def whole_followers_task(top_n, date, window_size):
user_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
count, get_results = user_search.search(query={'followers_count': {'$gt': FOLLOWERS_MIN_SUPPORT}}, sort_by=['-followers_count'], fields=['_id'], max_offset=top_n)
sorted_uids = []
for user in get_results():
sorted_uids.append(user['_id'])
return sorted_uids
def load_friendship_to_leveldb():
from xapian_weibo.xapian_backend import XapianSearch
s_user = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
query_dict = {
'_id': {
'$gt': 0,
}
}
count, get_results = s_user.search(query=query_dict, fields=['_id', 'friends', 'followers'])
print count
count = 0
ts = te = time.time()
for r in get_results():
uid = r['_id']
friends = r['friends']
followers = r['followers']
if friends and len(friends):
k = str(uid) + '_' + 'friends'
v = json.dumps(friends)
friendship_bucket.Put(k, str(v))
if followers and len(followers):
k = str(uid) + '_' + 'followers'
v = json.dumps(followers)
friendship_bucket.Put(k, str(v))
count += 1
if count % 10000 == 0:
te = time.time()
print count, '%s sec' % (te - ts)
ts = te
def get_user(uid):
user = {}
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user')
count,get_results = s.search(query={'_id': uid})
for r in get_results():
user['id'] = r['_id']
user['province'] = r['province']
user['bi_followers_count'] = r['bi_followers_count']
user['verified'] = r['verified']
user['description'] = r['description'].decode("utf-8")
user['friends_count'] = r['friends_count']
user['city'] = r['city']
user['gender'] = r['gender']
user['profile_image_url'] = r['profile_image_url']
user['verified_reason'] = r['verified_reason'].decode("utf-8")
user['followers_count'] = r['followers_count']
user['location'] = r['location'].decode("utf-8")
user['active'] = r['active']
user['statuses_count'] = r['statuses_count']
if r['name']:
user['name'] = r['name'].decode("utf-8")
else:
user['name'] = u'未知用户'
user['userField'] = u'未知领域'
break
if user == {}:
return None
else:
return user
def make_network(topic, date, window_size, max_size=100000, ts=False):
end_time = datetime2ts(date)
start_time = end_time - window2time(window_size)
g = nx.DiGraph()
#need repost index
topic = cut(s, topic.encode('utf-8'))
statuses_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)
query_dict = {'text': topic, 'timestamp': {'$gt': start_time, '$lt': end_time}}
if ts:
count, get_statuses_results = statuses_search.search(query=query_dict, field=['text', 'user', 'timestamp', 'retweeted_status'], max_offset=max_size)
else:
count, get_statuses_results = statuses_search.search(query=query_dict, field=['text', 'user', 'retweeted_status'], max_offset=max_size)
print 'topic statuses count %s' % count
if ts:
uid_ts = {}
for status in get_statuses_results():
try:
if status['retweeted_status']:
repost_uid = status['user']
rt_mid = status['retweeted_status']
repost_ts = int(status['timestamp'])
source_status = acquire_status_by_id(rt_mid)
source_uid = source_status['user']
source_ts = int(source_status['timestamp'])
if is_in_trash_list(repost_uid) or is_in_trash_list(source_uid):
continue
if repost_uid not in uid_ts:
uid_ts[repost_uid] = repost_ts
else:
if uid_ts[repost_uid] > repost_ts:
uid_ts[repost_uid] = repost_ts
if source_uid not in uid_ts:
uid_ts[source_uid] = source_ts
else:
if uid_ts[source_uid] > source_ts:
uid_ts[source_uid] = source_ts
g.add_edge(repost_uid, source_uid)
except (TypeError, KeyError):
continue
return uid_ts, g
else:
for status in get_statuses_results():
try:
if status['retweeted_status']:
repost_uid = status['user']
rt_mid = status['retweeted_status']
source_uid = acquire_status_by_id(rt_mid)['user']
if is_in_trash_list(repost_uid) or is_in_trash_list(source_uid):
continue
g.add_edge(repost_uid, source_uid)
except (TypeError, KeyError):
continue
return g
def search_test(date):
start_ts = datetime2ts(date)
end_ts = start_ts + 24*60*60
statuses_search = XapianSearch(path=XAPIAN_STATUSES_PATH, name='master_timeline_weibo', schema_version=2)
query_dict = {'timestamp': {'$gt': start_ts, '$lt': end_ts}}
