def update_operation_act_4(i):
u = solutions_optim_relea_2[i]
S, env, w, r = syst_sim(N, I_act + u, E_act, d_act, S0, Smax, env_min)
fig_4b.title = 'Supply deficit (max(0,d-Qreg_rel)) - Total = ' + str(
(np.sum((np.maximum(d_act - r, [0] * N)))).astype('int')) + ' ML'
fig_4d.title = 'Natural + pumped inflows - Total pumped vol = ' + str(
(np.sum(np.array(u))).astype('int')) + ' ML'
return S, u, r, i
def update_operation(i):
S, env, w, r = syst_sim(N, I_for + solutions_optim_relea[i], E_for,
d_for, S0, Smax, env_min)
fig_wd.title = 'Supply deficit (max(0,d-Qreg_rel)) - Average deficit = ' + str(
(sd_mean[i]).astype('int')) + ' ± ' + str(
(sd_std[i]).astype('int')) + ' ML'
fig_in.title = 'Natural + pumped inflows - Total pumped vol = {:.0f} ML'.format(
results2_optim_relea[i] / c)
return S, solutions_optim_relea[i], r, results1_optim_relea[
i], results2_optim_relea[i], i
def Interactive_Pareto_front(N,I_for,E_for,d_for,S0,Smax,Smin,env_min,c,solutions_optim_relea,results1_optim_relea,results2_optim_relea):
members_num = np.shape(I_for)[0]
population_size = np.shape(solutions_optim_relea)[0]
sdpen = np.zeros([members_num,population_size])
sdpen_mean = np.zeros(population_size)
sdpen_std = np.zeros(population_size)
sd = np.zeros([members_num,population_size])
sd_mean = np.zeros(population_size)
sd_std = np.zeros(population_size)
for i in range(population_size):
S_opt,env_opt,w_opt,r_opt = syst_sim(N,I_for+solutions_optim_relea[i],E_for,d_for,S0,Smax,env_min)
sdpen[:,i] = np.sum(np.maximum(d_for-r_opt,np.zeros(np.shape(d_for)))**2,axis = 1)
sdpen_mean[i] = np.mean(sdpen[:,i])
sdpen_std[i] = np.std(sdpen[:,i])
sd[:,i] = np.sum(np.maximum(d_for-r_opt,np.zeros(np.shape(d_for))),axis = 1)
sd_mean[i] = np.mean(sd[:,i])
sd_std[i] = np.std(sd[:,i])
# Interactive Pareto front
def update_operation(i):
S,env,w,r = syst_sim(N,I_for+solutions_optim_relea[i],E_for,d_for,S0,Smax,env_min)
fig_wd.title = 'Total supply deficit = '+str((sd_mean[i]).astype('int'))+' ± '+str((sd_std[i]).astype('int'))+' ML'
fig_in.title = 'Natural + pumped inflows - Total pumped vol = {:.0f} ML'.format(results2_optim_relea[i]/c)
return S,solutions_optim_relea[i],r,results1_optim_relea[i],results2_optim_relea[i],i
def solution_selected(change):
if pareto_front.selected == None:
pareto_front.selected = [0]
storage.y = update_operation(pareto_front.selected[0])[0]
deficit.y = np.maximum(d_for-update_operation(pareto_front.selected[0])[2],np.zeros(np.shape(d_for)))
pump_inflows.y = update_operation(pareto_front.selected[0])[1]
tot_inflows.y = update_operation(pareto_front.selected[0])[1] + I_for
pareto_front_ensemble.x = np.reshape([results2_optim_relea for i in range(0, members_num)],(members_num,population_size))[:,pareto_front.selected[0]]
pareto_front_ensemble.y = sdpen[:,pareto_front.selected[0]]
