},
"hue_saturation": {
"use": True,
"min": -10,
"max": 10
}
}
}
}
args.aug_settings = AttrDict(aug_settings) # Add to args.
# Logging
LOGGER = logging.getLogger(__name__)
log_file = os.path.join('logs', '{}.log'.format(args.exp_name))
os.makedirs('logs', exist_ok=True)
setup_logging(log_path=log_file, logger=LOGGER)
if __name__ == '__main__':
# Experiment directory.
experiment_path = os.path.join('experiments', args.exp_name)
os.makedirs(experiment_path, exist_ok=True)
# Set cudnn profiling, add torchvision path.
if torch.cuda.is_available():
cudnn.benchmark = True
os.environ['TORCH_HOME'] = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'torchvision')
# Initialise trainer.
trainer = CNNTrainer(args, logger=LOGGER)
sys.exit()
def main() -> None:
logger.info("--- start ---")
# CSVの差分抽出
diff_df: DifferenceDf = DifferenceDf(csv_path=path)
# csv作成
diff_df.execute()
# 依頼件数取得
logger.info(f"件数: {diff_df.row_count} 行")
if __name__ == "__main__":
try:
# OSチェック
check_platform()
# 引数をparse
path, enc, debug = parse_arg()
# loggerの設定
logger = setup_logging(conf_path=get_log_conf_path(is_debug=debug))
main()
except Exception:
logger.error(format_exc())
finally:
logger.info("--- end ---")
wd = os.getcwd()
sys.path.append(wd)
from lib import alpaca
from lib.utils import setup_logging
credentials = alpaca.load_paper_credentials()
api = alpaca_trade_api.REST(key_id=credentials['key_id'], secret_key=credentials['secret_key'], base_url=credentials['base_url'])
# setup logging
log_out = sys.argv[1] if len(sys.argv) > 1 else '/var/log/apps/alpaca/paper_trade_example.log'
if log_out == 'stdout':
logger = setup_logging()
else:
logger = setup_logging(file_name=log_out)
def _get_prices(symbols, end_dt, max_workers=5):
# Get the map of DataFrame price data from Alpaca's data API.
start_dt = end_dt - pandas.Timedelta('50 days')
start = start_dt.strftime('%Y-%m-%d')
end = end_dt.strftime('%Y-%m-%d')
def get_barset(symbols):
return api.get_barset(symbols, 'day', limit = 50, start=start, end=end)
# The maximum number of symbols we can request at once is 200.
barset = None
if attempts_checkout:
checkout_lst = CheckoutList()
try:
for attempt in attempts_checkout:
checkout_lst.append((attempt, client, conn))
#Execute the checkout procedure for each item
pool.map(checkout_procedure, checkout_lst)
transaction.commit()
except:
transaction.abort()
raise
time.sleep(config.getint('checkout', 'mins_to_wait') * 60)
pool.close
if __name__ == '__main__':
config = utils.balaio_config_from_env()
utils.setup_logging(config)
models.Session.configure(bind=models.create_engine_from_config(config))
print('Start checkout process...')
main(config)
"""
filepath = event.pathname
if not os.path.basename(filepath).startswith('_'):
if not zipfile.is_zipfile(filepath):
logger.info('Invalid zipfile: %s.' % filepath)
return None
logger.debug('Adding %s to checkin processing pool.' % filepath)
self.pool.add_job(filepath)
if __name__ == '__main__':
# App bootstrapping:
# Setting up the app configuration, logging and SqlAlchemy Session.
config = utils.balaio_config_from_env()
utils.setup_logging(config)
models.Session.configure(bind=models.create_engine_from_config(config))
# Setting up PyInotify event watcher.
wm = pyinotify.WatchManager()
handler = EventHandler(config=config)
notifier = pyinotify.Notifier(wm, handler)
wm.add_watch(config.get('monitor', 'watch_path').split(','),
mask,
rec=config.getboolean('monitor', 'recursive'),
auto_add=config.getboolean('monitor', 'recursive'))
logger.info('Watching %s.' % config.get('monitor', 'watch_path'))
notifier.loop()
def logLevel(level=None):
utils.setup_logging(root_folder, log_level = level)
"""
parsed_l = data.parse_input_line(l)
if parsed_l is None:
return None
reddit, text, url = parsed_l
if not data.is_image_url(url):
return False, reddit, text, text, url
doc = tokenizer(text)
tokens = [t.lower_ for t in doc]
text_parsed = " ".join(tokens)
return True, reddit, text, text_parsed, url
if __name__ == "__main__":
utils.setup_logging()
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data", default=data.DEFAULT_DATA_FILE,
help="Data file to read, default=" + data.DEFAULT_DATA_FILE)
parser.add_argument("-o", "--output", default=data.DEFAULT_OUTPUT_PREFIX,
help="Output data prefix to produce. Suffixes will be added "
"to this path, default=" + data.DEFAULT_OUTPUT_PREFIX)
parser.add_argument("--limit", type=int, help="Optional limit of input lines to process, default=No limit")
args = parser.parse_args()
# using only tokenizer from spacy, this is much faster
nlp = English()
tokenizer = nlp.Defaults.create_tokenizer(nlp)
data_path = pathlib.Path(args.data)
import os
import sys
import argparse
from datetime import datetime
import backtrader
import pandas
wd = os.getcwd()
sys.path.append(wd)
from lib import alpaca
from lib.utils import setup_logging
# setup logging
logger = setup_logging()
# moving average strategy
class MovingAverageStretegy(backtrader.Strategy):
params = dict(day_period=15, week_period=5)
def log(self, txt, dt=None):
dt = dt or self.datas[0].datetime.date(0)
logger.info('%s, %s' % (dt.isoformat(), txt))
def __init__(self):
sma_day = backtrader.indicators.SMA(self.data0,
period=self.p.day_period)
sma_week = backtrader.indicators.SMA(self.data1,
period=self.p.week_period)
setattr(settings, 'num_input_channels', 1)
elif settings.dataset == 'kitti_2015':
setattr(settings, 'left_img_folder', 'image_2')
setattr(settings, 'right_img_folder', 'image_3')
setattr(settings, 'disparity_folder', 'disp_noc_0')
setattr(settings, 'num_val', 40)
setattr(settings, 'num_input_channels', 3)
# Python logging.
LOGGER = logging.getLogger(__name__)
exp_dir = join('experiments', '{}'.format(settings.exp_name))
log_file = join(exp_dir, 'log.log')
os.makedirs(exp_dir, exist_ok=True)
os.makedirs(join(exp_dir, 'qualitative_samples'), exist_ok=True)
setup_logging(log_path=log_file, log_level=settings.log_level, logger=LOGGER)
settings_file = join(exp_dir, 'settings.log')
with open(settings_file, 'w') as the_file:
the_file.write(str(settings))
# Set random seed.
# NOTE: The seed affects the train/val split if patch locations data is
# created, useful for reproducing results..
random.seed(settings.seed)
# Model.
device = '/cpu:0' if tfe.num_gpus() == 0 else '/gpu:0'
global_step = tf.Variable(0, trainable=False)
with tf.device(device):
def logLevel(level=None):
utils.setup_logging(root_folder, log_level=level)