old_contents: import matplotlib.pyplot as plt
  import numpy as np
  mu, sigma = 0, 0.1
  noise = np.random.normal(mu, sigma, 200)/5
  x = np.linspace(-1, 1, 200)
  x2 = np.linspace(-0.7, 0.7, 200)
  delta = 1*np.exp(-(x-0)**2/2)/(np.sqrt(2*np.pi)*1)
  y = 0.5 - 0.8*x2**2 + noise + delta/2
  y2 = 1.3*delta + noise - 0.1
  fig = plt.figure()
  ax = fig.add_subplot(111)
  ax2 = fig.add_subplot(111)
  ax.fill_between(x2, y1=y, y2=y2, edgecolor='red', facecolor='blue', alpha=0.3)
  ax2.plot(x, y2, color='black')
  ax.set_xlabel('x')
  ax.set_ylabel('y')
  plt.show()
  
new_contents: import matplotlib.pyplot as plt
  import numpy as np
  mu, sigma = 0, 0.1
  noise = np.random.normal(mu, sigma, 200)/5
  x = np.linspace(-1, 1, 200)
  x2 = np.linspace(-0.7, 0.7, 200)
  delta = 1*np.exp(-(x-0)**2/2)/(np.sqrt(2*np.pi)*1)
  y = 0.5 - 0.8*x2**2 + noise + delta/2
  y2 = 1.3*delta + noise - 0.1
  fig = plt.figure('large deviate')
  ax = fig.add_subplot(111)
  ax2 = fig.add_subplot(111)
  ax.fill_between(x2, y1=y, y2=y2, edgecolor='red', facecolor='blue', alpha=0.3)
  ax2.plot(x, y2, color='black')
  ax.set_xlabel('x')
  ax.set_ylabel('y')
  plt.show()
  
subject: update thesis code