tools/perf/cli_tools/pinboard/pinboard.py
def GetItemsToUpdate(state):
  """Select jobs with new data to download and cached data for existing jobs.

  This also filters out old revisions to keep only recent (6 months) data.

  Returns:
    new_items: A list of job items from which to get data.
    cached_df: A DataFrame with existing cached data, may be None.
  """
  from_date = str(TimeAgo(months=6).date())
  new_items = [item for item in state if item['timestamp'] > from_date]
  df = GetCachedDataset()
  if df is not None:
    recent_revisions = set(item['revision'] for item in new_items)
    df = df[df['revision'].isin(recent_revisions)]
    known_revisions = set(df['revision'])
    new_items = [
        item for item in new_items if item['revision'] not in known_revisions]
  return new_items, df
Select jobs with new data to download and cached data for existing jobs.

  This also filters out old revisions to keep only recent (6 months) data.

  Returns:
    new_items: A list of job items from which to get data.
    cached_df: A DataFrame with existing cached data, may be None.
  
Select jobs with new data to download and cached data for existing jobs.
This also filters out old revisions to keep only recent (6 months) data.
Select jobs with new data to download and cached data for existing jobs . This also filters out old revisions to keep only recent ( 6 months ) data .
def GetItemsToUpdate(state):
  from_date = str(TimeAgo(months=6).date())
  new_items = [item for item in state if item['timestamp'] > from_date]
  df = GetCachedDataset()
  if df is not None:
    recent_revisions = set(item['revision'] for item in new_items)
    df = df[df['revision'].isin(recent_revisions)]
    known_revisions = set(df['revision'])
    new_items = [
        item for item in new_items if item['revision'] not in known_revisions]
  return new_items, df
def GetItemsToUpdate ( state ) : from_date = str ( TimeAgo ( months = 6 ) . date ( ) ) new_items = [ item for item in state if item [ 'timestamp' ] > from_date ] df = GetCachedDataset ( ) if df is not None : recent_revisions = set ( item [ 'revision' ] for item in new_items ) df = df [ df [ 'revision' ] . isin ( recent_revisions ) ] known_revisions = set ( df [ 'revision' ] ) new_items = [ item for item in new_items if item [ 'revision' ] not in known_revisions ] return new_items , df
Select jobs with new data to download and cached data for existing jobs.
Select jobs with new data to download and cached data for existing jobs .
"""Select jobs with new data to download and cached data for existing jobs.

  This also filters out old revisions to keep only recent (6 months) data.

  Returns:
    new_items: A list of job items from which to get data.
    cached_df: A DataFrame with existing cached data, may be None.
  """
A list of job items from which to get data.
cached_df: A DataFrame with existing cached data, may be None.
A list of job items from which to get data . cached_df : A DataFrame with existing cached data may be None .
new_items
state