


```python
import numpy as np
import matplotlib.pyplot as plt
from Basilisk.utilities.MonteCarlo import Controller, RetentionPolicy, Dispersion
from Basilisk.utilities.MonteCarlo import scenario_AttFeedback

def run(show_plots):
    # Create a Monte Carlo simulation controller
    monteCarlo = Controller()

    # Set the simulation and execution functions
    monteCarlo.setSimulationFunction(scenario_AttFeedback.run)
    monteCarlo.setExecutionFunction(scenario_AttFeedback.execute)

    # Set the execution count, archive directory, and seed dispersion
    monteCarlo.setExecutionCount(100)
    monteCarlo.setArchiveDir('MonteCarlo_Archive')
    monteCarlo.setSeedDispersion(Dispersion.Uniform())

    # Set the thread count, verbosity, and variable casting
    monteCarlo.setThreadCount(4)
    monteCarlo.setVerbosity(True)
    monteCarlo.setVariableCasting(True)

    # Define a list of dispersions
    dispList = [Dispersion.Uniform(-10, 10, 100),
                Dispersion.Normal(0, 1, 100),
                Dispersion.Cauchy(0, 1, 100)]

    # Add dispersions to the Monte Carlo controller
    for disp in dispList:
        monteCarlo.addDispersion(disp)

    # Create a retention policy
    retentionPolicy = RetentionPolicy()

    # Add message logs to the retention policy
    retentionPolicy.addMessageLog(scenario_AttFeedback.scenarioMessageLog)

    # Set the data callback
    retentionPolicy.setDataCallback(scenario_AttFeedback.logFunction)

    # Add the retention policy to the Monte Carlo controller
    monteCarlo.addRetentionPolicy(retentionPolicy)

    # Execute the simulations
    monteCarlo.executeSimulations()

    # Execute callbacks if show_plots is True
    if show_plots:
        monteCarlo.executeCallbacks()

    # Return
    return

def displayPlots(data, retentionPolicy):
    # Extract time and states from the data
    time = data[:, 0]
    states = data[:, 1:]

    # Plot the states against time
    plt.plot(time, states)
    plt.xlabel('Time')
    plt.ylabel('State')
    plt.title('State vs. Time')
    plt.show()

if __name__ == '__main__':
    run(True)
```

This code defines a function 'run' that takes a boolean parameter 'show_plots'. Inside this function, it creates a Monte Carlo simulation controller, sets its simulation and execution functions, execution count, archive directory, seed dispersion, thread count, verbosity, variable casting, and dispersion magnitude file. It also defines a list of dispersions and adds them to the Monte Carlo controller. Then, it creates a retention policy, adds message logs to it, sets its data callback, and adds it to the Monte Carlo controller. The function executes the simulations, executes callbacks if 'show_plots' is True, and returns. The code also defines a function 'displayPlots' that takes 'data' and 'retentionPolicy' as parameters, extracts time and states from the data, and plots the states against time. Finally, it calls the 'run' function with 'True' as the argument if the script is run as the main program. Source: assistant
EOT: true