def parse(self, s): if not s: return s = str(s) buf = io.BytesIO(s) config = ConfigParser() config.readfp(buf) # Read all data from the configuration file in the _config dict. for section in config.sections(): items = dict(config.items(section)) if section == "DEFAULT": section = "main" try: self._config[section].update(items) except KeyError: self._config[section] = items # Update the logger, because the logging configuration may # have been altered. logger.setup_logging(self)
def main():
setup_logging(__file__)
try:
all_stocks = StockInfo.objects().timeout(False)
except Exception, e:
logging.error("Error when query StockInfo:" + str(e))
raise e
def run(self): """ Runs the pakfire daemon with provided settings. """ # Read the configuration file for the daemon. self.config = config.ConfigDaemon() logger.setup_logging(self.config) # Create daemon instance. d = daemon.PakfireDaemon(self.config) try: d.run() # We cannot just kill the daemon, it needs a smooth shutdown. except (SystemExit, KeyboardInterrupt): d.shutdown()
def main():
root_path = os.path.dirname(os.path.abspath(__file__))
os.chdir(root_path)
result = do_input_validation()
result.verbose = True
setup_logging(logfile="bt_report.log", scrnlog=result.verbose)
logger.info("Running Script with -> Vertica Server: %s, Vertica User: %s, Customer ID: %s, DeviceType ID: %s, "
"Date: %s, CSV File: %s, Verbose: %s" % (result.server, result.username, result.customer,
result.device_type_id, result.start_date, result.csv_file,
result.verbose))
date_list, end_date = get_dates(start_date=result.start_date)
date_formatter = datetime.strptime(end_date, TIME_FORMAT_MDY)
year_month_format = "%s%02d" % (date_formatter.year, date_formatter.month)
report_name = "50_Network Intrusion Prevention_Detection Service Report_%s" % year_month_format
report_output_file_path = get_output_file_path(base_path=root_path, template_file=report_name)
logger.info("Report Generation Started. Result file: %s" % report_output_file_path)
# print report_output_file_path
try:
vertica_db_instance = VerticaDatabase(
server=result.server,
user=result.username,
password=result.password
)
final_data_dict, severity_records, top_5_alarms = fetch_records(
db_instance=vertica_db_instance,
customer_id=result.customer,
device_type_ids=result.device_type_id,
start_date=result.start_date,
end_date=end_date,
date_range=date_list,
csv_file_path=result.csv_file
)
# print top_5_alarms
# print final_data_dict
workbook = ExcelWriter(report_output_file_path)
sheet_name = "50_NIDS_IPS_Report_%s" % year_month_format
workbook.write_to_document_file(sheet_name=sheet_name, date_str=end_date)
workbook.write_data_worksheet(sheet_name="DATA", data=final_data_dict, top_alarms=top_5_alarms)
workbook.draw_top5_charts(sheet_name="TOP 5")
workbook.write_main_worksheet(sheet_name="MAIN", data=severity_records, start_date=result.start_date,
end_date=end_date)
workbook.close()
logger.info("Report Generation Completed. Result file: %s" % report_output_file_path)
print("Report Generation Completed. Result file: %s" % report_output_file_path)
except Exception, ex:
logger.exception(ex)
sys.exit()
def __init__(self, service=None):
""" Constructor to create ServiceInstanceTemplate object
@param vsm object on which ServiceInstanceTemplate has to be configured
"""
super(ServiceInstanceTemplate, self).__init__()
self.log = logger.setup_logging(self.__class__.__name__)
self.schema_class = 'service_instance_template_schema.ServiceInstanceTemplateSchema'
self.set_connection(service.get_connection())
self.set_create_endpoint("/si/service/" + str(service.id) + "/serviceinstancetemplate")
self.id = None
self.update_as_post = False
def __init__(self, vsm=None):
""" Constructor to create EventThresholds object
@param vsm object on which EventThresholds object has to be configured
"""
super(EventThresholds, self).__init__()
self.log = logger.setup_logging(self.__class__.__name__)
self.schema_class = 'event_thresholds_schema.EventThresholdsSchema'
self.set_connection(vsm.get_connection())
self.set_create_endpoint("/firewall/stats/eventthresholds")
self.set_read_endpoint("/firewall/stats/eventthresholds")
self.create_as_put = True
def __init__(self, config):
"""
class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving
and logging module.
:param config: Dict containing configurations, hyperparameters for training. contents of `config.json` file for example.
:param resume: String, path to the checkpoint being loaded.
:param modification: Dict keychain:value, specifying position values to be replaced from config dict.
:param run_id: Unique Identifier for training processes. Used to save checkpoints and training log. Timestamp is being used as default
"""
# load config file and apply modification
run_id = config.run_id
if config.device is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = config.device
self._config = config
# set save_dir where trained model and log will be saved.
save_dir = Path(self.config['trainer']['save_dir'])
exper_name = self.config['name']
if run_id is None: # use timestamp as default run-id
run_id = datetime.now().strftime(r'%m%d_%H%M%S')
self._save_dir = save_dir / exper_name / 'models' #/ run_id
self._log_dir = save_dir / exper_name / 'log' #/ run_id
# make directory for saving checkpoints and log.
# exist_ok = run_id == ''
exist_ok = os.path.isdir(self.save_dir)
self.save_dir.mkdir(parents=True, exist_ok=exist_ok)
self.log_dir.mkdir(parents=True, exist_ok=exist_ok)
# configure logging module
setup_logging(self.log_dir)
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
def __init__(self, config, resume=None, modification=None, run_id=None):
"""
解包json文件，处理训练、断点保存、记录模块的超参数
class to parse configuration json file. Handles hyperparameters for training, initializations of modules, checkpoint saving
and logging module.
:param config: Dict containing configurations, hyperparameters for training. contents of `config.json` file for example.
:param resume: String, path to the checkpoint being loaded.
:param modification: Dict keychain:value, specifying position values to be replaced from config dict.
:param run_id: Unique Identifier for training processes. Used to save checkpoints and training log. Timestamp is being used as default
"""
# load config file and apply modification
self._config = _update_config(config, modification)
self.resume = resume
# set save_dir where trained model and log will be saved.
save_dir = Path(self.config['trainer']['save_dir']) # 模型保存路径
exper_name = self.config['name'] # 任务名
if run_id is None: # use timestamp as default run-id
run_id = datetime.now().strftime(r'%m%d_%H%M%S') # 任务id: 月日_时分秒
self._save_dir = save_dir / 'models' / exper_name / run_id # 模型保存路径
self._log_dir = save_dir / 'log' / exper_name / run_id # 记录路径
# make directory for saving checkpoints and log.
exist_ok = run_id == ''
self.save_dir.mkdir(parents=True, exist_ok=exist_ok) # 创建路径
self.log_dir.mkdir(parents=True, exist_ok=exist_ok)
# save updated config file to the checkpoint dir
write_json(self.config, self.save_dir / 'config.json') # 将保存后的模型路径存入json中，并进行保存
# configure logging module
setup_logging(self.log_dir) # 根据logger_config.json文件，配置log的方式，
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
def __init__(self, config, resume=None, modification=None, run_id=None):
"""
class to parse configuration json file. Handles hyperparameters for training,
initializations of modules, checkpoint saving and logging module.
:param config: Dict containing configurations, hyperparameters for training.
contents of `config.json` file for example.
