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tracinginsights
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0377dae
1
Parent(s):
82528df
Update main.py
Browse files
main.py
CHANGED
@@ -1,10 +1,769 @@
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from git import Repo
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import os
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9 |
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# from git import Repo
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# import os
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# GITHUB_PAT = os.environ['GITHUB']
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# if not os.path.exists('repo_directory'):
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# # os.mkdir('repo_directory')
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# Repo.clone_from(f'https://tracinginsights:{GITHUB_PAT}@github.com/TracingInsights/fastf1api.git', 'repo_directory' )
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# from repo_directory.main import *
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import concurrent.futures
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import datetime
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import functools
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import math
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import os
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from io import BytesIO
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import fastf1
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import numpy as np
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import pandas as pd
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import requests
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import streamlit as st
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from fastapi import Depends, FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse, HTMLResponse
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from fastf1.ergast import Ergast
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from pydantic import BaseModel, Field
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from sqlalchemy.orm import Session
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# from . import accelerations, database, models, utils
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import accelerations
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import database
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import models
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import utils
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FASTF1_CACHE_DIR = os.environ["FASTF1_CACHE_DIR"]
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fastf1.Cache.enable_cache(FASTF1_CACHE_DIR)
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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database.Base.metadata.create_all(bind=database.engine)
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def get_db():
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try:
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db = database.SessionLocal()
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yield db
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finally:
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db.close()
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class RacePace(BaseModel):
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year: int
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event: str
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session: str
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Driver: str
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LapTime: float
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Diff: float
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Team: str
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fill: str
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# @functools.cache
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@app.get("/racepace/{year}/{event}/{session}", response_model=None)
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async def average_race_pace(
