt_p)
		A_gpu = CuArray(A)
		colors_A_gpu = similar(A_gpu)
		enemiesInA_gpu = CuArray(enemiesInA)
		agentsInA_gpu = CuArray(agentsInA)
		
		threads_x = min(max_threads, m)  # Limit to max_threads threads in the x dimension
		threads_y = min(max_threads, n)  # Limit to max_threads threads in the y dimension
		blocks_x = ceil(Int, m / threads_x)
		blocks_y = ceil(Int, n / threads_y)
		
		@cuda threads=(threads_x, threads_y) blocks=(blocks_x, blocks_y) color_kernel2(colors_A_gpu, alt_p_gpu, A_gpu, enemiesInA_gpu, agentsInA_gpu, m, n, max_height, power)
		
		return collect(colors_A_gpu)
	end
end

# ╔═╡ 4fc38e89-e776-46af-9f5f-e005b997821b
# ╠═╡ skip_as_script = true
#=╠═╡
begin
	min_v2 = 10/(max_height+30)
	max_v2 = (max_height+10)/(max_height+30)
	bush_v2 = (max_height+20)/(max_height+30)
	custom_colorscale2 = [
		(0, "#3bff00"),  # Green
		(min_v2 - 0.000000001, "#3bff00"),  # Green
		(min_v2, "#222224"),  # Blue
		(min_v2 + 1*(max_v2-min_v2)/5, "#3E2163"),  # Blue
		(min_v2 + 2*(max_v2-min_v2)/5, "#88236A"),# Yellow
		(min_v2 + 3*(max_v2-min_v2)/5, "#D04544"),# Yellow
		(min_v2 + 4*(max_v2-min_v2)/5, "#F78D1E"),# Yellow
		(max_v2 - 0.000000001, "#F1E760"),# Yellow
		(max_v2, "#ffffff"),  # White
		(bush_v2, "#ffffff"),  # White
		(bush_v2+0.01, "#000000"),  # Black
		(1, "#000000"),  # Black
	]
end
  ╠═╡ =#

# ╔═╡ fd1d674b-9d98-45fe-9159-3a447ace6af6
#=╠═╡
[min_v2, max_v2, bush_v2]
  ╠═╡ =#

# ╔═╡ 2ba11cce-52b6-445a-aac5-7f45d5697376
function print_agents(agents_obj)
	m = size(agents_obj)[1]
	for i in 1:m
		println(agents_obj[i].XY())
	end
end	

# ╔═╡ d091b339-af6d-4118-ac00-f2679372e21d
# ╠═╡ show_logs = false
begin
	agent1 = Agent(unique_id=1, x=3, y=4, view_sight=3, gather_sight=2, env_len=10)
	agent2 = Agent(unique_id=2, x=6, y=9, view_sight=3, gather_sight=2, env_len=10)
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
	println(step_agents([agent1, agent2], topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents([agent1, agent2])
end

# ╔═╡ 8a9cf010-3fad-4318-bb6c-97602cb040aa
agent1.XY()

# ╔═╡ db9c87a1-2af5-49b6-9335-3e15069e8822
# ╠═╡ show_logs = false
begin
	Random.seed!(seed+100)
	agent_pos = rand(1:L, (n_agent,2));
	# agent_pos = fill(25, (n_agent,2));
	agents = fill(0, (n_agent, 3))
	agent_objs = []

	for i in 1:n_agent
		agents[i, 1], agents[i, 2], agents[i, 3] = i, agent_pos[i, 1], agent_pos[i, 2];
		print(agents[i, [1, 2, 3]])
		push!(agent_objs, Agent(unique_id=agents[i, 1], x=agents[i, 2], y=agents[i, 3], view_sight=11, gather_sight=5, env_len=L))
		print_agents([agent_objs[i]])
	end
	md"Generating random enemy clusters. The look like so..."
end

# ╔═╡ d79c24f7-55c1-48ed-a974-a4ffa863ef40
agents

# ╔═╡ c20331aa-5da4-48d8-9285-62595d15c340
# ╠═╡ show_logs = false
begin
	println(step_agents(agent_objs, topo_bush_python_gpu(topo, A, enemies), [], seed))
	print_agents(agent_objs)
end

