cadCAD/demos/predator_prey_hunter.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from decimal import Decimal\n",
"import numpy as np\n",
"from datetime import timedelta\n",
"\n",
"from SimCAD import configs\n",
"from SimCAD.configuration import Configuration\n",
"from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
" ep_time_step"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sim_config = {\n",
" 'N': 1,\n",
" 'T': range(100000)\n",
"}\n",
"seed = {}\n",
"env_processes = {}\n",
"initial_condition = {\n",
" 'Prey': float(10),\n",
" 'Predator': float(10),\n",
" 'timestamp': '2018-01-01 00:00:00'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Behaviors\n",
"# There are no behaviors in this example\n",
"\n",
"# Mechanisms\n",
"# There are no mechanisms in this example\n",
"\n",
"# Parameters\n",
"alfa = 1.1e-3\n",
"beta = 0.4e-3\n",
"gama = 0.4e-3\n",
"delta = 0.1e-3\n",
"\n",
"# Exogenous States\n",
"def prey_model(step, sL, s, _input):\n",
" y = 'Prey'\n",
" x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']\n",
" return (y, x)\n",
"\n",
"def predator_model(step, sL, s, _input):\n",
" y = 'Predator'\n",
" x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']\n",
" return (y, x)\n",
"\n",
"ts_format = '%Y-%m-%d %H:%M:%S'\n",
"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
"def time_model(step, sL, s, _input):\n",
" y = 'timestamp'\n",
" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
" return (y, x)\n",
"\n",
"\n",
"exogenous_states = exo_update_per_ts(\n",
" {\n",
" 'Prey': prey_model,\n",
" 'Predator': predator_model,\n",
" 'timestamp': time_model\n",
" }\n",
")\n",
"\n",
"\n",
"mechanisms = {\n",
"}\n",
"\n",
"\n",
"configs.append(\n",
" Configuration(\n",
" sim_config=sim_config,\n",
" state_dict=initial_condition,\n",
" seed=seed,\n",
" exogenous_states=exogenous_states,\n",
" env_processes=env_processes,\n",
" mechanisms=mechanisms\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Behaviors\n",
"def hunter(step, sL, s):\n",
" kill = 0\n",
" if (s['Predator'] > 2 * s['Prey']):\n",
" kill = s['Predator']*0.5\n",
" return {'value': kill}\n",
"\n",
"def dummy_behavior(step, sL, s):\n",
" return {'value': 0}\n",
"\n",
"# Mechanisms\n",
"def hunt(step, sL, s, _input):\n",
" y = 'Predator'\n",
" x = s['Predator'] - _input['value']\n",
" return (y, x)\n",
"\n",
"\n",
"# Parameters\n",
"alfa = 1.1e-3\n",
"beta = 0.4e-3\n",
"gama = 0.4e-3\n",
"delta = 0.1e-3\n",
"\n",
"# Exogenous States\n",
"def prey_model(step, sL, s, _input):\n",
" y = 'Prey'\n",
" x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']\n",
" return (y, x)\n",
"\n",
"def predator_model(step, sL, s, _input):\n",
" y = 'Predator'\n",
" x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']\n",
" return (y, x)\n",
"\n",
"ts_format = '%Y-%m-%d %H:%M:%S'\n",
"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
"def time_model(step, sL, s, _input):\n",
" y = 'timestamp'\n",
" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
" return (y, x)\n",
"\n",
"\n",
"exogenous_states = exo_update_per_ts(\n",
" {\n",
"# 'Prey': prey_model,\n",
"# 'Predator': predator_model,\n",
" 'timestamp': time_model\n",
" }\n",
")\n",
"\n",
"\n",
"mechanisms = {\n",
" 'nature': {\n",
" 'behaviors': {\n",
" 'dummy': dummy_behavior\n",
" },\n",
" 'states': { \n",
" 'Prey': prey_model,\n",
" 'Predator': predator_model\n",
" }\n",
" \n",
" },\n",
" 'hunt_season': {\n",
" 'behaviors': {\n",
" 'hunter': hunter\n",
" },\n",
" 'states': { \n",
" 'Predator': hunt\n",
" }\n",
" }\n",
"}\n",
"\n",
"\n",
"configs.append(\n",
" Configuration(\n",
" sim_config=sim_config,\n",
" state_dict=initial_condition,\n",
" seed=seed,\n",
" exogenous_states=exogenous_states,\n",
" env_processes=env_processes,\n",
" mechanisms=mechanisms\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"multi_proc: [<SimCAD.configuration.Configuration object at 0x113eac908>, <SimCAD.configuration.Configuration object at 0x113ecd128>]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from tabulate import tabulate\n",
"\n",
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
"from SimCAD import configs\n",
"\n",
"exec_mode = ExecutionMode()\n",
"\n",
"\n",
"multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
"run = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
"results = run.main()\n",
"for raw_result, tensor_field in results:\n",
" result = pd.DataFrame(raw_result)\n",
" result.plot('timestamp', ['Prey','Predator'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"a = pd.DataFrame(results[0][0])\n",
"b = pd.DataFrame(results[1][0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.017634498287318664"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a['Prey'].min()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.2929648442097132"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b['Prey'].min()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}