def find_topic(cls, topic_id, start_date, end_date=None):
""" Find showable graph for time window and topic_id. """
# Parse end date to match database values
end_date = DateUtils.last_second_of_day(end_date if end_date else start_date)
# Retrieve topic graph
graph = ShowableGraphDAO().find(topic_id, start_date, end_date)
# Normalize node size
nodes = graph['nodes']
sizes = list(map(lambda node: node['size'], nodes))
max_size = max(sizes)
# Normalize to a (0,1] vector
for node in nodes:
node['size'] = (node['size'] / max_size)
# Subtract minimum value to get effective [0,1) vector and transform to wanted interval
sizes = list(map(lambda node: node['size'], nodes))
min_size = min(sizes)
max_size = max(sizes) - min_size
for node in nodes:
node['size'] = ((node['size'] - min_size)/max_size)*(cls.MAX_SIZE - cls.MIN_SIZE) + cls.MIN_SIZE
return graph
def find_topic(cls, topic_id, start_date, end_date):
end_date = DateUtils.last_second_of_day(end_date if end_date else start_date)
document = TopicUsageDAO().find(topic_id, start_date, end_date)
tweet_id = HashtagDAO().first_known_usage_tweet_id(topic_id)
return HashtagUsageResponseMapper.map_one(document, tweet_id)
def find_hashtag(cls, hashtag_name, start_date, end_date):
# Parse end date to match database values
end_date = DateUtils.last_second_of_day(end_date if end_date else start_date)
document = HashtagUsageDAO().find(hashtag_name, start_date, end_date)
tweet_id = HashtagDAO().first_known_usage_tweet_id(hashtag_name)
return HashtagUsageResponseMapper.map_one(document, tweet_id)