# ╔═╡ 44688b66-caae-46a1-85d7-a4c76a7838f2
# ╠═╡ show_logs = false
print_agents(agent_objs)

# ╔═╡ b81ecc1b-ae19-4e8c-82c3-061377dbc857
begin
	# Function to determine the unit vector components based on the given dx, dy, and rotation angle
	function get_unitDxDY(dx, dy, rotateByPiby2=0)
		angle = atan(dy,dx) + rotateByPiby2 * pi/2# % pi

		# print(" angle=",angle/pi*360.)
		
		# if dx<0
		# 	angle = (angle + pi) % pi
		# end

		# print("\n\t   (",@sprintf("%.3f",dx),",",@sprintf("%.3f",dy),") a=",@sprintf("%.3f",angle/pi*180.))
				
		# Determine the direction in the Moore neighborhood
		if angle < -7*pi/8 || angle >= 7*pi/8
			dx, dy = -1, 0  # Move left
			# print(" left")
		elseif -7*pi/8 <= angle < -5*pi/8
			dx, dy = -1, -1   # Move down-left
			# print(" down-left")
		elseif -5*pi/8 <= angle < -3*pi/8
			dx, dy = 0, -1   # Move down
			# print(" down")
		elseif -3*pi/8 <= angle < -1*pi/8
			dx, dy = 1, -1   # Move bottom-right
			# print(" bottom-right")
		elseif -1*pi/8 <= angle < pi/8
			dx, dy = 1, 0   # Move right
			# print(" right")
		elseif pi/8 <= angle < 3*pi/8
			dx, dy = 1, 1  # Move top-right
			# print(" top-right")
		elseif 3*pi/8 <= angle < 5*pi/8
			dx, dy = 0, 1  # Move top
			# print(" top")
		elseif 5*pi/8 <= angle < 7*pi/8
			dx, dy = -1, 1  # Move top-left
			# print(" top-left")
		end
		return dx, dy
	end

	# Function to calculate the Gibbs-Boltzmann probability
	function gibbs_boltzmann_probability(energy_difference, temperature)
	    return exp(-energy_difference / temperature)
	end
	
	# Function to calculate the Euclidean distance between two points
	function distance(x1, y1, x2, y2)
	    return sqrt((x2 - x1)^2 + (y2 - y1)^2)
	end

	# Function to avoid collision between enemies
	function avoid_collision(enemiesAtT, e, dx, dy, min_distance, temperature, collision)
	    x_new = enemiesAtT[e, 1] + dx
	    y_new = enemiesAtT[e, 2] + dy
	
	    for i in 1:size(enemiesAtT, 1)
	        if i != e
	            x_other = enemiesAtT[i, 1]
	            y_other = enemiesAtT[i, 2]
	
				if distance(x_new, y_new, x_other, y_other) < (min_distance + enemiesAtT[i, 3])
					# print("col (", e, ",", i, ") R=(",enemiesAtT[e, 1] - x_other, ",", enemiesAtT[e, 2] - y_other,")")
					# dx, dy = [enemiesAtT[e, 1] - x_other, enemiesAtT[e, 2] - y_other]
					
					# Adjust movement to avoid collision based on the relative size of enemies
					if enemiesAtT[i,3] >= enemiesAtT[e, 3]
						# Randomly choose a direction to move away from the colliding enemy
						if rand()<0.5
							dx, dy = get_unitDxDY(enemiesAtT[e, 1] - x_other, enemiesAtT[e, 2] - y_other)
						elseif rand()<0.5							
							dx, dy = get_unitDxDY(enemiesAtT[e, 1] - x_other, enemiesAtT[e, 2] - y_other, 1)
						else							
							dx, dy = get_unitDxDY(enemiesAtT[e, 1] - x_other, enemiesAtT[e, 2] - y_other, -1)
						end
						
						# print(" dxdy=(",dx,",",dy,")")
						# println("\t\t\ttemp=", temperature, ", T*1.1=", temperature * 1.1)
						
						collision = true
						# Adjust the temperature to avoid collision
						temperature = min(temperature * 1.7, 100)
						
