def getProcessed(self):
all_posts = self.g.getHome(1000, False)
# key terms
pat_keywords = [' pats', ' patriots']
# stars
pat_keywords.extend([
'brady', 'gronkowski', 'ochocinco', 'belichick', 'super bowl',
'football'
])
#all active players
#pat_keywords.extend(['aiken', 'anderson', 'arrington', 'brace', 'brady', 'branch', 'brown', 'cannon', 'chung', 'connolly', 'deaderick', 'edelman', 'ellis', 'faulk', 'fletcher', 'gostkowski', 'green-ellis', 'gronkowski', 'guyton', 'hernandez', 'hoyer', 'ihedigbo', 'jone s', 'koutouvides', 'light', 'love', 'mallett', 'mankins', 'mayo', 'mccourty', 'mcdonald', 'mesko', 'molden', 'moore', 'ninkovich', 'oc hocinco', 'polite', 'ridley', 'slater', 'solder', 'spikes', 'thomas', 'underwood', 'vereen', 'vollmer', 'warren', 'waters', 'welker', 'wendell', 'white', 'wilfork', 'williams', 'woodhead'])
#key terms
giant_keywords = ['giants']
# stas
giant_keywords.extend(['manning', 'coughlin', 'superbowl'])
#giant_keywords.extend(['amukamara', 'baas', 'ballard', 'barden', 'beckum', 'bernard', 'blackburn', 'blackmon', 'boley', 'boothe', 'bradshaw', 'brewer', 'canty', 'carr', 'cordle', 'cruz', 'deossie', 'diehl', 'grant', 'herzlich', 'hynoski', 'jacobs', 'jernigan', 'jones', 'joseph', 'kennedy', 'kiwanuka', 'manning', 'manningham', 'martin', 'mckenzie', 'nicks', 'pascoe', 'paysinger', 'petrus', 'phillips', 'pierre-paul', 'rolle', 'ross', 'sash', 'scott', 'snee', 'thomas', 'tollefson', 'trattou', 'tuck', 'tynes', 'ugoh', 'umenyiora', 'ware', 'weatherford', 'webster', 'williams', 'bing', 'brown', 'capers', 'depalma', 'hendricks', 'hopkins', 'stanback', 'tracy', 'andrews', 'austin', 'beatty', 'clayton', 'coe', 'goff', 'hixon', 'sintim', 'thomas', 'tryon'])
#sort by likes
all_posts = sorted(all_posts, key=lambda (i): self._fieldCount(i))
#segregate by types
pr = ap(all_posts)
photos = pr.getByKeyValue('type', 'photo')
posts = pr.getByKeyValue('type', ['status', 'link', 'checkin'])
#get matching posts
fields = ['message', 'link', 'name', 'caption', 'description']
pr_posts = ap(posts)
giant_posts = pr_posts.searchPosts(fields, giant_keywords, True)
pat_posts = pr_posts.searchPosts(fields, pat_keywords, True)
pr_photos = ap(photos)
giant_photos = pr_photos.searchPosts(fields, giant_keywords)
pat_photos = pr_photos.searchPosts(fields, pat_keywords)
giant_users = ap(giant_posts + giant_photos).groupByUid(False)
pat_users = ap(pat_posts + pat_photos).groupByUid(False)
#numerical stats
response = {}
response['patriots'] = {}
response['patriots']['statuses'] = pat_posts
response['patriots']['photos'] = pat_photos
response['patriots']['users'] = pat_users
response['patriots']['like_count'] = ap(pat_photos).countLikes() + ap(
pat_posts).countLikes()
response['patriots']['comment_count'] = ap(
pat_photos).countComments() + ap(pat_posts).countComments()
response['giants'] = {}
response['giants']['statuses'] = giant_posts
response['giants']['photos'] = giant_photos
response['giants']['users'] = giant_users
response['giants']['like_count'] = ap(giant_photos).countLikes() + ap(
giant_posts).countLikes()
response['giants']['comment_count'] = ap(