pareto_front_ensemble.unselected_style={'opacity': 0.1}
pareto_front_ensemble.selected_style={'opacity': 0.1}
pareto_front_ensemble.opacity = [0.1]*members_num
x_sc_pf = LinearScale()
y_sc_pf = LinearScale(min = 0,max = 4000)
x_ax_pf = Axis(label='Total Pumping Cost [£]', scale=x_sc_pf)
y_ax_pf = Axis(label='Total Squared Deficit [ML^2]', scale=y_sc_pf, orientation='vertical')
pareto_front = plt.scatter(results2_optim_relea[:],results1_optim_relea[:],scales={'x': x_sc_pf, 'y': y_sc_pf},colors=['deepskyblue'], interactions={'hover':'tooltip','click': 'select'})
pareto_front.unselected_style={'opacity': 0.8}
pareto_front.selected_style={'fill': 'red', 'stroke': 'yellow', 'width': '1125px', 'height': '125px'}
if pareto_front.selected == []:
pareto_front.selected = [0]
pareto_front_ensemble = plt.Scatter(x=np.reshape([results2_optim_relea for i in range(0, members_num)],(members_num,population_size))[:,pareto_front.selected[0]],
y=sdpen[:,pareto_front.selected[0]],scales={'x': x_sc_pf, 'y': y_sc_pf},
colors=['red'],
interactions={'hover':'tooltip','click': 'select'})
pareto_front_ensemble.unselected_style={'opacity': 0.1}
pareto_front_ensemble.selected_style={'opacity': 0.1}
pareto_front_ensemble.opacity = [0.1]*members_num
fig_pf = plt.Figure(marks=[pareto_front,pareto_front_ensemble],title = 'Pareto front', axes=[x_ax_pf, y_ax_pf],layout={'width': '500px', 'height': '500px'},
animation_duration=500)
pareto_front.observe(solution_selected,'selected')
S,env,w,r = syst_sim(N,I_for+solutions_optim_relea[pareto_front.selected[0]],E_for,d_for,S0,Smax,env_min)
x_sc_in = OrdinalScale(min=1,max=N)
y_sc_in = LinearScale(min=0,max=100)
x_ax_in = Axis(label='week', scale=x_sc_in)
y_ax_in = Axis(label='ML/week', scale=y_sc_in, orientation='vertical')
x_sc_st = LinearScale(min=0,max=N)
y_sc_st = LinearScale(min=0,max=160)
x_ax_st = Axis(label='week', scale=x_sc_st)#,tick_values=[0.5,1.5,2.5,3.5])
y_ax_st = Axis(label='ML', scale=y_sc_st, orientation='vertical')
x_sc_wd = LinearScale(min=0.5,max=N+0.5)
y_sc_wd = LinearScale(min=0,max=100);
x_ax_wd = Axis(label='week', scale=x_sc_wd,tick_values=[1,2,3,4,5,6,7,8])
y_ax_wd = Axis(label='ML/week', scale=y_sc_wd, orientation='vertical')
pump_inflows = plt.Lines(x=np.arange(1,N+1),
y=solutions_optim_relea[pareto_front.selected[0]],
scales={'x': x_sc_in, 'y': y_sc_in},
colors=['orange'],
opacities = [1],
stroke_width = 1,
marker = 'circle',
marker_size = 10,
labels = ['pump (Qreg_inf)'],
fill = 'bottom',
fill_opacities = [1],
fill_colors = ['orange']*members_num*N)
tot_inflows = plt.Lines(x = np.arange(1,N+1),
y = solutions_optim_relea[pareto_front.selected[0]]+I_for,
scales={'x': x_sc_wd, 'y': y_sc_wd},
colors=['blue'],
opacities = [1]*members_num,
stroke_width = 0.5,
marker = 'circle',
marker_size = 10,
