new_contents: from __future__ import print_function, absolute_import, division
  
  import numpy as np
  import pandas as pd
  
  from mousestyles import data
  
  def path_index(movement, stop_threshold, min_path_length):
      r"""
      Return a list object containing start and end indices 
      for a specific movement. Each element in the list is 
      a list containing two indices: the first element is 
      the start index and the second element is the end index.
  
      Parameters
      ----------
      movement : pandas.DataFrame
          CT, CX, CY coordinates and homebase status 
          for the unique combination of strain, mouse and day
      stop_threshold : int
          positive integer indicating the path cutoff criteria
          if the time difference between two observations is 
          less than this threhold, they will be in the same path
  
      min_path_length : int
          positive integer indicating how many observations in 
          a path
  
      Returns
      -------
      paths index : a list containing the indices for all paths
  
      Examples
      --------
      >>> movement = data.load_movement(1, 2, 1)
      >>> paths = path_index(movement, 1, 1)
      [[0, 2], [6, 8], [107, 113], [129, 131], [144, 152]]
      """
      # check if all inputs are positive integers
      conditions_value = [stop_threshold <= 0, min_path_length <= 0]
      conditions_type = [type(stop_threshold) != int,
                         type(min_path_length) != int]
      if any(conditions_value):
          raise ValueError("Input values need to be positive")
      if any(conditions_type):
          raise TypeError("Input values need to be integer")
  
      # Pull out time variable
      T = movement['t'].ravel()
      # Calculate time differences
      TD = np.diff(T)
      path = []
      
      # index
      i = 0
      while i < len(TD):
          start_index = i
          # If time difference is less than stop_threshold
          # start to track the index in this path
          while TD[i] < stop_threshold:
              i += 1
              if TD[i] == len(TD): break
          end_index = i
          
          # Check whether start index is equal to end index
          # If they are equal jump to next index
          if start_index == end_index: next
          else:
              path.append([start_index, end_index])
          
          i += 1
      path = [p for p in path if (p[1] - p[0]) > min_path_length]
      
      return(path)
subject: add function that output the path indices