mirror of
https://github.com/flobz/psa_car_controller.git
synced 2026-08-21 17:06:18 +00:00
184 lines
7.8 KiB
Python
184 lines
7.8 KiB
Python
from copy import deepcopy
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from typing import List, Tuple
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import dash_bootstrap_components as dbc
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import dash_table
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import numpy as np
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from dash_core_components import Graph
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from dash_table.Format import Format, Scheme, Symbol
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from dateutil.relativedelta import relativedelta
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from pandas import DataFrame
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import plotly.express as px
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import plotly.graph_objects as go
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from Trip import Trips
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from pandas import options as pandas_options
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import dash_html_components as html
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def unix_time_millis(dt):
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return int(dt.timestamp())
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def get_marks_from_start_end(start, end):
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nb_marks = 10
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result = []
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time_delta = int((end - start).total_seconds() / nb_marks)
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current = start
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if time_delta > 0:
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while current <= end:
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result.append(current)
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current += relativedelta(seconds=time_delta)
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result[-1] = end
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if time_delta < 3600 * 24:
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if time_delta > 3600:
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date_f = '%x %Hh'
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else:
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date_f = '%x %Hh%M'
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else:
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date_f = '%x'
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marks = {}
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for date in result:
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marks[unix_time_millis(date)] = str(date.strftime(date_f))
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return marks
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return None
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consumption_fig = None
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consumption_df = None
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trips_map = None
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consumption_fig_by_speed = None
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consumption_graph_by_temp = None
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table_fig = None
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pandas_options.display.float_format = '${:.2f}'.format
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info = ""
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battery_info = dbc.Alert("No data to show", color="danger")
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battery_table = None
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def get_figures(trips: Trips, charging: Tuple[dict]):
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global consumption_fig, consumption_df, trips_map, consumption_fig_by_speed, table_fig, info, battery_info, \
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battery_table, consumption_graph_by_temp
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lats = []
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lons = []
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names = []
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for trip in trips:
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for points in trip.positions:
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lats = np.append(lats, points.longitude)
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lons = np.append(lons, points.latitude)
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names = np.append(names, [str(trip.start_at)])
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lats = np.append(lats, None)
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lons = np.append(lons, None)
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names = np.append(names, None)
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trips_map = px.line_mapbox(lat=lats, lon=lons, hover_name=names,
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mapbox_style="stamen-terrain", zoom=12)
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# table
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nb_format = Format(precision=2, scheme=Scheme.fixed, symbol=Symbol.yes)
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table_fig = dash_table.DataTable(
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id='trips-table',
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sort_action='native',
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# sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
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columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
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{'id': 'duration', 'name': 'duration', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" min").precision(0)},
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{'id': 'speed_average', 'name': 'average speed', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" km/h").precision(0)},
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{'id': 'consumption_km', 'name': 'average consumption', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" kWh/100km")},
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{'id': 'consumption_fuel_km', 'name': 'average consumption fuel', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" L/100km")},
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{'id': 'distance', 'name': 'distance', 'type': 'numeric',
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'format': nb_format.symbol_suffix(" km").precision(1)},
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{'id': 'mileage', 'name': 'mileage', 'type': 'numeric',
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'format': nb_format.symbol_suffix(" km").precision(1)}],
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data=[tr.get_info() for tr in trips[::-1]],
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page_size=50
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)
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# consumption_fig
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consumption_df = DataFrame.from_records(trips.get_long_trips())
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consumption_fig = px.line(consumption_df, x="date", y="consumption", title='Consumption of the car')
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consumption_fig.update_layout(yaxis_title="Consumption kWh/100Km")
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consumption_fig_by_speed = px.histogram(consumption_df, x="speed", y="consumption_km", histfunc="avg",
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title="Consumption by speed")
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consumption_fig_by_speed.update_traces(xbins_size=15)
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consumption_fig_by_speed.update_layout(bargap=0.05)
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consumption_fig_by_speed.add_trace(
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go.Scatter(mode="markers", x=consumption_df["speed"], y=consumption_df["consumption_km"],
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name="Trips"))
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consumption_fig_by_speed.update_layout(xaxis_title="average Speed km/h", yaxis_title="Consumption kWh/100Km")
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kw_per_km = float(consumption_df["consumption_km"].mean())
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info = "Average consumption: {:.1f} kWh/100km".format(kw_per_km)
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# charging
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charging_data = DataFrame.from_records(charging)
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try:
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co2_data = charging_data[charging_data["co2"] > 0]
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co2_per_kw = co2_data["co2"].sum() / co2_data["kw"].sum()
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except (ZeroDivisionError, KeyError):
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co2_per_kw = 0
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co2_per_km = co2_per_kw * kw_per_km / 100
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try:
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charge_speed = 3600 * charging_data["kw"].mean() / \
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(charging_data["stop_at"] - charging_data["start_at"]).mean().total_seconds()
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except (TypeError, KeyError): # when there is no data yet:
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charge_speed = 0
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battery_info = dash_table.DataTable(
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id='battery_info',
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sort_action='native',
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columns=[{'id': 'name', 'name': ''},
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{'id': 'value', 'name': ''}],
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style_header={'display': 'none'},
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style_data={'border': '0px'},
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data=[{"name": "Average emission:", "value": "{:.1f} g/km".format(co2_per_km)},
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{"name": " ", "value:": "{:.1f} g/kWh".format(co2_per_kw)},
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{"name": "Average charge speed:", "value": "{:.3f} kW".format(charge_speed)}])
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battery_info = html.Div(children=[html.Tr(
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[
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html.Td('Average emission:', rowSpan=2),
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html.Td("{:.1f} g/km".format(co2_per_km)),
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]
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),
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html.Tr(
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[
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"{:.1f} g/kWh".format(co2_per_kw),
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]
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),
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html.Tr(
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[
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html.Td("Average charge speed:"),
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html.Td("{:.3f} kW".format(charge_speed))
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]
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)
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])
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battery_table = dash_table.DataTable(
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id='battery-table',
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sort_action='native',
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sort_by=[{'column_id': 'start_at', 'direction': 'desc'}],
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columns=[{'id': 'start_at', 'name': 'start at', 'type': 'datetime'},
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{'id': 'stop_at', 'name': 'stop at', 'type': 'datetime'},
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{'id': 'start_level', 'name': 'start level', 'type': 'numeric'},
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{'id': 'end_level', 'name': 'end level', 'type': 'numeric'},
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{'id': 'co2', 'name': 'CO2', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" g/kWh").precision(1)},
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{'id': 'kw', 'name': 'consumption', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" kWh").precision(3)}],
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data=charging,
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)
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consumption_by_temp_df = consumption_df[consumption_df["consumption_by_temp"].notnull()]
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if len(consumption_by_temp_df) > 0:
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consumption_fig_by_temp = px.histogram(consumption_by_temp_df, x="consumption_by_temp", y="consumption_km",
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histfunc="avg", title="Consumption by temperature")
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consumption_fig_by_temp.update_traces(xbins_size=2)
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consumption_fig_by_temp.update_layout(bargap=0.05)
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consumption_fig_by_temp.add_trace(
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go.Scatter(mode="markers", x=consumption_by_temp_df["consumption_by_temp"],
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y=consumption_by_temp_df["consumption_km"], name="Trips"))
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consumption_fig_by_temp.update_layout(xaxis_title="average temperature in °C",
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yaxis_title="Consumption kWh/100Km")
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consumption_graph_by_temp = Graph(figure=consumption_fig_by_temp, id="consumption_fig_by_temp")
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else:
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consumption_graph_by_temp = Graph(style={'display': 'none'})
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