def dual_learning_update(actor, critic, target_actor, target_critic, params,
rank):
mongo = MongoDB()
actor.train()
query = {'training_round': params['training_round'], 'rank': rank}
projection = {
'obs': 1,
'state': 1,
'betsize_mask': 1,
'action_mask': 1,
'action': 1,
'reward': 1,
'_id': 0
}
data = mongo.get_data(query, projection)
for i in range(params['learning_rounds']):
policy_losses = []
losses = []
for poker_round in data:
update_actor_critic(poker_round, critic, target_critic, actor,
target_actor, params)
soft_update(critic, target_critic, params['device'])
soft_update(actor, target_actor, params['device'])
mongo.close()
del data
return actor, critic, params
class SearchIndex(object):
"""docstring for SearchIndex"""
def __init__(self):
self.schema = schema
self.collect = MongoDB()
def create_index(self):
if not os.path.exists("index"):
os.mkdir("index")
index = create_in("index", schema)
writer = index.writer()
for post in self.collect.find():
writer.update_document(title=post['title'],
content=post['content'],
nid=unicode(post["_id"]),
url=post['url'])
writer.commit()
def get_index_info(self, query):
index = open_dir("index")
with index.searcher() as searcher:
parser = QueryParser("content", index.schema)
my_query = parser.parse(query)
result = []
resp = searcher.search(my_query, limit=None)
if resp:
for one in resp:
post = self.collect.find_one(ObjectId(one["nid"]))
result.append({"title":one['title'], "url":post['url'], "content":one.highlights("content")})
# return one['title']
# print one["title"]
# print post['url']
# print one.highlights("content")
# print "###############"
return result, len(result)
def batch_learning_update(actor, critic, target_actor, target_critic, params):
mongo = MongoDB()
actor.train()
query = {'training_round': params['training_round']}
projection = {
'obs': 1,
'state': 1,
'betsize_mask': 1,
'action_mask': 1,
'action': 1,
'reward': 1,
'_id': 0
}
db_data = mongo.get_data(query, projection)
trainloader = return_trajectoryloader(db_data)
for _ in range(params['learning_rounds']):
losses = []
for i, data in enumerate(trainloader):
critic_loss = update_actor_critic_batch(data, actor, critic,
target_actor,
target_critic, params)
losses.append(critic_loss)
# print(f'Learning Round {i}, critic loss {sum(losses)}, policy loss {sum(policy_losses)}')
mongo.close()
return actor, critic, params
def plot_critic_values(training_round=0):
query = {
# 'position':args.position,
# 'training_round':args.run
}
projection = {'values': 1, 'reward': 1, 'action': 1, '_id': 0}
mongo = MongoDB()
# for position in [pdt.PositionStrs.SB,pdt.PositionStrs.BB]:
# query['position'] = position
data = mongo.get_data(query, projection)
rewards = []
actions = []
values = []
for point in data:
rewards.append(point['reward'])
values.append(point['values'])
actions.append(point['action'])
M = len(values)
# plot value loss over time
interval = M // 4
values = np.vstack(values)
rewards = np.vstack(rewards)
actions = np.array(actions)
mask = np.zeros((actions.size, pdt.Action.RAISE), dtype=bool)
mask[np.arange(actions.size), actions] = 1
critic_loss = values[mask].reshape(M, 1) - rewards
critic_loss_rolling_mean = []
for i in range(len(critic_loss) - interval):
critic_loss_rolling_mean.append(np.mean(critic_loss[i:interval + i]))
plot_data(f'Critic loss ', [critic_loss_rolling_mean], ['Values'])
def process_item(self, item, spider):
mongo_ins = MongoDB()
if isinstance(item, TopicBriefItem):
if mongo_ins.check_topic_brief_exisit(item.get('target')) is False:
mongo_ins.topic_brief.insert(dict(item))
elif isinstance(item, TopicInfoItem):
if mongo_ins.check_topic_info_exisit(item.get('target')) is False:
mongo_ins.topic_info.insert(dict(item))
def __init__(self):
self.mysql = MySQL()
