def __init__(self):
# load network
G, dike_list, dike_branch = funs_generate_network.get_network()
# Load hydrological statistics:
A = pd.read_excel('./data/hydrology/werklijn_params.xlsx')
lowQ, highQ = werklijn_inv([0.992, 0.99992], A)
Qpeaks = np.unique(np.asarray([np.random.uniform(lowQ, highQ)/6 for _ in range(0, 30)]))[::-1]
# Probabiltiy of exceedence for the discharge @ Lobith (i.e. times 6)
p_exc = 1 - werklijn_cdf(Qpeaks*6, A)
self.Qpeaks = Qpeaks
self.p_exc = p_exc
self.A = A
self.G = G
self.dikelist = dike_list
self.dike_branch = dike_branch
# Accounting for the discharge reduction due to upstream dike breaches
self.sb = False
# Planning window [y]
self.n = 50
# Step of dike increase [cm]
self.step = 10
# Time step correction: Q is a mean daily value expressed in m3/s
self.timestepcorr = 24*60*60
ema_logging.info('model initialized')
def __init__(self):
# planning steps
self.num_planning_steps = 3
self.num_events = 30
# load network
G, dike_list, dike_branch, planning_steps = funs_generate_network.get_network(
self.num_planning_steps)
# Load hydrological statistics:
self.A = pd.read_excel('./data/hydrology/werklijn_params.xlsx')
lowQ, highQ = werklijn_inv([0.992, 0.99992], self.A)
self.Qpeaks = np.unique(
np.asarray([
np.random.uniform(lowQ, highQ) / 6
for _ in range(0, self.num_events)
]))[::-1]
# Probabiltiy of exceedence for the discharge @ Lobith (i.e. times 6)
self.p_exc = 1 - werklijn_cdf(self.Qpeaks * 6, self.A)
self.G = G
self.dikelist = dike_list
self.dike_branch = dike_branch
self.planning_steps = planning_steps
# Accounting for the discharge reduction due to upstream dike breaches
self.sb = True
# Planning window [y], reasonable for it to be a multiple of num_planning_steps
self.n = 200
# Years in planning step:
self.y_step = self.n // self.num_planning_steps
# Step of dike increase [m]
self.dh = 0.1
# Time step correction: Q is a mean daily value expressed in m3/s
self.timestepcorr = 24 * 60 * 60