old_contents: import os
  import sys
  sys.path.append(os.getcwd() + "/../")
  import skimage.io
  import wget
  from matplotlib import pyplot as plt
  from decode import video_decoder as vd
  from extract import feature_extractor as fe
  from demo_features import RGBToGray
  from demo_features import BatchOP
  
  # Meeting Notes
  # Before presenting demo, review the 2 specified data structures
  # Make everyone cd into directory containing this demo.py file
  # Make everyone open this demo.py file in his/her/their favorite editor
  # Make everyone run each of the commands below in terminal
  
  # Figure out if you already have test_video.mp4 in the required directory
  vid_path = os.getcwd() + '/../test/test_data/'
  
  # Download test_video.mp4 into directory if it doesn't already exist
  if not os.path.exists(vid_path + 'test_video.mp4'):
      wget.download("https://s3.amazonaws.com/testcodas/test_video.mp4", vid_path)
  
  # Decode video to build dataset
  batch_list = vd.decode_mpeg(os.getcwd() + '/../test/test_data/test_video.mp4',
                              batch_size=2, end_idx=10)
  
  print('Batch_list contains {0} batches!'.format(len(batch_list)))
  
  # Extract features (e.g. Grayscale and "BatchOP") from data
  data_structure = fe.extract_features(batch_list,
                                       [RGBToGray, BatchOP])
  
  # Now, let's access  some of the features extracted from frames
  imgs = [data_structure[0]['input']['frame'],
          data_structure[0]['input']['grayscale']]
  skimage.io.imshow_collection(imgs)
  plt.show()
  
  # You can access particular features and keys in the data structure, too!
  for key, thing in data_structure[0]['input'].items():
      print(key)
  
new_contents: import os
  import sys
  sys.path.append(os.getcwd() + "/../")
  import skimage.io
  import wget
  from matplotlib import pyplot as plt
  from decode import video_decoder as vd
  from extract import feature_extractor as fe
  from demo_features import RGBToGray
  from demo_features import BatchOP
  
  # Meeting Notes
  # Before presenting demo, review the 2 specified data structures
  # Make everyone cd into directory containing this demo.py file
  # Make everyone open this demo.py file in his/her/their favorite editor
  # Make everyone run each of the commands below in terminal
  
  # Figure out if you already have test_video.mp4 in the required directory
  vid_path = os.getcwd() + '/../test/test_data/'
  
  # Download test_video.mp4 into directory if it doesn't already exist
  if not os.path.exists(vid_path + 'test_video.mp4'):
      wget.download("https://s3.amazonaws.com/codasimageanalysis/test_video.mp4", vid_path)
  
  # Decode video to build dataset
  batch_list = vd.decode_mpeg(os.getcwd() + '/../test/test_data/test_video.mp4',
                              batch_size=2, end_idx=10)
  
  print('Batch_list contains {0} batches!'.format(len(batch_list)))
  
  # Extract features (e.g. Grayscale and "BatchOP") from data
  data_structure = fe.extract_features(batch_list,
                                       [RGBToGray, BatchOP])
  
  # Now, let's access  some of the features extracted from frames
  imgs = [data_structure[0]['input']['frame'],
          data_structure[0]['input']['grayscale']]
  skimage.io.imshow_collection(imgs)
  plt.show()
  
  # You can access particular features and keys in the data structure, too!
  for key, thing in data_structure[0]['input'].items():
      print(key)
  
subject: add sphinx quickstart