def upload_test(self):
start_time = time.time()
q = Manager().Queue()
plist = []
for i in range(self.upload_user):
proc = Process(target=self.upload_one_user, args=(q,))
plist.append(proc)
for proc in plist:
proc.start()
for proc in plist:
proc.join()
while True:
if q.empty():
break
else:
if q.get() == 0:
self.upload_success += 1
else:
self.upload_fail += 1
use_time = time.time() - start_time
table = PrettyTable(["key", "value"])
table.add_row(["One File Size (M)", self.upload_file_size])
table.add_row(["All File Size (M)", self.upload_file_size * self.upload_number * self.upload_user])
table.add_row(["Process Count(user)", self.upload_user])
table.add_row(["Upload Count", self.upload_number * self.upload_user])
table.add_row(["Interval Time(s)", self.upload_time])
table.add_row(["Success count", self.upload_success])
table.add_row(["Fail count", self.upload_fail])
table.add_row(["Success ratio (%)",
(round(self.upload_success / float(self.upload_number * self.upload_user), 4) * 100)])
table.add_row(["Use time (s)", "%.2f" % use_time])
print table
def main():
arg_parser = argparse.ArgumentParser(description='bbd compressing program')
arg_parser.add_argument('-compress_from_dir', type=str, default='.',
help='directory where needs to be compressed')
arg_parser.add_argument('-compress_to_dir', type=str, default='.',
help='directory where puts compressed file')
arg_parser.add_argument('-compress_method', default='bz2', choices=['bz2', 'gz'],
help='the method of compressing, '
'support bz2 and gz, bz2 is default')
arg_parser.add_argument('-compress_dir_match', default=None,
help='regular expressions what matches '
'which directories can be compressed')
arg_parser.add_argument('-compress_file_match', default=None,
help='regular expressions what matches '
'which files can be compressed')
args = arg_parser.parse_args()
kwargs = dict()
kwargs['compress_from_dir'] = os.path.abspath(args.compress_from_dir)
kwargs['compress_to_dir'] = os.path.abspath(args.compress_to_dir)
kwargs['compress_method'] = args.compress_method
kwargs['compress_dir_match'] = args.compress_dir_match
kwargs['compress_file_match'] = args.compress_file_match
print('Operating parameters are as follows:')
print('\t' + '\n\t'.join(['{}: {}'.format(k, v) for k, v in kwargs.items()]))
if check_compress_proc_is_alive():
return
if kwargs['compress_from_dir'] == kwargs['compress_to_dir']:
print(kwargs['compress_from_dir'], kwargs['compress_to_dir'])
compress_to_dir = os.path.join(kwargs['compress_to_dir'], 'flume_compressed_data')
kwargs['compress_to_dir'] = compress_to_dir
os.makedirs(compress_to_dir, exist_ok=True)
max_worker = cpu_count() if cpu_count() <= 8 else 8
pool_cls = Pool
compressed_queue = Manager().Queue()
print('using multi processes to compress files')
path_mgr = PathUtils(**kwargs)
compressed_data_dir = Path(kwargs['target_dir']) / 'bbd_compressed_data_dir'
compress_method = kwargs['compress_method']
for file_path in path_mgr.match_need_compress_files():
from_path = str(file_path.absolute())
to_path = str((compressed_data_dir / file_path.name).absolute())
compressed_queue.put((from_path, to_path, compress_method))
if compressed_queue.empty():
print('there is no file need to be compressed, waiting for next checking')
return
multi_workers(max_worker=max_worker, pool_cls=pool_cls, work=compress_file,
compressed_queue=compressed_queue)
def upload_begin(self):
plist = []
q = Manager().Queue()
with open(self.list_path, 'r') as fp:
for i in fp:
if not i:
break
md5_crc32 = i.strip()[:41]
if md5_crc32 not in self.tmp_list and len(md5_crc32) == 41:
self.tmp_list.append(md5_crc32)
self.upload_num += 1
print self.upload_num
for md5_crc32_list in self.chunks(self.tmp_list, self.work_count):
proc = Process(target=self.upload_file, args=(q, md5_crc32_list,))
plist.append(proc)
for proc in plist:
proc.start()
for proc in plist:
proc.join()
while True:
if q.empty():
break
else:
r = q.get()
if r == 0:
self.success += 1
elif r == 1:
self.fail += 1
elif r == 2:
self.download_fail += 1
else:
pass
use_time = time.time() - self.start_time
table = PrettyTable(["key", "value"])
table.add_row(["Upload Count", len(set(self.tmp_list))])
table.add_row(["Success count", self.success])
table.add_row(["Fail count", self.fail])
table.add_row(["Download Fail", self.download_fail])
table.add_row(["Use time (s)", "%.2f" % use_time])
print table
def startServer(host, port, options):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
s.bind((host, port))
s.listen(0)
queue = Manager().Queue()
while True:
print "main: waiting for connection"
conn, addr = s.accept()
print 'main: Connected by', addr
data = conn.recv(1024)
print 'received port request'
p = Process(target = serverNewClient, args = (queue, options, ))
p.start()
while queue.empty():
time.sleep(0.05)
print "queue is still empty"
port = queue.get()
conn.sendall(str(port) + '\r\n')
print "assigned port %d to new client" % port
# 往线程池加入2个task
#f1 = pool.submit(return_future,"hello")
# with Manager() as mgr:
# l = mgr.list()
# l=[]
# l.append(0)
###q =Queue()用process开子进程时，且非pool时用
q = Manager().Queue() #pool时用 #*
# f2 = pool.submit(return_future,q) #*
###p=Process(target=return_future, args=(q,))
###p.start()
#p.join()
while 1:
tim = time.time()
print('c')
print('main:', os.getpid())
time.sleep(0.4)
if not e.is_set(): #未置位就增加一个子进程到pool
f2 = pool.submit(return_future, q, e)
if not q.empty():
value = q.get(True)
d = value[0] + 1
print
print("d=", d)
print
if d == 11:
break
# pool.terminate()
print(time.time() - tim)
def getData(q):
while True:
print('move data %s out from queue'%(q.get()))
time.sleep(2)
if __name__ == '__main__':
q = Manager().Queue()
pool = Pool(8)
for i in range(8):
pool.apply_async(generateData, args=(q,))
# generator = Process(target=generateData, args=(q,))
pool.close()
mover = Process(target=getData, args=(q,))
# generator.start()
mover.start()
pool.join()
# generator.join()
while True:
if q.empty() == True:
break
mover.terminate()# because the while
def calc_factor_loading(cls,
start_date,
end_date=None,
month_end=True,
save=False,
**kwargs):
"""
计算指定日期的样本个股的因子载荷, 并保存至因子数据库
Parameters:
--------
:param start_date: datetime-like, str
开始日期, 格式: YYYY-MM-DD or YYYYMMDD
:param end_date: datetime-like, str
结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD
:param month_end: bool, 默认为True
如果为True, 则只计算月末时点的因子载荷
:param save: bool, 默认为True
是否保存至因子数据库
:param kwargs:
'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False
:return: dict
因子载荷
"""
# 取得交易日序列及股票基本信息表
start_date = Utils.to_date(start_date)
if end_date is not None:
end_date = Utils.to_date(end_date)
trading_days_series = Utils.get_trading_days(start=start_date,
end=end_date)
else:
trading_days_series = Utils.get_trading_days(end=start_date,
ndays=1)
all_stock_basics = CDataHandler.DataApi.get_secu_basics()
# 遍历交易日序列, 计算DASTD因子载荷
dict_dastd = None
for calc_date in trading_days_series:
if month_end and (not Utils.is_month_end(calc_date)):
continue
logging.info('[%s] Calc DASTD factor loading.' %
Utils.datetimelike_to_str(calc_date))
# 遍历个股, 计算个股的DASTD因子值
s = (calc_date - datetime.timedelta(
days=risk_ct.DASTD_CT.listed_days)).strftime('%Y%m%d')
stock_basics = all_stock_basics[all_stock_basics.list_date < s]
ids = [] # 个股代码list
dastds = [] # DASTD因子值list
if 'multi_proc' not in kwargs:
kwargs['multi_proc'] = False
if not kwargs['multi_proc']:
# 采用单进程计算DASTD因子值
for _, stock_info in stock_basics.iterrows():
logging.info(
"[%s] Calc %s's DASTD factor loading." %
(calc_date.strftime('%Y-%m-%d'), stock_info.symbol))
dastd_data = cls._calc_factor_loading(
stock_info.symbol, calc_date)
if dastd_data is not None:
ids.append(dastd_data['code'])
dastds.append(dastd_data['dastd'])
else:
# 采用多进程并行计算DASTD因子值
q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷
p = Pool(4) # 进程池, 最多同时开启4个进程
for _, stock_info in stock_basics.iterrows():
p.apply_async(cls._calc_factor_loading_proc,
args=(
stock_info.symbol,
calc_date,
q,
))
p.close()
p.join()
while not q.empty():
dastd_data = q.get(True)
ids.append(dastd_data['code'])
dastds.append(dastd_data['dastd'])
date_label = Utils.get_trading_days(start=calc_date, ndays=2)[1]
dict_dastd = {
'date': [date_label] * len(ids),
'id': ids,
'factorvalue': dastds
}
if save:
Utils.factor_loading_persistent(
cls._db_file,
Utils.datetimelike_to_str(calc_date, dash=False),
dict_dastd, ['date', 'id', 'factorvalue'])
# 暂停180秒
logging.info('Suspending for 180s.')
