def lca(): session_name = 'Indiana-IUC' mynetwork = IndianaNetwork(file_name = "INDIANA2019", n_assets = -1, settings = settings, is_deck = True, is_superstructure = True, is_substructure = True) mynetwork.load_network() mynetwork.set_current_budget_limit(10000) mynetwork.set_budget_limit_model(Linear(X0 = 10000, drift = 0, settings = settings)) mynetwork.set_npv_budget_limit(100000) simulator = MainSimulator(settings = settings) lca = LCA(lca_name = session_name, settings = settings, network = mynetwork, simulator = simulator, random = False, is_hazard = False, n_simulations = 5, should_report = True) return lca
def lca_instance():
# Creating the settings instance
settings = GeneralSettings()
# Creating the network
session_name = 'IndianaSHM'
mynetwork = DummySHMNetwork(file_name="INDIANA2019",
settings=settings,
n_assets=1,
is_deck=False,
is_superstructure=True,
is_substructure=False)
mynetwork.load_network()
mynetwork.set_current_budget_limit(100000)
mynetwork.set_budget_limit_model(
Linear(X0=100000, drift=0, settings=settings))
mynetwork.set_npv_budget_limit(10000)
# Creating the simulator
simulator = MainSimulator(settings=settings)
# shaping the main LCA
lca = LCA(lca_name=session_name,
settings=settings,
network=mynetwork,
simulator=simulator,
random=True,
is_hazard=True,
n_simulations=10,
should_report=True)
return lca
def lca_for_validation(mrrs, q_out):
for mrr in mrrs:
session_name = 'Indiana'
mynetwork = IndianaNetwork("INDIANA2019",
n_assets=1,
is_deck=False,
is_superstructure=True,
is_substructure=False)
mynetwork.load_network()
mynetwork.set_current_budget_limit(100000)
mynetwork.set_budget_limit_model(Linear(X0=100000, drift=0))
mynetwork.set_npv_budget_limit(10000)
mynetwork.assets[0].mrr_model.set_mrr(np.atleast_2d(mrr))
simulator = MainSimulator()
lca = LCA(network=mynetwork,
lca_name=session_name,
simulator=simulator,
random=False,
is_hazard=False,
n_simulations=1000)
lca.run()
results = lca.get_network_npv()
q_out.put(mrr + results)
def lca(mrr=None):
session_name = 'Indiana'
mynetwork = IndianaNetwork("INDIANA2019",
n_assets=1,
is_deck=False,
is_superstructure=True,
is_substructure=False)
mynetwork.load_network()
if not mrr is None:
mynetwork.assets[0].mrr_model.set_mrr(mrr)
mynetwork.set_current_budget_limit(100000)
mynetwork.set_budget_limit_model(Linear(X0=100000, drift=0))
mynetwork.set_npv_budget_limit(10000)
simulator = MainSimulator()
lca = LCA(network=mynetwork,
lca_name=session_name,
simulator=simulator,
random=False,
is_hazard=False,
n_simulations=500)
return lca
class DummyUserCost(BaseUserCost):
def __init__(self, **params):
super().__init__(**params)
self.linear_model = Linear(X0=1, drift=0, settings=self.settings)
def predict_series(self, project_duration, random=True):
'''Method for predicting the user costs in time'''
return 3 * self.linear_model.predict_series(random)
# The out put will be 1000 dollars
class RetrofitCosts(BaseAgencyCost):
def __init__(self):
self.linear_model = Linear(X0=1, drift=0, settings=self.settings)
pass
def retrofit_costs(self, random):
check_random(random)
return 100 * self.linear_model.predict_series(random) / 1000
def predict_series(self, random):
return {BINAR: self.retrofit_costs(random)}
class RetrofitCosts(BaseAgencyCost):
def __init__(self):
self.linear_model = Linear(X0=1, drift=0, settings=self.settings)
pass
def first_inspection_cost(self, random):
return 1 * self.linear_model.predict_series(random) / 1000
def further_inspection_cost(self, random):
return 10 * self.linear_model.predict_series(random) / 1000
def action_costs(self, random):
return 100 * self.linear_model.predict_series(random) / 1000
def predict_series(self, random):
assert isinstance(random, bool), 'random must be boolean'
return {
INSP1: self.first_inspection_costs(random),
INSP2: self.further_inspection_costs(random),
DOMNT: self.action_costs(random)
}
def lca(): session_name = 'BuildingRetrofit' mynetwork = BuildingNetwork(None, n_assets = 5) mynetwork.load_network() mynetwork.set_current_budget_limit(100000) mynetwork.set_budget_limit_model(Linear(X0 = 100000, drift = 0)) mynetwork.set_npv_budget_limit(10000) simulator = DummyRiskAnalyzer() lca = LCA(network = mynetwork, lca_name = session_name, simulator = simulator, random = False, is_hazard = False, n_simulations = 1) return lca
def lca_instance(): # Creating the settings instance settings = GeneralSettings() # Creating the network session_name = 'IndianaTest' mynetwork = IndianaNetwork(file_name = "INDIANA2019", settings = settings, n_assets = 1, is_deck = True, is_superstructure = True, is_substructure = True) mynetwork.load_network() new_mrr = np.array([[1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1]]) mynetwork.assets[0].mrr_model.set_mrr(new_mrr) mynetwork.set_current_budget_limit(100000) mynetwork.set_budget_limit_model(Linear(X0 = 100000, drift = 0, settings = settings)) mynetwork.set_npv_budget_limit(10000) # Creating the simulator simulator = MainSimulator(settings = settings) # shaping the main LCA lca = LCA(lca_name = session_name, settings = settings, network = mynetwork, simulator = simulator, random = True, is_hazard = True, n_simulations = 20, should_report = True) return lca
def __init__(self):
self.linear_model = Linear(X0=1, drift=0, settings=self.settings)
pass
def __init__(self, **params):
super().__init__(**params)
self.linear_model = Linear(X0=1, drift=0, settings=self.settings)