:param resume: String, path to the checkpoint being loaded.
:param modification: Dict keychain:value, specifying position values to
be replaced from config dict.
:param run_id: Unique Identifier for training processes.
Used to save checkpoints and training log. Timestamp used as default
"""
# load config file and apply modification
self._config = _update_config(config, modification)
self.resume = resume
# set save_dir where trained model and log will be saved.
save_dir = Path(self.config["trainer"]["save_dir"])
exper_name = self.config["name"]
if run_id is None: # use timestamp as default run-id
run_id = datetime.now().strftime(r"%m%d_%H%M%S")
self._save_dir = save_dir / "models" / exper_name / run_id
self._log_dir = save_dir / "log" / exper_name / run_id
# make directory for saving checkpoints and log.
exist_ok = run_id == ""
self.save_dir.mkdir(parents=True, exist_ok=exist_ok)
self.log_dir.mkdir(parents=True, exist_ok=exist_ok)
# save updated config file to the checkpoint dir
write_json(self.config, self.save_dir / "config.json")
# configure logging module
setup_logging(self.log_dir)
self.log_levels = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG}
def __init__(self, args, mode, timestamp=True):
args = args.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.resume:
self.resume = Path(args.resume)
self.cfg_fname = self.resume / 'config.json'
# self.cfg_fname = Path(args.config)
else:
msg_no_cfg = "Config file must be specified"
assert args.config is not None, msg_no_cfg
self.resume = None
self.cfg_fname = Path(args.config)
self._config = read_json(self.cfg_fname)
if mode == 'train':
save_dir = Path(self.config['trainer']['save_dir'])
if self.resume:
self._save_dir = self.resume
self._log_dir = self.resume
else:
self._save_dir = save_dir / args.logdir
self._log_dir = save_dir / args.logdir
else:
if args.mode == 'val':
save_dir = Path(self.config['trainer']['save_dir'])
self._save_dir = self.resume / "val"
self._log_dir = self.resume / "val"
self._config['data_loader']['args']['mode'] = 'val'
elif args.mode == 'test':
save_dir = Path(self.config['trainer']['save_dir'])
self._save_dir = self.resume / "test"
self._log_dir = self.resume / "test"
self._config['data_loader']['args']['mode'] = 'test'
#model_name = self.cfg_fname.parent.stem
#exper_name = f"{model_name}-{self.cfg_fname.stem}"
#self._save_dir = save_dir / logdir
#self._log_dir = save_dir / logdir
#self._exper_name = exper_name
self._args = args
self.save_dir.mkdir(parents=True, exist_ok=True)
self.log_dir.mkdir(parents=True, exist_ok=True)
write_json(self.config, self._save_dir / 'config.json')
self.log_path = setup_logging(self.log_dir)
def __init__(self, args):
args = args.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.resume:
self.resume = Path(args.resume)
self.cfg_fname = self.resume.parent / 'config.json'
else:
self.resume = None
self.cfg_fname = Path(args.config)
assert self.cfg_fname is not None, "Configuration file need to be specified. Add '-c config.json', for example."
self.config = read_json(self.cfg_fname)
self.exper_name = self.config['name']
# set save_dir where trained model and log will be saved.
save_dir = Path(self.config['trainer']['save_dir'])
timestamp = datetime.now().strftime(
'%m%d_%H%M%S') # if timestamp else ''
self.save_dir = save_dir / 'models' / self.exper_name / timestamp
self.log_dir = save_dir / 'log' / self.exper_name / timestamp
self.save_dir.mkdir(parents=True)
self.log_dir.mkdir(parents=True)
# copy the config file to the checkpoint dir # NOTE: str() can be removed from here on python 3.6
copyfile(str(self.cfg_fname), str(self.save_dir / 'config.json'))
# configure logging module
setup_logging(self.log_dir)
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
def __init__(self, line_sz, total_sz, logger=None, console=True, filelog=False, level=logging.ERROR):
import sys
assert isinstance(line_sz, int) \
and isinstance(total_sz, int) \
and line_sz > 0 \
and total_sz > 0
self.line_size = line_sz
self.total_size = total_sz
self.line_count = total_sz / line_sz
if logger is None:
from logger import setup_logging
self.logger = setup_logging(console=console, logfile='mem_simulation.log', filelog=filelog, level=level)
else:
self.logger = logger
self.line_szbit = int(math.log(self.line_size, 2))
self.total_szbit = int(math.log(self.total_size, 2))
def __init__(self, args, timestamp=True):
args = args.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.resume:
self.resume = Path(args.resume)
self.cfg_fname = self.resume.parent / 'config.json'
# self.cfg_fname = Path(args.config)
else:
msg_no_cfg = "Config file must be specified"
assert args.config is not None, msg_no_cfg
self.resume = None
self.cfg_fname = Path(args.config)
self._config = read_json(self.cfg_fname)
if "trainer" in self.config:
save_dir = Path(self.config['trainer']['save_dir'])
else:
save_dir = Path(self.config['tester']['save_dir'])
timestamp = datetime.now().strftime(
r"%Y-%m-%d_%H-%M-%S") if timestamp else ""
model_name = self.cfg_fname.parent.stem
exper_name = f"{model_name}-{self.cfg_fname.stem}"
self._save_dir = save_dir / 'models' / exper_name / timestamp
self._log_dir = save_dir / 'log' / exper_name / timestamp
self._exper_name = exper_name
self._args = args
self.save_dir.mkdir(parents=True, exist_ok=True)
self.log_dir.mkdir(parents=True, exist_ok=True)
write_json(self.config, self._save_dir / 'config.json')
self.log_path = setup_logging(self.log_dir)
from logger import setup_logging
from message_handler import render_msg, parse_data
import asyncio
from random import randrange
log = setup_logging(__name__)
class ChatClient(asyncio.Protocol):
message = 'This is the message. It will be echoed.'
client = 'CLIENT{:0>3}'.format(randrange(0,1000))
transport = None
def connection_made(self, transport):
self.transport = transport
log.debug("Setting up connection!")
# Do Register
self.register()
res = True
while(res):
res = self.send_msg()
log.debug("Disconnecting from server!")
self.transport.close()
def data_received(self, data):
mstring = data.decode()
log.debug('Data received: [{}]'.format(mstring))
"""
This will write the timestamp, meter power value, PV power value, sum of the powers to a CSV file.