						# println("\t\t\ttemp=", temperature)
					end
	                break
	            end
	        end
	    end
	
	    return dx, dy, temperature, collision
	end

	seed_value = 42
	Random.seed!(seed_value)
end

# ╔═╡ 88a7ce82-2280-4b00-8e97-0bff66696b24
md"Clock $(@bind t Clock())"

# ╔═╡ 7382f5ff-0c87-4d1d-b45f-80286353135f
Markdown.parse("``t=$(t)\\ \\text{ticks}``")

# ╔═╡ fa304120-14f9-4c1a-a430-0438db6743f3
# ╠═╡ show_logs = false
begin
	function gradient_ascend(enemies, t)
		enemiesAtT = copy(enemies)
		enemiesAtT_m, _ = size(enemiesAtT)
		surfacePlot = []
		for ti in 2:t
			for e in 1:enemiesAtT_m
				i, j = enemiesAtT[e, [1,2]]
				slopeHere = slope[i* Int(n/L), j* Int(n/L)]
				r = enemiesAtT[e, 3]
				# dx = ceil(slopeHere[1] * n/L)
				# dy = ceil(slopeHere[2] * n/L)
				
				angle = atan(slopeHere[2], slopeHere[1])
				
				# Determine the direction in the Moore neighborhood
				if angle < -7*pi/8 || angle >= 7*pi/8
					dx, dy = -1, 0  # Move left
				elseif -7*pi/8 <= angle < -5*pi/8
					dx, dy = -1, -1   # Move down-left
				elseif -5*pi/8 <= angle < -3*pi/8
					dx, dy = 0, -1   # Move down
				elseif -3*pi/8 <= angle < -1*pi/8
					dx, dy = 1, -1   # Move bottom-right
				elseif -1*pi/8 <= angle < pi/8
					dx, dy = 1, 0   # Move right
				elseif pi/8 <= angle < 3*pi/8
					dx, dy = 1, 1  # Move top-right
				elseif 3*pi/8 <= angle < 5*pi/8
					dx, dy = 0, 1  # Move top
				elseif 5*pi/8 <= angle < 7*pi/8
					dx, dy = -1, 1  # Move top-left
				end
				
				enemiesAtT[e, 1] = max(min(enemiesAtT[e, 1] + dx, L-r), r+1)
				enemiesAtT[e, 2] = max(min(enemiesAtT[e, 2] + dy, L-r), r+1)
			end
			enemiesInA = gen_e_in_A(enemiesAtT, n, L)
			
			function colors_alias2(x, y)
				return color(x, y, alt_p, A, enemiesInA)
			end
			
			x = 1:n
			y = 1:n
			
		end
			surfacePlot = PlutoPlotly.surface(x = x, y = y, z=transpose(topo_bush_gpu(topo, A, enemies, true)), colorscale=custom_colorscale, surfacecolor = transpose(color_gpu(alt_p, A, enemiesInA, max_height, power)), ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n], xlabel="X", ylabel="Y", zlabel="Z", showscale=false)
		for e in 1:enemiesAtT_m
			println(e, "(", enemiesAtT[e, 1], ", ", enemiesAtT[e, 2], ") ", enemiesAtT[e, 3], " ", slope[enemiesAtT[e, 1], enemiesAtT[e, 2]])
		end
		return surfacePlot
	end
	surfacePlot = gradient_ascend(enemies, t)

	layout = PlutoPlotly.Layout(
	    scene = attr(
	        xaxis = attr(range=[0, n]),
	        yaxis = attr(range=[0, n]),
	        zaxis = attr(range=[0, L]),
			
			width=1050, height=1050,
			
	        camera = attr(
	            eye = attr(x=0, y=0, z=0.85),  # Set the camera position
	            center = attr(x=0, y=0, z=0),  # Set the center point to look at
    			up=attr(x=0, y=1, z=0),
	        )
		)
	)
	# PlutoPlotly.plot(surfacePlot, layout)
end