time.sleep(180)
return dict_dastd
def calc_factor_loading(cls,
start_date,
end_date=None,
month_end=True,
save=False,
**kwargs):
"""
计算指定日期的样本个股的因子载荷, 并保存至因子数据库
Parameters:
--------
:param start_date: datetime-like, str
开始日期, 格式: YYYY-MM-DD or YYYYMMDD
:param end_date: datetime-like, str
结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD
:param month_end: bool, 默认为True
如果为True, 则只计算月末时点的因子载荷
:param save: bool, 默认True
是否保存至因子数据库
:param kwargs:
:return: dict
因子载荷
"""
# 取得交易日序列及股票基本信息表
start_date = Utils.to_date(start_date)
if end_date is not None:
end_date = Utils.to_date(end_date)
trading_days_series = Utils.get_trading_days(start=start_date,
end=end_date)
else:
trading_days_series = Utils.get_trading_days(end=start_date,
ndays=1)
all_stock_basics = CDataHandler.DataApi.get_secu_basics()
# 遍历交易日序列, 计算筹码分布因子载荷
dict_beta = {}
dict_hsigma = {}
for calc_date in trading_days_series:
if month_end and (not Utils.is_month_end(calc_date)):
continue
logging.info('[%s] Calc BETA factor loading.' %
Utils.datetimelike_to_str(calc_date))
# 遍历个股, 计算个股BETA因子值
s = (calc_date - datetime.timedelta(days=180)).strftime('%Y%m%d')
stock_basics = all_stock_basics[all_stock_basics.list_date < s]
ids = [] # 个股代码list
betas = [] # BETA因子值
hsigmas = [] # HSIGMA因子值
# 采用单进程计算BETA因子和HSIGMA因子值,
# for _, stock_info in stock_basics.iterrows():
# logging.info("[%s] Calc %s's BETA and HSIGMA factor data." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol))
# beta_data = cls._calc_factor_loading(stock_info.symbol, calc_date)
# if beta_data is not None:
# ids.append(beta_data['code'])
# betas.append(beta_data['beta'])
# hsigmas.append(beta_data['hsigma'])
# 采用多进程并行计算BETA因子和HSIGMA因子值
q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷
p = Pool(4) # 进程池, 最多同时开启4个进程
for _, stock_info in stock_basics.iterrows():
p.apply_async(cls._calc_factor_loading_proc,
args=(
stock_info.symbol,
calc_date,
q,
))
p.close()
p.join()
while not q.empty():
beta_data = q.get(True)
ids.append(beta_data['code'])
betas.append(beta_data['beta'])
hsigmas.append(beta_data['hsigma'])
date_label = Utils.get_trading_days(calc_date, ndays=2)[1]
dict_beta = {
'date': [date_label] * len(ids),
'id': ids,
'factorvalue': betas
}
dict_hsigma = {
'date': [date_label] * len(ids),
'id': ids,
'factorvalue': hsigmas
}
if save:
Utils.factor_loading_persistent(
cls._db_file,
Utils.datetimelike_to_str(calc_date, dash=False),
dict_beta, ['date', 'id', 'factorvalue'])
hsigma_path = os.path.join(factor_ct.FACTOR_DB.db_path,
risk_ct.HSIGMA_CT.db_file)
Utils.factor_loading_persistent(
hsigma_path,
Utils.datetimelike_to_str(calc_date, dash=False),
dict_hsigma, ['date', 'id', 'factorvalue'])
# 休息180秒
logging.info('Suspending for 180s.')
time.sleep(180)
return dict_beta
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs):
"""
计算指定日期的样本个股的因子载荷，并保存至因子数据库
Parameters
--------
:param start_date: datetime-like, str
开始日期，格式：YYYY-MM-DD or YYYYMMDD
:param end_date: datetime-like, str
结束日期，如果为None，则只计算start_date日期的因子载荷，格式：YYYY-MM-DD or YYYYMMDD
:param month_end: bool，默认True
如果为True，则只计算月末时点的因子载荷
:param save: bool，默认False
是否保存至因子数据库
:param kwargs:
'multi_proc': bool, True=采用多进程并行计算, False=采用单进程计算, 默认为False
:return: 因子载荷，DataFrame
--------
因子载荷，DataFrame
0. date: 日期
1. id: 证券symbol
2. m0: 隔夜时段动量
3. m1: 第一个小时动量
4. m2: 第二个小时动量
5. m3: 第三个小时动量
6. m4: 第四个小时动量
7. m_normal: 传统动量
"""
# 取得交易日序列及股票基本信息表
start_date = Utils.to_date(start_date)
if end_date is not None:
end_date = Utils.to_date(end_date)
trading_days_series = Utils.get_trading_days(start=start_date, end=end_date)
else:
trading_days_series = Utils.get_trading_days(end=start_date, ndays=1)
# all_stock_basics = CDataHandler.DataApi.get_secu_basics()
# 遍历交易日序列，计算日内动量因子值
dict_intraday_momentum = None
for calc_date in trading_days_series:
if month_end and (not Utils.is_month_end(calc_date)):
continue
# 计算日内各时段动量因子
dict_intraday_momentum = {'date': [], 'id': [], 'm0': [], 'm1': [],
'm2': [], 'm3': [], 'm4': [], 'm_normal': []}
# 遍历个股，计算个股日内动量值
s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d')
stock_basics = Utils.get_stock_basics(s)
if 'multi_proc' not in kwargs:
kwargs['multi_proc'] = False
if not kwargs['multi_proc']:
# 采用单进程进行计算
for _, stock_info in stock_basics.iterrows():
momentum_data = cls._calc_factor_loading(stock_info.symbol, calc_date)
if momentum_data is not None:
logging.info("[%s] %s's intraday momentum = (%0.4f,%0.4f,%0.4f,%0.4f,%0.4f,%0.4f)" % (calc_date.strftime('%Y-%m-%d'),stock_info.symbol, momentum_data.m0, momentum_data.m1, momentum_data.m2, momentum_data.m3, momentum_data.m4, momentum_data.m_normal))
dict_intraday_momentum['id'].append(Utils.code_to_symbol(stock_info.symbol))
dict_intraday_momentum['m0'].append(round(momentum_data.m0, 6))
dict_intraday_momentum['m1'].append(round(momentum_data.m1, 6))
dict_intraday_momentum['m2'].append(round(momentum_data.m2, 6))
dict_intraday_momentum['m3'].append(round(momentum_data.m3, 6))
dict_intraday_momentum['m4'].append(round(momentum_data.m4, 6))
dict_intraday_momentum['m_normal'].append(round(momentum_data.m_normal, 6))
else:
# 采用多进程并行计算日内动量因子载荷
q = Manager().Queue() # 队列，用于进程间通信，存储每个进程计算的因子载荷
p = Pool(4) # 进程池，最多同时开启4个进程
for _, stock_info in stock_basics.iterrows():
p.apply_async(cls._calc_factor_loading_proc, args=(stock_info.symbol, calc_date, q,))
p.close()
p.join()
while not q.empty():
momentum_data = q.get(True)
dict_intraday_momentum['id'].append(momentum_data[0])
dict_intraday_momentum['m0'].append(round(momentum_data[1], 6))
dict_intraday_momentum['m1'].append(round(momentum_data[2], 6))
dict_intraday_momentum['m2'].append(round(momentum_data[3], 6))
dict_intraday_momentum['m3'].append(round(momentum_data[4], 6))
dict_intraday_momentum['m4'].append(round(momentum_data[5], 6))
dict_intraday_momentum['m_normal'].append(round(momentum_data[6], 6))
date_label = Utils.get_trading_days(calc_date, ndays=2)[1]
dict_intraday_momentum['date'] = [date_label] * len(dict_intraday_momentum['id'])
# 保存因子载荷至因子数据库
if save:
# Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_intraday_momentum)
cls._save_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_intraday_momentum, 'periodmomentum', factor_type='raw')
# 计算日内各时段动量因子的Rank IC值向量, 并保存
cls._calc_periodmomentum_ic(calc_date, 'month')
# 计算最优化权重
if alphafactor_ct.INTRADAYMOMENTUM_CT['optimized']:
cls._optimize_periodmomentum_weight(calc_date)
# 计算合成日内动量因子
if alphafactor_ct.INTRADAYMOMENTUM_CT['synthesized']:
logging.info('[%s] calc synthetic intraday momentum factor loading.' % Utils.datetimelike_to_str(calc_date))
dict_intraday_momentum = {'date': [], 'id': [], 'factorvalue': []}
# 读取日内个时段动量因子值
# period_momentum_path = os.path.join(SETTINGS.FACTOR_DB_PATH, alphafactor_ct.INTRADAYMOMENTUM_CT.db_file, 'raw/periodmomentum')
# df_factor_loading = Utils.read_factor_loading(period_momentum_path, Utils.datetimelike_to_str(calc_date, False))
df_factor_loading = cls._get_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), factor_name='periodmomentum', factor_type='raw', drop_na=False)
if df_factor_loading.shape[0] <= 0:
logging.info("[%s] It doesn't exist intraday momentum factor loading." % Utils.datetimelike_to_str(calc_date))
return
df_factor_loading.fillna(0, inplace=True)
# 读取因子最优权重
factor_weight = cls._get_factor_weight(calc_date)
if factor_weight is None:
logging.info("[%s] It doesn't exist factor weight.")
return
# 计算合成动量因子, 合成之前先对日内各时段动量因子进行去极值和标准化处理
arr_factor_loading = np.array(df_factor_loading[['m0', 'm1', 'm2', 'm3', 'm4']])
arr_factor_loading = Utils.normalize_data(arr_factor_loading, treat_outlier=True)
arr_factor_weight = np.array(factor_weight.drop('date')).reshape((5, 1))
arr_synthetic_factor = np.dot(arr_factor_loading, arr_factor_weight)
dict_intraday_momentum['date'] = list(df_factor_loading['date'])
dict_intraday_momentum['id'] = list(df_factor_loading['id'])
dict_intraday_momentum['factorvalue'] = list(arr_synthetic_factor.astype(float).round(6).reshape((arr_synthetic_factor.shape[0],)))
# 标准化合成动量因子
df_std_intradaymonmentum = Utils.normalize_data(pd.DataFrame(dict_intraday_momentum), columns='factorvalue', treat_outlier=True, weight='eq')
# 保存合成因子
if save:
# Utils.factor_loading_persistent(synthetic_db_file, Utils.datetimelike_to_str(calc_date, False), dict_intraday_momentum)
cls._save_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_intraday_momentum, 'IntradayMomentum', factor_type='raw', columns=['date', 'id', 'factorvalue'])
cls._save_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), df_std_intradaymonmentum, 'IntradayMomentum', factor_type='standardized', columns=['date', 'id', 'factorvalue'])
# 休息360秒
logging.info('Suspending for 360s.')
