def test_run_model(self): wd = r"../models" model_file = r"/Wolf Sheep Predation.nlogo" model = NetLogoModel("predPreyNetlogo", wd=wd, model_file=model_file) model.run_length = 1000 model.uncertainties = [ RealParameter("grass-regrowth-time", 10, 100), CategoricalParameter("grass?", ("true", "false")) ] model.outcomes = [ TimeSeriesOutcome('sheep'), TimeSeriesOutcome('wolves') ] model.model_init(Policy('no policy')) case = {"grass-regrowth-time": 35, "grass?": "true"} model.run_model(case) _ = model.retrieve_output() model.cleanup()
def _build_executable_model(mediator, context): # set the name of the EMAModel name = mediator.name # set the dir path for the model model_dir_path = mediator.model['dir'] # set the name of the model model_file = mediator.model['name'] # set the dir path where NetLogo is located netlogo_home = mediator.netlogo['dir'] # set the NetLogo version netlogo_version = mediator.netlogo['version'] # create the NetLogoModel object ema_model = NetLogoModel(name=name, wd=model_dir_path, model_file=model_file, netlogo_home=netlogo_home, netlogo_version=netlogo_version, gui=False) # set the max run length of this model ema_model.run_length = context.max_run_length # set the number of replications ema_model.replications = context.num_replications uncertainties, outcomes, constants = _determine_parameters( mediator, context) ema_model.uncertainties = uncertainties ema_model.outcomes = outcomes ema_model.constants = constants return ema_model
def test_run_model(self): wd = r"../models" model_file = r"/Wolf Sheep Predation.nlogo" model = NetLogoModel("predPreyNetlogo", wd=wd, model_file=model_file) model.run_length = 1000 model.uncertainties = [RealParameter("grass-regrowth-time", 10, 100), CategoricalParameter("grass?", ("true", "false")) ] model.outcomes = [TimeSeriesOutcome('sheep'), TimeSeriesOutcome('wolves')] model.model_init(Policy('no policy')) case = {"grass-regrowth-time": 35, "grass?": "true"} model.run_model(case) _ = model.retrieve_output() model.cleanup()
import numpy as np import pandas as pd import pyNetLogo import os from ema_workbench.connectors.netlogo import NetLogoModel ema_logging.log_to_stderr(ema_logging.INFO) if __name__ == '__main__': model = NetLogoModel("PoRModel", wd="./", model_file='git/model/PoR_Model.nlogo') model.run_length = 31 model.replications = 10 #netlogo = pyNetLogo.NetLogoLink(gui=False) #netlogo.load_model(os.path.abspath('git/model/PoR_Model.nlogo')) #netlogo.command('setup') # set levers model.levers = [ RealParameter('total-available-subsidy', 0, 100000000), RealParameter('subsidy-for-industries', 0, 200), RealParameter('total-subsidy-increase-for-target', 0, 15), RealParameter('industry-subsidy-increase-for-target', 0, 15), RealParameter('extensible-storage-price', 0, 50) ]
from ema_workbench.connectors.netlogo import NetLogoModel from ema_workbench import (RealParameter, ema_logging, perform_experiments, TimeSeriesOutcome, save_results) from ema_workbench.em_framework.evaluators import MultiprocessingEvaluator from ema_workbench.analysis.plotting import lines from ema_workbench.analysis.plotting_util import BOXPLOT if __name__ == '__main__': #turn on logging ema_logging.log_to_stderr(ema_logging.INFO) model = NetLogoModel('predprey', wd="./models/predatorPreyNetlogo", model_file="Wolf Sheep Predation.nlogo") model.run_length = 100 model.replications = 1 model.uncertainties = [ RealParameter("grass-regrowth-time", 1, 99), RealParameter("initial-number-sheep", 1, 200), RealParameter("initial-number-wolves", 1, 200), RealParameter("sheep-reproduce", 1, 20), RealParameter("wolf-reproduce", 1, 20), ] model.outcomes = [ TimeSeriesOutcome('sheep'), TimeSeriesOutcome('wolves'), TimeSeriesOutcome('grass'), TimeSeriesOutcome('TIME')
from ema_workbench.connectors.netlogo import NetLogoModel from ema_workbench.em_framework import (TimeSeriesOutcome, RealParameter, perform_experiments) from ema_workbench.util import ema_logging from ema_workbench.analysis import plotting, plotting_util if __name__ == '__main__': #turn on logging ema_logging.log_to_stderr(ema_logging.DEBUG) model = NetLogoModel('predprey', wd="./models/predatorPreyNetlogo", model_file="Wolf Sheep Predation.nlogo") model.run_length = 100 model.uncertainties = [RealParameter("grass-regrowth-time", 1, 99), RealParameter("initial-number-sheep", 1, 200), RealParameter("initial-number-wolves", 1, 200), RealParameter("sheep-reproduce", 1, 20), RealParameter("wolf-reproduce", 1, 20), ] model.outcomes = [TimeSeriesOutcome('sheep'), TimeSeriesOutcome('wolves'), TimeSeriesOutcome('grass') ] #perform experiments n = 100 results = perform_experiments(model, n, parallel=True)