# ╔═╡ 6e9fadb4-879f-435c-bec4-45ed80866825
begin
	# Main function for gradient ascent while avoiding collisions
	function gradient_ascend_avoidCollision2(enemies, t)
		Random.seed!(seed)
		enemiesAtT = copy(enemies)
		# agentsAtT = copy(agents)
		# agent_objs_local = copy(agent_objs)
		# print_agents(agent_objs_local)
		enemiesAtT_m, _ = size(enemiesAtT)
		surfacePlot = []
		# discovered_enemies = []
		
		# Initialize the enemy temperatures
		enem_T=fill(6.0, (n_enem, 1)) 
		
		# Iterate over time steps
		for ti in 1:t
			# println()
			# print_agents(agent_objs_local)
			
			# println(agentsAtT)
			# if ti>1
			# 	agentsAtT, discovered_enemies = step_agents(agent_objs_local, topo_bush_python_gpu(topo, A, enemiesAtT), discovered_enemies, seed)
	
			# 	# print_agents(agent_objs_local)
			# 	println(agentsAtT)
				
			# 	println(discovered_enemies)
			# end

			# println(ti % 1)
			
			# Iterate over enemies
			for e in 1:enemiesAtT_m
				# print(ti, ": gbP(", e, ") = ", @sprintf("%.3f",gibbs_boltzmann_probability(2.0, enem_T[e])), ", T = ", @sprintf("%.3f",enem_T[e]))
				# println("\n\tT0(",e,")=", enem_T[e])
				i, j = enemiesAtT[e, [1,2]]
				slopeHere = slope[i* Int(n/L), j* Int(n/L)]
				r = enemiesAtT[e, 3]
				# collision = false
				# dx = ceil(slopeHere[1] * n/L)
				# dy = ceil(slopeHere[2] * n/L)
				
				# dx = slopeHere[1]
				# dy = slopeHere[2]

				dx, dy = get_unitDxDY(slopeHere[1], slopeHere[2])

				# Check and adjust movement to avoid collision
				if ti>1 && (ti % 1 == 0)
					# print("\n\t",e,":(",i," ",j,") D=(",dx, " ", dy, ") ")
					
					# Check and adjust movement to avoid collision
					min_distance = 10 + r
					dx, dy, enem_T[e], collision = avoid_collision(enemiesAtT, e, dx, dy, min_distance, enem_T[e], false)
					# print("\n\t    coll?", collision)
				end
				
				
				if ti>1 && (ti % 1 == 0)
					metropolis = rand()
					# Check Gibbs Boltzmann probability
					if metropolis < gibbs_boltzmann_probability(2.0, enem_T[e]) && !(collision)
						# print("\tmetropolis trip ", metropolis)
						
						# Take the direction which reduces altitude
						dx, dy = -dx, -dy
						enem_T[e] = min(enem_T[e] * 1.01, 30)
					end

					# print(" mp=",@sprintf("%.3f",metropolis),",trip?", metropolis < gibbs_boltzmann_probability(2.0, enem_T[e]) && !(collision), "(",max(min(enemiesAtT[e, 1] + dx, L-r), r+1)," ",max(min(enemiesAtT[e, 2] + dy, L-r), r+1),") D=(", dx, " ", dy,")\n")

					# Update enemy positions based on movement
					# if the enemy is near the boundary, bring it in (X)
					if ((enemiesAtT[e, 1] + dx) > L-r) || ((enemiesAtT[e, 1] + dx) < r+1)
						if rand()<0.5 # 50% net probability to move inside
							enemiesAtT[e, 1] = enemiesAtT[e, 1] - dx
						elseif rand()<1/3 # 16.67% net probability to move down
							enemiesAtT[e, 2] = enemiesAtT[e, 2] - 1
						elseif rand()<1/2 # 16.67% net probability to move up
							enemiesAtT[e, 2] = enemiesAtT[e, 2] + 1
						end
					else
						enemiesAtT[e, 1] = enemiesAtT[e, 1] + dx
					end
					# if the enemy is near the boundary, bring it in (Y)
					if ((enemiesAtT[e, 2] + dy) > L-r) || ((enemiesAtT[e, 2] + dy) < r+1)
						if rand()<0.5 # 50% net probability to move inside
							enemiesAtT[e, 2] = enemiesAtT[e, 2] - dy
						elseif rand()<1/3 # 16.67% net probability to move left
							enemiesAtT[e, 1] = enemiesAtT[e, 1] - 1
						elseif rand()<1/2 # 16.67% net probability to move right
							enemiesAtT[e, 1] = enemiesAtT[e, 1] + 1
						end
					else
						enemiesAtT[e, 2] = enemiesAtT[e, 2] + dy
					end
					