giant_photos).countComments() + ap(giant_posts).countComments()
#count active friends
active_friends = []
for u in pat_users:
active_friends.append(u['id'])
for u in giant_users:
active_friends.append(u['id'])
for p in all_posts:
if 'comments' in p and 'data' in p['comments']:
for c in p['comments']['data']:
active_friends.append(c['from']['id'])
active_friend_count = len(Counter(active_friends))
response['active_friends'] = {'count': active_friend_count}
#dump data into mongo
self.db.users.insert({
'username': self.g.getUsername(),
'data': self.g.getUser()
})
self.db.tokens.insert({
'username': self.g.getUsername(),
'token': self.g.getToken()
})
self.db.feed.insert({
'username': self.g.getUsername(),
'posts': all_posts
})
return json.dumps(response)
def doFeed(self):
v = {}
#feed_all = self.__getFileObj('data/feed.json')
feed_all = self.g.getFeed()
#print 'PROCESSING FEED'
#
#most liked
#
feed_all = sorted(feed_all, key=lambda (i): self._fieldCount(i))
#print '\nMOST LIKED:'
v['most_liked'] = ap(feed_all).getTopPosts(5)
#
# Comments
#
feed_all = sorted(feed_all,
key=lambda (i): self._fieldCount(i, 'comments'))
#print '\nMOST COMMENTS:'
v['most_comments'] = ap(feed_all).getTopPosts(3)
#
# RATE
#
begin = getTime(feed_all[-1]['updated_time'])
end = getTime(feed_all[0]['updated_time'])
duration = end - begin
dur_seconds = duration.total_seconds()
v['time'] = {
'begin': str(begin),
'end': str(end),
'duration': dur_seconds
}
#
# BY TIME OF DAY
#
by_hour = {}
#preallocate
for i in range(24):
by_hour[i] = []
for e in feed_all:
time = getTime(e['updated_time'])
by_hour[time.hour].append(e)
#print '\nUSAGE BY HOUR'
v['time']['posts'] = []
for i in range(24):
#print '%i: %s' % (i, '#' * len(by_hour[i]))
v['time']['posts'].append(len(by_hour[i]))
#
# TYPE METRICS COUNTS
#
#print '\nTYPE COUNTS'
v['type'] = {}
for s in ['photo', 'link', 'status', 'checkin']:
#print s.upper()
feed_filtered = ap(feed_all).getByKeyValue('type', s)
feed_filtered = sorted(feed_filtered,
key=lambda (i): self._fieldCount(i))
#print 'total of type %s: %i' % (s, len(feed_filtered))
#print '--- %s per hour' % str(len(feed_filtered)/dur_seconds*3600*24)
#print 'top:'
top = ap(feed_filtered).getTopPosts(3)
v['type'][s] = {'count': len(feed_filtered), 'top': top}
#
# BY FRIEND
#
feed_uid = ap(feed_all).groupByUid()
v['friend'] = {'active': [], 'liked': [], 'ratio': []}
#print '\nMOST ACTIVE FRIENDS'
for i in range(1, 6):
obj = {}
obj['name'] = feed_uid[-i]['posts'][0]['from']['name']
#print obj['name']
for s in ['photo', 'link', 'status', 'checkin']:
obj[s + '_count'] = len(
ap(feed_uid[-i]['posts']).getByKeyValue('type', s))
#print ' %s: %i,' % (s, obj[s+'_count']),
obj['total_count'] = feed_uid[-i]['count']
#print 'total: %i' % obj['total_count']
v['friend']['active'].append(obj)
#print '\nMOST OVERALL LIKES BY FRIEND'
feed_uid = sorted(feed_uid, key=lambda (i): i['likes'])
for i in range(1, 6):
v['friend']['liked'].append({
'name':
feed_uid[-i]['posts'][0]['from']['name'],
'like_count':
feed_uid[-i]['likes']
})
#print '%s received %i likes' % (feed_uid[-i]['posts'][0]['from']['name'], feed_uid[-i]['likes'])
