def __init__(self, simulation, emitter):
self.simulation = simulation
self.hmap = simulation.hmap
self.emitter = emitter
self.coords = np.array(
[self.hmap.hmap[i].coords for i in range(self.hmap.n**2)])
self.colors = [
'green' if self.hmap.hmap[i].state == 'terrain' else 'steelblue'
for i in range(self.hmap.n**2)
]
ax_x = Axis(label="x", scale=bqplot.LinearScale(min=0, max=2))
ax_y = Axis(label="y",
scale=bqplot.LinearScale(min=0, max=2),
orientation="vertical",
side="left")
self.grid_scat = plt.scatter(x=self.coords[:, 0],
y=self.coords[:, 1],
colors=self.colors,
default_size=3,
default_opacities=[.3],
marker='rectangle')
self.particles_scat = plt.scatter(
x=[p.x for p in simulation.particles],
y=[p.y for p in simulation.particles])
self.fig = plt.Figure(marks=[self.grid_scat, self.particles_scat],
axes=[ax_x, ax_y],
animation_duration=100,
padding_x=.05,
padding_y=.05)
self.out = widgets.Output()
def on_value_change(change):
t = change['new']
if t == 1:
self.simulation.reset()
self.emitter.reset()
self.hmap.reset()
self.out.clear_output()
self.particles_scat.x = [p.x for p in self.simulation.particles]
self.particles_scat.y = [p.y for p in self.simulation.particles]
self.simulation.update_particles()
p = self.emitter.emit()
if p:
self.simulation.add_particle(p)
self.slider = widgets.IntSlider(min=0,
max=1000,
step=1,
continuous_update=True)
self.play = widgets.Play(min=1, max=1000, interval=150)
self.slider.observe(on_value_change, 'value')
widgets.jslink((self.play, 'value'), (self.slider, 'value'))
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
self.scale_x = bqplot.LinearScale(min=limits[0][0], max=limits[0][1])
self.scale_y = bqplot.LinearScale(min=limits[1][0], max=limits[1][1])
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y, 'rotation': self.scale_rotation,
'size': self.scale_size, 'opacity': self.scale_opacity}
margin = {'bottom': 30, 'left': 60, 'right': 0, 'top': 0}
self.figure = plt.figure(self.figure_key, fig=self.figure, scales=self.scales, fig_margin=margin)
self.figure.layout.min_width = '900px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x ** 2
self._fix_scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.image = bqplot_image.Image(scales=self.scales, src=src, x=self.scale_x.min, y=self.scale_y.max,
width=self.scale_x.max - self.scale_x.min, height=-(self.scale_y.max - self.scale_y.min))
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales, size=x, marker="arrow")
self.panzoom = bqplot.PanZoom(scales={'x': [self.scale_x], 'y': [self.scale_y]})
self.figure.interaction = self.panzoom
# self.figure.axes[0].label = self.x
# self.figure.axes[1].label = self.y
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.control_widget, self.figure])
self.create_tools()
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
self.scale_x = bqplot.LinearScale(min=limits[0][0].item(), max=limits[0][1].item())
self.scale_y = bqplot.LinearScale(min=limits[1][0].item(), max=limits[1][1].item())
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {'x': self.scale_x, 'y': self.scale_y, 'rotation': self.scale_rotation,
'size': self.scale_size, 'opacity': self.scale_opacity}
margin = {'bottom': 30, 'left': 60, 'right': 0, 'top': 0}
self.figure = plt.figure(self.figure_key, fig=self.figure, scales=self.scales, fig_margin=margin)
self.figure.layout.min_width = '900px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x ** 2
self._fix_scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.image = bqplot_image.Image(scales=self.scales, src=src, x=self.scale_x.min, y=self.scale_y.max,
width=self.scale_x.max - self.scale_x.min, height=-(self.scale_y.max - self.scale_y.min))
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x, y, visible=False, rotation=x, scales=self.scales, size=x, marker="arrow")
self.panzoom = bqplot.PanZoom(scales={'x': [self.scale_x], 'y': [self.scale_y]})
self.figure.interaction = self.panzoom
# self.figure.axes[0].label = self.x
# self.figure.axes[1].label = self.y
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.control_widget, self.figure])
self.create_tools()
def LoadScatterPlotEvaluation(Y, Y_hat, prediction_size):
"""Prepara debajo de la celda una gráfica para observar visualmente el
resultado de una predicción.
