text: import numpy as np
  import matplotlib.pyplot as plt
  from scipy.stats import norm
  import pylab
  import scipy.stats as stats
  
  pylab.clf()
  # Plot between -10 and 10 with .001 steps.
  x = np.arange(0, 1, 0.001)
  # Mean = 0, SD = 2.
  
  plt.subplot(2,2,1)
  plt.xlabel('Evaluator Expertise $\mathbf{a_{p}}$', fontsize=14)
  plt.ylabel('Proportion of Crowd', fontsize=14)
  plt.title('Case I: Homogenous Crowds')
  plt.plot(x, norm.pdf(x,.8,.02), linewidth=2, color='gray',linestyle='--')
  plt.plot(x, norm.pdf(x,.3,.02), linewidth=2, color='gray',linestyle=':')
  plt.plot(x, norm.pdf(x,.5,.02), linewidth=2, color='gray')
  
  plt.subplot(2,2,2)
  plt.xlabel('Evaluator Expertise $\mathbf{a_{p}}$', fontsize=14)
  plt.ylabel('Proportion of Crowd', fontsize=14)
  plt.title('Case II: Heterogeneous Crowds')
  scale=.1
  y1=np.array(stats.truncnorm.pdf(x,-.2/scale,.8/scale,.2,scale))
  scale=.4
  y2=np.array(stats.truncnorm.pdf(x,-.2/scale,.8/scale,.2,scale))
  plt.plot(x, norm.pdf(x,.2,.2), linewidth=2, color='gray',linestyle=':')
  plt.plot(x, y1, linewidth=2, color='gray',linestyle='--')
  plt.plot(x, y2, linewidth=2, color='gray')
  
  pylab.setp(plt.subplot(2,2,1).get_yticklabels(), visible=False)
  pylab.setp(plt.subplot(2,2,2).get_yticklabels(), visible=False)
  plt.tight_layout()
  pylab.savefig('crowd_expertises.png')
  