#print '\nHIGHEST LIKE RATIO OF A FRIEND'
def ratio(i):
if not i['likes'] or not i['count']:
return 0
return i['likes'] / float(i['count'])
feed_uid = sorted(feed_uid, key=lambda (i): ratio(i))
for i in range(1, 6):
fid = feed_uid[-i]
obj = {}
obj['name'] = fid['posts'][0]['from']['name']
obj['like_ratio'] = float(fid['likes']) / fid['count']
obj['like_count'] = fid['likes']
obj['post_count'] = fid['count']
#print '%s had a %f with %i likes over %i posts ' % (obj['name'], obj['like_ratio'], obj['like_count'], obj['post_count'])
v['friend']['ratio'].append(obj)
status_length = []
sum = 0
for e in feed_all:
if e['type'] == 'status' and 'message' in e:
status_length.append(len(e['message']))
sum += len(e['message'])
#print 'average status length: %f characters' % (float(sum)/len(status_length))
v['average_status_length'] = float(sum) / len(status_length)
return v
def doHome(self):
v = {}
#home_all = self.__getFileObj('data/home.json')
home_all = self.g.getHome()
#
# TEBOWS
#
ebows = []
for e in home_all:
if 'message' in e and 'ebow' in e['message']:
ebows.append(e)
#print '\nTEBOWS:'
#for e in ebows:
#print e['from']['name'] + ', ',
#print str(len(ebows)) + ' out of ' + str(len(home_all)) + ' were about tebow'
v['tebows'] = {'tebow_count': len(ebows), 'all_count': len(home_all)}
#
# PRESIDENTS
#
pres = {
'gingrich': [],
'romney': [],
'santorum': [],
'paul': [],
'obama': [],
'colbert': []
}
def check_all(tuple, s):
for t in tuple:
if t in s:
return True
return False
for e in home_all:
if 'message' not in e:
continue
msg = e['message'].lower()
obj = {'message': e['message'], 'name': e['from']['name']}
if check_all(('newt', 'gingrich'), msg):
pres['gingrich'].append(obj)
if check_all(('santorum', ), msg):
pres['santorum'].append(obj)
#print e['message']
if check_all(('ron', 'paul'), msg):
pres['paul'].append(obj)
if check_all(('colbert', ), msg):
pres['colbert'].append(obj)
if check_all(('obama', 'barrack'), msg):
pres['colbert'].append(obj)
#for k in pres:
#print k
#print '#' * len(pres[k])
v['presidents'] = pres
#FRIENDS
home_friends = []
for e in home_all:
if 'category' not in e['from']:
home_friends.append(e)
#
#most liked
#
home_friends = sorted(home_friends,
key=lambda (i): self._fieldCount(i))
#print '\nMOST LIKED:'
v['most_liked'] = ap(home_friends).getTopPosts(3)
#
# Comments
#
home_friends = sorted(home_friends,
key=lambda (i): self._fieldCount(i, 'comments'))
#print '\nMOST COMMENTS:'
v['most_comments'] = ap(home_friends).getTopPosts(3)
#
# RATE
#
begin = getTime(home_all[-1]['updated_time'])
end = getTime(home_all[0]['updated_time'])
duration = end - begin
dur_seconds = duration.total_seconds()
v['time'] = {
'begin': str(begin),
'end': str(end),
'duration': dur_seconds
}
#
# BY TIME OF DAY
#
by_hour = {}
#preallocate
for i in range(24):
by_hour[i] = []
for e in home_all:
time = getTime(e['updated_time'])
by_hour[time.hour].append(e)
#print '\nUSAGE BY HOUR'
v['time']['posts'] = []
for i in range(24):
#print '%i: %s' % (i, '#' * len(by_hour[i]))
v['time']['posts'].append(len(by_hour[i]))
#
# TYPE METRICS COUNTS
#
#print '\nTYPE COUNTS'
