def get_figures(trips: Trips, charging: List[dict]):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info, \
battery_table, consumption_graph_by_temp
lats = []
lons = []
names = []
for trip in trips:
for points in trip.positions:
lats = np.append(lats, points.latitude)
lons = np.append(lons, points.longitude)
names = np.append(names, [str(trip.start_at)])
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
trips_map = px.line_mapbox(lat=lats,
lon=lons,
hover_name=names,
mapbox_style="stamen-terrain",
zoom=12)
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes) # pylint: disable=no-member
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='native',
sort_by=[{
'column_id': 'id',
'direction': 'desc'
}],
columns=[{
'id': 'id',
'name': '#',
'type': 'numeric'
}, {
'id': 'start_at',
'name': 'start at',
'type': 'datetime'
}, {
'id':
'duration',
'name':
'duration',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" min").precision(0)
}, {
'id':
'speed_average',
'name':
'average speed',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" km/h").precision(0)
}, {
'id': 'consumption_km',
'name': 'average consumption',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")
}, {
'id': 'consumption_fuel_km',
'name': 'average consumption fuel',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" L/100km")
}, {
'id': 'distance',
'name': 'distance',
'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)
}, {
'id': 'mileage',
'name': 'mileage',
'type': 'numeric',
'format': nb_format
}, {
'id': 'altitude_diff',
'name': 'Altitude diff',
'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" m").precision(0)
}],
style_data_conditional=[{
'if': {
'column_id': ['altitude_diff']
},
'color': 'dodgerblue',
"text-decoration": "underline"
}],
data=trips.get_info(),
page_size=50)
# consumption_fig
consumption_df = DataFrame.from_records(trips.get_long_trips())
consumption_fig = px.histogram(consumption_df,
x="date",
y="consumption_km",
title='Consumption of the car',
histfunc="avg")
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
consumption_fig_by_speed = px.histogram(consumption_df,
x="speed",
y="consumption_km",
histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(
go.Scatter(mode="markers",
x=consumption_df["speed"],
y=consumption_df["consumption_km"],
name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h",
yaxis_title="Consumption kWh/100Km")
kw_per_km = float(consumption_df["consumption_km"].mean())
info = "Average consumption: {:.1f} kWh/100km".format(kw_per_km)
# charging
charging_data = DataFrame.from_records(charging)
co2_per_kw = __calculate_co2_per_kw(charging_data)
co2_per_km = co2_per_kw * kw_per_km / 100
try:
charge_speed = 3600 * charging_data["kw"].mean() / \
(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
price_kw = (charging_data["price"] / charging_data["kw"]).mean()
total_elec = kw_per_km * trips.get_distance() / 100
except (TypeError, KeyError,
ZeroDivisionError): # when there is no data yet:
charge_speed = 0
price_kw = 0
total_elec = 0
SUMMARY_CARDS["Average charge speed"]["text"] = f"{charge_speed:.2f} kW"
SUMMARY_CARDS["Average emission"]["text"] = [
html.P(f"{co2_per_km:.1f} g/km"),
html.P(f"{co2_per_kw:.1f} g/kWh")
]
SUMMARY_CARDS["Electricity consumption"]["text"] = [f"{total_elec:.0f} kWh", html.Br(), \
f"{total_elec * price_kw:.0f} {ElecPrice.currency}"]
SUMMARY_CARDS["Average consumption"][
"text"] = f"{consumption_df['consumption_km'].mean():.1f} kWh/100km"
battery_table = dash_table.DataTable(
id='battery-table',
sort_action='native',
sort_by=[{
'column_id': 'start_at',
'direction': 'desc'
}],
columns=[{
'id': 'start_at',
'name': 'start at',
'type': 'datetime'
}, {
'id': 'stop_at',
'name': 'stop at',
'type': 'datetime'
}, {
'id': 'start_level',
'name': 'start level',
'type': 'numeric'
}, {
'id': 'end_level',
'name': 'end level',
