def test_model_index():
from biorefineries.sugarcane import sugarcane_sys, flowsheet as f
import biosteam as bst
# Make sure repeated metrics raise an error
biorefinery = bst.process_tools.UnitGroup('Biorefinery',
sugarcane_sys.units)
heating_duty = bst.Metric('Heating duty', biorefinery.get_heating_duty,
'GJ/hr')
heating_duty_repeated = bst.Metric('Heating duty',
biorefinery.get_heating_duty, 'GJ/hr')
with pytest.raises(ValueError):
model = bst.Model(sugarcane_sys, [heating_duty, heating_duty_repeated])
model = bst.Model(sugarcane_sys, [heating_duty])
# Make sure repeated parameters raise an error
R301 = f.unit.R301
@model.parameter(element=R301)
def set_efficiency(efficiency):
R301.efficiency = efficiency
with pytest.raises(ValueError):
@model.parameter(element=R301)
def set_efficiency(efficiency):
R301.efficiency = efficiency
bst.default()
def test_model():
from biosteam.examples import ethanol_subsystem as ethanol_sys
from biosteam import main_flowsheet as F
import biosteam as bst
# Make sure repeated metrics raise an error
biorefinery = bst.process_tools.UnitGroup('Biorefinery', ethanol_sys.units)
heating_duty = bst.Metric('Heating duty', biorefinery.get_heating_duty,
'GJ/hr')
heating_duty_repeated = bst.Metric('Heating duty',
biorefinery.get_heating_duty, 'GJ/hr')
with pytest.raises(ValueError):
model = bst.Model(ethanol_sys, [heating_duty, heating_duty_repeated])
model = bst.Model(ethanol_sys, [heating_duty])
# Make sure repeated parameters raise an error
R301 = F.flowsheet.ethanol_subsystem_example.unit.R301
@model.parameter(element=R301)
def set_efficiency(efficiency):
R301.efficiency = efficiency
with pytest.raises(ValueError):
@model.parameter(element=R301)
def set_efficiency(efficiency):
R301.efficiency = efficiency
bst.process_tools.default()
def test_kolmogorov_smirnov_d(model):
# Test made by Yalin.
import numpy as np
import biosteam as bst
from chaospy import distributions as shape
bst.settings.set_thermo(['H2O', 'Ethanol'], cache=True)
s1 = bst.Stream('s1', H2O=100)
M1 = bst.MixTank(ins=s1)
M2 = bst.MixTank(ins=M1 - 0)
sys = bst.System('sys', path=(M1, M2))
model = bst.Model(sys)
baseline = 1
distribution = shape.Uniform(lower=0.5, upper=1.5)
@model.parameter(name='M1 tau',
element=M1,
kind='coupled',
distribution=distribution,
units='hr',
baseline=baseline)
def set_M1_tau(i):
M1.tau = i
baseline = 1.75
distribution = shape.Uniform(lower=1, upper=2)
@model.parameter(name='M2 tau',
element=M2,
kind='coupled',
distribution=distribution,
units='hr',
baseline=baseline)
def set_M2_tau(i):
M2.tau = i
model.metrics = [
bst.Metric(name='tau1', getter=lambda: M1.tau, units='hr', element=M1),
bst.Metric(name='tau2', getter=lambda: M2.tau, units='hr', element=M2),
]
np.random.seed(3221)
samples = model.sample(100, rule='L')
model.load_samples(samples)
model.evaluate()
D, p = model.kolmogorov_smirnov_d(
thresholds=[1, 1.5]) # Just make sure it works for now
def test_model_exception_hook():
import biosteam as bst
import pytest
from biorefineries import lipidcane as lc
from chaospy import distributions as shape
from warnings import simplefilter
import numpy as np
bst.settings.set_thermo(lc.chemicals)
simplefilter("ignore")
IRR_metric = bst.Metric('Internal rate of return',
lc.lipidcane_tea.solve_IRR)
metrics = [IRR_metric]
lipidcane_model = bst.Model(lc.lipidcane_sys, metrics)
baseline = lc.lipidcane.F_mass
distribution = shape.Triangle(-baseline, baseline,
2 * baseline) # Negative value should fail
@lipidcane_model.parameter(element=lc.lipidcane,
distribution=distribution,
units='kg/hr')
def set_lipidcane_flow_rate(flow_rate):
lc.lipidcane.F_mass = flow_rate
samples = lipidcane_model.sample(15, 'L')
lipidcane_model.load_samples(samples)
# Without an exception hook, the same behavior will result (NaN values for failed evaluations)
lipidcane_model.evaluate()
assert np.isnan(lipidcane_model.table.values).any()
InfeasibleRegion = bst.exceptions.InfeasibleRegion
# This will provide a more understandable IRR result for infeasible regions
def exception_hook(exception, sample):
if isinstance(exception, (InfeasibleRegion, ValueError, RuntimeError)):
return [0]
else:
raise exception
lipidcane_model.exception_hook = exception_hook
lipidcane_model.evaluate()
assert not np.isnan(lipidcane_model.table.values).any()
# This will raise an exception due to negative flow rates
def exception_hook(exception, sample):
if isinstance(exception, InfeasibleRegion):
raise exception
lipidcane_model.exception_hook = exception_hook
with pytest.raises(InfeasibleRegion):
lipidcane_model.evaluate()
# This will raise an exception regardless
def exception_hook(exception, sample):
raise exception
lipidcane_model.exception_hook = exception_hook
with pytest.raises(InfeasibleRegion):
lipidcane_model.evaluate()
# Here is another cool thing we could do in the case where
# some metrics are expected to fail
bad_metric = bst.Metric('bad metric', lambda: 1 / 0)
lipidcane_model.metrics = (IRR_metric, bad_metric)
lipidcane_model.load_samples(
samples) # Metrics changed, so need to reload sample
def exception_hook(exception, sample):
if not isinstance(exception, ZeroDivisionError): return
lc.lipidcane_sys.simulate()
values = []
for i in lipidcane_model.metrics:
try:
x = i()
except:
x = None
values.append(x)
return values
lipidcane_model.exception_hook = exception_hook
lipidcane_model.evaluate()
bad_metric_results = lipidcane_model.table[bad_metric.index]
IRR_metric_results = lipidcane_model.table[IRR_metric.index]
assert np.isnan(bad_metric_results).all()
assert not np.isnan(IRR_metric_results).all()