time.sleep(360)
return dict_intraday_momentum
class Queue_server(object):
'''
初始话公众号队列
@param Tuple wx_lists 公众号列表
'''
def __init__(self ,wx_lists=()):
self.__queue = Manager().Queue(-1)
self.init_wx_lists(wx_lists)
self.__fail_list = Manager().list()
'''
初始话公众号队列
@param Tuple wx_lists 公众号列表
'''
def init_wx_lists(self ,wx_lists=()):
for wx in wx_lists:
self.put(wx)
'''
添加元素
@param mixed value 要添加的元素
'''
def put(self ,value):
self.__queue.put(value)
'''
弹出元素
@return mixed
'''
def get(self):
if not self.empty():
return self.__queue.get()
return False
'''
获取队列
@return mixed
'''
def get_wx_lists_queue(self):
return self.__queue
'''
获取队列大小
@return int
'''
def get_size(self):
return self.__queue.qsize()
'''
队列是否为空
@return bool
'''
def empty(self):
return self.__queue.empty()
'''
添加失败数据
@param tuple wx_data 公众号信息
@return bool
'''
def put_fail_wx(self , wx_data):
self.__fail_list.append(wx_data)
'''
打印失败列表
'''
def print_fail_list(self ,flush=None):
if len(self.__fail_list) > 0 :
for fail in self.__fail_list:
self.put(fail)
print 'the fail wx : {0}' . format(fail)
if not flush:
self.__fail_list = Manager().list()
elif flush:
print 'all success'
#判断是否有错
def is_have_failed(self):
#判断是否有失败的公众号重新加入队列中
return not self.empty()
class MPResult(object):
"""
Sync result between processes
"""
MATCH = {} # id -> instance
def __init__(self, result):
from multiprocessing import Manager
# Test result instance
self.result = result
# Result queue
self.queue = Manager().Queue()
def __getattr__(self, item):
return getattr(self.result, item)
@staticmethod
def pack_result_storage(storage):
"""
Pack result from storage
"""
return [(get_master_id(s[0]), s[1]) for s in storage]
def unpack_result_storage(self, storage):
"""
Unpack result from storage
"""
unpack_storage = []
for master_id, message in storage:
unpack_storage.append(
(self.MATCH[master_id], message),
)
return unpack_storage
def match(self, suite):
"""
Match id of master process to instance
"""
self.MATCH[get_suite_master_id(suite)] = suite
def match(s):
for o in s:
if isinstance(o, BaseSuite):
self.MATCH[get_suite_master_id(o)] = o
match(o)
else:
self.MATCH[get_case_master_id(o)] = o
match(suite)
def save_result(self):
"""
Save result in queue
"""
self.queue.put(
(
(
self.pack_result_storage(self.result.errors),
self.pack_result_storage(self.result.skipped),
self.pack_result_storage(self.result.failures),
),
self.result.testsRun,
),
)
def make_result(self):
"""
Merge result from queue to result instance
"""
while not self.queue.empty():
(errors, skipped, failures), run_tests = self.queue.get()
self.result.errors.extend(self.unpack_result_storage(errors))
self.result.skipped.extend(self.unpack_result_storage(skipped))
self.result.failures.extend(self.unpack_result_storage(failures))
self.result.testsRun += run_tests
def query_request_new(kb_ids, issue, kb_vers_map):
"""
tips: cache形式：cache.set(box ,(kb, 0/1), 过期时间)
requirement: (1)查看boxs列表, 对比cache中的boxs, 若有空闲的box，则为其赋一个知识库；
若没有空闲box，则在cache中寻找该KB已有的box。
(2)取出请求中所有知识库对应的BOX的地址，单独起进程发送请求，请求结果放入queue
param: kb_ids 列表
return:
"""
# 取cache中已有的kb
logger.debug('start get add')
start_get_add = time()
kb_add_dict = cache.get_many(kb_ids)
no_kbs = set(kb_ids) - set(kb_add_dict.keys())
boxs = cache.get('boxs')
box_addr_dict = cache.get('box_infos')
box_kb_dict = cache.get_many(boxs)
box_kb_rest = list(
filter(lambda x: (x[1][0] in boxs) and (not x[1][1]),
box_kb_dict.items()))
boxs_idle = list(filter(lambda x: not cache.get(x), boxs))
logger.debug('boxs_idle:%s' % boxs_idle)
# 为cache中没有的kb赋予box
boxs_free = []
if kb_add_dict:
boxs_free = set(dict(boxs).keys()) - set(
dict(kb_add_dict.values()).keys())
else:
boxs_free = set(dict(boxs).keys())
if len(boxs_free) < len(no_kbs):
rest_kbs = no_kbs[len(boxs_free):]
kb_ids = set(kb_ids) - set(rest_kbs)
# 写入cache
boxs_free_info = filter(lambda x: x[0] in boxs_free, boxs)
temp_kb_box_list = list(zip(no_kbs, boxs_free_info))
cache_ret = map(lambda x: cache.set(x[0], x[1], 30 * 60), temp_kb_box_list)
logger.debug('cache_ret:%s' % list(cache_ret))
kb_add_dict = cache.get_many(kb_ids)
logger.debug('kb_add_dict:%s' % kb_add_dict)
logger.debug('------get address time:%.5f' % (time() - start_get_add))
logger.debug('start box-request ')
start_request = time()
num = len(kb_ids)
q = Manager().Queue()
p_list = []
for i in range(0, num):
kb = kb_ids[i]
version = kb_vers_map[kb]
add = kb_add_dict[kb][1]
logger.debug('Target:%s Add:%s' % (kb, add))
temp_p = Process(target=_writer, args=(q, kb, version, add, issue))
p_list.append(temp_p)
temp_p.start()
for pr in p_list:
pr.join()
logger.debug('------box-request time:%.5f' % (time() - start_request))
start_get_msg = time()
i = 0
ret = {'no_box': [], 'ans': [], 'not_match': [], 'fail': []}
while not q.empty():
msg = q.get()
if 'not_match' in msg.keys():
ret['not_match'].append(msg['not_match'])
elif 'fail' in msg.keys():
ret['fail'].append(msg['fail'])
else:
ret['ans'].append(msg)
logger.debug('------%d msg:%s' % (i, msg))
i += 1
logger.debug('------get answers time:%.5f' % (time() - start_get_msg))
# 异步写入zk
# set_box_zk.delay(temp_kb_box_list)
return ret
def find_pro_ite(ite,url,m_page,max_asin):
head_csv={'A': 'ID', 'B': 'Type', 'C': 'SKU', 'D': 'Name', 'E': 'Published', 'F': 'Is featured?', 'G': 'Visibility in catalog', 'H': 'Short description',
'I': 'Description', 'J': 'Date sale price starts', 'K': 'Date sale price ends', 'L': 'Tax status', 'M': 'Tax class', 'N': 'In stock?', 'O': 'Stock',
'P': 'Low stock amount', 'Q': 'Backorders allowed?', 'R': 'Sold individually?', 'S': 'Weight (kg)', 'T': 'Length (cm)', 'U': 'Width (cm)',
'V': 'Height (cm)', 'W': 'Allow customer reviews?', 'X': 'Purchase note', 'Y': 'Sale price', 'Z': 'Regular price', 'AA': 'Categories', 'AB': 'Tags',
'AC': 'Shipping class', 'AD': 'Images', 'AE': 'Download limit', 'AF': 'Download expiry days', 'AG': 'Parent', 'AH': 'Grouped products', 'AI': 'Upsells',
'AJ': 'Cross-sells', 'AK': 'External URL', 'AM': 'Button text', 'AL': 'Position', 'AN': 'Attribute 1 name', 'AO': 'Attribute 1 value(s)',
'AP': 'Attribute 1 visible', 'AQ': 'Attribute 1 global', 'AR': 'Attribute 2 name', 'AS': 'Attribute 2 value(s)', 'AT': 'Attribute 2 visible',
'AU': 'Attribute 2 global', 'AV': 'Meta: pf_size_chart', 'AW': 'Meta: _primary_term_product_cat'
}
headers= {
'Accept-Language':'en-us,en;q=0.9',
'accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',
'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.121 Safari/537.36',
'cookie':r'session-id-time=2082787201l; session-id=143-7283193-0434042; ubid-main=135-4071739-0100158; i18n-prefs=USD; session-token=F1LPasjCZIhIQ3/UGQzCK5nsUrdENnnmnEtR39OUktmBknNz2vdXXWxPOt7TEoa6vmDlLxrS6DtUP45Q//el4TorL3P/dxmPofxCIQNxJTdAm4VegAQpnjMFvd1iytSxmCo8A8EpGmHFurR6fehGlCCvQl1+XDM9qcGXvlg6bdKcz2LQv0xkdqye+tM3mHl+vQ8A39yGrNXr0Zdf0zh4t5AWSZAEtibFA7ijzLXESlwl85N8bV4MacnWQl46mWYM; csm-hit=tb:s-M0ECGT4J6MCDVSNCDN6F|1601001176170&t:1601001180260&adb:adblk_no'
}
#相关设置
url=url
csv_path="./pro/{ite}.csv".format(ite=ite)
asin_path="./asin/{ite}.txt".format(ite=ite)#可不修改
#新建或者清空asin文件
open(asin_path,"w",encoding="utf8",newline="")
#采集asin
for i in find_x():
if m_page == -1:
pass
elif i > m_page:
print('采集完{i}页的asin'.format(i=i))
break
else:
pass
try:
#获取下一页url和本页面的产品url
bs=find_bs(url,headers)
print('成功获取bs')
page_url,asins=find_page_url(bs,asin_path)
print('成功获取产品asin和下一页')
str_asins="".join(asins)
print("page_url-----",page_url)
print("asins------",str_asins)
except Exception as e:
print(e)
print("采集完毕！")
print("最后采集页面是",url)
break
url=page_url
#写入表头
with open(csv_path,"w",encoding='utf8',newline='') as f:
f_csv=csv.DictWriter(f,head_csv)
f_csv.writerow(head_csv)
with open(asin_path,'r',encoding='utf8') as f:
q_asin_list=set()
all_asin=[]
q_asins=Manager().Queue()
q = Manager().Queue()
q_asin=Manager().Queue()
for asin in f:
t=5
main_asin=re.sub("\n",'',asin)
try:
find_pro(csv_path,main_asin,headers,head_csv,t,q_asin,q_asin_list,q,q_asins,all_asin,max_asin)
print('采集完{asin}'.format(asin=main_asin))
except Exception as e:
print('采集{asin}失败,跳过'.format(asin=main_asin),e)