statuses_count, get_statuses_results = statuses_search.search(query=query_dict, field=['user', '_id', 'retweeted_status'])
count = 0
start_time = time.time()
for status in get_statuses_results():
count += 1
print 'total statuses %s' % count
def search_single():
search_start_ts = time.time()
xapian_weibo = XapianSearch(stub=stub_files, schema_version=5)
count, get_results = xapian_weibo.search(query={"text": keywords_arg}, fields=xapian_fields)
count = 0
search_end_ts = time.time()
print "search single %d" % (search_end_ts - search_start_ts)
tb = time.time()
ts = tb
for r in get_results():
if count % 10000 == 0:
te = time.time()
print "[%s] read speed: %s sec/per %s" % (datetime.now().strftime("%Y-%m-%d %H:%M:%S"), te - ts, 10000)
ts = te
count += 1
print count
def make(date, hour):
ts = datetime2ts(date)
start_ts = datetime2ts(date) + (hour-1)*60*60
end_ts = start_ts + hour*60*60
db_name = get_leveldb(ts, hour)
hourly_user_burst_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, db_name),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
statuses_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)
user_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
batch = leveldb.WriteBatch()
query_dict = {'timestamp': {'$gt': start_ts, '$lt': end_ts}, 'reposts_count': {'$gt': 100}}
statuses_count, get_statuses_results = statuses_search.search(query=query_dict, field=['user', '_id', 'reposts_count'])
print 'total statuses: %s' % statuses_count
print 'writing to levelDB %s...' % db_name
batch.Put('size', str(statuses_count))
count = 0
uid_burst = {}
for status in get_statuses_results():
if count % 10000 == 0:
print 'current count: %s' % count
uid = status['user']
reposts_count = status['reposts_count']
followers_count = 0
if uid not in uid_burst:
uid_burst[uid] = 0
reposts_count += uid_burst[uid]
uid_burst[uid] = reposts_count
batch.Put(str(uid), str(reposts_count))
count += 1
hourly_user_burst_bucket.Write(batch, sync=True)
print 'done.'
def test_search(stub_file):
search_start_ts = time.time()
xapian_weibo = XapianSearch(stub=stub_file, schema_version=5)
count, get_results = xapian_weibo.search(query={"text": keywords_arg}, fields=xapian_fields)
pid_num = os.getpid()
search_end_ts = time.time()
print "Working in Process #%d, %d, search uses %d seconds" % (pid_num, count, search_end_ts - search_start_ts)
fw = open("./cache/%s.txt" % pid_num, "w")
count = 0
tb = time.time()
ts = tb
for r in get_results():
fw.write("%s\n" % json.dumps(r))
if count % 10000 == 0:
te = time.time()
print "process [%s] write speed: %s sec/per %s" % (pid_num, te - ts, 10000)
ts = te
count += 1
fw.close()
return pid_num
def setUp(self):
self.n = 10000
self.s = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_weibo')
self.weibo_ids = self._load_weibo_ids_from_xapian(self.n)
class BenchXapianGetByIds(hurdles.BenchCase):
def setUp(self):
self.n = 10000
self.s = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_weibo')
self.weibo_ids = self._load_weibo_ids_from_xapian(self.n)
def tearDown(self):
pass
def _load_weibo_ids_from_xapian(self, limit):
begin_ts = time.mktime(datetime.datetime(2013, 1, 1).timetuple())
end_ts = time.mktime(datetime.datetime(2013, 1, 2).timetuple())
query_dict = {
'timestamp': {'$gt': begin_ts, '$lt': end_ts},
}
count, get_results = self.s.search(query=query_dict, max_offset=limit, fields=['_id'])
print count
ids = []
for r in get_results():
ids.append(r['_id'])
return ids
def bench_1(self):
for _id in self.weibo_ids:
query_dict = {'_id': _id}
count, get_results = self.s.search(query=query_dict, fields=['_id', 'text'])
def bench_10(self):
size = 10
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size: (i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict, fields=['_id', 'text'])
def bench_20(self):
size = 20
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size: (i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict, fields=['_id', 'text'])
def bench_30(self):
size = 30
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size: (i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict, fields=['_id', 'text'])
def bench_50(self):
size = 50
