def get_dropdown_list_vehicle_motor():
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"})
def get_odrive_histogram_plot_by_vehicle(code_structure=None,
filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
odrive_df["Immatriculation"] = (odrive_df["Immatriculation"].astype(str) +
" (" + odrive_df["Marque"] + " - " +
odrive_df["Modèle"] + ")")
vehicle_emissions = odrive_df.groupby(["Immatriculation"])
vehicle_emissions = vehicle_emissions.agg({
"Moyenne KM par mois":
pd.Series.mean,
"Emissions (g/an)":
pd.Series.mean
}).reset_index()
vehicle_emissions["Moyenne KM par mois"] = vehicle_emissions[
"Moyenne KM par mois"]
vehicle_emissions[
"Emissions (g/an)"] = vehicle_emissions["Emissions (g/an)"] / 12000
vehicle_emissions = vehicle_emissions.nlargest(50, "Moyenne KM par mois")
vehicle_emissions = vehicle_emissions.sort_values("Moyenne KM par mois",
ascending=[0])
fig = make_subplots(specs=[[{"secondary_y": False}]])
fig.add_trace(
go.Bar(
y=vehicle_emissions["Moyenne KM par mois"],
x=vehicle_emissions["Immatriculation"],
name="Distance parcourue (km/mois)",
),
secondary_y=False,
)
fig.add_trace(
go.Bar(y=vehicle_emissions["Emissions (g/an)"],
x=vehicle_emissions["Immatriculation"],
name="Emissions (kg/mois)"),
secondary_y=False,
)
fig.update_layout(plot_bgcolor="white",
template="plotly_white",
margin={
"t": 30,
"r": 30,
"l": 30
})
fig.update_yaxes(title_text="Emissions (kg/mois), Distance par mois (km)",
secondary_y=False)
# fig.update_yaxes(title_text="CO2 (g/km)", secondary_y=True)
fig.update_layout(barmode="group")
return fig
def get_donut_by_entity_type(code_structure=None):
odrive_df = ov.get_structure_data(code_structure)
prestation_df = odrive_df.groupby(
["Entité 3"])["Emissions (g/an)"].sum().reset_index()
prestation_df.entities = list(set(odrive_df["Entité 3"]))
fig = go.Figure(data=[
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
def get_odrive_scatter_plot_by_vehicle_type(code_structure=None,
filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
odrive_df["Marque"] = odrive_df["Marque"] + " " + odrive_df[
"Modèle"] + " (" + odrive_df["Motorisation"] + ")"
vehicle_emissions = odrive_df.groupby(["Marque"])
vehicle_emissions = vehicle_emissions.agg({
"Emissions (g/an)":
pd.Series.mean,
"km parcours par an":
pd.Series.mean,
"Motorisation":
lambda x: x.value_counts().index[0],
"Immatriculation":
pd.Series.nunique,
}).reset_index()
vehicle_emissions["Marque"] = (
vehicle_emissions["Marque"] + " (" +
vehicle_emissions["Immatriculation"].astype(str) + " véhicule(s))")
vehicle_emissions["Immatriculation"] = vehicle_emissions[
"Immatriculation"].add(5)
fig = go.Figure(data=[
go.Scatter(
x=vehicle_emissions["km parcours par an"],
y=vehicle_emissions["Emissions (g/an)"],
mode="markers",
marker_size=vehicle_emissions["Immatriculation"],
text=vehicle_emissions["Marque"],
)
]) # color=vehicle_emissions["Motorisation"],
fig.update_layout(
plot_bgcolor="white",
template="plotly_white",
margin={
"t": 30,
"r": 30,
"l": 30
},
xaxis=dict(title_text="Moyenne de distance parcourue par an (km)"),
yaxis=dict(title_text="Moyenne des emissions de CO2 par an (g)"),
)
return fig
def get_odrive_plot_vehicle_make(code_structure=None,
filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
odrive_df["categorie_emmission"] = odrive_df["CO2 (g/km)"].apply(
get_vehicle_category)
vehicles_df = odrive_df.groupby(
["categorie_emmission"])["Immatriculation"].nunique().reset_index()
fig = go.Figure(data=[
go.Pie(labels=vehicles_df["categorie_emmission"],
values=vehicles_df["Immatriculation"],
hole=0.3)
])
fig.update_layout(plot_bgcolor="white",
template="plotly_white",
margin={
"t": 30,
"r": 30,
"l": 30
})
return fig
def get_test_graph_odrive(code_structure=None, filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
prestation_df = odrive_df.groupby(
["Entité 3"])["km parcours par an"].sum().reset_index()
prestation_df.entities = list(set(odrive_df["Entité 3"]))
fig = go.Figure(data=[
go.Bar(y=prestation_df["km parcours par an"],
x=prestation_df["Entité 3"])
])
fig.update_layout(
plot_bgcolor="white",
template="plotly_white",
margin={
"t": 30,
"r": 30,
"l": 30
},
yaxis=dict(title_text="km parcours par an"),
)
return fig
def load_data(self):
self.chorus_dt_df = ch.get_structure_data(self.service.code_chorus)
self.odrive_df = ov.get_structure_data(self.service.code_odrive)
self.osfi_df = oh.get_structure_data(self.service.code_osfi)
def get_odrive_montly_kilometer(code_structure=None, filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
count_kilometers_per_month_per_vehicle = (
odrive_df["km parcours par an"].sum() /
12) / odrive_df["Immatriculation"].nunique()
return round(count_kilometers_per_month_per_vehicle, 2)
def get_kilometers_total_odive(code_structure=None, filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
count_kilometers = odrive_df["km parcours par an"].sum()
return round(count_kilometers, 2)
def get_total_emisions_vehicles(code_structure=None, filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
count_emissions = odrive_df["Emissions (g/an)"].sum()
return round(count_emissions / 1000, 2)
def get_fleet_vehicle(code_structure=None, filter_vehicle_type=None):
odrive_df = ov.get_structure_data(code_structure, filter_vehicle_type)
count_vehicles = odrive_df["Immatriculation"].nunique()
return round(count_vehicles, 2)