guassian.py

# The function that defines are Guassian function for a 
# random variable X who is normally distrubted with 
# mean 'm' and standard deviation 's'

def guass_func(x, m, s):
    a = 1 / (s * ((2 * math.pi) ** 0.5))
    b = -0.5 * (((x - m) / s) ** 2)
    return a * math.exp(b)
  
# Generate a linearly spaced range of x-values from -5 to 5 (1000 of them)
xs = np.linspace(-5, 5, 1000)
# Map our Guassian function with mean 0 and standard deviation 1 over each x in the range
ys = list(map(lambda x: guass_func(x, 0, 1), xs))

# We can then go on to plot this