for key in initial_conditions.keys():
initial_condition = initial_conditions[key]
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
s_s = initial_condition['s_s']
fill_percent = np.repeat(
[.99], n_dams4) #np.random.uniform(0,0.8,n_dams4).round(2)#
S = (S_max * fill_percent).tolist()
SSN5.set_dam_state(states=[1 for _ in range(n_dams4)])
SSN5.initialize(q=q, S=S, s_t=s_t, s_p=s_p, s_s=s_s)
dc_passive_S4, st_passive_S4 = SSN5.Run_256([0, te], forcing,
dam_params256)
out_passive_S4 = SSN5.CalculateOutflow(dam_params256,
st_passive_S4)
temp1 = dc_passive_S4.max(
) #dc_passive_S4[dc_passive_S4.index>120].max()
temp2 = out_passive_S4.max(
) #out_passive_S4[out_passive_S4.index>120].max()
dc_passive_Peak_S4 = dc_passive_Peak_S4.append(temp1,
ignore_index=True)
out_passive_Peak_S4 = out_passive_Peak_S4.append(temp2,
ignore_index=True)
print(
f'(Scenario-4)Storm:{dstorm} is simulated with given initial conditions!'
)
SSN5.dam_ids = dams5
H_spill, H_max, S_max, _alpha, diam, c1, c2, L_spill, L_crest = PrepareDamParams(
dams5)
dam_params256 = SSN5.init_dam_params256(H_spill, H_max, S_max, _alpha,
diam, c1, c2, L_spill, L_crest)
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
s_s = initial_condition['s_s']
fill_percent = np.repeat([0.0001], n_dams5)
S = (S_max * fill_percent).tolist()
SSN5.set_dam_state(states=[1 for _ in range(n_dams5)])
SSN5.initialize(q=q, S=S, s_t=s_t, s_p=s_p, s_s=s_s)
dc_passive_S5, st_passive_S5 = SSN5.Run_256([0, te], forcing,
dam_params256)
out_passive_S5 = SSN5.CalculateOutflow(dam_params256, st_passive_S5)
dc_passive_S5.to_csv(
f'/Users/gurbuz/Supp_DamStudy/final_ActiveControl/dc_passive_{save_ext}.csv'
)
st_passive_S5.to_csv(
f'/Users/gurbuz/Supp_DamStudy/final_ActiveControl/st_passive_{save_ext}.csv'
)
out_passive_S5.to_csv(
f'/Users/gurbuz/Supp_DamStudy/final_ActiveControl/out_passive_{save_ext}.csv'
)
print(f'Done passive in {time.time()-start}')
############################################################
start = time.time()
print('Running Random..')
SSN5_r = Watershed(Model=256)
te = len(forcing) - 1
for key in initial_conditions.keys():
initial_condition = initial_conditions[key]
q = initial_condition['q']
s_p = initial_condition['s_p']
s_t = initial_condition['s_t']
s_s = initial_condition['s_s']
fill_percent = np.repeat(
[.99], n_dams1) #()np.random.uniform(0,0.8,n_dams1).round(2)
S = (S_max * fill_percent).tolist()
SSN2.set_dam_state(states=[1 for _ in range(n_dams1)])
SSN2.initialize(q=q, S=S, s_t=s_t, s_p=s_p, s_s=s_s)
dc_passive_S1, st_passive_S1 = SSN2.Run_256([0, te], forcing,
dam_params256)
out_passive_S1 = SSN2.CalculateOutflow(dam_params256,
st_passive_S1)
temp1 = dc_passive_S1.max(
) #dc_passive_S1[dc_passive_S1.index>120].max()
temp2 = out_passive_S1.max(
) #out_passive_S1[out_passive_S1.index>120].max()
dc_passive_Peak_S1 = dc_passive_Peak_S1.append(temp1,
ignore_index=True)
out_passive_Peak_S1 = out_passive_Peak_S1.append(temp2,
ignore_index=True)
print(
f'(Scenario-1)Storm:{dstorm} is simulated with given initial conditions!'
)