"""
import csv
import os.path
import config as cfg
import logger
from datetime import datetime
simulator_log = logger.setup_logging("pv_simulator")
async def write(file_path: str, data: dict) -> None:
"""
:param file_path: path to the file
:param data: data with fields ['timestamp', 'meter_power_value', 'PV_power_value', 'sum_of_powers']
:return: None
"""
simulator_log.info("File writer requested for filepath %s" % file_path)
new_file = True
if os.path.isfile(file_path):
new_file = False
with open(file_path, mode='a+', newline='') as csv_file:
fieldnames = cfg.CSV_FIELDS
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
if new_file:
writer.writeheader()
writer.writerow(data)
simulator_log.info("File writer finished writing to file path %s" % file_path)
logger.info('Error setting relay state')
if __name__ == '__main__':
# Argument parsing
parser = argparse.ArgumentParser(
description='Start and stop a generator based on conditions')
parser.add_argument('-d',
'--debug',
help='set logging level to debug',
action='store_true')
parser.add_argument('-r',
'--retries',
help='Retries on error',
default=300,
type=int)
args = parser.parse_args()
print '-------- dbus_generator, v' + softwareversion + ' is starting up --------'
logger = setup_logging(args.debug)
# Have a mainloop, so we can send/receive asynchronous calls to and from dbus
DBusGMainLoop(set_as_default=True)
generator = DbusGenerator(args.retries)
# Start and run the mainloop
mainloop = gobject.MainLoop()
mainloop.run()
def main():
# parse arguments
aparser = argparse.ArgumentParser(description='Convert SystemRDL files to outputs like RTL')
aparser.add_argument('files', type=str, nargs='+', help='Input RDL files')
aparser.add_argument('--perl-preproc', action='store_true',
dest='perl_pp', help='Enable perl preprocessing')
aparser.add_argument('--print', action='store_true',
dest='v_print', help='Print register information')
aparser.add_argument('--lang', choices=['verilog', 'vhdl'], default='vhdl',
dest='lang', help='Select target language')
log_level_nos = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL]
log_level_names = [logging.getLevelName(i).lower() for i in log_level_nos]
log_levels = dict(zip(log_level_names, log_level_nos))
aparser.add_argument('--log-level', choices=log_level_names, default='INFO',
dest='log_level', type=str.lower, help='Select log level')
aparser.add_argument('--debug', action='store_true',
dest='debug', help=argparse.SUPPRESS)
args = aparser.parse_args()
# setup logger
logger.setup_logging(log_levels[args.log_level], args.debug, None)
log = logging.getLogger()
# preprocessing
log.info('Start preprocessing..')
data = ''
line_infos = []
for fn in args.files:
(data_pp, ln) = preproc(fn, args.perl_pp)
data += data_pp
line_infos += ln
log.info('Preprocessing done.')
log.handlers[0].formatter.line_info = Common.flatten(line_infos, [])
# parsing
log.info('Start parsing..')
inputstream = antlr4.InputStream(data)
lexer = SystemRDLLexer(inputstream)
lexer.removeErrorListeners()
lexer._listeners = [CustomErrorListener()]
stream = antlr4.CommonTokenStream(lexer)
parser = SystemRDLParser(stream)
parser._listeners = [CustomErrorListener()]
tree = parser.root()
listener = Listener(parser)
walker = antlr4.ParseTreeWalker()
walker.walk(listener, tree)
log.info('Parsing done.')
for am in listener.addrmaps:
log.info(f'{am.def_id} assigned address space till 0x{am.last_addr:x}')
if args.v_print:
am.pprint()
log.info(f'Generating RTL for AddrMap {am.def_id}..')
rtl_gen.generate_rtl(args.lang, am, listener.signals, listener.internal_signals)
log.info('Done.')
logging.shutdown()
def main():
setup_logging(__file__)
if not is_weekend():
logging.info("Start Collect %s Trading Data" % datetime.date.today())
collect_stock_daily_trading()
logging.info("Collect %s Trading Data Success" % datetime.date.today())
def main():
setup_logging(__file__, logging.WARNING)
if not is_weekend():
logging.info('Start Collect %s Trading Data' % datetime.date.today())
collect_stock_daily_trading()
logging.info('Collect %s Trading Data Success' % datetime.date.today())
import config
import daemon
import logger
import packages
import repository
import server
import transaction
import util
from system import system
from constants import *
from i18n import _
# Initialize a very simple logging that is removed when a Pakfire instance
# is started.
logger.setup_logging()
class Cli(object):
pakfire = base.Pakfire
def __init__(self):
self.parser = argparse.ArgumentParser(
description = _("Pakfire command line interface."),
)
self.parse_common_arguments()
self.parser.add_argument("--root", metavar="PATH",
default="/",
help=_("The path where pakfire should operate in."))
def main():
setup_logging(__file__, logging.WARNING)
if not is_weekend():
logging.info('Start Collect %s Trading Data' % datetime.date.today())
collect_stock_daily_trading()
logging.info('Collect %s Trading Data Success' % datetime.date.today())
def __init__(self, args, options='', timestamp=True, slave_mode=False):
# slave_mode - when calling the config parser form an existing process, we
# avoid reinitialising the logger and ignore sys.argv when argparsing.
# parse default and custom cli options
for opt in options:
args.add_argument(*opt.flags, default=None, type=opt.type)
if slave_mode:
args = args.parse_args(args=[])
else:
args = args.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
if args.resume and not slave_mode:
self.resume = Path(args.resume)
# self.cfg_fname = self.resume.parent / 'config.json'
else:
msg_no_cfg = "Config file must be specified"
assert args.config is not None, msg_no_cfg
self.resume = None
self.cfg_fname = Path(args.config)
config = self.load_config(self.cfg_fname)
self._config = _update_config(config, options, args)
if self._config.get("eval_config", False):
# validate path to evaluation file
eval_cfg_path = self._config.get("eval_config")
msg = f"eval_config was specified, but `{eval_cfg_path}` does not exist"
assert Path(self._config.get("eval_config")).exists(), msg
# set save_dir where trained model and log will be saved.
if "tester" in self.config:
save_dir = Path(self.config['tester']['save_dir'])
else:
save_dir = Path(self.config['trainer']['save_dir'])
timestamp = datetime.now().strftime(r"%Y-%m-%d_%H-%M-%S") if timestamp else ""
if slave_mode:
timestamp = f"{timestamp}-eval-worker"
exper_name = self.set_exper_name(args, config=config)
if getattr(args, "group_id", False):
subdir = Path(args.group_id) / f"seed-{args.group_seed}" / timestamp
else:
subdir = timestamp
self._save_dir = save_dir / 'models' / exper_name / subdir
self._web_log_dir = save_dir / 'web' / exper_name / subdir
self._log_dir = save_dir / 'log' / exper_name / subdir
self._exper_name = exper_name
self._args = args
# if set, remove all previous experiments with the current config
if vars(args).get("purge_exp_dir", False):
for dirpath in (self._save_dir, self._log_dir, self._web_log_dir):
config_dir = dirpath.parent
existing = list(config_dir.glob("*"))
print(f"purging {len(existing)} directories from config_dir...")
tic = time.time()
os.system(f"rm -rf {config_dir}")
print(f"Finished purge in {time.time() - tic:.3f}s")
self.save_dir.mkdir(parents=True, exist_ok=True)
self.log_dir.mkdir(parents=True, exist_ok=True)
# save updated config file to the checkpoint dir
write_json(self.config, self.save_dir / 'config.json')
# configure logging module
if not slave_mode:
self.log_path = setup_logging(self.log_dir)
self.log_levels = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG}
from message_handler import *
from logger import setup_logging
import asyncio
from itertools import count
# DEBUG STUFF
_d_conn_counter = 0 # Counter for conns.