self.mongo = MongoDB()
self.query = Query()
cfg = getConfig()
self.mysql.init_cfg(cfg)
self.mongo.init_cfg(cfg)
self._con = self.mysql.get_connection()
self._client = self.mongo.get_client()
self.query.init_db(self._con, self._client)
self._db = self._client[cfg.MONGODB_COLLECTION]
def plot_betsize_probabilities(training_round=0):
query = {'training_round': training_round}
projection = {'betsizes': 1, 'hand': 1, '_id': 0}
params = {'interval': 100}
mongo = MongoDB()
gametype = "Omaha"
# SB
for position in [pdt.PositionStrs.SB, pdt.PositionStrs.BB]:
query['position'] = position
data = mongo.get_data(query, projection)
betsize, unique_hands, unique_betsize = mongo.betsizeByHand(
data, params)
hand_labels = [
f'Hand {pdt.Globals.KUHN_CARD_DICT[hand]}' for hand in unique_hands
]
action_labels = [size for size in unique_betsize]
plot_frequencies(
f'{gametype}_betsize_probabilities_for_{query["position"]}',
betsize, hand_labels, action_labels)
def get(self):
db = MongoDB()
pics = []
#按CreateTime降序排列，最新照片在最上面，sort写法特殊，参考http://stackoverflow.com/questions/10242149/using-sort-with-pymongo
for i in db.collection.find({'MsgType': 'image'}).sort([('CreateTime', -1)]):
print i
pic_path = self.get_rela_pic_path(i.get('LocalPicUrl'))
pic_txt = i.get('ResponseMsg')
if not pic_path or not pic_txt:
continue
pics.append((pic_path, pic_txt))
self.render('img.html', pics=pics)
def export_quake_log(file, pattern):
db = MongoDB(database_name='quake', collection_name='games')
game_count = 0
game_name = 'game_{}'.format(game_count)
game = {'game': game_name, 'kills': []}
with open(file, "r") as f:
started_game = False
for row in f:
if INIT_GAME in row and started_game:
db.save(game)
game_count += 1
game_name = 'game_{}'.format(game_count)
game = {'game': game_name, 'kills': []}
if INIT_GAME in row:
started_game = True
if 'Kill:' in row and started_game:
regex_groups = re.search(pattern, row).groups()
kill_row = {
'player_killer':
regex_groups[GroupPattern.PLAYER_KILLER.value],
'player_dead':
regex_groups[GroupPattern.PLAYER_DEAD.value],
'type_gun': regex_groups[GroupPattern.TYPE_GUN.value]
}
game['kills'].append(kill_row)
db.save(game)
print('Successfully export Quake 3 Logs to MongoDB ')
def train_shared_model(agent_params,env_params,training_params,id,actor,critic):
actor.train()
critic.train()
pid = os.getpid()
print(f"Intiantiating process PID {pid}")
env = Poker(env_params)
nS = env.state_space
nO = env.observation_space
nA = env.action_space
nB = env.betsize_space
nC = nA - 2 + nB
print_every = (training_params['epochs']+1) // 5
seed = 154
agent = ParallelAgent(nS,nO,nA,nB,seed,agent_params,actor,critic)
training_data = copy.deepcopy(training_params['training_data'])
for e in range(1,training_params['epochs']+1):
last_state,state,obs,done,mask,betsize_mask = env.reset()
while not done:
if env.game == pdt.GameTypes.HISTORICALKUHN:
actor_outputs = agent(state,mask,betsize_mask) if env.rules.betsize == True else agent(state,mask)
else:
actor_outputs = agent(last_state,mask,betsize_mask) if env.rules.betsize == True else agent(last_state,mask)
last_state,state,obs,done,mask,betsize_mask = env.step(actor_outputs)
ml_inputs = env.ml_inputs()
agent.learn(ml_inputs)
ml_inputs = detach_ml(ml_inputs)
for position in ml_inputs.keys():
training_data[position].append(ml_inputs[position])
if id == 0 and e % print_every == 0:
print(f'PID {pid}, Epoch {e}')
mongo = MongoDB()
mongo.clean_db()
mongo.store_data(training_data,env.db_mapping,training_params['training_round'],env.game,id,training_params['epochs'])
def make_app():
load_dotenv()
settings = {
"template_path": os.path.join(os.path.dirname(__file__), "templates")
}
global db
db = MongoDB(os.getenv("MONGO_CONN_STRING"))
return tornado.web.Application([
(r"/", MainHandler),