labels = ['nat (I) + pump (Qreg_inf)'],
fill = 'bottom',
fill_opacities = [1/members_num]*members_num*N,
fill_colors = ['blue']*members_num*N)
fig_in = plt.Figure(marks = [tot_inflows,pump_inflows],axes=[x_ax_in, y_ax_in],layout={'max_width': '480px', 'max_height': '250px'},
scales={'x': x_sc_in, 'y': y_sc_in}, animation_duration=1000,legend_location = 'bottom-right')
storage = plt.plot(x=np.arange(0,N+1),y=S,scales={'x': x_sc_st, 'y': y_sc_st},
colors=['blue'], stroke_width = 0.1,
fill = 'bottom', fill_opacities = [0.1]*members_num)
max_storage = plt.plot(x=np.arange(0,N+1),
y=[Smax]*(N+1),
colors=['red'],
scales={'x': x_sc_st, 'y': y_sc_st})
max_storage_label = plt.label(text = ['Max storage'],
x=[0],
y=[Smax+10],
colors=['red'])
fig_st = plt.Figure(marks = [storage,max_storage,max_storage_label],
title = 'Reservoir storage volume',
axes=[x_ax_st, y_ax_st],
layout={'width': '1000px', 'height': '350px'},
animation_duration=1000,
scales={'x': x_sc_st, 'y': y_sc_st})
deficit = plt.Lines(x = np.arange(1,N+1),
y = np.maximum(d_for-r,np.zeros(np.shape(r))),
scales={'x': x_sc_wd, 'y': y_sc_wd},
colors=['red'],
stroke_width = 1,
opacities = [1]*members_num,
marker = 'circle',
marker_size = 10,
labels = ['max(0,d-Qreg_rel)'],
fill = 'bottom',
fill_opacities = [1/members_num]*members_num,
fill_colors = ['red']*members_num)
fig_wd = plt.Figure(marks = [deficit],axes=[x_ax_wd, y_ax_wd],
layout={'max_width': '480px', 'max_height': '250px'},
animation_duration=1000,
legend_location = 'bottom-right')
storage.y = update_operation(pareto_front.selected[0])[0]
deficit.y = np.maximum(d_for-update_operation(pareto_front.selected[0])[2],np.zeros(np.shape(d_for)))
pump_inflows.y = update_operation(pareto_front.selected[0])[1]
tot_inflows.y = update_operation(pareto_front.selected[0])[1] + I_for
storage.observe(solution_selected, ['x', 'y'])
deficit.observe(solution_selected, ['x', 'y'])
pump_inflows.observe(solution_selected, ['x', 'y'])
tot_inflows.observe(solution_selected, ['x', 'y'])
return fig_pf,fig_wd,fig_st,fig_in,pareto_front
def update_operation_2(i):
u = solutions_optim_relea_2[i]
S,env,w,r = syst_sim(N,I_sel+u,E,d_sel,S0,Smax,env_min)
fig_2b.title = 'Total supply deficit = '+str((np.sum((np.maximum(d_sel-r,[0]*N)))).astype('int'))+' ML'
fig_2d.title = 'Natural + pumped inflows - Total pumped vol = '+str((np.sum(np.array(u))).astype('int'))+' ML'
return S,u,r,i
def Interactive_Pareto_front_act(N,I_act,E_act,d_act,S0,Smax,Smin,env_min,c,solutions_optim_relea_2,results1_optim_relea_2,results2_optim_relea_2,sel_policy):
population_size = np.shape(solutions_optim_relea_2)[0]
sdpen_act_4 = np.zeros(population_size); pcost_act_4 = np.zeros(population_size)
for i in range(population_size):
pinfl_policy_4 = np.array(solutions_optim_relea_2[i])