'type': 'numeric'
}, {
'id':
'co2',
'name':
'CO2',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)
}, {
'id':
'kw',
'name':
'consumption',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" kWh").precision(2)
}, {
'id':
'price',
'name':
'price',
'type':
'numeric',
'format':
deepcopy(nb_format).symbol_suffix(" " +
ElecPrice.currency).precision(2),
'editable':
True
}],
data=charging,
style_data_conditional=[{
'if': {
'column_id': ['start_level', "end_level"]
},
'color': 'dodgerblue',
"text-decoration": "underline"
}, {
'if': {
'column_id': 'price'
},
'backgroundColor': 'rgb(230, 246, 254)'
}],
)
consumption_by_temp_df = consumption_df[
consumption_df["consumption_by_temp"].notnull()]
if len(consumption_by_temp_df) > 0:
consumption_fig_by_temp = px.histogram(
consumption_by_temp_df,
x="consumption_by_temp",
y="consumption_km",
histfunc="avg",
title="Consumption by temperature")
consumption_fig_by_temp.update_traces(xbins_size=2)
consumption_fig_by_temp.update_layout(bargap=0.05)
consumption_fig_by_temp.add_trace(
go.Scatter(mode="markers",
x=consumption_by_temp_df["consumption_by_temp"],
y=consumption_by_temp_df["consumption_km"],
name="Trips"))
consumption_fig_by_temp.update_layout(
xaxis_title="average temperature in °C",
yaxis_title="Consumption kWh/100Km")
consumption_graph_by_temp = html.Div(
Graph(figure=consumption_fig_by_temp),
id="consumption_graph_by_temp")
else:
consumption_graph_by_temp = html.Div(Graph(style={'display': 'none'}),
id="consumption_graph_by_temp")
return True
def update_format_table(
fixed_checkbox,
decimal_char,
precision,
si_prefix,
options_checklist,
nan,
scheme_dd,
symbol_pct_radio,
prefix,
suffix,
width,
fill_char,
align_val,
group_checkbox,
group_char,
groupings,
):
code = {}
formatted = Format()
if fixed_checkbox:
formatted = formatted.scheme(Scheme.fixed)
code[1] = ".scheme(Scheme.fixed)"
if decimal_char:
formatted = formatted.decimal_delimiter(decimal_char)
code[2] = f".decimal_delimiter('{decimal_char}')"
if precision:
formatted = formatted.precision(int(precision))
code[3] = f".precision({int(precision)})"
if si_prefix:
si_prefix = None if si_prefix == "None" else si_prefix
formatted = formatted.si_prefix(si_prefix)
code[4] = f".si_prefix({si_prefix})"
if symbol_pct_radio == "percentage":
formatted = formatted.scheme(Scheme.percentage)
code[5] = f".scheme(Scheme.percentage)"
if "plus_minus" in options_checklist:
formatted = formatted.sign(Sign.positive)
code[6] = f".sign(Sign.positive)"
if "parantheses" in options_checklist:
formatted = formatted.sign(Sign.parantheses)
code[7] = f".sign(Sign.parantheses)"
if "trim" in options_checklist:
formatted = formatted.trim(Trim.yes)
code[8] = f".trim(Trim.yes)"
if nan:
print("nan", nan)
formatted = formatted.nully(nan)
code[9] = f".nully('{nan}')"
if scheme_dd:
formatted = formatted.scheme(scheme_dd)
code[10] = f".scheme('{scheme_dd}')"
if symbol_pct_radio == "symbol":
formatted = formatted.symbol(Symbol.yes)
code[11] = f".symbol(Symbol.yes)"
if symbol_pct_radio == "none":
code[11] = None
code[5] = None
if prefix:
formatted = formatted.symbol_prefix(prefix)
code[12] = f".symbol_prefix('{prefix}')"
if suffix:
formatted = formatted.symbol_suffix(suffix)
code[13] = f".symbol_suffix('{suffix}')"
if width:
formatted = formatted.padding_width(int(width))
code[14] = f".padding_width({int(width)})"
if fill_char:
formatted = formatted.fill(fill_char)
code[15] = f".fill('{fill_char}')"
if align_val:
formatted = formatted.align(align_val)
code[16] = f".align('{align_val}')"
if group_checkbox:
formatted = formatted.group(Group.yes)
code[17] = f".group(Group.yes)"
if group_char:
formatted = formatted.group_delimiter(group_char)
code[18] = f".group_delimiter('{group_char}')"
if groupings:
formatted = formatted.groups(int(groupings))
code[19] = f".groups({int(groupings)})"
columns = [