_d_conn_rev = 0
# Logging
log = setup_logging(__name__)
# List of all connections
connections = {}
class ChatServer(asyncio.Protocol):
name = None # Name of client
transport = None # Transport i/f to
def connection_made(self, transport):
global _d_conn_counter
_d_conn_counter += 1
host, port = transport.get_extra_info('peername')
log.debug("Connection made from {1}:{2}. Total = {0}".format(
_d_conn_counter, host, port))
# Storing this connection...
self.transport = transport
import sys
import unittest
# our own packages
test_dir = os.path.dirname(__file__)
sys.path.insert(1, os.path.join(test_dir, '..', 'ext', 'velib_python', 'test'))
sys.path.insert(1, os.path.join(test_dir, '..'))
import dbus_systemcalc
import vedbus
from logger import setup_logging
from mock_dbus_monitor import MockDbusMonitor
from mock_dbus_service import MockDbusService
from mock_settings_device import MockSettingsDevice
dbus_systemcalc.logger = setup_logging()
class TestSystemCalcBase(unittest.TestCase):
def __init__(self, methodName='runTest'):
unittest.TestCase.__init__(self, methodName)
self._service = MockDbusService('com.victronenergy.system')
self._system_calc = dbus_systemcalc.SystemCalc(\
lambda x: MockDbusMonitor(x), \
lambda x: MockDbusService(x), \
lambda x, y: MockSettingsDevice(x, y))
self._monitor = self._system_calc._dbusmonitor
self._service = self._system_calc._dbusservice
self._settings = self._system_calc._settings
def _update_values(self):
#coding:utf-8
__author__ = 'liuyix'
from logger import setup_logging
logger = setup_logging(console=True, logfile='locate_src.log', filelog=True)
def find_src(trace_name, filter_name, result_file):
import linecache
with open(filter_name) as filter:
with open(result_file, 'w') as result:
last_src_info = []
for line in filter:
if line.isspace():
continue
if not line.split()[0].strip().isdigit():
result.write(line)
else:
src_info = linecache.getline(trace_name, int(line.split()[-1])).split(' : ')[-3:]
if src_info != last_src_info:
last_src_info = src_info
result.write(line.rstrip() + ' ' + ' '.join(src_info))
else:
result.write(line)
def usage():
help_message = 'locate_src.py 修改后的trace文件 filtered_file result_file'
print help_message
from core_modules.election_news_classifier.election_classifier import SemanticFeatureExtractor
from logger import setup_logging
__author__ = 'pralav'
def classify_news(path='/news_classified.pkl',articles=None,logging=None):
vec_name="new_vectorizers.pkl"
mod_name="new_models.pkl"
kbest_name="new_kbests.pkl"
sem = SemanticFeatureExtractor(logging=logging,vec_name=vec_name,model_name=mod_name,kbest_name=kbest_name)
# sem.train_examples()
sem.classify_db_articles_batch(reset=False,limit=5000)
if __name__ == '__main__':
logging=setup_logging()
classify_news(logging=logging)
def __del__(self): # Reset logging. logger.setup_logging()
if hybrid:
csvhelper.write(['spm total', 'spm block', 'spm miss', 'cache total', 'cache line', 'cache miss'])
test_hybrid(p, csvhelper)
if cache:
csvhelper.write(['cache size', 'cache line', 'miss'])
test_cache(p, csvhelper)
trace_fobj.close()
def simple_trace():
trace='./simple_matrix/thread0-trace.out'
trace_file = open(trace, 'r')
file_line_count = wccount(trace)
trace_begin = int(68365.0 / 268569.0 * file_line_count)
trace_end = int(151920.0 / 268569.0 * file_line_count)
phase = trace_file.readlines()[trace_begin:trace_end]
logger.info("line count: %d", file_line_count)
logger.info("begin: %d, end: %d", trace_begin, trace_end)
return phase
if __name__ == "__main__":
import sys
from logger import setup_logging
logger = setup_logging(console=False, logfile='mem_simulation.log')
#do_simple_test()
#test_cache()
#filter_result_parser()
#extract_phases()
simulate_main()
import datetime
import calendar
# our own packages
test_dir = os.path.dirname(__file__)
sys.path.insert(0, test_dir)
sys.path.insert(1, os.path.join(test_dir, '..', 'ext', 'velib_python', 'test'))
sys.path.insert(1, os.path.join(test_dir, '..'))
import dbus_generator
import gobject
from logger import setup_logging
from mock_dbus_monitor import MockDbusMonitor
from mock_dbus_service import MockDbusService
from mock_settings_device import MockSettingsDevice
dbus_generator.logger = setup_logging()
class MockGenerator(dbus_generator.Generator):
def _create_dbus_monitor(self, *args, **kwargs):
return MockDbusMonitor(*args, **kwargs)
def _create_settings(self, *args, **kwargs):
return MockSettingsDevice(*args, **kwargs)
def _create_dbus_service(self):
return MockDbusService('com.victronenergy.generator.startstop0')
class TestGeneratorBase(unittest.TestCase):
def __init__(self, methodName='runTest'):
import logging
import numpy as np
import pandas as pd
from sklearn.metrics import classification_report
from logger import setup_logging
from classifier.classifier import IsReturningCustomerClassifier as Classifier
setup_logging(
filename="main"
)
logger = logging.getLogger(__name__)
def get_feature_importance(model):
"""
Get feature importances
Args:
model : model
Returns:
feature importances
"""
importances = model.feature_important
return importances
def get_classification_report(actual, predictions):
"""
Get classification report, f1 score
self.utils.save_file_joblib(
params, "%s/%s_%s.npy" % (out_path, model_name, e))
self.utils.save_file_joblib(
enc_params,
"%s/encoding_%s_%s.npy" % (out_path, model_name, e))
all_params = (self.max_chars, self.num_filters, self.encoder_size,
self.decoder_size, self.char_vocab_size,
self.grad_clip, self.norm_constraint,
self.learning_rate)
self.utils.save_file_joblib(
all_params,
"%s/encoding_%s_%s_global.npy" % (out_path, model_name, e))
if __name__ == '__main__':
logging = setup_logging(save_path=LOG_PATH + "/training.log")
parser = argparse.ArgumentParser(description='Trainer')
parser.add_argument('-m',
'--model_name',
help='Model Name',
dest='model_name',
default='tweet_word')
parser.add_argument('-f',
'--num_filters',
help='No. of filters',
dest='num_filters',
default=256,
type=int)
parser.add_argument('-L',
'--encoder_len',
def __init__(self, args, options='', timestamp=True):
# parse default and custom cli options
for opt in options:
args.add_argument(*opt.flags, default=None, type=opt.type)
args = args.parse_args()
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
self.bert_config_path = None
if args.resume:
self.resume = Path(args.resume)
self.cfg_fname = self.resume.parent / 'config.json'
self.bert_config_path = str(self.resume.parent / 'BertConfig.json')
else:
msg_no_cfg = "Configuration file need to be specified. Add '-c config.json', for example."
