hoge.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:771accee14e3f67147be65c405ce7a08c1121bcb4c3f29cb02fb048615467f1b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from ecolisim import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "init_ipynb()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script>\n",
        " if(window['d3'] === undefined ||\n",
        "    window['THREE'] === undefined ||\n",
        "    window['Elegans'] === undefined){\n",
        "      var paths = {\n",
        "\t  d3: 'http://cdnjs.cloudflare.com/ajax/libs/d3/3.4.4/d3.min',\n",
        "\t  THREE: 'http://cdnjs.cloudflare.com/ajax/libs/three.js/r66/three.min',\n",
        "          Elegans: 'https://rawgit.com/domitry/elegans/ecolisim/release/elegans'\n",
        "      };\n",
        "\n",
        "      var shim = {\n",
        "\t    'THREE': {exports: 'THREE'}\n",
        "      };\n",
        "      \n",
        "      console.log('Begin loading all JavaScript libs...');\n",
        "      require.config({paths: paths, shim: shim});\n",
        "\n",
        "      require(['d3', 'THREE'], function(d3, THREE){\n",
        "\t    window['d3'] = d3;\n",
        "\t    window['THREE'] = THREE;\n",
        "\t    console.log('Finished loading d3.js and Three.js.');\n",
        "\n",
        "        require(['Elegans'], function(Elegans){\n",
        "          window['Elegans'] = Elegans;\n",
        "\t      console.log('Finished loading Elegans.js ;)');\n",
        "          console.log(\"dispatch events...\");\n",
        "\t      var event = document.createEvent(\"HTMLEvents\");\n",
        "\t      event.initEvent(\"load_elegans\", true, false);\n",
        "\t      window.dispatchEvent(event);\n",
        "        });\n",
        "      });\n",
        "    }\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f8c0f388310>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = Simulator(switch=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state = sim.state"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m1 = MaterialPoint(pos=[2.4e-6, 2.5e-6,0])\n",
      "m2 = MaterialPoint(pos=[0,0,0])\n",
      "m3 = MaterialPoint(pos=[0,5.0e-6, 0])\n",
      "m4 = MaterialPoint(pos=[0,0,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e1 = Ecoli(m1,m2, state.config, species={A: 3, B: 5})\n",
      "e2 = Ecoli(m4,m3, state.config, species={A: 5, B: 3})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state.add(e1)\n",
      "state.add(e2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Normal Parameter"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.run(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "divide!\n",
        "divided!\n",
        "divide!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "divided!\n",
        "divide!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "divided!\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<html>\n",
        "  <head>\n",
        "    <script type=\"text/javascript\">\n",
        "     (function(){\n",
        "       /*\n",
        "       data -> [{df: {x: [0,1,2,..,4], y: , z: , height, x_rad, y_rad, z_rad}, t: 0}, {}]\n",
        "\t*/\n",
        "\n",
        "       function render(){\n",
        "\t console.log(\"Begin rendering...\");\n",
        "\t var options = {\"player\": true, \"switch\": true, \"range\": {\"y\": [0, 5e-05], \"x\": [0, 5e-05], \"z\": [0, 5e-05]}, \"autorange\": false, \"space_mode\": \"wireframe\"};\n",
        "\t var stage = new Elegans.Stage(d3.select(\"#\"+\"viz55b906f9-6c9b-48bf-a25b-2e721ff53c5f\")[0][0], options);\n",
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1.8653702833697757e-06, 1.9076831694739757e-06, 1.9499960555781755e-06, 1.9923089416823754e-06, 3.2803534945817685e-06, 3.2778909539614189e-06, 3.2754284133410697e-06, 3.2729658727207201e-06, 3.2705033321003705e-06, 3.2680407914800213e-06, 3.2655782508596717e-06, 3.2631157102393221e-06, 3.260653169618973e-06, 3.2581906289986233e-06, 3.2557280883782737e-06, 3.2532655477579246e-06, 3.250803007137575e-06, 1.2546818369086328e-06, 1.2579730671588471e-06, 1.2612642974090617e-06, 1.2645555276592761e-06, 1.2678467579094907e-06, 1.271137988159705e-06, 1.2744292184099196e-06, 1.109565500047189e-06, 1.1364666312057744e-06, 1.1633677623643598e-06, 1.190268893522945e-06, 1.2171700246815304e-06, 1.2440711558401158e-06, 