Args :
Y (:obj: `numpy.array`): datos reales.
Y_hat (:obj: `numpy.array`): datos predichos.
prediction_size (int): número de años predichos.
"""
n = int(Y.shape[0] / prediction_size)
colors = []
if prediction_size >= 1:
colors.append(1)
if prediction_size >= 2:
colors.append(2)
if prediction_size >= 3:
colors.append(3)
if prediction_size >= 4:
colors.append(4)
if prediction_size >= 5:
colors.append(5)
colors = np.array(colors * n)
axes_options = {
'x': dict(label='Valor real (alumnos)'),
'y': dict(label='Valor predicho (alumnos)'),
'color': dict(label='Año', side='right')
}
scatter2 = plt.scatter(Y,
Y_hat,
color=colors,
stroke='black',
axes_options=axes_options)
def build_widgets(self, *args, **kwargs):
# residuals plot
self.residuals_fig = plt.figure(title='Residuals vs Predicted Values',
layout=Layout(width='960px',
height='600px',
overflow_x='hidden',
overflow_y='hidden'))
axes_options = {
'y': {
'label': 'Residual',
'tick_format': '0.1f'
},
'x': {
'label': 'Predicted Value'
}
}
self.residuals_plot = plt.scatter([], [],
colors=['yellow'],
default_size=16,
stroke='black',
axes_options=axes_options)
# zero line
plt.hline(level=0, colors=['limegreen'], stroke_width=3)
self.widgets_layout = HBox([self.residuals_fig])
def LoadEvaluationPlot(result, prediction_size, conjunto, modelo):
"""Prepara debajo de la celda una gráfica que detalla visualmente el resultado
de la evaluación de un conjunto de datos utilizando un modelo específico.
Args:
result (:obj: `TestResult`): instancia de TestResult con los resultados
de la evaluación.
prediction_size (int): número de años predichos
conjunto (str): conjunto de datos evaluado.
modelo (str): método de predicción utilizado.
"""
n = int(result.Y.shape[0] / prediction_size)
m = max(np.amax(result.Y_hat), np.amax(result.Y))
colors = []
if prediction_size >= 1:
colors.append(1)
if prediction_size >= 2:
colors.append(2)
if prediction_size >= 3:
colors.append(3)
if prediction_size >= 4:
colors.append(4)
if prediction_size >= 5:
colors.append(5)
colors = np.array(colors * n)
fig = plt.figure(
title='Proyección de matrícula escolar en %s utilizando %s' %
(conjunto, modelo),
legend_location='top-left',
fig_margin=dict(top=50, bottom=70, left=100, right=100))
plt.scales(
scales={
'color':
OrdinalColorScale(
colors=['Green', 'DodgerBlue', 'Yellow', 'Orange', 'Red'])
})
axes_options = {
'x': dict(label='Valor real (alumnos)'),
'y': dict(label='Valor predicho (alumnos)'),
'color': dict(label='Año', side='right')
}
scatter2 = plt.scatter(result.Y,
result.Y_hat,
color=colors,
stroke='black',
axes_options=axes_options)
plt.plot(x=np.array([0, m]),
y=np.array([0, m]),
labels=['Línea base de predicción'],
display_legend=True)
def generate_scatter(df,
title="",
scientific_notation=False,
small_xlabel=True):
fig = plt.figure(title=title)
x_vals = df.index.values.tolist()
if len(x_vals) > 5:
small_xlabel = True
x_titles = []
for val in x_vals:
if len(val.split(" ")) < 3:
x_titles.append(val)
else:
x_titles.append(" ".join(val.split(" ")[:2]))
scatter = plt.scatter(x=x_titles, y=df)
if small_xlabel:
fig.axes[0].tick_style = {"font-size": "6"}
if not scientific_notation:
fig.axes[1].tick_format = ".1f"
return fig
def addTimeseries(inMap,path,bands,new_plot):
px_series = xr.open_dataset(path)
date_start = px_series.t.min().values
date_end = px_series.t.max().values
color = ['blue','red','green','yellow']
for i,b in enumerate(bands):
