Improve Model.evaluate function about biosteam HOT 4 CLOSED

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
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):
        return [0]
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()

# Note that the possibilities are infinite here...