new_contents: import pyprobml_utils as pml
  import numpy as np
  from scipy.stats import uniform, laplace, norm
  import matplotlib.pyplot as plt
  
  n = 2000
  x = np.arange(-4, 4, 0.01)
  y1 = norm.pdf(x, 0, 1)
  y2 = uniform.pdf(x, -2, 4)
  y3 = laplace.pdf(x, 0, 1)
  
  plt.plot(x, y1, color='blue')
  plt.plot(x, y2, color='green')
  plt.plot(x, y3, color='red')
  pml.savefig('1D.pdf')
  plt.savefig('1D.pdf')
  plt.show()
  
  x1 = np.random.normal(0, 1, n).reshape(n, 1)
  x2 = np.random.normal(0, 1, n).reshape(n, 1)
  plt.scatter(x1, x2, marker='.', color='blue')
  plt.gca().set_aspect('equal')
  plt.xlim(-4,4)
  plt.ylim(-4,4)
  plt.title("Gaussian")
  pml.savefig('Gaussian.pdf')
  plt.savefig('Gaussian.pdf')
  plt.show()
  
  x1 = np.random.laplace(0, 1, n).reshape(n, 1)
  x2 = np.random.laplace(0, 1, n).reshape(n, 1)
  plt.scatter(x1, x2, marker='.', color='red')
  plt.gca().set_aspect('equal')
  plt.xlim(-8,8)
  plt.ylim(-8,8)
  plt.title("Laplace")
  pml.savefig('Laplace.pdf')
  plt.savefig('Laplace.pdf')
  plt.show()
  
  x1 = np.random.uniform(-2, 2, n).reshape(n, 1)
  x2 = np.random.uniform(-2, 2, n).reshape(n, 1)
  plt.scatter(x1, x2, marker='.', color='green')
  plt.gca().set_aspect('equal')
  plt.xlim(-2.5, 2.5)
  plt.ylim(-2, 2)
  plt.title("Uniform")
  pml.savefig('Uniform.pdf')
  plt.savefig('Uniform.pdf')
  plt.show()
  
subject: Convert subSuperGaussPlot to python (#491)