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year: int, event: str | int, session: str, db: Session = Depends(get_db)
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) -> any:
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race_pace_data = (
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db.query(models.RacePace)
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.filter_by(year=year, event=event, session=session)
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.all()
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)
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if race_pace_data:
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print("Fetching from Database")
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if not race_pace_data:
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print("Writing to Database")
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f1session = fastf1.get_session(
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year,
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event,
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session,
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# backend="fastf1",
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# force_ergast=False,
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)
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f1session.load(telemetry=False, weather=False, messages=False)
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laps = f1session.laps
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laps = laps.loc[laps.LapNumber > 1]
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laps = laps.pick_track_status(
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"1",
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)
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laps["LapTime"] = laps.Sector1Time + laps.Sector2Time + laps.Sector3Time
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# convert LapTime to seconds
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laps["LapTime"] = laps["LapTime"].apply(lambda x: x.total_seconds())
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laps = laps.loc[laps.LapTime < laps.LapTime.min() * 1.07]
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df = (
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laps[["LapTime", "Driver"]].groupby("Driver").mean().reset_index(drop=False)
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)
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df = df.sort_values(by="LapTime").reset_index(drop=True)
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df["LapTime"] = df["LapTime"].round(3)
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df["Diff"] = (df["LapTime"] - df["LapTime"].min()).round(3)
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teams = laps[["Driver", "Team"]].drop_duplicates().reset_index(drop=True)
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# join teams and df
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df = df.merge(teams, on="Driver", how="left")
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car_colors = utils.team_colors(year)
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df["fill"] = df["Team"].map(car_colors)
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df_json = df.to_dict("records")
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# save the data to the database
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for record in df.to_dict("records"):
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race_pace = models.RacePace(**record)
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db.add(race_pace)
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db.commit()
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return {"racePace": df_json}
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return {"racePace": [dict(race_pace) for race_pace in race_pace_data]}
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@functools.cache
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@app.get("/topspeed/{year}/{event}/{session}", response_model=None)
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async def top_speed(year: int, event: str | int, session: str) -> any:
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f1session = fastf1.get_session(year, event, session)
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f1session.load(telemetry=False, weather=False, messages=False)
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laps = f1session.laps
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team_colors = utils.team_colors(year)
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fastest_speedtrap = (
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laps[["SpeedI1", "SpeedI2", "SpeedST", "SpeedFL"]]
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.idxmax(axis=1)
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.value_counts()
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.index[0]
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)
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speed_df = (
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laps[[fastest_speedtrap, "Driver", "Compound", "Team"]]
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.groupby("Driver")
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.max()
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.sort_values(fastest_speedtrap, ascending=False)
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.reset_index()
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)
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# add team colors to dataframe
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speed_df["fill"] = speed_df["Team"].apply(lambda x: team_colors[x])
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# rename fastest speedtrap column to TopSpeed
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speed_df.rename(columns={fastest_speedtrap: "TopSpeed"}, inplace=True)
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# remove nan values in any column
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speed_df = speed_df.dropna()
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# Convert to int
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speed_df["TopSpeed"] = speed_df["TopSpeed"].astype(int)
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speed_dict = speed_df.to_dict(orient="records")
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return {"topSpeed": speed_dict}
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@functools.cache
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@app.get("/overtakes/{year}/{event}", response_model=None)
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181 |
+
def get_overtakes(year: int, event: str) -> any:
|
182 |
+
def get_overtakes_df(year, event):
|
183 |
+
if year == 2023:
|
184 |
+
url = "https://docs.google.com/spreadsheets/d/1M4aepPJaIfdqE9oU3L-2CQqKIyubLXG4Q4cqWnyqxp4/export?format=csv"
|
185 |
+
if year == 2022:
|
186 |
+
url = "https://docs.google.com/spreadsheets/d/1cuS3B6hk4iQmMaRQoMTcogIInJpavnV7rKuEsiJnEbU/export?format=csv"
|
187 |
+
if year == 2021:
|
188 |
+
url = "https://docs.google.com/spreadsheets/d/1ANQnPVkefRmvzrmGvEqXoqQ4dBfgcI_R9FPg-0BcM34/export?format=csv"
|
189 |
+
if year == 2020:
|
190 |
+
url = "https://docs.google.com/spreadsheets/d/1eG9WTkXKzFT4NMh-WqHOMs5G0UuPGnb6wP4CnFD8uzY/export?format=csv"
|
191 |
+
if year == 2019:
|
192 |
+
url = "https://docs.google.com/spreadsheets/d/10nHg7BIs5ySh_dE9uuIz2lq-gRWcg02tIMr0EPgPvJs/export?format=csv"
|
193 |
+
if year == 2018:
|
194 |
+
url = "https://docs.google.com/spreadsheets/d/1MyAwQdczccdca_FAIiZKkqZNauNh3ts99JZ278S2OKc/export?format=csv"
|
195 |
+
|
196 |
+
response = requests.get(url, timeout=10)
|
197 |
+
df = pd.read_csv(BytesIO(response.content))
|
198 |
+
df = df[["Driver", event]]
|
199 |
+
# replace NaNs with 0s
|
200 |
+
df = df.fillna(0)
|
201 |
+
# convert numbers to ints
|
202 |
+
df[event] = df[event].astype(int)
|
203 |
+
# replace event with "overtakes"
|
204 |
+
df = df.rename(columns={event: "overtakes"})
|
205 |
+
return df
|
206 |
+
|
207 |
+
def get_overtaken_df(year, event):
|
208 |
+
if year == 2023:
|
209 |
+
url = "https://docs.google.com/spreadsheets/d/1wszzx694Ot-mvA5YrFCpy3or37xMgnC0XpE8uNnJLWk/export?format=csv"
|
210 |
+
if year == 2022:
|
211 |
+
url = "https://docs.google.com/spreadsheets/d/19_XFDD3BZDIQVkNE4bG6dwuKvMaO4g5HNaUARGaJwhE/export?format=csv"