					# enemiesAtT[e, 1] = max(min(enemiesAtT[e, 1] + dx, L-r), r+1)
					# enemiesAtT[e, 2] = max(min(enemiesAtT[e, 2] + dy, L-r), r+1)
				end
				

					enem_T[e] *= 0.95
				# if ti % 10 == 0
				# end
					
			end
			
			enemiesInA = gen_e_in_A(enemiesAtT, n, L)
			# agentsInA = gen_a_in_A(agentsAtT, n, L)

			# Update the surface plot for visualization
			x = 1:n
			y = 1:n
			surfacePlot = PlutoPlotly.surface(x = x, y = y, z=transpose(topo_bush_gpu(topo, A, enemiesAtT, true)), colorscale=custom_colorscale, surfacecolor = transpose(color_gpu(alt_p, A, enemiesInA, max_height, power)), ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n], xlabel="X", ylabel="Y", zlabel="Z", showscale=false)
		end
		# for e in 1:enemiesAtT_m
			# println(e, "(", enemiesAtT[e, 1], ", ", enemiesAtT[e, 2], ") ", enemiesAtT[e, 3], " ", slope[enemiesAtT[e, 1], enemiesAtT[e, 2]])
		# end
		return surfacePlot
	end
	# print_agents(agent_objs)			
	# println(agents)
	surfacePlot2 = gradient_ascend_avoidCollision2(enemies, t2)

	PlutoPlotly.plot(surfacePlot2, layout)
end

# ╔═╡ 282cd2e0-8b45-4625-af65-49f2167b1dc4
md"Clock t = $t"

# ╔═╡ 6d80d171-2ef7-4646-a289-cdeea175221e
# ╠═╡ show_logs = false
#=╠═╡
begin
	# Main function for gradient ascent while avoiding collisions
	function gradient_ascend_avoidCollision(enemies, agents, agent_objs, t)
		Random.seed!(seed)
		enemiesAtT = copy(enemies)
		agentsAtT = copy(agents)
		agent_objs_local = copy(agent_objs)
		print_agents(agent_objs_local)
		enemiesAtT_m, _ = size(enemiesAtT)
		surfacePlot = []
		discovered_enemies = []
		
		# Initialize the enemy temperatures
		enem_T=fill(6.0, (n_enem, 1)) 
		
		# Iterate over time steps
		for ti in 1:t
			println()
			# print_agents(agent_objs_local)
			
			println(agentsAtT)
			if ti>1
				agentsAtT, discovered_enemies = step_agents(agent_objs_local, topo_bush_python_gpu(topo, A, enemiesAtT), discovered_enemies, seed)
	
				# print_agents(agent_objs_local)
				println(agentsAtT)
				
				println(discovered_enemies)
			end

			println(ti % 3)
			
			# Iterate over enemies
			for e in 1:enemiesAtT_m
				# print(ti, ": gbP(", e, ") = ", @sprintf("%.3f",gibbs_boltzmann_probability(2.0, enem_T[e])), ", T = ", @sprintf("%.3f",enem_T[e]))
				# println("\n\tT0(",e,")=", enem_T[e])
				i, j = enemiesAtT[e, [1,2]]
				slopeHere = slope[i* Int(n/L), j* Int(n/L)]
				r = enemiesAtT[e, 3]
				# collision = false
				# dx = ceil(slopeHere[1] * n/L)
				# dy = ceil(slopeHere[2] * n/L)
				
				# dx = slopeHere[1]
				# dy = slopeHere[2]

				dx, dy = get_unitDxDY(slopeHere[1], slopeHere[2])

