def performExperiments():
ema_logging.LOG_FORMAT = '[%(name)s/%(levelname)s/%(processName)s] %(message)s'
ema_logging.log_to_stderr(ema_logging.INFO)
model = Model('SimulateER', function=simulate_ER) # instantiate the model
# specify uncertainties
model.uncertainties = [RealParameter("p", 0.1, 1.0)]
model.uncertainties = [RealParameter("f", 0.1, 0.9)]
model.levers = [IntegerParameter("n", 10, 100)]
# specify outcomes
model.outcomes = [ScalarOutcome('cc')]
model.constants = [Constant('replications', 10)]
n_scenarios = 10
n_policies = 10
res = perform_experiments(model, n_scenarios, n_policies)
experiments, outcomes = res
data = experiments[['n', 'p', 'f']]
data.to_csv('out.csv', index=False)
return data
def _get_exploratory_results(mediator, context):
# turn logging on
ema_logging.log_to_stderr(ema_logging.INFO)
# create a EMA Model Object
ema_model = _build_executable_model(mediator, context)
with MultiprocessingEvaluator(ema_model,
n_processes=context.num_processes,
maxtasksperchild=4) as evaluator:
# run model using EMA
results = evaluator.perform_experiments(context.num_experiments)
return results
This file illustrated the use the EMA classes for a contrived vensim
example
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
chamarat <c.hamarat (at) tudelft (dot) nl>
'''
from __future__ import (division, unicode_literals, absolute_import,
print_function)
from ema_workbench import (TimeSeriesOutcome, perform_experiments,
RealParameter, ema_logging)
from ema_workbench.connectors.vensim import VensimModel
if __name__ == "__main__":
#turn on logging
ema_logging.log_to_stderr(ema_logging.INFO)
#instantiate a model
wd = r'./models/vensim example'
vensimModel = VensimModel("simpleModel", wd=wd,
model_file=r'\model.vpm')
vensimModel.uncertainties = [RealParameter("x11", 0, 2.5),
RealParameter("x12", -2.5, 2.5)]
vensimModel.outcomes = [TimeSeriesOutcome('a')]
results = perform_experiments(vensimModel, 1000, parallel=True)
import statsmodels.api as sm
from patsy.highlevel import dmatrices
from ema_workbench import (Model, RealParameter, TimeSeriesOutcome,
ScalarOutcome, perform_experiments, ema_logging,
SequentialEvaluator)
from ema_workbench.analysis import plotting, plotting_util
from ema_workbench.em_framework.evaluators import LHS, SOBOL, MORRIS
from ema_workbench.analysis import feature_scoring
from ema_workbench.analysis.scenario_discovery_util import RuleInductionType
from ema_workbench.em_framework.salib_samplers import get_SALib_problem
from SALib.analyze import sobol
from ema_workbench import ema_logging
ema_logging.log_to_stderr(ema_logging.INFO)
def pred_prey(prey_birth_rate=0.025,
predation_rate=0.0015,
predator_efficiency=0.002,
predator_loss_rate=0.06,
initial_prey=50,
initial_predators=20,
dt=0.25,
final_time=365,
reps=1):
#Initial values
predators = np.zeros((reps, int(final_time / dt) + 1))
prey = np.zeros((reps, int(final_time / dt) + 1))
'''
Created on May 26, 2015
@author: jhkwakkel
'''
from __future__ import (print_function)
import matplotlib.pyplot as plt
import ema_workbench.analysis.cart as cart
from ema_workbench import ema_logging, load_results
ema_logging.log_to_stderr(level=ema_logging.INFO)
def classify(data):
# get the output for deceased population
result = data['deceased population region 1']
# if deceased population is higher then 1.000.000 people,
# classify as 1
classes = result[:, -1] > 1000000
return classes
# load data
fn = './data/1000 flu cases with policies.tar.gz'
results = load_results(fn)
experiments, results = results
''' Created on May 26, 2015 @author: jhkwakkel ''' import matplotlib.pyplot as plt import ema_workbench.analysis.cart as cart from ema_workbench import ema_logging, load_results ema_logging.log_to_stderr(level=ema_logging.INFO) default_flow = 2.178849944502783e7 # load data fn = './data/5000 runs WCM.tar.gz' results = load_results(fn) x, outcomes = results ooi = 'throughput Rotterdam' outcome = outcomes[ooi] / default_flow y = outcome < 1 cart_alg = cart.CART(x, y) cart_alg.build_tree() # print cart to std_out print(cart_alg.stats_to_dataframe()) print(cart_alg.boxes_to_dataframe()) # visualize
