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kaizu/Intermidiate.ipynb

Last active August 29, 2015 14:16
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Intermidiate.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:126c95dc824bc0b4b4591b003d04987fbd00b5bdbf98f3bdaf9c5044df269820"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from ecell4 import *\n",
"viz.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",
"\td3: 'http://cdnjs.cloudflare.com/ajax/libs/d3/3.4.4/d3.min',\n",
"\tTHREE: 'http://cdnjs.cloudflare.com/ajax/libs/three.js/r66/three.min'\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",
"\twindow['d3'] = d3;\n",
"\twindow['THREE'] = THREE;\n",
"\tconsole.log('Finished loading d3.js and Three.js.');\n",
"\n",
"\t// URL shown below (rawgit.com) should be replaced!\n",
"\tvar script = d3.select(\"head\")\n",
"\t .append(\"script\")\n",
"\t .attr(\"src\", 'https://rawgit.com/domitry/elegans/master/release/elegans.js')\n",
"\t .attr(\"async\", true);\n",
"\n",
"\tscript[0][0].onload = script[0][0].onreadystatechange = function(){\n",
"\t var event = document.createEvent(\"HTMLEvents\");\n",
"\t event.initEvent(\"load_elegans\", true, false);\n",
"\t window.dispatchEvent(event);\n",
"\t console.log('Finished loading Elegans.js ;)');\n",
"\t};\n",
" });\n",
"}\n",
"</script>\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f13a91d5690>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with species_attributes():\n",
" A | B | C | {'D': '1'}\n",
"\n",
"with reaction_rules():\n",
" A + B > C | 0.01\n",
"\n",
"m = get_model()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = meso.MesoscopicWorld(Real3(10, 1, 1), Integer3(10, 1, 1))\n",
"w.bind_to(m)\n",
"w.add_molecules(Species('A'), 1200, Integer3(2, 0, 0))\n",
"w.add_molecules(Species('B'), 1200, Integer3(7, 0, 0))\n",
"cfg = viz.plot_world(w, radius=0.0025)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<html>\n",
" <head>\n",
" <script type=\"text/javascript\">\n",
" (function(){\n",
" function render(){\n",
"\t console.log(\"Begin rendering...\");\n",
"\t var model = {\"plots\": [{\"data\": {\"y\": [0.2958397804759443, 0.8819412803277373, 0.3558190499898046, 0.28622744767926633, 0.8115108436904848, 0.8970536980777979, 0.43049115617759526, 0.4391883707139641, 0.13175553060136735, 0.2560071398038417, 0.3942911841440946, 0.775970181915909, 0.598708426579833, 0.8534273409750313, 0.08226676704362035, 0.4036276482511312, 0.04061475279740989, 0.7016472208779305, 0.5391036365181208, 0.11707997997291386, 0.09541700198315084, 0.8546969783492386, 0.7094354201108217, 0.8261895170435309, 0.49769481061957777, 0.6930329096503556, 0.17002618522383273, 0.07921674149110913, 0.9707943869289011, 0.22186476620845497, 0.6242039387580007, 0.37783670285716653, 0.4754146889317781, 0.05554818129166961, 0.323813816299662, 0.5942050588782877, 0.5897772223688662, 0.5717377483379096, 0.68629230116494, 0.9948819102719426, 0.5265813355799764, 0.8719956218264997, 0.8530562971718609, 0.12122454098425806, 0.9267961443401873, 0.2737335681449622, 0.034842640394344926, 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0.8626532424241304, 0.5744527797214687, 0.1617841706611216, 0.2168785638641566, 0.49552004924044013, 0.13234497886151075, 0.6998753056395799, 0.7975326438900083, 0.7946510168258101, 0.5958276824094355, 0.02055078325793147, 0.7916917197871953, 0.006014055805280805, 0.3309205244295299, 0.6106704252306372, 0.9332096902653575, 0.8360827486030757, 0.023915016558021307, 0.4531180616468191, 0.29058545315638185, 0.8582588373683393, 0.09122389578260481, 0.8975225586909801, 0.30136020737700164, 0.6816118110436946, 0.01987437973730266, 0.9287108168937266, 0.3569079532753676, 0.07768935826607049, 0.9008167868014425, 0.7828332635108382, 0.6504237116314471, 0.4242856837809086, 0.2779266918078065]}, \"type\": \"Particles\", \"options\": {\"color\": \"#1f78b4\", \"name\": \"B\", \"size\": 0.075}}], \"options\": {\"width\": 500, \"range\": {\"y\": [-4.5, 5.5], \"x\": [0.0, 10.0], \"z\": [-4.5, 5.5]}, \"grid\": true, \"autorange\": false, \"height\": 500}};\n",
"\t Elegans.Embed.parse(\"#\"+\"vizc0f5d31a-d668-40a1-ba21-f1f7214c4f32\",model).render();\n",
" }\n",
"\n",
" var before_render = function(){\n",
"\t if(!(['d3', 'THREE', 'Elegans'].some(function(lib_name){\n",
"\t if(window[lib_name] === undefined){\n",
"\t console.log(lib_name + ' is not loaded.');\n",
"\t window.addEventListener(\"load_elegans\", before_render);\n",
"\t return true;\n",
"\t }\n",
"\t return false;\n",