while q.empty()==False:
q.get()
print('成功清空q')
FPS_clock = pygame.time.Clock()
game_state = state.GameState()
game_gui = gui.GUI(game_state)
game_event_handler = event_handler.EventLogic(game_state, game_gui)
game_gui.add_handler(game_event_handler)
game_gui.draw(game_state.get_state())
pygame.display.update()
commandQueue = Manager().Queue()
listeningProcess = Process(target=voice_listener, args=(game_event_handler, commandQueue,))
while True:
game_gui.draw(game_state.get_state())
game_event_handler.event_handler()
if game_state.get_state() == "SSH season voice mode" or game_state.get_state() == "Web season voice mode":
if not game_event_handler.queue.empty():
val = game_event_handler.queue.get()
if val:
listeningProcess.start()
else:
listeningProcess.terminate()
listeningProcess.join()
listeningProcess = Process(target=voice_listener, args=(game_event_handler, commandQueue,))
if not commandQueue.empty():
voice_command = commandQueue.get()
try:
game_event_handler.pipi.say(voice_command %
game_gui.bool_to_text[str(game_gui.light_to_string[voice_command])])
except KeyError:
pass
pygame.display.update()
FPS_clock.tick(30)
class HostScanner:
def __init__(self,
function,
number,
method,
port_range=None,
ip_range=None,
verbose=False,
write_json=False,
json_file='./result.json'):
self.function = function
self.number = number
self.method = method
self.port_range = port_range
self.ip_range = ip_range
self.write_json = write_json
self._verbose = verbose
self.json_file = json_file
self._PoolExecutor = ThreadPoolExecutor if self.method == 'thread' else ProcessPoolExecutor
self.que = Manager().Queue(10)
def _update_json_file(self):
if self._verbose:
print(f"Write the results in the queue to {self.json_file}")
json_update = {}
while not self.que.empty():
json_update.update(self.que.get())
if os.path.exists(self.json_file):
with open(self.json_file, 'r') as fr:
json_content = json.loads(fr.read())
json_content.update(json_update)
with open(self.json_file, 'w') as fw:
fw.write(json.dumps(json_content, indent=4))
else:
with open(self.json_file, 'w') as fw:
fw.write(json.dumps(json_update, indent=4))
def _ping_host_ip(self, ip):
if self._verbose:
print("pid is %s" % os.getpid())
try:
res = subprocess.call('ping -c 2 -t 2 %s' % ip,
shell=True,
stdout=subprocess.PIPE)
status = 'Active' if res == 0 else 'Inactive'
print(f'{ip} {status}')
if self.write_json:
if self.method == 'proc':
with WRITE_LOCK_PROC:
if self.que.full():
self._update_json_file()
elif self.method == 'thread':
with WRITE_LOCK_THREAD:
if self.que.full():
self._update_json_file()
self.que.put({ip: status})
except Exception as e:
print('Failed to get status for {}: {}'.format(ip, e))
def _scan_host_port(self, port):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
s.connect((self.ip_range, port))
print(f'{port} OPEN')
if self.write_json:
if self.method == 'proc':
with WRITE_LOCK_PROC:
if self.que.full():
self._update_json_file()
elif self.method == 'thread':
with WRITE_LOCK_THREAD:
if self.que.full():
self._update_json_file()
self.que.put({port: 'OPEN'})
except Exception as e:
# Ignore the failed port
pass
finally:
s.close()
def _runMultiWorks(self):
with self._PoolExecutor(self.number) as Executor:
if self.function == 'tcp':
print(f'The scanned host is {self.ip_range}')
Executor.map(self._scan_host_port, list(self.port_range))
elif self.function == 'ping':
Executor.map(self._ping_host_ip, self.ip_range)
def run(self):
# In order to support multiple debugging,
# delete the generated json file in the first run
if os.path.exists(self.json_file):
os.remove(self.json_file)
if self._verbose:
print('Start')
print('*' * 20)
start_time = time.time()
self._runMultiWorks()
end_time = time.time()
if self._verbose:
print('*' * 20)
print('End')
print("Total time spent: %0.2f" % (end_time - start_time))
if self.write_json:
print("Writing into {}".format(self.json_file))
self._update_json_file()
def query_request(kb_ids, issue, kb_vers_map):
"""
requirement: (1)cache中查找知识库的地址，cache中没有的，则为其在cache中没有被其他kb占用的box中选取BOX，并异步写入其TARGET
(2)遍历/B/vm/下的子节点，找到空闲节点，为其赋一个知识库，单独起进程发送请求，请求结果放入queue
param: kb_ids 列表
return:
"""
# 取cache中已有的kb
logger.debug('start get add')
start_get_add = time()
kb_add_dict = cache.get_many(kb_ids)
no_kbs = set(kb_ids) - set(kb_add_dict.keys())
logger.debug('no_kbs:%s' % no_kbs)
# 为cache中没有的kb赋予box
boxs = [('/B/83c4ee846cf2/B70/', '192.168.30.187:8000/70'),
('/B/83c4ee846cf2/B74/', '192.168.30.187:8000/74'),
('/B/83c4ee846cf2/B73/', '192.168.30.187:8000/73'),
('/B/83c4ee846cf2/B72/', '192.168.30.187:8000/72'),
('/B/83c4ee846cf2/B71/', '192.168.30.187:8000/71'),
('/B/83c4ee846cf2/B30/', '192.168.30.187:8000/30'),
('/B/83c4ee846cf2/B23/', '192.168.30.187:8000/23'),
('/B/83c4ee846cf2/B22/', '192.168.30.187:8000/22'),
('/B/83c4ee846cf2/B21/', '192.168.30.187:8000/21'),
('/B/83c4ee846cf2/B20/', '192.168.30.187:8000/20'),
('/B/83c4ee846cf2/B27/', '192.168.30.187:8000/27'),
('/B/83c4ee846cf2/B26/', '192.168.30.187:8000/26'),
('/B/83c4ee846cf2/B25/', '192.168.30.187:8000/25'),
('/B/83c4ee846cf2/B24/', '192.168.30.187:8000/24'),
('/B/83c4ee846cf2/B66/', '192.168.30.187:8000/66'),
('/B/83c4ee846cf2/B67/', '192.168.30.187:8000/67'),
('/B/83c4ee846cf2/B64/', '192.168.30.187:8000/64'),
('/B/83c4ee846cf2/B29/', '192.168.30.187:8000/29'),
('/B/83c4ee846cf2/B65/', '192.168.30.187:8000/65'),
('/B/83c4ee846cf2/B28/', '192.168.30.187:8000/28'),
('/B/83c4ee846cf2/B68/', '192.168.30.187:8000/68'),
('/B/83c4ee846cf2/B69/', '192.168.30.187:8000/69'),
('/B/83c4ee846cf2/B5/', '192.168.30.187:8000/5'),
('/B/83c4ee846cf2/B4/', '192.168.30.187:8000/4'),
('/B/83c4ee846cf2/B81/', '192.168.30.187:8000/81'),
('/B/83c4ee846cf2/B3/', '192.168.30.187:8000/3'),
('/B/83c4ee846cf2/B80/', '192.168.30.187:8000/80'),
('/B/83c4ee846cf2/B2/', '192.168.30.187:8000/2'),
('/B/83c4ee846cf2/B83/', '192.168.30.187:8000/83'),
('/B/83c4ee846cf2/B9/', '192.168.30.187:8000/9'),
('/B/83c4ee846cf2/B82/', '192.168.30.187:8000/82'),
('/B/83c4ee846cf2/B8/', '192.168.30.187:8000/8'),
('/B/83c4ee846cf2/B85/', '192.168.30.187:8000/85'),
('/B/83c4ee846cf2/B7/', '192.168.30.187:8000/7'),
('/B/83c4ee846cf2/B84/', '192.168.30.187:8000/84'),
('/B/83c4ee846cf2/B6/', '192.168.30.187:8000/6'),
('/B/83c4ee846cf2/B40/', '192.168.30.187:8000/40'),
('/B/83c4ee846cf2/B41/', '192.168.30.187:8000/41'),
('/B/83c4ee846cf2/B32/', '192.168.30.187:8000/32'),
('/B/83c4ee846cf2/B31/', '192.168.30.187:8000/31'),
('/B/83c4ee846cf2/B34/', '192.168.30.187:8000/34'),
('/B/83c4ee846cf2/B33/', '192.168.30.187:8000/33'),
('/B/83c4ee846cf2/B36/', '192.168.30.187:8000/36'),
('/B/83c4ee846cf2/B35/', '192.168.30.187:8000/35'),
('/B/83c4ee846cf2/B38/', '192.168.30.187:8000/38'),
('/B/83c4ee846cf2/B37/', '192.168.30.187:8000/37'),
('/B/83c4ee846cf2/B75/', '192.168.30.187:8000/75'),
('/B/83c4ee846cf2/B76/', '192.168.30.187:8000/76'),
('/B/83c4ee846cf2/B39/', '192.168.30.187:8000/39'),
('/B/83c4ee846cf2/B77/', '192.168.30.187:8000/77'),
('/B/83c4ee846cf2/B78/', '192.168.30.187:8000/78'),
('/B/83c4ee846cf2/B79/', '192.168.30.187:8000/79'),
('/B/83c4ee846cf2/B1/', '192.168.30.187:8000/1'),
('/B/83c4ee846cf2/B19/', '192.168.30.187:8000/19'),
('/B/83c4ee846cf2/B17/', '192.168.30.187:8000/17'),
('/B/83c4ee846cf2/B18/', '192.168.30.187:8000/18'),
('/B/83c4ee846cf2/B90/', '192.168.30.187:8000/90'),
('/B/83c4ee846cf2/B51/', '192.168.30.187:8000/51'),
('/B/83c4ee846cf2/B11/', '192.168.30.187:8000/11'),
('/B/83c4ee846cf2/B52/', '192.168.30.187:8000/52'),
('/B/83c4ee846cf2/B12/', '192.168.30.187:8000/12'),
('/B/83c4ee846cf2/B50/', '192.168.30.187:8000/50'),
('/B/83c4ee846cf2/B10/', '192.168.30.187:8000/10'),
('/B/83c4ee846cf2/B15/', '192.168.30.187:8000/15'),
('/B/83c4ee846cf2/B16/', '192.168.30.187:8000/16'),
('/B/83c4ee846cf2/B13/', '192.168.30.187:8000/13'),
('/B/83c4ee846cf2/B14/', '192.168.30.187:8000/14'),
('/B/83c4ee846cf2/B49/', '192.168.30.187:8000/49'),
('/B/83c4ee846cf2/B48/', '192.168.30.187:8000/48'),
('/B/83c4ee846cf2/B47/', '192.168.30.187:8000/47'),
('/B/83c4ee846cf2/B46/', '192.168.30.187:8000/46'),
('/B/83c4ee846cf2/B45/', '192.168.30.187:8000/45'),