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size: (i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict, fields=['_id', 'text'])
class BenchXapianGetByIds(hurdles.BenchCase):
def setUp(self):
self.n = 10000
self.s = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/',
name='master_timeline_weibo')
self.weibo_ids = self._load_weibo_ids_from_xapian(self.n)
def tearDown(self):
pass
def _load_weibo_ids_from_xapian(self, limit):
begin_ts = time.mktime(datetime.datetime(2013, 1, 1).timetuple())
end_ts = time.mktime(datetime.datetime(2013, 1, 2).timetuple())
query_dict = {
'timestamp': {
'$gt': begin_ts,
'$lt': end_ts
},
}
count, get_results = self.s.search(query=query_dict,
max_offset=limit,
fields=['_id'])
print count
ids = []
for r in get_results():
ids.append(r['_id'])
return ids
def bench_1(self):
for _id in self.weibo_ids:
query_dict = {'_id': _id}
count, get_results = self.s.search(query=query_dict,
fields=['_id', 'text'])
def bench_10(self):
size = 10
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size:(i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict,
fields=['_id', 'text'])
def bench_20(self):
size = 20
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size:(i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict,
fields=['_id', 'text'])
def bench_30(self):
size = 30
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size:(i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict,
fields=['_id', 'text'])
def bench_50(self):
size = 50
for i in xrange(self.n / size):
query_dict = {
'$or': [],
}
for _id in self.weibo_ids[i * size:(i + 1) * size]:
query_dict['$or'].append({'_id': _id})
count, get_results = self.s.search(query=query_dict,
fields=['_id', 'text'])
if not line:
break
else:
record=line.split()
swds.append(record[0])
classes=["education","culture", "fashion",'entertainment',"finance", "media", "sports", "technology"]
##cc = opencc.OpenCC('mix2s')##繁简体转换
####generate seed users
###period
b=datetime.datetime(2012,10,1)
tb=time.mktime(b.timetuple())
e=datetime.datetime(2013,10,1)
te=time.mktime(e.timetuple())
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline')##search by index
s1 = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline')##search for original tweet
total_uid_set = set()
u_seed=[]##seed users
u_cls={}##user and class mapping
w_user={}##word segmentation
n_s=0##number of seed users for each class
sw_cls={}##seed user words statistics for each class
sw_cls=cinitialize(classes,sw_cls,2)
sw={}##words from all seed users
wp_cls={}##protowords for each class
wp_cls=cinitialize(classes,wp_cls,1)
Nswds=0
exception=0
for area in classes:
from operator import itemgetter, attrgetter
import pymongo
import datetime
import time
import sys
import leveldb
import os
from xapian_weibo.xapian_backend import XapianSearch
LEVELDBPATH = '/home/mirage/leveldb'
global_user_field_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'linhao_global_user_field_20131012'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
xapian_search_weibo = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)##search by index
xapian_search_user = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)##search by index
mbr = {"culture":0, "entertainment":0, "fashion":0,'education':0,"finance":0, "sports":0, "technology":0,'media':0}
fields_value = ['culture', 'education', 'entertainment', 'fashion', 'finance', 'media', 'sports', 'technology']
def readProtoUser():
f=open("/home/mirage/linhao/project_bishe/weibo/profile/user_classify/protou.txt","r")
protou={}
for line in f:
area=line.split(":")[0]
if area not in protou:
protou[area]=set()
for u in (line.split(":")[1]).split():
protou[area].add(int(u))
return protou
# -*- coding: utf-8 -*-
from xapian_weibo.xapian_backend import XapianSearch
import leveldb
import datetime
import time
import os
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline')
LEVELDBPATH = '/home/mirage/leveldb'
weibo_multi_sentiment_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'huyue_weibo_multi_sentiment'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