				# Check and adjust movement to avoid collision
				if ti>1 && (ti % 3 == 0)
					# print("\n\t",e,":(",i," ",j,") D=(",dx, " ", dy, ") ")
					
					# Check and adjust movement to avoid collision
					min_distance = 10 + r
					dx, dy, enem_T[e], collision = avoid_collision(enemiesAtT, e, dx, dy, min_distance, enem_T[e], false)
					# print("\n\t    coll?", collision)
				end
				
				
				if ti>1 && (ti % 3 == 0)
					metropolis = rand()
					# Check Gibbs Boltzmann probability
					if metropolis < gibbs_boltzmann_probability(2.0, enem_T[e]) && !(collision)
						# print("\tmetropolis trip ", metropolis)
						
						# Take the direction which reduces altitude
						dx, dy = -dx, -dy
						enem_T[e] = min(enem_T[e] * 1.01, 30)
					end

					# print(" mp=",@sprintf("%.3f",metropolis),",trip?", metropolis < gibbs_boltzmann_probability(2.0, enem_T[e]) && !(collision), "(",max(min(enemiesAtT[e, 1] + dx, L-r), r+1)," ",max(min(enemiesAtT[e, 2] + dy, L-r), r+1),") D=(", dx, " ", dy,")\n")

					# Update enemy positions based on movement
					# if the enemy is near the boundary, bring it in (X)
					if ((enemiesAtT[e, 1] + dx) > L-r) || ((enemiesAtT[e, 1] + dx) < r+1)
						if rand()<0.5 # 50% net probability to move inside
							enemiesAtT[e, 1] = enemiesAtT[e, 1] - dx
						elseif rand()<1/3 # 16.67% net probability to move down
							enemiesAtT[e, 2] = enemiesAtT[e, 2] - 1
						elseif rand()<1/2 # 16.67% net probability to move up
							enemiesAtT[e, 2] = enemiesAtT[e, 2] + 1
						end
					else
						enemiesAtT[e, 1] = enemiesAtT[e, 1] + dx
					end
					# if the enemy is near the boundary, bring it in (Y)
					if ((enemiesAtT[e, 2] + dy) > L-r) || ((enemiesAtT[e, 2] + dy) < r+1)
						if rand()<0.5 # 50% net probability to move inside
							enemiesAtT[e, 2] = enemiesAtT[e, 2] - dy
						elseif rand()<1/3 # 16.67% net probability to move left
							enemiesAtT[e, 1] = enemiesAtT[e, 1] - 1
						elseif rand()<1/2 # 16.67% net probability to move right
							enemiesAtT[e, 1] = enemiesAtT[e, 1] + 1
						end
					else
						enemiesAtT[e, 2] = enemiesAtT[e, 2] + dy
					end
					
					# enemiesAtT[e, 1] = max(min(enemiesAtT[e, 1] + dx, L-r), r+1)
					# enemiesAtT[e, 2] = max(min(enemiesAtT[e, 2] + dy, L-r), r+1)
				end
				

					enem_T[e] *= 0.95
				# if ti % 10 == 0
				# end
					
			end
			
			enemiesInA = gen_e_in_A(enemiesAtT, n, L)
			agentsInA = gen_a_in_A(agentsAtT, n, L)

			# Update the surface plot for visualization
			x = 1:n
			y = 1:n
			surfacePlot = PlutoPlotly.surface(x = x, y = y, z=transpose(topo_bush_gpu(topo, A, enemiesAtT, true)), colorscale=custom_colorscale2, surfacecolor = transpose(color_gpu2(alt_p, A, enemiesInA, agentsInA, max_height, power)), ratio=1, zlim=[0,L], xlim=[0,n], ylim=[0,n], xlabel="X", ylabel="Y", zlabel="Z", showscale=false)
		end
		# for e in 1:enemiesAtT_m
			# println(e, "(", enemiesAtT[e, 1], ", ", enemiesAtT[e, 2], ") ", enemiesAtT[e, 3], " ", slope[enemiesAtT[e, 1], enemiesAtT[e, 2]])
		# end
		return surfacePlot
	end
	# print_agents(agent_objs)			
	println(agents)
	surfacePlot1 = gradient_ascend_avoidCollision(enemies, agents, agent_objs, t)

