found.damage = champion.damage
found.objective_score = champion.objective_score
found.tower_score = champion.tower_score
found.won = champion.won
found.pick_rate = (champion.seen / float(match_num))
found.num_seen = champion.seen
db.session.add(found)
db.session.commit()
db.session.commit()
def get_match_count():
LOGGING.push("Getting match count.")
match_num = db.session.query(Match).count()
LOGGING.push("*'" + str(match_num) + "'* matches found in the database.")
return match_num
def analyze_bans():
"""Analyze banning patterns, looking at what popular bans, and when."""
pass
if __name__ == '__main__':
LOGGING.push("Analyzing database.")
analyze_champions()
LOGGING.push("Database has been analyzed.")
and
# TODO(Need to encode this into unicode.)
len(str(status.text.encode("unicode_escape"))) < 1000
):
LOGGING.push("*" + status.user.name + "*: " + LOGGING.clean(status.text))
Tweet.store_tweet(status)
self.num_tweets += 1
if self.num_tweets % 100 == 0:
LOGGING.push("*" + str(self.num_tweets) + "* tweets have been collected.")
def on_error(self, status_code):
LOGGING.push("Received error with status code: #", str(status_code) + "#. Disconnecting from stream.")
return False
def on_timeout(self):
LOGGING.push("Server timed out.")
return True
if __name__ == "__main__":
stream_listener = TweetStreamListener()
stream = tweepy.Stream(auth=API.auth, listener=stream_listener)
LOGGING.push("Starting to collect the sample stream.")
stream.sample()
tweepy.Cursor(API.search, q=category, count=100).items(1000)
)
for status in cursor:
if (
db.session.query(Tweet).filter_by(
tweet_id=status.id
).count() == 0 and
# TODO(Need to encode this into unicode.)
len(str(status.text.encode('unicode_escape'))) < 1000
):
LOGGING.push(
"*" + status.user.name + "*: " + LOGGING.clean(status.text)
)
Tweet.store_tweet(status)
def crawl_search():
"""Crawls the search."""
for category in CATEGORIES:
crawl_category(category)
# Stem out into related common words with searches?
if __name__ == "__main__":
LOGGING.push("Starting to crawl the search.")
crawl_search()
LOGGING.push("Finished crawling the search.")
# algorithm for assigning tier lists. Look at popularity on twitter?
for champion in champions:
# True forces the methods to force updates
champion.get_score(True)
champion.get_counters(True)
champion.get_assists(True)
# TODO(Use adjustments attribute to assign miscellaneous overall stats)
# ChampionDatas as verticies. Directed edges against champions to
# depict wins against that champion. Champion w/ highest out
# degree is the most likely to win in the meta.
# Can start storing specified MMR for games and create a single
# source shortest path to a desired MMR.
# Adjust all champions scores to model a normal distribution.
# Adjustment scores are used for this.
def approximate_normal():
"""Approximate the normal distribution with champion scores."""
pass
if __name__ == "__main__":
LOGGING.push("Starting adjustments.")
make_adjustments()
LOGGING.push("Adjustments have been completed.")
LOGGING.push("Now starting to approximate normal.")
approximate_normal()
LOGGING.push("Finished approximating normal.")