def ajax_get_current_hot_weibo():
task_name = request.args.get('task_name', '')
update_time = request.args.get("update_time", "")
pinyin_task_name = pinyin.get(task_name.encode('utf-8'),
format='strip',
delimiter="_")
index_name = "stimulation_" + pinyin_task_name
index_type = "stimulation_results"
es_results = es_prediction.get(index=index_name,
doc_type=index_type,
id=update_time)["_source"]
results = json.loads(es_results["current_hot_weibo"])
attribute_list = [
"comment", "uid", "text", "uname", "fansnum", "retweet", "mid", "geo",
"photo_url", "statusnum", "timestamp"
]
return_list = []
for each in results:
new_dict = dict()
for item in attribute_list:
new_dict[item] = each[item]
return_list.append(new_dict)
return json.dumps(return_list)
def ajax_get_future_user_info():
task_name = request.args.get('task_name','')
update_time = request.args.get("update_time","")
pinyin_task_name = pinyin.get(task_name.encode('utf-8'), format='strip', delimiter="_")
index_name = "stimulation_"+pinyin_task_name
index_type = "stimulation_results"
es_results = es_prediction.get(index=index_name, doc_type=index_type, id=update_time)["_source"]
results = es_results["future_user_info"]
return results
def get_task_detail():
task_name = request.args.get('task_name','')
pinyin_task_name = pinyin.get(task_name.encode('utf-8'), format='strip', delimiter="_")
task_detail = es_prediction.get(index=index_manage_interfere_task, doc_type=type_manage_interfere_task,id=pinyin_task_name)["_source"]
task_dict = dict()
attribute_list = ["task_name", "remark", "submit_user","submit_time","start_time", "stop_time","update_time","finish","should_keywords","must_keywords"]
for item in attribute_list:
task_dict[item] = task_detail[item]
return json.dumps(task_dict)
def ajax_show_social_sensors():
results = []
task_detail = es_prediction.get(index="manage_sensing_task",
doc_type="task",
id="social_sensing_task")["_source"]
uid_list = json.loads(task_detail["social_sensors"])
profile_results = es_user_profile.mget(index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list})["docs"]
for item in profile_results:
'''
tmp = []
if item["found"]:
tmp.append(item["_source"]["nick_name"])
tmp.append(item["_source"]["uid"])
tmp.append(item["_source"]["user_location"])
tmp.append(item["_source"]["fansnum"])
tmp.append(item["_source"]["statusnum"])
tmp.append(item["_source"]["friendsnum"])
else:
tmp.append(item["_id"])
tmp.append(item["_id"])
tmp.append('')
tmp.append('')
tmp.append('')
tmp.append('')
results.append(tmp)
'''
if item["found"]:
results.append(item['_source'])
else:
item_new = {}
item_new['photo_url'] = ''
item_new['nick_name'] = ''
item_new['uid'] = item['_id']
item_new['sex'] = ''
item_new['fansnum'] = ''
item_new['friendsnum'] = ''
item_new['user_location'] = ''
item_new['create_at'] = ''
results.append(item_new)
return json.dumps(results)
def ajax_delete_social_sensors():
finish = ["0"]
delete_user = request.args.get("delete_users", '') # &.join
task_detail = es_prediction.get(index="manage_sensing_task",
doc_type="task",
id="social_sensing_task")["_source"]
sensors = json.loads(task_detail["social_sensors"])
if delete_user:
uid_list = delete_user.split("&")
new_list = set(sensors) - set(uid_list)
task_detail["social_sensors"] = json.dumps(list(new_list))
es_prediction.index(index="manage_sensing_task",
doc_type="task",
id="social_sensing_task",
body=task_detail)
finish = ["1"]
return json.dumps(finish)
def ajax_get_hot_user():
task_name = request.args.get('task_name','')
update_time = request.args.get("update_time","")
pinyin_task_name = pinyin.get(task_name.encode('utf-8'), format='strip', delimiter="_")
index_name = "stimulation_"+pinyin_task_name
index_type = "stimulation_results"
