Code Breakdown

1. Importing Libraries and Setting Up the App

The code begins by importing necessary libraries and setting up the Flask app:

from flask import Flask, render_template, request, jsonify
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
import io
import base64
import matplotlib
matplotlib.use('Agg')  # Use the non-GUI Agg backend

app = Flask(__name__)

Explanation: This block of code imports various libraries that are essential for different functionalities in the app:

• Flask: Used for setting up the web framework.
• NumPy and Pandas: Used for handling numerical data and dataframes.
• Random: Used for generating random numbers, which are essential for simulating race conditions.
• Matplotlib: Used for generating visualizations like histograms and lap time graphs.

2. Driver Ratings and Information

Next, the code defines the driver ratings and information:

driver_ratings = {
    'VER': {'Pace': 95, 'Racecraft': 95, 'Awareness': 94, 'Experience': 92},
    'HAM': {'Pace': 93, 'Racecraft': 96, 'Awareness': 93, 'Experience': 97},
    'LEC': {'Pace': 92, 'Racecraft': 88, 'Awareness': 86, 'Experience': 72},
    ...
}

driver_info = {
    '1': 'VER',
    '44': 'HAM',
    '63': 'RUS',
    ...
}

Explanation: This section defines the drivers' ratings across four key attributes: Pace, Racecraft, Awareness, and Experience. These attributes are crucial for determining how each driver will perform during the race. The `driver_info` dictionary maps driver numbers to their respective codes (e.g., '1' for Verstappen as 'VER').

3. Simulation Logic

The heart of the app is the race simulation logic:

def run_simulation(driver, strategy, num_simulations, initial_position):
    driver_abbr = driver_info[driver]
    num_laps = 70
    ...
    for _ in range(num_simulations):
        current_tire_index = 0  # Start with the first tire in the sequence
        race_lap_times = {d: [] for d in driver_info.keys()}
        for lap in range(1, num_laps + 1):
            if lap == 1:
                sorted_drivers = list(driver_info.keys())
                sorted_drivers.remove(driver)
                sorted_drivers.insert(initial_position - 1, driver)
            else:
                sorted_drivers = sorted(race_lap_times.keys(), key=lambda d: race_lap_times[d][-1] if race_lap_times[d] else float('inf'))

            for idx, d in enumerate(sorted_drivers):
                ratings = driver_ratings[driver_info[d]]
                prime_time = driver_prime_times[driver_info[d]]
                ...

Explanation: The `run_simulation` function handles the core simulation logic. Here’s what happens:

• It loops through a set number of simulations to get a range of possible outcomes.
• Each lap is simulated by calculating lap times based on driver ratings, tire wear, and other factors.
• The lap times are adjusted for pit stops, track evolution, and random events, creating a dynamic and realistic race environment.
• Finally, drivers are sorted by their total race times to determine their finishing positions.

4. Plotting Results

After the simulations are run, the results are visualized using Matplotlib:

def plot_histogram(positions):
    plt.figure(figsize=(10, 6))
    plt.hist(positions, bins=range(1, 22), edgecolor='black', alpha=0.7)
    plt.xlabel('Finishing Position')
    plt.ylabel('Frequency')
    plt.title('Finishing Positions Histogram')

    img = io.BytesIO()
    plt.savefig(img, format='png')
    img.seek(0)
    img_url = base64.b64encode(img.getvalue()).decode('utf8')
    plt.close()

    return 'data:image/png;base64,{}'.format(img_url)

Explanation: This function creates histograms to display the distribution of finishing positions for the simulated races. The histogram provides a visual summary of where drivers are most likely to finish, which can be compared to actual race outcomes. The generated plot is then converted into a base64 string so that it can be easily rendered in the web application.

5. Serving the Application

The final part of the code is responsible for serving the Flask application:

@app.route('/')
def index():
    return render_template('index.html', drivers=driver_info)

@app.route('/simulate', methods=['POST'])
def simulate():
    data = request.json
    driver = data['driver']
    strategy = data['strategy']
    num_simulations = int(data['num_simulations'])
    initial_position = int(data['initial_position'])

    results = run_simulation(driver, strategy, num_simulations, initial_position)
    img = plot_histogram(results)

    return jsonify({'image': img})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5005, debug=True, use_reloader=False)

Explanation: This section includes the Flask routes that handle requests:

• The `index` route serves the main page where users can input their simulation parameters.
• The `simulate` route processes the input data, runs the simulation, and returns the results as a JSON object, including the histogram image.
• The application is then run on a specified host and port, making it accessible to users.