	PlutoPlotly.plot(surfacePlot1, layout)
end
  ╠═╡ =#

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    ConstructionBaseUnitfulExt = "ConstructionBase"
    InverseFunctionsUnitfulExt = "InverseFunctions"

    [deps.Unitful.weakdeps]
    ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
    InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"

[[deps.UnitfulLatexify]]
deps = ["LaTeXStrings", "Latexify", "Unitful"]
git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd"
uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728"
version = "1.6.3"

[[deps.UnsafeAtomics]]
git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
version = "0.2.1"

[[deps.UnsafeAtomicsLLVM]]
deps = ["LLVM", "UnsafeAtomics"]
git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e"
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
version = "0.1.3"

[[deps.Unzip]]
git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78"
uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d"
version = "0.2.0"

[[deps.VersionParsing]]
git-tree-sha1 = "58d6e80b4ee071f5efd07fda82cb9fbe17200868"
uuid = "81def892-9a0e-5fdd-b105-ffc91e053289"
version = "1.3.0"

[[deps.Vulkan_Loader_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Wayland_jll", "Xorg_libX11_jll", "Xorg_libXrandr_jll", "xkbcommon_jll"]
git-tree-sha1 = "2f0486047a07670caad3a81a075d2e518acc5c59"
uuid = "a44049a8-05dd-5a78-86c9-5fde0876e88c"
version = "1.3.243+0"

[[deps.Wayland_jll]]
deps = ["Artifacts", "EpollShim_jll", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "7558e29847e99bc3f04d6569e82d0f5c54460703"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.21.0+1"

[[deps.Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4528479aa01ee1b3b4cd0e6faef0e04cf16466da"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.25.0+0"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
git-tree-sha1 = "24b81b59bd35b3c42ab84fa589086e19be919916"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.11.5+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.XZ_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "522b8414d40c4cbbab8dee346ac3a09f9768f25d"
uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800"
version = "5.4.5+0"

[[deps.Xorg_libICE_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "e5becd4411063bdcac16be8b66fc2f9f6f1e8fe5"
uuid = "f67eecfb-183a-506d-b269-f58e52b52d7c"
version = "1.0.10+1"

[[deps.Xorg_libSM_jll]]
deps = ["Libdl", "Pkg", "Xorg_libICE_jll"]
git-tree-sha1 = "4a9d9e4c180e1e8119b5ffc224a7b59d3a7f7e18"
uuid = "c834827a-8449-5923-a945-d239c165b7dd"
version = "1.2.3+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.8.6+0"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.11+0"

[[deps.Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "34d526d318358a859d7de23da945578e8e8727b7"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.4+0"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"

[[deps.Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"

[[deps.Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"

[[deps.Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.1+0"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "b4bfde5d5b652e22b9c790ad00af08b6d042b97d"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.15.0+0"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "730eeca102434283c50ccf7d1ecdadf521a765a4"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.2+0"

[[deps.Xorg_xcb_util_cursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_jll", "Xorg_xcb_util_renderutil_jll"]
git-tree-sha1 = "04341cb870f29dcd5e39055f895c39d016e18ccd"
uuid = "e920d4aa-a673-5f3a-b3d7-f755a4d47c43"
version = "0.1.4+0"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.6+0"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.39.0+0"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.5.0+0"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+0"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "49ce682769cd5de6c72dcf1b94ed7790cd08974c"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.5+0"

[[deps.eudev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"]
git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba"
uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06"
version = "3.2.9+0"

[[deps.fzf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "47cf33e62e138b920039e8ff9f9841aafe1b733e"
uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
version = "0.35.1+0"

[[deps.gperf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033"
uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70"
version = "3.1.1+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.4.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+0"

[[deps.libevdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22"
uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc"
version = "1.11.0+0"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libinput_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"]
git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce"
uuid = "36db933b-70db-51c0-b978-0f229ee0e533"
version = "1.18.0+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.mtdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11"
uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e"
version = "1.1.6+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "1.4.1+1"
"""

# ╔═╡ Cell order:
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