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tpike3/advanced tutorial.ipynb

Created November 11, 2016 19:45
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advanced tutorial.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from mesa import Agent, Model\n",
"from mesa.time import RandomActivation\n",
"import random\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from mesa.space import MultiGrid\n",
"import numpy as np\n",
"from mesa.datacollection import DataCollector\n",
"from mesa.batchrunner import BatchRunner\n",
"from mesa.visualization.modules import CanvasGrid\n",
"from mesa.visualization.ModularVisualization import ModularServer\n",
"from mesa.visualization.modules import ChartModule\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_gini(model):\n",
" agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
" x = sorted(agent_wealths)\n",
" N = model.num_agents\n",
" B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))\n",
" return (1 + (1/N) - 2*B)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MoneyAgent(Agent):\n",
" \"\"\"An agent with fixed initial wealth.\"\"\"\n",
" def __init__(self, unique_id, model):\n",
" super().__init__(unique_id, model)\n",
" self.wealth = 1\n",
" \n",
" # agent move to cell based on moore neighborhood\n",
" def move(self):\n",
" possible_steps = self.model.grid.get_neighborhood(\n",
" self.pos,\n",
" moore=True,\n",
" include_center=False)\n",
" new_position = random.choice(possible_steps)\n",
" self.model.grid.move_agent(self, new_position) \n",
" \n",
" # interact with agents in grid\n",
" def give_money(self):\n",
" cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
" if len(cellmates) > 1:\n",
" other = random.choice(cellmates)\n",
" other.wealth += 1\n",
" self.wealth -= 1\n",
" \n",
" # Agent steps \n",
" def step(self):\n",
" self.move()\n",
" if self.wealth > 0:\n",
" self.give_money()\n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MoneyModel(Model):\n",
" \"\"\"A model with some number of agents.\"\"\"\n",
" def __init__(self, N, width, height):\n",
" self.num_agents = N\n",
" self.grid = MultiGrid(width, height, True)\n",
" self.schedule = RandomActivation(self)\n",
" self.running = True\n",
" \n",
" # Create agents\n",
" for i in range(self.num_agents):\n",
" a = MoneyAgent(i, self)\n",
" self.schedule.add(a)\n",
" \n",
" # Add the agent to a random grid cell\n",
" x = random.randrange(self.grid.width)\n",
" y = random.randrange(self.grid.height)\n",
" self.grid.place_agent(a, (x, y))\n",
" \n",
" self.datacollector = DataCollector(\n",
" model_reporters={\"Gini\": compute_gini},\n",
" agent_reporters={\"Wealth\": lambda a: a.wealth})\n",
" \n",
" def step(self):\n",
" self.datacollector.collect(self)\n",
" '''Advance the model by one step.'''\n",
" self.schedule.step()\n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = MoneyModel(50, 10, 10)\n",
"for i in range(20):\n",
" model.step()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x220a548d828>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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7NJLUSU4GkCS1anyGzkw0ktRJ9mgkSa2yRyNJatX49GicdSZJapU9GknqJIfO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\nJLXKRCNJapWJRpLUKhONJKlV/x9oy2t2pQ4/2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x220a539e550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"agent_counts = np.zeros((model.grid.width, model.grid.height))\n",
"for cell in model.grid.coord_iter():\n",
" cell_content, x, y = cell\n",
" agent_count = len(cell_content)\n",
" agent_counts[x][y] = agent_count\n",
"plt.imshow(agent_counts, interpolation='nearest')\n",
"plt.colorbar()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = MoneyModel(50, 10, 10)\n",
"for i in range(100):\n",
" model.step()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def agent_portrayal(agent):\n",
" portrayal = {\"Shape\": \"circle\",\n",
" \"Color\": \"red\",\n",
" \"Filled\": \"true\",\n",
" \"Layer\": 0,\n",
" \"r\": 0.5}\n",
" \n",
" \n",
" if agent.wealth > 0:\n",
" portrayal[\"Color\"] = \"red\"\n",
" portrayal[\"Layer\"] = 0\n",
" else:\n",
" portrayal[\"Color\"] = \"grey\"\n",
" portrayal[\"Layer\"] = 1\n",
" portrayal[\"r\"] = 0.2\n",
" return portrayal\n",
" \n",
" \n",
" return portrayal"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grid = CanvasGrid(agent_portrayal, 10, 10, 500, 500)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Interface starting at http://127.0.0.1:8889\n"
]
},
{
"ename": "RuntimeError",
"evalue": "IOLoop is already running",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-b57517a62713>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m 100, 10, 10)\n\u001b[1;32m 10\u001b[0m \u001b[0mserver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8889\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mserver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mC:\\Users\\Tom\\Anaconda3\\lib\\site-packages\\mesa\\visualization\\ModularVisualization.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, port)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Interface starting at http://127.0.0.1:{PORT}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPORT\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlisten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0mtornado\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mioloop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIOLoop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mC:\\Users\\Tom\\Anaconda3\\lib\\site-packages\\zmq\\eventloop\\ioloop.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mZMQIOLoop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 163\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mZMQError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrno\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mETERM\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Tom\\Anaconda3\\lib\\site-packages\\tornado\\ioloop.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 750\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 751\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_running\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 752\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"IOLoop is already running\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 753\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_logging\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stopped\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: IOLoop is already running"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Socket opened!\n",
"{\"type\":\"reset\"}\n",
"{\"type\":\"get_step\",\"step\":1}\n",
"{\"type\":\"get_step\",\"step\":2}\n",
"{\"type\":\"get_step\",\"step\":3}\n",
"{\"type\":\"get_step\",\"step\":4}\n",
"{\"type\":\"get_step\",\"step\":5}\n",
"{\"type\":\"get_step\",\"step\":6}\n",
"{\"type\":\"get_step\",\"step\":7}\n",
"{\"type\":\"get_step\",\"step\":8}\n",
"{\"type\":\"get_step\",\"step\":9}\n",
"{\"type\":\"get_step\",\"step\":10}\n",
"{\"type\":\"get_step\",\"step\":11}\n",
"{\"type\":\"get_step\",\"step\":12}\n",
"{\"type\":\"get_step\",\"step\":13}\n",
"{\"type\":\"get_step\",\"step\":14}\n",
"{\"type\":\"get_step\",\"step\":15}\n",
"{\"type\":\"get_step\",\"step\":16}\n",
"{\"type\":\"get_step\",\"step\":17}\n",
"{\"type\":\"get_step\",\"step\":18}\n",
"{\"type\":\"get_step\",\"step\":19}\n",
"{\"type\":\"get_step\",\"step\":20}\n",
"{\"type\":\"get_step\",\"step\":21}\n",
"{\"type\":\"get_step\",\"step\":22}\n",
"{\"type\":\"get_step\",\"step\":23}\n",
"{\"type\":\"get_step\",\"step\":24}\n",
"{\"type\":\"get_step\",\"step\":25}\n"
]
}
],
"source": [
"\n",
"chart = ChartModule([{\"Label\": \"Gini\",\n",
" \"Color\": \"Black\"}],\n",
" data_collector_name='datacollector')\n",
"\n",
"server = ModularServer(MoneyModel,\n",
" [grid],\n",
" \"Money Model\",\n",
" 100, 10, 10)\n",
"server.port = 8889\n",
"server.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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