('/B/83c4ee846cf2/B44/', '192.168.30.187:8000/44'),
('/B/83c4ee846cf2/B43/', '192.168.30.187:8000/43'),
('/B/83c4ee846cf2/B42/', '192.168.30.187:8000/42'),
('/B/83c4ee846cf2/B88/', '192.168.30.187:8000/88'),
('/B/83c4ee846cf2/B89/', '192.168.30.187:8000/89'),
('/B/83c4ee846cf2/B86/', '192.168.30.187:8000/86'),
('/B/83c4ee846cf2/B87/', '192.168.30.187:8000/87'),
('/B/83c4ee846cf2/B60/', '192.168.30.187:8000/60'),
('/B/83c4ee846cf2/B61/', '192.168.30.187:8000/61'),
('/B/83c4ee846cf2/B62/', '192.168.30.187:8000/62'),
('/B/83c4ee846cf2/B63/', '192.168.30.187:8000/63'),
('/B/83c4ee846cf2/B58/', '192.168.30.187:8000/58'),
('/B/83c4ee846cf2/B57/', '192.168.30.187:8000/57'),
('/B/83c4ee846cf2/B59/', '192.168.30.187:8000/59'),
('/B/83c4ee846cf2/B54/', '192.168.30.187:8000/54'),
('/B/83c4ee846cf2/B53/', '192.168.30.187:8000/53'),
('/B/83c4ee846cf2/B56/', '192.168.30.187:8000/56'),
('/B/83c4ee846cf2/B55/', '192.168.30.187:8000/55'),
('/B/d204c1d12b8a/B70/', '192.168.30.186:8000/70'),
('/B/d204c1d12b8a/B74/', '192.168.30.186:8000/74'),
('/B/d204c1d12b8a/B73/', '192.168.30.186:8000/73'),
('/B/d204c1d12b8a/B72/', '192.168.30.186:8000/72'),
('/B/d204c1d12b8a/B71/', '192.168.30.186:8000/71'),
('/B/d204c1d12b8a/B30/', '192.168.30.186:8000/30'),
('/B/d204c1d12b8a/B23/', '192.168.30.186:8000/23'),
('/B/d204c1d12b8a/B22/', '192.168.30.186:8000/22'),
('/B/d204c1d12b8a/B21/', '192.168.30.186:8000/21'),
('/B/d204c1d12b8a/B20/', '192.168.30.186:8000/20'),
('/B/d204c1d12b8a/B27/', '192.168.30.186:8000/27'),
('/B/d204c1d12b8a/B26/', '192.168.30.186:8000/26'),
('/B/d204c1d12b8a/B25/', '192.168.30.186:8000/25'),
('/B/d204c1d12b8a/B24/', '192.168.30.186:8000/24'),
('/B/d204c1d12b8a/B66/', '192.168.30.186:8000/66'),
('/B/d204c1d12b8a/B67/', '192.168.30.186:8000/67'),
('/B/d204c1d12b8a/B64/', '192.168.30.186:8000/64'),
('/B/d204c1d12b8a/B29/', '192.168.30.186:8000/29'),
('/B/d204c1d12b8a/B65/', '192.168.30.186:8000/65'),
('/B/d204c1d12b8a/B28/', '192.168.30.186:8000/28'),
('/B/d204c1d12b8a/B68/', '192.168.30.186:8000/68'),
('/B/d204c1d12b8a/B69/', '192.168.30.186:8000/69'),
('/B/d204c1d12b8a/B5/', '192.168.30.186:8000/5'),
('/B/d204c1d12b8a/B4/', '192.168.30.186:8000/4'),
('/B/d204c1d12b8a/B81/', '192.168.30.186:8000/81'),
('/B/d204c1d12b8a/B3/', '192.168.30.186:8000/3'),
('/B/d204c1d12b8a/B80/', '192.168.30.186:8000/80'),
('/B/d204c1d12b8a/B2/', '192.168.30.186:8000/2'),
('/B/d204c1d12b8a/B83/', '192.168.30.186:8000/83'),
('/B/d204c1d12b8a/B9/', '192.168.30.186:8000/9'),
('/B/d204c1d12b8a/B82/', '192.168.30.186:8000/82'),
('/B/d204c1d12b8a/B8/', '192.168.30.186:8000/8'),
('/B/d204c1d12b8a/B85/', '192.168.30.186:8000/85'),
('/B/d204c1d12b8a/B7/', '192.168.30.186:8000/7'),
('/B/d204c1d12b8a/B84/', '192.168.30.186:8000/84'),
('/B/d204c1d12b8a/B6/', '192.168.30.186:8000/6'),
('/B/d204c1d12b8a/B40/', '192.168.30.186:8000/40'),
('/B/d204c1d12b8a/B41/', '192.168.30.186:8000/41'),
('/B/d204c1d12b8a/B32/', '192.168.30.186:8000/32'),
('/B/d204c1d12b8a/B31/', '192.168.30.186:8000/31'),
('/B/d204c1d12b8a/B34/', '192.168.30.186:8000/34'),
('/B/d204c1d12b8a/B33/', '192.168.30.186:8000/33'),
('/B/d204c1d12b8a/B36/', '192.168.30.186:8000/36'),
('/B/d204c1d12b8a/B35/', '192.168.30.186:8000/35'),
('/B/d204c1d12b8a/B38/', '192.168.30.186:8000/38'),
('/B/d204c1d12b8a/B37/', '192.168.30.186:8000/37'),
('/B/d204c1d12b8a/B75/', '192.168.30.186:8000/75'),
('/B/d204c1d12b8a/B76/', '192.168.30.186:8000/76'),
('/B/d204c1d12b8a/B39/', '192.168.30.186:8000/39'),
('/B/d204c1d12b8a/B77/', '192.168.30.186:8000/77'),
('/B/d204c1d12b8a/B78/', '192.168.30.186:8000/78'),
('/B/d204c1d12b8a/B79/', '192.168.30.186:8000/79'),
('/B/d204c1d12b8a/B1/', '192.168.30.186:8000/1'),
('/B/d204c1d12b8a/B19/', '192.168.30.186:8000/19'),
('/B/d204c1d12b8a/B17/', '192.168.30.186:8000/17'),
('/B/d204c1d12b8a/B18/', '192.168.30.186:8000/18'),
('/B/d204c1d12b8a/B90/', '192.168.30.186:8000/90'),
('/B/d204c1d12b8a/B51/', '192.168.30.186:8000/51'),
('/B/d204c1d12b8a/B11/', '192.168.30.186:8000/11'),
('/B/d204c1d12b8a/B52/', '192.168.30.186:8000/52'),
('/B/d204c1d12b8a/B12/', '192.168.30.186:8000/12'),
('/B/d204c1d12b8a/B50/', '192.168.30.186:8000/50'),
('/B/d204c1d12b8a/B10/', '192.168.30.186:8000/10'),
('/B/d204c1d12b8a/B15/', '192.168.30.186:8000/15'),
('/B/d204c1d12b8a/B16/', '192.168.30.186:8000/16'),
('/B/d204c1d12b8a/B13/', '192.168.30.186:8000/13'),
('/B/d204c1d12b8a/B14/', '192.168.30.186:8000/14'),
('/B/d204c1d12b8a/B49/', '192.168.30.186:8000/49'),
('/B/d204c1d12b8a/B48/', '192.168.30.186:8000/48'),
('/B/d204c1d12b8a/B47/', '192.168.30.186:8000/47'),
('/B/d204c1d12b8a/B46/', '192.168.30.186:8000/46'),
('/B/d204c1d12b8a/B45/', '192.168.30.186:8000/45'),
('/B/d204c1d12b8a/B44/', '192.168.30.186:8000/44'),
('/B/d204c1d12b8a/B43/', '192.168.30.186:8000/43'),
('/B/d204c1d12b8a/B42/', '192.168.30.186:8000/42'),
('/B/d204c1d12b8a/B88/', '192.168.30.186:8000/88'),
('/B/d204c1d12b8a/B89/', '192.168.30.186:8000/89'),
('/B/d204c1d12b8a/B86/', '192.168.30.186:8000/86'),
('/B/d204c1d12b8a/B87/', '192.168.30.186:8000/87'),
('/B/d204c1d12b8a/B60/', '192.168.30.186:8000/60'),
('/B/d204c1d12b8a/B61/', '192.168.30.186:8000/61'),
('/B/d204c1d12b8a/B62/', '192.168.30.186:8000/62'),
('/B/d204c1d12b8a/B63/', '192.168.30.186:8000/63'),
('/B/d204c1d12b8a/B58/', '192.168.30.186:8000/58'),
('/B/d204c1d12b8a/B57/', '192.168.30.186:8000/57'),
('/B/d204c1d12b8a/B59/', '192.168.30.186:8000/59'),
('/B/d204c1d12b8a/B54/', '192.168.30.186:8000/54'),
('/B/d204c1d12b8a/B53/', '192.168.30.186:8000/53'),
('/B/d204c1d12b8a/B56/', '192.168.30.186:8000/56'),
('/B/d204c1d12b8a/B55/', '192.168.30.186:8000/55')]
boxs_free = []
if kb_add_dict:
boxs_free = set(dict(boxs).keys()) - set(
dict(kb_add_dict.values()).keys())
else:
boxs_free = set(dict(boxs).keys())
if len(boxs_free) < len(no_kbs):
rest_kbs = no_kbs[len(boxs_free):]
kb_ids = set(kb_ids) - set(rest_kbs)
# 写入cache
boxs_free_info = filter(lambda x: x[0] in boxs_free, boxs)
temp_kb_box_list = list(zip(no_kbs, boxs_free_info))
cache_ret = map(lambda x: cache.set(x[0], x[1], 30 * 60), temp_kb_box_list)
logger.debug('cache_ret:%s' % list(cache_ret))
kb_add_dict = cache.get_many(kb_ids)
logger.debug('kb_add_dict:%s' % kb_add_dict)
logger.debug('------get address time:%.5f' % (time() - start_get_add))
logger.debug('start box-request ')
start_request = time()
num = len(kb_ids)
q = Manager().Queue()
p_list = []
for i in range(0, num):
kb = kb_ids[i]
version = kb_vers_map[kb]
add = kb_add_dict[kb][1]
logger.debug('Target:%s Add:%s' % (kb, add))
temp_p = Process(target=_writer, args=(q, kb, version, add, issue))
p_list.append(temp_p)
temp_p.start()
for pr in p_list:
pr.join()
logger.debug('------box-request time:%.5f' % (time() - start_request))
start_get_msg = time()
i = 0
ret = {'no_box': [], 'ans': [], 'not_match': [], 'fail': []}
while not q.empty():
msg = q.get()
if 'not_match' in msg.keys():
ret['not_match'].append(msg['not_match'])
elif 'fail' in msg.keys():
ret['fail'].append(msg['fail'])
else:
ret['ans'].append(msg)
logger.debug('------%d msg:%s' % (i, msg))
i += 1
logger.debug('------get answers time:%.5f' % (time() - start_get_msg))
# 异步写入zk
# set_box_zk.delay(temp_kb_box_list)
return ret
def query_request_0(kb_ids, issue, kb_vers_map):
"""
requirement: (1)zk中查找/B/节点下的vm
(2)遍历/B/vm/下的子节点，找到空闲节点，为其赋一个知识库，单独起进程发送请求，请求结果放入queue
param: kb_ids 列表
return:
"""
try:
zk = KazooClient(hosts=ZOOKEEPER['HOST'])
zk.start()
except Exception as e:
err_log.error(e)
raise Exception(1910)
_node_list = zk.get_children('/B/')
logger.debug('vm:%s' % _node_list)
q = Manager().Queue()
ret = {'no_box': [], 'ans': [], 'not_match': [], 'fail': []}
p_list = []
random.shuffle(_node_list)
for vm in _node_list:
box_list = zk.get_children('/B/' + vm + '/')
random.shuffle(box_list)
for box in box_list:
node = '/B/' + vm + '/' + box + '/'
_str, _ = zk.get(node)
_dict = json.loads(_str)
if _dict['status'] == '0':
target = kb_ids.pop()
logger.debug('------Target:%s Add:%s' % (target, _dict['Add']))
temp_p = Process(target=_writer,
args=(q, target, kb_vers_map[target],
_dict['Add'], issue))
