ACCESS_TOKEN = sys.argv[1]
except IndexError, e:
print >> sys.stderr, \
"Could not either find access token in 'facebook.access_token' or parse args."
ACCESS_TOKEN = login()
# process the results of the following fql query to create a json output suitable for
# consumption by a simple hierarchical tree widget:
fql = FQL(ACCESS_TOKEN)
q = \
"""select name, current_location, hometown_location from user where uid in
(select target_id from connection where source_id = me() and target_type = 'user')"""
results = fql.query(q)
# First, read over the raw fql query and create a hierarchical maps that groups people by
# where they live now compared to their hometown. we'll simply tabulate frequencies, but
# you could easily grab additional data in the fql and use it for many creative situations
current_by_hometown = {}
for r in results:
if r['current_location'] != None:
current_location = r['current_location']['city'] + ', ' \
+ r['current_location']['state']
else:
current_location = 'Unknown'
if r['hometown_location'] != None:
hometown_location = r['hometown_location']['city'] + ', ' \
# doesn't interpret any characters in it. You may also need to escape
# the # character
ACCESS_TOKEN = sys.argv[1]
except IndexError, e:
print >> sys.stderr, \
"Could not either find access token in 'facebook.access_token' or parse args."
ACCESS_TOKEN = login()
fql = FQL(ACCESS_TOKEN)
# get friend ids
q = \
'select target_id from connection where source_id = me() and target_type =\'user\''
my_friends = [str(t['target_id']) for t in fql.query(q)]
# now get friendships amongst your friends. note that this api appears to return
# arbitrarily truncated results if you pass in more than a couple hundred friends
# into each part of the query, so we perform (num friends)/N queries and aggregate
# the results to try and get complete results
# Warning: this can result in a several API calls and a lot of data returned that
# you'll have to process
mutual_friendships = []
N = 50
for i in range(len(my_friends) / N + 1):
q = 'select uid1, uid2 from friend where uid1 in (%s) and uid2 in (%s)' \
% (','.join(my_friends), ','.join(my_friends[i * N:(i + 1) * N]))
mutual_friendships += fql.query(q)
# doesn't interpret any characters in it. You may also need to escape
# the # character
ACCESS_TOKEN = sys.argv[1]
except IndexError, e:
print >> sys.stderr, \
"Could not either find access token in 'facebook.access_token' or parse args."
ACCESS_TOKEN = login()
fql = FQL(ACCESS_TOKEN)
# get friend ids
q = \
'select target_id from connection where source_id = me() and target_type =\'user\''
my_friends = [str(t['target_id']) for t in fql.query(q)]
# now get friendships amongst your friends. note that this api appears to return
# arbitrarily truncated results if you pass in more than a couple hundred friends
# into each part of the query, so we perform (num friends)/N queries and aggregate
# the results to try and get complete results
# Warning: this can result in a several API calls and a lot of data returned that
# you'll have to process
mutual_friendships = []
N = 50
for i in range(len(my_friends) / N + 1):
q = 'select uid1, uid2 from friend where uid1 in (%s) and uid2 in (%s)' \
% (','.join(my_friends), ','.join(my_friends[i * N:(i + 1) * N]))
mutual_friendships += fql.query(q)
for i in range(len(groups['data'])):
print '%s) %s' % (i, groups['data'][i]['name'])
choice = int(raw_input('Pick a group, any group: '))
gid = groups['data'][choice]['id']
# Find the friends in the group
fql = FQL(ACCESS_TOKEN)
q = \
"""select uid from group_member where gid = %s and uid in
(select target_id from connection where source_id = me() and target_type = 'user')
""" \
% (gid, )
uids = [u['uid'] for u in fql.query(q)]
# Filter the previously generated output for these ids
filtered_rgraph = [n for n in rgraph if n['id'] in uids]
# Trim down adjancency lists for anyone not appearing in the graph.
# Note that the full connection data displayed as HTML markup
# in "connections" is still preserved for the global graph.
for n in filtered_rgraph:
n['adjacencies'] = [a for a in n['adjacencies'] if a in uids]
if not os.path.isdir('out'):
os.mkdir('out')
for i in range(len(groups['data'])):
print '%s) %s' % (i, groups['data'][i]['name'])
choice = int(raw_input('Pick a group, any group: '))
gid = groups['data'][choice]['id']
# Find the friends in the group
fql = FQL(ACCESS_TOKEN)
q = \
"""select uid from group_member where gid = %s and uid in
(select target_id from connection where source_id = me() and target_type = 'user')
""" \
% (gid, )
uids = [u['uid'] for u in fql.query(q)]
# Filter the previously generated output for these ids
filtered_rgraph = [n for n in rgraph if n['id'] in uids]
# Trim down adjancency lists for anyone not appearing in the graph.
# Note that the full connection data displayed as HTML markup
# in "connections" is still preserved for the global graph.
for n in filtered_rgraph:
n['adjacencies'] = [a for a in n['adjacencies'] if a in uids]
if not os.path.isdir('out'):
os.mkdir('out')