fastest_lap.py

import json
import os

import fastf1
import numpy as np
import pandas as pd
import requests
import utils

YEAR = 2021


events = [
    'Bahrain Grand Prix', 

  'Emilia Romagna Grand Prix', 
    'Portuguese Grand Prix', 'Spanish Grand Prix', 'Monaco Grand Prix', 
    'Azerbaijan Grand Prix', 'French Grand Prix', 'Styrian Grand Prix',
    'Austrian Grand Prix', 'British Grand Prix', 'Hungarian Grand Prix', 
    'Belgian Grand Prix', 'Dutch Grand Prix', 'Italian Grand Prix',
    'Russian Grand Prix', 'Turkish Grand Prix', 
    'United States Grand Prix',
    'Mexico City Grand Prix', 
    'São Paulo Grand Prix', 'Qatar Grand Prix', 
    'Saudi Arabian Grand Prix', 'Abu Dhabi Grand Prix', 
]


def sessions_available(year: int, event: str | int) -> any:
    # get sessions available for a given year and event
    event = str(event)
    data = utils.LatestData(year)
    sessions = data.get_sessions(event)
    return sessions


def fastest_lap(year: int, event: str | int, session: str) -> any:
    f1session = fastf1.get_session(year, event, session)
    f1session.load(telemetry=False, weather=False, messages=False)
    laps = f1session.laps

    drivers = pd.unique(laps["Driver"])

    list_fastest_laps = list()

    for drv in drivers:
        drvs_fastest_lap = laps.pick_driver(drv).pick_fastest()
        list_fastest_laps.append(drvs_fastest_lap)

    df = (
        fastf1.core.Laps(list_fastest_laps)
        .sort_values(by="LapTime")
        .reset_index(drop=True)
    )

    pole_lap = df.pick_fastest()
    df["Diff"] = df["LapTime"] - pole_lap["LapTime"]

    # car_colors = utils.team_colors(year)

    # df["fill"] = df["Team"].map(car_colors)

    # convert timedelta to float and round to 3 decimal places
    df["Diff"] = df["Diff"].dt.total_seconds().round(3)
    df["LapTime"] = df["LapTime"].dt.total_seconds().round(3)
    df = df[
        [
            "Driver",
            "LapTime",
            "Diff",
            "Team",
        ]
    ]

    # remove nan values in any column
    df = df.dropna()

    df_json = df.to_dict("records")

    return {"fastest": df_json}


# Your list of events
events_list = events

# Loop through each event
for event in events_list:
    sessions = sessions_available(YEAR, event)
    if event == "Russian Grand Prix":
        sessions = ["Practice 1", "Practice 2", "Qualifying", "Race"]
    if event == "Saudi Arabian Grand Prix":
        sessions = ["Practice 1", "Practice 2", "Practice 3","Qualifying", "Race"] # removing
    
    for session in sessions:
        fastest_lap_dict = fastest_lap(YEAR, event, session)

        # Specify the file path where you want to save the JSON data
        file_path = f"{event}/{session}/fastest_lap.json"

        # Save the dictionary to a JSON file
        with open(file_path, "w") as json_file:
            json.dump(fastest_lap_dict, json_file)

        print(f"Dictionary saved to {file_path}")