text: # Draw samples from the distribution:
  
  loc, scale = 10, 1
  s = np.random.logistic(loc, scale, 10000)
  import matplotlib.pyplot as plt
  count, bins, ignored = plt.hist(s, bins=50)
  
  # #   plot against distribution
  
  def logist(x, loc, scale):
      return np.exp((loc-x)/scale)/(scale*(1+np.exp((loc-x)/scale))**2)
  lgst_val = logist(bins, loc, scale)
  plt.plot(bins, lgst_val * count.max() / lgst_val.max())
  plt.show()
  