user_daily_sentiment_count_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'linhao_user_daily_sentiment_count'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
emotions_kv = {'happy': 1, 'angry': 2, 'sad': 3}
total_days = 89
today = datetime.datetime.today()
now_ts = time.mktime(datetime.datetime(today.year, today.month, today.day, 2, 0).timetuple())
now_ts = int(now_ts)
during = 24 * 3600
begin_ts = now_ts - total_days * during
query_dict = {
'timestamp': {'$gt': begin_ts, '$lt': now_ts}
}
count, get_results = s.search(query=query_dict, fields=['user', 'id', 'timestamp'])
print count
def setUp(self):
self.weibo_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_weibo')
self.user_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
self.begin_ts = time.mktime(datetime.datetime(2011, 12, 1).timetuple())
self.end_ts = time.mktime(datetime.datetime(2011, 12, 31).timetuple())
tks = [
token for token in s.participle(cut_filter(text))
if 3 < len(token[0]) < 30 or token[0] in single_word_whitelist
]
if cx:
return tks
else:
return [tk[0] for tk in tks]
##加载分词工具结束
##加载xapian读取用户的认证类型
XAPIAN_USER_DATA_PATH = '/home/xapian/xapian_user/'
xs = XapianSearch(path=XAPIAN_USER_DATA_PATH,
name='master_timeline_user',
schema_version=1)
def read_by_xapian(xs, uid): #根据用户id，去xapian里面查找该用户的背景信息
count, get_results = xs.search(query={'_id': uid})
if count:
for r in get_results():
return r
else:
return 'other'
##加载xapian数据结束
class BenchXapianR(hurdles.BenchCase):
def setUp(self):
self.weibo_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/',
name='master_timeline_weibo')
self.user_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/',
name='master_timeline_user',
schema_version=1)
self.begin_ts = time.mktime(datetime.datetime(2011, 12, 1).timetuple())
self.end_ts = time.mktime(datetime.datetime(2011, 12, 31).timetuple())
def tearDown(self):
pass
"""
def bench_load_users(self):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.user_x.search(query=query_dict, fields=['_id', 'name'])
print count
def bench_load_users_then_sort(self):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.user_x.search(query=query_dict, fields=['_id', 'name'], sort_by=['created_at'])
print count
def bench_load_weibos(self):
query_dict = {
'timestamp': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.weibo_x.search(query=query_dict, fields=['_id', 'user'])
print count
"""
def bench_get_results_weibos(self):
query_dict = {
'timestamp': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
_, get_results = self.weibo_x.search(query=query_dict,
fields=['_id', 'user'])
for r in get_results():
_id = r['_id']
def bench_get_results_users(self, *args, **kwargs):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
_, get_results = self.user_x.search(query=query_dict,
fields=['_id', 'name'])
for r in get_results():
_id = r['_id']
"""
# -*- coding:utf-8 -*-
import time
import datetime
from xapian_weibo.xapian_backend import XapianSearch
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
begin_ts = time.mktime(datetime.datetime(2011, 1, 1).timetuple())
end_ts = time.mktime(datetime.datetime(2011, 12, 31).timetuple())
"""
query_dict = {
'created_at': {
'$gt': begin_ts,
'$lt': end_ts,
}
}
count, get_results = s.search(query=query_dict, max_offset=1, fields=['_id', 'name'], sort_by=['created_at'])
print count
for r in get_results():
print r['_id'], r['name']
"""
"""
query_dict = {
'$or': [
{'_id': 1934744637},
# -*- coding: utf-8 -*-
from xapian_weibo.xapian_backend import XapianSearch
'''
search_weibo = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)
print search_weibo.search(query={'_id': {'gt': 0, 'lt': 30000000000000000000000000}}, count_only=True)
search_user = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
print search_user.search(query={'_id': {'gt': 0, 'lt': 300000000000000}}, count_only=True)
'''
begin_ts = 0
end_ts = 12349873894898
query_dict = {
'timestamp': {'$gt': begin_ts, '$lt': end_ts},
}
xapian_search_sentiment = XapianSearch(path='/opt/xapian_weibo/data/20130807', name='master_timeline_sentiment', schema_version=3)
print xapian_search_sentiment.search(query=query_dict, count_only=True)
import leveldb
