notebook-grey-scot.ipynb

scott.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reaction-Diffusion\n",
    "\n",
    "Model for simulating two different reagents, labeled `A` and `B`. `A` is the limiting reagent or food / enery it's fed into the simulation at a certain rate. Represented as $f(1-A)$. `B` is the celular growth, two cells react with one `A` to create another `B`. `B` is removed via a kill function. Diffusion is modeled via a 2D lapace transform. \n",
    "\n",
    "$$A' = A + (D_{a}\\nabla^{2} A - AB^{2} + f(1-A)) \\Delta t$$\n",
    "$$B' = B + (D_{b}\\nabla^{2} B + AB^{2} - (k + f)B) \\Delta t$$\n",
    "\n",
    "$A$ and $B$ are both normalized scalars, representing the saturation of each reagent. Each grid cell has both $A$ and $B$ values.\n",
    "\n",
    "$A'$ and $B'$ are the calculated saturations.\n",
    "\n",
    "Diffusion: Modeled via a normal scalar $D_{a}$ and $D_{b}$ along with the 2D Laplace operator $\\nabla^{2}$. From Wikipedia:\n",
    ">In a Cartesian coordinate system, the Laplacian is given by the sum of second partial derivatives of the function with respect to each independent variable. In other coordinate systems such as cylindrical and spherical coordinates, the Laplacian also has a useful form.\n",
    "\n",
    "Reaction: Modeled via the $AB^{2}$ terms, it represents the consumption of $A$ and the production of $B$. \n",
    "\n",
    "Feed / Kill: Modeled via the normal scalars $f$ and $k$ respectively. We limit $A$'s feed rate to $1-A$ to ensure it doesn't exceed 1. $B$'s kill rate is modeled as $(k + f)B$ to prevent the kill rate from being _less_ than the feed rate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "???? (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function ∇²(A::Array{Float64,2})\n",
    "    \"\"\" Compute the Laplace Transform via Direct Convolution \"\"\"\n",
    "    k_ℒ = Float64[[0.05 0.2 0.05]\n",
    "                  [0.2  -1 0.2 ]\n",
    "                  [0.05 0.2 0.05]]\n",
    "    A′ = zeros(size(A))\n",
    "    \n",
    "    Threads.@threads for y = 1:size(A, 2)\n",
    "        Threads.@threads for x = 1:size(A,1)\n",
    "            # Bound A\n",
    "            by_A = max(1, y - 1) : min(size(A,2), y + 1)\n",
    "            bx_A = max(1, x - 1) : min(size(A,1), x + 1)\n",
    "\n",
    "            # Bound Laplace Kernel\n",
    "            by_ℒ = 1 + ????(y, 1) : size(k_ℒ, 2) - ????(y, size(A, 2))\n",
    "            bx_ℒ = 1 + ????(x, 1) : size(k_ℒ, 1) - ????(x, size(A, 1))\n",
    "\n",
    "            # Calculate subarray views\n",
    "            v_A = view(A, by_A, bx_A)\n",
    "            v_ℒ = view(k_ℒ, by_ℒ, bx_ℒ)\n",
    "\n",
    "            A′[y, x] = sum(v_ℒ .* v_A)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return A′\n",
    "end\n",
    "\n",
    "function ????(i, j)\n",
    "    \"\"\" Kronecker delta function, fancy name, simple function ????\"\"\"\n",
    "    return i == j ? 1 : 0\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Define Constants \"\"\"\n",
    "\n",
    "function A_prime(A::Array{Float64,2}, B::Array{Float64,2}, DA::Float64, f::Float64, t::Float64)\n",
    "    # Pre-compute Laplace\n",
    "    ∇²A = ∇²(A)\n",
    "    A′ = zeros(size(A))\n",
    "    Threads.@threads for y = 1:size(A, 2)\n",
    "        for x = 1:size(A,1)\n",
    "            diffusion = DA * ∇²A[x,y] \n",
    "            consumption = A[x,y] * B[x,y]^2\n",
    "            creation = f * (1 - A[x,y])\n",
    "            A′[x,y] = A[x,y] + (diffusion - consumption + creation) * t\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return A′\n",
    "end\n",
    "\n",
    "function B_prime(A::Array{Float64,2}, B::Array{Float64,2}, DB::Float64, f::Float64, k::Float64, t::Float64)\n",