|
212 |
+
if year == 2021:
|
213 |
+
url = "https://docs.google.com/spreadsheets/d/1dQBHnd3AXEPNH5I75cjbzAAzi9ipqGk3v9eZT9eYKS4/export?format=csv"
|
214 |
+
if year == 2020:
|
215 |
+
url = "https://docs.google.com/spreadsheets/d/1snyntPMxYH4_KHSRI96AwBoJQrPbX6OanJAcqbYyW-Y/export?format=csv"
|
216 |
+
if year == 2019:
|
217 |
+
url = "https://docs.google.com/spreadsheets/d/11FfFkXErJg7F22iVwJo9XfLFAWucMBVlzL1qUGWxM3s/export?format=csv"
|
218 |
+
if year == 2018:
|
219 |
+
url = "https://docs.google.com/spreadsheets/d/1XJXAEyRpRS_UwLHzEtN2PdIaFJYGWSN6ypYN8Ecwp9A/export?format=csv"
|
220 |
+
|
221 |
+
response = requests.get(url, timeout=10)
|
222 |
+
df = pd.read_csv(BytesIO(response.content))
|
223 |
+
df = df[["Driver", event]]
|
224 |
+
# replace NaNs with 0s
|
225 |
+
df = df.fillna(0)
|
226 |
+
# convert numbers to ints
|
227 |
+
df[event] = df[event].astype(int)
|
228 |
+
df = df.rename(columns={event: "overtaken"})
|
229 |
+
return df
|
230 |
+
|
231 |
+
overtakes = get_overtakes_df(year, event)
|
232 |
+
overtaken = get_overtaken_df(year, event)
|
233 |
+
df = overtakes.merge(overtaken, on="Driver")
|
234 |
+
|
235 |
+
# remove drivers with 0 overtakes and 0 overtaken
|
236 |
+
df = df[(df["overtakes"] != 0) | (df["overtaken"] != 0)]
|
237 |
+
|
238 |
+
# sort in the decreasing order of overtakes
|
239 |
+
df = df.sort_values(
|
240 |
+
by=["overtakes", "overtaken"], ascending=[False, True]
|
241 |
+
).reset_index(drop=True)
|
242 |
+
# convert to dictionary
|
243 |
+
df_dict = df.to_dict(orient="records")
|
244 |
+
|
245 |
+
return {"overtakes": df_dict}
|
246 |
+
|
247 |
+
|
248 |
+
@functools.cache
|
249 |
+
@app.get("/fastest/{year}/{event}/{session}", response_model=None)
|
250 |
+
async def fastest_lap(year: int, event: str | int, session: str) -> any:
|
251 |
+
f1session = fastf1.get_session(year, event, session)
|
252 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
253 |
+
laps = f1session.laps
|
254 |
+
|
255 |
+
drivers = pd.unique(laps["Driver"])
|
256 |
+
|
257 |
+
list_fastest_laps = list()
|
258 |
+
|
259 |
+
for drv in drivers:
|
260 |
+
drvs_fastest_lap = laps.pick_driver(drv).pick_fastest()
|
261 |
+
list_fastest_laps.append(drvs_fastest_lap)
|
262 |
+
|
263 |
+
df = (
|
264 |
+
fastf1.core.Laps(list_fastest_laps)
|
265 |
+
.sort_values(by="LapTime")
|
266 |
+
.reset_index(drop=True)
|
267 |
+
)
|
268 |
+
|
269 |
+
pole_lap = df.pick_fastest()
|
270 |
+
df["Diff"] = df["LapTime"] - pole_lap["LapTime"]
|
271 |
+
|
272 |
+
car_colors = utils.team_colors(year)
|
273 |
+
|
274 |
+
df["fill"] = df["Team"].map(car_colors)
|
275 |
+
|
276 |
+
# convert timedelta to float and round to 3 decimal places
|
277 |
+
df["Diff"] = df["Diff"].dt.total_seconds().round(3)
|
278 |
+
df = df[["Driver", "LapTime", "Diff", "Team", "fill"]]
|
279 |
+
|
280 |
+
# remove nan values in any column
|
281 |
+
df = df.dropna()
|
282 |
+
|
283 |
+
df_json = df.to_dict("records")
|
284 |
+
|
285 |
+
return {"fastest": df_json}
|
286 |
+
|
287 |
+
|
288 |
+
# @st.cache_data
|
289 |
+
|
290 |
+
|
291 |
+
@app.get("/wdc", response_model=None)
|
292 |
+
async def driver_standings() -> any:
|
293 |
+
YEAR = 2023 # datetime.datetime.now().year
|
294 |
+
df = pd.DataFrame(
|
295 |
+
pd.read_html(f"https://www.formula1.com/en/results.html/{YEAR}/drivers.html")[0]
|
296 |
+
)
|
297 |
+
df = df[["Driver", "PTS", "Car"]]
|
298 |
+
# reverse the order
|
299 |
+
df = df.sort_values(by="PTS", ascending=True)
|
300 |
+
|
301 |
+
# in Driver column only keep the last 3 characters
|
302 |
+
df["Driver"] = df["Driver"].str[:-5]
|
303 |
+
|
304 |
+
# add colors to the dataframe
|
305 |
+
car_colors = utils.team_colors(YEAR)
|
306 |
+
df["fill"] = df["Car"].map(car_colors)
|
307 |
+
|
308 |
+
# remove rows where points is 0
|
309 |
+
df = df[df["PTS"] != 0]
|
310 |
+
df.reset_index(inplace=True, drop=True)
|
311 |
+
df.rename(columns={"PTS": "Points"}, inplace=True)
|
312 |
+
|
313 |
+
return {"WDC": df.to_dict("records")}
|
314 |
+
|
315 |
+
|
316 |
+
# @st.cache_data
|
317 |
+
|
318 |
+
|
319 |
+
@app.get("/", response_model=None)
|
320 |
+
async def root():
|
321 |
+
return HTMLResponse(
|
322 |
+
content="""<iframe src="https://tracinginsights-f1-analysis.hf.space" frameborder="0" style="width:100%; height:100%;" scrolling="yes" allowfullscreen:"yes"></iframe>""",