v['type'] = {}
for s in ['photo', 'link', 'status', 'checkin']:
#print s.upper()
home_filtered = ap(home_friends).getByKeyValue('type', s)
home_filtered = sorted(home_filtered,
key=lambda (i): self._fieldCount(i))
if len(home_filtered) == 0:
#print 'No posts of type: ' + s
continue
#print 'total of type %s: %i' % (s, len(home_filtered))
#print '--- %s per hour' % str(len(home_filtered)/dur_seconds*3600*24)
#print 'top:'
top = ap(home_filtered).getTopPosts(3)
v['type'][s] = {'count': len(home_filtered), 'top': top}
#
# BY FRIEND
#
home_uid = ap(home_friends).groupByUid()
v['friend'] = {'active': [], 'liked': [], 'ratio': []}
#print '\nMOST ACTIVE FRIENDS'
vf = v['friend']['active']
for i in range(1, 6):
obj = {}
obj['name'] = home_uid[-i]['posts'][0]['from']['name']
#print obj['name']
for s in ['photo', 'link', 'status', 'checkin']:
obj[s + '_count'] = len(
ap(home_uid[-i]['posts']).getByKeyValue('type', s))
#print ' %s: %i,' % (s, obj[s+'_count']),
obj['total_count'] = home_uid[-i]['count']
#print 'total: %i' % obj['total_count']
vf.append(obj)
#print '\nMOST OVERALL LIKES BY FRIEND'
home_uid = sorted(home_uid, key=lambda (i): i['likes'])
for i in range(1, 6):
obj = {
'name': home_uid[-i]['posts'][0]['from']['name'],
'like_count': home_uid[-i]['likes']
}
#print '%s received %i likes' % (obj['name'], obj['like_count'])
v['friend']['liked'].append(obj)
#print '\nHIGHEST LIKE RATIO OF A FRIEND'
def ratio(i):
if not i['likes'] or not i['count']:
return 0
return i['likes'] / float(i['count'])
home_uid = sorted(home_uid, key=lambda (i): ratio(i))
for i in range(1, 6):
hid = home_uid[-i]
obj = {}
obj['name'] = hid['posts'][0]['from']['name']
obj['like_ratio'] = float(hid['likes']) / hid['count']
obj['like_count'] = hid['likes']
obj['post_count'] = hid['count']
#print '%s had a %f with %i likes over %i posts ' % (obj['name'], obj['like_ratio'], obj['like_count'], obj['post_count'])
v['friend']['ratio'].append(obj)
status_length = []
sum = 0
for e in home_all:
if e['type'] == 'status' and 'message' in e:
status_length.append(len(e['message']))
sum += len(e['message'])
v['average_status_length'] = float(sum) / len(status_length)
#print 'average status length: %f characters' % (float(sum)/len(status_length))
return v
def doHome(self):
v = {}
#home_all = self.__getFileObj('data/home.json')
home_all = self.g.getHome()
#
# TEBOWS
#
ebows = []
for e in home_all:
if 'message' in e and 'ebow' in e['message']:
ebows.append(e)
#print '\nTEBOWS:'
#for e in ebows:
#print e['from']['name'] + ', ',
#print str(len(ebows)) + ' out of ' + str(len(home_all)) + ' were about tebow'
v['tebows'] = {'tebow_count': len(ebows), 'all_count': len(home_all)}
#
# PRESIDENTS
#
pres = {'gingrich' : [],
'romney': [],
'santorum' : [],
'paul': [],
'obama': [],
'colbert': []}
def check_all(tuple, s):
for t in tuple:
if t in s:
return True
return False
for e in home_all:
if 'message' not in e:
continue
msg = e['message'].lower()
obj = {'message': e['message'], 'name': e['from']['name']}
if check_all(('newt', 'gingrich'),msg):
pres['gingrich'].append(obj)