p_list.append(temp_p)
temp_p.start()
if not kb_ids:
break
if not kb_ids:
break
else:
if kb_ids:
ret['no_box'] = kb_ids
for pr in p_list:
pr.join()
zk.stop()
i = 0
while not q.empty():
msg = q.get()
if 'not_match' in msg.keys():
ret['not_match'].append(msg['not_match'])
elif 'fail' in msg.keys():
ret['fail'].append(msg['fail'])
else:
ret['ans'].append(msg)
logger.debug('------%d msg:%s' % (i, msg))
i += 1
logger.debug('get answers finished')
return ret
# Set up Processes
number_of_processes = 16
for i in range(number_of_processes):
worker = MD5Cracker(work_queue, global_namespace)
worker.start()
workers.append(worker)
print "Target Hash: {}".format(hash)
maxChars = 13
while_count = 1
for baseWidth in range(1, maxChars + 1):
while global_namespace.finished is False:
if work_queue.empty():
print "checking passwords width [" + `baseWidth` + "]"
# set is width, position, baseString
work_queue.put({'width': baseWidth, 'position': 0, 'baseString': ""})
break
else:
if while_count % 10 == 0:
global_namespace.count = 0
while_count = 1
else:
print "{:,d} passwords/sec".format(global_namespace.count/while_count)
while_count += 1
print "Queue Size: {}".format(work_queue.qsize())
def main():
import argparse
import logging
import os
import yaml
parser = argparse.ArgumentParser()
parser.add_argument('classifier')
parser.add_argument('--postprocess', action="store_true",
help='Run postprocessing, close blobs and remove noise')
parser.add_argument('videolist', help='A file listed all the videos to be indexed')
parser.add_argument('cores', type=int, help='Number of processes of paralellism')
args = parser.parse_args()
logging.basicConfig(level=logging.WARNING,
format="%(asctime)s - %(message)s")
classifier = zipfile.ZipFile(args.classifier)
global forest0, svmmodels, training_bosts, hist0
forest0, hist0, forest1, hist1, training_bosts, svmmodels, prior = \
load_from_classifier(classifier)
classifier.close()
KEY_FRAME_PERIOD = 2 # in seconds
#queue = Queue.Queue()
#data_queue = Queue.Queue()
queue = Manager().Queue()
data_queue = Manager().Queue()
for processes in [4]:
video_list = open(args.videolist, 'r')
log_file = open('statistics%d.txt' % processes, 'w')
fps = 0
fps_count = 0
for video_file in video_list:
video_file = video_file.strip()
name = os.path.splitext(video_file)[0]
file_path = os.path.join(VIDEO_RESOURCE, video_file)
log_file.write(file_path+"\n")
capture = cv.CaptureFromFile(file_path)
frame_rate = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)
total_frames = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT)
log_file.write("frame rate: %.3f, total frames: %d\n" % (frame_rate, total_frames))
start_time0 = time.time()
key_frame_counter = 0
frame = cv.QueryFrame(capture)
os.makedirs("tmp")
while frame:
cv.SaveImage("tmp/" + name + "%d.png" % key_frame_counter, frame)
for i in xrange(int(KEY_FRAME_PERIOD * frame_rate)):
frame = cv.QueryFrame(capture)
key_frame_counter += 1
for i in xrange(key_frame_counter):
data_queue.put(i)
start_time = time.time()
ps = []
for group in xrange(processes):
p = Process(target = calculate_class, args=(name, queue, data_queue, ))
#p = threading.Thread(target = calculate_class, args=(name, queue, data_queue, ))
p.start()
ps.append(p)
for p in ps:
p.join()
elapse_time = time.time() - start_time
accuracy_file = open('360.txt', 'w')
while not queue.empty():
q_entry = queue.get()
frame_counter = q_entry[0]
ILP = q_entry[1]
accuracy_file.write('%d' % frame_counter)
for class_index, score in enumerate(ILP):
accuracy_file.write(',%.02f' % score)
accuracy_file.write('\n')
accuracy_file.close()
os.system("rm -rf tmp")
log_file.write("decoding time: %.2f, total time: %.2f, key frames: %d, frame per sec: %.3f\n" \
% (start_time - start_time0, elapse_time, key_frame_counter, key_frame_counter / elapse_time))
fps += key_frame_counter / elapse_time
fps_count += 1
#time.sleep(10)
video_list.close()
log_file.write("average fps: %.3f\n" % (fps/fps_count))
log_file.close()
def sub_cmd_multisearch(args):
if not (args.m and args.sc):
exit(1)
config = xq.get_strategy_config(args.sc)
pprint.pprint(config)
module_name = config["module_name"].replace("/", ".")
class_name = config["class_name"]
symbol = config['symbol']
md = DBMD(args.m, kl.KLINE_DATA_TYPE_JSON)
start_time, end_time = get_time_range(md, symbol, args.r)
count = args.count
cpus = cpu_count()
print("count: %s, cpus: %s" % (count, cpus) )
result_q = Manager().Queue()#Manager中的Queue才能配合Pool
task_q = Manager().Queue()#Manager中的Queue才能配合Pool
for index in range(count):
task_q.put(index)
print('Parent process %s.' % os.getpid())
p = Pool(cpus)
for i in range(cpus):
#p.apply_async(child_process_test, args=(i, task_q, result_q))
p.apply_async(child_process, args=(i, task_q, result_q, args.m, config, module_name, class_name, start_time, end_time))
print('Waiting for all subprocesses done...')
p.close()
start_time = datetime.now()
result = []
while len(result) < count:
if result_q.empty():
time.sleep(1)
else:
value = result_q.get()
print("result value: ", value)
result.append(value)
sys.stdout.write(
" %d/%d, cost: %s, progress: %g%% \r"
% (
len(result),
count,
datetime.now() - start_time,
round((len(result) / count) * 100, 2)
)
)
sys.stdout.flush()
print("")
#print("result queue(len: %s)" % (result_q.qsize()))
p.join()
print('All subprocesses done.')
sorted_rs = sorted(result, key=lambda x: x[1][0], reverse=True)
for r in sorted_rs:
#print("r: ", r)
info = "%6s %30s %s " % r
print(info)
def query_request_z(kb_ids, issue, kb_vers_map):
"""
requirement: (1)cache中查找知识库的地址，cache中没有的，则为其在cache中没有被其他kb占用的box中选取BOX，并异步写入其TARGET
(2)遍历/B/vm/下的子节点，找到空闲节点，为其赋一个知识库，单独起进程发送请求，请求结果放入queue
param: kb_ids 列表
return:
"""
# 取cache中已有的kb
# start_get_add = time()
# kb_add_dict = cache.get_many(kb_ids)
# no_kbs = set(kb_ids) - set(kb_add_dict.keys())
# logger.debug('no_kbs:%s' % no_kbs)
# 为cache中没有的kb赋予box
if cache.ttl("boxs") == 0:
cache.set("boxs", str(_acquire_zk_node()), timeout=None)
boxs = eval(cache.get("boxs"))
add_dict = list(dict(boxs).values())
# boxs_free = []
# if kb_add_dict:
# boxs_free = set(dict(boxs).keys()) - set(dict(kb_add_dict.values()).keys())
# else:
# boxs_free = set(dict(boxs).keys())
# if len(boxs_free) < len(no_kbs):
# rest_kbs = no_kbs[len(boxs_free):]
# kb_ids = set(kb_ids) - set(rest_kbs)
# 写入cache
# boxs_free_info = filter(lambda x: x[0] in boxs_free, boxs)
# temp_kb_box_list = list(zip(no_kbs, boxs_free_info))
# cache_ret = map(lambda x: cache.set(x[0], x[1], 30 * 60), temp_kb_box_list)
# logger.debug('cache_ret:%s' % list(cache_ret))
# kb_add_dict = cache.get_many(kb_ids)
# logger.debug('kb_add_dict:%s' % kb_add_dict)
# logger.debug('------get address time:%.5f' % (time() - start_get_add))
# start_request = time()
num = len(kb_ids)
global lock_label
while True:
if lock_label:
lock_label = False
break
global top_label
seed = int(top_label)
temp = (seed + 1) % len(boxs)
top_label = temp
lock_label = True
logger.debug(seed)
q = Manager().Queue()
p_list = []
for i in range(0, num):
kb = kb_ids[i]
version = kb_vers_map[kb]
# add = kb_add_dict[kb][1]
add = add_dict[(seed + i) % len(boxs)]
# logger.debug('Target:%s Add:%s' % (kb, add))
temp_p = Process(target=_writer, args=(q, kb, version, add, issue))
p_list.append(temp_p)
temp_p.start()
for pr in p_list:
pr.join()
# logger.debug('test.q is ' + q.empty())
# logger.debug('------box-request time:%.5f' % (time() - start_request))
# start_get_msg = time()
i = 0
ret = {'no_box': [], 'ans': [], 'not_match': [], 'fail': []}
while not q.empty():
msg = q.get()
if 'not_match' in msg.keys():
ret['not_match'].append(msg['not_match'])
elif 'fail' in msg.keys():
ret['fail'].append(msg['fail'])
else:
ret['ans'].append(msg)
logger.debug('------%d msg:%s' % (i, msg))
i += 1
# logger.debug('------get answers time:%.5f' % (time() - start_get_msg))
# 异步写入zk
# set_box_zk.delay(temp_kb_box_list)
return ret
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs):
"""
计算指定日期的样本个股的因子载荷, 并保存至因子数据库
Parameters:
--------
:param start_date: datetime-like, str
开始日期, 格式: YYYY-MM-DD or YYYYMMDD