], **settings)
def plot_action_frequencies(actiontype, handtype, training_round=0):
print(actiontype, handtype)
query = {'training_round': training_round}
projection = {'action': 1, 'hand_strength': 1, 'hand': 1, '_id': 0}
data_params = {'interval': 100}
mongo = MongoDB()
# gametype = mongo.get_gametype(training_round)
gametype = "Omaha"
for position in [pdt.PositionStrs.SB, pdt.PositionStrs.BB]:
query['position'] = position
data = mongo.get_data(query, projection)
if handtype == pdt.VisualHandTypes.HAND:
actions, hands, unique_actions = mongo.actionByHand(
data, data_params)
else:
actions, hands, unique_actions = mongo.actionByHandStrength(
data, data_params)
hand_labels = HAND_LABELS_DICT[actiontype](hands)
action_labels = [pdt.ACTION_DICT[act] for act in unique_actions]
plot_frequencies(
f'{gametype}_action_{handtype}_for_{query["position"]}', actions,
hand_labels, action_labels)
def __init__(self, tn=None, location=None, dbname=None):
name = self.__class__.__name__
self.tablename = tn.strip(
) if tn is not None and len(tn) else name.lower()
if location is None and dbname is None:
if mongodb is not None:
self.mc = mongodb[self.tablename]
else:
raise Exception('Unable to find available dbname')
elif location is None and dbname is not None:
self.mc = connection[dbname][self.tablename]
else:
self.location = location
self.dbname = dbname
self.mc = MongoDB.db_connection(self.location,
self.dbname)[self.tablename]
class TaskTracker(object):
def __init__(self, filename):
self._dynamic_import(filename)
def _is_exist(self, filename):
if not os.path.exists(filename):
raise IOError(
"IOError: File '%s' does not exists." % filename)
def _dynamic_import(self, filename):
self._is_exist(filename)
package_name = _package_parse(filename)
self.export_module = __import__(package_name, fromlist=['*'])
def _do_function(self, target_prefix):
return [
getattr(self.export_module, attr)
for attr in dir(self.export_module)
if re.match(target_prefix, attr)]
def yaml_read(self):
yamlpath = self.export_module.settings
self._is_exist(yamlpath)
raw_yaml_file = open(yamlpath)
self.settings = yaml.load(raw_yaml_file)
def connect_database(self):
self.db = MongoDB(self.settings.get("server"))
def disconnect_database(self):
self.db.disconnect()
def create_setup_data(self):
self.db.set_models(self.settings["scheme"])
self.db.setup_run(self.settings["setup"])
def destory_setup_data(self):
self.db.destroy_run()
def previous_run(self):
for f in self._do_function("setup_"):
f()
def __set_fast_data(img_file_path, lbl):
payload = list()
db_handle = MongoDB()
feature_vector = FeatureExtractor().get_features(img_file_path)
feature_map = dict()
key_p = os.path.splitext(os.path.basename(img_file_path))
key = key_p[0] + '_' + key_p[1][1:] + '_' + str(
int(time.time() * 1000.0))
key = key.replace('.', '_')
feature_map['file'] = key
feature_map['label'] = lbl
feature_map['feature'] = feature_vector
payload.append(feature_map)
try:
db_handle.to_db(payload=payload,
key=None,
db=MONGO_HOPS_DB,
collection=MONGO_XRAY_COLLECTION)
payload.clear()
db_handle.close()
except Exception as e:
db_handle.close()
print(img_file_path)
print("Ignoring Exception : " + str(e))
def task_get_matches(record):
"""
For a given userId, set of coordinates, and datetime
find all users that were nearby within 1 minute of
user identified by userId
"""
db = MongoDB().connect()
# Hack b/c of Celery's serialization converting
# datetimes to strings
record['time'] = parser.parse(record['time'])
locs = db.user_locations.find({
'loc': {
'$geoNear': {
'$geometry': record['loc'],
'$maxDistance': 150,
},
},
'userId': {
'$ne': record['userId']
},
'time': {
'$gte': record['time'] - timedelta(seconds=30),
'$lte': record['time'] + timedelta(seconds=30)
},
})
for loc in locs:
# For each match, create two records.
criteria = {'from': record['userId'], 'to': loc['userId']}
new_values = criteria.copy()
new_values.update({'time': record['time']})
# Update existing or create new record
db.matches.update(criteria, new_values, upsert=True)
# Symmetrical - both users get a match made `to` them `from` the other user
reversed_criteria = {'from': loc['userId'], 'to': record['userId']}
new_values.update(reversed_criteria)
db.matches.update(reversed_criteria, new_values, upsert=True)
from cleantext import clean
from scrapy import (Spider, Request)
from uuid import uuid4
root_url = 'https://www.metacritic.com'
genres = [
'adventure', 'fighting', 'first-person', 'flight', 'party', 'platformer',
'puzzle', 'racing', 'real-time', 'role-playing', 'simulation', 'sports',
'strategy', 'third-person', 'turn-based', 'wargame', 'wrestling', 'action'
]
genre_url = '/browse/games/genre/date/'
query_str = '/all?view=condensed'
critic_reviews = 'critic'
user_reviews = 'user'
batch_size = 100
database = MongoDB()
database.open()
meta_db = database.get_database('metacritic')
def now():
from datetime import timezone, datetime
return int(datetime.now(tz=timezone.utc).timestamp() * 1000)
def is_none(d):
for k, v in d.items():
if v is None:
return True
return False
import hashlib
import traceback
import tornado.web
import tornado.ioloop
from tornado import ioloop, gen
from lxml import etree
from utils import *
from db import MongoDB
from db import RedisDB
from logger_helper import logger
from wxpy import *
logger = logging.getLogger('wxpyInterface')
TURING_BOT = TuringBot()
MONGO_OBJ = MongoDB()
REDIS_OBJ = RedisDB()
global bot
@gen.coroutine
def cur_wxpy_mode():
wxpy_mode = REDIS_OBJ.client.get('WXPY_MODE')
logger.info('Current mode: %s' % wxpy_mode)
bot.self.send_msg('Current mode: %s' % wxpy_mode)
@gen.coroutine
def register_task_reminder():
if get_wxpy_mode() != 'GAI':
logger.info('Not GAI mode, pass the reminder')
def save_to_mongo(self,dic):
mon = MongoDB()
if not mon.find({'内容':self.weibo_content}):
mon.insert(dic)
from db import MongoDB
import scrapy
db = MongoDB()
# BBC article model
class BBCArticleItem(scrapy.Item):
_id = scrapy.Field() # preset id field for mongo db
title = scrapy.Field()
date = scrapy.Field()
time = scrapy.Field()
section = scrapy.Field()
content = scrapy.Field()
url = scrapy.Field()
def save(self):
# write data to database
db.insert_article(self)
def update(self, updates={}):
db.update_article(self, updates)
@classmethod
def fetch_by_title(cls, title):
results = db.find_article_by_title(title)
return cls.parse_result(results)
@classmethod
def fetch_by_section(cls, section):
results = db.find_articles_by_section(section)
from db import MongoDB
from flask import Flask, request, render_template
from model import Node
import json
import threading
import time
import requests
app = Flask(__name__)
mongodb = MongoDB()
thread = threading.Thread(target=mongodb.find_vacant)
thread.start()
@app.route('/')
def index():
list = mongodb.get_all()
return render_template('index.html', rooms=list.get('nodes'))
@app.route('/ping', methods=['POST'])
def ping():
if request.method == 'POST':
node_id = request.headers.get('node-id')
node_floor = request.headers.get('node-floor')
print 'Incoming ping from node ' + node_id
incoming_node = Node(node_id, node_floor)
success = mongodb.add_or_update(incoming_node)
def __init__(self):
self.schema = schema
self.collect = MongoDB()
from flask import escape
from config import Config, ProductionConfig, DevelopmentConfig
from db import MySQL, MongoDB, Query
mysql = MySQL()
mongoDB = MongoDB()
query = Query()
def html_escape(inp):
return str(escape(inp))
def toHourandMin(timeDel):
secs = timeDel.total_seconds()
hours = int(secs / 3600)
minutes = int(secs / 60) % 60
return "%02d:%02d" % (hours, minutes)
def getConfig():
cfg = ProductionConfig
if Config.ENABLE_DEV:
cfg = DevelopmentConfig
return cfg
if row["done"] == 1:
log("%s already processed, skipping!" % row["Subreddit"])
continue
subreddit = row["Subreddit"]
log("Gathering post ID's for %s" % row["Subreddit"])
pushshift = Pushshift(sys.argv[1], sys.argv[2], subreddit)
post_ids = pushshift._post_ids
reddit = Reddit(db, subreddit, post_ids)
log("Gathering datasets")
reddit.gather()
# Get a List of files in working directory
all_datasets = listdir(am._historical_dir)
dataset_names = [
"comments_{}.csv".format(subreddit),
"posts_{}.csv".format(subreddit)
]
if any(dataset in all_datasets for dataset in dataset_names):
log("Processing: " + subreddit)
loaded = am.load_datasets(subreddit)
if loaded:
am.comments_and_posts()
am.wordfreq()
am.bigramfreq()
am.currency_mentions()
log("*" * 30)
db.mark_subreddit_done(subreddit)
db.reset_subreddit_done()
import socket
import threading
import time
from http.server import HTTPServer
from handler import FumbleHandler
from db import MongoDB
MongoDB().recreate()
addr = ('0.0.0.0', 8000)
sock = socket.socket()
# One socket, many threads
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind(addr)
sock.listen(5)
class Thread(threading.Thread):
def __init__(self, i):
super().__init__()
self.i = i
# ctrl-c will kill process
self.daemon = True
self.start()
def run(self):
server = HTTPServer(addr, FumbleHandler, False)
server.socket = sock
def __init__(self):
self.neo_db = NeoDB()
self.mongo_db = MongoDB()
self.query_history = []
post = {"author": random_data(8),
"text": ' '.join(random_data(10) for _ in range(10)),
"tags": ["mongodb", "python", "pymongo"],
"date": datetime.datetime.utcnow()}
return post
def status(obj, msg=''):
print()
print('-'*60, msg.upper(), '-'*60)
if obj:
pprint.pprint(obj)
# req - create connection only
m0 = MongoDB()
status(m0.get_current_status(), 'only connection')
# req - create database with validation
m0.create_db('try000')
status(m0.get_current_status(), 'only database')
# 2
m = MongoDB(database_name='try001')
status(m.get_current_status(), 'only database - part 2')
# req - drop db by name
mm = MongoDB(database_name='DELETE_ME')
mm.create_collection(collection_name='Temporary')
mm.insert(get_dummy_data())
status(mm.get_database_names(), msg='create and delete db')
print('collection created - ', mm.get_collection_names())
mm.drop_db(database_name='DELETE_ME')
def connect_database(self):
self.db = MongoDB(self.settings.get("server"))
# manager = mp.Manager()
# return_dict = manager.dict()
# online training
seed = 123
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
actor = env_networks['actor'](seed, nS, nA, nB, agent_params)
critic = env_networks['critic'][args.critic](seed, nS, nA, nB,
agent_params)
del env
actor.share_memory() #.to(device)
critic.share_memory() #.to(device)
processes = []
num_processes = mp.cpu_count()
if args.clean:
print('Cleaning db')
mongo = MongoDB()
mongo.clean_db()
del mongo
for i in range(num_processes): # No. of processes
p = mp.Process(target=train_shared_model,
args=(agent_params, env_params, training_params, i,
actor, critic))
p.start()
processes.append(p)
for p in processes:
p.join()
basepath = os.path.abspath(sys.argv[0])
torch.save(
actor.state_dict(),
os.path.join(basepath, 'checkpoints/Historical_kuhn' + '_actor'))
torch.save(
def __init__(self):
self.db = MongoDB()
self.duration = 2000
class dbHandler():
def __init__(self):
self.mysql = MySQL()
self.mongo = MongoDB()
self.query = Query()
cfg = getConfig()
self.mysql.init_cfg(cfg)
self.mongo.init_cfg(cfg)
self._con = self.mysql.get_connection()
self._client = self.mongo.get_client()
self.query.init_db(self._con, self._client)
self._db = self._client[cfg.MONGODB_COLLECTION]
def getQuery(self):
return self.query
def getallMAC(self, table): #/
allmac = self._db[table].find().distinct("message.MAC")
return allmac
def getallSensor(self, table, mac): #/
result = self._db[table].find(
{"$and": [{
"message.MAC": mac
}, {
"message.s": {
"$exists": True
}
}]})
alltopic = result.distinct("topic")
