old_contents: import time
  import sys
  sys.path.append('..')
  
  from sklearn.covariance import GraphLassoCV
  from inverse_covariance import (
      StatisticalPower,
      QuicGraphLassoCV, 
      QuicGraphLassoEBIC,
  )
  from matplotlib import pyplot as plt
  
  
  '''
  A few comments to do on phone with M:
  - why do I still need to threshold, and by how much?
  - what about that ebic thing that fixed the situation?
  - In the case of model-average, do we threshold the proportion matrix to 
    determine a sparsity?  How does that work?
  '''
  
  
  '''
  # EBIC, gamma=0.3
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoEBIC(gamma=0.3),
          n_features=50,
          n_trials=100,
          verbose=True,
      )
  sp.fit()
  sp.show()
  plt.title('gamma = 0.3')
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoEBIC(gamma=0.0),
          n_features=50,
          n_trials=100,
          verbose=True,
      )
  sp.fit()
  sp.show()
  plt.title('gamma = 0')
  '''
  
  '''
  start = time.time()
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=100,
          n_trials=100,
          n_jobs=1,
          verbose=True,
      )
  sp.fit()
  end = time.time()
  print 'Elapsed time for single thread {}'.format(end - start)
  
  start = time.time()
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=100,
          n_trials=100,
          n_jobs=4,
          verbose=True,
      )
  sp.fit()
  end = time.time()
  print 'Elapsed time for 4 thread {}'.format(end - start)
  '''
  
  '''
  # GraphLassoCV CV, 
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=50,
          n_trials=100,
          n_jobs=4,
          verbose=True,
      )
  sp.fit()
  sp.show()
  '''
  
  # GraphLassoCV CV, 
  sp = StatisticalPower(
          model_selection_estimator=GraphLassoCV(),
          n_features=50,
          n_trials=100,
          verbose=True,
      )
  sp.fit()
  sp.show()
  
  
  raw_input()
new_contents: import time
  import sys
  sys.path.append('..')
  
  from sklearn.covariance import GraphLassoCV
  from inverse_covariance import (
      StatisticalPower,
      QuicGraphLassoCV, 
      QuicGraphLassoEBIC,
  )
  from matplotlib import pyplot as plt
  
  
  '''
  A few comments to do on phone with M:
  - why do I still need to threshold, and by how much?
  - what about that ebic thing that fixed the situation?
  - In the case of model-average, do we threshold the proportion matrix to 
    determine a sparsity?  How does that work?
  '''
  
  
  '''
  # QuicGraphLassoEBIC, gamma=0.3
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoEBIC(gamma=0.3),
          n_features=50,
          n_trials=100,
          verbose=True,
      )
  sp.fit()
  sp.show()
  plt.title('gamma = 0.3')
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoEBIC(gamma=0.0),
          n_features=50,
          n_trials=100,
          verbose=True,
      )
  sp.fit()
  sp.show()
  plt.title('gamma = 0')
  '''
  
  '''
  start = time.time()
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=100,
          n_trials=100,
          n_jobs=1,
          verbose=True,
      )
  sp.fit()
  end = time.time()
  print 'Elapsed time for single thread {}'.format(end - start)
  
  start = time.time()
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=100,
          n_trials=100,
          n_jobs=4,
          verbose=True,
      )
  sp.fit()
  end = time.time()
  print 'Elapsed time for 4 thread {}'.format(end - start)
  '''
  
  '''
  # QuicGraphLassoCV CV, 
  sp = StatisticalPower(
          model_selection_estimator=QuicGraphLassoCV(),
          n_features=50,
          n_trials=100,
          n_jobs=4,
          verbose=True,
      )
  sp.fit()
  sp.show()
  '''
  
  # GraphLassoCV CV, 
  sp = StatisticalPower(
          model_selection_estimator=GraphLassoCV(),
          n_features=50,
          n_trials=100,
          verbose=True,
          penalty_='alpha_',
      )
  sp.fit()
  sp.show()
  
  
  raw_input()
subject: Add sklearn graphlassocv example, just for testing