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 batch_setting_unit_params(df, model, unit, exclude=()):
for para in df.index:
if para in exclude: continue
b = getattr(unit, para)
lower = float(df.loc[para]['low'])
upper = float(df.loc[para]['high'])
dist = df.loc[para]['distribution']
if dist == 'uniform':
D = shape.Uniform(lower=lower, upper=upper)
elif dist == 'triangular':
D = shape.Triangle(lower=lower, midpoint=b, upper=upper)
elif dist == 'constant':
continue
else:
raise ValueError(
f'Distribution {dist} not recognized for unit {unit}.')
su_type = type(unit).__name__
if su_type.lower() == 'lagoon':
su_type = f'{unit.design_type.capitalize()} lagoon'
name = f'{su_type} {para}'
model.parameter(setter=AttrSetter(unit, para),
name=name,
element=unit,
kind='coupled',
units=df.loc[para]['unit'],
baseline=b,
distribution=D)
def baseline_uniform(baseline, ratio):
return shape.Uniform(baseline * (1 - ratio), baseline * (1 + ratio))
U101 = system.U101
D = baseline_uniform(2205, 0.1)
@param(name='Flow rate',
element=U101,
kind='coupled',
units='dry U.S. ton/day',
baseline=2205,
distribution=D)
def set_feedstock_flow_rate(rate):
U101.feedstock_flow_rate = rate
D = shape.Uniform(0.9, 1)
@param(name='Plant uptime',
element='TEA',
kind='isolated',
units='%',
baseline=0.96,
distribution=D)
def set_operating_days(uptime):
lactic_tea.operating_days = 365 * uptime
special_price = {
# stream distribution min max
'feedstock': ('Uniform', (0.0529, 0.117)),
distribution=shape.Triangle(0.02, 0.048, 0.111),
units='USD/kg')
def set_feedstock_price(price):
cs.cornstover.price = price
@parameter(element='Operation',
distribution=shape.Triangle(21, 30, 30),
units='year')
def set_plant_life(plant_life):
start = cs.cornstover_tea.duration[0]
cs.cornstover_tea.duration = (start, start + plant_life)
@parameter(element='Operation',
distribution=shape.Uniform(7920, 8610),
units='hours')
def set_annual_operating_hours(annual_operating_hours):
cs.cornstover_tea.operating_days = annual_operating_hours / 24.
@parameter(element='Economics',
distribution=shape.Triangle(7, 10, 22),
units='%')
def set_discount_rate(discount_rate):
cs.cornstover_tea.IRR = discount_rate / 100.
# TODO: Find what type of depreciation schedules
# are used (hopefully MARCS) and whether a 30 yr
# depreciation is realistic for MARCS.
__all__ = ('model_bsm1', 'model_2dv', 'model_ss', 'run_uncertainty',
'analyze_timeseries', 'run_wdiff_init')
bsm1 = bm.bsm1
s = bm.system
#%%
# =============================================================================
# model with all uncertain variables
# =============================================================================
model_bsm1 = qs.Model(system=bsm1, exception_hook='raise')
########## Add Uncertainty Parameters ##########
param = model_bsm1.parameter
get_uniform_w_frac = lambda b, frac: shape.Uniform(lower=b * (1 - frac),
upper=b * (1 + frac))
cmps = s.cmps
PE = s.PE
b = 0.08
D = shape.Triangle(lower=0.04, midpoint=b, upper=0.12)
@param(name='Biomass N content i_XB',
element=PE,
kind='coupled',
units='g N/g COD',
baseline=b,
distribution=D)
def set_i_XB(i):