allnum = result.distinct("message.s")
res = []
for s in allnum:
for topic in alltopic:
res.append({"s": s, "topic": topic})
return res
def getallTopic(self, table, mac): #/
result = self._db[table].find_one(
{"$or": [{
"message.MAC": mac
}, {
"message.s": mac
}]})
return ['topic', result["topic"], 'MAC', result["message"]["MAC"]]
def getLastVAL(self, sid, mac, _type="ct"):
createdAt = 0
res = None
query = {"message.MAC": mac}
if (_type.split("_") == "dm"):
query["message.s"] = sid
rawCT = self._db[_type].find(query).sort("created_at", -1).limit(1)
for CT in rawCT:
if (CT["created_at"] > createdAt):
res = CT
createdAt = CT["created_at"]
if (_type.split("_")[0] == "dm"):
return (res["message"]["AL1"] + res["message"]["AL2"] +
res["message"]["AL3"]) / 3
return res["message"]["a"]
def getLastPIR(self, mac, _type="pir"):
createdAt = 0
res = {}
for t in self.getTable({"sensor_type": _type}):
rawPIR = self._db[t].find({
"message.MAC": mac
}).sort("created_at", -1).limit(1)
for PIR in rawPIR:
if (PIR["created_at"] > createdAt):
res = PIR
createdAt = PIR["created_at"]
return res
def getTable(self, devFind={}): #/
res = []
for _type in self._db["iot_type"].find(devFind):
name = _type["sensor_type"]
if (_type["device_type"] != ''):
name = name + "_" + _type["device_type"]
res.append(name)
return res
class Logics(object):
db = MongoDB()
@classmethod
def wrap_dict(cls, data):
if isinstance(data, (list, tuple)):
lst = []
for i in data:
lst.append(cls.wrap_dict(i))
return lst
elif isinstance(data, dict):
return AttrDict(data)
else:
return data
@classmethod
def wrap_member(cls, m, status=False, history=False, infos=False):
""" 装饰成员, 将成员换成AttrDict
Arguments:
`m` - 成员
`status` - 是否装载状态
`history` - 是否装载消息历史
`infos` - 是否装载信息
"""
if isinstance(m, (list, tuple)):
lst = []
for i in m:
lst.append(
cls.wrap_member(i, status=status, history=history,
infos=infos))
return lst
elif isinstance(m, dict):
m = AttrDict(m)
if infos:
m["infos"] = cls.wrap_dict(
list(cls.db[const.INFO].find({"mid": m._id})))
if history:
m["history"] = cls.wrap_dict(
list(cls.db[const.HISTORY].find({"from_member.$id": m._id})))
if status:
m["status"] = cls.wrap_dict(
list(cls.db[const.STATUS].find({"mid": m._id})))
return m
else:
return m
@classmethod
def get_with_nick(cls, nick, **kwargs):
""" 根据昵称获取成员
Arguments:
`nick` - 成员昵称
"""
m = cls.db[const.MEMBER].find_one({"nick": nick})
return cls.wrap_member(m, **kwargs)
@classmethod
def get_one(cls, jid, **kwargs):
""" 获取一个成员
Arguments:
`jid` - 成员jid
"""
email = get_email(jid)
return cls.wrap_member(cls.db[const.MEMBER].find_one({"email": email}),
**kwargs)
@classmethod
def add(cls, jid, nick=None, show=None):
""" 添加一个成员
Arguments:
`jid` - 成员jid
`nick` - 昵称
`show` - stanze.show
"""
if cls.get_one(jid):
return
if not nick:
nick = get_email(jid).split("@")[0]
doc = {"email": get_email(jid), "nick": nick, "isonline": True,
"join_date": now()}
mid = cls.db[const.MEMBER].insert(doc)
cls.db[const.STATUS].insert({"mid": mid, "statustext": show,
"resource": jid.resource,
"status": const.ONLINE})
return cls.get_one(jid)
@classmethod
def drop(cls, jid):
""" 删除一个成员
Arguments:
`jid` - 成员jid
"""
m = cls.get_one(jid)
try:
cls.db[const.MEMBER].remove({"email": get_email(jid)})
cls.db[const.STATUS].remove({"mid": m._id})
cls.db[const.INFO].remove({"mid": m._id})
except:
traceback.print_exc()
return
@classmethod
def get_members(cls, remove=None, **kwargs):
""" 获取所有成员
Arguments:
`remove` - 排除成员
"""
remove_email = get_email(remove)
if remove:
ms = cls.db[const.MEMBER].find({"email": {"$ne": remove_email}})
return cls.wrap_member(list(ms), **kwargs)
ms = cls.db[const.MEMBER].find()
return cls.wrap_member(list(ms), **kwargs)
@classmethod
def modify_nick(cls, jid, nick):
""" 修改成员昵称
Arguments:
`jid` - jid
`nick` - 新昵称
Return:
False // 昵称已存在
True // 更改昵称成功
"""
m = cls.get_one(jid)
if not m:
return False
if m:
exists = cls.get_with_nick(nick)
if exists:
return False
