{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Blog home](http://rajeshrinet.github.io/blog)\n",
    "$$\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gray-Scott reaction–diffusion equation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reaction we are modeling is \n",
    "$$\n",
    "u + 2v \\rightarrow 3v\n",
    "$$\n",
    "Gray-Scott reaction-diffusion equation is used to model the reaction\n",
    "$$\n",
    "\\frac{\\partial u}{\\partial t} = \\underbrace{D_u \\boldsymbol\\nabla^2 u}_{\\substack{\\text{diffuse }u\\\\\\text{ for concentration to spread}}} - \\underbrace{uv^2}_{\\text{probability of reaction}} + \\underbrace{F(1-u)}_{\\substack{\\text{add more of u }  \\\\\\text{with a bound 1}}}\n",
    "$$\n",
    "$$\n",
    "\\frac{\\partial v}{\\partial t} = \\underbrace{D_v \\boldsymbol\\nabla^2 v}_{\\substack{\\text{diffuse }v\\\\\\text{ for concentration to spread}}} + \\underbrace{uv^2}_{\\text{probability of reaction}} - \\underbrace{(F+K)v}_{\\substack{\\text{remove } v \\text{ from reaction}  \\\\\\text{  with a bound 0}}}\n",
    "$$\n",
    "\n",
    "The system shows very rich set of patterns as the parameters for equations are varied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (1.16.4)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/site-packages (3.1.0)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/Cellar/ipython/7.5.0/libexec/vendor/lib/python3.7/site-packages (from matplotlib) (2.8.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib) (2.4.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: six in /usr/local/Cellar/ipython/7.5.0/libexec/vendor/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.8.0)\n"
     ]
    }
   ],
   "source": [
    "# Install a pip package in the current Jupyter kernel\n",
    "import sys\n",
    "!{sys.executable} -m pip install numpy matplotlib scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.sparse import spdiags \n",
    "\n",
    "\n",
    "\n",
    "class GrayScott():\n",
    "    \"\"\"Class to solve Gray-Scott Reaction-Diffusion equation\"\"\"\n",
    "    def __init__(self, N):\n",
    "        self.N = N\n",
    "        self.u = np.ones((N, N), dtype=np.float128)\n",
    "        self.v = np.zeros((N, N), dtype=np.float128)\n",
    "    \n",
    "    \n",
    "    def laplacian(self):\n",
    "        \"\"\"Construct a sparse matrix that applies the 5-point discretization\"\"\"\n",
    "        N = self.N\n",
    "        e=np.ones(N**2)\n",
    "        e2=([1]*(N-1)+[0])*N\n",
    "        e3=([0]+[1]*(N-1))*N\n",
    "        A=spdiags([-4*e,e2,e3,e,e],[0,-1,1,-N,N],N**2,N**2)\n",
    "        return A\n",
    "\n",