1.2709722869987012e-06, 1.2978734181572866e-06, 1.324774549315872e-06, 1.3516756804744574e-06, 9.839884411724626e-07, 9.8521534430450885e-07, 9.8644224743655489e-07, 9.8766915056860114e-07, 9.8889605370064739e-07, 9.9012295683269364e-07, 9.9134985996473967e-07, 9.9257676309678592e-07, 9.9380366622883217e-07, 2.0427969525043638e-06, 2.038059483622336e-06, 2.0333220147403082e-06, 2.0285845458582808e-06, 2.023847076976253e-06], \"fill\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.9961565875226187, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.9995986267147265, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003317549101199599, 0.003317549101199599, 0.003317549101199599, 0.003317549101199599, 0.003317549101199599]}, \"t\": 10}];\n",
        "\n",
        "\t console.log(data);\n",
        "\n",
        "\t var colors = [\"#8dd3c7\",\"#bebada\",\"#fb8072\",\"#80b1d3\",\"#fdb462\",\"#b3de69\",\"#fccde5\",\"#d9d9d9\",\"#bc80bd\",\"#ccebc5\",\"#ffed6f\",\"#ffffb3\"];\n",
        "\n",
        "\t if(options['switch']){\n",
        "\t   Elegans.DataBase.add(\"database-particles\", data, \"df\", \"t\", 0)\n",
        "\t   console.log(\"rendering toggle switch\");\n",
        "\t   stage.add(new Elegans.Particles(\"database-particles\", {\n",
        "\t     color: [\"#d73027\", \"#4575b4\"],\n",
        "\t     fill_by: 'fill',\n",
        "\t     fill_by_range: [0.0, 1.0],\n",
        "\t     name: \"particles\"\n",
        "\t   }));\n",
        "\t }else{\n",
        "\t   for(var i=0; i< data.length; i++){\n",
        "\t     Elegans.DataBase.add(\"database-particles\"+String(i), data[i], \"df\", \"t\", 0)\n",
        "\t     stage.add(new Elegans.Particles(\"database-particles\"+String(i), {\n",
        "\t       color: colors[i],\n",
        "\t       name: \"particles\"\n",
        "\t     }));\n",
        "\t   }\n",
        "\t }\n",
        "\n",
        "\t stage.render();\n",
        "       }\n",
        "\n",
        "       var before_render = function(){\n",
        "         if(!(['d3', 'THREE', 'Elegans'].some(function(lib_name){\n",
        "           if(window[lib_name] === undefined){\n",
        "             console.log(lib_name + ' is not loaded.');\n",
        "             window.addEventListener(\"load_elegans\", before_render);\n",
        "             return true;\n",
        "           }\n",
        "           return false;\n",
        "         }))){\n",
        "           render();\n",
        "         };\n",
        "       };\n",
        "\n",
        "       before_render();\n",
        "     })();\n",
        "    </script>\n",
        "  </head>\n",
        "  <body>\n",
        "    <div id=\"viz55b906f9-6c9b-48bf-a25b-2e721ff53c5f\"></div>\n",
        "  </body>\n",
        "</html>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f8c0a9c43d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.state.objects[0].sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cjkSqukBXGc0E+mKmy/4NeB5TApiNmTk1HhhqHRtn7Y/DrMx2P8VXJ4lUCx+u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\nZJj9LVrAyJFm+5dfTMLydeo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       "text": [
        "<matplotlib.figure.Figure at 0x7fee621a2810>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.state.objects[1].sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fee620f1f90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.state.objects[2].sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RvZKISBjZSRJg1rj+JMBzewfiXQVUxpQm3sWUHhoBuzED9vZYx6cBzX0+38za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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f40a8e17950>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Another Parameter"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = Simulator(switch=True)\n",
      "state = sim.state\n",
      "m1 = MaterialPoint(pos=[2.4e-6, 2.5e-6,0])\n",
      "m2 = MaterialPoint(pos=[0,0,0])\n",
      "m3 = MaterialPoint(pos=[0,5.0e-6, 0])\n",
      "m4 = MaterialPoint(pos=[0,0,0])\n",
      "e1 = Ecoli(m1,m2, state.config, species={A: 3, B: 5})\n",
      "e2 = Ecoli(m4,m3, state.config, species={A: 5, B: 3})\n",
      "state.add(e1)\n",
      "state.add(e2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gillespie_constants.update({\n",
      "    \"alpha1\" : 10.0,\n",
      "    \"alpha2\" : 10.0,\n",
      "    \"beta\" : 1.1,\n",