df_conversion_efficiencies = db_file.parse('ConversionEfficiencies')
df_conversion_units = db_file.parse('ConversionUnits')
df_demand = db_file.parse('Demand')
df_locations = db_file.parse('Locations')
df_max_converted = db_file.parse('MaxConverted')
df_max_flow = db_file.parse('MaxFlow')
df_network = db_file.parse('Network')
df_storage_units = db_file.parse('StorageUnits')
df_supply = db_file.parse('Supply')
df_supply_units = db_file.parse('SupplyUnits')
df_time_periods = db_file.parse('TimePeriods')
#model = Model('simpleModel', function=optim_model)
###############################################################################
#ema_logging.LOG_FORMAT = '[%(name)s/%(levelname)s/%(processName)s] %(message)s'
ema_logging.log_to_stderr(ema_logging.DEBUG)
##############################################################################
model = PyomoModel('simpleModel', wd='./data') # instantiate the model
##############################################################################
# specify uncertainties
model.uncertainties = [
CategoricalParameter('demand_index',
(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)),
RealParameter('dev_rate_wind', 0.011,
0.033), #0.022 is estimated base value
RealParameter('dev_rate_solar', 0.025,
0.075), #0.05 is estimated base value
RealParameter("dev_rate_storage_elec", 0.025,
0.075), #0.05 is estimated base value
'''
Created on 20 sep. 2011
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
import numpy as np
import matplotlib.pyplot as plt
from ema_workbench import load_results, ema_logging
from ema_workbench.analysis.pairs_plotting import (pairs_lines, pairs_scatter,
pairs_density)
ema_logging.log_to_stderr(level=ema_logging.DEFAULT_LEVEL)
# load the data
fh = './data/1000 flu cases no policy.tar.gz'
experiments, outcomes = load_results(fh)
# transform the results to the required format
# that is, we want to know the max peak and the casualties at the end of the
# run
tr = {}
# get time and remove it from the dict
time = outcomes.pop('TIME')
for key, value in outcomes.items():
if key == 'deceased population region 1':
tr[key] = value[:, -1] #we want the end value
else:
"""
"""
from __future__ import unicode_literals, print_function, absolute_import, division
# Created on Jul 23, 2016
#
# .. codeauthor::jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
from ema_workbench import RealParameter, TimeSeriesOutcome, ema_logging, perform_experiments
from ema_workbench.connectors import PysdModel
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.DEBUG)
mdl_file = "./models/pysd/Teacup.mdl"
model = PysdModel(mdl_file=mdl_file)
model.uncertainties = [RealParameter("Room Temperature", 33, 120)]
model.outcomes = [TimeSeriesOutcome("Teacup Temperature")]
perform_experiments(model, 100)
def main():
hybridmodel = Model('hybridmodel', function=hybridloop)
hybridmodel.uncertainties = [
IntegerParameter("inputpowerfactor", 15, 25), #7 13
IntegerParameter("inputLNGprice", 200, 1000),
IntegerParameter("inputtransferprice", 50, 300),
IntegerParameter("inputCapincrease", 1000, 3000),
IntegerParameter("inputCapincreasetime", 1, 2),
IntegerParameter("inputLNGCapincrease", 1000, 3000),
IntegerParameter("inputLNGCapincreasetime", 1, 2),
# RealParameter("DemandBalanceSupplyEnergyPrice", 0.4, 0.7),
RealParameter("MaximumChangeinDemand", 0.4, 0.7),
RealParameter("SupplyElasticityGas", 0.06, 0.07),
RealParameter("SupplyElasticityOil", 0.1, 0.2),
RealParameter("SupplyElasticityCoal", 0.1, 0.2),
RealParameter("SupplyElasticityNuclear", 0.007, 0.017),
RealParameter("SupplyElasticityBiofuel", 0.1, 0.2),
RealParameter("SupplyElasticityOR", 0.15, 0.3),
IntegerParameter("EconomicGrowthScenario", 1, 3),