es_results = es_prediction.get(index=index_name, doc_type=index_type, id=update_time)["_source"]
future_results = json.loads(es_results["future_results"])
results = []
for start_uid, end_dict in future_results.iteritems():
tmp_uid_list = end_dict.keys()
tmp_uid_list.append(start_uid)
tmp_profile = es_user_profile.mget(index=profile_index_name, doc_type=profile_index_type,body={"ids":tmp_uid_list})["docs"]
tmp_profile_dict = dict()
for item in tmp_profile:
if item["found"]:
nick_name = item["_source"]["nick_name"]
fansnum = item["_source"]["fansnum"]
else:
nick_name = start_uid
fansnum = ''
tmp_iter = dict()
tmp_iter["nick_name"] = nick_name
tmp_iter["fansnum"] = fansnum
tmp_iter["uid"] = item["_id"]
if item["_id"] != start_uid:
tmp_iter["retweeted"] = end_dict[item["_id"]]
tmp_profile_dict[item["_id"]] = tmp_iter
tmp = []
tmp.extend([start_uid, tmp_profile_dict[start_uid]["nick_name"], tmp_profile_dict[start_uid]["fansnum"]])
tmp.append(len(end_dict))
tmp.append(int(sum(end_dict.values())))
tmp_profile_dict.pop(start_uid)
tmp_sorted_profile = sorted(tmp_profile_dict.values(), key=lambda x:x["retweeted"], reverse=True)
tmp.append(tmp_sorted_profile)
results.append(tmp)
results = sorted(results, key=lambda x:x[3], reverse=True)
return json.dumps(results)
def ajax_add_social_sensor():
finish = ["0"]
add_user = request.args.get("add_users", '') # &.join
task_detail = es_prediction.get(index="manage_sensing_task",
doc_type="task",
id="social_sensing_task")["_source"]
sensors = json.loads(task_detail["social_sensors"])
if add_user:
uid_list = add_user.split("，") #改成了“，”
if uid_list:
in_set = set(uid_list) & set(sensors)
out_set = set(uid_list) - set(sensors)
if out_set:
new_list = list(set(uid_list) | set(sensors))
task_detail["social_sensors"] = json.dumps(new_list)
es_prediction.index(index="manage_sensing_task",
doc_type="task",
id="social_sensing_task",
body=task_detail)
finish = ["1"]
results = [list(in_set), list(out_set)]
#return json.dumps(results)
return json.dumps(finish)
def get_origin_weibo_detail(ts, size, order, message_type=1):
topic_value_dict = json.loads(r.get("topic_value_dict"))
task_detail = es_prediction.get(index=index_sensing_task,
doc_type=_id,
id=ts)['_source']
mid_value = json.loads(task_detail['mid_topic_value'])
duplicate_dict = json.loads(task_detail['duplicate_dict'])
tmp_duplicate_dict = dict()
for k, v in duplicate_dict.iteritems():
try:
tmp_duplicate_dict[v].append(k)
except:
tmp_duplicate_dict[v] = [k, v]
if message_type == 1:
weibo_detail = json.loads(task_detail['origin_weibo_detail'])
elif message_type == 2:
weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
else:
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query": {
"filtered": {
"filter": {
"terms": {
"mid": mid_list
}
}
}
},
"size": 1000,
"sort": {
"timestamp": {
"order": "desc"
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts - DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
sort_results = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(
index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list},
fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {
"nick_name": item["fields"]["nick_name"][0],
"photo_url": item["fields"]["photo_url"][0]
}
else:
portrait_dict[item['_id']] = {
"nick_name": item['_id'],
"photo_url": ""
}
if order == "total":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[1],
reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[2],
reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[3],
reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
results_dict = dict()
mid_index_dict = dict()
for item in sorted_list: # size
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid, ''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