x_data = px_series.t.values
y_data = px_series.to_array().loc[dict(variable=b)][:,0,0].values
x_data = x_data[~np.isnan(y_data)]
y_data = y_data[~np.isnan(y_data)]
x_data = x_data[y_data!=0]
y_data = y_data[y_data!=0]
axes_options = {'x': {'label':'Time', 'side':'bottom', 'num_ticks':8, 'tick_format':'%b %y'}, 'y': {'orientation':'vertical', 'side':'left', 'num_ticks':10}}
if i==0:
title = ''
for x in bands:
title += (x + ' ')
title += ' timeseries'
if new_plot:
inMap.figure = bqplt.figure(title=title,layout={'max_height': '250px', 'width': '600px'})
else:
if inMap.figure is not None:
pass
else:
inMap.figure = bqplt.figure(title=title,layout={'max_height': '250px', 'width': '600px'})
scatt = bqplt.scatter(x_data, y_data, labels=[b], display_legend=True, colors=[color[i]], default_size=30, axes_options=axes_options)
widget_control = WidgetControl(widget=inMap.figure, position='bottomright')
if inMap.figure_widget is not None:
inMap.map.remove_control(inMap.figure_widget)
inMap.figure_widget = widget_control
inMap.map.add_control(inMap.figure_widget)
return
def Interactive_Pareto_front(simtime,I,E,d,S0,Smax,ms,env_min, demand_plot,solutions_optim_relea,results1_optim_relea,results2_optim_relea):
def syst_sim(simtime,I,E,d,S0,Smax,env_min,Qreg):
# Declare output variables
S = [0]*(simtime+1) # reservoir storage in ML
spill = [0]*(simtime) # spillage in ML
env = [env_min]*(simtime) # environmental compensation flow
S[0] = S0 # initial storage
for t in range(simtime): # Loop for each time-step (week)
# If at week t the inflow (I) is lower than the minimum environmental compensation (env_min),
# then the environmental compensation (env) = inflow (I)
if env_min >= I[t] :
env[t] = I[t]
# If the minimum environmental compensation is higher than the water resource available (S + I - E)
# then the environmental compensation is equal to the higher value between 0 and the resource available
if env_min >= S[t] + I[t] - E[t]:
env[t] = max(0,S[t] + I[t] - E[t]) # S[t] = Smin then env[t] = I[t] and S[t+1] < Smin
# If the demand is higher than the water resource available (S + I - E - env)
# then the release for water supply is equal to the higher value between 0 and the resource available
if d[t] >= S[t] + I[t] - E[t] - env[t]:
Qreg[t] = min(Qreg[t],max(0,S[t] + I[t] - E[t] - env[t]))
# The spillage is equal to the higher value between 0 and the resource available exceeding the reservoir capacity
spill[t] = max(0,S[t] + I[t] - Qreg[t] - env[t] - E[t] - Smax)
# The final storage (initial storage in the next step) is equal to the storage + inflow - outflows
S[t+1] = S[t] + I[t] - Qreg[t] - env[t]- E[t] - spill[t]
return S,env,spill,Qreg
def update_operation_4(i):
Qreg = solutions_optim_relea[i]
S,env,spill,Qreg1 = syst_sim(simtime,I,E,d,S0,Smax,env_min,Qreg)
lspen = np.sum((np.maximum(ms-S,[0]*(simtime+1)))).astype('int')
fig_4a.title = 'Reservoir storage - Minimum storage violation = '+str(lspen)+' ML'
sdpen = (np.sum((np.maximum(d-Qreg1,[0]*simtime))**2)).astype('int')
fig_4b.title = 'Supply vs Demand - Total squared deficit = '+str(sdpen)+' ML^2'
return S,env,spill,Qreg1
def solution_selected(change):
if pareto_front.selected == None:
pareto_front.selected = [0]
y_vals_4a = update_operation_4(pareto_front.selected[0])[0]
storage_4.y = y_vals_4a
y_vals_4b = update_operation_4(pareto_front.selected[0])[3]
releases_4.y = y_vals_4b
x_sc_pf = LinearScale();y_sc_pf = LinearScale()