assert args.config is not None, msg_no_cfg
self.resume = None
self.cfg_fname = 'config' / Path(args.config)
# load config file and apply custom cli options
config = read_json(self.cfg_fname)
self._config = _update_config(config, options, args)
# set save_dir where trained model and log will be saved.
self.base_save_dir = Path(self.config['trainer']['save_dir'])
self.exper_name = self.config['processor']['args']['data_name'] + \
'_' + self.config['arch']['type']
timestamp = datetime.now().strftime(
r'%m%d_%H%M%S') if timestamp else ''
self._save_dir = self.base_save_dir / 'models' / self.exper_name / timestamp
self._log_dir = self.base_save_dir / 'log' / self.exper_name / timestamp
self.log_dir.mkdir(parents=True, exist_ok=True)
# configure logging module
setup_logging(self.log_dir)
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
setup_seed(self._config['seed'])
self.debug_mode = args.debug if "debug" in args else False
self.all = args.all if "all" in args else False
self.reset = args.reset if "reset" in args else False
self.search_mode = args.searchMode if "searchMode" in args else "disable"
self.gradient_accumulation_steps = self.config['trainer'][
'gradient_accumulation_steps']
if self.search_mode != 'disable':
self.config['trainer']['tensorboardX'] = False
if self.all:
self.config["data_loader"]["args"]["validation_split"] = 0.0
if self.debug_mode:
self.config["trainer"]["epochs"] = 2
def main():
setup_logging(__file__, logging.WARNING)
date = setup_argparse()
logging.info('Start Collect %s Trading Data' % datetime.date.today())
collect_stock_daily_trading(date)
logging.info('Collect %s Trading Data Success' % datetime.date.today())
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""
config
"""
import sys
import yaml
import logging
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
from logger import setup_logging
setup_logging()
class YamlConfigReader(object):
"""
YamlConfigReader
"""
def __init__(self, config_file):
self.logger = logging.getLogger("YamlConfigReader")
self.config_file = config_file
self.config_dict = self.load_conf(self.config_file)
def load_conf(self, config_file):
"""
load_conf
"""
try:
with open(config_file, 'rt') as f:
config_dict = yaml.load(f)
r = v
else:
r += v
return r
if __name__ == "__main__":
# Argument parsing
parser = argparse.ArgumentParser(
description='Converts readings from AC-Sensors connected to a VE.Bus device in a pvinverter ' +
'D-Bus service.'
)
parser.add_argument("-d", "--debug", help="set logging level to debug",
action="store_true")
args = parser.parse_args()
print("-------- dbus_systemcalc, v" + softwareVersion + " is starting up --------")
logger = setup_logging(args.debug)
# Have a mainloop, so we can send/receive asynchronous calls to and from dbus
DBusGMainLoop(set_as_default=True)
systemcalc = SystemCalc()
# Start and run the mainloop
logger.info("Starting mainloop, responding only on events")
mainloop = gobject.MainLoop()
mainloop.run()
import boto3
import requests
import pytz
from Crypto.Cipher import AES
from Crypto.Util import Counter
from logger import setup_logging
from pyspark.sql import Row, SparkSession
from pyspark.sql.types import *
from pyspark.sql import functions as F
keys_map = {} # for caching dks key
the_logger = setup_logging(
log_level=os.environ["ADG_LOG_LEVEL"].upper()
if "ADG_LOG_LEVEL" in os.environ else "INFO",
log_path="${log_path}",
)
class CollectionData:
def __init__(self, collection_name, staging_hive_table, tag_value):
self.collection_name = collection_name
self.staging_hive_table = staging_hive_table
self.tag_value = tag_value
def main():
database_name = get_staging_db_name()
secrets_response = retrieve_secrets()
collections = get_collections(secrets_response)
stock_info.business_scope = survey_table[32].text
stock_info.company_introduce = survey_table[31].text
stock_info.area = survey_table[23].text
core_concept_url = core_concept.format(query_id)
concept_html = send_request(core_concept_url)
concept_soup = BeautifulSoup(concept_html, 'lxml').find('div', class_='summary').find('p').text
stock_info.market_plate = concept_soup.replace(u'要点一：所属板块 ', '').replace(u'。', '').split(u'，')
stock_info.update_time = datetime.datetime.now()
stock_info.save()
def start_collect_detail():
try:
all_stocks = StockInfo.objects().timeout(False)
except Exception, e:
logging.error('Error when query StockInfo:' + str(e))
raise e
for i in all_stocks:
try:
collect_company_survey(i)
except Exception, e:
logging.error('Error when collect %s data: %s' % (i.stock_number, e))
time.sleep(random.random())
if __name__ == '__main__':
setup_logging(__file__)
start_collect_detail()
def create_app(dev_server=False):
app = Flask(__name__)
app.config.from_envvar('SETTINGS_FILE')
app.jinja_options['extensions'].append('jinja2.ext.do')
if install_logging:
logger.setup_logging(app)
for extension in (cdn, csrf, cache, db, mail, assets, toolbar):
extension.init_app(app)
migrate.init_app(app, db, render_as_batch=True)
login_manager.setup_app(app, add_context_processor=True)
app.login_manager.login_view = 'users.login'
from models.user import User
from models import site_state, feature_flag
@login_manager.user_loader
def load_user(userid):
user = User.query.filter_by(id=userid).first()
if user:
_request_ctx_stack.top.user_email = user.email
return user
if app.config.get('TICKETS_SITE'):
gocardless.environment = app.config['GOCARDLESS_ENVIRONMENT']
gocardless.set_details(
app_id=app.config['GOCARDLESS_APP_ID'],
app_secret=app.config['GOCARDLESS_APP_SECRET'],
access_token=app.config['GOCARDLESS_ACCESS_TOKEN'],
merchant_id=app.config['GOCARDLESS_MERCHANT_ID'])
stripe.api_key = app.config['STRIPE_SECRET_KEY']
@app.before_request
def load_per_request_state():
site_state.get_states()
feature_flag.get_db_flags()
if app.config.get('DEBUG'):
# Prevent staging site from being displayed on Google
@app.after_request
def send_noindex_header(response):
response.headers['X-Robots-Tag'] = 'noindex, nofollow'
return response
# Prevent DB connections and random numbers being shared
ppid = os.getpid()
@app.before_request
def fix_shared_state():
if os.getpid() != ppid:
db.engine.dispose()
random.seed()
@app.before_request
def simple_cache_warning():
if not dev_server and app.config.get('CACHE_TYPE', 'null') == 'simple':
logging.warn(
'Per-process cache being used outside dev server - refreshing will not work'
)
@app.after_request
def send_security_headers(response):
use_hsts = app.config.get('HSTS', False)
if use_hsts:
max_age = app.config.get('HSTS_MAX_AGE', 3600 * 24 * 7 * 4)
response.headers[
'Strict-Transport-Security'] = 'max-age=%s' % max_age
response.headers['X-Frame-Options'] = 'deny'
response.headers['X-Content-Type-Options'] = 'nosniff'
return response
@app.errorhandler(404)
def handle_404(e):
return render_template('errors/404.html'), 404
@app.errorhandler(500)
def handle_500(e):
return render_template('errors/500.html'), 500
from apps.common import load_utility_functions
load_utility_functions(app)
from apps.base import base
from apps.users import users
from apps.tickets import tickets
from apps.payments import payments
from apps.cfp import cfp
from apps.cfp_review import cfp_review
from apps.schedule import schedule
from apps.arrivals import arrivals
from apps.admin import admin
app.register_blueprint(base)
app.register_blueprint(users)
app.register_blueprint(tickets)
app.register_blueprint(payments)
app.register_blueprint(cfp)
app.register_blueprint(cfp_review, url_prefix='/cfp-review')
app.register_blueprint(schedule)
app.register_blueprint(arrivals, url_prefix='/arrivals')
app.register_blueprint(admin, url_prefix='/admin')
return app
import logging
from queue import QueueManager, Queue
from logger import setup_logging
if __name__ == '__main__':
os.chdir(os.path.dirname(os.path.abspath(sys.argv[0])))
try:
from tendo import singleton
me = singleton.SingleInstance()
except Exception, e:
print "Another instance of script is running. Exiting!!!"