{"name": i, "id": i, "type": "numeric", "format": formatted} for i in df.columns
]
code = ["formatted = formatted"] + list(code.values())
code = [] if code == ["formatted = formatted", None, None] else code
to_json = ["formatted = "] + [str(formatted.to_plotly_json())]
return columns, code, to_json
def get_figures(trips: Trips, charging: Tuple[dict]):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info, \
battery_table, consumption_graph_by_temp
lats = []
lons = []
names = []
for trip in trips:
for points in trip.positions:
lats = np.append(lats, points.longitude)
lons = np.append(lons, points.latitude)
names = np.append(names, [str(trip.start_at)])
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
trips_map = px.line_mapbox(lat=lats, lon=lons, hover_name=names,
mapbox_style="stamen-terrain", zoom=12)
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes)
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='native',
# sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
{'id': 'duration', 'name': 'duration', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" min").precision(0)},
{'id': 'speed_average', 'name': 'average speed', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" km/h").precision(0)},
{'id': 'consumption_km', 'name': 'average consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")},
{'id': 'consumption_fuel_km', 'name': 'average consumption fuel', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" L/100km")},
{'id': 'distance', 'name': 'distance', 'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)},
{'id': 'mileage', 'name': 'mileage', 'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)}],
data=[tr.get_info() for tr in trips[::-1]],
page_size=50
)
# consumption_fig
consumption_df = DataFrame.from_records(trips.get_long_trips())
consumption_fig = px.line(consumption_df, x="date", y="consumption", title='Consumption of the car')
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
consumption_fig_by_speed = px.histogram(consumption_df, x="speed", y="consumption_km", histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(
go.Scatter(mode="markers", x=consumption_df["speed"], y=consumption_df["consumption_km"],
name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h", yaxis_title="Consumption kWh/100Km")
kw_per_km = float(consumption_df["consumption_km"].mean())
info = "Average consumption: {:.1f} kWh/100km".format(kw_per_km)
# charging
charging_data = DataFrame.from_records(charging)
co2_per_kw = __calculate_co2_per_kw(charging_data)
co2_per_km = co2_per_kw * kw_per_km / 100
try:
charge_speed = 3600 * charging_data["kw"].mean() / \
(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
except (TypeError, KeyError): # when there is no data yet:
charge_speed = 0
battery_info = dash_table.DataTable(
id='battery_info',
sort_action='native',
columns=[{'id': 'name', 'name': ''},
{'id': 'value', 'name': ''}],
style_header={'display': 'none'},
style_data={'border': '0px'},
data=[{"name": "Average emission:", "value": "{:.1f} g/km".format(co2_per_km)},
{"name": " ", "value:": "{:.1f} g/kWh".format(co2_per_kw)},
{"name": "Average charge speed:", "value": "{:.3f} kW".format(charge_speed)}])
battery_info = html.Div(children=[html.Tr(
[
html.Td('Average emission:', rowSpan=2),
html.Td("{:.1f} g/km".format(co2_per_km)),
]
),
html.Tr(
[
"{:.1f} g/kWh".format(co2_per_kw),
]
),
html.Tr(
[
html.Td("Average charge speed:"),
html.Td("{:.3f} kW".format(charge_speed))
]
)
])
battery_table = dash_table.DataTable(
id='battery-table',
sort_action='native',
sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
{'id': 'stop_at', 'name': 'stop at', 'type': 'datetime'},
{'id': 'start_level', 'name': 'start level', 'type': 'numeric'},
{'id': 'end_level', 'name': 'end level', 'type': 'numeric'},
{'id': 'co2', 'name': 'CO2', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)},