x_ax_pf = Axis(label='Total squared deficit [ML^2]', scale=x_sc_pf)
y_ax_pf = Axis(label='Minimum storage violation [ML]', scale=y_sc_pf, orientation='vertical')
pareto_front = plt.scatter(results1_optim_relea[:],results2_optim_relea[:],scales={'x': x_sc_pf, 'y': y_sc_pf},
colors=['deepskyblue'], interactions={'hover':'tooltip','click': 'select'})
pareto_front.unselected_style={'opacity': 0.4}
pareto_front.selected_style={'fill': 'red', 'stroke': 'yellow', 'width': '1125px', 'height': '125px'}
def_tt = Tooltip(fields=['x', 'y'],labels=['Water deficit', 'Min storage'], formats=['.1f', '.1f'])
pareto_front.tooltip=def_tt
fig_pf = plt.Figure(marks = [pareto_front],title = 'Interactive Pareto front', axes=[x_ax_pf, y_ax_pf],
layout={'width': '500px', 'height': '500px'}, animation_duration=1000)
if pareto_front.selected == []:
pareto_front.selected = [0]
pareto_front.observe(solution_selected,'selected')
S,env,w,u1 = syst_sim(simtime,I,E,d,S0,Smax,env_min,solutions_optim_relea[pareto_front.selected[0]])
x_sc_1 = LinearScale();y_sc_1 = LinearScale(min=0,max=35);x_ax_1 = Axis(label='week', scale=x_sc_1);y_ax_1 = Axis(label='ML/week', scale=y_sc_1, orientation='vertical')
x_sc_2a = LinearScale(min=0,max=simtime);y_sc_2a = LinearScale(min=0,max=200);x_ax_2a = Axis(label='week', scale=x_sc_2a,tick_values=np.arange(8)+0.5);y_ax_2a = Axis(label='ML', scale=y_sc_2a, orientation='vertical')
max_storage_2 = plt.plot(x=np.arange(0,simtime+1),y=[Smax]*(simtime+1),colors=['red'],scales={'x': x_sc_2a, 'y': y_sc_2a})
max_storage_label_2 = plt.label(text = ['Max storage'], x=[0],y=[Smax+15],colors=['red'])
min_storage_3 = plt.plot(np.arange(0,simtime+1),ms,scales={'x': x_sc_2a, 'y': y_sc_2a},colors=['red'],opacities = [1],line_style = 'dashed',
fill = 'bottom',fill_opacities = [0.4],fill_colors = ['red'], stroke_width = 1)
min_storage_label_3 = plt.label(text = ['Min storage'], x=[0],y=[ms[0]-10],colors=['red'])
storage_4 = Lines(x=range(simtime+1),y=S,scales={'x': x_sc_2a, 'y': y_sc_2a}, fill = 'bottom',fill_opacities = [0.7]*simtime,
fill_colors = ['blue'])
fig_4a = plt.Figure(marks = [min_storage_3,storage_4,max_storage_2,max_storage_label_2,min_storage_label_3],
axes=[x_ax_2a, y_ax_2a],layout={'width': '480px', 'height': '250px'},animation_duration=1000)
releases_4 = plt.bar(np.arange(1,simtime+1),u1,colors=['green'],opacities = [0.7]*simtime,scales={'x': x_sc_1, 'y': y_sc_1},
labels = ['release'], display_legend = True, stroke_width = 1)
fig_4b = plt.Figure(marks = [demand_plot, releases_4], axes=[x_ax_1, y_ax_1],layout={'width': '480px', 'height': '250px'},
animation_duration=1000,legend_location = 'top-left',legend_style = {'fill': 'white', 'opacity': 0.5})
storage_4.y = update_operation_4(pareto_front.selected[0])[0]
releases_4.y = update_operation_4(pareto_front.selected[0])[3]
storage_4.observe(solution_selected, ['x', 'y'])
releases_4.observe(solution_selected, ['x', 'y'])
return fig_4a,fig_4b,fig_pf
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 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
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
def fix(v):
# bqplot is picky about float and numpy scalars
if hasattr(v, 'item'):
return v.item()
else:
return v
self.scale_x = bqplot.LinearScale(min=fix(limits[0][0]),
max=fix(limits[0][1]),
allow_padding=False)
self.scale_y = bqplot.LinearScale(min=fix(limits[1][0]),