sys.exit(2)
try:
setup_logging("Notification", os.path.join("data", "log"), logfile="queue.log", scrnlog = False)
logger = logging.getLogger("Notification")
logger.info("cron job initiated")
if(len(sys.argv) <= 1):
#process Queue
logger.info("Processing queue")
queue_manager = QueueManager()
for case_id, queue_item in queue_manager.get_current_queue().iteritems():
logger.info("Processing Queue. Case ID: %s", case_id)
queue_item.data.print_data()
main_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"main.py")
print os.system("python '%s' -i %s" % (main_file_path, str(case_id)) )
elif len(sys.argv) == 2:
if(sys.argv[1].lower() == "--export"):
self.logger.info(
"Writing %s failed ingestion(s) out to %s" % (len(self.failed_ingestions), outfile))
writer.writerow(dict((fn,fn) for fn in fieldnames))
for f in self.failed_ingestions:
writer.writerow(f)
if __name__ == '__main__':
# Separate the task and arguments.
task, args = sys.argv[1], sys.argv[2:]
# Setup Logging
log_file_name = "_".join((
"ingestion", task, datetime.today().strftime('%Y_%m_%d_%H_%M_%S'),
)) + ".log"
logger.setup_logging(
log_file_name=log_file_name,
send_mail="-no-email" not in args and EMAIL['enabled'])
main_logger = logging.getLogger('Main')
# If the -h argument is passed at the command line, display the internal documentation and exit.
if "-h" in sys.argv:
sys.stdout.write(INTERNAL_DOCUMENTATION)
log_and_exit(0)
# Run the task with the arguments.
perform = Task(args)
if task in Task.valid_tasks:
main_logger.info("-")
main_logger.info(
"Running ingestion task '%s' with command-line arguments '%s'" % (
task, " ".join(args)))
def main(config):
cfg_trainer = config['trainer_colab'] if config[
'colab'] == True else config['trainer']
run_id = config['resume'].split('/')[-2]
file_name = config['resume'].split('/')[-1].split('.')[0]
output_dir = os.path.join(cfg_trainer['output_dir'], run_id, file_name)
(os.path.exists(output_dir) or os.makedirs(
output_dir, exist_ok=True)) and rmdir(output_dir, remove_parent=False)
setup_logging(output_dir)
logger = logging.getLogger('test')
use_gpu = cfg_trainer['n_gpu'] > 0 and torch.cuda.is_available()
device = torch.device('cuda:0' if use_gpu else 'cpu')
map_location = "cuda:0" if use_gpu else torch.device('cpu')
datamanager, _ = build_datamanager(config['type'], config['data'])
model, _ = build_model(config,
num_classes=len(
datamanager.datasource.get_attribute()))
logger.info('Loading checkpoint: {} ...'.format(config['resume']))
checkpoint = torch.load(config['resume'], map_location=map_location)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
model.to(device)
preds = []
labels = []
with tqdm(total=len(datamanager.get_dataloader('test'))) as epoch_pbar:
with torch.no_grad():
for batch_idx, (data, _labels) in enumerate(
datamanager.get_dataloader('test')):
data, _labels = data.to(device), _labels.to(device)
out = model(data)
_preds = torch.sigmoid(out)
preds.append(_preds)
labels.append(_labels)
epoch_pbar.update(1)
preds = torch.cat(preds, dim=0)
labels = torch.cat(labels, dim=0)
preds = preds.cpu().numpy()
labels = labels.cpu().numpy()
# # get best threshold
# from sklearn.metrics import roc_curve, auc, precision_recall_curve
# precision = dict()
# recall = dict()
# thresholds_pr = dict()
# pr_auc = dict()
# best_threshold = dict()
# fpr = dict()
# tpr = dict()
# roc_auc = dict()
# thresholds_roc = dict()
# for i in range(len(datamanager.datasource.get_attribute())):
# precision[i], recall[i], thresholds_pr[i] = precision_recall_curve(labels[:, i], preds[:, i])
# pr_auc[i] = auc(recall[i], precision[i])
# best_threshold[i] = np.argmax((2 * precision[i] * recall[i]) / (precision[i] + recall[i]))
# fpr[i], tpr[i], thresholds_roc[i] = roc_curve(labels[:, i], preds[:, i])
# roc_auc[i] = auc(fpr[i], tpr[i])
# fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)
# ax1.plot(recall[i], precision[i], label='Precision-Recall Curve, mAP: %f' % pr_auc[i])
# ax1.scatter(
# recall[i][best_threshold[i]],
# precision[i][best_threshold[i]],
# marker='o',
# color='black',
# label='Best threshold %f' % (thresholds_pr[i][best_threshold[i]]))
# ax1.set_xlabel('Recall')
# ax1.set_ylabel('Precision')
# ax1.set_title('Attribute: %s' % datamanager.datasource.get_attribute()[i])
# # ax1.legend(loc="lower right")
# fig, ax2 = plt.subplots(122)
# ax2.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % (roc_auc[i]))
# ax2.plot([0, 1], [0, 1], 'k--')
# ax2.scatter(fpr[i][best_threshold[i]], tpr[i][best_threshold[i]], marker='o', color='black', label='Best threshold %f' % (thresholds[i][best_threshold[i]]))
# ax2.set_xlim([0.0, 1.0])
# ax2.set_ylim([0.0, 1.05])
# ax2.set_xlabel('False Positive Rate')
# ax2.set_ylabel('True Positive Rate')
# ax2.set_title('Attribute: %s' % datamanager.datasource.get_attribute()[i])
# # ax2.legend(loc="lower right")
# plt.show()
result_label, result_instance = recognition_metrics(labels, preds)
log_test(logger.info, datamanager.datasource.get_attribute(),
datamanager.datasource.get_weight('test'), result_label,
result_instance)
from flask.ext.assets import Environment, Bundle
from flask_wtf import CsrfProtect
import gocardless
import stripe
import logging
import logger
logging.basicConfig(level=logging.NOTSET)
app = Flask(__name__)
csrf = CsrfProtect(app)
app.config.from_envvar('SETTINGS_FILE')
logger.setup_logging(app)
db = SQLAlchemy(app)
mail = Mail(app)
login_manager = LoginManager()
assets = Environment(app)
css_all = Bundle('css/main.css',
output='gen/packed.css', filters='cssmin')
css_admin = Bundle('css/admin.css',
output='gen/admin-packed.css', filters='cssmin')
css_print = Bundle('css/print.css',
output='gen/print-packed.css', filters='cssmin')
assets.register('css_all', css_all)
assets.register('css_admin', css_admin)
assets.register('css_print', css_print)
from flask.ext.assets import Environment, Bundle
from flask_wtf import CsrfProtect
import gocardless
import stripe
import logging
import logger
logging.basicConfig(level=logging.NOTSET)
app = Flask(__name__)
csrf = CsrfProtect(app)
app.config.from_envvar('SETTINGS_FILE')
logger.setup_logging(app)
db = SQLAlchemy(app)
mail = Mail(app)
login_manager = LoginManager()
assets = Environment(app)
css_all = Bundle('css/main.css', output='gen/packed.css', filters='cssmin')
css_admin = Bundle('css/admin.css',
output='gen/admin-packed.css',
filters='cssmin')
css_print = Bundle('css/print.css',
output='gen/print-packed.css',
filters='cssmin')
assets.register('css_all', css_all)
assets.register('css_admin', css_admin)
def create_app(dev_server=False):
app = Flask(__name__)
app.config.from_envvar('SETTINGS_FILE')
app.jinja_options['extensions'].append('jinja2.ext.do')
if install_logging:
logger.setup_logging(app)
for extension in (cdn, csrf, cache, db, mail, assets, toolbar):
extension.init_app(app)
migrate.init_app(app, db, render_as_batch=True)
login_manager.setup_app(app, add_context_processor=True)