S_act_4,env_act_4,w_act_4,r_act_4 = syst_sim(N,I_act+pinfl_policy_4,E_act,d_act,S0,Smax,env_min)
sdpen_act_4[i] = (np.sum((np.maximum(d_act-r_act_4,[0]*N))**2)).astype('int')
pcost_act_4[i] = (np.sum(np.array(pinfl_policy_4)*c)).astype('int')
def update_operation_act_4(i):
u = solutions_optim_relea_2[i]
S,env,w,r = syst_sim(N,I_act+u,E_act,d_act,S0,Smax,env_min)
fig_4b.title = 'Total supply deficit = '+str((np.sum((np.maximum(d_act-r,[0]*N)))).astype('int'))+' ML'
fig_4d.title = 'Natural + pumped inflows - Total pumped vol = '+str((np.sum(np.array(u))).astype('int'))+' ML'
return S,u,r,i
def solution_selected_act_4(change):
if pareto_front_act_4.selected == None:
pareto_front_act_4.selected = [0]
deficit_4.y = np.maximum(d_act-update_operation_act_4(pareto_front_act_4.selected[0])[2], [0]*N)
storage_4.y = update_operation_act_4(pareto_front_act_4.selected[0])[0]
pump_inflows_4.y = [update_operation_act_4(pareto_front_act_4.selected[0])[1]]
tot_inflows_4.y = [update_operation_act_4(pareto_front_act_4.selected[0])[1]+I_act[0]]
def on_hover_4pf(self, target):
hover_elem_id = list(target.values())[1]['index']
def on_element_click_4pf(self, target):
click_elem_id = list(target.values())[1]['index']
colors = ['deepskyblue']*population_size
colors[click_elem_id] = 'red'
pareto_front_4.colors = colors
x_sc_2pf = LinearScale()
y_sc_2pf = LinearScale()
x_ax_2pf = Axis(label='Total Pumping Cost [£]',
scale=x_sc_2pf)
y_ax_2pf = Axis(label='Total Squared Deficit [ML^2]',
scale=y_sc_2pf,
orientation='vertical')
pareto_front_4 = plt.scatter(results2_optim_relea_2[:],
results1_optim_relea_2[:],
scales={'x': x_sc_2pf, 'y': y_sc_2pf},
colors=['deepskyblue'],
opacity = [0.11]*population_size,
interactions={'hover':'tooltip'})
pareto_front_4.tooltip = None
pareto_front_act_4 = plt.scatter(pcost_act_4[:],sdpen_act_4[:],scales={'x': x_sc_2pf, 'y': y_sc_2pf},
colors=['green'], interactions={'hover':'tooltip'})
pareto_front_act_4.unselected_style = {'opacity': 0}
pareto_front_act_4.selected_style = {'fill': 'black', 'stroke': 'black', 'width': '1125px', 'height': '125px'}
pareto_front_4.selected_style = {'fill': 'red', 'stroke': 'red', 'width': '1125px', 'height': '125px'}
pareto_front_act_4.tooltip = None
fig_4pf = plt.Figure(marks = [pareto_front_4,pareto_front_act_4 ],title = 'Pareto front', axes=[x_ax_2pf, y_ax_2pf],
layout={'width': '500px', 'height': '500px'}, animation_duration=1000)
if pareto_front_act_4.selected == []:
pareto_front_4.selected = [sel_policy]
pareto_front_act_4.selected = [sel_policy]
pareto_front_act_4.observe(solution_selected_act_4,'selected')
pareto_front_act_4.on_hover(on_hover_4pf)
pareto_front_4.on_hover(on_hover_4pf)
x_sc_2b = OrdinalScale(min=1,
max=N)
y_sc_2b = LinearScale(min=0,
max=100)
x_ax_2b = Axis(label='week',
scale=x_sc_2b)
y_ax_2b = Axis(label='ML/week',
scale=y_sc_2b,