max=fix(limits[1][1]),
allow_padding=False)
self.scale_rotation = bqplot.LinearScale(min=0, max=1)
self.scale_size = bqplot.LinearScale(min=0, max=1)
self.scale_opacity = bqplot.LinearScale(min=0, max=1)
self.scales = {
'x': self.scale_x,
'y': self.scale_y,
'rotation': self.scale_rotation,
'size': self.scale_size,
'opacity': self.scale_opacity
}
margin = {'bottom': 35, 'left': 60, 'right': 5, 'top': 5}
self.figure = plt.figure(self.figure_key,
fig=self.figure,
scales=self.scales,
fig_margin=margin)
self.figure.layout.min_width = '600px'
plt.figure(fig=self.figure)
self.figure.padding_y = 0
x = np.arange(0, 10)
y = x**2
self._fix_scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales)
self._fix_scatter.visible = False
# self.scale_rotation = self.scales['rotation']
src = "" # vaex.image.rgba_to_url(self._create_rgb_grid())
# self.scale_x.min, self.scale_x.max = self.limits[0]
# self.scale_y.min, self.scale_y.max = self.limits[1]
self.core_image = widgets.Image(format='png')
self.core_image_fix = widgets.Image(format='png')
self.image = bqplot.Image(scales=self.scales, image=self.core_image)
self.figure.marks = self.figure.marks + [self.image]
# self.figure.animation_duration = 500
self.figure.layout.width = '100%'
self.figure.layout.max_width = '500px'
self.scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales,
size=x,
marker="arrow")
self.panzoom = bqplot.PanZoom(scales={
'x': [self.scale_x],
'y': [self.scale_y]
})
self.figure.interaction = self.panzoom
for axes in self.figure.axes:
axes.grid_lines = 'none'
axes.color = axes.grid_color = axes.label_color = blackish
self.figure.axes[0].label = str(plot.x)
self.figure.axes[1].label = str(plot.y)
self.scale_x.observe(self._update_limits, "min")
self.scale_x.observe(self._update_limits, "max")
self.scale_y.observe(self._update_limits, "min")
self.scale_y.observe(self._update_limits, "max")
self.observe(self._update_scales, "limits")
self.image.observe(self._on_view_count_change, 'view_count')
self.control_widget = widgets.VBox()
self.widget = widgets.VBox(children=[self.figure])
self.create_tools()
def create_widget(self, output, plot, dataset, limits):
self.plot = plot
self.output = output
self.dataset = dataset
self.limits = np.array(limits).tolist()
def fix(v):
# bqplot is picky about float and numpy scalars
if hasattr(v, 'item'):
return v.item()
else:
return v
self.scale_x = bqplot.LinearScale(min=fix(limits[0][0]),
max=fix(limits[0][1]),
allow_padding=False)
self.scale_y = bqplot.LinearScale(min=fix(limits[1][0]),
max=fix(limits[1][1]),
allow_padding=False)
self.scales = {'x': self.scale_x, 'y': self.scale_y}
self.figure = plt.figure(self.figure_key,
fig=self.figure,
scales=self.scales)
self.figure.layout.width = 'calc(100% - 400px)'
self.figure.layout.min_height = '800px'
plt.figure(fig=self.figure)
#self.figure.padding_y = 0
x = np.arange(0, 10)
y = x**2
self.core_image = widgets.Image(format='png')
self.core_image_fix = widgets.Image(format='png')
self.image = bqplot.Image(scales=self.scales, image=self.core_image)
# triggered by regular mouse click not a brush selector
self.image.on_element_click(self.click_to_zoom)
self.figure.marks = self.figure.marks + [self.image]
self.scatter = s = plt.scatter(x,
y,
visible=False,
rotation=x,
scales=self.scales,
size=x,
marker="arrow")
self.panzoom = bqplot.PanZoom(scales={
'x': [self.scale_x],
'y': [self.scale_y]
})
self.figure.interaction = self.panzoom