    "\n",
    "    def initialise(self):\n",
    "        \"\"\"Setting up the initial condition\"\"\"\n",
    "        N, N2, r = self.N, np.int(self.N/2), 16\n",
    "        \n",
    "        self.u += 0.02*np.random.random((N,N))\n",
    "        self.v += 0.02*np.random.random((N,N))\n",
    "        \n",
    "        self.u[N2-r:N2+r, N2-r:N2+r] = 0.50\n",
    "        self.v[N2-r:N2+r, N2-r:N2+r] = 0.25\n",
    "        return  \n",
    "\n",
    "    \n",
    "    def integrate(self, Nt, Du, Dv, F, K, L):\n",
    "        \"\"\"Integrate the resulting system of equations using the Euler method\"\"\"\n",
    "        u = self.u.reshape((N*N))\n",
    "        v = self.v.reshape((N*N))\n",
    "\n",
    "        #evolve in time using Euler method\n",
    "        for i in range(Nt):\n",
    "            uvv = u*v*v\n",
    "            u += (Du*L.dot(u) - uvv +  F *(1-u))\n",
    "            v += (Dv*L.dot(v) + uvv - (F+K)*v  )\n",
    "        \n",
    "        self.u = u\n",
    "        self.v = v\n",
    "        return\n",
    "        \n",
    "    \n",
    "    def configPlot(self):\n",
    "        \"\"\"Plotting business\"\"\"\n",
    "        u = self.u\n",
    "        v = self.v\n",
    "        f = plt.figure(figsize=(25, 10), dpi=400, facecolor='w', edgecolor='k');\n",
    "        sp =  f.add_subplot(1, 2, 1 );\n",
    "        plt.pcolor(u.reshape((N, N)), cmap=plt.cm.RdBu)\n",
    "        plt.axis('tight')\n",
    "\n",
    "        sp =  f.add_subplot(1, 2, 2 );\n",
    "        plt.pcolor(v.reshape((N, N)), cmap=plt.cm.RdBu)\n",
    "        plt.axis('tight')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bacteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 10000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 256\n",
    "rdSolver = GrayScott(N)\n",
    "L = rdSolver.laplacian()\n",
    "Du, Dv, F, K = 0.14, 0.06, 0.035, 0.065\n",
    "Nt = 32000 \n",
    "\n",
    "rdSolver.initialise()\n",
    "rdSolver.integrate(Nt, Du, Dv, F, K, L)\n",
    "rdSolver.configPlot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16384x16384 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 81662 stored elements (5 diagonals) in DIAgonal format>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N=128\n",
    "rdSolver = GrayScott(N)\n",