orientation='vertical')
deficit_4 = plt.Lines(x=np.arange(1,N+1),
y=np.maximum(d_act-r_act_4,[0]*N),
colors=['red'],
opacities = [1],
stroke_width = 0.5,
marker = 'circle',
marker_size = 15,
labels = ['max(0,d-Qreg_rel)'],
fill = 'bottom',
fill_opacities = [0.5],
fill_colors = ['red'],
display_legend = False,
scales={'x': x_sc_2b, 'y': y_sc_2b})
fig_4b = plt.Figure(marks = [deficit_4],
axes=[x_ax_2b, y_ax_2b],
layout={'width': '480px', 'height': '250px'},
scales={'x': x_sc_2b, 'y': y_sc_2b},
animation_duration=1000,
legend_location = 'bottom-right',
legend_style = {'fill': 'white', 'opacity': 0.5})
x_sc_2c = LinearScale(min=0,
max=N)
y_sc_2c = LinearScale(min=0,
max=160)
x_ax_2c = Axis(label='week',
scale=x_sc_2c)
y_ax_2c = Axis(label='ML',
scale=y_sc_2c,
orientation='vertical')
max_storage_2 = plt.plot(x=np.arange(0,N+1),
y=[Smax]*(N+1),
colors=['red'],
scales={'x': x_sc_2c, 'y': y_sc_2c})
max_storage_label_2 = plt.label(text = ['Max storage'],
x=[0],
y=[Smax+10],
colors=['red'])
storage_4 = plt.Lines(x=np.arange(0,N+1),
y=S_act_4,
colors=['blue'],
scales={'x': x_sc_2c, 'y': y_sc_2c},
fill = 'bottom',fill_opacities = [0.8],
fill_colors = ['blue'])
fig_4c = plt.Figure(marks = [storage_4,max_storage_2,max_storage_label_2],
title = 'Reservoir storage (s)',
axes=[x_ax_2c, y_ax_2c],
layout={'width': '1000px', 'height': '350px'},
animation_duration=1000,
scales={'x': x_sc_2c, 'y': y_sc_2c})
x_sc_2d = OrdinalScale(min=1,
max=N)
y_sc_2d = LinearScale(min=0,
max=100)
x_ax_2d = Axis(label='week',
scale=x_sc_2d)
y_ax_2d = Axis(label='ML/week',
scale=y_sc_2d,
orientation='vertical')
pump_inflows_4 = plt.Lines(x=np.arange(1,N+1),
y=[pinfl_policy_4],
colors=['orange'],
opacities = [1],
stroke_width = 0.5,
marker = 'circle',
marker_size = 15,
fill = 'bottom',
fill_opacities = [1],
fill_colors = ['orange'],
labels = ['pump (Qreg_inf)'],
display_legend = True,
scales={'x': x_sc_2d, 'y': y_sc_2d})
tot_inflows_4 = plt.Lines(x=np.arange(1,N+1),
y=[pinfl_policy_4+I_act[0]],
colors=['blue'],
opacities = [1],
stroke_width = 1,
marker = 'circle',
marker_size = 15,
fill = 'bottom',
fill_opacities = [0.5],
fill_colors = ['blue'],
labels = ['nat (I) + pump (Qreg_inf)'],
display_legend = True,
scales={'x': x_sc_2d, 'y': y_sc_2d})
fig_4d = plt.Figure(marks = [tot_inflows_4,pump_inflows_4],
title = 'Natural + pumped inflows',
axes=[x_ax_2d, y_ax_2d],
layout={'width': '480px', 'height': '250px'},
scales={'x': x_sc_2d, 'y': y_sc_2d},
animation_duration=1000,
legend_location = 'top',
legend_style = {'fill': 'white', 'opacity': 0.5})
deficit_4.y = np.maximum(d_act-update_operation_act_4(sel_policy)[2],[0]*N)
storage_4.y = update_operation_act_4(sel_policy)[0]
pump_inflows_4.y = [update_operation_act_4(sel_policy)[1]]
tot_inflows_4.y = [update_operation_act_4(sel_policy)[1]+I_act[0]]