for axes in self.figure.axes:
axes.grid_lines = 'none'
axes.color = axes.grid_color = axes.label_color = blackish
self.figure.axes[0].label = plot.x_label
self.figure.axes[1].label = plot.y_label
self.figure.axes[1].scale = bqplot.LinearScale(min=0,
max=self.scale_y.max -
self.scale_y.min,
allow_padding=False)
self.stuck_ctr = 0
self.base_address = widgets.Label(
value=f"Base address: 0x{int(self.limits[1][0]):X}")
self.zoom_args = widgets.Text(description="Zoom Args",
value=self.zoom_args_string(),
disabled=True,
style={'description_width': 'initial'},
layout=widgets.Layout(width='50%'))
self.curr_action = Action.other
self.undo_actions = list()
self.redo_actions = list()
self.counter = 2
self.scale_x.observe(self._update_limits)
self.scale_y.observe(self._update_limits)
self.widget = widgets.VBox(
[self.figure, self.base_address, self.zoom_args])
self.create_tools()
import numpy as np import bqplot.pyplot as plt size = 100 plt.figure(title='Scatter plot with colors') plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size)) plt.show()
def plot(self,
x,
y,
plot_type=None,
overlay=False,
position='bottomright',
min_width=None,
max_width=None,
min_height=None,
max_height=None,
**kwargs):
"""Creates a plot based on x-array and y-array data.
Args:
x (numpy.ndarray or list): The x-coordinates of the plotted line.
y (numpy.ndarray or list): The y-coordinates of the plotted line.
plot_type (str, optional): The plot type can be one of "None", "bar", "scatter" or "hist". Defaults to None.
overlay (bool, optional): Whether to overlay plotted lines on the figure. Defaults to False.
position (str, optional): Position of the control, can be ‘bottomleft’, ‘bottomright’, ‘topleft’, or ‘topright’. Defaults to 'bottomright'.
min_width (int, optional): Min width of the widget (in pixels), if None it will respect the content size. Defaults to None.
max_width (int, optional): Max width of the widget (in pixels), if None it will respect the content size. Defaults to None.
min_height (int, optional): Min height of the widget (in pixels), if None it will respect the content size. Defaults to None.
max_height (int, optional): Max height of the widget (in pixels), if None it will respect the content size. Defaults to None.
"""
if self.plot_widget is not None:
plot_widget = self.plot_widget
else:
plot_widget = widgets.Output(layout={'border': '1px solid black'})
plot_control = WidgetControl(widget=plot_widget,
position=position,
min_width=min_width,
max_width=max_width,
min_height=min_height,
max_height=max_height)
self.plot_widget = plot_widget
self.plot_control = plot_control
self.add_control(plot_control)
if max_width is None:
max_width = 500
if max_height is None:
max_height = 300
if (plot_type is None) and ('markers' not in kwargs.keys()):
kwargs['markers'] = 'circle'
with plot_widget:
try:
fig = plt.figure(1, **kwargs)
if max_width is not None:
fig.layout.width = str(max_width) + 'px'
if max_height is not None:
fig.layout.height = str(max_height) + 'px'
plot_widget.clear_output(wait=True)
if not overlay:
plt.clear()
if plot_type is None:
if 'marker' not in kwargs.keys():
kwargs['marker'] = 'circle'
plt.plot(x, y, **kwargs)
elif plot_type == 'bar':
plt.bar(x, y, **kwargs)
elif plot_type == 'scatter':
plt.scatter(x, y, **kwargs)
elif plot_type == 'hist':
plt.hist(y, **kwargs)
plt.show()
except Exception as e:
print(e)
print("Failed to create plot.")
def add_scat(self, *args, **kwargs):
return plt.scatter(*args, figure=self.fig, **kwargs)
def Interactive_policy_auto(N,
I_hist, e_hist,
s_0, s_min, s_max,
u_0, u_1, u_mean, u_max,
env_min, d_hist,
rc,
results1_optim,results2_optim,sol_optim):
# Function to update the release policy when clicking on the points of the Pareto front
def update_operating_policy_2(i):
u_ref,s_ref_1,s_ref_2 = sol_optim[i]
x0 = [0, u_0]
x1 = [s_ref_1, u_ref]
x2 = [s_ref_2, u_ref]
x3 = [1, u_1]
param = [x0, x1, x2, x3, u_mean]
u_frac = four_points_policy(param)/u_mean
Qreg = {'releases' : {'file_name' : 'Reservoir_operating_policy.Operating_policy_functions',
'function' : 'four_points_policy',
'param': param},
'inflows' : [],
'rel_inf' : []}
Qenv, Qspill, u, I_reg, s, E = Res_sys_sim(I_hist, e_hist, s_0, s_min, s_max, env_min, d_hist, Qreg)
MSV = (np.sum((np.maximum(rc-s,[0]*(N+1))))).astype('int')
fig_2c.title = 'Reservoir storage volume - MSV = '+str(MSV)+' ML'
TSD = (np.sum((np.maximum(d_hist-u,[0]*N))**2)).astype('int')
fig_2b.title = 'Supply vs Demand - Total squared deficit = '+str(TSD)+' ML^2'
return u_frac, Qenv, Qspill, u, I_reg, s
# Function to update the figures when clicking on the points of the Pareto front
def update_figure_2(change):
policy_function.y = update_operating_policy_2(pareto_front.selected[0])[0]
releases.y = update_operating_policy_2(pareto_front.selected[0])[3]
storage.y = update_operating_policy_2(pareto_front.selected[0])[5]
# Fig_pf: Pareto front
x_sc_pf = LinearScale();y_sc_pf = LinearScale()
x_ax_pf = Axis(label='Total squared deficit [ML^2]', scale=x_sc_pf)
y_ax_pf = Axis(label='Minimum storage violation [ML]', scale=y_sc_pf, orientation='vertical')
pareto_front = plt.scatter(results1_optim[:],results2_optim[:],
scales={'x': x_sc_pf, 'y': y_sc_pf},
colors=['deepskyblue'],
interactions={'hover':'tooltip','click': 'select'})
pareto_front.unselected_style={'opacity': 0.4}
pareto_front.selected_style={'fill': 'red', 'stroke': 'yellow', 'width': '1125px', 'height': '125px'}
def_tt = Tooltip(fields=['index','x', 'y'],
labels=['index','Water deficit', 'Min storage'],
formats=['.d','.1f', '.1f'])
pareto_front.tooltip=def_tt
fig_pf = plt.Figure(marks = [pareto_front],title = 'Interactive Pareto front',
axes=[x_ax_pf, y_ax_pf],
layout={'width': '400px', 'height': '400px'}, animation_duration=1000)
if pareto_front.selected == []:
pareto_front.selected = [0]
pareto_front.observe(update_figure_2,'selected')
# Initial simulation applting the point of the Pareto Fron selected by default
u_ref,s_ref_1,s_ref_2 = sol_optim[pareto_front.selected[0]]
x0 = [0, u_0]
x1 = [s_ref_1, u_ref]
x2 = [s_ref_2, u_ref]
x3 = [1, u_1]
param = [x0, x1, x2, x3, u_mean]
u_frac = four_points_policy(param)/u_mean
Qreg = {'releases' : {'file_name' : 'Reservoir_operating_policy.Operating_policy_functions',
'function' : 'four_points_policy',
'param': param},
'inflows' : [],
