gaussian_analysis.py

import numpy as np

# implement the function gaussian_analysis

def gaussian_analysis(loc, scale, lower_bound, upper_bound):
    if not (isinstance(loc, (int, float)) and isinstance(scale, (int, float)) and
            isinstance(lower_bound, (int, float)) and isinstance(upper_bound, (int, float))):
        raise ValueError("loc, scale, lower_bound, and upper_bound must be integers or floats.")
        
    if lower_bound >= upper_bound:
        raise ValueError("lower_bound must be smaller than upper_bound.")
        
    samples = np.random.normal(loc, scale, 100)
    filtered_samples = samples[(samples > lower_bound) & (samples < upper_bound)]
    
    mean = np.mean(filtered_samples)
    std = np.std(filtered_samples)
    
    return (mean, std)

if __name__ == "__main__":
    # use this for your own testing!

    pass