:param end_date: datetime-like, str
结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD
:param month_end: bool, 默认为True
如果为True, 则只计算月末时点的因子载荷
:param save: bool, 默认为True
是否保存至因子数据库
:param kwargs:
'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False
:return: dict
因子载荷数据
"""
# 取得交易日序列及股票基本信息表
start_date = Utils.to_date(start_date)
if end_date is not None:
end_date = Utils.to_date(end_date)
trading_days_series = Utils.get_trading_days(start=start_date, end=end_date)
else:
trading_days_series = Utils.get_trading_days(end=start_date, ndays=1)
all_stock_basics = CDataHandler.DataApi.get_secu_basics()
# 遍历交易日序列, 计算LIQUIDITY因子载荷
dict_raw_liquidity = None
for calc_date in trading_days_series:
if month_end and (not Utils.is_month_end(calc_date)):
continue
dict_stom = None
dict_stoq = None
dict_stoa = None
dict_raw_liquidity = None
logging.info('[%s] Calc Liquidity factor loading.' % Utils.datetimelike_to_str(calc_date))
# 遍历个股，计算个股LIQUIDITY因子值
s = (calc_date - datetime.timedelta(days=risk_ct.LIQUIDITY_CT.listed_days)).strftime('%Y%m%d')
stock_basics = all_stock_basics[all_stock_basics.list_date < s]
ids = []
stoms = []
stoqs = []
stoas = []
raw_liquidities = []
if 'multi_proc' not in kwargs:
kwargs['multi_proc'] = False
if not kwargs['multi_proc']:
# 采用单进程计算LIQUIDITY因子值
for _, stock_info in stock_basics.iterrows():
logging.debug("[%s] Calc %s's LIQUIDITY factor loading." % (Utils.datetimelike_to_str(calc_date, dash=True), stock_info.symbol))
liquidity_data = cls._calc_factor_loading(stock_info.symbol, calc_date)
if liquidity_data is not None:
ids.append(liquidity_data['code'])
stoms.append(liquidity_data['stom'])
stoqs.append(liquidity_data['stoq'])
stoas.append(liquidity_data['stoa'])
raw_liquidities.append(liquidity_data['liquidity'])
else:
# 采用多进程计算LIQUIDITY因子值
q = Manager().Queue()
p = Pool(4)
for _, stock_info in stock_basics.iterrows():
p.apply_async(cls._calc_factor_loading_proc, args=(stock_info.symbol, calc_date, q,))
p.close()
p.join()
while not q.empty():
liquidity_data = q.get(True)
ids.append(liquidity_data['code'])
stoms.append(liquidity_data['stom'])
stoqs.append(liquidity_data['stoq'])
stoas.append(liquidity_data['stoa'])
raw_liquidities.append(liquidity_data['liquidity'])
date_label = Utils.get_trading_days(start=calc_date, ndays=2)[1]
dict_stom = dict({'date': [date_label]*len(ids), 'id': ids, 'factorvalue': stoms})
dict_stoq = dict({'date': [date_label]*len(ids), 'id': ids, 'factorvalue': stoqs})
dict_stoa = dict({'date': [date_label]*len(ids), 'id': ids, 'factorvalue': stoas})
dict_raw_liquidity = dict({'date': [date_label]*len(ids), 'id': ids, 'factorvalue': raw_liquidities})
# 读取Size因子值, 将流动性因子与Size因子正交化
size_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, risk_ct.SIZE_CT.db_file)
df_size = Utils.read_factor_loading(size_factor_path, Utils.datetimelike_to_str(calc_date, dash=False))
df_size.drop(columns='date', inplace=True)
df_size.rename(columns={'factorvalue': 'size'}, inplace=True)
df_liquidity = pd.DataFrame(dict({'id': ids, 'liquidity': raw_liquidities}))
df_liquidity = pd.merge(left=df_liquidity, right=df_size, how='inner', on='id')
arr_liquidity = Utils.normalize_data(Utils.clean_extreme_value(np.array(df_liquidity['liquidity']).reshape((len(df_liquidity), 1))))
arr_size = Utils.normalize_data(Utils.clean_extreme_value(np.array(df_liquidity['size']).reshape((len(df_liquidity), 1))))
model = sm.OLS(arr_liquidity, arr_size)
results = model.fit()
df_liquidity['liquidity'] = results.resid
df_liquidity.drop(columns='size', inplace=True)
df_liquidity.rename(columns={'liquidity': 'factorvalue'}, inplace=True)
df_liquidity['date'] = date_label
# 保存因子载荷
if save:
str_date = Utils.datetimelike_to_str(calc_date, dash=False)
factor_header = ['date', 'id', 'factorvalue']
Utils.factor_loading_persistent(cls._db_file, 'stom_{}'.format(str_date), dict_stom, factor_header)
Utils.factor_loading_persistent(cls._db_file, 'stoq_{}'.format(str_date), dict_stoq, factor_header)
Utils.factor_loading_persistent(cls._db_file, 'stoa_{}'.format(str_date), dict_stoa, factor_header)
Utils.factor_loading_persistent(cls._db_file, 'rawliquidity_{}'.format(str_date), dict_raw_liquidity, factor_header)
Utils.factor_loading_persistent(cls._db_file, str_date, df_liquidity.to_dict('list'), factor_header)
# 暂停180秒
# logging.info('Suspending for 180s.')
# time.sleep(180)
return dict_raw_liquidity
def calc_factor_loading(cls,
start_date,
end_date=None,
month_end=True,
save=False,
**kwargs):
"""
计算指定日期的样本个股的因子载荷，并保存至因子数据库
Parameters
--------
:param start_date: datetime-like, str
开始日期
:param end_date: datetime-like, str，默认None
结束日期，如果为None，则只计算start_date日期的因子载荷
:param month_end: bool，默认True
只计算月末时点的因子载荷，该参数只在end_date不为None时有效，并且不论end_date是否为None，都会计算第一天的因子载荷
:param save: 是否保存至因子数据库，默认为False
:param kwargs:
'multi_proc': bool, True=采用多进程并行计算, False=采用单进程计算, 默认为False
:return: 因子载荷，DataFrame
--------
因子载荷,DataFrame
0: id, 证券ID
1: factorvalue, 因子载荷
如果end_date=None，返回start_date对应的因子载荷数据
如果end_date!=None，返回最后一天的对应的因子载荷数据
如果没有计算数据，返回None
"""
# 1.取得交易日序列及股票基本信息表
start_date = Utils.to_date(start_date)
if end_date is not None:
end_date = Utils.to_date(end_date)
trading_days_series = Utils.get_trading_days(start=start_date,
end=end_date)
else:
trading_days_series = Utils.get_trading_days(end=start_date,
ndays=1)
# all_stock_basics = CDataHandler.DataApi.get_secu_basics()
# 2.遍历交易日序列，计算APM因子载荷
dict_apm = None
for calc_date in trading_days_series:
dict_apm = {'date': [], 'id': [], 'factorvalue': []}
if month_end and (not Utils.is_month_end(calc_date)):
continue
# 2.1.遍历个股，计算个股APM.stat统计量，过去20日收益率，分别放进stat_lst,ret20_lst列表中
s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d')
stock_basics = Utils.get_stock_basics(s)
stat_lst = []
ret20_lst = []
symbol_lst = []
if 'multi_proc' not in kwargs:
kwargs['multi_proc'] = False
if not kwargs['multi_proc']:
# 采用单进程计算
for _, stock_info in stock_basics.iterrows():
stat_i = cls._calc_factor_loading(stock_info.symbol,
calc_date)
ret20_i = Utils.calc_interval_ret(stock_info.symbol,
end=calc_date,
ndays=20)
if stat_i is not None and ret20_i is not None:
stat_lst.append(stat_i)
ret20_lst.append(ret20_i)
symbol_lst.append(
Utils.code_to_symbol(stock_info.symbol))
logging.info('APM of %s = %f' %
(stock_info.symbol, stat_i))
else:
# 采用多进程并行计算
q = Manager().Queue()
p = Pool(4) # 最多同时开启4个进程
for _, stock_info in stock_basics.iterrows():
p.apply_async(cls._calc_factor_loading_proc,
args=(
stock_info.symbol,
calc_date,
q,
))
p.close()
p.join()
while not q.empty():
apm_value = q.get(True)
symbol_lst.append(apm_value[0])
stat_lst.append(apm_value[1])
ret20_lst.append(apm_value[2])
assert len(stat_lst) == len(ret20_lst)
assert len(stat_lst) == len(symbol_lst)
# 2.2.构建APM因子
# 2.2.1.将统计量stat对动量因子ret20j进行截面回归：stat_j = \beta * Ret20_j + \epsilon_j
# 残差向量即为对应个股的APM因子
# 截面回归之前，先对stat统计量和动量因子进行去极值和标准化处理
stat_arr = np.array(stat_lst).reshape((len(stat_lst), 1))
ret20_arr = np.array(ret20_lst).reshape((len(ret20_lst), 1))
stat_arr = Utils.clean_extreme_value(stat_arr)
stat_arr = Utils.normalize_data(stat_arr)
ret20_arr = Utils.clean_extreme_value(ret20_arr)
ret20_arr = Utils.normalize_data(ret20_arr)
# 回归分析
# ret20_arr = sm.add_constant(ret20_arr)
apm_model = sm.OLS(stat_arr, ret20_arr)
apm_result = apm_model.fit()
apm_lst = list(np.around(apm_result.resid, 6)) # amp因子载荷精确到6位小数
assert len(apm_lst) == len(symbol_lst)
# 2.2.2.构造APM因子字典，并持久化
date_label = Utils.get_trading_days(calc_date, ndays=2)[1]
dict_apm = {
'date': [date_label] * len(symbol_lst),
'id': symbol_lst,
'factorvalue': apm_lst
}
df_std_apm = Utils.normalize_data(pd.DataFrame(dict_apm),
columns='factorvalue',
treat_outlier=True,
weight='eq')
if save:
# Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_apm)
cls._save_factor_loading(cls._db_file,
Utils.datetimelike_to_str(calc_date,
dash=False),
dict_apm,
'APM',
factor_type='raw',
columns=['date', 'id', 'factorvalue'])
cls._save_factor_loading(cls._db_file,
Utils.datetimelike_to_str(calc_date,
dash=False),
df_std_apm,
'APM',
factor_type='standardized',
columns=['date', 'id', 'factorvalue'])