if message_type == 1:
temp.append(1)
elif message_type == 2:
temp.append(3)
else:
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
temp.append(iter_text['timestamp'])
temp.append(mid_value[mid])
temp.append(mid)
results.append(temp)
count_n += 1
results = sorted(results,
key=operator.itemgetter(-4, -2, -6),
reverse=True) # -4 -2 -3
sort_results = []
count = 0
for item in results:
sort_results.append([item])
mid_index_dict[item[-1]] = count
count += 1
if tmp_duplicate_dict:
remove_list = []
value_list = tmp_duplicate_dict.values() # [[mid, mid], ]
for item in value_list:
tmp = []
for mid in item:
if mid_index_dict.get(mid, 0):
tmp.append(mid_index_dict[mid])
if len(tmp) > 1:
tmp_min = min(tmp)
else:
continue
tmp.remove(tmp_min)
for iter_count in tmp:
sort_results[tmp_min].extend(sort_results[iter_count])
remove_list.append(sort_results[iter_count])
if remove_list:
for item in remove_list:
sort_results.remove(item)
return sort_results
def get_retweet_weibo_detail(ts, size, text_type, type_value):
task_detail = es_prediction.get(index=index_sensing_task,
doc_type=_id,
id=ts)['_source']
origin_weibo_detail = json.loads(task_detail['origin_weibo_detail'])
retweeted_weibo_detail = json.loads(task_detail['retweeted_weibo_detail'])
mid_list = []
mid_list.extend(origin_weibo_detail.keys())
mid_list.extend(retweeted_weibo_detail.keys())
query_body = {
"query": {
"filtered": {
"filter": {
"bool": {
"must": [{
"range": {
"timestamp": {
"gte": ts - time_interval,
"lt": ts
}
}
}, {
"terms": {
"root_mid": mid_list
}
}]
}
}
}
},
"sort": {
"timestamp": {
"order": "desc"
}
},
"size": 100
}
if text_type == "message_type":
query_body['query']['filtered']['filter']['bool']['must'].append(
{"term": {
text_type: type_value
}})
if text_type == "sentiment":
#if isinstance(type_value, str):
if len(type_value) == 1:
query_body['query']['filtered']['filter']['bool']['must'].append(
{"term": {
text_type: type_value
}})
else:
query_body['query']['filtered']['filter']['bool']['must'].append(
{"terms": {
text_type: type_value
}})
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts - time_interval)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
# 1. 查询微博
if datetime == datetime_1 and exist_es:
search_results = es_text.search(index=index_name,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
elif datetime != datetime_1 and exist_es_1:
search_results = es_text.search(index=index_name_1,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
else:
search_results = []
#print search_results
# 2. 获取微博相关信息
results = []
uid_list = []
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
if uid_list:
portrait_result = es_profile.mget(
index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list},
fields=['nick_name', 'photo_url'])["docs"]
for i in range(len(uid_list)):
item = search_results[i]['_source']
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
temp.append(item['uid'])
if portrait_result[i]['found']:
temp.append(portrait_result[i]["fields"]["nick_name"][0])
temp.append(portrait_result[i]["fields"]["photo_url"][0])
else:
temp.append(item['uid'])
temp.append("")
temp.append(item["text"])
#print item['text']
temp.append(item["sentiment"])
temp.append(ts2date(item['timestamp']))
temp.append(item['geo'])
temp.append(item["message_type"])
results.append(temp)
return results
def ajax_get_diffusion_path():
task_name = request.args.get('task_name', '')
update_time = request.args.get("update_time", "")
pinyin_task_name = pinyin.get(task_name.encode('utf-8'),
format='strip',
delimiter="_")
index_name = "stimulation_" + pinyin_task_name
index_type = "stimulation_results"
es_results = es_prediction.get(index=index_name,
doc_type=index_type,
id=update_time)["_source"]
#print 'keys::::::::',es_results.keys()