|
323 |
+
status_code=200,
|
324 |
+
)
|
325 |
+
|
326 |
+
|
327 |
+
# @st.cache_data
|
328 |
+
|
329 |
+
|
330 |
+
@app.get("/years", response_model=None)
|
331 |
+
async def years_available() -> any:
|
332 |
+
# make a list from 2018 to current year
|
333 |
+
current_year = datetime.datetime.now().year
|
334 |
+
years = list(range(2018, current_year + 1))
|
335 |
+
# reverse the list to get the latest year first
|
336 |
+
years.reverse()
|
337 |
+
years = [{"label": str(year), "value": year} for year in years]
|
338 |
+
return {"years": years}
|
339 |
+
|
340 |
+
|
341 |
+
# format for events {"events":[{"label":"Saudi Arabian Grand Prix","value":2},{"label":"Bahrain Grand Prix","value":1},{"label":"Pre-Season Testing","value":"t1"}]}
|
342 |
+
|
343 |
+
# @st.cache_data
|
344 |
+
|
345 |
+
|
346 |
+
@app.get("/{year}", response_model=None)
|
347 |
+
async def events_available(year: int) -> any:
|
348 |
+
# get events available for a given year
|
349 |
+
data = utils.LatestData(year)
|
350 |
+
events = data.get_events()
|
351 |
+
events = [{"label": event, "value": event} for i, event in enumerate(events)]
|
352 |
+
events.reverse()
|
353 |
+
|
354 |
+
return {"events": events}
|
355 |
+
|
356 |
+
|
357 |
+
# format for sessions {"sessions":[{"label":"FP1","value":"FP1"},{"label":"FP2","value":"FP2"},{"label":"FP3","value":"FP3"},{"label":"Qualifying","value":"Q"},{"label":"Race","value":"R"}]}
|
358 |
+
|
359 |
+
|
360 |
+
# @st.cache_data
|
361 |
+
@functools.cache
|
362 |
+
@app.get("/{year}/{event}", response_model=None)
|
363 |
+
async def sessions_available(year: int, event: str | int) -> any:
|
364 |
+
# get sessions available for a given year and event
|
365 |
+
data = utils.LatestData(year)
|
366 |
+
sessions = data.get_sessions(event)
|
367 |
+
sessions = [{"label": session, "value": session} for session in sessions]
|
368 |
+
|
369 |
+
return {"sessions": sessions}
|
370 |
+
|
371 |
+
|
372 |
+
# format for drivers {"drivers":[{"color":"#fff500","label":"RIC","value":"RIC"},{"color":"#ff8700","label":"NOR","value":"NOR"},{"color":"#c00000","label":"VET","value":"VET"},{"color":"#0082fa","label":"LAT","value":"LAT"},{"color":"#787878","label":"GRO","value":"GRO"},{"color":"#ffffff","label":"GAS","value":"GAS"},{"color":"#f596c8","label":"STR","value":"STR"},{"color":"#787878","label":"MAG","value":"MAG"},{"color":"#0600ef","label":"ALB","value":"ALB"},{"color":"#ffffff","label":"KVY","value":"KVY"},{"color":"#fff500","label":"OCO","value":"OCO"},{"color":"#0600ef","label":"VER","value":"VER"},{"color":"#00d2be","label":"HAM","value":"HAM"},{"color":"#ff8700","label":"SAI","value":"SAI"},{"color":"#00d2be","label":"BOT","value":"BOT"},{"color":"#960000","label":"GIO","value":"GIO"}]}
|
373 |
+
|
374 |
+
# @st.cache_data
|
375 |
+
|
376 |
+
|
377 |
+
@functools.cache
|
378 |
+
@app.get("/strategy/{year}/{event}", response_model=None)
|
379 |
+
async def get_strategy(year: int, event: str | int) -> any:
|
380 |
+
f1session = fastf1.get_session(year, event, "R")
|
381 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
382 |
+
laps = f1session.laps
|
383 |
+
|
384 |
+
drivers_list = pd.unique(laps["Driver"])
|
385 |
+
|
386 |
+
drivers = pd.DataFrame(drivers_list, columns=["Driver"])
|
387 |
+
drivers["FinishOrder"] = drivers.index + 1
|
388 |
+
|
389 |
+
# Get the LapNumber of the first lap of each stint
|
390 |
+
first_lap = (
|
391 |
+
laps[["Driver", "Stint", "Compound", "LapNumber"]]
|
392 |
+
.groupby(["Driver", "Stint", "Compound"])
|
393 |
+
.first()
|
394 |
+
.reset_index()
|
395 |
+
)
|
396 |
+
# Add FinishOrder to first_lap
|
397 |
+
first_lap = pd.merge(first_lap, drivers, on="Driver")
|
398 |
+
# change LapNumber to LapStart
|
399 |
+
first_lap = first_lap.rename(columns={"LapNumber": "LapStart"})
|
400 |
+
# reduce the lapstart by 1
|
401 |
+
first_lap["LapStart"] = first_lap["LapStart"] - 1
|
402 |
+
|
403 |
+
# find the last lap of each stint
|
404 |
+
last_lap = (
|
405 |
+
laps[["Driver", "Stint", "Compound", "LapNumber"]]
|
406 |
+
.groupby(["Driver", "Stint", "Compound"])
|
407 |
+
.last()
|
408 |
+
.reset_index()
|
409 |
+
)
|
410 |
+
# change LapNumber to LapEnd
|
411 |
+
last_lap = last_lap.rename(columns={"LapNumber": "LapEnd"})
|
