old_contents: import sys
  import os
  import numpy as np
  
  from skcycling.utils import load_power_from_fit
  from skcycling.power_profile import Rpp
  
  # The first input argument corresponding to the data path
  data_path = sys.argv[1]
  
  # The second input argument is the storage directory
  storage_path = sys.argv[2]
  
  # We can create a list of all the *.fit files present inside that directory
  # Create a list with the files to considered
  filenames = []
  for root, dirs, files in os.walk(data_path):
      for file in files:
          if file.endswith('.fit'):
              filenames.append(os.path.join(root, file))
  
  max_duration_rpp = 30
  rpp_rider = Rpp(max_duration_rpp=max_duration_rpp)
  # Open each file and fit
  for idx_file, filename in enumerate(filenames):
      print 'Process file #{} over {}'.format(idx_file+1, len(filenames))
      # Open the file
      power_ride = load_power_from_fit(filename)
      # Fit the ride
      rpp_rider.fit(power_ride)
  
  # Create a directory to store the data if it is not existing
  if not os.path.exists(storage_path):
      os.makedirs(storage_path)
  
  # Store the data somewhere
  np.save(os.path.join(storage_path, 'profile.npy'), rpp_rider.rpp_)
  
new_contents: import sys
  import os
  import numpy as np
  
  from skcycling.utils import load_power_from_fit
  from skcycling.restoration import denoise
  from skcycling.power_profile import Rpp
  
  # The first input argument corresponding to the data path
  data_path = sys.argv[1]
  
  # The second input argument is the storage directory
  storage_path = sys.argv[2]
  
  # We can create a list of all the *.fit files present inside that directory
  # Create a list with the files to considered
  filenames = []
  for root, dirs, files in os.walk(data_path):
      for file in files:
          if file.endswith('.fit'):
              filenames.append(os.path.join(root, file))
  
  max_duration_rpp = 30
  rpp_rider = Rpp(max_duration_rpp=max_duration_rpp)
  # Open each file and fit
  for idx_file, filename in enumerate(filenames):
      print 'Process file #{} over {}'.format(idx_file+1, len(filenames))
      # Open the file
      power_ride = load_power_from_fit(filename)
      # Reject the outliers using thresholding method
      power_ride = denoise.outliers_rejection(power_ride)
      # Fit the ride
      rpp_rider.fit(power_ride)
  
  # Create a directory to store the data if it is not existing
  if not os.path.exists(storage_path):
      os.makedirs(storage_path)
  
  # Store the data somewhere
  np.save(os.path.join(storage_path, 'profile.npy'), rpp_rider.rpp_)
  
subject: Update the pipeline to take into account the outlier rejection method to compute the RPP