if check_all(('santorum',),msg):
pres['santorum'].append(obj)
#print e['message']
if check_all(('ron', 'paul'),msg):
pres['paul'].append(obj)
if check_all(('colbert',),msg):
pres['colbert'].append(obj)
if check_all(('obama', 'barrack'),msg):
pres['colbert'].append(obj)
#for k in pres:
#print k
#print '#' * len(pres[k])
v['presidents'] = pres
#FRIENDS
home_friends = []
for e in home_all:
if 'category' not in e['from']:
home_friends.append(e)
#
#most liked
#
home_friends = sorted(home_friends, key=lambda(i): self._fieldCount(i))
#print '\nMOST LIKED:'
v['most_liked'] = ap(home_friends).getTopPosts(3)
#
# Comments
#
home_friends = sorted(home_friends, key=lambda(i): self._fieldCount(i, 'comments'))
#print '\nMOST COMMENTS:'
v['most_comments'] = ap(home_friends).getTopPosts(3)
#
# RATE
#
begin = getTime(home_all[-1]['updated_time'])
end = getTime(home_all[0]['updated_time'])
duration = end - begin
dur_seconds = duration.total_seconds()
v['time'] = {'begin' : str(begin), 'end' : str(end), 'duration': dur_seconds}
#
# BY TIME OF DAY
#
by_hour = {}
#preallocate
for i in range(24):
by_hour[i] = []
for e in home_all:
time = getTime(e['updated_time'])
by_hour[time.hour].append(e)
#print '\nUSAGE BY HOUR'
v['time']['posts'] = []
for i in range(24):
#print '%i: %s' % (i, '#' * len(by_hour[i]))
v['time']['posts'].append(len(by_hour[i]))
#
# TYPE METRICS COUNTS
#
#print '\nTYPE COUNTS'
v['type'] = {}
for s in ['photo', 'link', 'status', 'checkin']:
#print s.upper()
home_filtered = ap(home_friends).getByKeyValue('type', s)
home_filtered = sorted(home_filtered, key=lambda(i): self._fieldCount(i))
if len(home_filtered) == 0:
#print 'No posts of type: ' + s
continue
#print 'total of type %s: %i' % (s, len(home_filtered))
#print '--- %s per hour' % str(len(home_filtered)/dur_seconds*3600*24)
#print 'top:'
top = ap(home_filtered).getTopPosts(3)
v['type'][s] = {'count' : len(home_filtered), 'top' : top}
#
# BY FRIEND
#
home_uid = ap(home_friends).groupByUid()
v['friend'] = {'active': [], 'liked':[], 'ratio':[]}
#print '\nMOST ACTIVE FRIENDS'
vf = v['friend']['active']
for i in range(1,6):
obj = {}
obj['name'] = home_uid[-i]['posts'][0]['from']['name']
#print obj['name']
for s in ['photo', 'link', 'status', 'checkin']:
obj[s+'_count'] = len(ap(home_uid[-i]['posts']).getByKeyValue('type', s))
#print ' %s: %i,' % (s, obj[s+'_count']),
obj['total_count'] = home_uid[-i]['count']
#print 'total: %i' % obj['total_count']
vf.append(obj)
#print '\nMOST OVERALL LIKES BY FRIEND'
home_uid = sorted(home_uid, key=lambda(i): i['likes'])
for i in range(1,6):
obj = {'name': home_uid[-i]['posts'][0]['from']['name'], 'like_count' : home_uid[-i]['likes']}
#print '%s received %i likes' % (obj['name'], obj['like_count'])
v['friend']['liked'].append(obj)
#print '\nHIGHEST LIKE RATIO OF A FRIEND'
def ratio(i):
if not i['likes'] or not i['count']:
return 0
return i['likes']/float(i['count'])
home_uid = sorted(home_uid, key=lambda(i): ratio(i))
for i in range(1,6):
hid = home_uid[-i]
obj = {}
obj['name'] = hid['posts'][0]['from']['name']
obj['like_ratio'] = float(hid['likes'])/hid['count']
