def get_user_timeline(user_name):
out = db_handler(host_='localhost', port_=27017, db_name_='ttr_exp')
engine = tweepy_engine(out=out)
user = engine.get_user_info(user_name)
user._id = user.name_
out.save_user(user.serialise())
return user
from analysing_data import markov_chain_machine
from analysing_data.markov_chain_machine import markov_chain
import text_proc.text_processing as tp
from analysing_data.booster import db_mc_handler
from model.db import db_handler
from search_engine.twitter_engine import tweepy_engine
from analysing_data.mc_difference_logic import diff_markov_chains
import tools
__author__ = '4ikist'
db = db_handler(host_='localhost', port_=27017, db_name_='ttr_exp')
boost = db_mc_handler()
engine = tweepy_engine(out=db)
def get_users_data(user_name1, user_name2):
user1 = engine.get_user_info(user_name1)
user2 = engine.get_user_info(user_name2)
db.save_user(user1.serialise())
db.save_user(user2.serialise())
timeline1 = tools.flush(user1.timeline, by_what=lambda x: tp.get_words(x['text'], is_normalise=True))[:10]
timeline2 = tools.flush(user2.timeline, by_what=lambda x: tp.get_words(x['text'], is_normalise=True))[:10]
print len(timeline1)
print len(timeline2)
mc1 = markov_chain_machine.create_model(timeline1, user_name1, boost)
mc2 = markov_chain_machine.create_model(timeline2, user_name2, boost)
return mc1, mc2
def load_users_by_star_friend(star_name):
engine = tweepy_engine(out=db_handler(host_='localhost', port_=27027, db_name_='ttr_test'))
engine.get_relations_of_user(star_name)
from analysing_data import mc_difference_logic
from analysing_data.booster import db_mc_handler
from analysing_data.markov_chain_machine import markov_chain
import loggers
from model.db import db_handler
from search_engine import twitter_engine
from search_engine.twitter_engine import tweepy_engine
import tools
from visualise import vis_machine
__author__ = '4ikist'
db_ = db_handler(truncate=False)
api_engine = twitter_engine.tweepy_engine(out=db_)
booster = db_mc_handler(truncate=False)
vis_processor = vis_machine
log = loggers.logger
def model_splitter(message):
message_ = message.split()
return message_
def process_names(file_name, class_name):
"""
get from file ser names, scrapping saving and forming markov chains for any user timeline
"""