#results = es_results["diffusion"]
results = json.loads(es_results["diffusion_path"])
# uid_set = set()
# root_uid_set = set()
# for k,v in results.iteritems():
# uid_set.add(k)
# uid_set = uid_set|set(v)
# root_uid_set.add(k)
# uid_list = list(uid_set)
# root_uid_list = list(root_uid_set)
# user_info = dict()
# if uid_list:
# es_results = es_user_profile.mget(index=profile_index_name, doc_type=profile_index_type,body={"ids":uid_list})["docs"]
# for item in es_results:
# tmp = dict()
# if item["found"]:
# item = item["_source"]
# tmp["uid"] = item["uid"]
# tmp["photo_url"] = item["photo_url"]
# if item["nick_name"]:
# tmp["nick_name"] = item["nick_name"]
# else:
# tmp["nick_name"] = item["uid"]
# tmp["fansnum"] = item["fansnum"]
# tmp["friendsnum"] = item["friendsnum"]
# tmp["statusnum"] = item["statusnum"]
# tmp["location"] = item["user_location"]
# else:
# tmp["uid"] = item["_id"]
# tmp["photo_url"] = ""
# tmp["nick_name"] = item["_id"]
# tmp["fansnum"] = ""
# tmp["friendsnum"] = ""
# tmp["statusnum"] = ""
# tmp["location"] = ""
# user_info[tmp["uid"]] = tmp
# results_dic_list = []
# for root_uid,uid_list in results.iteritems():
# results_dic_list_item = {}
# results_dic_list_item["root_uid"] = root_uid
# results_dic_list_item["uid_list"] = uid_list
# results_dic_list.append(results_dic_list_item)
# return json.dumps([results_dic_list, user_info])
# uid_name_list = []
# for uid in uid_list:
# name = user_info[uid]["nick_name"]
# if uid in root_uid_list:
# if name :
# uid_name_list.append([1,name])
# else:
# uid_name_list.append([1,uid])
# else:
# if name :
# uid_name_list.append([2,name])
# else:
# uid_name_list.append([2,uid])
# link_list = []
# for root_uid,uid_list in results.iteritems():
# for uid in uid_list:
# root_name =
return json.dumps(results)
def get_sensitive_text_detail(task_name, ts, user, order):
_id = user + '-' + task_name
task_detail = es.get(index=index_sensing_task, doc_type=_id,
id=ts)['_source']
weibo_detail = json.loads(task_detail['sensitive_weibo_detail'])
weibo_detail_list = []
if weibo_detail:
for iter_mid, item in weibo_detail.iteritems():
tmp = []
tmp.append(iter_mid)
tmp.append(item[iter_mid])
tmp.append(item['retweeted'])
tmp.append(item['comment'])
weibo_detail_list.append(tmp)
mid_list = weibo_detail.keys()
results = []
query_body = {
"query": {
"filtered": {
"filter": {
"terms": {
"mid": mid_list
}
}
}
}
}
index_list = []
datetime = ts2datetime(ts)
datetime_1 = ts2datetime(ts - DAY)
index_name = flow_text_index_name_pre + datetime
exist_es = es_text.indices.exists(index_name)
if exist_es:
index_list.append(index_name)
index_name_1 = flow_text_index_name_pre + datetime_1
exist_es_1 = es_text.indices.exists(index_name_1)
if exist_es_1:
index_list.append(index_name_1)
if index_list and mid_list:
search_results = es_text.search(index=index_list,
doc_type=flow_text_index_type,
body=query_body)["hits"]["hits"]
else:
search_results = []
uid_list = []
text_dict = dict() # 文本信息
portrait_dict = dict() # 背景信息
if search_results:
for item in search_results:
uid_list.append(item["_source"]['uid'])
text_dict[item['_id']] = item['_source'] # _id是mid
if uid_list:
portrait_result = es_profile.mget(
index=profile_index_name,
doc_type=profile_index_type,
body={"ids": uid_list},
fields=['nick_name', 'photo_url'])["docs"]
for item in portrait_result:
if item['found']:
portrait_dict[item['_id']] = {
"nick_name": item["fields"]["nick_name"][0],
"photo_url": item["fields"]["photo_url"][0]
}
else:
portrait_dict[item['_id']] = {
"nick_name": item['_id'],
"photo_url": ""
}
if order == "total":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[1],
reverse=True)
elif order == "retweeted":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[2],
