Emerson Matson
Moves the large data set into a more compact, workable dataset for analysis.
"""
from sqlalchemy import func, Integer
from league import db
from league.models import Champion
from league.models import ChampionData
from league.models import Match
from prettylog import PrettyLog
LOGGING = PrettyLog()
"""
TODO(Optimize this query for large databases.)
Window the query.
http://stackoverflow.com/
questions/7389759/
memory-efficient-built-in-sqlalchemy-iterator-generator
Filter the query to the most recent matches (30 days?)
Limit the query to a good amount (10 million?)
Prune the database every so often.
"""
def analyze_champions():
"""Analyzes the champion database and condenses the statistics."""
Emerson Matson
Collects tweets from the Twitter sample stream.
"""
import tweepy
from prettylog import PrettyLog
from twitter import db
from twitter.models import Tweet
from analysis.connection import API
LOGGING = PrettyLog()
class TweetStreamListener(tweepy.StreamListener):
"""Configures the Stream Listener for storage"""
num_tweets = 0
# TODO(Consider using PrettyLog for this.)
def on_connect(self):
LOGGING.push("Successfully connected to Twitter streaming API.")
def on_status(self, status):
if (
db.session.query(Tweet).filter_by(tweet_id=status.id).count() == 0
and
import tweepy
from prettylog import PrettyLog
from twitter import app
from twitter import db
from twitter.models import Tweet
from analysis.connection import AUTH
API = tweepy.API(AUTH, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
CATEGORIES = app.config['CATEGORIES']
LOGGING = PrettyLog()
def limit_handled(cursor):
"""Limits the cursor's accesses with respect to the rate limit.
Args:
cursor: Cursor to limit.
Notes:
Once the rate limit is reached, program execution waits 15 minutes.
"""
while True:
"""
remaining = int(API.last_response.getheader('x-rate-limit-remaining'))
"""
Champion Score Analysis
Emerson Matson
Analyzes individual champion (and role's) score.
"""
from league.models import ChampionData
from prettylog import PrettyLog
LOGGING = PrettyLog()
def make_adjustments():
"""Takes all champion data and creates a score."""
champions = ChampionData.query.all()
# TODO(Look at external sources for better analysis.)
# Look at pro players? Look at nerfplz.com and analyze their
# 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