import os
import random
import sys
LEVELDBPATH = '/home/mirage/leveldb'
global_user_field_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'linhao_global_user_field'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
user_daily_field_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'linhao_user_daily_field'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
from xapian_weibo.xapian_backend import XapianSearch
user_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
query_dict = {
'_id':{
'$gt': 0,
'$lt': 100000000000000000000
}
}
count, get_results = user_search.search(query=query_dict)
print 'user count: ', count
fields_value = ['culture', 'education', 'entertainment', 'fashion', 'finance', 'media', 'sports', 'technology']
count = 0
for r in get_results():
uid = r['_id']
# -*- coding: utf-8 -*-
import json
import time
from xapian_weibo.xapian_backend import XapianSearch
XAPIAN_USER_DATA_PATH = '/home/xapian/xapian_user/'
xs = XapianSearch(path=XAPIAN_USER_DATA_PATH, name='master_timeline_user', schema_version=1)
with open('total_users.json', 'w') as f:
record = 1
ts = time.time()
tm = ts
for count, item in enumerate(xs.iter_all_docs()):#遍历数组的索引值和元素
f.write(json.dumps(item) + '\n')
if (count + 1) % 100000 == 0:
te = time.time()
span = round(te - tm)
print '%s chunk spend: %s' % (record, span)
record += 1
tm = time.time()
print 'total docs count: ', (count+1)
te = time.time()
total_span = round(te - ts)
print 'total spend: %s' % total_span
class BenchXapianR(hurdles.BenchCase):
def setUp(self):
self.weibo_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_weibo')
self.user_x = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/', name='master_timeline_user', schema_version=1)
self.begin_ts = time.mktime(datetime.datetime(2011, 12, 1).timetuple())
self.end_ts = time.mktime(datetime.datetime(2011, 12, 31).timetuple())
def tearDown(self):
pass
"""
def bench_load_users(self):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.user_x.search(query=query_dict, fields=['_id', 'name'])
print count
def bench_load_users_then_sort(self):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.user_x.search(query=query_dict, fields=['_id', 'name'], sort_by=['created_at'])
print count
def bench_load_weibos(self):
query_dict = {
'timestamp': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
count, get_results = self.weibo_x.search(query=query_dict, fields=['_id', 'user'])
print count
"""
def bench_get_results_weibos(self):
query_dict = {
'timestamp': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
_, get_results = self.weibo_x.search(query=query_dict, fields=['_id', 'user'])
for r in get_results():
_id = r['_id']
def bench_get_results_users(self, *args, **kwargs):
query_dict = {
'created_at': {
'$gt': self.begin_ts,
'$lt': self.end_ts,
}
}
_, get_results = self.user_x.search(query=query_dict, fields=['_id', 'name'])
for r in get_results():
_id = r['_id']
"""
# -*- coding: utf-8 -*-
import pymongo, time, codecs, datetime
try:
from xapian_weibo.xapian_backend import XapianSearch
statuses_search = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)
except:
pass
def con_database():
DB_HOST = '219.224.135.60'
DB_PORT = 27017
DB_USER = '******'
DB_PWD = 'root'
connection = pymongo.Connection(DB_HOST, DB_PORT)
db = connection.admin
db.authenticate(DB_USER, DB_PWD)
return connection.test_crawler_liwenwen
def main(uid, startdate, enddate):
startts = date2ts(startdate)
endts = date2ts(enddate)
db = con_database()
print db.users.find({'uid': str(uid), 'ts':{'$gte': startts, '$lte': endts}}).count()
cursor = db.users.find({'uid': str(uid), 'ts':{'$gte': startts, '$lte': endts}})
for weibo in cursor:
print weibo
def date2ts(date):
return int(time.mktime(time.strptime(date, '%Y-%m-%d')))
def calculate_topic(kw):
#初始化
topic_info = {}
topic_index = {}
date_list = []
perday_count_list = []
topic_rel_blog = []
topic_url = []
topic_participents = []
topic_leader = []
topic_date = []
blogs_sum = 0
comments_sum = 0
topic_ori_blog = []
city_count={}
