crystal.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:5a69a947565c24e2a5c33559b2c7d88c1f2501b1ca4e32798f4c7c43c6aa523b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "class agent:\n",
      "    id_number\n",
      "    getLocation()\n",
      "    changeLocation()\n",
      "    category\n",
      "        carbon, iron, sulfur, seed (2)\n",
      "\n",
      "class swarm:\n",
      "    vector of agents\n",
      "        computeNextMove(agent locations, rules)\n",
      "        \n",
      "rules:\n",
      "    if carbon:\n",
      "        go toward the largest group of carbons\n",
      "    if sulfur:\n",
      "        go toward the nearest unpaired sulfur\n",
      "        after pairing: go toward nearest iron\n",
      "    if iron:\n",
      "        go toward the seed\n",
      "        never be next to another iron"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import pylab as plt\n",
      "from matplotlib import animation\n",
      "from JSAnimation import IPython_display\n",
      "\n",
      "swarm_params = { 'Ncarbon': 6, 'Nsulfur': 8, 'Niron': 5,'Nseed': 1 }\n",
      "color_dict = { 'carbon': 'k', 'sulfur' : 'y', 'iron' : 'g', 'seed' :'r' }\n",
      "xlims = [-5,5]\n",
      "ylims = [-5,5]\n",
      "\n",
      "class agent():\n",
      "    def __init__(self,id_number,pars):\n",
      "        self.id = id_number\n",
      "        self.xpos = pars['x_init']\n",
      "        self.ypos = pars['y_init']\n",
      "        self.cat = pars['cat']\n",
      "        self.fixed = pars['fixed']\n",
      "        return None\n",
      "    \n",
      "    def update_position(self, newx, newy):\n",
      "        self.xpos = newx\n",
      "        self.ypos = newy\n",
      "        return None\n",
      "    \n",
      "    def checkBCs(self):\n",
      "        if self.xpos > xlims[1]:\n",
      "            self.update_position(-5,self.ypos)\n",
      "        if self.xpos < xlims[0]:\n",
      "            self.update_position(5,self.ypos)\n",
      "        if self.ypos > ylims[1]:\n",
      "            self.update_position(self.xpos,-5)\n",
      "        if self.ypos < ylims[0]:\n",
      "            self.update_position(self.xpos, 5)\n",
      "    \n",
      "    def moveRight(self):\n",
      "        newx = self.xpos + 1\n",
      "        newy = self.ypos\n",
      "        self.update_position(newx,newy)\n",
      "        \n",
      "    def moveLeft(self):\n",
      "        newx = self.xpos - 1\n",
      "        newy = self.ypos\n",
      "        self.update_position(newx,newy)\n",
      "        \n",
      "    def moveUp(self):\n",
      "        newx = self.xpos\n",
      "        newy = self.ypos + 1\n",
      "        self.update_position(newx,newy)\n",
      "        \n",
      "    def moveDown(self):\n",
      "        newx = self.xpos \n",
      "        newy = self.ypos - 1\n",
      "        self.update_position(newx,newy)\n",
      "    \n",
      "    \n",
      "def agentSep(agent1,agent2):\n",
      "    return np.sqrt((agent1.xpos-agent2.xpos)**2+(agent1.ypos-agent2.ypos)**2)\n",
      "\n",
      "class swarm():\n",
      "    \n",
      "    def __init__(self, pars):\n",
      "        \n",
      "        self.members = []\n",
      "        self.fig = plt.figure()\n",
      "        self.ax = self.fig.gca()\n",
      "        \n",
      "        for i in np.arange(pars['Ncarbon']):\n",
      "            temp = agent(i,{'x_init':i, 'y_init':0, 'cat': 'carbon', 'fixed': False})\n",
      "            self.members.append(temp)\n",
      "        \"\"\"     \n",
      "        for i in np.arange(pars['Nsulfur']):\n",
      "            temp = agent(i,{'x_init':i, 'y_init':1, 'cat': 'sulfur', 'fixed': False})\n",
      "            self.members.append(temp)\n",
      "            \n",
      "        for i in np.arange(pars['Niron']):\n",
      "            temp = agent(i,{'x_init':i, 'y_init':2, 'cat': 'iron', 'fixed': False})\n",
      "            self.members.append(temp)\n",
      "        \"\"\"    \n",
      "        for i in np.arange(pars['Nseed']):\n",
