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()
#model.outcomes = [ScalarOutcome('lastvalue-co2-emitted-to-air-global', ScalarOutcome.MINIMIZE),
# ScalarOutcome('sum-co2-emitted-to-air-global', ScalarOutcome.MAXIMIZE),
# ScalarOutcome('sum-subsidy-to-por-global', ScalarOutcome.MINIMIZE),
# ScalarOutcome('sum-subsidy-to-industries-global', ScalarOutcome.MINIMIZE)]
#np.max is used as the outcome is a list of one value
#-> it doesn't matter which function is used to extract it, np.max was chosen arbitrarily
model.outcomes = [
ScalarOutcome('2050 yearly CO2 emitted',
ScalarOutcome.MINIMIZE,
variable_name='lastvalue-co2-emitted-to-air-global',
function=np.max),
ScalarOutcome('sum-co2-emitted-to-air-global',
ScalarOutcome.MAXIMIZE,
variable_name='sum-co2-emitted-to-air-global',
function=np.max),
ScalarOutcome('sum-subsidy-to-por-global',
ScalarOutcome.MINIMIZE,
variable_name='sum-subsidy-to-por-global',
function=np.max),
ScalarOutcome('sum-subsidy-to-industries-global',
ScalarOutcome.MINIMIZE,
variable_name='sum-subsidy-to-industries-global',
function=np.max)
]
convergence = [
HyperVolume(minimum=[0, 0, 0, 0], maximum=[1e9, 1e9, 1e8, 1e8]),
EpsilonProgress()
]
with SequentialEvaluator(model) as evaluator:
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')
]
#perform experiments
n = 10
with MultiprocessingEvaluator(model) as evaluator:
results = perform_experiments(model, n, evaluator=evaluator)
fn = r'./data/{} runs.tar.gz'.format(n)
save_results(results, fn)
print "finish"
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)
model.uncertainties = [RealParameter('clustering-resources', 0, 1),
RealParameter('clustering-PCFs', 0, 1),
RealParameter('clustering-SCFs', 0, 1),
RealParameter('clustering-ECFs', 0, 1),
RealParameter('width-distributions-resources', 0, 1),
#RealParameter('width-distributions-PCFs', 0, 1),
RealParameter('width-distributions-SCFs', 0, 1),
RealParameter('width-distributions-ECFs', 0, 1),
RealParameter('social-network-size', 2, 8),
RealParameter('network-ratio', 0, 1)
]
model.outcomes = [TimeSeriesOutcome('level-of-achievability'),
TimeSeriesOutcome('personal-conversion-factor-shortage'),
TimeSeriesOutcome('social-conversion-factor-shortage'),
TimeSeriesOutcome('environmental-conversion-factor-shortage'),
TimeSeriesOutcome('TIME')]
#perform experiments
n = 100
with MultiprocessingEvaluator(model) as evaluator:
#results = evaluator.perform_experiments(scenarios=500)
results = perform_experiments(model, n, evaluator=evaluator)
fn = r'./data/{}_runs_cap_approach.tar.gz'.format(n)
save_results(results, fn)
RealParameter('clustering-resources', 0, 1),
RealParameter('clustering-PCFs', 0, 1),
RealParameter('clustering-SCFs', 0, 1),
RealParameter('clustering-ECFs', 0, 1),
RealParameter('width-distributions-resources', 0, 1),
#RealParameter('width-distributions-PCFs', 0, 1),
RealParameter('width-distributions-SCFs', 0, 1),
RealParameter('width-distributions-ECFs', 0, 1),
RealParameter('social-network-size', 2, 8),
RealParameter('network-ratio', 0, 1)
]
model.outcomes = [
TimeSeriesOutcome('level-of-achievability'),
TimeSeriesOutcome('personal-conversion-factor-shortage'),
TimeSeriesOutcome('social-conversion-factor-shortage'),
TimeSeriesOutcome('environmental-conversion-factor-shortage'),
TimeSeriesOutcome('TIME')
]
#perform experiments
n = 100
with MultiprocessingEvaluator(model) as evaluator:
#results = evaluator.perform_experiments(scenarios=500)
results = perform_experiments(model, n, evaluator=evaluator)
fn = r'./data/{}_runs_cap_approach.tar.gz'.format(n)
save_results(results, fn)
print "finish"
