def mloc_users(users_and_coords):
"""
pick users with good home locations from geotweets
"""
users, locs = _untangle_users_and_coords(users_and_coords)
selected = []
for uid,user in users.iteritems():
spots = locs[uid]
if len(spots)<=2: continue
if user['followers_count']==0 and user['friends_count']==0: continue
median = utils.median_2d(spots)
dists = [utils.coord_in_miles(median,spot) for spot in spots]
if numpy.median(dists)>50:
continue #user moves too much
user['mloc'] = median
selected.append(user)
random.shuffle(selected)
return selected
def mloc_reject_count(users_and_coords):
"""
count the number of users we ignored in mloc_users. (This job was done to
calculate a number for the paper, and is almost trash.)
"""
results = collections.defaultdict(int)
users, locs = _untangle_users_and_coords(users_and_coords)
for uid,user in users.iteritems():
spots = locs[uid]
if len(spots)<=2:
results['spots']+=1
continue
median = utils.median_2d(spots)
dists = [utils.coord_in_miles(median,spot) for spot in spots]
if numpy.median(dists)>50:
results['moves']+=1
elif user['followers_count']==0 and user['friends_count']==0:
results['counts']+=1
else:
results['good']+=1
return results.iteritems()