      "            temp = agent(i,{'x_init':i, 'y_init':3, 'cat': 'seed', 'fixed': True})\n",
      "            self.members.append(temp)\n",
      "    \n",
      "    def checkMove(self,current,newx,newy):\n",
      "        for other in self.members:\n",
      "            if newx == other.xpos and newy == other.ypos:\n",
      "                return 1\n",
      "        return 0\n",
      "    \n",
      "    def timeEvolve(self, N):\n",
      "        counter = 0\n",
      "        while counter < N:\n",
      "            for member in self.members:\n",
      "                if not member.fixed:\n",
      "                    test = np.random.rand(1)\n",
      "                    if test >= 0 and test < 0.25:\n",
      "                        member.moveLeft()\n",
      "                    if test >= 0.25 and test < 0.5:\n",
      "                        member.moveRight()\n",
      "                    if test >= 0.5 and test < 0.75:\n",
      "                        member.moveUp()\n",
      "                    if test >= 0.75 and test <= 1:\n",
      "                        member.moveDown()\n",
      "                    member.checkBCs()\n",
      "                    member.fixed = self.shouldFixQ(member)\n",
      "            counter += 1\n",
      "                    \n",
      "                \n",
      "    def shouldFixQ(self,member):\n",
      "        for other in self.members:\n",
      "            if agentSep(member,other) == 1 and other.fixed:\n",
      "                return True\n",
      "        return False\n",
      "            \n",
      "    def showCurrentState(self):\n",
      "        self.ax.cla()\n",
      "        self.ax.set_xlim(xlims[0],xlims[1])\n",
      "        self.ax.set_ylim(ylims[0],ylims[1])\n",
      "        xvec = []\n",
      "        yvec = []\n",
      "        colvec = []\n",
      "        for member in self.members:\n",
      "            xvec.append(member.xpos)\n",
      "            yvec.append(member.ypos)\n",
      "            colvec.append(color_dict[member.cat])\n",
      "        scat = self.ax.scatter(xvec,yvec,c=colvec,)\n",
      "        return scat\n",
      "        \n",
      "    def animInit(self):\n",
      "        self.ax.grid(b=True, which='major', color='0.65',linestyle='-')\n",
      "        \n",
      "    def timeEvolveAndShowCurrentState(self):\n",
      "        print '1'\n",
      "        self.timeEvolve(1)\n",
      "        print '2'\n",
      "        scat = self.showCurrentState()\n",
      "        return scat\n",
      "        \n",
      "    def animateSwarm(self):\n",
      "        anim = animation.FuncAnimation(self.fig, self.timeEvolveAndShowCurrentState, init_func=self.animInit,\n",
      "                               frames=xrange(200))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classroom = swarm(swarm_params)\n",
      "classroom.timeEvolve(300)\n",
      "classroom.showCurrentState()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 169,
       "text": [
        "<matplotlib.collections.PathCollection at 0x109119890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x109144690>"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classroom = swarm(swarm_params)\n",
      "classroom.animateSwarm()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJXwX/B5doRo039wRHAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109116b50>"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def main():\n",
      "    numframes = 100\n",
      "    numpoints = 10\n",
      "    color_data = np.random.random((numframes, numpoints))\n",
      "    x, y, c = np.random.random((3, numpoints))\n",
      "\n",
      "    fig = plt.figure()\n",
      "    scat = plt.scatter(x, y, c=c, s=100)\n",
      "\n",
      "    ani = animation.FuncAnimation(fig, update_plot, frames=xrange(numframes),\n",
      "                                  fargs=(color_data, scat))\n",
      "    plt.show()\n",
      "\n",
      "def update_plot(i, data, scat):\n",
      "    scat.set_array(data[i])\n",
      "    return scat,\n",
      "\n",
      "main()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}