deficit_4.observe(solution_selected_act_4, ['x', 'y'])
storage_4.observe(solution_selected_act_4, ['x', 'y'])
pump_inflows_4.observe(solution_selected_act_4, ['x', 'y'])
tot_inflows_4.observe(solution_selected_act_4, ['x', 'y'])
return fig_4b,fig_4c,fig_4d,fig_4pf
def Interactive_Pareto_front_det(N,I_sel,E,d_sel,S0,Smax,Smin,env_min,c,solutions_optim_relea_2,results1_optim_relea_2,results2_optim_relea_2):
def update_operation_2(i):
u = solutions_optim_relea_2[i]
S,env,w,r = syst_sim(N,I_sel+u,E,d_sel,S0,Smax,env_min)
fig_2b.title = 'Total supply deficit = '+str((np.sum((np.maximum(d_sel-r,[0]*N)))).astype('int'))+' ML'
fig_2d.title = 'Natural + pumped inflows - Total pumped vol = '+str((np.sum(np.array(u))).astype('int'))+' ML'
return S,u,r,i
def solution_selected_2(change):
if pareto_front_2.selected == None:
pareto_front_2.selected = [0]
deficit_2.y = np.maximum(d_sel-update_operation_2(pareto_front_2.selected[0])[2],[0]*N)
storage_2.y = update_operation_2(pareto_front_2.selected[0])[0]
pump_inflows_2.y = [update_operation_2(pareto_front_2.selected[0])[1]]
tot_inflows_2.y = [update_operation_2(pareto_front_2.selected[0])[1]+I_sel[0]]
x_sc_2pf = LinearScale();y_sc_2pf = LinearScale()
x_ax_2pf = Axis(label='Total Pumping Cost [£]', scale=x_sc_2pf)
y_ax_2pf = Axis(label='Total Squared Deficit [ML^2]', scale=y_sc_2pf, orientation='vertical')
pareto_front_2 = plt.scatter(results2_optim_relea_2[:],results1_optim_relea_2[:],scales={'x': x_sc_2pf, 'y': y_sc_2pf},
colors=['deepskyblue'], interactions={'hover':'tooltip','click': 'select'})
pareto_front_2.unselected_style = {'opacity': 0.4}
pareto_front_2.selected_style = {'fill': 'red', 'stroke': 'yellow', 'width': '1125px', 'height': '125px'}
def_tt = Tooltip(fields=['x', 'y','index'],labels=['Pumping cost','Squared deficit', 'sol index'],
formats=['.1f', '.1f', '.0f'])
pareto_front_2.tooltip = def_tt
fig_2pf = plt.Figure(marks = [pareto_front_2],title = 'Pareto front', axes=[x_ax_2pf, y_ax_2pf],
layout={'width': '500px', 'height': '500px'}, animation_duration=1000)
if pareto_front_2.selected == []:
pareto_front_2.selected = [0]
pareto_front_2.observe(solution_selected_2,'selected')
S,env,w,r = syst_sim(N,I_sel+solutions_optim_relea_2[pareto_front_2.selected[0]],E,d_sel,S0,Smax,env_min)
x_sc_2b = OrdinalScale(min=1,max=N);y_sc_2b = LinearScale(min=0,max=100)
x_ax_2b = Axis(label='week', scale=x_sc_2b)
y_ax_2b = Axis(label='ML/week', scale=y_sc_2b, orientation='vertical')
deficit_2 = plt.Lines(x=np.arange(1,N+1),
y=np.maximum(d_sel-r,[0]*N),
colors=['red'],
opacities = [1],
stroke_width = 0.5,
marker = 'circle',
marker_size = 15,
labels = ['max(0,d-Qreg_rel)'],
fill = 'bottom',
fill_opacities = [0.5],
fill_colors = ['red'],
display_legend = False,
scales={'x': x_sc_2b, 'y': y_sc_2b})
fig_2b = plt.Figure(marks = [deficit_2],
