random.uniform(low, high) How to choose the low and high values?

I have generated 10 random exponential functions with some restrictions. I chose the lower and higher values of the a, b and c (see below) randomly.

a = random.uniform(0, 5) 
b = random.uniform(1, 6) 
c = random.uniform(-5, 5)

Actually I used the random uniform ranges from another example. Now I need to explain why I used this ranges, does somebody know how these ranges are dependent on the exponential functions? I only figured out that the computational cost is higher when these values are very high, but otherwise I can use any number that I want and the outputs are exactly the same..

# Restrictions
max_value = 2
min_length = 10
n_functions = 10

# Noise
mu = 0
sigma = 0.01

#%% Create exponentials
curve_list = []
curve_count = 0
id_count = 0
df = pd.DataFrame(columns=['x', 'y', 'id'])
while(1):
    df_ = pd.DataFrame(columns=['x', 'y', 'id'])
    
    a = random.uniform(0, 5) 
    b = random.uniform(1, 6) 
    c = random.uniform(-5, 5)
    
    if (max_value-c) > 0:
        len_func = np.log((max_value-c)/a)/np.log(b)
    else:
        len_func = 0

    if len_func > min_length:
        x = np.arange(0, len_func, 0.1)
        func = a*(b**x)+c
    
    
        if func[0] > 0: 
            noise = np.random.normal(mu, sigma, [len(func)])
 
            df_.x = func+noise
            df_.y = np.arange(len(func)-1, -1, -1)
            df_.id = id_count
            
            df = pd.concat([df, df_])
            
            id_count = id_count + 1
            curve_count = curve_count + 1
            if curve_count == n_functions+1:
                break