old_contents: import numpy as np
  import matplotlib
  matplotlib.use("Qt5Agg")
  import matplotlib.pyplot as plt
  from MPLAnimator import Animator
  
  def naive_estimator(x, data, h):
      n = sum(1 for d in data if x-h/2 < d <= x+h/2)
      N = len(data)
      return n/(N*h)
  
  data = [0.5, 0.7, 0.8, 1.9, 2.4, 6.1, 6.2, 7.3]
  xs = np.arange(0, 8, 0.01)
  h = 2
  hist = [naive_estimator(x, data, h) for x in xs]
  
  def setup():
      plt.gcf().set_size_inches(8,6)
      plt.suptitle("Naive Estimator for h = {}".format(h))
  
  def frame(i):
      plt.cla()
  
      # plot original data
      plt.plot(xs, hist)
      plt.plot(data, [0]*len(data), 'xk')
      plt.axhline(0, color='k', linewidth=0.5)
  
      # calculate current interval
      x = i / 10
      x1, x2 = x-h/2, x+h/2
  
      # calculate relative width for visualization
      axis_to_data = plt.gca().transAxes + plt.gca().transData.inverted()
      bottom = axis_to_data.transform((0, 0))[1]
      top = -bottom
  
      # plot visualization lines
      plt.plot([x, x], [0, hist[i * 10]], '--C3')
      plt.plot([x1, x1], [bottom, top], 'C3', linewidth=0.5)
      plt.plot([x2, x2], [bottom, top], 'C3', linewidth=0.5)
      plt.plot([x1, x2], [0, 0], 'C3', linewidth=0.5)
      plt.fill_between([x1, x2], bottom, top, color='C3', alpha=0.3)
      plt.plot(x, hist[i * 10], '.C3')
  
      # highlight data in interval
      highlight_data = [d for d in data if x1 < d <= x2]
      plt.plot(highlight_data, [0]*len(highlight_data), 'oC3')
  
      plt.xlim(-0.5, 8.5)
  
  
  
  a = Animator(name='NaiveEstimator', setup_handle=setup)
  a.setFrameCallback(frame_handle=frame, max_frame=80)
  a.run(clear=False, prerendered=True)
  
new_contents: import numpy as np
  import matplotlib
  matplotlib.use("Qt5Agg")
  import matplotlib.pyplot as plt
  from MPLAnimator import Animator
  
  def naive_estimator(x, data, h):
      n = sum(1 for d in data if x-h/2 < d <= x+h/2)
      N = len(data)
      return n/(N*h)
  
  data = [0.5, 0.7, 0.8, 1.9, 2.4, 6.1, 6.2, 7.3]
  xs = np.arange(0, 8, 0.01)
  h = 2
  hist = [naive_estimator(x, data, h) for x in xs]
  
  def setup():
      plt.gcf().set_size_inches(8,6)
      plt.suptitle("Naive Estimator for h = {}".format(h))
  
  def frame(i):
      plt.cla()
  
      # plot original data
      plt.plot(xs, hist)
      plt.plot(data, [0]*len(data), 'xk')
      plt.axhline(0, color='k', linewidth=0.5)
  
      # calculate current interval
      x = i / 10
      x1, x2 = x-h/2, x+h/2
  
      # calculate relative width for visualization
      axis_to_data = plt.gca().transAxes + plt.gca().transData.inverted()
      bottom = axis_to_data.transform((0, 0))[1]
      top = -bottom
  
      # plot visualization lines
      plt.plot([x, x], [0, hist[i * 10]], '--C3')
      plt.plot([x1, x1], [bottom, top], 'C3', linewidth=0.5)
      plt.plot([x2, x2], [bottom, top], 'C3', linewidth=0.5)
      plt.plot([x1, x2], [0, 0], 'C3', linewidth=0.5)
      plt.fill_between([x1, x2], bottom, top, color='C3', alpha=0.3)
      plt.plot(x, hist[i * 10], '.C3')
  
      # highlight data in interval
      highlight_data = [d for d in data if x1 < d <= x2]
      plt.plot(highlight_data, [0]*len(highlight_data), 'oC3')
  
      plt.xlim(-0.5, 8.5)
  
  
  a = Animator(name='NaiveEstimator', setup_cb=setup)
  a.setFrameCallback(frame_cb=frame, max_frame=80)
  a.run(clear=False, prerendered=True)
  
subject: Fix the example.