import numpy as np
import random
import pandas as pd


def simulate_attacker_guesses(num_guesses, grid_size, min_selections, max_selections, restricted_cells, bias_points):
    """
    Simulate an attacker guessing passwords considering bias points influenced by image elements.

    :param num_guesses: Number of guesses to simulate
    :param grid_size: Size of the grid (number of rows and columns)
    :param min_selections: Minimum number of cells to be selected for a password
    :param max_selections: Maximum number of cells that can be selected for a password
    :param restricted_cells: List of cells that cannot be selected
    :param bias_points: Points that are more likely to be chosen due to image influence
    :return: List of guessed passwords
    """
    guesses = []

    # Create a probability distribution favoring the bias points
    total_points = grid_size * grid_size
    base_prob = 1 / total_points
    bias_prob_increase = 0.1  # How much more likely are we to choose a bias point
    probabilities = np.full((grid_size, grid_size), base_prob)

    for point in bias_points:
        probabilities[point] += bias_prob_increase
    probabilities /= probabilities.sum()  # Normalize to sum to 1

    for _ in range(num_guesses):
        num_selections = random.randint(min_selections, max_selections)
        guess = []
        while len(guess) < num_selections:
            flat_index = np.random.choice(total_points, p=probabilities.flatten())
            row, col = divmod(flat_index, grid_size)
            cell = (row, col)
            if cell not in restricted_cells and cell not in guess:
                guess.append(cell)
        guesses.append(guess)

    return guesses


# Update these variables based on the actual bias points from your image
bias_points = [(1, 2), (2, 3), (4, 5), (5, 6)]  # Replace with actual bias points
grid_size = 8
min_selections = 4
max_selections = 10
restricted_cells = [(0, 0), (7, 0), (0, 7), (7, 7), (3, 3), (3, 4), (4, 3), (4, 4)]
num_guesses = 1100

# Simulate attacker guesses
attacker_guesses = simulate_attacker_guesses(num_guesses, grid_size, min_selections, max_selections, restricted_cells,
                                             bias_points)

# Save the guesses to an Excel file
df_guesses = pd.DataFrame({'Guessed Password': attacker_guesses})
df_guesses.to_excel('attacker_guesses.xlsx', index=False)

print("Attacker guesses have been saved to 'attacker_guesses.xlsx'.")