reverse=True)
elif order == "comment":
sorted_list = sorted(weibo_detail_list,
key=lambda x: x[3],
reverse=True)
else:
sorted_list = weibo_detail_list
count_n = 0
for item in sorted_list:
mid = item[0]
iter_text = text_dict.get(mid, {})
temp = []
# uid, nick_name, photo_url, text, sentiment, timestamp, geo, common_keywords, message_type
if iter_text:
uid = iter_text['uid']
temp.append(uid)
iter_portrait = portrait_dict.get(uid, {})
if iter_portrait:
temp.append(iter_portrait['nick_name'])
temp.append(iter_portrait['photo_url'])
else:
temp.extend([uid, ''])
temp.append(iter_text["text"])
temp.append(iter_text["sentiment"])
temp.append(ts2date(iter_text['timestamp']))
temp.append(iter_text['geo'])
temp.append(iter_text['message_type'])
temp.append(item[2])
temp.append(item[3])
temp.append(iter_text.get('sensitive', 0))
count_n += 1
results.append(temp)
if results and order == "ts":
results = sorted(results, key=lambda x: x[5], reverse=True)
if results and order == "sensitive":
results = sorted(results, key=lambda x: x[-1], reverse=True)
return results
def get_weibo_content(topic,start_ts,end_ts,opinion,sort_item='timestamp'): #微博内容
weibo_dict = {}
#a = json.dumps(opinion)
#opinion = '圣保罗_班底_巴西_康熙'
#[u'毛泽东', u'纪念日', u'亲人', u'毛泽东思想', u'万岁']
#opinion = json.dumps(opinion)
#opinion = '毛泽东_纪念日_亲人_毛泽东思想_万岁'
# opinion_str = opinion[0]
# opinion_str = '_'.join(opinion)
# for i in range(1,len(opinion)):
# opinion_str = opinion_str + '_' + opinion_str
# query_body = {
# 'query':{
# 'bool':{
# 'must':[
# {'wildcard':{'keys':opinion}},
# {'term':{'name':topic}},
# {'range':{'start_ts':{'lte':start_ts}}},
# {'range':{'end_ts':{'gte':end_ts}}}
# ]
# }
# },
# 'size':100000
# } #没有查到uid 每次的id不一样
# print 'opinion:::::::;;',opinion
# start_ts = int(start_ts)
query_body = {
'query':{
'bool':{
'must':[
# {'wildcard':{'keys':'*'+opinion+'*'}},
{"match_phrase":{"keys": opinion}},
#{'term':{'keys':opinion}},
{'term':{'name':topic}},
{'range':{'start_ts':{'gte':start_ts}}},
{'range':{'end_ts':{'lte':end_ts}}}
]
}
},
'size':1000000
} #没有查到uid 每次的id不一样
# query_body = {
# 'query':{
# 'match_all':{}
# },
# 'size':1000000
# }
print 'query_body:::::;',query_body
weibos = es.search(index=subopinion_index_name,doc_type=subopinion_index_type,body=query_body)['hits']['hits']
#print weibo_es,subopinion_index_name,subopinion_index_type,query_body
print len(weibos)
#keys_list = []
for weibo in weibos:
print weibo['_source'].keys()
print 'start_ts:::::::::',weibo["_source"]["start_ts"]
print 'end_ts:::::::::::',weibo["_source"]["end_ts"]
print 'name:::::::::::::',weibo['_source']["name"]
print 'keys:::::::::::::',weibo["_source"]["keys"]
#print 'keys_list:::::::',keys_list
print 'opinion:::::',opinion
print 'start_ts::::',start_ts
print 'end_ts::::::',end_ts
print 'topic:::::::',topic
if weibos:
# print 'weibos:::::::::::;',weibos[0]['_source']['keys']
weibos = json.loads(weibos[0]['_source']['cluster_dump_dict'])
for weibo in weibos.values():#jln0825
weibo = weibo[0]
weibo_content = {}
weibo_content['text'] = weibo['text']
weibo_content['uid'] = weibo['uid']
weibo_content['timestamp'] = full_datetime2ts(weibo['datetime'])
weibo_content['comment'] = weibo['comment']
weibo_content['retweeted'] = weibo['retweeted']
weibo_content['mid'] = weibo['id']
try:
user = es.get(index=profile_index_name,doc_type=profile_index_type,id=weibo_content['uid'])['_source']
weibo_content['uname'] = user['nick_name']
weibo_content['photo_url'] = user['photo_url']
except:
weibo_content['uname'] = 'unknown'
weibo_content['photo_url'] = 'unknown'
weibo_dict[weibo_content['mid']] = weibo_content
results = sorted(weibo_dict.items(),key=lambda x:x[1][sort_item],reverse=True)
#print results
return results
else:
return 'no results'