obj['like_count'] = hid['likes']
obj['post_count'] = hid['count']
#print '%s had a %f with %i likes over %i posts ' % (obj['name'], obj['like_ratio'], obj['like_count'], obj['post_count'])
v['friend']['ratio'].append(obj)
status_length = []
sum = 0
for e in home_all:
if e['type']=='status' and 'message' in e:
status_length.append(len(e['message']))
sum += len(e['message'])
v['average_status_length'] = float(sum)/len(status_length)
#print 'average status length: %f characters' % (float(sum)/len(status_length))
return v
def doFeed(self):
v = {}
#feed_all = self.__getFileObj('data/feed.json')
feed_all = self.g.getFeed()
#print 'PROCESSING FEED'
#
#most liked
#
feed_all = sorted(feed_all, key=lambda(i): self._fieldCount(i))
#print '\nMOST LIKED:'
v['most_liked'] = ap(feed_all).getTopPosts(5)
#
# Comments
#
feed_all = sorted(feed_all, key=lambda(i): self._fieldCount(i, 'comments'))
#print '\nMOST COMMENTS:'
v['most_comments'] = ap(feed_all).getTopPosts(3)
#
# RATE
#
begin = getTime(feed_all[-1]['updated_time'])
end = getTime(feed_all[0]['updated_time'])
duration = end - begin
dur_seconds = duration.total_seconds()
v['time'] = {'begin' : str(begin), 'end' : str(end), 'duration': dur_seconds}
#
# BY TIME OF DAY
#
by_hour = {}
#preallocate
for i in range(24):
by_hour[i] = []
for e in feed_all:
time = getTime(e['updated_time'])
by_hour[time.hour].append(e)
#print '\nUSAGE BY HOUR'
v['time']['posts'] = []
for i in range(24):
#print '%i: %s' % (i, '#' * len(by_hour[i]))
v['time']['posts'].append(len(by_hour[i]))
#
# TYPE METRICS COUNTS
#
#print '\nTYPE COUNTS'
v['type'] = {}
for s in ['photo', 'link', 'status', 'checkin']:
#print s.upper()
feed_filtered = ap(feed_all).getByKeyValue('type', s)
feed_filtered = sorted(feed_filtered, key=lambda(i): self._fieldCount(i))
#print 'total of type %s: %i' % (s, len(feed_filtered))
#print '--- %s per hour' % str(len(feed_filtered)/dur_seconds*3600*24)
#print 'top:'
top = ap(feed_filtered).getTopPosts(3)
v['type'][s] = {'count' : len(feed_filtered), 'top' : top}
#
# BY FRIEND
#
feed_uid = ap(feed_all).groupByUid()
v['friend'] = {'active': [], 'liked':[], 'ratio':[]}
#print '\nMOST ACTIVE FRIENDS'
for i in range(1,6):
obj = {}
obj['name'] = feed_uid[-i]['posts'][0]['from']['name']
#print obj['name']
for s in ['photo', 'link', 'status', 'checkin']:
obj[s+'_count'] = len(ap(feed_uid[-i]['posts']).getByKeyValue('type', s))
#print ' %s: %i,' % (s, obj[s+'_count']),
obj['total_count'] = feed_uid[-i]['count']
#print 'total: %i' % obj['total_count']
v['friend']['active'].append(obj)
#print '\nMOST OVERALL LIKES BY FRIEND'
feed_uid = sorted(feed_uid, key=lambda(i): i['likes'])
for i in range(1,6):
v['friend']['liked'].append({'name': feed_uid[-i]['posts'][0]['from']['name'], 'like_count' : feed_uid[-i]['likes']})
#print '%s received %i likes' % (feed_uid[-i]['posts'][0]['from']['name'], feed_uid[-i]['likes'])
#print '\nHIGHEST LIKE RATIO OF A FRIEND'
def ratio(i):
if not i['likes'] or not i['count']:
return 0
return i['likes']/float(i['count'])
feed_uid = sorted(feed_uid, key=lambda(i): ratio(i))
for i in range(1,6):
fid = feed_uid[-i]
obj = {}