app.login_manager.login_view = 'users.login'
from models.user import User
from models import site_state, feature_flag
@login_manager.user_loader
def load_user(userid):
user = User.query.filter_by(id=userid).first()
if user:
_request_ctx_stack.top.user_email = user.email
return user
if app.config.get('TICKETS_SITE'):
gocardless.environment = app.config['GOCARDLESS_ENVIRONMENT']
gocardless.set_details(app_id=app.config['GOCARDLESS_APP_ID'],
app_secret=app.config['GOCARDLESS_APP_SECRET'],
access_token=app.config['GOCARDLESS_ACCESS_TOKEN'],
merchant_id=app.config['GOCARDLESS_MERCHANT_ID'])
stripe.api_key = app.config['STRIPE_SECRET_KEY']
@app.before_request
def load_per_request_state():
site_state.get_states()
feature_flag.get_db_flags()
if app.config.get('DEBUG'):
# Prevent staging site from being displayed on Google
@app.after_request
def send_noindex_header(response):
response.headers['X-Robots-Tag'] = 'noindex, nofollow'
return response
# Prevent DB connections and random numbers being shared
ppid = os.getpid()
@app.before_request
def fix_shared_state():
if os.getpid() != ppid:
db.engine.dispose()
random.seed()
@app.before_request
def simple_cache_warning():
if not dev_server and app.config.get('CACHE_TYPE', 'null') == 'simple':
logging.warn('Per-process cache being used outside dev server - refreshing will not work')
@app.after_request
def send_security_headers(response):
use_hsts = app.config.get('HSTS', False)
if use_hsts:
max_age = app.config.get('HSTS_MAX_AGE', 3600 * 24 * 7 * 4)
response.headers['Strict-Transport-Security'] = 'max-age=%s' % max_age
response.headers['X-Frame-Options'] = 'deny'
response.headers['X-Content-Type-Options'] = 'nosniff'
return response
@app.errorhandler(404)
def handle_404(e):
return render_template('errors/404.html'), 404
@app.errorhandler(500)
def handle_500(e):
return render_template('errors/500.html'), 500
from apps.common import load_utility_functions
load_utility_functions(app)
from apps.base import base
from apps.users import users
from apps.tickets import tickets
from apps.payments import payments
from apps.cfp import cfp
from apps.cfp_review import cfp_review
from apps.schedule import schedule
from apps.arrivals import arrivals
from apps.admin import admin
app.register_blueprint(base)
app.register_blueprint(users)
app.register_blueprint(tickets)
app.register_blueprint(payments)
app.register_blueprint(cfp)
app.register_blueprint(cfp_review, url_prefix='/cfp-review')
app.register_blueprint(schedule)
app.register_blueprint(arrivals, url_prefix='/arrivals')
app.register_blueprint(admin, url_prefix='/admin')
return app
def __init__(self, config, modification=None, timestamp=None):
# load config file and apply modification
self._config = _update_config(config, modification)
# logger
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
verbosity = self['trainer']['verbosity']
msg_verbosity = 'verbosity option {} is invalid. Valid options are {}.'.format(
verbosity, self.log_levels.keys())
assert verbosity in self.log_levels, msg_verbosity
self._logger = logging.getLogger('default')
self._logger.setLevel(self.log_levels[verbosity])
# set save_dir where trained model and log will be saved.
run_id = timestamp
save_dir = Path(self.config['trainer']['save_dir'])
exper_name = self.config['name']
dir = save_dir / exper_name / run_id
self._dir = dir
self._log_dir = dir / 'log'
self._save_dir = dir / 'models'
self._tensors_dir = dir / 'tensors'
self._samples_dir = dir / 'samples'
self._im_dir = dir / 'images'
self._fields_dir = dir / 'fields'
self._grids_dir = dir / 'grids'
self._norms_dir = dir / 'norms'
# segmentation IDs
self.structures_dict = {
'left_thalamus': 10,
'left_caudate': 11,
'left_putamen': 12,
'left_pallidum': 13,
'brain_stem': 16,
'left_hippocampus': 17,
'left_amygdala': 18,
'left_accumbens': 26,
'right_thalamus': 49,
'right_caudate': 50,
'right_putamen': 51,
'right_pallidum': 52,
'right_hippocampus': 53,
'right_amygdala': 54,
'right_accumbens': 58
}
# make directories for saving checkpoints and log.
exist_ok = run_id == ''
self.log_dir.mkdir(parents=True, exist_ok=exist_ok)
self.save_dir.mkdir(parents=True, exist_ok=exist_ok)
self.tensors_dir.mkdir(parents=True, exist_ok=exist_ok)
self.samples_dir.mkdir(parents=True, exist_ok=exist_ok)
samples_VI_dir = self.samples_dir / 'VI'
samples_MCMC_dir = self.samples_dir / 'MCMC'
samples_VI_dir.mkdir(parents=True, exist_ok=exist_ok)
samples_MCMC_dir.mkdir(parents=True, exist_ok=exist_ok)
self.im_dir.mkdir(parents=True, exist_ok=exist_ok)
self.fields_dir.mkdir(parents=True, exist_ok=exist_ok)
self.grids_dir.mkdir(parents=True, exist_ok=exist_ok)
self.norms_dir.mkdir(parents=True, exist_ok=exist_ok)
# configure logging
setup_logging(self.log_dir)
# save updated config file to the checkpoint dir
self.config_str = json.dumps(self.config, indent=4,
sort_keys=False).replace('\n', '')
write_json(self.config, dir / 'config.json')
if not is_duplicate(smt):
smt.save()
def is_duplicate(smt):
duplicate_data = SMT.objects(Q(stock_number=smt.stock_number) &
Q(stock_name=smt.stock_name) &
Q(rz_net_buy_amount=smt.rz_net_buy_amount) &
Q(rq_repay_volume=smt.rq_repay_volume) &
Q(rq_sell_volume=smt.rq_sell_volume) &
Q(rq_remaining_volume=smt.rq_remaining_volume) &
Q(rz_repay_amount=smt.rz_repay_amount) &
Q(rz_buy_amount=smt.rz_buy_amount) &
Q(rz_remaining_amount=smt.rz_remaining_amount))
if duplicate_data:
return True
else:
return False
if __name__ == '__main__':
setup_logging(__file__, logging.WARNING)
logging.info('Start to collect stock margin trading')
for url in rzrq_api:
try:
collect_margin_trading(url)
except Exception, e:
logging.error('Collect margin trading %s failed:%s' % (url, e))
logging.info('collect stock margin trading success')
parser = argparse.ArgumentParser(
description='dummy dbus service'
)
parser.add_argument("-n", "--name",
help="the D-Bus service you want me to claim",
type=str, default="com.victronenergy.battery.socketcan_can0")
parser.add_argument("-i", "--instance",
help="DeviceInstance",
type=int, default=0)
args = parser.parse_args()
print(__file__ + " is starting up, use -h argument to see optional arguments")
logger = setup_logging(debug=True)
# Have a mainloop, so we can send/receive asynchronous calls to and from dbus
DBusGMainLoop(set_as_default=True)
s = DbusDummyService(
servicename=args.name,
deviceinstance=args.instance,
paths={
'/Alarms/CellImbalance': {'initial': 0},
'/Alarms/HighChargeCurrent': {'initial': 0},
'/Alarms/HighChargeTemperature': {'initial': 0},
'/Alarms/HighDischargeCurrent': {'initial': 0},
'/Alarms/HighTemperature': {'initial': 0},
'/Alarms/HighVoltage': {'initial': 0},
'/Alarms/InternalFailure': {'initial': 0},
self.logger.info(
"Writing %s failed ingestion(s) out to %s" % (len(self.failed_ingestions), outfile))
writer.writerow(dict((fn,fn) for fn in fieldnames))
for f in self.failed_ingestions:
writer.writerow(f)
if __name__ == '__main__':