html = '''<select name="province" id="province" defvalue="11"><option value="34">安徽</option><option value="11">北京</option><option value="50">重庆</option><option value="35">福建</option><option value="62">甘肃</option>
<option value="44">广东</option><option value="45">广西</option><option value="52">贵州</option><option value="46">海南</option><option value="13">河北</option>
<option value="23">黑龙江</option><option value="41">河南</option><option value="42">湖北</option><option value="43">湖南</option><option value="15">内蒙古</option><option value="32">江苏</option>
<option value="36">江西</option><option value="22">吉林</option><option value="21">辽宁</option><option value="64">宁夏</option><option value="63">青海</option><option value="14">山西</option><option value="37">山东</option>
<option value="31">上海</option><option value="51">四川</option><option value="12">天津</option><option value="54">西藏</option><option value="65">新疆</option><option value="53">云南</option><option value="33">浙江</option>
<option value="61">陕西</option><option value="71">台湾</option><option value="81">香港</option><option value="82">澳门</option><option value="400">海外</option><option value="100">其他</option></select>'''
province_soup = BeautifulSoup(html)
for province in province_soup.findAll('option'):
pp = province.string
if pp == u'海外' or pp == u'其他':
continue
city_count[pp] = 0
gt = calendar.timegm(datetime(2012, 1, 1).timetuple())
lt = calendar.timegm(datetime(2012, 1, 10).timetuple())
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo', schema_version=2)
count, get_results = s.search(query={'text': [u'%s'%kw], 'timestamp': {'$gt': gt, '$lt': lt}}, sort_by=['timestamp'], fields=['text', 'timestamp','reposts_count','comments_count','user', 'terms', '_id','retweeted_status','bmiddle_pic','geo','source','attitudes_count'])
for r in get_results():
# 获取时间与每天微博数量
temp_date = date.fromtimestamp(r['timestamp'])
if len(date_list) == 0:
date_list.append(temp_date)
perday_count_list.append(1)
else:
if temp_date < date_list[-1]:
if temp_date in date_list:
temp_index = date_list.index(temp_date)
perday_count_list[temp_index] += 1
else:
i = 0
while i < len(date_list):
if temp_date < date_list[0]:
date_list.insert(0,temp_date)
perday_count_list.insert(0,1)
break
else:
if temp_date > date_list[i] and temp_date < date_list[i+1]:
date_list.insert(i+1,temp_date)
perday_count_list.insert(i+1,1)
break
else:
i += 1
if temp_date == date_list[-1]:
perday_count_list[-1] += 1
if temp_date > date_list[-1]:
timedelta = date(2000,1,2)-date(2000,1,1)
while date_list[-1] != temp_date:
temp_date1 = date_list[-1] + timedelta
date_list.append(temp_date1)
perday_count_list.append(0)
perday_count_list[-1] = 1
if r['user']:
uid = int(r['user'])
user = get_user(uid)
if user != None:
if user not in topic_participents:
topic_participents.append(user)
if r['retweeted_status'] == None:
temp_ori = {}
temp_ori['status'] = r
temp_ori['user'] = user
topic_ori_blog.append(temp_ori)
if r['reposts_count'] != None and r['comments_count'] != None:
rc = r['reposts_count'] + r['comments_count']
if rc > 1500:
topic_leader.append(user)
if r['reposts_count'] > 1000:
temp = {}
temp['status'] = r
temp['status']['created_at'] = datetime.fromtimestamp(r['timestamp'])
temp['status']['text'] = r['text'].decode("utf-8")
temp['status']['source'] = re.match('<.*?>(.*)<.*?>', r['source']).group(1).decode("utf-8")
temp['user'] = user
topic_rel_blog.append(temp)
if r['bmiddle_pic']:
topic_url.append(r['bmiddle_pic'])
if r['geo'] != None and r['geo'].has_key('province_name'):
p = r['geo']['province_name'].split('省')[0]
if p == u'海外' or p == u'其他':
pass
else:
city_count[p] += 1
elif user['location']:
p = user['location'].split(' ')[0]
if p == u'海外' or p == u'其他':
pass
else:
city_count[p] += 1
else:
pass
else:
pass
comments_sum = comments_sum + r['comments_count']
blogs_sum += 1
timedelta = len(date_list)
avg = blogs_sum/float(timedelta)
i = 0
persistent_index = 0
temp_sudden = 0
while i < int(timedelta):
if perday_count_list[i] > avg:
persistent_index += 1
temp_sudden = perday_count_list[i]-avg+temp_sudden
i += 1
else:
i += 1
sudden_index = '%10.2f'%(temp_sudden/float(blogs_sum))
coverage_index = '%10.2f'%((blogs_sum + comments_sum)/(24*float(timedelta)))
media_index = 0