    "    ∇²B = ∇²(B)\n",
    "    B′ = zeros(size(B))\n",
    "    Threads.@threads for y = 1:size(B, 2)\n",
    "        for x = 1:size(B,1)\n",
    "            diffusion = DB * ∇²B[x,y]\n",
    "            production = A[x,y] * B[x,y]^2\n",
    "            destruction =  (k + f) * B[x,y]\n",
    "            B′[x,y] = B[x,y] + (diffusion + production - destruction) * t\n",
    "        end\n",
    "    end\n",
    "    return B′\n",
    "end;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "AssertionError: Cannot allocate before CuArrays has been initialized.",
     "output_type": "error",
     "traceback": [
      "AssertionError: Cannot allocate before CuArrays has been initialized.",
      "",
      "Stacktrace:",
      " [1] CuArray{Float32,2,P} where P(::UndefInitializer, ::Tuple{Int64,Int64}) at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/memory.jl:120",
      " [2] Type at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:98 [inlined]",
      " [3] Type at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:99 [inlined]",
      " [4] ones at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:292 [inlined]",
      " [5] ones(::Int64, ::Int64) at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:294",
      " [6] top-level scope at ./In[7]:33"
     ]
    }
   ],
   "source": [
    "using CuArrays\n",
    "using Plots; gr()\n",
    "clibrary(:cmocean)\n",
    "\n",
    "DA = 1.0     # Diffusion Rate of A\n",
    "DB = 0.5 # Diffusion Rate of B\n",
    "# f  = 0.055  # Feed rate of A\n",
    "# k  = 0.062  # Kill rate of B\n",
    "\n",
    "# f = 0.0545\n",
    "# k = 0.062\n",
    "\n",
    "# f = 0.029 # Mazes\n",
    "# k = 0.057\n",
    "\n",
    "# theta\n",
    "f=0.038\n",
    "k=0.061\n",
    "\n",
    "\n",
    "# f = 0.0620 # U-skates\n",
    "# k = 0.0777\n",
    "\n",
    "# Devil's model\n",
    "# f=0.066\n",
    "# k=0.050\n",
    "\n",
    "w, h = 256, 256\n",
    "\n",
    "USE_CHECKPOINT = false\n",
    "if USE_CHECKPOINT == false\n",
    "    \n",
    "    g_A = CuArrays.ones(w, h)\n",
    "    g_B = CuArrays.zeros(w, h)\n",
    "    \n",
    "    hh, hw = Int(h//2), Int(w//2)\n",
    "    \n",
    "    #Seed B.\n",
    "#     sz = 5\n",
    "#     for y = hh-sz:hh+sz, x = hw-sz:hw+sz\n",
    "#         g_B[x,y] = 1.0\n",
    "#     end\n",
    "\n",
    "    sz = 12\n",
    "    for y = 1+sz:h-sz, x = 1+sz:w-sz\n",
    "        if rand() > 0.90\n",
    "            g_B[x,y] = 1.0\n",
    "            g_B[x+1, y+1] = 1.0\n",
    "            g_B[x, y+1] = 1.0\n",
    "            g_B[x+1, y] = 1.0\n",
    "        end\n",
    "    end\n",
    "    \n",
    "else\n",
    "    g_B = b_steps[end]\n",
    "    a_A = a_steps[end]\n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Run a few steps\n",
    "b_steps = [g_B]\n",
    "a_steps = [g_A]\n",
    "\n",
    "\n",
    "trials = 10\n",
    "for i in 1:trials\n",
    "    g_A = A_prime(g_A, g_B, DA, f, 1.0)\n",
    "    g_B = B_prime(g_A, g_B, DB, f, k, 1.0)\n",
    "     \n",
    "#     if i % 20 == 0\n",
    "#         heatmap(g_B, fmt=:png,aspect_ratio=1, grid=false, c=:haline)\n",
    "#         savefig(\"mitosis/cells/$i.png\")\n",
    "#         heatmap(g_A, fmt=:png,aspect_ratio=1, grid=false, c=:haline)\n",
    "#         savefig(\"mitosis/food/$i.png\")\n",
    "#     end\n",
    "    \n",
    "    if i % 20 == 0\n",
    "        push!(b_steps, g_B)\n",
    "        push!(a_steps, g_A)\n",
    "    end\n",
    "end\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Saved animation to \n",
      "│   fn = /home/tmoll/notebooks/Julia/mitosis/bubbles3.gif\n",
      "└ @ Plots /home/tmoll/.julia/packages/Plots/Iuc9S/src/animation.jl:95\n"
     ]
    }
   ],
   "source": [
