old_contents: # test_generate_datasets.py
  from __future__ import print_function
  import os
  
  import pytest
  import matplotlib.pyplot as plt
  
  from ..estimation import KaplanMeierFitter, NelsonAalenFitter
  from ..generate_datasets import exponential_survival_data
  
  
  def test_exponential_data_sets_correct_censor():
      print(os.environ)
      N = 20000
      censorship = 0.2
      T, C = exponential_survival_data(N, censorship, scale=10)
      assert abs(C.mean() - (1 - censorship)) < 0.02
  
  
  @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
  def test_exponential_data_sets_fit():
      N = 20000
      T, C = exponential_survival_data(N, 0.2, scale=10)
      naf = NelsonAalenFitter()
      naf.fit(T, C).plot()
      plt.title("Should be a linear with slope = 0.1")
  
  
  @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
  def test_kmf_minimum_observation_bias():
      N = 250
      kmf = KaplanMeierFitter()
      T, C = exponential_survival_data(N, 0.2, scale=10)
      B, _ = exponential_survival_data(N, 0.0, scale=2)
      kmf.fit(T, C, entry=B)
      kmf.plot()
      plt.title("Should have larger variances in the tails")
  
new_contents: # test_generate_datasets.py
  from __future__ import print_function
  import os
  
  import pytest
  import matplotlib.pyplot as plt
  
  from ..estimation import KaplanMeierFitter, NelsonAalenFitter
  from ..generate_datasets import exponential_survival_data
  
  
  def test_exponential_data_sets_correct_censor():
      print(os.environ)
      N = 20000
      censorship = 0.2
      T, C = exponential_survival_data(N, censorship, scale=10)
      assert abs(C.mean() - (1 - censorship)) < 0.02
  
  
  @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
  def test_exponential_data_sets_fit():
      N = 20000
      T, C = exponential_survival_data(N, 0.2, scale=10)
      naf = NelsonAalenFitter()
      naf.fit(T, C).plot()
      plt.title("Should be a linear with slope = 0.1")
  
  
  @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
  def test_kmf_minimum_observation_bias():
      N = 250
      kmf = KaplanMeierFitter()
      T, C = exponential_survival_data(N, 0.1, scale=10)
      B = 0.01*T
      kmf.fit(T, C, entry=B)
      kmf.plot()
      plt.title("Should have larger variances in the tails")
  
subject: fix this test to use new interpretation