old_contents: import numpy as np
  
  try:
      import statsmodels.api as sm
  except ImportError:
      print("Example requires statsmodels")
      sys.exit(0)
  
  from pymc import *
  
  # Generate data
  size = 50
  true_intercept = 1
  true_slope = 2
  
  x = np.linspace(0, 1, size)
  y = true_intercept + x*true_slope + np.random.normal(scale=.5, size=size)
  
  data = dict(x=x, y=y)
  
  with Model() as model:
      glm.glm('y ~ x', data)
  
  
  def run(n=2000):
      if n == "short":
          n = 50
      import matplotlib.pyplot as plt
  
      with model:
          trace = sample(n, Slice(model.vars))
  
      plt.plot(x, y, 'x')
      glm.plot_posterior_predictive(trace)
      # plt.show()
  
  if __name__ == '__main__':
      run()
  
  
  
new_contents: import numpy as np
  import sys
  
  try:
      import statsmodels.api as sm
  except ImportError:
      print("Example requires statsmodels")
      sys.exit(0)
  
  from pymc import *
  
  # Generate data
  size = 50
  true_intercept = 1
  true_slope = 2
  
  x = np.linspace(0, 1, size)
  y = true_intercept + x*true_slope + np.random.normal(scale=.5, size=size)
  
  data = dict(x=x, y=y)
  
  with Model() as model:
      glm.glm('y ~ x', data)
  
  
  def run(n=2000):
      if n == "short":
          n = 50
      import matplotlib.pyplot as plt
  
      with model:
          trace = sample(n, Slice(model.vars))
  
      plt.plot(x, y, 'x')
      glm.plot_posterior_predictive(trace)
      # plt.show()
  
  if __name__ == '__main__':
      run()
  
  
  
subject: add missing sys import