# Separate the task and arguments.
task, args = sys.argv[1], sys.argv[2:]
# Setup Logging
log_file_name = "_".join((
"ingestion", task, datetime.today().strftime('%Y_%m_%d_%H_%M_%S'),
)) + ".log"
logger.setup_logging(
log_file_name=log_file_name,
send_mail="-no-email" not in args and EMAIL['enabled'],
info_to_console="-v" in args)
main_logger = logging.getLogger('Main')
logging.getLogger("requests").setLevel(logging.WARNING)
# If the -h argument is passed at the command line, display the internal documentation and exit.
if "-h" in sys.argv:
sys.stdout.write(INTERNAL_DOCUMENTATION)
log_and_exit(0)
# Run the task with the arguments.
perform = Task(args)
if task in Task.valid_tasks:
task_start_time = datetime.now()
main_logger.info(
"Running ingestion task '%s' with command-line arguments '%s'" % (
import simulation
import step
import cuboid
import vessel
import options
import process
import grid
import scoring
import entity
# singleton modules are not classes
import toolbox
import computer
import logger
logger.setup_logging()
try:
reload
except NameError:
# Python 3
from imp import reload
# inject test_shim before we load the domain
from sys import modules
try:
test_shim = modules['test_shim']
except KeyError:
import test_shim as test_shim
reload(cuboid)
def __del__(self):
# Reset logging.
logger.setup_logging()
def __init__(self, args, options='', timestamp=True):
# parse default and custom cli options
for opt in options:
args.add_argument(*opt.flags, default=None, type=opt.type)
args = args.parse_args()
self._name = None
self._out_name = None
if hasattr(args, 'device'):
if args.device:
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
self.cfg_fname = None
if hasattr(args, 'resume'):
if args.resume:
self.resume = Path(args.resume)
if hasattr(args, 'config') and args.config is not None:
self.cfg_fname = Path(args.config)
else:
self.cfg_fname = self.resume.parent / 'config.json'
if self.cfg_fname is None:
if hasattr(args, 'config'):
msg_no_cfg = "Configuration file need to be specified. Add '-c config.json', for example."
assert args.config is not None, msg_no_cfg
self.resume = None
self.cfg_fname = Path(args.config)
if hasattr(args, 'name'):
if args.name:
self._name = str(args.name)
if hasattr(args, 'oname'):
if args.oname:
self._out_name = str(args.oname)
# load config file and apply custom cli options
config = read_json(self.cfg_fname)
self._config = _update_config(config, options, args)
# set save_dir where trained model and log will be saved.
save_dir = Path(self.config['trainer']['save_dir'])
timestamp = datetime.now().strftime(
r'%d%m%y_%H%M%S') if timestamp else ''
exper_name = self.config['name']
self._save_dir = save_dir / 'models' / exper_name / timestamp
self._log_dir = save_dir / 'log' / exper_name / timestamp
self._temp_dir = save_dir / 'temp' / exper_name / timestamp
self.save_dir.mkdir(parents=True, exist_ok=True)
self.log_dir.mkdir(parents=True, exist_ok=True)
self.temp_dir.mkdir(parents=True, exist_ok=True)
# save updated config file to the checkpoint dir
write_json(self.config, self.save_dir / 'config.json')
# configure logging module
setup_logging(self.log_dir)
self.log_levels = {
0: logging.WARNING,
1: logging.INFO,
2: logging.DEBUG
}
def main():
signal.signal(signal.SIGINT, exit)
parser = argparse.ArgumentParser(
description='Publishes values from the D-Bus to an MQTT broker')
parser.add_argument('-d',
'--debug',
help='set logging level to debug',
action='store_true')
parser.add_argument('-q',
'--mqtt-server',
nargs='?',
default=None,
help='name of the mqtt server')
parser.add_argument('-u',
'--mqtt-user',
default=None,
help='mqtt user name')
parser.add_argument('-P',
'--mqtt-password',
default=None,
help='mqtt password')
parser.add_argument(
'-c',
'--mqtt-certificate',
default=None,
help='path to CA certificate used for SSL communication')
parser.add_argument('-b', '--dbus', default=None, help='dbus address')
parser.add_argument('-k',
'--keep-alive',
default=60,
help='keep alive interval in seconds',
type=int)
parser.add_argument(
'-i',
'--init-broker',
action='store_true',
help='Tries to setup communication with VRM MQTT broker')
args = parser.parse_args()
print("-------- dbus_mqtt, v{} is starting up --------".format(
SoftwareVersion))
logger = setup_logging(args.debug)
# This allows us to use gobject code in new threads
gobject.threads_init()
mainloop = gobject.MainLoop()
# Have a mainloop, so we can send/receive asynchronous calls to and from dbus
DBusGMainLoop(set_as_default=True)
keep_alive_interval = args.keep_alive if args.keep_alive > 0 else None
handler = DbusMqtt(mqtt_server=args.mqtt_server,
ca_cert=args.mqtt_certificate,
user=args.mqtt_user,
passwd=args.mqtt_password,
dbus_address=args.dbus,
keep_alive_interval=keep_alive_interval,
init_broker=args.init_broker,
debug=args.debug)
# Handle SIGUSR1 and dump a stack trace
signal.signal(signal.SIGUSR1, dumpstacks)
# Start and run the mainloop
try:
mainloop.run()
except KeyboardInterrupt:
pass
try:
all_stocks = StockInfo.objects()
except Exception, e:
logging.error('Error when query StockInfo:' + str(e))
raise e
stocks_count = len(all_stocks)
skip = 0
while skip < stocks_count:
try:
stocks = StockInfo.objects().skip(skip).limit(query_step)
except Exception, e:
logging.error('Error when query skip %s StockInfo:%s' % (skip, e))
stocks = []
for i in stocks:
try:
collect_company_survey(i)
except Exception, e:
logging.error('Error when collect %s data: %s' % (i.stock_number, e))
time.sleep(random.random())
skip += query_step
if __name__ == '__main__':
setup_logging(__file__, logging.WARNING)
logging.info('Start to collect stock detail info')
start_collect_detail()
logging.info('Collect stock detail info Success')
def log():
logger.setup_logging(40, False, None)
yield logging.getLogger()
logging.shutdown()