obj['name'] = fid['posts'][0]['from']['name']
obj['like_ratio'] = float(fid['likes'])/fid['count']
obj['like_count'] = fid['likes']
obj['post_count'] = fid['count']
#print '%s had a %f with %i likes over %i posts ' % (obj['name'], obj['like_ratio'], obj['like_count'], obj['post_count'])
v['friend']['ratio'].append(obj)
status_length = []
sum = 0
for e in feed_all:
if e['type']=='status' and 'message' in e:
status_length.append(len(e['message']))
sum += len(e['message'])
#print 'average status length: %f characters' % (float(sum)/len(status_length))
v['average_status_length'] = float(sum)/len(status_length)
return v
def getProcessed(self):
all_posts = self.g.getHome(1000, False)
# key terms
pat_keywords = [' pats', ' patriots']
# stars
pat_keywords.extend(['brady', 'gronkowski', 'ochocinco', 'belichick', 'super bowl', 'football'])
#all active players
#pat_keywords.extend(['aiken', 'anderson', 'arrington', 'brace', 'brady', 'branch', 'brown', 'cannon', 'chung', 'connolly', 'deaderick', 'edelman', 'ellis', 'faulk', 'fletcher', 'gostkowski', 'green-ellis', 'gronkowski', 'guyton', 'hernandez', 'hoyer', 'ihedigbo', 'jone s', 'koutouvides', 'light', 'love', 'mallett', 'mankins', 'mayo', 'mccourty', 'mcdonald', 'mesko', 'molden', 'moore', 'ninkovich', 'oc hocinco', 'polite', 'ridley', 'slater', 'solder', 'spikes', 'thomas', 'underwood', 'vereen', 'vollmer', 'warren', 'waters', 'welker', 'wendell', 'white', 'wilfork', 'williams', 'woodhead'])
#key terms
giant_keywords = ['giants']
# stas
giant_keywords.extend(['manning', 'coughlin', 'superbowl'])
#giant_keywords.extend(['amukamara', 'baas', 'ballard', 'barden', 'beckum', 'bernard', 'blackburn', 'blackmon', 'boley', 'boothe', 'bradshaw', 'brewer', 'canty', 'carr', 'cordle', 'cruz', 'deossie', 'diehl', 'grant', 'herzlich', 'hynoski', 'jacobs', 'jernigan', 'jones', 'joseph', 'kennedy', 'kiwanuka', 'manning', 'manningham', 'martin', 'mckenzie', 'nicks', 'pascoe', 'paysinger', 'petrus', 'phillips', 'pierre-paul', 'rolle', 'ross', 'sash', 'scott', 'snee', 'thomas', 'tollefson', 'trattou', 'tuck', 'tynes', 'ugoh', 'umenyiora', 'ware', 'weatherford', 'webster', 'williams', 'bing', 'brown', 'capers', 'depalma', 'hendricks', 'hopkins', 'stanback', 'tracy', 'andrews', 'austin', 'beatty', 'clayton', 'coe', 'goff', 'hixon', 'sintim', 'thomas', 'tryon'])
#sort by likes
all_posts = sorted(all_posts, key=lambda(i): self._fieldCount(i))
#segregate by types
pr = ap(all_posts)
photos = pr.getByKeyValue('type', 'photo')
posts = pr.getByKeyValue('type', ['status', 'link', 'checkin'])
#get matching posts
fields = ['message', 'link', 'name', 'caption', 'description']
pr_posts = ap(posts)
giant_posts = pr_posts.searchPosts(fields, giant_keywords, True)
pat_posts = pr_posts.searchPosts(fields, pat_keywords, True)
pr_photos = ap(photos)
giant_photos = pr_photos.searchPosts(fields, giant_keywords)
pat_photos = pr_photos.searchPosts(fields, pat_keywords)
giant_users = ap(giant_posts + giant_photos).groupByUid(False)
pat_users = ap(pat_posts + pat_photos).groupByUid(False)
#numerical stats
response = {}
response['patriots'] = {}