    "clibrary(:cmocean)\n",
    "\n",
    "# Save a gif\n",
    "anim = @animate for i=1:length(b_steps)\n",
    "    heatmap(b_steps[i], clims=[0,1], legend=false, showaxis=false, size=(1000,800), c=:curl)\n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "filename = \"/home/tmoll/notebooks/Julia/mitosis/bubbles3.gif\"\n",
    "gif(anim, filename, fps = 5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](http://localhost:8888/files/Media/diffusion2.gif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap(b_steps[end], fmt=:png,aspect_ratio=1, grid=false, c=:topo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_checkpoint = b_steps[end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = zeros(21,21)\n",
    "grid[5,5] = 1.0\n",
    "grid[5,6] = 1.0\n",
    "grid[5,4] = 1.0\n",
    "\n",
    "\n",
    "plots = [heatmap(grid, clims=(0,1), aspect_ratio=1.0, legend=false, showaxis=false)]\n",
    "\n",
    "trials = 50\n",
    "for i in 1:trials\n",
    "    grid = ∇²(grid)\n",
    "    #grid = rand(5,5)\n",
    "    if i % 5 == 0\n",
    "        push!(plots, heatmap(grid, clims=(0,1), aspect_ratio=1.0, legend=false, showaxis=false))\n",
    "    end\n",
    "end\n",
    "    \n",
    "plot(plots..., layout=length(plots),legend=false, color=:isolum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "w, h = 25, 25\n",
    "A = zeros(w, h)\n",
    "\n",
    "A[12,12] = 1.0\n",
    "A[13,12] = 1.0\n",
    "A[14,13] = 1.0\n",
    "A[15,12] = 1.0\n",
    "\n",
    "# for y = 4:8, x = 4:8\n",
    "#     A[x,y] = 1.0\n",
    "# end\n",
    "\n",
    "\n",
    "steps = []\n",
    "for i in 1:10\n",
    "    A = ∇²(A)\n",
    "    push!(steps, A)\n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "anim = @animate for i=1:length(steps)\n",
    "    heatmap(steps[i], clims=[0,1])\n",
    "end\n",
    "\n",
    "filename = \"/Users/tmoll/Repos/SCRATCH/py3_shit/Media/diffusion.gif\"\n",
    "\n",
    "gif(anim, filename, fps = 5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Traceur\n",
    "\n",
    "@trace g_A = A_prime(rand(50, 50), rand(50,50), DA, f, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.000661 seconds (7.63 k allocations: 722.125 KiB)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "50×50 Array{Float64,2}:\n",
       " -0.424209     0.0194635   -0.222443    …  -0.337504    -0.643974 \n",
       " -0.262765     0.244229     0.521218       -0.187223     0.4498   \n",
       " -0.345182    -0.463556    -0.342448       -0.102444    -0.0771404\n",
       " -0.0534933    0.0905613    0.641942        0.136142    -0.408528 \n",
       " -0.511935     0.360683    -0.415698        0.153024    -0.119035 \n",
       "  0.293863     0.163213    -0.231668    …  -0.403282     0.0566041\n",
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       " -0.207264     0.220008    -0.244267       -0.0225047   -0.0501158\n",
       "  ⋮                                     ⋱                         \n",
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       "  0.0512171    0.0834571   -0.380777    …  -0.440719     0.267235 \n",
       " -0.244963     0.231167     0.0479666       0.316136    -0.548687 \n",
       " -0.544454    -0.0601096   -0.344388        0.00464107  -0.0680374\n",
       " -0.278486     0.290964    -0.213929       -0.0761556    0.256143 \n",
       "  0.300686    -0.59679      0.306015       -0.297306    -0.745348 "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function ∇²_st(A::Array{Float64,2})\n",
    "    \"\"\" Compute the Laplace Transform via Direct Convolution \"\"\"\n",
    "    k_ℒ = Float64[[0.05 0.2 0.05]\n",
    "                  [0.2  -1 0.2 ]\n",
    "                  [0.05 0.2 0.05]]\n",
    "    A′ = zeros(size(A))\n",
    "    \n",