{'id': 'kw', 'name': 'consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh").precision(3)}],
data=charging,
)
consumption_by_temp_df = consumption_df[consumption_df["consumption_by_temp"].notnull()]
if len(consumption_by_temp_df) > 0:
consumption_fig_by_temp = px.histogram(consumption_by_temp_df, x="consumption_by_temp", y="consumption_km",
histfunc="avg", title="Consumption by temperature")
consumption_fig_by_temp.update_traces(xbins_size=2)
consumption_fig_by_temp.update_layout(bargap=0.05)
consumption_fig_by_temp.add_trace(
go.Scatter(mode="markers", x=consumption_by_temp_df["consumption_by_temp"],
y=consumption_by_temp_df["consumption_km"], name="Trips"))
consumption_fig_by_temp.update_layout(xaxis_title="average temperature in °C",
yaxis_title="Consumption kWh/100Km")
consumption_graph_by_temp = Graph(figure=consumption_fig_by_temp, id="consumption_fig_by_temp")
else:
consumption_graph_by_temp = Graph(style={'display': 'none'})
def get_figures(car: Car):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, \
battery_table, consumption_fig_by_temp
lats = [42, 41]
lons = [1, 2]
names = ["undefined", "undefined"]
trips_map = px.line_mapbox(lat=lats, lon=lons, hover_name=names, zoom=12, mapbox_style="style.json")
trips_map.add_trace(go.Scattermapbox(
mode="markers",
marker={"symbol": "marker", "size": 20},
lon=[lons[0]], lat=[lats[0]],
showlegend=False, name="Last Position"))
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes) # pylint: disable=no-member
style_cell_conditional = []
if car.is_electric():
style_cell_conditional.append({'if': {'column_id': 'consumption_fuel_km', }, 'display': 'None', })
if car.is_thermal():
style_cell_conditional.append({'if': {'column_id': 'consumption_km', }, 'display': 'None', })
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='custom',
sort_by=[{'column_id': 'id', 'direction': 'desc'}],
columns=[{'id': 'id', 'name': '#', 'type': 'numeric'},
{'id': 'start_at_str', 'name': 'start at', 'type': 'datetime'},
{'id': 'duration', 'name': 'duration', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" min").precision(0)},
{'id': 'speed_average', 'name': 'average speed', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" km/h").precision(0)},
{'id': 'consumption_km', 'name': 'average consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")},
{'id': 'consumption_fuel_km', 'name': 'average consumption fuel', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" L/100km")},
{'id': 'distance', 'name': 'distance', 'type': 'numeric',
'format': nb_format.symbol_suffix(" km").precision(1)},
{'id': 'mileage', 'name': 'mileage', 'type': 'numeric',
'format': nb_format},
{'id': 'altitude_diff', 'name': 'Altitude diff', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" m").precision(0)}
],
style_data_conditional=[
{
'if': {'column_id': ['altitude_diff']},
'color': 'dodgerblue',
"text-decoration": "underline"
}
],
style_cell_conditional=style_cell_conditional,
data=[],
page_size=50
)
# consumption_fig
consumption_fig = px.histogram(x=[0], y=[1], title='Consumption of the car',
histfunc="avg")
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km", xaxis_title="date")
consumption_fig_by_speed = px.histogram(data_frame=[{"start_at": 1, "speed_average": 2}], x="start_at",
y="speed_average", histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(go.Scatter(mode="markers", x=[0],
y=[0], name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h", yaxis_title="Consumption kWh/100Km")
# battery_table
battery_table = dash_table.DataTable(
id='battery-table',
sort_action='custom',
sort_by=[{'column_id': 'start_at_str', 'direction': 'desc'}],
columns=[{'id': 'start_at_str', 'name': 'start at', 'type': 'datetime'},
{'id': 'stop_at_str', 'name': 'stop at', 'type': 'datetime'},