'rel_inf' : []}
Qenv, Qspill, u, I_reg, s, E = Res_sys_sim(I_hist, e_hist, s_0, s_min, s_max, env_min, d_hist, Qreg)
# Fig 2a: Policy function
s_frac = np.arange(0,1.01,0.01)
x_sc_2a = LinearScale(min=0,max=1); y_sc_2a = LinearScale(min=0,max=u_max/u_mean);
x_ax_2a = Axis(label='Storage fraction', scale=x_sc_2a);
y_ax_2a = Axis(label='Release fraction', scale=y_sc_2a, orientation='vertical')
policy_function = Lines(x = s_frac,
y = u_frac ,
colors = ['blue'],
scales = {'x': x_sc_2a, 'y': y_sc_2a})
fig_2a = plt.Figure(marks = [policy_function],
title = 'Policy function',
axes=[x_ax_2a, y_ax_2a],
layout={'width': '400px', 'height': '375px'},
animation_duration=1000,
scales={'x': x_sc_2a, 'y': y_sc_2a})
policy_function.observe(update_figure_2, ['x', 'y'])
# Fig 2b: Releases vs Demand
x_sc_2b = LinearScale(min=0,max=N); y_sc_2b = LinearScale(min=0,max=u_max);
x_ax_2b = Axis(label='week', scale=x_sc_2b); y_ax_2b = Axis(label='ML/week', scale=y_sc_2b, orientation='vertical')
demand = Bars(x = np.arange(1,N+1),
y = d_hist,
colors = ['gray'],
scales = {'x': x_sc_2b, 'y': y_sc_2b})
releases = Bars(x = np.arange(1,N+1),
y = u,
colors = ['green'],
scales = {'x': x_sc_2b, 'y': y_sc_2b})
TSD = (np.sum((np.maximum(d_hist-u,[0]*N))**2)).astype('int')
fig_2b = plt.Figure(marks = [demand, releases],
title = 'Supply vs Demand - TSD = '+str(TSD)+' ML^2',
axes=[x_ax_2b, y_ax_2b],
layout={'width': '950px', 'height': '250px'},
animation_duration=1000,
scales={'x': x_sc_2b, 'y': y_sc_2b})
releases.observe(update_figure_2, ['x', 'y'])
# Fig 2c: Storage
x_sc_2c = LinearScale(); y_sc_2c = LinearScale(min=0,max=200);
x_ax_2c = Axis(label='week', scale=x_sc_2c); y_ax_2c = Axis(label='ML', scale=y_sc_2c, orientation='vertical')
storage = 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 = plt.plot(x=np.arange(0,N+1),
y=[s_max]*(N+1),
colors=['red'],
scales={'x': x_sc_2c, 'y': y_sc_2c})
max_storage_label = plt.label(text = ['Max storage'],
x=[0],
y=[s_max+15],
colors=['red'])
min_storage = plt.plot(np.arange(0,N+1),rc,
scales={'x': x_sc_2c, 'y': y_sc_2c},
colors=['red'],opacities = [1],
line_style = 'dashed',
fill = 'bottom',fill_opacities = [0.4],fill_colors = ['red'], stroke_width = 1)
min_storage_label = plt.label(text = ['Min storage'],
x=[0],
y=[rc[0]-10],
colors=['red'])
MSV = (np.sum((np.maximum(rc-s,[0]*(N+1))))).astype('int')
fig_2c = plt.Figure(marks = [storage,max_storage,max_storage_label,
min_storage,min_storage_label],
title = 'Reservoir storage volume - MSV = '+str(MSV)+' ML',
axes=[x_ax_2c, y_ax_2c],
layout={'width': '950px', 'height': '250px'},
animation_duration=1000,
scales={'x': x_sc_2c, 'y': y_sc_2c})
storage.observe(update_figure_2, ['x', 'y'])
return fig_pf, fig_2a,fig_2b,fig_2c
import numpy as np import bqplot.pyplot as plt size = 100 plt.figure(title="Scatter plot with colors") plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size)) plt.show()
