mirror of
https://github.com/flobz/psa_car_controller.git
synced 2026-08-22 17:36:15 +00:00
247 lines
11 KiB
Python
247 lines
11 KiB
Python
from copy import deepcopy
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from typing import List
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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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import plotly.express as px
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import plotly.graph_objects as go
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from pandas import DataFrame
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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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from libs.car import Car
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from libs.elec_price import ElecPrice
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from trip import Trips, Trip
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from web.db import Database
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def unix_time_millis(date):
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return int(date.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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# pylint: disable=invalid-name
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ERROR_DIV = dbc.Alert("No data to show, there is probably no trips recorded yet", color="danger")
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PADDING_TOP = {"padding-top": "1em"}
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consumption_fig = ERROR_DIV
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consumption_df = ERROR_DIV
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trips_map = ERROR_DIV
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consumption_fig_by_speed = ERROR_DIV
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consumption_graph_by_temp = ERROR_DIV
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table_fig = ERROR_DIV
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pandas_options.display.float_format = '${:.2f}'.format
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info = ""
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battery_info = ERROR_DIV
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battery_table = None
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SUMMARY_CARDS = {"Average consumption": {"text": None, "src": "static/images/consumption.svg"},
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"Average emission": {"text": None, "src": "static/images/pollution.svg"},
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"Average charge speed": {"text": None, "src": "static/images/battery-charge-line.svg"},
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"Electricity consumption": {"text": None, "src": "static/images/electricity bill.svg"}
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}
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# pylint: disable=too-many-locals
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def get_figures(trips: Trips, charging: List[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.latitude)
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lons = np.append(lons, points.longitude)
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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) # pylint: disable=no-member
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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': 'id', 'direction': 'desc'}],
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columns=[{'id': 'id', 'name': '#', 'type': 'numeric'},
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{'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},
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{'id': 'altitude_diff', 'name': 'Altitude diff', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" m").precision(0)}
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],
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style_data_conditional=[
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{
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'if': {'column_id': ['altitude_diff']},
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'color': 'dodgerblue',
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"text-decoration": "underline"
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}
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],
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data=trips.get_info(),
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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.histogram(consumption_df, x="date", y="consumption_km", title='Consumption of the car',
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histfunc="avg")
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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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co2_per_kw = __calculate_co2_per_kw(charging_data)
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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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price_kw = (charging_data["price"] / charging_data["kw"]).mean()
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total_elec = kw_per_km * trips.get_distance() / 100
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except (TypeError, KeyError, ZeroDivisionError): # when there is no data yet:
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charge_speed = 0
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price_kw = 0
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total_elec = 0
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SUMMARY_CARDS["Average charge speed"]["text"] = f"{charge_speed:.2f} kW"
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SUMMARY_CARDS["Average emission"]["text"] = [html.P(f"{co2_per_km:.1f} g/km"), html.P(f"{co2_per_kw:.1f} g/kWh")]
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SUMMARY_CARDS["Electricity consumption"]["text"] = [f"{total_elec:.0f} kWh", html.Br(), \
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f"{total_elec * price_kw:.0f} {ElecPrice.currency}"]
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SUMMARY_CARDS["Average consumption"]["text"] = f"{consumption_df['consumption_km'].mean():.1f} kWh/100km"
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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(2)},
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{'id': 'price', 'name': 'price', 'type': 'numeric',
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'format': deepcopy(nb_format).symbol_suffix(" " + ElecPrice.currency).precision(2), 'editable': True}
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],
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data=charging,
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style_data_conditional=[
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{
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'if': {'column_id': ['start_level', "end_level"]},
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'color': 'dodgerblue',
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"text-decoration": "underline"
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},
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{
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'if': {'column_id': 'price'},
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'backgroundColor': 'rgb(230, 246, 254)'
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}
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],
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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 = html.Div(Graph(figure=consumption_fig_by_temp), id="consumption_graph_by_temp")
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else:
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consumption_graph_by_temp = html.Div(Graph(style={'display': 'none'}), id="consumption_graph_by_temp")
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return True
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def __calculate_co2_per_kw(charging_data):
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try:
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co2_data = charging_data[charging_data["co2"] > 0]
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co2_kw_sum = co2_data["kw"].sum()
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if co2_kw_sum > 0:
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return co2_data["co2"].sum() / co2_kw_sum
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except KeyError:
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return 0
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return 0
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def get_battery_curve_fig(row: dict, car: Car):
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start_date = Database.convert_datetime_from_string(row["start_at"])
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stop_at = Database.convert_datetime_from_string(row["stop_at"])
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conn = Database.get_db()
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res = Database.get_battery_curve(conn, start_date, car.vin)
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conn.close()
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res.insert(0, {"level": row["start_level"], "date": start_date})
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res.append({"level": row["end_level"], "date": stop_at})
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battery_curves = []
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speed = 0
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for x in range(1, len(res)):
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start_level = res[x - 1]["level"]
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end_level = res[x]["level"]
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speed = car.get_charge_speed(start_level, end_level, (res[x]["date"] - res[x - 1]["date"]).total_seconds())
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battery_curves.append({"level": start_level, "speed": speed})
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battery_curves.append({"level": row["end_level"], "speed": speed})
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fig = px.line(battery_curves, x="level", y="speed")
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fig.update_layout(xaxis_title="Battery %", yaxis_title="Charging speed in kW")
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return html.Div(Graph(figure=fig))
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def get_altitude_fig(trip: Trip):
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conn = Database.get_db()
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res = list(map(list, conn.execute("SELECT mileage, altitude FROM position WHERE Timestamp>=? and Timestamp<=?;",
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(trip.start_at, trip.end_at)).fetchall()))
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start_mileage = res[0][0]
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for line in res:
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line[0] = line[0] - start_mileage
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fig = px.line(res, x=0, y=1)
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fig.update_layout(xaxis_title="Distance km", yaxis_title="Altitude m")
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conn.close()
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return html.Div(Graph(figure=fig))
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