412 |
+
|
413 |
+
# combine first_lap and last_lap
|
414 |
+
stint_laps = pd.merge(first_lap, last_lap, on=["Driver", "Stint", "Compound"])
|
415 |
+
# to cover for outliers
|
416 |
+
stint_laps["fill"] = "white"
|
417 |
+
|
418 |
+
stint_laps["fill"] = stint_laps["Compound"].map(
|
419 |
+
{
|
420 |
+
"SOFT": "red",
|
421 |
+
"MEDIUM": "yellow",
|
422 |
+
"HARD": "white",
|
423 |
+
"INTERMEDIATE": "blue",
|
424 |
+
"WET": "green",
|
425 |
+
}
|
426 |
+
)
|
427 |
+
|
428 |
+
# sort by FinishOrder
|
429 |
+
stint_laps = stint_laps.sort_values(by=["FinishOrder"], ascending=[True])
|
430 |
+
|
431 |
+
stint_laps_dict = stint_laps.to_dict("records")
|
432 |
+
|
433 |
+
return {"strategy": stint_laps_dict}
|
434 |
+
|
435 |
+
|
436 |
+
@functools.cache
|
437 |
+
@app.get("/lapchart/{year}/{event}/{session}", response_model=None)
|
438 |
+
async def lap_chart(
|
439 |
+
year: int,
|
440 |
+
event: str | int,
|
441 |
+
session: str,
|
442 |
+
) -> any:
|
443 |
+
ergast = Ergast()
|
444 |
+
|
445 |
+
race_names_df = ergast.get_race_schedule(season=year, result_type="pandas")
|
446 |
+
event_number = race_names_df[race_names_df["raceName"] == event]["round"].values[0]
|
447 |
+
drivers_df = ergast.get_driver_info(
|
448 |
+
season=year, round=event_number, result_type="pandas"
|
449 |
+
)
|
450 |
+
laptimes_df = ergast.get_lap_times(
|
451 |
+
season=year, round=event_number, result_type="pandas", limit=2000
|
452 |
+
).content[0]
|
453 |
+
laptimes_df = pd.merge(laptimes_df, drivers_df, how="left", on="driverId")
|
454 |
+
|
455 |
+
results_df = ergast.get_race_results(
|
456 |
+
season=year, round=event_number, result_type="pandas"
|
457 |
+
).content[0]
|
458 |
+
results_df = results_df[["driverCode", "constructorName"]]
|
459 |
+
|
460 |
+
# merge results_df on laptime_df
|
461 |
+
laptimes_df = pd.merge(laptimes_df, results_df, how="left", on="driverCode")
|
462 |
+
|
463 |
+
team_colors = utils.team_colors(year)
|
464 |
+
# add team_colors to laptimes_df
|
465 |
+
laptimes_df["fill"] = laptimes_df["constructorName"].map(team_colors)
|
466 |
+
|
467 |
+
# rename number as x and position as y
|
468 |
+
laptimes_df.rename(
|
469 |
+
columns={"number": "x", "position": "y", "driverCode": "id"}, inplace=True
|
470 |
+
)
|
471 |
+
|
472 |
+
lap_chart_data = []
|
473 |
+
|
474 |
+
for driver in laptimes_df["id"].unique():
|
475 |
+
data = laptimes_df[laptimes_df["id"] == driver]
|
476 |
+
fill = data["fill"].values[0]
|
477 |
+
data = data[["x", "y"]]
|
478 |
+
data_dict = data.to_dict(orient="records")
|
479 |
+
driver_dict = {"id": driver, "fill": fill, "data": data_dict}
|
480 |
+
# add this to all_data
|
481 |
+
lap_chart_data.append(driver_dict)
|
482 |
+
|
483 |
+
lap_chart_dict = {"lapChartData": lap_chart_data}
|
484 |
+
|
485 |
+
return lap_chart_dict
|
486 |
+
|
487 |
+
|
488 |
+
@functools.cache
|
489 |
+
@app.get("/{year}/{event}/{session}", response_model=None)
|
490 |
+
async def session_drivers(year: int, event: str | int, session: str) -> any:
|
491 |
+
# get drivers available for a given year, event and session
|
492 |
+
f1session = fastf1.get_session(year, event, session)
|
493 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
494 |
+
|
495 |
+
laps = f1session.laps
|
496 |
+
team_colors = utils.team_colors(year)
|
497 |
+
# add team_colors dict to laps on Team column
|
498 |
+
laps["color"] = laps["Team"].map(team_colors)
|
499 |
+
|
500 |
+
unique_drivers = laps["Driver"].unique()
|
501 |
+
|
502 |
+
drivers = [
|
503 |
+
{
|
504 |
+
"color": laps[laps.Driver == driver].color.iloc[0],
|
505 |
+
"label": driver,
|
506 |
+
"value": driver,
|
507 |
+
}
|
508 |
+
for driver in unique_drivers
|
509 |
+
]
|
510 |
+
|
511 |
+
return {"drivers": drivers}
|
512 |
+
|
513 |
+
|
514 |
+
@functools.cache
|
515 |
+
@app.get("/laps/{year}/{event}/{session}", response_model=None)
|
516 |
+
async def get_driver_laps_data(year: int, event: str | int, session: str) -> any:
|
517 |
+
# get drivers available for a given year, event and session
|
518 |
+
f1session = fastf1.get_session(year, event, session)
|
519 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
520 |
+
laps = f1session.laps
|
521 |
+
team_colors = utils.team_colors(year)
|
522 |
+
# add team_colors dict to laps on Team column
|
523 |
+
