old_contents: '''
      config choosing data and training the neural net
  '''
  
  
  '''
      constants for how to create labels, 
      from OpenStreetMap way (road) info in PBF files
  '''
  # enough to cover NAIPs around DC/Maryland/Virginia
  PBF_FILE_URLS = ['http://download.geofabrik.de/north-america/us/maryland-latest.osm.pbf',
                   'http://download.geofabrik.de/north-america/us/virginia-latest.osm.pbf',
                   'http://download.geofabrik.de/north-america/us/district-of-columbia-latest.osm.pbf']
  
  # tile the NAIP and training data into NxN tiles with this dimension
  TILE_SIZE = 64
  
  # the number of pixels to count as road, 
  # on each side of of the centerline pixels
  PIXELS_BESIDE_WAYS = 1
  
  # to count an NxN tile as being "On" for roads,
  # N*.25 pixels on that tiles must have been classified as roads
  PERCENT_OF_TILE_HEIGHT_TO_ACTIVATE = .50
  
  '''
      constants for NAIP imagery to use   
  '''
  # the bands to use from the NAIP for analysis (R G B IR)
  BANDS_TO_USE = [1,1,1,1]
  
  # set this to None to get different tifs to analyze
  HARDCODED_NAIP_LIST = [
                    'm_3807708_ne_18_1_20130924.tif',
                    'm_3807708_nw_18_1_20130904.tif',
                    'm_3807708_se_18_1_20130924.tif',
                    'm_3807708_se_18_1_20130924.tif'
                    ]
  
  # values to create the S3 bucket path for some maryland NAIPs
  # you can get random NAIPS from here, or the exact HARDCODED_NAIP_LIST above
  # \todo document how to configure some of these
  NAIP_STATE = 'md'
  NAIP_YEAR = '2013'
  NAIP_RESOLUTION = '1m'
  NAIP_SPECTRUM = 'rgbir' 
  NAIP_GRID = '38077'
  
  # set this to a value between 1 and 10 or so,
  # and unset HARDCODED_NAIP_LIST, to get some different NAIPs
  NUMBER_OF_NAIPS = -1
  
  # set this to True for production data science, False for debugging infrastructure
  # speeds up downloads and matrix making when False
  RANDOMIZE_NAIPS = False
  
  '''
      constants for training neural net  
  '''
  # the remainder is allocated as test data
  PERCENT_FOR_TRAINING_DATA = .93
  
  # the number of batches to train the neural net
  # @lacker recommends 3-5K for statistical significance, as rule of thumb
  # can achieve 70% accuracy with 5000 so far
  NUMBER_OF_BATCHES = 5000
  
  # the number of tiles for each training round
  BATCH_SIZE = 100
  
  # the patch size for both the 32 and 64 feature convolutions
  # used with an NxN tile, where N has usually been 64
  CONVOLUTION_PATCH_SIZE = 5
  
  
  
new_contents: '''
      config choosing data and training the neural net
  '''
  
  
  '''
      constants for how to create labels, 
      from OpenStreetMap way (road) info in PBF files
  '''
  # enough to cover NAIPs around DC/Maryland/Virginia
  PBF_FILE_URLS = ['http://download.geofabrik.de/north-america/us/maryland-latest.osm.pbf',
                   'http://download.geofabrik.de/north-america/us/virginia-latest.osm.pbf',
                   'http://download.geofabrik.de/north-america/us/district-of-columbia-latest.osm.pbf']
  
  # tile the NAIP and training data into NxN tiles with this dimension
  TILE_SIZE = 64
  
  # the number of pixels to count as road, 
  # on each side of of the centerline pixels
  PIXELS_BESIDE_WAYS = 1
  
  # to count an NxN tile as being "On" for roads,
  # N*.25 pixels on that tiles must have been classified as roads
  PERCENT_OF_TILE_HEIGHT_TO_ACTIVATE = .50
  
  '''
      constants for NAIP imagery to use   
  '''
  # the bands to use from the NAIP for analysis (R G B IR)
  BANDS_TO_USE = [1,1,1,1]
  
  # set this to None to get different tifs to analyze
  HARDCODED_NAIP_LIST = [
                    'm_3807708_ne_18_1_20130924.tif',
                    #'m_3807708_nw_18_1_20130904.tif',
                    #'m_3807708_se_18_1_20130924.tif',
                    #'m_3807708_se_18_1_20130924.tif'
                    ]
  
  # values to create the S3 bucket path for some maryland NAIPs
  # you can get random NAIPS from here, or the exact HARDCODED_NAIP_LIST above
  # \todo document how to configure some of these
  NAIP_STATE = 'md'
  NAIP_YEAR = '2013'
  NAIP_RESOLUTION = '1m'
  NAIP_SPECTRUM = 'rgbir' 
  NAIP_GRID = '38077'
  
  # set this to a value between 1 and 10 or so,
  # and unset HARDCODED_NAIP_LIST, to get some different NAIPs
  NUMBER_OF_NAIPS = -1
  
  # set this to True for production data science, False for debugging infrastructure
  # speeds up downloads and matrix making when False
  RANDOMIZE_NAIPS = False
  
  '''
      constants for training neural net  
  '''
  # the remainder is allocated as test data
  PERCENT_FOR_TRAINING_DATA = .93
  
  # the number of batches to train the neural net
  # @lacker recommends 3-5K for statistical significance, as rule of thumb
  # can achieve 70% accuracy with 5000 so far
  NUMBER_OF_BATCHES = 100
  
  # the number of tiles for each training round
  BATCH_SIZE = 100
  
  # the patch size for both the 32 and 64 feature convolutions
  # used with an NxN tile, where N has usually been 64
  CONVOLUTION_PATCH_SIZE = 5
  
  
  
subject: move from laptop