    "rdSolver.laplacian()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coral patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KSkriiiuuyLoWAACoyRtvvBHHHXdcrF27Nu313XffPfr37x/NmzePlStXxttvvx0VFRUpv5s8eXIUFRXFrbfeWtclAwA0arvttlt07NgxOnbsGBERq1evjsWLF6ft0SIifve738Xf/va3uPfee6OkpCSreyZp31//CgBQWOqjf02SlStXxtFHHx3Lli1Le33XXXeNffbZJ1q3bh2rV6+ON998M+0AmIceeihOP/30ePDBB6Np06Z1XTYAADRaZkEAANDYJOmdEAAAGi6zIAAAIDvO0gEAFBazIPSvAACFxCwIsyCSwIfJKVjXXHNN2oOo7dq1i4kTJ8bo0aOjbdu2f4+XlZXFgw8+GFdeeWUsXrx4uzVLly6Ns88+Ox5//HH/kAAAUK158+bltL5bt25ZrXv99dfj/PPPT7shNXLkyJgwYUL0799/u/gbb7wRP/rRj+Kee+7ZLl5ZWRnjxo2L/fbbL/bbb7+s6gEAoGF69NFH46qrrkqJN2nSJC666KK45JJLomfPnttde/7552PixInx6KOPbhffunVrnHXWWTF//vzo0qVLrWv56KOP4qyzzkp7EPWEE06IH/7whzF06NDt4kuXLo2f//znccstt6RsuE2YMCEOPPDAOO6442pdCwAAheHmm2+OAw88MOv1bdq0yXrtxo0b4/TTT0/7Iv/QoUPjuuuui6OPPjqKior+Hv/444/jN7/5Tfz3f/93St972223xZAhQ2LcuHFZ1wQAwPZat24dp512Whx11FHxL//yL9GnT5+U33z22WcxY8aM+OlPfxrz589Puf6Xv/wlLrzwwpg8eXKt75+kfX/9KwBA8tV3/7qjq666KoYPH571+ubNm2e9try8PL797W+nPYi69957x49//OM45ZRTorj4H0cl161bF1OmTIkf/vCHsX79+u3WPPzwwzFx4sS49tprs64JAAConlkQAADUB7MgAABoyMyCAACgkJkFAQBAder7LF2S9v31rwAAyVff/euOzIIgIqKoMt1fNJBwCxYsiMGDB0d5efl28d69e8fs2bPT/gP7pU2bNsXpp58es2bNSrl20003xYUXXpj3egEAKEz/vKnypfr4E6qioiIOOeSQeOmll7aLN2nSJCZPnhxjx46tdv1vf/vbuOCCC1JqHzp0aDz//PNp/3sCALDztGvXLmXjo0ePHjF48OAYMmRIDB48ODp06BBDhgxJWTt37twYNmxYXur4/PPPo3///vHBBx9sF2/RokXcd999ceKJJ1a7/uqrr067UXPGGWfEtGnTal3PGWecEffee29KfOLEiTFp0qRq186YMSNOP/30lBeievbsGW+++Wa0bNmy1vUAAPCFpPSvw4YNiyeffLLO8tfWpZdeGj//+c9T4mPHjo1bb711u5egdvTSSy/F8ccfn3IQYNddd43FixdHp06d8l4vAEBDN27cuLj99tsjIuLQQw+N888/P84888xo1apVRusrKiri2muvjYkTJ6ZcKyoqimeeeSYOO+ywjOtJ2r6//hUAIFmS1r+OGjUqfv/7328XmzJlSowaNSrjHPl00003xcUXX5wSP+mkk2LatGmxyy67VLn23XffjWOOOSblIGvTpk3j1VdfjYEDB+a9XgAAaOzMggAAYGcwCwIAgJ0hKWfpzIIAACATSelfzYIAAKA6STtLl7R9f/0rAECyJK1/NQuCqjSp7wIgG//5n/+ZchC1VatW8fDDD1d7EPXL3913332x7777ply75pprYuPGjXmtFQAAcnX33XenbEhFREyaNKnGDamIiPPOOy/ty/ovvvhi3HPPPfkoEQCAHPTo0SNOPfXUuPbaa2PmzJmxevXqWLZsWdx3331x5ZVXxvHHHx/t27ev8zp+8YtfpBxEjYiYPHlyjQdRIyL+3//7fzF69OiU+PTp0+O5556rVS3PPPNM2oOoY8aMqfEgakTE8OHDY/LkySnxZcuWxQ033FCrWgAA2F5S+tckef/99+PXv/51SvzrX/96TJ48udoX+SMiDjrooLj33nujSZPtX+PZsGFDXHPNNXmtFQCgMTn88MPjsccei+eeey5GjRqV8Yv8EV8cEr3mmmvi+uuvT7lWWVkZEyZMqFUtSdr3178CACRTkvrXJKmqzxwwYEBMnz692oOoERF9+/aNmTNnpvyuvLw8Lr300rzWCgAAfMEsCAAAGpMkvRMCAED+JeUsnVkQAABkIin9a5I4SwcAkExJOkuXpH1//SsAQDIlqX9NErMgkqWosrKysr6LgNp4+eWX46CDDkqJX3/99XH55ZdnnGfevHlx2GGHpcR//vOfxyWXXJJTjQAANAxFRUUpsfr4E2q//faLhQsXbhcbMGBAvPbaa9G0adOMcmzbti0OOOCAeOONN1Jy//Wvf81brQAA1I2lS5dG7969U+Jz586NYcOG5Zx/8+bN0bNnz1i9evV28RNOOCFmzpyZcZ7169fHXnvtFatWrdoufsopp8SDDz6YcZ6TTz45ZsyYsV2sY8eOsWTJkmjTpk3GeU444YSYNWtWSp5ly5ZFixYtMs4DAEDt1HX/GhExbNiwePLJJ+ssf21cdNFFcfPNN28Xa9GiRbz55pvRq1evjPN873vfi9tuu227WLNmzWLp0qXRuXPnfJQKANBorFixIrp27ZqXXIcddljMmzdvu1iTJk1i5cqV0bFjx4xyJGnfX/8KAJA8SetfR40aFb///e+3i02ZMiVGjRqVlxpr42c/+1lcdtllKfF58+bFIYccknGe66+/Pn7wgx+kxF9++eUYPHhwTjUCAAD/YBYEAAA7i1kQAAAkhVkQZkEAABQSsyCcpQMAqG9JO0uXpH1//SsAQPIkrX81C4KqNKnvAqC2Jk+enBJr3759XHzxxbXKc+ihh8bxxx+fEv/tb3+bdW0AAJBvzz33XMqGVETEVVddlfGGVEREcXFxXHXVVSnx119/PZ5//vmcagQAoPD97//+b8pB1IiISZMm1SpP27ZtY/z48SnxGTNmxEcffZRRjg8//DDtAdhLLrmkVgdRI9LXv2rVqrj//vtrlQcAAKqyZcuWuPPOO1Pio0aNqtWL/BEREyZMiOLi4u1ipaWlKS99AQBQs3y9yB8RaV98r6ioSBmEV5Uk7fvrXwEAkilJ/WvSpDvndcIJJ9TqIGpExL/927/F7rvvnlF+AAAge2ZBAADQmCTpnRAAABousyAAACA7ztIBACRTks7SJWnfX/8KAJBMSepfk8YsiGTxYXIKyrZt22L69Okp8ZEjR8Yuu+xS63znnXdeSuytt96KV199Nav6AAAg3+65556UWPv27eO0006rda7TTz897R/Sd999d1a1AQDQcKTrOwcOHBgHH3xwrXONGTMm5QWqioqK+NOf/pTR+qlTp0ZFRcV2seLi4hgzZkytaznkkENiwIABKXE9MAAA+TJjxoz47LPPUuLnn39+rXN17949TjjhhJS4/hUAoH59/etfj6KiopT4smXLMlqfpH1//SsAQMOXa/+aJK+88kosXrw4JZ5N/7rLLrvEOeeckxKfNm1abNu2Lav6AACA7ZkFAQBAY5Okd0IAAGi4zIIAAIDsOEsHANDwmQWRnv4VACCZzIJIzyyI/PBhcgrK888/H2vXrk2Jn3766VnlGz58eLRs2TIlPnPmzKzyAQBAvqXrTUeMGBElJSW1zlVSUhIjRozI6B4AADQepaWlMWfOnJR4ts9dO3bsGEcccURKPNO+M93vjjjiiNhjjz2yqueMM85Iic2ZMydKS0uzygcAAP8sXf/au3fvGDRoUFb50vWvr7/+eqxYsSKrfAAA5K5169ax2267pcQ//vjjjNYnad9f/woA0PDl2r8mSbr+tWXLlnHiiSdmlS9d/7pmzZp44YUXssoHAABszywIAAAamyS9EwIAQMNkFgQAAGTPWToAgIbPLIiq6V8BAJLHLIiqmQWROx8mp6DMnTs3JbbLLrvEoYcemlW+Fi1axGGHHZYST/fiFQAA7GzLly+Pd999NyV+zDHHZJ0z3dp33nknli9fnnVOAAAK2wsvvBCff/55SjzffeczzzwTZWVl1a4rLS2NZ599ts5r2bRpk80kAADyIt17DPnuXyO8xwAAUN/SHRxt0qTm17CTtu+vfwUAaByy7V+TJl3/evjhh0eLFi2yynfooYem/aih/hUAAPLDLAgAABqTpL0TAgBAw2QWBAAAZM9ZOgCAxsEsiMxridC/AgDUN7Mg0jMLIneF978iGrWXXnopJTZo0KC0/0hmaujQoSmxV199Net8AACQL+n634iIgw8+OOuc6frfiIhXXnkl65wAABS2dH1ncXFxDB48OOuc6frOLVu2xBtvvFHtuoULF8aWLVtS4rn0wAceeGA0bdo0Ja4HBgAgV2vWrIn33nsvJZ5L/9qtW7fo3LlzSlz/CgBQfzZt2hSrV69Oiafr23aUpH1//SsAQOOQS/+aJJWVlWn7ylz612bNmsUBBxyQEte/AgBAfpgFAQBAY5Kkd0IAAGi4zIIAAIDsOEsHANA4mAVRNf0rAEDymAVRNbMgcufD5BSUv/71rymxAQMG5JRz4MCBKbF169bFsmXLcsoLAEDDtnnz5vjggw9i/vz58cYbb8TKlStj69ateb1Huv63ZcuW0bdv36xz9uvXL1q0aJESX7BgQdY5AQAobOn6zr59+6btGzOV7rlrRM19Z7paInJ7DrzLLrvEnnvuWetaAAAoXNu2bYuVK1fG66+/HgsWLIjly5fHxo0b836fuuhfI9L30/pXAID68+STT0ZFRUVKPN1zxx0lad9f/woA0Djk0r9moqKiIlatWhWLFi2K+fPnx9KlS+Ozzz7LS+5/tmzZsli/fn1KXP8KAADJZRYEAABJYRYEAAANhVkQAAA0NGZBAACQT2ZBVE//CgCQLGZBVE//mpvi+i4AMlVaWhrLly9Pief6j2FVf9C/99570bNnz5xyAwDQ8Pzrv/5rPPPMM/HGG2+k/WO9Z8+ecfjhh8cRRxwRZ5xxRnTo0CHre7377rspsT59+kRRUVHWOYuKiqJPnz6xaNGi7eLvvfde1jkBAChs6frOXJ+7durUKVq1ahWbNm3aLl5T35multatW0enTp1yqqdv377x9ttv16oWAAAKzy233BJXX311vPTSS2mHB+6+++5x2GGHxeGHHx7f/OY346tf/WpO90vXv0bUzXsM+lcAgPozZcqUlFhJSUkcd9xxNa5N0r6//hUAoHHIpX+tzp///Oe466674vnnn087/K9169ZxyCGHxNe+9rUYPnx4DBkyJKf77cz+dfny5bFt27YoLnbUEgAAsmUWBAAASWAWBAAADY1ZEAAANBRmQQAAUBfMgqie/hUAIFnMgqieWRC5aVLfBUCmli9fnvZl/65du+aUt6r1S5cuzSkvAAAN029+85t4/fXX0/amERHLli2Lu+++O77//e9Hjx494oILLoh33nknq3ul60lz7X+ryqH/BQBovOqq7+zSpUtG96rpero8taUHBgBoHKZNmxbPPPNM2oOoERFr1qyJhx56KH7wgx/EPvvsE8OHD48nnngi6/ul6ymbN2+e05DCiPT964cffhjbtm3LKS8AALW3YMGCuO+++1LiRx11VLRt27bG9Una99e/AgA0fLn2r9WZOXNmzJ49O+1B1IiIjRs3xuzZs2PSpElx0EEHxRFHHBH3339/1verqr+ti3Nk5eXlaT+gCAAAZM4sCAAAksAsCAAAGhqzIAAAaCjMggAAIN/MgsiuFv0rAED9MAuiZmZB5MaHySkYq1evThv/yle+klPeqtZXdT8AAMjU5s2bY/LkybH//vvH5MmTa70+XU+aa/8bEdGpU6eM7gUAQOOQpL4zSbUAANCwVVZWxsMPPxxHHXVUXHTRRbFly5Za50jXU3bs2DHn2tL1rxUVFbFmzZqccwMAkLny8vL43ve+l3ZY9WWXXZZRjiQ989S/AgA0bPnoX/PpmWeeiVNPPTXOPPPMWLduXa3Xp+tfi4qKcu5h0/WvVd0PAADInFkQAAAUGrMgAAAoBEnqO5NUCwAADZtZEAAA1MQsiOxr0b8CAOx8ZkFkxiyI3PgwOQWjqj9K27Ztm1Pepk2bRqtWrVLif/vb33LKCwAAX/r888/jggsuiO985ztRWVmZ8bp0PXCu/W9ERJs2bVJi+l8AgMbp888/j61bt6bE66vv3Jk98ObNm2Pz5s055wYAoPDdfPPNccghh9T6OenO7F8jPMcFANjZfvSjH8Xzzz+fEh8+fHgce+yxGeVI0r6//hUAoGHLR/9aF6ZPnx77779/vPfee7Val65/bdWqVTRt2jSnevSvAABQN8yCAACgUJkFAQBAUpkFYRYEAABmQQAAkJ5ZENnXkkk9AADkl1kQmdG/5qa4vguATG3cuDFtvHXr1jnnbtWqVWzatGm72I7/GQCAxqtZs2Zx2GGHxTHHHBMDBgyI/v37R4cOHaJNmzaxdevWWLt2bbz77rvx7LPPxr333hvz589Pm+fOO++M9u3bxw033JDRfdP1wPnqf3ek/wUAaJzq+rnrjmrqO3dmD/xlPS1btsw5PwAA9aeoqCj222+/OO6442LQoEGx7777RqdOnaJNmzbRpEmTWLNmTaxYsSLmzZsXs2bNipkzZ0ZFRUVKngULFsQpp5wSjz/+eMY9Yn30rwAA7ByzZs2KH/7whynxtm3bxi233JJxniTt++tfAQAarnz1r+n069cvjj/++Bg8eHDsu+++0b1799h1112jWbNmsWbNmvjkk0/ihRdeiNmzZ8f//u//RllZWUqODz74IE444YR49tlnY4899sjovvpXAAAoLGZBAABQX8yCAACgoTILwiwIAIBCZxYEAAB1wSyI3GrJpB4AAPLHLIjM6V9z48PkFIx0/xhFRBQX5/4/45KSkpRYaWlpznkBAChshx56aIwdOzbOOuusKv+ALSkpidatW0f37t1j2LBhMWHChHjiiSfi+9//frz11lspv//lL38ZgwYNiu985zs13j9dD6z/BQAgn5L23HVn9sCZ1AMAQHL16dMnjj322Bg1alR069atyt917tw5OnfuHEOGDImLL7443nnnnRg/fnw89NBDKb+dN29eXHjhhXHHHXdkVIP+FQCgYVq0aFkGarcAACAASURBVFGcddZZaYeY3HrrrdGjR4+McyVp31//CgDQMOWzf/1S586d4/LLL48xY8bEXnvtVeXvvvKVr8RXvvKVGDhwYJx33nnx0UcfxVVXXRVTpkxJ+e2SJUvi29/+djz++OMZ1aB/BQCAwpK0d5IBAGj4zIIAAKChS9pzV+9yAACQKbMgAACoK2ZB5F5LJvUAAJAfZkHUjv41N03quwDIVHl5edp406ZNc86dLse2bdtyzgsAQGF77rnnYuzYsVUeRK3KsGHD4tVXX40zzjgj7fUrr7wyNm/eXGOedD2w/hcAgHxK2nPXndkDZ1IPAADJdccdd8RVV11V7UHUdPbcc8/4y1/+Ej/5yU/SXv/9738fr732Wka59K8AAA3PihUr4qSTTor169enXLv44ovj29/+dq3yJWnfX/8KANDw5Lt//dKPf/zjuP7666s9iJpOly5d4o477oi77747bZ84Z86cePDBBzPKpX8FAIDCkrR3kgEAaPjMggAAoKFL2nNX73IAAJApsyAAAKgLZkHkp5ZM6gEAIHdmQdSe/jU3PkxOwSguLk4bz8f/s6fLUVJSknNeAAAar5YtW8Zdd90VRx11VMq1FStWxE033VRjjnQ9sP4XAIB8Stpz153ZA2dSDwAADddll10WEyZMSIlXVFSkjaejfwUAaFhWr14dxx13XCxbtizl2je+8Y244YYbap0zSfv++lcAgIalLvrXfDn77LPj1ltvTXvtyiuvzCiH/hUAAApL0t5JBgCA6pgFAQBAIUjac1fvcgAAsLOYBQEAwI7MgshfLZnUAwBAbsyCyI7+NTc+TE7BaN68edp4aWlpzrnT5ajqfgAAkKlmzZrFb3/722jWrFnKtXvvvbfG9el6Uv0vAAD5lLTnrjuzB86kHgAAGrZJkybF3nvvnRJ/7LHHYsOGDTWu178CADQca9eujeOOOy4WLVqUcu24446LqVOnRtOmTWudN0n7/vpXAICGo67613waN25cHH300SnxN954I956660a1+tfAQCgsCTtnWQAAKiJWRAAACRd0p67epcDAICdySwIAAC+ZBZEfmvJpB4AALJnFkT29K+58WFyCsauu+6aNp7JBlBN0uVo06ZNznkBAKBv375x9tlnp8Rfeuml+PTTT6tdm64H1v8CAJBPSXvuujN74KruBwBA41FcXBxXXHFFSrysrCwee+yxGtfv7P7Vc1wAgLrx2WefxfHHHx+vvfZayrUjjzwy7r///qxfTE/Svr/+FQCgYajL/jXfrr766rTxhx9+uMa1+lcAACgsSXsnGQAAMmEWBAAASZa0565mQQAAsDOZBQEAQIRZEHVRSyb1AACQHbMgcqN/zY0Pk1Mw2rdvnza+bt26nPJu2bIltm7dmvH9AACgtk4++eSUWEVFRbzyyivVrkvXk+ba/1aVQ/8LANA4NWvWLFq3bp0Sr6++c2f2wG3atImSkpKccwMAUNhOOumkKCoqSom/9NJLNa7dmf1rVfcDACA3GzdujBNPPDFt/3fYYYfFQw89FC1btsw6f5L2/fWvAACFr67713z72te+Fu3atUuJZ/v8taozYLWhfwUAgLphFgQAAIXKLAgAAJLKLAizIAAAGjuzIAAAGjezIPSvAACFxCwIsyDqmw+TUzA6deqUNv7xxx/nlLeq9VXdDwAAamvw4MFp45988km169L1pLn2v1Xl0P8CADReSeo7k1QLAACNQ8eOHaN79+4p8Zqe30ak7ylXr14dFRUVOdWUrn9t3rx52pe2AADI3qZNm+Kkk06K5557LuXa0KFDY+bMmWmH+dVGkp556l8BAArbzuhf8624uDgGDhyYEs/2+WuEc2QAAJBUengAAAqVWRAAACRZkvrOJNUCAEDjYBYEAEDjZRaE/hUAoJCYBfEF58jqlw+TUzC6dOkSzZs3T4l/8MEHOeWtan3v3r1zygsAAF/q2LFj2viqVauqXZeuJ821/60qh/4XAKDxqou+s7y8PD766KOM7lXT9RUrVkR5eXlO9eiBAQCoTrpnuDU9v41I31OWl5fHihUrcqonXf/aq1evKCoqyikvAAD/8Pnnn8fJJ58cTz/9dMq1Aw88MGbNmhVt2rTJ+T5J2vfXvwIAFK6d1b/WhXw+f42om3NkLVq0cBgVAAByZBYEAACFyiwIAACSzCwIAAAaO7MgAAAaH7MgvqB/BQAoDGZB/INZEPXLh8kpGEVFRdG3b9+U+OLFi3PKu2TJkrTxPffcM6e8AABQk9LS0mqv9+vXLyW2bNmyGtdVZ+vWrWn/kE53LwAAGod0vWCuz12XLl0aZWVlGd2rputlZWWxdOnSnOpJ9xxYDwwAQHUyeQ5bVU9ZF+8x6F8BAPJn8+bNMWLEiHjiiSdSrg0cODAee+yxaNeuXV7ulaR9f/0rAEBh2pn9686StOeve+65p2EqAACQI7MgAABoaMyCAAAgCcyCAACAVEl7F1n/CgCQP2ZBbE//CgCQbGZBbM8siPrlw+QUlEGDBqXEFixYkFPO+fPnp8S6desWHTp0yCkvAAB8adWqVWnjbdq0qXZduv63vLw8Fi5cmHUtr7/+epSXl6fEDzjggKxzAgBQ2NL1nR9++GGsWbMm65zpnrtG1Nx3pqslIrfnwJ9++ml8+OGHta4FAIDGI90z3Jqe30ZE7LffflFcXJwSz6V/raysTLte/woAkB9btmyJb3zjG/H444+nXBswYEA8/vjjsfvuu+ftfkna99e/AgAUnp3dv9aFbJ+/7rHHHtG1a9eUeF2cI9O/AgBAfpgFAQBAITILAgCAJDMLAgCAxs4sCACAxsMsCP0rAEAhMQvCLIik8WFyCsrBBx+cElu0aFGsX78+65zz5s3L6D4AAJCtV155JW28R48e1a4bMmRINGmS+mfbc889l3Ut6frfpk2bxuDBg7POCQBAYavqeWi++84OHTpEnz59ql3Xt2/faN++fZ3XEuE5MAAAX1i9enUsX748JV7T89uIiBYtWsTAgQNT4rn0rwsXLowNGzakxPWvAAC527p1a5x22mnx2GOPpVzr379/PP7443n/kEmS9v31rwAAhaU++td827ZtW/z1r39NiWfy/DUifV+ZS/+6bt26ePPNNzO6DwAAUHtmQQAAUIjMggAAIMnMggAAoDEzCwIAoPEwC0L/CgBQSMyCMAsiiXyYnIJy7LHHpsTKy8tj9uzZWeVbtWpVzJ8/P6P7AABAtv7yl7+kjQ8aNKjade3atYshQ4akxGfNmpV1LenWDhkyJNq1a5d1TgAACtuAAQPiK1/5Sko8333nMcccE0VFRdWuKyoqimOOOabOa+nUqVMMGDAg65wAADQcDz30UFRWVqbEa3p++6V07xfMmTMntm3bllU96frXkpKSOPLII7PKBwDAF0pLS+OMM86ImTNnplzbe++9Y86cOdGxY8e83zdp+/76VwCAwlBf/Wu+Pf3007Fu3bqUeC7PX+fPnx+rV6/Oqp7Zs2dHRUVFRvcBAABqzywIAAAKkVkQAAAkmVkQAAA0ZmZBAAA0DmZBfEH/CgBQGMyC+IJZEMnjw+QUlP79+0ffvn1T4lOnTs0q35///OeUTaWioqI4+eSTs8oHAAA7evfdd+Oee+5Jiffr1y+6d+9e4/pTTjklJfboo4/G2rVra13LmjVr4rHHHkuJjxgxota5AABoOKp6Jjp9+vQoLy+vdb6FCxfGG2+8kRLPtO9M1wMvXLgwFi1aVOtatm3bFtOnT8/oHgAAND7btm2L66+/PiVeVFQURx11VEY50vWWa9eujUcffTSrmv70pz+lxIYNGxa77rprVvkAAPii7zvrrLPioYceSrm21157xdy5c6NTp051dv8k7fvrXwEAkq+++9d8uvbaa9PG0w2pTmf48OEpQ68rKipi2rRpWdWTrn/t169ffPWrX80qHwAAsD2zIAAAKDRmQQAAkHRmQQAA0FiZBQEA0DjU91m6JO37618BAJKvvvvXfDILouHxYXIKzrnnnpsSu//++2P58uW1ylNZWRk333xzSnzYsGHRrVu3rOsDAIAvlZaWxnnnnRdlZWUp19L1temMHDky5Q/p0tLSuO2222pdz+TJk6O0tHS7WFFRUZxzzjm1zgUAQMOSrj/9+OOPs9rAuemmm1JirVu3jm984xsZrT/11FOjVatWKfFf//rXta5l+vTp8cknn6TEM+3HAQBo2CZNmhSLFy9OidfmvYHDDz88evXqlRLPpn99/vnn45VXXkmJ618BALJXXl4eZ599dtx///0p1/bcc8+YM2dOdO7cuU5rSNK+v/4VACDZktC/5sv//M//xJw5c1Liffr0iUMPPTSjHD169Ih/+Zd/SYnfdNNNKR8nrMny5cvjgQceSInrXwEAIL/MggAAoFCYBQEAQKEwCwIAgMbILAgAgIYvCWfpkrTvr38FAEi2JPSv+WIWRMPkw+QUnPPPPz+aNWu2XaysrCyuuuqqWuWZMmVKvPXWWynxiy66KKf6AAAofDNnzoy//e1vOeXYvHlzjBw5MubOnZtybdddd40LL7wwozy9e/eOk046KSX+05/+tFY1fvrpp/GTn/wkJX7yySen3WgCAKBxOfLII2PAgAEp8WuuuSblxabqvP3223HHHXekxEePHp32gGk6rVq1ilGjRqXE77jjjrQHBaqydevWuPrqq1PiAwcOTLtZBQBAYXjppZfi7bffzjnPz372s/jRj36U9toVV1yRcZ6ioqK0z3tnzZqV9vlwVSorK+Pyyy9PiXfs2DG+9a1vZZwHAIB/qKioiO985zsxffr0lGt9+vSJuXPnRteuXeu8jiTt++tfAQCSKwn961tvvRUvv/xyznnuueee+N73vpf22uWXX54yrKU66c55vfnmm/G73/2uVjVNmDAhtm3btl2sefPmcd5559UqDwAAUD2zIAAAqGtmQQAA0NiYBQEAQKEwCwIAgEwl4SxdRLL2/fWvAADJlYT+1SwIauLD5BScLl26xJgxY1Lif/jDH2LatGkZ5Vi8eHFccsklKfEBAwbEN7/5zZxrBACgsN12223Ro0ePuOiii+L555+PysrKWq1/8sknY/DgwWkfCEREXHfdddG+ffuM86UbvLJmzZoYPXp0VFRU1Li+vLw8Ro8eHWvXrt0uXlRUFBMmTMi4DgAAGq6ioqK0feeSJUvSPktN5/PPP4+RI0dGWVnZdvEWLVrEpZdeWqt6LrvssmjevPl2sbKyshg5cmRs3rw5oxzjx4+Pd955JyVe28GGAAAky7PPPhv9+/ePb37zm/HQQw/Fli1barX+3XffjREjRsRll12W9vrpp58exx13XK1yXnDBBbHHHnukxEePHh2ffvppRjl+8pOfxFNPPZUSv/TSS6NFixa1qgcAgC8OS44dOzbuvvvulGu9e/eOuXPnRrdu3XZaPUna99e/AgAkT1L617feeisOOuigOOqoo2Lq1KmxYcOGWq1fuXJljBs3Ls4555woLy9PuX7QQQfFuHHjapXztNNOi3333TclXtU7AelMnTo17rzzzpT42LFjo3PnzrWqBwAAqJ5ZEAAA1DWzIAAAaGzMggAAoFCYBQEAQCaScpbuS0na99e/AgAkT1L6V7MgqElRZW3fqoYE+PTTT2PvvfeONWvWbBdv3rx5TJkyJc4+++wq186fPz9GjBgRH374Ycq1OXPmxFFHHZX3egEAKCzf/OY344EHHvj7f+7SpUscf/zxccABB8TAgQOjZ8+e0aZNm2jTpk2UlZXF2rVr45133olnn302pk+fHvPnz68y97e+9a2YOnVqrWv67ne/G3/4wx9S4t/+9rfj9ttvj1122SXtuk2bNsWYMWPiz3/+c9qcv/vd72pdCwAAO9/SpUujd+/eKfG5c+fGsGHD8nafYcOGxZNPPpkSv+SSS+K///u/o7i4OO26Tz/9NM4888x44oknUq5NnDgxJk2aVOtaJk6cGP/1X/+VEj/66KNj6tSp0aFDh7Trtm3bFpdeemn86le/Srl25JFHpq0RAID8qsv+9Ze//GWMHz/+7/+5devW8fWvfz2GDBkSAwcOjL322ivatWsXbdq0iSZNmsTatWtjxYoVMW/evJg5c2bMnDmzypf999prr3jhhReiXbt2ta5rypQpaQdrDxw4MB544IHo1atXlWt/9rOfxf/9v/83ZTBiv379YuHChdGsWbNa1wMA0NhddNFFcfPNN6fEd9lll7jjjjuiZ8+eOd+jefPmMWjQoIx/n6R9f/0rAECyJKV/vf/+++PUU0/dbs3RRx8dQ4cOjYEDB0b//v1jt912izZt2kRJSUmsW7cuPv7443jhhRdi9uzZcd9996UMsP5Sx44d48UXX8zqv8ucOXPimGOOSYn36NEjHnzwwdh///2rXHvXXXfFmDFjorS0dLv47rvvHosXL67Vx2UAAIDMmAUBAEBdMgsCAICkMQviH8yCAABIPrMg/sFZOgCA+pGUs3T/LEn7/vpXAIBkSUr/ahYENfFhcgrWjv/A/bMTTzwxLrjggjjkkEOiQ4cOsWHDhliwYEHcdddd8bvf/S7tP2zjx4+PX/ziF3VdNgAABWDHw6j58q1vfSv++Mc/RklJSa3Xrlu3LgYNGhRLly5NudajR48YP358nHjiiX/fEHrvvffikUceiV/84hdpB7H07t07Xn311axeqgIAIL+qelE/X95///1qXxzasZZBgwbFunXrUq7ts88+MX78+DjmmGOiW7duUVZWFkuWLIkHHnggfvWrX6UMD4yIOPjgg+Ppp5/OqgcuLS2NI444Il588cWUa+3bt4//+I//iBEjRkS/fv2iuLg4li9fHrNnz45f/vKX8eabb6as2W233WL+/Pl52aQDAGjM6rt/3fEwar7su+++MWvWrOjatWvWOU499dS4//77U+KtWrWKCy+8MM4444zYZ599onnz5rFy5cp46qmn4sYbb4wXXnghZU2zZs3i2WefjSFDhmRdDwBAY9arV69YtmxZnd6jZ8+eaffwq5K0fX/9KwBAciSlf63urFYuunXrFo888kjsu+++WecYP358/PKXv0yJl5SUxJgxY2LkyJExcODAaN26daxevTrmzZsXt912W8yaNSttvgceeCBGjBiRdT0AAED1zIIAAKCumAUBAMDOVN9n6XasxSwIAACqU9/9q1kQAABkIiln6f5Z0vb99a8AAMmRlP7VLAhq4sPkFLQf//jHceWVV+ac5+STT4777rsvqxeiAABoePJ9GLVNmzZxww03xJgxY3LK8/rrr8ewYcPSvuRfG+3bt48nnngiBgwYkFMeAADyo75f5t/RnDlzYvjw4bFly5ac7tunT5946qmncnqZf8WKFXHEEUfE+++/n1MtLVu2jBkzZsRRRx2VUx4AAOq/f833YdQmTZrEv//7v8d1110XLVq0yCnXZ599Fsccc0y8/PLLOdd05513xjnnnJNTHgCAxiwpL/PvKEn7/vpXAIDkSEr/WheHUc8999y48cYbc/54SllZWZx66qkxY8aMnGv68Y9/HFdccUXOeQAAgOqZBQEAQF0wCwIAgJ2pvs/S7cgsCAAAqlPf/atZEAAAZCIpZ+l2lKR9f/0rAEByJKV/NQuCmjSp7wIgFz/4wQ/ihhtuiKZNm2ad49xzz43p06c7iAoAwN8NHjw4OnTokHOe3XffPcaPHx8LFy7M+SBqRMR+++0XTzzxRE4vevXp08dBVAAAqnX00UfHzJkzc+qJDzzwwHjiiSdyOogaEdG1a9d48sknY9CgQVnn2GOPPWLmzJkOogIANBB77rln9OnTJ+c8zZo1i7POOivmzZsXv/jFL3I+iBrxxWD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      "text/plain": [
       "<Figure size 10000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 256\n",
    "rdSolver = GrayScott(N)\n",
    "L = rdSolver.laplacian()\n",
    "Du, Dv, F, K = 0.16, 0.08, 0.060, 0.062 \n",
    "Nt = 32000 \n",
    "\n",
    "rdSolver.initialise()\n",
    "rdSolver.integrate(Nt, Du, Dv, F, K, L)\n",
    "rdSolver.configPlot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spirals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 10000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 256\n",
    "rdSolver = GrayScott(N)\n",
    "L = rdSolver.laplacian()\n",
    "Du, Dv, F, K = 0.12, 0.08, 0.020, 0.050\n",
    "Nt = 32000 \n",
    "\n",
    "rdSolver.initialise()\n",
    "rdSolver.integrate(Nt, Du, Dv, F, K, L)\n",
    "rdSolver.configPlot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zebrafish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 10000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 256\n",
    "rdSolver = GrayScott(N)\n",
    "L = rdSolver.laplacian()\n",
    "Du, Dv, F, K = 0.16, 0.08, 0.035, 0.060\n",
    "Nt = 32000 \n",
    "\n",
    "rdSolver.initialise()\n",
    "rdSolver.integrate(Nt, Du, Dv, F, K, L)\n",
    "rdSolver.configPlot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}