def evaluate(self, thorough=True):
"""
Evaluate metric over the argument sample space and save values to ``table``.
Parameters
----------
thorough : bool
If True, simulate the whole system with each sample.
If False, simulate only the affected parts of the system.
"""
speed_up()
samples = self._samples
if samples is None: raise RuntimeError('must load samples before evaluating')
evaluate_sample = self._evaluate_sample_thorough if thorough else self._evaluate_sample_smart
self._table[self._metric_indices] = [evaluate_sample(samples[i]) for i in self._index]
def evaluate(self, thorough=True):
"""Evaluate metric over the argument sample space and save values to ``table``.
Parameters
----------
thorough : bool
If True, simulate the whole system with each sample.
If False, simulate only the affected parts of the system.
"""
speed_up()
# Setup before simulation
funcs = [i.getter for i in self._metrics]
samples = self._samples
if samples is None:
raise ValueError(
'must load samples or distribution before evaluating')
index = self._index
values = np.zeros([len(index), len(funcs)])
zip_ = zip
if thorough:
if self._setters:
setters = self._setters
else:
self._setters = setters = [p.setter for p in self._params]
simulate = self._system.simulate
for i in index:
for f, s in zip_(setters, samples[i]):
f(s)
simulate()
values[i] = [i() for i in funcs]
else:
update = self._update
for i in index:
update(samples[i])
values[i] = [i() for i in funcs]
self.table[var_indices(self._metrics)] = values
def evaluate(self, thorough=True, jit=True, notify=False):
"""
Evaluate metric over the argument sample space and save values to `table`.
Parameters
----------
thorough : bool
If True, simulate the whole system with each sample.
If False, simulate only the affected parts of the system.
jit : bool
Whether to JIT compile functions with Numba to speed up simulation.
notify=False : bool, optional
If True, notify elapsed time after each sample evaluation.
"""
if jit: speed_up()
samples = self._samples
if samples is None:
raise RuntimeError('must load samples before evaluating')
evaluate_sample = self._evaluate_sample_thorough if thorough else self._evaluate_sample_smart
table = self.table
if notify:
from biosteam.utils import TicToc
timer = TicToc()
timer.tic()
def evaluate(sample, count=[0]):
count[0] += 1
values = evaluate_sample(sample)
print(f"{count} Elapsed time: {timer.elapsed_time:.0f} sec")
return values
else:
evaluate = evaluate_sample
table[self._metric_indices] = [
evaluate(samples[i]) for i in self._index
]
def update_productivity(productivity):
R301.productivity = productivity
R302.productivity = productivity * R302.ferm_ratio
for unit in (R301, R302):
unit._design()
unit._cost()
solve_TEA()
# %%
# =============================================================================
# Regular strain
# =============================================================================
bst.speed_up()
R301.set_titer_limit = True
R301.neutralization = True
R401.bypass = False
S402.bypass = False
run_number = 0
MPSPs_regular = [[], [], []]
NPVs_regular = [[], [], []]
print('\n-------- Regular Strain --------')
for i in titer_range:
for j in yield_range:
limits[0].append(i)
limits[1].append(j)
# Baseline productivity