cls.db[const.MEMBER].update({"_id": m._id},
{"$set": {"nick": nick, "last_change": now()},
"$push": {"used_nick": nick}})
cls.set_info(jid, const.INFO_CHANGE_NICK_TIMES,
int(cls.get_info(jid,
const.INFO_CHANGE_NICK_TIMES,
0).value) + 1)
return True
@classmethod
def get_one_status(cls, jid):
m = cls.get_one(jid)
if not m:
return False, False
return cls.db[const.STATUS].find_one({"resource": jid.resource},
{"mid": m._id}), m
@classmethod
def set_online(cls, jid, show=None):
""" 设置成员在线
Arguments:
`jid` - 成员jid
`show` - stanza.show
"""
status, m = cls.get_one_status(jid)
if not m:
return False
if status:
cls.db[const.STATUS].update({"_id": status.get("_id")},
{"$set": {"statustext": show}})
else:
cls.db[const.STATUS].insert({"status": const.ONLINE,
"statustext": show,
"resource": jid.resource,
"mid": m._id})
return True
@classmethod
def set_offline(cls, jid):
status, m = cls.get_one_status(jid)
if not m or not status:
return False
cls.db[const.STATUS].remove({"_id": status.get("_id")})
@classmethod
def _get_info(cls, jid=None, key=None, default=None, is_global=False):
""" 获取成员选项
Arguments:
`jid` - jid
`key` - 选项键
`default` - 默认值
"""
cond = {"key": key, "is_global": is_global}
m = None
if jid:
m = cls.get_one(jid)
if not m:
return AttrDict(dict(key=key, value=default, is_global=is_global)), False, None
cond.update(mid=m._id)
info = cls.db[const.INFO].find_one(cond)
from_db = True
if not info:
info = dict(key=key, value=default, is_global=is_global)
from_db = False
return AttrDict(info), from_db, m
@classmethod
def get_info(cls, jid, key, default=None):
return cls._get_info(jid, key, default)[0]
@classmethod
def set_info(cls, jid, key, value):
""" 设置成员选项
Arguments:
`jid` - jid
`key` - 选项键
`value` - 选项值
"""
info, f, m = cls._get_info(jid, key)
if f:
cls.db[const.INFO].update({"_id": info._id},
{"$set": {"value": value}})
else:
cls.db[const.INFO].insert({"key": key, "value": value,
"is_global": False,
"pubdate": now(),
"mid": m._id})
return info
@classmethod
def get_today_rp(cls, jid):
""" 获取今日rp """
rp = None
rp_date = Logics.get_info(jid, const.INFO_RP_DATE).value
if rp_date:
try:
rp_date = datetime.fromtimestamp(float(rp_date))
except:
rp_date = time.time() - 86400
rp_date = datetime.fromtimestamp(float(rp_date))
now = datetime.now()
if now.year == rp_date.year and now.month == rp_date.month and \
now.day == rp_date.day:
rp = Logics.get_info(jid, const.INFO_RP).value
return rp
@classmethod
def set_today_rp(cls, jid, rp):
cls.set_info(jid, const.INFO_RP, rp)
cls.set_info(jid, const.INFO_RP_DATE, time.time())
cls.db[const.MEMBER].update({"email": get_email(jid)},
{"$push": {"rps": {"value": rp, "date": now()}}})
@classmethod
def get_global_info(cls, key, default=None):
""" 获取全局选项
Arguments:
`key` - 选项键
`default` - 默认值
"""
return cls._get_info(key=key, default=default, is_global=True)[0]
@classmethod
def set_global_info(cls, key, value):
""" 设置全局选项
Arguments:
`key` - 选项键
`value` - 选项值
"""
info, f, _ = cls._get_info(key=key, is_global=True)
if f:
cls.db[const.INFO].update({"_id": info._id},
{"$set": {"value": value}})
else:
cls.db[const.INFO].insert({"key": key, "value": value,
"pubdate": now(), "is_global": True})
return info
@classmethod
def add_history(cls, jid, to_jid, content):
m = cls.get_one(jid)
cls.db[const.MEMBER].update(
{"_id": m._id}, {"$set": {"last_say": now()}})
cls.db[const.HISTORY].insert(
{"from_member": cls.db.ref(const.MEMBER, m._id),
"to_member": to_jid, "content": content,
"pubdate": now()})
@classmethod
def get_history(cls, jid=None, starttime=None):
""" 获取历史信息
Arguments:
`jid` - 发送人
`to` - 接收人
`starttime` - 开始时间
"""
condition = {"to_member": "all"}
if jid:
m = cls.get_one(jid)
condition.update({"from_member.$id": m._id})
if starttime:
condition.update(pubdate={"$gte": starttime})
return cls.db.deref(list(cls.db[const.HISTORY].find(condition)
.sort("pubdate", cls.db.asc)))
@classmethod
def is_online(cls, jid):
m = cls.get_one(jid, status=True)
