domitry/hoge.ipynb

## hoge.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ecell4 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = NetworkModel()\n",
    "m.add_species_attribute(Species(\"A\", \"0.01\", \"1\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = lattice.LatticeWorld(Real3(1,1,1), 0.005)\n",
    "w.bind_to(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w.add_molecules(Species(\"A\"), 100, AABB(Real3(0.45, 0.45, 0.45), Real3(0.55, 0.55, 0.55)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# viz.plot_world(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sim = lattice.LatticeSimulator(w)\n",
    "obs = FixedIntervalHistoryObserver(0.0001, [\"A\"], 64)\n",
    "sim.run(0.01, obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy; numpy.sum(obs.data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "  <head>\n",
       "    <script type=\"text/javascript\">\n",
       "     (function(){\n",
       "       if(window[\"THREE\"] === undefined || window[\"d3\"] === undefined){\n",
       "\t require.config({\n",
       "\t   paths:{\n",
       "\t     d3: [\n",
       "\t       'https://cdnjs.cloudflare.com/ajax/libs/d3/3.4.4/d3.min',\n",
       "\t       'custom/d3.min'\n",
       "\t     ],\n",
       "\t     THREE: [\n",
       "\t       'https://cdnjs.cloudflare.com/ajax/libs/three.js/r66/three.min',\n",
       "\t       'custom/three.min'\n",
       "\t     ],\n",
       "             Elegans: [\n",
       "\t       'https://rawgit.com/domitry/elegans/master/release/elegans',\n",
       "\t       'custom/elegans'\n",
       "\t     ]\n",
       "\t   },\n",
       "\t   shim:{\n",
       "\t     THREE: {\n",
       "\t       exports: 'THREE'\n",
       "\t     },\n",
       "\t     Elegans: {\n",
       "\t       deps: ['THREE'],\n",
       "\t       exports: 'Elegans'\n",
       "\t     }\n",
       "\t   }\n",
       "\t });\n",
       "\n",
       "\t require([\"d3\", \"THREE\"], function(d3){\n",
       "\t   window[\"d3\"] = d3;\n",
       "\n",
       "\t   d3.select(document)\n",
       "\t     .on(\"contextmenu\", function(){\n",
       "\t     d3.selectAll(\"canvas\").style(\"z-index\", 10);\n",
       "\t     d3.selectAll(\".download_menu\").style(\"z-index\", 100);\n",
       "\t   });\n",
       "\t });\n",
       "       }\n",
       "\n",
       "       require([\"Elegans\"], function(){\n",
       "\t console.log(\"Begin rendering...\");\n",
       "\t var model = {\"plots\": [{\"data\": \"data:image/png;base64,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\", \"type\": \"Volume\", \"options\": {\"f_per_row\": 8.0, \"name\": \"\", \"f_per_column\": 8.0, \"height\": 64, \"width\": 64, \"depth\": 64}}], \"options\": {\"save_image\": false, \"grid\": false}};\n",
       "\t Elegans.Embed.parse(\"#\"+\"viz9bdd066b-7fa8-4ef9-8000-f6470da503f1\",model).render();\n",
       "       });\n",
       "     })();\n",
       "    </script>\n",
       "  </head>\n",
       "  <body>\n",
       "    <div id=\"viz9bdd066b-7fa8-4ef9-8000-f6470da503f1\"></div>\n",
       "  </body>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz.plot_dense_array(obs.data(), input_histogram=True, colors=[\"#31a354\"], length=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# viz.plot_world(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from ecell4 import *"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"m = NetworkModel()\n",
	"m.add_species_attribute(Species(\"A\", \"0.01\", \"1\"))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"w = lattice.LatticeWorld(Real3(1,1,1), 0.005)\n",
	"w.bind_to(m)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"w.add_molecules(Species(\"A\"), 100, AABB(Real3(0.45, 0.45, 0.45), Real3(0.55, 0.55, 0.55)))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# viz.plot_world(w)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"sim = lattice.LatticeSimulator(w)\n",
	"obs = FixedIntervalHistoryObserver(0.0001, [\"A\"], 64)\n",
	"sim.run(0.01, obs)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"10100"
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"import numpy; numpy.sum(obs.data())"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {
	"collapsed": false,
	"scrolled": true
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<html>\n",
	" <head>\n",
	" <script type=\"text/javascript\">\n",
	" (function(){\n",
	" if(window[\"THREE\"] === undefined \|\| window[\"d3\"] === undefined){\n",
	"\t require.config({\n",
	"\t paths:{\n",
	"\t d3: [\n",
	"\t 'https://cdnjs.cloudflare.com/ajax/libs/d3/3.4.4/d3.min',\n",
	"\t 'custom/d3.min'\n",
	"\t ],\n",
	"\t THREE: [\n",
	"\t 'https://cdnjs.cloudflare.com/ajax/libs/three.js/r66/three.min',\n",
	"\t 'custom/three.min'\n",
	"\t ],\n",
	" Elegans: [\n",
	"\t 'https://rawgit.com/domitry/elegans/master/release/elegans',\n",
	"\t 'custom/elegans'\n",
	"\t ]\n",
	"\t },\n",
	"\t shim:{\n",
	"\t THREE: {\n",
	"\t exports: 'THREE'\n",
	"\t },\n",
	"\t Elegans: {\n",
	"\t deps: ['THREE'],\n",
	"\t exports: 'Elegans'\n",
	"\t }\n",
	"\t }\n",
	"\t });\n",
	"\n",
	"\t require([\"d3\", \"THREE\"], function(d3){\n",
	"\t window[\"d3\"] = d3;\n",
	"\n",
	"\t d3.select(document)\n",
	"\t .on(\"contextmenu\", function(){\n",
	"\t d3.selectAll(\"canvas\").style(\"z-index\", 10);\n",
	"\t d3.selectAll(\".download_menu\").style(\"z-index\", 100);\n",
	"\t });\n",
	"\t });\n",
	" }\n",
	"\n",
	" require([\"Elegans\"], function(){\n",
	"\t console.log(\"Begin rendering...\");\n",
	"\t var model = {\"plots\": [{\"data\": \"data:image/png;base64,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\", \"type\": \"Volume\", \"options\": {\"f_per_row\": 8.0, \"name\": \"\", \"f_per_column\": 8.0, \"height\": 64, \"width\": 64, \"depth\": 64}}], \"options\": {\"save_image\": false, \"grid\": false}};\n",
	"\t Elegans.Embed.parse(\"#\"+\"viz9bdd066b-7fa8-4ef9-8000-f6470da503f1\",model).render();\n",
	" });\n",
	" })();\n",
	" </script>\n",
	" </head>\n",
	" <body>\n",
	" <div id=\"viz9bdd066b-7fa8-4ef9-8000-f6470da503f1\"></div>\n",
	" </body>\n",
	"</html>"
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"viz.plot_dense_array(obs.data(), input_histogram=True, colors=[\"#31a354\"], length=64)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 45,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# viz.plot_world(w)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}