IntegerParameter("EnergyIntensityScenario", 1, 3),
RealParameter("CO2coal", 93.46, 113.67),
RealParameter("CO2oil", 59.58, 102.12),
RealParameter("Variancepower", -5.0, -0.1),
IntegerParameter("POil", 8900, 9100),
IntegerParameter("PCoal", 2800, 3100),
IntegerParameter("PBio", 29000, 32000),
IntegerParameter("PNuc", 16000, 17000),
IntegerParameter("POR", 19000, 22000),
IntegerParameter("PGasE", 6500, 7000),
IntegerParameter("PGasNA", 2500, 2700),
IntegerParameter("PGasSCA", 2500, 2700),
IntegerParameter("PGasCIS", 6500, 7000),
IntegerParameter("PGasME", 7000, 8000),
IntegerParameter("PGasAF", 7000, 8000),
IntegerParameter("PGasAP", 7000, 8000)
]
hybridmodel.outcomes = [
TimeSeriesOutcome("EU_GasSup"),
TimeSeriesOutcome("EU_GasDem"),
TimeSeriesOutcome("EU_GasCon"),
TimeSeriesOutcome("EU_OilSup"),
TimeSeriesOutcome("EU_OilDem"),
TimeSeriesOutcome("EU_OilCon"),
TimeSeriesOutcome("EU_CoalSup"),
TimeSeriesOutcome("EU_CoalDem"),
TimeSeriesOutcome("EU_CoalCon"),
TimeSeriesOutcome("EU_NucSup"),
TimeSeriesOutcome("EU_NucDem"),
# TimeSeriesOutcome("EU_NucCon"),
TimeSeriesOutcome("EU_BioSup"),
TimeSeriesOutcome("EU_BioDem"),
TimeSeriesOutcome("EU_BioCon"),
TimeSeriesOutcome("EU_ORSup"),
TimeSeriesOutcome("EU_ORDem"),
# TimeSeriesOutcome("EU_ORCon"),
TimeSeriesOutcome("EU_EDem"),
TimeSeriesOutcome("EU_ESup"),
TimeSeriesOutcome("EU_GDP"),
TimeSeriesOutcome("EU_CO2"),
TimeSeriesOutcome("EU_RusGas"),
TimeSeriesOutcome("EU_EUGI"),
TimeSeriesOutcome("EU_GIC"),
TimeSeriesOutcome("EU_RGperAG"),
TimeSeriesOutcome("EU_RGperTES"),
TimeSeriesOutcome("EU_RGperGC"),
TimeSeriesOutcome("EU_GICperBBTU"),
TimeSeriesOutcome("Oil_Price"),
TimeSeriesOutcome("Coal_Price"),
TimeSeriesOutcome("Bio_Price"),
TimeSeriesOutcome("Gas_PriceE"),
TimeSeriesOutcome("Nuc_PriceE"),
TimeSeriesOutcome("OR_PriceE"),
TimeSeriesOutcome("FuncpriceGas"),
TimeSeriesOutcome("FuncpriceOil"),
TimeSeriesOutcome("FuncpriceCoal")
]
hybridmodel.levers = [
IntegerParameter("EnergyUnion", 0, 1),
IntegerParameter("CO2Cost", 0, 1)
]
# In[ ]:
ema_logging.log_to_stderr(ema_logging.INFO)
with MultiprocessingEvaluator(hybridmodel) as evaluator:
results = evaluator.perform_experiments(scenarios=1,
policies=4,
levers_sampling='ff')
# In[1]:
save_results(results, r'./1000 runs V30.tar.gz')
"""
Created on Fri Dec 09 14:59:03 2016
@author: Pedro
"""
from __future__ import (division, print_function, absolute_import,
unicode_literals)
from ema_workbench import (RealParameter, ScalarOutcome, ema_logging,
perform_experiments)
from ema_workbench.connectors.excel import ExcelModel
if __name__ == "__main__":
ema_logging.log_to_stderr(level=ema_logging.DEBUG)
model = ExcelModel("excelModel", wd="./models",
model_file= '/excelModel.xlsx')
model.uncertainties = [RealParameter("B2", 0.04, 0.2), #we can refer to a cell in the normal way
RealParameter("B3", 4000,6000), # we can also use named cells
RealParameter("B4", 0.9,1.1),
RealParameter("B5", 0.9,1.1)]
#specification of the outcomes
model.outcomes = [ScalarOutcome("B7")] # we can also use named range
#name of the sheet
model.sheet = "EMA"
results = perform_experiments(model, 100, parallel=True, reporting_interval=1)
'''
Created on 20 sep. 2011
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
import numpy as np
import matplotlib.pyplot as plt
from ema_workbench import load_results, ema_logging
from ema_workbench.analysis.pairs_plotting import (pairs_lines, pairs_scatter,
pairs_density)
ema_logging.log_to_stderr(level=ema_logging.DEFAULT_LEVEL)
# load the data
fh = './data/1000 flu cases no policy.tar.gz'
experiments, outcomes = load_results(fh)
# transform the results to the required format
# that is, we want to know the max peak and the casualties at the end of the
# run
tr = {}
# get time and remove it from the dict
time = outcomes.pop('TIME')
for key, value in outcomes.items():
if key == 'deceased population region 1':
tr[key] = value[:,-1] #we want the end value
else:
