324 lines
68 KiB
Plaintext
324 lines
68 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from decimal import Decimal\n",
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"import numpy as np\n",
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"from datetime import timedelta\n",
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"\n",
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"from SimCAD import configs\n",
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"from SimCAD.configuration import Configuration\n",
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"from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \\\n",
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" ep_time_step"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"sim_config = {\n",
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" 'N': 1,\n",
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" 'T': range(100000)\n",
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"}\n",
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"seed = {}\n",
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"env_processes = {}\n",
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"initial_condition = {\n",
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" 'Prey': float(10),\n",
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" 'Predator': float(10),\n",
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" 'timestamp': '2018-01-01 00:00:00'\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# Behaviors\n",
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"# There are no behaviors in this example\n",
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"\n",
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"# Mechanisms\n",
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"# There are no mechanisms in this example\n",
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"\n",
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"# Parameters\n",
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"alfa = 1.1e-3\n",
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"beta = 0.4e-3\n",
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"gama = 0.4e-3\n",
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"delta = 0.1e-3\n",
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"\n",
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"# Exogenous States\n",
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"def prey_model(step, sL, s, _input):\n",
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" y = 'Prey'\n",
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" x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']\n",
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" return (y, x)\n",
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"\n",
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"def predator_model(step, sL, s, _input):\n",
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" y = 'Predator'\n",
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" x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']\n",
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" return (y, x)\n",
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"\n",
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"ts_format = '%Y-%m-%d %H:%M:%S'\n",
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"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
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"def time_model(step, sL, s, _input):\n",
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" y = 'timestamp'\n",
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" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
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" return (y, x)\n",
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"\n",
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"\n",
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"exogenous_states = exo_update_per_ts(\n",
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" {\n",
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" 'Prey': prey_model,\n",
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" 'Predator': predator_model,\n",
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" 'timestamp': time_model\n",
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" }\n",
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")\n",
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"\n",
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"\n",
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"mechanisms = {\n",
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"}\n",
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"\n",
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"\n",
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"configs.append(\n",
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" Configuration(\n",
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" sim_config=sim_config,\n",
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" state_dict=initial_condition,\n",
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" seed=seed,\n",
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" exogenous_states=exogenous_states,\n",
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" env_processes=env_processes,\n",
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" mechanisms=mechanisms\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Behaviors\n",
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"def hunter(step, sL, s):\n",
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" kill = 0\n",
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" if (s['Predator'] > 2 * s['Prey']):\n",
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" kill = s['Predator']*0.5\n",
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" return {'value': kill}\n",
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"\n",
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"def dummy_behavior(step, sL, s):\n",
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" return {'value': 0}\n",
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"\n",
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"# Mechanisms\n",
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"def hunt(step, sL, s, _input):\n",
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" y = 'Predator'\n",
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" x = s['Predator'] - _input['value']\n",
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" return (y, x)\n",
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"\n",
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"\n",
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"# Parameters\n",
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"alfa = 1.1e-3\n",
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"beta = 0.4e-3\n",
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"gama = 0.4e-3\n",
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"delta = 0.1e-3\n",
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"\n",
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"# Exogenous States\n",
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"def prey_model(step, sL, s, _input):\n",
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" y = 'Prey'\n",
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" x = s['Prey'] + alfa*s['Prey'] - beta*s['Prey']*s['Predator']\n",
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" return (y, x)\n",
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"\n",
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"def predator_model(step, sL, s, _input):\n",
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" y = 'Predator'\n",
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" x = s['Predator'] + delta*s['Prey']*s['Predator'] - gama*s['Predator']\n",
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" return (y, x)\n",
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"\n",
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"ts_format = '%Y-%m-%d %H:%M:%S'\n",
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"t_delta = timedelta(days=0, minutes=0, seconds=1)\n",
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"def time_model(step, sL, s, _input):\n",
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" y = 'timestamp'\n",
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" x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)\n",
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" return (y, x)\n",
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"\n",
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"\n",
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"exogenous_states = exo_update_per_ts(\n",
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" {\n",
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"# 'Prey': prey_model,\n",
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"# 'Predator': predator_model,\n",
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" 'timestamp': time_model\n",
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" }\n",
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")\n",
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"\n",
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"\n",
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"mechanisms = {\n",
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" 'nature': {\n",
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" 'behaviors': {\n",
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" 'dummy': dummy_behavior\n",
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" },\n",
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" 'states': { \n",
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" 'Prey': prey_model,\n",
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" 'Predator': predator_model\n",
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" }\n",
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" \n",
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" },\n",
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" 'hunt_season': {\n",
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" 'behaviors': {\n",
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" 'hunter': hunter\n",
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" },\n",
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" 'states': { \n",
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" 'Predator': hunt\n",
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" }\n",
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" }\n",
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"}\n",
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"\n",
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"\n",
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"configs.append(\n",
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" Configuration(\n",
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" sim_config=sim_config,\n",
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" state_dict=initial_condition,\n",
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" seed=seed,\n",
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" exogenous_states=exogenous_states,\n",
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" env_processes=env_processes,\n",
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" mechanisms=mechanisms\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"multi_proc: [<SimCAD.configuration.Configuration object at 0x10c95c358>, <SimCAD.configuration.Configuration object at 0x10c9b0b00>]\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"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": [
|
|
"2.6177520081711023"
|
|
]
|
|
},
|
|
"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
|
|
}
|