axes=[x_ax_2b, y_ax_2b],
layout={'width': '480px', 'height': '250px'},
scales={'x': x_sc_2b, 'y': y_sc_2b},
animation_duration=1000,
legend_location = 'bottom-right',
legend_style = {'fill': 'white', 'opacity': 0.5})
x_sc_2c = LinearScale(min=0,max=N)
y_sc_2c = LinearScale(min=0,max=160)
x_ax_2c = Axis(label='week',
scale=x_sc_2c)
y_ax_2c = Axis(label='ML',
scale=y_sc_2c,
orientation='vertical')
storage_2 = plt.Lines(x=np.arange(0,N+1),
y=S,colors=['blue'],
scales={'x': x_sc_2c, 'y': y_sc_2c},
fill = 'bottom',
fill_opacities = [0.8],
fill_colors = ['blue'])
max_storage_2 = plt.plot(x=np.arange(0,N+1),
y=[Smax]*(N+1),
colors=['red'],
scales={'x': x_sc_2c, 'y': y_sc_2c})
max_storage_label_2 = plt.label(text = ['Max storage'],
x=[0],
y=[Smax+10],
colors=['red'])
fig_2c = plt.Figure(marks = [storage_2,max_storage_2,max_storage_label_2],
title = 'Reservoir storage (s)',
axes=[x_ax_2c, y_ax_2c],
layout={'width': '1000px', 'height': '350px'},
animation_duration=1000,
scales={'x': x_sc_2c, 'y': y_sc_2c})
x_sc_2d = OrdinalScale(min=1,
max=N)
y_sc_2d = LinearScale(min=0,
max=100)
x_ax_2d = Axis(label='week',
scale=x_sc_2d)
y_ax_2d = Axis(label='ML/week',
scale=y_sc_2d,
orientation='vertical')
# Stacked bars
pump_inflows_2 = plt.Lines(x=np.arange(1,N+1),
y=(solutions_optim_relea_2[pareto_front_2.selected[0]]),
colors=['orange'],
opacities = [1],
stroke_width = 0.5,
marker = 'circle',
marker_size = 15,
fill = 'bottom',
fill_opacities = [1],
fill_colors = ['orange'],
labels = ['pump (Qreg_inf)'],
display_legend = True,
scales={'x': x_sc_2d, 'y': y_sc_2d})
tot_inflows_2 = plt.Lines(x=np.arange(1,N+1),
y=(solutions_optim_relea_2[pareto_front_2.selected[0]]+I_sel[0]),
colors=['blue'],
opacities = [1],
stroke_width = 1,
marker = 'circle',
marker_size = 15,
fill = 'bottom',
fill_opacities = [0.5],
fill_colors = ['blue'],
labels = ['nat (I) + pump (Qreg_inf)'],
display_legend = True,
scales={'x': x_sc_2d, 'y': y_sc_2d})
fig_2d = plt.Figure(marks = [tot_inflows_2,pump_inflows_2],
title = 'Natural + pumped inflows',
axes=[x_ax_2d, y_ax_2d],
layout={'width': '480px', 'height': '250px'},
scales={'x': x_sc_2d, 'y': y_sc_2d},
animation_duration=1000,
legend_location = 'top',
legend_style = {'fill': 'white', 'opacity': 0.5})
deficit_2.y = np.maximum(d_sel-update_operation_2(pareto_front_2.selected[0])[2],[0]*N)
storage_2.y = update_operation_2(pareto_front_2.selected[0])[0]
pump_inflows_2.y = [update_operation_2(pareto_front_2.selected[0])[1]]
tot_inflows_2.y = [update_operation_2(pareto_front_2.selected[0])[1]+I_sel[0]]
deficit_2.observe(solution_selected_2, ['x', 'y'])
storage_2.observe(solution_selected_2, ['x', 'y'])
pump_inflows_2.observe(solution_selected_2, ['x', 'y'])
tot_inflows_2.observe(solution_selected_2, ['x', 'y'])
return fig_2pf,fig_2b,fig_2c,fig_2d,pareto_front_2