    "    for y = 1:size(A, 2)\n",
    "        for x = 1:size(A,1)\n",
    "            # Bound A\n",
    "            by_A = max(1, y - 1) : min(size(A,2), y + 1)\n",
    "            bx_A = max(1, x - 1) : min(size(A,1), x + 1)\n",
    "\n",
    "            # Bound Laplace Kernel\n",
    "            by_ℒ = 1 + ????(y, 1) : size(k_ℒ, 2) - ????(y, size(A, 2))\n",
    "            bx_ℒ = 1 + ????(x, 1) : size(k_ℒ, 1) - ????(x, size(A, 1))\n",
    "\n",
    "            # Calculate subarray views\n",
    "            v_A = view(A, by_A, bx_A)\n",
    "            v_ℒ = view(k_ℒ, by_ℒ, bx_ℒ)\n",
    "\n",
    "            A′[y, x] = sum(v_ℒ .* v_A)\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return A′\n",
    "end\n",
    "\n",
    "a = rand(50,50)\n",
    "@time ∇²(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{Symbol,1}:\n",
       " :Plots      \n",
       " :cmocean    \n",
       " :misc       \n",
       " :colorcet   \n",
       " :colorbrewer"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clibraries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "1"
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "Pkg.Types.PkgError",
     "evalue": "https://github.com/JuliaGPU/CuArrays.jl is not a valid packagename.\nThe argument appears to be a URL or path, perhaps you meant `Pkg.add(PackageSpec(url=\"...\"))` or `Pkg.add(PackageSpec(path=\"...\"))`.",
     "output_type": "error",
     "traceback": [
      "https://github.com/JuliaGPU/CuArrays.jl is not a valid packagename.\nThe argument appears to be a URL or path, perhaps you meant `Pkg.add(PackageSpec(url=\"...\"))` or `Pkg.add(PackageSpec(path=\"...\"))`.",
      "",
      "Stacktrace:",
      " [1] pkgerror(::String) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/Types.jl:112",
      " [2] check_package_name(::String, ::Symbol) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/API.jl:28",
      " [3] iterate at ./none:0 [inlined]",
      " [4] collect(::Base.Generator{Array{String,1},getfield(Pkg.API, Symbol(\"##22#23\"))}) at ./array.jl:606",
      " [5] #add#21 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/API.jl:65 [inlined]",
      " [6] add at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/API.jl:65 [inlined]",
      " [7] #add#20(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(Pkg.API.add), ::String) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/API.jl:64",
      " [8] add(::String) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Pkg/src/API.jl:64",
      " [9] top-level scope at In[18]:1"
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   "source": [
    "using Pkg; Pkg."
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "AssertionError: Cannot allocate before CuArrays has been initialized.",
     "output_type": "error",
     "traceback": [
      "AssertionError: Cannot allocate before CuArrays has been initialized.",
      "",
      "Stacktrace:",
      " [1] CuArray{Float32,1,P} where P(::UndefInitializer, ::Tuple{Int64}) at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/memory.jl:120",
      " [2] convert at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:98 [inlined]",
      " [3] adapt_storage at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:240 [inlined]",
      " [4] adapt_structure at /home/tmoll/.julia/packages/Adapt/aeQPS/src/Adapt.jl:9 [inlined]",
      " [5] adapt at /home/tmoll/.julia/packages/Adapt/aeQPS/src/Adapt.jl:6 [inlined]",
      " [6] cu(::Array{Float64,1}) at /home/tmoll/.julia/packages/CuArrays/ir1DU/src/array.jl:288",
      " [7] top-level scope at In[11]:2"
     ]
    }
   ],
   "source": [
    "using CuArrays\n",
    "cu(rand(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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