Buckets:
| 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 top_speed(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 | |
| # team_colors = utils.team_colors(year) | |
| fastest_speedtrap = ( | |
| laps[["SpeedI1", "SpeedI2", "SpeedST", "SpeedFL"]] | |
| .idxmax(axis=1) | |
| .value_counts() | |
| .index[0] | |
| ) | |
| speed_df = ( | |
| laps[[fastest_speedtrap, "Driver", "Compound", "Team"]] | |
| .groupby("Driver") | |
| .max() | |
| .sort_values(fastest_speedtrap, ascending=False) | |
| .reset_index() | |
| ) | |
| # add team colors to dataframe | |
| # speed_df["fill"] = speed_df["Team"].apply(lambda x: team_colors[x]) | |
| # rename fastest speedtrap column to TopSpeed | |
| speed_df.rename(columns={fastest_speedtrap: "TopSpeed"}, inplace=True) | |
| # remove nan values in any column | |
| speed_df = speed_df.dropna() | |
| # Convert to int | |
| speed_df["TopSpeed"] = speed_df["TopSpeed"].astype(int) | |
| speed_dict = speed_df.to_dict(orient="records") | |
| return {"topSpeed": speed_dict} | |
| 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 | |
| # 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: | |
| top_speed_dict = top_speed(YEAR, event, session) | |
| # Specify the file path where you want to save the JSON data | |
| file_path = f"{event}/{session}/top_speeds.json" | |
| # Save the dictionary to a JSON file | |
| with open(file_path, "w") as json_file: | |
| json.dump(top_speed_dict, json_file) | |
| print(f"Dictionary saved to {file_path}") | |
Xet Storage Details
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- 2.94 kB
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