本文整理汇总了Python中weibopy.api.API.counts方法的典型用法代码示例。如果您正苦于以下问题:Python API.counts方法的具体用法?Python API.counts怎么用?Python API.counts使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weibopy.api.API
的用法示例。
在下文中一共展示了API.counts方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Test
# 需要导入模块: from weibopy.api import API [as 别名]
# 或者: from weibopy.api.API import counts [as 别名]
class Test(unittest.TestCase):
consumer_key = ""
consumer_secret = ""
def __init__(self):
""" constructor """
def getAtt(self, key):
try:
return self.obj.__getattribute__(key)
except Exception as e:
print(e)
return ""
def setAccessToken(self, key, secret):
self.auth = OAuthHandler(self.consumer_key, self.consumer_secret)
self.auth.setAccessToken(key, secret)
self.api = API(self.auth, source=self.consumer_key)
def basicAuth(self, source, username, password):
self.authType = "basicauth"
self.auth = BasicAuthHandler(username, password)
self.api = API(self.auth, source=source)
def counts(self):
counts = self.api.counts(ids="1864372538,1484854960,1877120192")
for count in counts:
self.obj = count
mid = self.getAtt("id")
comments = self.getAtt("comments")
rt = self.getAtt("rt")
print("mentions---" + str(mid) + ":" + str(comments) + ":" + str(rt))
示例2: handle
# 需要导入模块: from weibopy.api import API [as 别名]
# 或者: from weibopy.api.API import counts [as 别名]
def handle(self, *args, **options):
print('Start classifying influence area\n')
YORO_CONSUMER_KEY = settings.SINA_CONSUMER_KEY
YORO_CONSUMER_SECRET = settings.SINA_CONSUMER_SECRET
auth = OAuthHandler(YORO_CONSUMER_KEY, YORO_CONSUMER_SECRET)
auth.setToken('128021658f2bfdae185d89bdffb3cede','1713185d5c8208e8f1ef27a1f484ebc9')
api = API(auth)
mid_list = []
rt_count = 0 #Count of status that retweet my status
ids_list = []
tried_count = 0
while True:
timeline = api.user_timeline(count=200,user_id=1275017594)
if len(timeline) == 0:
tried_count += 1
#print 'try again in getting timeline'
else:
break
if tried_count > 3:
raise Exception('weibo api error. No timeline got')
break
for line in timeline:
text = getAtt(line, 'text')
retweet = getAtt(line, 'retweet')
if retweet:
text += getAtt(retweet, 'text')
if is_fashion(text):
mid_list.append(str(getAtt(line, "id")))
if len(mid_list) == 20:
ids_list.append(','.join(mid_list))
mid_list = []
if mid_list: #append the remaining ids
ids_list.append(','.join(mid_list))
for ids in ids_list:
counts = api.counts(ids=ids)
for obj in counts:
rt_count += getAtt(obj, 'rt')
sample_size = (len(ids_list)-1)*20 + len(ids_list[-1]) if ids_list else 0
influence_count = long(rt_count * 50 * 3990 * 1.0/(sample_size+1)) #indirect influence
influence_count += 3899834 #direct influence
print influence_count
print('Finished classifying influence area\n')
示例3: compute
# 需要导入模块: from weibopy.api import API [as 别名]
# 或者: from weibopy.api.API import counts [as 别名]
def compute(str_inf_pk):
try:
inf = Influence.objects.get(pk=str_inf_pk)
auth = OAuthHandler(settings.SINA_CONSUMER_KEY, settings.SINA_CONSUMER_SECRET)
#auth.setToken(inf.sina_key, inf.sina_secret)
auth.setToken('128021658f2bfdae185d89bdffb3cede', '1713185d5c8208e8f1ef27a1f484ebc9') #Calculating for celebrity
api = API(auth)
followed_dict = {}
if inf.follower_count > 1000: #only for popular users that have too many people retweet his status
followed, cursors = api.friends(count=200, cursor=-1)
while True:
for f in followed:
followed_dict[getAtt(f, 'screen_name')] = 1
if cursors[1] == 0:
break
followed, cursors = api.friends(count=200, cursor=cursors[1])
"""TODO:This part seems to be unstable;Sometimes it doens work"""
#Calculate the most influenced friends through mentions api
mentions = api.mentions(count=200)
friends_dict = {}
friends_image_dict = {}
for m in mentions:
user = getAtt(m, 'user')
screen_name = getAtt(user, 'screen_name')
if screen_name in friends_dict:
friends_dict[screen_name] += 1
else:
if screen_name == inf.screen_name:
continue
friends_dict[screen_name] = 1
friends_image_dict[screen_name] = getAtt(user, 'profile_image_url')
friends = sorted(friends_dict.iteritems(), key=operator.itemgetter(1), reverse=True)
if followed_dict:
friends_list = []
backup_list = []
for f in friends:
if f[0] in followed_dict:
friends_list.append(f[0])
else:
backup_list.append(f[0])
if len(friends_list) == 4:
break
friends_list.extend(backup_list[:4-len(friends_list)])
else:
friends_list = [f[0] for f in friends[:4]]
influence_friends = ','.join(friends_list)
if influence_friends:
inf.influence_friends = influence_friends