"\t }))){\n",
"\t render();\n",
"\t };\n",
" };\n",
"\n",
" before_render();\n",
" })();\n",
" </script>\n",
" </head>\n",
" <body>\n",
" <div id=\"vizc0f5d31a-d668-40a1-ba21-f1f7214c4f32\"></div>\n",
" </body>\n",
"</html>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f13a1c46e90>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sim = meso.MesoscopicSimulator(w)\n",
"obs1 = NumberObserver(('A', 'B', 'C')) # XXX: saves the numbers after every steps\n",
"sim.run(5, obs1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = ode.ODEWorld(Real3(10, 1, 1))\n",
"w.bind_to(m)\n",
"w.add_molecules(Species('A'), 1200)\n",
"w.add_molecules(Species('B'), 1200)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sim = ode.ODESimulator(w)\n",
"obs2 = FixedIntervalNumberObserver(0.05, ('A', 'B', 'C')) # XXX: saves the numbers after every steps\n",
"sim.run(5, obs2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"viz.plot_number_observer(obs1, '-', obs2, '--')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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aMbV1L6oWt9E1NpGRMHWqSgw3b8KTJ9Cihaq3VLUquLhoHaFJJDZR+BBzmXGA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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f13a1c55e90>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with species_attributes():\n",
" A | B | C | {'D': '1'}\n",
"\n",
"with reaction_rules():\n",
" A + B > C | 1.0\n",
"\n",
"m = get_model()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = lattice.LatticeWorld(Real3(2, 1, 1), 0.005)\n",
"w.bind_to(m)\n",
"w.add_molecules(Species('A'), 120)\n",
"w.add_molecules(Species('B'), 120)\n",
"obs1 = FixedIntervalNumberObserver(0.005, ('A', 'B', 'C'))\n",
"sim = lattice.LatticeSimulator(w)\n",
"sim.run(1.0, obs1)\n",
"# viz.plot_world(w)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = ode.ODEWorld(Real3(2, 1, 1))\n",
"w.bind_to(m)\n",
"w.add_molecules(Species('A'), 120)\n",
"w.add_molecules(Species('B'), 120)\n",
"obs2 = FixedIntervalNumberObserver(0.005, ('A', 'B', 'C'))\n",
"sim = ode.ODESimulator(w)\n",
"sim.run(1.0, obs2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"viz.plot_number_observer(obs1, '-', obs2, '--')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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cIUSGkVqC6GP+OdkZgdiiQ90KjOvZDm7cgGee0Tsch/P28uSVZz6j2+IOHLtw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\n2QghXFvm6YPIl89y3+XLEB0NxYo5Px4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"text": [
"<matplotlib.figure.Figure at 0x7f13a1c12250>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with species_attributes():\n",
" A | {'D': '1', 'location': 'C'}\n",
"\n",
"m = get_model()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w = lattice.LatticeWorld(Real3(1, 1, 1), 0.005)\n",
"w.bind_to(m)\n",
"sph = Sphere(Real3(0.5, 0.5, 0.5), 0.45)\n",
"print(w.add_structure(Species('C'), sph)) # will print 539805\n",
"# viz.plot_world(w)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"539805\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w.add_molecules(Species('A'), 120)\n",
"# viz.plot_world(w, species_list=('A',)) # visualize A-molecules only"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pid_list = [pid for pid, p in w.list_particles(Species('A'))[: 10]]\n",
"obs = FixedIntervalTrajectoryObserver(1e-3, pid_list)\n",
"sim = lattice.LatticeSimulator(w)\n",
"sim.run(1, obs)\n",
"cfg = viz.plot_trajectory(obs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<html>\n",
" <head>\n",
" <script type=\"text/javascript\">\n",
" (function(){\n",
" function render(){\n",
"\t console.log(\"Begin rendering...\");\n",
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"\t Elegans.Embed.parse(\"#\"+\"viz206c8dbc-a50d-42c9-86f1-9013a5045903\",model).render();\n",
" }\n",
"\n",
" var before_render = function(){\n",
"\t if(!(['d3', 'THREE', 'Elegans'].some(function(lib_name){\n",
"\t if(window[lib_name] === undefined){\n",
"\t console.log(lib_name + ' is not loaded.');\n",
"\t window.addEventListener(\"load_elegans\", before_render);\n",
"\t return true;\n",
"\t }\n",
"\t return false;\n",
"\t }))){\n",
"\t render();\n",
"\t };\n",
" };\n",
"\n",
" before_render();\n",
" })();\n",
" </script>\n",
" </head>\n",
" <body>\n",
" <div id=\"viz206c8dbc-a50d-42c9-86f1-9013a5045903\"></div>\n",
" </body>\n",
"</html>"
],
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{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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