{'id': 'start_level', 'name': 'start level', 'type': 'numeric'},
{'id': 'end_level', 'name': 'end level', 'type': 'numeric'},
{'id': 'co2', 'name': 'CO2', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)},
{'id': 'kw', 'name': 'consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kWh").precision(2)},
{'id': 'price', 'name': 'price', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" " + ElecPrice.currency).precision(2), 'editable': True}
],
data=[],
style_data_conditional=[
{
'if': {'column_id': ['start_level', "end_level"]},
'color': 'dodgerblue',
"text-decoration": "underline"
},
{
'if': {'column_id': 'price'},
'backgroundColor': '#ABE2FB'
}
],
)
consumption_fig_by_temp = px.histogram(x=[0], y=[0],
histfunc="avg", title="Consumption by temperature")
consumption_fig_by_temp.update_traces(xbins_size=2)
consumption_fig_by_temp.update_layout(bargap=0.05)
consumption_fig_by_temp.add_trace(
go.Scatter(mode="markers", x=[0],
y=[0], name="Trips"))
consumption_fig_by_temp.update_layout(xaxis_title="average temperature in °C",
yaxis_title="Consumption kWh/100Km")
return True
def get_figures(trips: List[Trip], charging: List[dict]):
global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info
lats = []
lons = []
names = []
for trip in trips:
for points in trip.positions:
lats = np.append(lats, points.longitude)
lons = np.append(lons, points.latitude)
names = np.append(names, [str(trip.start_at)])
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
trips_map = px.line_mapbox(lat=lats, lon=lons, hover_name=names,
mapbox_style="stamen-terrain", zoom=12)
# table
nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes)
table_fig = dash_table.DataTable(
id='trips-table',
sort_action='native',
sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
{'id': 'duration', 'name': 'duration', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" min").precision(0)},
{'id': 'speed_average', 'name': 'average speed', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" km/h")},
{'id': 'consumption_km', 'name': 'average consumption', 'type': 'numeric',
'format': deepcopy(nb_format).symbol_suffix(" kw/100km")},
{'id': 'distance', 'name': 'distance', 'type': 'numeric', 'format': nb_format.symbol_suffix(" km")}],
data=[tr.get_info() for tr in trips],
)
# consumption_fig
consumption_df = DataFrame.from_records([tr.get_consumption() for tr in trips])
consumption_fig = px.line(consumption_df, x="date", y="consumption", title='Consumption of the car')
consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
consum_df_by_speed = DataFrame.from_records(
[{"speed": tr.speed_average, "consumption": tr.consumption_km} for tr in trips])
consumption_fig_by_speed = px.histogram(consum_df_by_speed, x="speed", y="consumption", histfunc="avg",
title="Consumption by speed")
consumption_fig_by_speed.update_traces(xbins_size=15)
consumption_fig_by_speed.update_layout(bargap=0.05)
consumption_fig_by_speed.add_trace(
go.Scatter(mode="markers", x=consum_df_by_speed["speed"], y=consum_df_by_speed["consumption"],
name="Trips"))
consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h", yaxis_title="Consumption kWh/100Km")
kw_per_km = float(consumption_df.mean(numeric_only=True))
info = "Average consumption: {:.1f} kW/100km".format(kw_per_km)
# charging
charging_data = DataFrame.from_records(charging)
try:
co2_per_kw = charging_data["co2"].sum() / charging_data["kw"].sum()
except ZeroDivisionError:
co2_per_kw = 0
co2_per_km = co2_per_kw * kw_per_km / 100
try:
charge_speed = 3600 * charging_data["kw"].mean() / \
(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
except TypeError: # when there is no data yet:
charge_speed = 0
battery_info = html.Div(children=[html.P("Average gC02/kW: {:.1f}".format(co2_per_kw)),
html.P("Average gC02/km: {:1f}".format(co2_per_km)),
html.P("Average Charge SPEED {:1f} kW/h".format(charge_speed))])