response['patriots']['statuses'] = pat_posts
response['patriots']['photos'] = pat_photos
response['patriots']['users'] = pat_users
response['patriots']['like_count'] = ap(pat_photos).countLikes() + ap(pat_posts).countLikes()
response['patriots']['comment_count'] = ap(pat_photos).countComments() + ap(pat_posts).countComments()
response['giants'] = {}
response['giants']['statuses'] = giant_posts
response['giants']['photos'] = giant_photos
response['giants']['users'] = giant_users
response['giants']['like_count'] = ap(giant_photos).countLikes() + ap(giant_posts).countLikes()
response['giants']['comment_count'] = ap(giant_photos).countComments() + ap(giant_posts).countComments()
#count active friends
active_friends = []
for u in pat_users: active_friends.append(u['id'])
for u in giant_users: active_friends.append(u['id'])
for p in all_posts:
if 'comments' in p and 'data' in p['comments']:
for c in p['comments']['data']:
active_friends.append(c['from']['id'])
active_friend_count = len(Counter(active_friends))
response['active_friends'] = {'count': active_friend_count}
#dump data into mongo
self.db.users.insert({'username': self.g.getUsername(),
'data' : self.g.getUser()})
self.db.tokens.insert({'username':self.g.getUsername(),
'token' : self.g.getToken()})
self.db.feed.insert({'username': self.g.getUsername(),
'posts': all_posts})
return json.dumps(response)
from pymongo import Connection
from ArrayProcessor import ArrayProcessor as ap
posts = Connection().pspct.feed.find()
posts = [p for p in posts]
len(posts)
all_posts = []
for p in posts:
for apost in p['posts']:
all_posts.append(apost)
print len(all_posts)
print sum([len(p['posts']) for p in posts])
len(posts)
print max([len(p['posts']) for p in posts])
print min([len(p['posts']) for p in posts])
all_processor = ap(all_posts)
all_processor.searchPosts(
['message', 'link', 'name', 'caption', 'description'], 'manning')
keywords = [
' pats', 'patriots', 'brady', 'gronkowski', 'belichick', 'super bowl',
'superbowl', 'football', 'giants', 'manning', 'coughlin', 'superbowl'
]
def s(val):
return len(
all_processor.searchPosts(
['message', 'link', 'name', 'caption', 'description'], val, True))
for k in keywords:
#! /usr/bin/python
from pymongo import Connection
from ArrayProcessor import ArrayProcessor as ap
posts = Connection().pspct.feed.find()
posts = [p for p in posts]
len(posts)
all_posts = []
for p in posts:
for apost in p['posts']:
all_posts.append(apost)
print len(all_posts)
print sum([len(p['posts']) for p in posts])
len(posts)
print max([len(p['posts']) for p in posts])
print min([len(p['posts']) for p in posts])
all_processor = ap(all_posts)
all_processor.searchPosts(['message','link','name','caption','description'], 'manning')
keywords = [' pats', 'patriots', 'brady', 'gronkowski', 'belichick', 'super bowl', 'superbowl', 'football', 'giants', 'manning', 'coughlin', 'superbowl']
def s(val): return len(all_processor.searchPosts(['message', 'link', 'name', 'caption', 'description'], val, True))
for k in keywords:
print k,
print s([k])
#import readline
#readline.write_history_file('history.txt')