return bool([status.status for status in m.status if status.status])
@classmethod
def empty_status(cls):
cls.db[const.STATUS].remove()
@classmethod
def get_all_rps(cls, starttime=None, endtime=None):
condition = {"key": const.INFO_RP}
if starttime or endtime:
condition["pubdate"] = {}
if starttime:
condition["pubdate"].update({"$gt": starttime})
if endtime:
condition["pubdate"].update({"$lte": endtime})
return list(cls.db[const.INFO].find(condition))
@classmethod
def get_today_rps(cls):
now = now()
starttime = datetime(now.year, now.month, now.day)
endtime = datetime(now.year, now.month, now.day, 23, 59, 59)
return sorted(cls.get_all_rps(starttime, endtime), key=lambda x: x["value"])
@classmethod
def add_honor(cls, jid, value, typ, item, desc):
m = cls.get_with_nick(jid)
doc = {"getdate": now(), "date": now(), "type": typ, "desc": desc,
"mid": m._id, "item": item, "value": value}
cls.db[const.HONOR].insert(doc)
@classmethod
def get_honor(cls, m):
honors = cls.db[const.HONOR].find({"mid": m._id})
return list(honors)
@classmethod
def get_honor_str(cls, m):
honors = cls.get_honor(m)
body = u""
for h in honors:
date = h.get("date").strftime("%Y/%m/%d")
body += u"{0} {1}为{2}达到{3}, 获得成就"\
.format(date, h.get("item"), h.get("value"),
h.get("desc"))
return body
def __init__(self):
self.db_obj = MongoDB()
self.msg_dic = {}
import time
from datetime import datetime
import schedule
from analyzer import BangumiAnalyzer
from conf import conf
from crawler import BangumiCrawler
from db import MongoDB
from utils import logger
if __name__ == '__main__':
logger.info('Hello! This is Bangumi-Provider :)')
client = MongoDB(conf)
crawler = BangumiCrawler(client, conf)
analyzer = BangumiAnalyzer(client, conf)
def jobs():
crawler.crawl()
analyzer.analyze()
if conf.SCHEDULE_ENABLE:
logger.info(
'Running with Schedule Enabled, Tasks Schedule Every Day. Now: %s, Next Schedule: %s.'
% (datetime.now(), conf.SCHEDULE_CRON_AT))
schedule.every().day.at(conf.SCHEDULE_CRON_AT).do(jobs)
while True:
schedule.run_pending()
time.sleep(1)
class Menu():
def __init__(self):
self.neo_db = NeoDB()
self.mongo_db = MongoDB()
self.query_history = []
def main_menu(self):
print("*******SELECT AN OPTION*************")
print("************************************")
print("* 1 - Create DBs (Mongo and Neo4j)**")
print("* 2 - Perform Query for Answer 1 **")
print("* 3 - Perform Query for Answer 2 **")
print("* 4 - Query MongoDB **")
print("* 5 - Query Neo4j **")
print("* 6 - Query History **")
print("* 7 - Import CSV file to DBs **")
print("* 8 - Quit the APP **")
print("************************************")
print("NOTE: Please run Option 1 before any others")
def parse_choice(self, choice):
if(choice=="1"): #todo: show all the user
self.neo_db.setup()
self.mongo_db.setupv2()
input("Press Enter to continue...")
if(choice=="2"):
self.get_user_list()
if(choice=="3"):
print(self.mongo_db.return_users()) #todo: return each user once
user = input("\nSelect User you would like to find \ntrusted colleagues-of-colleagues \nwith one or more interests: ")
print(self.mongo_db.answer_for_question2(user))
input("Press Enter to continue...")
pass
if(choice=="4"):
query = input("Write Query for MongoDB: ")
self.query_history.append(query)
pass
if(choice=="5"):
query = input("Write Query for Neo4j: ")
self.query_history.append(query)
pass
if(choice=="6"):
print(self.query_history)
pass
if(choice=="7"):
print("Import CSV file ... \n")
print("For example: /home/Navid/Documents/BigData/project1_sample/user.csv\n")
filepath = input ("enter file path:")
self.mongo_db.import_content(filepath)
pass
if(choice=="8"):
quit()
def get_user_list(self):
print( self.neo_db.get_all_users() )
user = input("SELECT User (Type Name): ")
company = input("SELECT Company Name of Same User: "******"Press ENTER to Continue...")
def run(self):
print("Welcome To Collaboration NET DB")
while(True):
self.main_menu()
choice = input("Choice: ")
self.parse_choice(choice)