laps["color"] = laps["Team"].map(team_colors)
|
524 |
+
|
525 |
+
# combine Driver and LapNumber as a new column
|
526 |
+
laps["label"] = (
|
527 |
+
laps["Driver"]
|
528 |
+
+ "-"
|
529 |
+
+ laps["LapNumber"].astype(int).astype(str)
|
530 |
+
+ "-"
|
531 |
+
+ str(year)
|
532 |
+
+ "-"
|
533 |
+
+ event
|
534 |
+
+ "-"
|
535 |
+
+ session
|
536 |
+
)
|
537 |
+
laps["value"] = (
|
538 |
+
laps["Driver"]
|
539 |
+
+ "-"
|
540 |
+
+ laps["LapNumber"].astype(int).astype(str)
|
541 |
+
+ "-"
|
542 |
+
+ str(year)
|
543 |
+
+ "-"
|
544 |
+
+ event
|
545 |
+
+ "-"
|
546 |
+
+ session
|
547 |
+
)
|
548 |
+
|
549 |
+
laps = laps[["value", "label", "color"]]
|
550 |
+
|
551 |
+
driver_laps_dict = laps.to_dict("records")
|
552 |
+
|
553 |
+
return {"laps": driver_laps_dict}
|
554 |
+
|
555 |
+
|
556 |
+
# format for chartData {"chartData":[{"lapnumber":1},{
|
557 |
+
# "VER":91.564,
|
558 |
+
# "VER_compound":"SOFT",
|
559 |
+
# "VER_compound_color":"#FF5733",
|
560 |
+
# "lapnumber":2
|
561 |
+
# },{"lapnumber":3},{"VER":90.494,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":4},{"lapnumber":5},{"VER":90.062,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":6},{"lapnumber":7},{"VER":89.815,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":8},{"VER":105.248,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":9},{"lapnumber":10},{"VER":89.79,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":11},{"VER":145.101,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":12},{"lapnumber":13},{"VER":89.662,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":14},{"lapnumber":15},{"VER":89.617,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":16},{"lapnumber":17},{"VER":140.717,"VER_compound":"SOFT","VER_compound_color":"#FF5733","lapnumber":18}]}
|
562 |
+
|
563 |
+
# @st.cache_data
|
564 |
+
|
565 |
+
|
566 |
+
@functools.cache
|
567 |
+
@app.get("/{year}/{event}/{session}/{driver}", response_model=None)
|
568 |
+
async def laps_data(year: int, event: str | int, session: str, driver: str) -> any:
|
569 |
+
# get drivers available for a given year, event and session
|
570 |
+
f1session = fastf1.get_session(year, event, session)
|
571 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
572 |
+
laps = f1session.laps
|
573 |
+
team_colors = utils.team_colors(year)
|
574 |
+
# add team_colors dict to laps on Team column
|
575 |
+
|
576 |
+
drivers = laps.Driver.unique()
|
577 |
+
# for each driver in drivers, get the Team column from laps and get the color from team_colors dict
|
578 |
+
drivers = [
|
579 |
+
{
|
580 |
+
"color": team_colors[laps[laps.Driver == driver].Team.iloc[0]],
|
581 |
+
"label": driver,
|
582 |
+
"value": driver,
|
583 |
+
}
|
584 |
+
for driver in drivers
|
585 |
+
]
|
586 |
+
|
587 |
+
driver_laps = laps.pick_driver(driver)
|
588 |
+
driver_laps["LapTime"] = driver_laps["LapTime"].dt.total_seconds()
|
589 |
+
# remove rows where LapTime is null
|
590 |
+
driver_laps = driver_laps[driver_laps.LapTime.notnull()]
|
591 |
+
compound_colors = {
|
592 |
+
"SOFT": "#FF0000",
|
593 |
+
"MEDIUM": "#FFFF00",
|
594 |
+
"HARD": "#FFFFFF",
|
595 |
+
"INTERMEDIATE": "#00FF00",
|
596 |
+
"WET": "#088cd0",
|
597 |
+
}
|
598 |
+
|
599 |
+
driver_laps_data = []
|
600 |
+
|
601 |
+
for _, row in driver_laps.iterrows():
|
602 |
+
if row["LapTime"] > 0:
|
603 |
+
lap = {
|
604 |
+
f"{driver}": row["LapTime"],
|
605 |
+
f"{driver}_compound": row["Compound"],
|
606 |
+
f"{driver}_compound_color": compound_colors[row["Compound"]],
|
607 |
+
"lapnumber": row["LapNumber"],
|
608 |
+
}
|
609 |
+
else:
|
610 |
+
lap = {"lapnumber": row["LapNumber"]}
|
611 |
+
|
612 |
+
driver_laps_data.append(lap)
|
613 |
+
|
614 |
+
return {"chartData": driver_laps_data}
|
615 |
+
|
616 |
+
|
617 |
+
@functools.cache
|
618 |
+
@app.get("/laptimes/{year}/{event}/{session}/{driver}", response_model=None)
|
619 |
+
async def get_laps_data(year: int, event: str | int, session: str, driver: str) -> any:
|
620 |
+
# get drivers available for a given year, event and session
|
621 |
+
f1session = fastf1.get_session(year, event, session)
|
622 |
+
f1session.load(telemetry=False, weather=False, messages=False)
|
623 |
+
laps = f1session.laps
|
624 |
+
team_colors = utils.team_colors(year)
|
625 |
+
# add team_colors dict to laps on Team column
|
