# -*- coding: utf-8 -*-

import os

import sys

import networkx as nx

import redis

import twitter

from recipe__oauth_login import oauth_login

from recipe__setwise_operations import get_redis_id

SCREEN_NAME = sys.argv[1]

t = oauth_login()

_id = str(t.users.show(screen_name=SCREEN_NAME)['id'])

g = nx.Graph() # An undirected graph

r = redis.Redis()

# Compute all ids for nodes appearing in the graph. Let's assume you've

# adapted recipe__crawl to harvest all of the friends and friends' friends

# for a user so that you can build a graph to inspect how these

# friendships relate to one another

# Create a collection of ids for a person and all of this person's friends

ids = [_id] + list(r.smembers(get_redis_id('friend_ids', user_id=_id)))

# Process each id in the collection such that edges are added to the graph

# for each of current_id's friends if those friends are also

# friends of SCREEN_NAME. In the end, you get an ego graph of

# SCREEN_NAME and SCREEN_NAME's friends, but you also see connections that

# existing amongst SCREEN_NAME's friends as well

for current_id in ids:

print >> sys.stderr, 'Processing user with id', current_id

try:

friend_ids = list(r.smembers(get_redis_id('friend_ids', user_id=current_id)))

friend_ids = [fid for fid in friend_ids if fid in ids]

except Exception, e:

print >> sys.stderr, 'Encountered exception. Skipping', current_id

for friend_id in friend_ids:

print >> sys.stderr, 'Adding edge %s => %s' % (current_id, friend_id,)

g.add_edge(current_id, friend_id)

# Optionally, pickle the graph to disk...

if not os.path.isdir('out'):

os.mkdir('out')

f = os.path.join('out', SCREEN_NAME + '-friendships.gpickle')

nx.write_gpickle(g, f)

print >> sys.stderr, 'Pickle file stored in', f