friends_images = [friends_image_dict[f] for f in friends_list]
else:
friends_images = []
#Calculates the quality of followers; Also get average followers count of sampled followers
total = 0
active_count = 0
counted = 0
followers, cursors = api.followers(user_id=inf.sina_account, cursor=-1, count=200)
while True: #next_cursor is not zero means not the end
for f in followers:
counted += 1
f_follower_count = getAtt(f, 'followers_count')
#f_friends_count = getAtt(f, 'friends_count')
f_status_count = getAtt(f, 'statuses_count')
if ((f_follower_count > 50 or f_status_count > 50) and f_follower_count + f_status_count > 75
or f_follower_count + f_status_count > 150):
active_count += 1
total += f_follower_count
break #200 samples should be enough
#if cursors[1] == 0:
# break
#followers, cursors = api.followers(user_id=inf.sina_account, cursor=cursors[1], count=200)
avg_follower_of_followers = total*1.0/counted if counted !=0 else 0
active_follower_ratio = active_count * 1.0 /counted if counted !=0 else 0
inf.active_follower_count = int(math.ceil(active_follower_ratio*inf.follower_count))
#Calculates the average rt_count of each tweet based on 200 samples
mid_list = []
rt_count = 0 #Count of status that retweet my status
ids_list = []
timeline = api.user_timeline(user_id=inf.sina_account, count=200)
for line in timeline:
mid_list.append(str(getAtt(line, "id")))
if len(mid_list) == 20:
ids_list.append(','.join(mid_list))
mid_list = []
if mid_list: #append the remaining ids
ids_list.append(','.join(mid_list))
if inf.status_count > 0 and not ids_list:
raise Exception('weibo api fails')
for ids in ids_list:
counts = api.counts(ids=ids)
for obj in counts:
rt_count += getAtt(obj, 'rt')
sample_size = (len(ids_list)-1)*20 + len(ids_list[-1]) if ids_list else 0
average_rt_count = rt_count*1.0/sample_size if sample_size != 0 else 0
inf.average_rt_count = average_rt_count
#.........这里部分代码省略.........
示例4: classify_influence
# 需要导入模块: from weibopy.api import API [as 别名]
# 或者: from weibopy.api.API import counts [as 别名]
#.........这里部分代码省略.........
ids_list = []
tweet_list = [] #Store the text of tweet and retweet
rt_count_list = []
tried_count = 0
while True:
timeline = api.user_timeline(user_id=inf.sina_account, count=200)
if len(timeline) == 0 and inf.status_count >0:
tried_count += 1
print 'try again in getting timeline'
else:
break
if tried_count > 3:
raise Exception('weibo api error. No timeline got')
break
for line in timeline:
text = getAtt(line, 'text')
retweet = getAtt(line, 'retweeted_status')
retweet_text = getAtt(retweet, 'text')
if retweet_text:
text += retweet_text
tweet_list.append(text)
mid_list.append(str(getAtt(line, "id")))
if len(mid_list) == 20:
ids_list.append(','.join(mid_list))
mid_list = []
if mid_list: #append the remaining ids
ids_list.append(','.join(mid_list))
if inf.status_count > 0 and not ids_list:
raise Exception('weibo api fails')
tweet_list_correct = []
correct_index = 20
for ids in ids_list:
counts = api.counts(ids=ids)
if len(counts) == 0:
print 'error in counts!'
correct_index += 20
continue
for obj in counts:
rt_count_list.append(getAtt(obj, 'rt'))
tweet_list_correct.extend(tweet_list[correct_index-20:correct_index])
correct_index += 20
if len(tweet_list_correct) == 0 or len(tweet_list_correct) != len(rt_count_list):
raise Exception('weibo api fails')
print 'length of tweet list and rt_count list', len(tweet_list_correct), len(rt_count_list)
#Remedy for those user who has posted less than 200 status
amplify_ratio = 1.0 if len(tweet_list_correct) == 200 else 200.0/len(tweet_list_correct)
for i in range(len(tweet_list_correct)):
print i
#This number 100 should be replaced by avg_follower_count
#Use math.sqrt to boost those tweet that has not been retweeted,
#and smooth the effect of famous people tweeting about things not related to them
added_count = (rt_count_list[i]*100 + math.sqrt(inf.follower_count)) * amplify_ratio
assigned_area = {}
try: #In Unix environment
from sinal import signal, SIGALRM, alarm #@UnresolvedImport
def handler(signum, frame):
#print 'Signal handler called with signal', signum
raise Exception("This code block runs for too long time!")
signal(SIGALRM, handler)
alarm(3)
mmseg_text = mmseg.Algorithm(tweet_list_correct[i].encode('utf-8'))
alarm(0) #cancel the alarm after finised
except ImportError: # In windows, SIGALRM, alarm is not available in signal module
mmseg_text = mmseg.Algorithm(tweet_list_correct[i].encode('utf-8'))
except: #mmseg halts for too long, process next tweet