626 |
+
|
627 |
+
drivers = laps.Driver.unique()
|
628 |
+
# for each driver in drivers, get the Team column from laps and get the color from team_colors dict
|
629 |
+
drivers = [
|
630 |
+
{
|
631 |
+
"color": team_colors[laps[laps.Driver == driver].Team.iloc[0]],
|
632 |
+
"label": driver,
|
633 |
+
"value": driver,
|
634 |
+
}
|
635 |
+
for driver in drivers
|
636 |
+
]
|
637 |
+
|
638 |
+
driver_laps = laps.pick_driver(driver)
|
639 |
+
driver_laps["LapTime"] = driver_laps["LapTime"].dt.total_seconds()
|
640 |
+
driver_laps = driver_laps[["Driver", "LapTime", "LapNumber", "Compound"]]
|
641 |
+
|
642 |
+
# remove rows where LapTime is null
|
643 |
+
driver_laps = driver_laps[driver_laps.LapTime.notnull()]
|
644 |
+
|
645 |
+
driver_laps_dict = driver_laps.to_dict("records")
|
646 |
+
|
647 |
+
return {"chartData": driver_laps_dict}
|
648 |
+
|
649 |
+
|
650 |
+
# @st.cache_data
|
651 |
+
|
652 |
+
|
653 |
+
@functools.cache
|
654 |
+
@app.get("/{year}/{event}/{session}/{driver}/{lap_number}", response_model=None)
|
655 |
+
async def telemetry_data(
|
656 |
+
year: int, event: str | int, session: str, driver: str, lap_number: int
|
657 |
+
) -> any:
|
658 |
+
f1session = fastf1.get_session(year, event, session)
|
659 |
+
f1session.load(telemetry=True, weather=False, messages=False)
|
660 |
+
laps = f1session.laps
|
661 |
+
|
662 |
+
driver_laps = laps.pick_driver(driver)
|
663 |
+
driver_laps["LapTime"] = driver_laps["LapTime"].dt.total_seconds()
|
664 |
+
|
665 |
+
# get the telemetry for lap_number
|
666 |
+
selected_lap = driver_laps[driver_laps.LapNumber == lap_number]
|
667 |
+
|
668 |
+
telemetry = selected_lap.get_telemetry()
|
669 |
+
|
670 |
+
lon_acc, lat_acc = accelerations.compute_accelerations(telemetry)
|
671 |
+
telemetry["lon_acc"] = lon_acc
|
672 |
+
telemetry["lat_acc"] = lat_acc
|
673 |
+
|
674 |
+
telemetry["Time"] = telemetry["Time"].dt.total_seconds()
|
675 |
+
|
676 |
+
laptime = selected_lap.LapTime.values[0]
|
677 |
+
data_key = f"{driver} - Lap {int(lap_number)} - {year} {session} [laptime]"
|
678 |
+
|
679 |
+
telemetry["DRS"] = telemetry["DRS"].apply(lambda x: 1 if x in [10, 12, 14] else 0)
|
680 |
+
|
681 |
+
brake_tel = []
|
682 |
+
drs_tel = []
|
683 |
+
gear_tel = []
|
684 |
+
rpm_tel = []
|
685 |
+
speed_tel = []
|
686 |
+
throttle_tel = []
|
687 |
+
time_tel = []
|
688 |
+
track_map = []
|
689 |
+
lon_acc_tel = []
|
690 |
+
lat_acc_tel = []
|
691 |
+
|
692 |
+
for _, row in telemetry.iterrows():
|
693 |
+
brake = {
|
694 |
+
"x": row["Distance"],
|
695 |
+
"y": row["Brake"],
|
696 |
+
}
|
697 |
+
brake_tel.append(brake)
|
698 |
+
|
699 |
+
drs = {
|
700 |
+
"x": row["Distance"],
|
701 |
+
"y": row["DRS"],
|
702 |
+
}
|
703 |
+
drs_tel.append(drs)
|
704 |
+
|
705 |
+
gear = {
|
706 |
+
"x": row["Distance"],
|
707 |
+
"y": row["nGear"],
|
708 |
+
}
|
709 |
+
gear_tel.append(gear)
|
710 |
+
|
711 |
+
rpm = {
|
712 |
+
"x": row["Distance"],
|
713 |
+
"y": row["RPM"],
|
714 |
+
}
|
715 |
+
rpm_tel.append(rpm)
|
716 |
+
|
717 |
+
speed = {
|
718 |
+
"x": row["Distance"],
|
719 |
+
"y": row["Speed"],
|
720 |
+
}
|
721 |
+
speed_tel.append(speed)
|
722 |
+
|
723 |
+
throttle = {
|
724 |
+
"x": row["Distance"],
|
725 |
+
"y": row["Throttle"],
|
726 |
+
}
|
727 |
+
throttle_tel.append(throttle)
|
728 |
+
|
729 |
+
time = {
|
730 |
+
"x": row["Distance"],
|
731 |
+
"y": row["Time"],
|
732 |
+
}
|
733 |
+
time_tel.append(time)
|
734 |
+
|
735 |
+
lon_acc = {
|
736 |
+
"x": row["Distance"],
|
737 |
+
"y": row["lon_acc"],
|
738 |
+
}
|
739 |
+
lon_acc_tel.append(lon_acc)
|
740 |
+
|
741 |
+
lat_acc = {
|
742 |
+
"x": row["Distance"],
|
743 |
+
"y": row["lat_acc"],
|
744 |
+
}
|
745 |
+
lat_acc_tel.append(lat_acc)
|
746 |
|
747 |
+
track = {
|
748 |
+
"x": row["X"],
|
749 |
+
"y": row["Y"],
|
750 |
+
}
|
751 |
+
track_map.append(track)
|
752 |
|
753 |
+
telemetry_data = {
|
754 |
+
"telemetryData": {
|
755 |
+
"brake": brake_tel,
|
756 |
+
"dataKey": data_key,
|
757 |
+
"drs": drs_tel,
|
758 |
+
"gear": gear_tel,
|
759 |
+
"rpm": rpm_tel,
|
760 |
+
"speed": speed_tel,
|
761 |
+
"throttle": throttle_tel,
|
762 |
+
"time": time_tel,
|
763 |
+
"lon_acc": lon_acc_tel,
|
764 |
+
"lat_acc": lat_acc_tel,
|
765 |
+
"trackMap": track_map,
|
766 |
+
}
|
767 |
+
}
|
768 |
|
769 |
+
return telemetry_data
|