<----SKIPPED LINES---->




  if (
      running_hours >= min_hours and
      not message_queue and
      not json_desc_dict.get('radio_range_flights') and
      # script /home/pi/splitflap/backup.sh creates temp file in this
      # directory; after it is copied to the NAS, it is deleted
      not os.listdir('/media/backup')):
    msg = ('All quiet reboot triggered based on %d days (%d hours); '
           'actual runtime: %.2f hours' %
           (restart_days, min_hours, running_hours))
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True

  # Wait another half day
  restart_days += 0.5
  min_hours = restart_days * HOURS_IN_DAY
  if (
      running_hours > min_hours and
      not message_queue and
      int(EpochDisplayTime(time.time(), '%-H')) >= 4):
    msg = ('Early morning reboot triggered based on %.1f (%d hours); '
           'actual runtime: %.2f hours' %
           (restart_days, min_hours, running_hours))
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True

  if 'soft_reboot' in configuration:
    msg = 'Soft reboot requested via web form'
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True
    RemoveSetting(configuration, 'soft_reboot')

  if 'end_process' in configuration:
    msg = 'Process end requested via web form'
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    RemoveSetting(configuration, 'end_process')





                            <----SKIPPED LINES---->




  if initial_memory_dump:
    tracemalloc.start(initial_frame_count)

  startup_time = time.time()
  json_desc_dict = {}

  init_timing.append((time.time(), 3))
  flights = UnpickleObjectFromFile(
      PICKLE_FLIGHTS, True, max_days=MAX_INSIGHT_HORIZON_DAYS, heartbeat=True)
  # Clear the loaded flight of any cached data, identified by keys
  # with a specific suffix, since code fixes may change the values for
  # some of those cached elements
  init_timing.append((time.time(), 4))
  for flight in flights:
    for key in list(flight.keys()):
      if key.endswith(CACHED_ELEMENT_PREFIX):
        flight.pop(key)
  init_timing.append((time.time(), 5))
  # We have no use for the memory-hogging persistent_path key of historical
  # flights, so let's remove all those keys and save a lot of memory

  for flight in flights:
    if 'persistent_path' in flight:
      del flight['persistent_path']

  screen_history = UnpickleObjectFromFile(PICKLE_SCREENS, True, max_days=2)

  # If we're displaying just a single insight message, we want it to be
  # something unique, to the extent possible; this dict holds a count of
  # the diff types of messages displayed so far
  insight_message_distribution = {}

  # bootstrap the flight insights distribution from a list of insights on each
  # flight (i.e.: flight['insight_types'] for a given flight might look like
  # [1, 2, 7, 9], or [], to indicate which insights were identified; this then
  # transforms that into {0: 25, 1: 18, ...} summing across all flights.
  missing_insights = []
  for flight in flights:
    if 'insight_types' not in flight:
      missing_insights.append('%s on %s' % (
          DisplayFlightNumber(flight), DisplayTime(flight, '%x %X')))
    distribution = flight.get('insight_types', [])
    for key in distribution:
      insight_message_distribution[key] = (




                            <----SKIPPED LINES---->

                            <----SKIPPED LINES---->




  if (
      running_hours >= min_hours and
      not message_queue and
      not json_desc_dict.get('radio_range_flights') and
      # script /home/pi/splitflap/backup.sh creates temp file in this
      # directory; after it is copied to the NAS, it is deleted
      not os.listdir('/media/backup')):
    msg = ('All quiet reboot triggered based on %d days (%d hours); '
           'actual runtime: %.2f hours' %
           (restart_days, min_hours, running_hours))
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True

  # Wait another half day
  restart_days += 0.5
  min_hours = restart_days * HOURS_IN_DAY
  if (
      running_hours > min_hours and
      not message_queue and
      6 >= int(EpochDisplayTime(time.time(), '%-H')) >= 4):
    msg = ('Early morning reboot triggered based on %.1f (%d hours); '
           'actual runtime: %.2f hours' %
           (restart_days, min_hours, running_hours))
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True

  if 'soft_reboot' in configuration:
    msg = 'Soft reboot requested via web form'
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    reboot = True
    RemoveSetting(configuration, 'soft_reboot')

  if 'end_process' in configuration:
    msg = 'Process end requested via web form'
    SHUTDOWN_SIGNAL = msg
    Log(msg)
    RemoveSetting(configuration, 'end_process')





                            <----SKIPPED LINES---->




  if initial_memory_dump:
    tracemalloc.start(initial_frame_count)

  startup_time = time.time()
  json_desc_dict = {}

  init_timing.append((time.time(), 3))
  flights = UnpickleObjectFromFile(
      PICKLE_FLIGHTS, True, max_days=MAX_INSIGHT_HORIZON_DAYS, heartbeat=True)
  # Clear the loaded flight of any cached data, identified by keys
  # with a specific suffix, since code fixes may change the values for
  # some of those cached elements
  init_timing.append((time.time(), 4))
  for flight in flights:
    for key in list(flight.keys()):
      if key.endswith(CACHED_ELEMENT_PREFIX):
        flight.pop(key)
  init_timing.append((time.time(), 5))
  # We have no use for the memory-hogging persistent_path key of historical
  # flights, so let's remove all those keys and save a lot of memory
  if flights:
    for flight in flights[:-1]:
      if 'persistent_path' in flight:
        del flight['persistent_path']

  screen_history = UnpickleObjectFromFile(PICKLE_SCREENS, True, max_days=2)

  # If we're displaying just a single insight message, we want it to be
  # something unique, to the extent possible; this dict holds a count of
  # the diff types of messages displayed so far
  insight_message_distribution = {}

  # bootstrap the flight insights distribution from a list of insights on each
  # flight (i.e.: flight['insight_types'] for a given flight might look like
  # [1, 2, 7, 9], or [], to indicate which insights were identified; this then
  # transforms that into {0: 25, 1: 18, ...} summing across all flights.
  missing_insights = []
  for flight in flights:
    if 'insight_types' not in flight:
      missing_insights.append('%s on %s' % (
          DisplayFlightNumber(flight), DisplayTime(flight, '%x %X')))
    distribution = flight.get('insight_types', [])
    for key in distribution:
      insight_message_distribution[key] = (




                            <----SKIPPED LINES---->