# # 2.3.构建PureAPM因子
# # 将stat_arr转换为DataFrame, 此时的stat_arr已经经过了去极值和标准化处理
# df_stat = DataFrame(stat_arr, index=symbol_lst, columns=['stat'])
# # 取得提纯的因变量因子
# df_dependent_factor = cls.get_dependent_factors(calc_date)
# # 将df_stat和因变量因子拼接
# df_data = pd.concat([df_stat, df_dependent_factor], axis=1, join='inner')
# # OLS回归，提纯APM因子
# arr_data = np.array(df_data)
# pure_apm_model = sm.OLS(arr_data[:, 0], arr_data[:, 1:])
# pure_apm_result = pure_apm_model.fit()
# pure_apm_lst = list(np.around(pure_apm_result.resid, 6))
# pure_symbol_lst = list(df_data.index)
# assert len(pure_apm_lst) == len(pure_symbol_lst)
# # 构造pure_apm因子字典，并持久化
# dict_pure_apm = {'date': [date_label]*len(pure_symbol_lst), 'id': pure_symbol_lst, 'factorvalue': pure_apm_lst}
# pure_apm_db_file = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.APM_CT.pure_apm_db_file)
# if save:
# Utils.factor_loading_persistent(pure_apm_db_file, calc_date.strftime('%Y%m%d'), dict_pure_apm)
# # 休息360秒
# logging.info('Suspended for 360s.')
# time.sleep(360)
return dict_apm
def main():
import argparse
import logging
import os
import yaml
import cv
global processes
global forest0, svmmodels, training_bosts, hist0
parser = argparse.ArgumentParser()
parser.add_argument('classifier')
parser.add_argument('cores', type=int, help='Number of processes of paralellism')
parser.add_argument('--postprocess', action="store_true",
help='Run postprocessing, close blobs and remove noise')
args = parser.parse_args()
logging.basicConfig(level=logging.WARNING,
format="%(asctime)s - %(message)s")
classifier = zipfile.ZipFile(args.classifier)
forest0, hist0, forest1, hist1, training_bosts, svmmodels, prior = \
load_from_classifier(classifier)
classifier.close()
processes = args.cores
pool = Pool(processes = processes)
KEY_FRAME_PERIOD = 2 # in seconds
q = Manager().Queue()
total_frame = 0
new_flag = True
while True:
if not new_flag:
print "wait..."
time.sleep(1)
stream_list = get_list(CLOUDLET_RESOURCE, STREAM_RESOURCE)
new_flag = False
prev_stream = None
for stream in stream_list:
if stream.get("stream_description").find("denatured") == -1 or stream.get("stream_description").find("video") == -1 or stream.get("stream_description").find("pstf") != -1:
prev_stream = stream
continue
ILP_max = []
for i in xrange(len(CLASSES)):
ILP_max.append(0)
ILP_list = []
for i in xrange(len(CLASSES)):
ILP_list.append([])
path, name = stream.get("path").replace("mnt", "cloudletstore").rsplit('/', 1)
print os.path.join(path, name)
path_p, name_p = prev_stream.get("path").replace("mnt", "cloudletstore").rsplit('/', 1)
print os.path.join(path_p, name_p)
statinfo = os.stat(os.path.join(path_p, name_p))
prev_stream = stream
if statinfo.st_size == 0:
continue
new_flag = True
frame_rate = 30
capture = cv.CaptureFromFile(os.path.join(path, name))
frame_rate = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FPS)
total_frames = cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_COUNT)
frame = cv.QueryFrame(capture)
print frame_rate, total_frames
print capture
start_time = time.time()
key_frame_counter_base = 0
while frame:
process_num = 0
while frame:
cv.SaveImage("indexing" + "%d.png" % process_num, frame)
for i in xrange(int(KEY_FRAME_PERIOD * frame_rate)):
frame = cv.QueryFrame(capture)
process_num += 1
if process_num == processes:
break
pool.map(calculate_class, [(q, x) for x in xrange(key_frame_counter_base, key_frame_counter_base + process_num)])
while not q.empty():
q_entry = q.get()
key_frame_counter = q_entry[0]
ILP = q_entry[1]
for class_index, score in enumerate(ILP):
if score > SCORE_THRESHOLD:
ILP_list[class_index].append((key_frame_counter * int(KEY_FRAME_PERIOD * frame_rate) + 1, score))
print (CLASSES[class_index], "%.02f" % score),
if score > ILP_max[class_index]:
ILP_max[class_index] = score
print
key_frame_counter_base += process_num
for class_index, frame_list in enumerate(ILP_list):
if not frame_list:
continue
frame_list_split = split_frame_list(frame_list, int(KEY_FRAME_PERIOD * frame_rate) * 2)
for frame_list, local_max_score in frame_list_split:
tag_entry = {}
tag_entry["tag"] = CLASSES[class_index] + ":%d" % (ILP_max[class_index] * 100)
tag_entry["tag_value"] = local_max_score
tag_entry["offset"] = frame_list[0] / frame_rate
tag_entry["duration"] = (frame_list[-1] - frame_list[0]) / frame_rate
tag_entry["segment"] = stream.get("segment")
print tag_entry
ret_dict = post(CLOUDLET_RESOURCE, TAG_RESOURCE, tag_entry)
if stream.get("stream_description").find("pstf") == -1:
stream_entry = {"stream_description": stream.get("stream_description") + "pstf;"}
ret_dict = put(CLOUDLET_RESOURCE, stream.get("resource_uri"), stream_entry)
elapse_time = time.time() - start_time
print "max score:"
print [(CLASSES[class_index], "%.02f" % score) for class_index, score in enumerate(ILP_max)]
print "total time: %.2f, key frames: %d, frame per sec: %.2f" \
% (elapse_time, key_frame_counter_base, key_frame_counter_base / elapse_time)
print
print('退出')
break
if __name__ == '__main__':
print('主进程开始')
#构建了一个任务队列
# pageQueue = Queue(300)
pageQueue = Manager().Queue(300)
for i in range(1, 50):
pageQueue.put(i)
#构建一个结果队列，存储获取的响应结果
# dataQueue = Queue()
dataQueue = Manager().Queue()
#打印，查看队列是否为空
print(pageQueue.empty(), dataQueue.empty())
downloadProcess = []
for i in range(0, 3):
p1 = Process(target=getdata, args=(pageQueue, dataQueue))
p1.start()
downloadProcess.append(p1)
for process in downloadProcess:
process.join()
print('总数', dataQueue.empty(), dataQueue.qsize())
parseProcess = []
for i in range(0, 3):
parse1 = Process(target=parsedata, args=(dataQueue, ))
def launch_expeditions( self , task_request_list , moon_name_list=None ):
global expedition
# ---[ 1 ]------------------------------------------------------
self.log.show( 'Checking Moon list sent by user' )
working_moons = []
if not moon_name_list :
self.log.show( 'Traveling to available Moons on Orbit' )
working_moons = self.orbit.values()
else :
self.log.show( 'Traveling to ' + str( moon_name_list ) )
working_moons = [ self.orbit.get_moon( moon_name ) for moon_name in moon_name_list ]
# ---[ 2 ]------------------------------------------------------
self.log.show( 'Build Thread-safe Queues with no maximum size' )
recv_queue = Manager().Queue( )#len(task_request_list) )
send_queue = Manager().Queue( )#len(task_request_list) )
# ---[ 3 ]------------------------------------------------------
self.log.show( 'Enqueue tasks on "send_queue" object' )
for task_obj in task_request_list :
send_queue.put_nowait( str(task_obj) ) # "Normal" Objects are note thread safe!
self.log.show( 'send_queue = ' + str(send_queue.qsize())+'/'+str(len(task_request_list)) + 'tasks')
# ---[ 4 ]------------------------------------------------------
self.log.show( 'Starting up Process Pool' )
pool = Pool(processes=len(working_moons))
for moon in working_moons :
#running_expeditions.append( Process( target=expedition , args=(self.name , moon.name , moon.ip , moon.port , taskrequest_queue , taskresponse_queue, ) ) ) # Process Object
pool.apply_async( func=expedition , args=(self.name , moon.name , moon.ip , moon.port , send_queue , recv_queue , ) )
# ---[ 5 ]------------------------------------------------------
pool.close()
pool.join()
self.log.show( 'recv_queue = '+ str(recv_queue.qsize())+'/'+str(len(task_request_list)) + 'tasks' )
tmp = []
while not recv_queue.empty() :
tmp.append( recv_queue.get() )
self.log.show( 'closing queue' )
self.log.show( 'return results' )
return tmp
def update_heatmap(slaid,
model,
mpp_inve,
li_ij_tissue,
wh_inve,
num_ij,
n_proc,
batch_size,
transphorm,
use_gpu,
is_debug,
slide_id_ij_input_output_last_tissue,
one_over_n_sliding=1):
# make zero heatmap
im_heatmap_ij = make_zero_heatmap(num_ij, one_over_n_sliding)
im_count_ij = make_zero_heatmap(num_ij, one_over_n_sliding)
# set the model as test mode
model.eval()
# for each tissue position
if n_proc > 0:
li_xy_inve_tissue = [
tuple(map(lambda a, b: a * b, ij_tissue, wh_inve))
for ij_tissue in li_ij_tissue
]
queue_size = batch_size * n_proc
queue = Manager().Queue(queue_size)
pool = Pool(n_proc)
split_points = []
for i in range(n_proc):
split_points.append(li_xy_inve_tissue[i::n_proc])
result = pool.map_async(
read_region_tissue,
[(queue, slaid, mpp_inve, wh_inve, li_xy_inve, transphorm)
for li_xy_inve in split_points])
li_ij, li_patch_inve = [], []
while True:
if queue.empty():
if not result.ready():
time.sleep(0.5)
elif result.ready() and 0 == len(li_patch_inve):
break
else:
patch_inve, i, j = queue.get()
li_ij.append((i, j))
li_patch_inve.append(patch_inve)
if len(li_patch_inve) == batch_size or \
(result.ready() and queue.empty() and len(li_patch_inve) > 0):
batch = Variable(torch.FloatTensor(np.stack(li_patch_inve)),
volatile=True)
if use_gpu:
batch = batch.cuda()
start_time = time.time()
output = model(batch)
elapsed_batch = time.time() - start_time
output = output.cpu().data.numpy()[:, 0]
logging.debug(f'elapsed time for computing one batch is ' +
f'{elapsed_batch:.3f}')
n_img_in_this_batch = batch.size(0)
for ii in range(n_img_in_this_batch):
i, j = li_ij[ii]
im_heatmap_ij[j, i] = output[ii]
logging.debug(queue.qsize())
li_ij, li_patch_inve = [], []
if not result.successful():
logging.debug('[!] Error: something wrong in result.')
pool.close()
pool.join()
else:
im_heatmap_ij = update_heatmap_no_parallel(
slaid, model, im_heatmap_ij, mpp_inve, transphorm, li_ij_tissue,
wh_inve, batch_size, use_gpu, is_debug,
slide_id_ij_input_output_last_tissue)
dummy = 0
return im_heatmap_ij, im_count