top_medias = []
medias = db.session.query(Media)
for media in medias:
media_name = media.mediaName
top_medias.append(media_name)
media_list = []
for r in topic_ori_blog:
tmedia = []
tmedia.append(r['user']['name'])
x = r['status']['comments_count']+r['status']['reposts_count']
tmedia.append(x)
media_list.append(tmedia)
sorted(media_list, key=lambda tmedia: tmedia[1],reverse = True)
if len(media_list) >= 20:
m = 0
while m < 20:
if media_list[m][0] in top_medias:
media_index += 1
m += 1
else:
m += 1
else:
m = 0
while m < len(media_list):
if media_list[m][0] in top_medias:
media_index += 1
m += 1
else:
m += 1
leader_index = len(topic_leader)
work_list = []
work_count = []
fields = db.session.query(Field)
for field in fields:
field_name = field.fieldName
work_list.append(field_name)
work_count.append(0)
for r in topic_participents:
k = 0
while k < len(work_list):
if r['userField'] == work_list[k]:
work_count[k] += 1
break
else:
k += 1
topic_index['persistent_index'] = persistent_index
topic_index['sudden_index'] = sudden_index
topic_index['coverage_index'] = coverage_index
topic_index['media_index'] = media_index
topic_index['leader_index'] = leader_index
map_data = province_color_map(city_count)
topic_info['topic_poster'] = topic_participents[0]['name']
topic_info['topic_post_date'] = date_list[0]
topic_info['topic_leader_count'] = len(topic_leader)
topic_info['topic_participents'] = len(topic_participents)
topic_info['blogs_sum'] = blogs_sum
topic_info['topic_ori_blog_count'] = len(topic_ori_blog)
topic_info['topic_url'] = topic_url
topic_info['perday_count_list'] = perday_count_list
topic_info['date_list'] = date_list
topic_info['topic_rel_blog'] = topic_rel_blog
topic_info['geo'] = map_data
topic_info['topic_leader'] = topic_leader
topic_info['topic_working_list'] = work_list
topic_info['topic_working_count'] = work_count
topic_info['topic_index'] = topic_index
topic_info['gt'] = gt
topic_info['lt'] = lt
return topic_info
def setUp(self):
self.n = 10000
self.s = XapianSearch(path='/home/arthas/dev/xapian_weibo/data/',
name='master_timeline_weibo')
self.weibo_ids = self._load_weibo_ids_from_xapian(self.n)
# -*- coding: utf-8 -*-
from xapian_weibo.xapian_backend_extra import Schema
from xapian_weibo.xapian_backend import XapianSearch
import leveldb
import datetime
import time
import os
LEVELDBPATH = '/home/mirage/leveldb'
weibo_daily_sentiment_count_global_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'lijun_weibo_daily_sentiment_count_global'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
total_days = 90
emotions_kv = {'happy': 1, 'angry': 2, 'sad': 3}
s = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_sentiment', schema=Schema, schema_version=1)
today = datetime.datetime.today()
now_ts = time.mktime(datetime.datetime(today.year, today.month, today.day, 2, 0).timetuple())
now_ts = int(now_ts)
during = 24 * 3600
for i in xrange(-total_days + 1, 1):
begin_ts = now_ts + during * (i - 1)
end_ts = now_ts + during * i
print i, begin_ts, end_ts
for emotion in emotions_kv.keys():
query_dict = {
'timestamp': {'$gt': begin_ts, '$lt': end_ts},
'sentiment': emotions_kv[emotion],
}
import os
from xapian_weibo.xapian_backend import XapianSearch
from operator import itemgetter
import datetime
import time
import leveldb
LEVELDBPATH = '/home/mirage/leveldb'
global_user_field_bucket = leveldb.LevelDB(os.path.join(LEVELDBPATH, 'linhao_global_user_field_20131012'),
block_cache_size=8 * (2 << 25), write_buffer_size=8 * (2 << 25))
xapian_search_weibo = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_weibo') # search by index
xapian_search_user = XapianSearch(path='/opt/xapian_weibo/data/', name='master_timeline_user', schema_version=1) # search by index
fields_value = ['culture', 'education', 'entertainment', 'fashion', 'finance', 'media', 'sports', 'technology']
def readProtoUser():
protou = {}
with open("/home/mirage/linhao/project_bishe/weibo/profile/user_classify/protou.txt") as f:
for line in f:
area = line.split(":")[0]
if area not in protou:
protou[area] = set()
for u in line.split(":")[1].split():
protou[area].add(int(u))
return protou
