},
xaxis=xaxis_format)
return fig
select_prestation_type = dcc.Dropdown(id="select-prestation_type",
options=[{
"label": "Train",
"value": "T"
}, {
"label": "Avion",
"value": "A"
}])
cards = dbc.CardDeck([
build_card_indicateur("Nombre de trajets", "Nombre de trajets", "2 300"),
build_card_indicateur("Emissions (eCO2)", "Emissions (eCO2)", "2M"),
build_card_indicateur("Indicateur X", "Indicateur X", "XX"),
build_card_indicateur("Indicateur Y", "Indicateur Y", "YY"),
])
layout = html.Div(
[
dbc.Row(html.P("", id="values-selected")),
# Cards row
dbc.Row([
dbc.Col([
dbc.Card(
dbc.CardBody([
html.H3("Filtres"),
html.Br(),
select_travel_year = dbc.Checklist(
options=ch.year_options, value=[p["value"] for p in ch.year_options], id="select-travel-year", inline=True,
)
select_unreliable = dbc.Checklist(
options=[{"label": "Inclure les trajets peu fiables", "value": False},],
value=[True],
id="select-unreliable",
switch=True,
)
cards = dbc.CardDeck(
[
build_card_indicateur("Émissions (kg eqCO2)", "0", "kpi-emissions"),
build_card_indicateur("Nombre de trajets", "0", "kpi-trips"),
build_card_indicateur("Distance totale (km)", "0", "kpi-distance"),
]
)
layout = html.Div(
[
dbc.Row(
[
dbc.Col(
[
dbc.Jumbotron(
[
html.P(
"Les émissions des trajets sont obtenues en croisant le facteur d'émission du "
go.Pie(labels=prestation_df["Entité 3"],
values=prestation_df["Emissions (g/an)"],
hole=0.3)
])
fig.update_layout(plot_bgcolor="white",
template="plotly_white",
margin={
"t": 30,
"r": 30,
"l": 30
})
return fig
cards = dbc.CardDeck([
build_card_indicateur("Nombre de véhicules", "XX"),
build_card_indicateur("Emissions (CO2)", "YY"),
build_card_indicateur("Indicateur X", "XX"),
build_card_indicateur("Indicateur Y", "YY"),
])
select_odrive_vehicle_type = dcc.Dropdown(
id="select_odrive_vehicle_type",
options=[
{
"label": "Electrique",
"value": "E"
},
{
"label": "Essence",
"value": "F"
odrive_df = ov.get_structure_data()
motors = list(set(odrive_df["Motorisation"]))
options = []
value = []
for m in motors:
if m is not None and m is not np.nan:
options.append({"label": m, "value": m})
value.append(m)
return dcc.Checklist(id="odrive_select_vehicle_type",
options=options,
value=value,
labelStyle={"display": "block"})
cards = dbc.CardDeck([
build_card_indicateur("Emissions en CO2 par an (kg)", "0",
"odrive_total_emissions"),
build_card_indicateur("Nombre de véhicules", "0",
"odrive_fleet_vehicle_number_odrive"),
build_card_indicateur("Distance parcourue par an (km)", "0",
"odrive_kilometers_total"),
build_card_indicateur("Distance par mois par véhicule (km)", "0",
"odrive_montly_kilometer"),
])
layout = html.Div(
[
# Cards row
dbc.Row([
dbc.Col([
html.B("", id="selected-odrive-vehicle_type"),
dbc.Jumbotron([