      "    \"gamma\" : 1.1,\n",
      "    \"c1\" : 1,\n",
      "    \"c2\" : 1\n",
      "})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.run(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "divide!\n",
        "divided!\n",
        "divide!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "divided!\n",
        "divide!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "divided!\n",
        "divide!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "divided!\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<html>\n",
        "  <head>\n",
        "    <script type=\"text/javascript\">\n",
        "     (function(){\n",
        "       /*\n",
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        "\t*/\n",
        "\n",
        "       function render(){\n",
        "\t console.log(\"Begin rendering...\");\n",
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3.4281253798862782e-07, 3.3477600273806294e-07, 3.2673946748749806e-07, 3.1870293223693312e-07], \"fill\": [0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 0.8571428571428571, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.1111111111111111, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 0.3333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 0.02640923761385775, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.2857142857142857, 0.0, 0.0, 0.0, 0.0, 0.6571733086231297, 0.6571733086231297, 0.6571733086231297, 0.6571733086231297, 0.6571733086231297]}, \"t\": 15}];\n",
        "\n",
        "\t console.log(data);\n",
        "\n",
        "\t var colors = [\"#8dd3c7\",\"#bebada\",\"#fb8072\",\"#80b1d3\",\"#fdb462\",\"#b3de69\",\"#fccde5\",\"#d9d9d9\",\"#bc80bd\",\"#ccebc5\",\"#ffed6f\",\"#ffffb3\"];\n",
        "\n",
        "\t if(options['switch']){\n",
        "\t   Elegans.DataBase.add(\"database-particles\", data, \"df\", \"t\", 0)\n",
        "\t   console.log(\"rendering toggle switch\");\n",
        "\t   stage.add(new Elegans.Particles(\"database-particles\", {\n",
        "\t     color: [\"#d73027\", \"#4575b4\"],\n",
        "\t     fill_by: 'fill',\n",
        "\t     fill_by_range: [0.0, 1.0],\n",
        "\t     name: \"particles\"\n",
        "\t   }));\n",
        "\t }else{\n",
        "\t   for(var i=0; i< data.length; i++){\n",
        "\t     Elegans.DataBase.add(\"database-particles\"+String(i), data[i], \"df\", \"t\", 0)\n",
        "\t     stage.add(new Elegans.Particles(\"database-particles\"+String(i), {\n",
        "\t       color: colors[i],\n",
        "\t       name: \"particles\"\n",
        "\t     }));\n",
        "\t   }\n",
        "\t }\n",
        "\n",
        "\t stage.render();\n",
        "       }\n",
        "\n",
        "       var before_render = function(){\n",
        "         if(!(['d3', 'THREE', 'Elegans'].some(function(lib_name){\n",
        "           if(window[lib_name] === undefined){\n",
        "             console.log(lib_name + ' is not loaded.');\n",
        "             window.addEventListener(\"load_elegans\", before_render);\n",
        "             return true;\n",
        "           }\n",
        "           return false;\n",
        "         }))){\n",
        "           render();\n",
        "         };\n",
        "       };\n",
        "\n",
        "       before_render();\n",
        "     })();\n",
        "    </script>\n",
        "  </head>\n",
        "  <body>\n",
        "    <div id=\"viz22e25b6b-2b6e-4de6-b777-3d30435375c8\"></div>\n",
        "  </body>\n",
        "</html>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f4261e53fd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.state.objects[0].sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dWIirMjJwjxvrd5yxFFd6qRoJIYELmaZpB46W4lrF2BC0hFA84jzBcYiVfE3u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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4261e53fd0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim.state.objects[1].sim.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3Kz+yFJVRo8QhlKMSsiz/7LAS9o2gZ07QOF/L/BRJ1Bm+dpBAcowuR84Wl+we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\nxOQXuAQH42oGvZyTi3QZlfpKkRZCLufJkc1kK1IIhkoUQiXTS3t7A2/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       "text": [
        "<matplotlib.figure.Figure at 0x7f425c2d6a50>"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}