def vrocli(verbose=False):
""" vRealize Automation coder/command line interface
"""
if verbose:
logger.setLevel(logging.DEBUG)
else:
logger.setLevel(logging.INFO)
def main(_):
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
if FLAGS.debug:
logger.setLevel(logging.DEBUG)
run_training(FLAGS.debug)
def main():
# parse cli args
args = parse_args(sys.argv[1:])
# set log level
if args.verbose is 1:
logger.setLevel(logging.INFO)
elif args.verbose > 1:
logger.setLevel(logging.DEBUG)
args.func(args)
def init_inference_session(args):
options = aclruntime.session_options()
if args.acl_json_path != None:
options.acl_json_path = args.acl_json_path
if args.debug == True:
logger.setLevel(logging.DEBUG)
options.log_level = 1
session = aclruntime.InferenceSession(args.model, args.device_id, options)
set_session_options(session, args)
logger.debug("session info:{}".format(session))
return session
def main():
cherrypy.config.update({
'log.screen': True,
'log.access_file': '',
'log.error_file': ''
})
telebot.logger.setLevel(logging.INFO)
logger.setLevel(logging.INFO)
sh = logging.StreamHandler()
sh.setLevel(logging.INFO)
fh = logging.FileHandler('alphabot.log', encoding='utf-8')
fh.setLevel(logging.INFO)
formatter = logging.Formatter(u'[%(asctime)s]: %(message)s')
sh.setFormatter(formatter)
fh.setFormatter(formatter)
logger.addHandler(sh)
logger.addHandler(fh)
'''
Automatic posting of birthday greetings in chat
'''
t1 = threading.Thread(target=birthday_method)
t1.daemon = True
t1.start()
bot.set_update_listener(listener)
# logger.info("Waiting 5 minutes before the start...")
# print("Start")
# threading.Timer(5*60, onStartProcessing).start()
# cherrypy.quickstart(WebhookServer(), config.WEBHOOK_URL_PATH, {'/': {}})
telegram_polling()
class Parameters:
program_dir = Path(abspath(__file__)).parents[1]
config_dir = 'config'
config_name = 'default_config.json'
config_path = join(program_dir, config_dir, config_name)
#try to load the config file
try:
#load json file here
config_dict = load_json_file(config_path)
except FileNotFoundError:
logger.critical(f'config file was not found at: {config_path}')
exit(1)
except Exception as e:
logger.critical(f'failed to load config file: {e}')
exit(1)
if is_valid_config(config_dict):
#set all parameters here
root_dir = Path(abspath(__file__)).parents[1]
whiskybroker_base_url = config_dict['whiskybroker base url']
email_address = config_dict['email address']
data_dir = join(root_dir, config_dict['data dir'])
logging_level, status = get_logging_level_from_parameter(
config_dict['general logging level'])
#TODO: maybe change the below logging to debug
logging.info(f'logging level parsing status: {status}')
logger.info(f'setting general logging level to: {logging_level}')
logger.setLevel(logging_level)
else:
#TODO: check why the config dict was not valid, and return any missing keys/wrong values
logger.critical(f'config file contained something wrong...')
exit(1)
def main(argv=None):
opts = create_parser().parse_args(argv)
opts.users = None
logger.setLevel(getattr(logging, opts.loglevel))
logger_handler.setLevel(getattr(logging, opts.loglevel))
try:
cloud = cloud_connect(opts.config)
if opts.cmd == 'vms':
print "\n".join(sorted(cloud))
else:
if opts.cmd == 'start':
for name in opts.vmnames:
cloud.start_vm(name, opts.users, opts.prepare)
elif opts.cmd == 'stop':
for name in opts.vmnames:
cloud.stop_vm(name, timeout1=opts.wait_time)
elif opts.cmd == 'login':
assert len(opts.vmnames) == 1
cloud.login_to_vm(opts.vmnames[0], opts.users)
elif opts.cmd == 'list':
for domain in cloud.list_vms():
try:
all_ips = ", ".join(cloud.get_vm_ips(domain.name()))
except socket.error as err:
if err.errno != errno.EPERM:
raise
all_ips = "Not enought permissions for arp-scan"
print "{0:>5} {1:<15} => {2}".format(
domain.ID(), domain.name(), all_ips)
elif opts.cmd == 'wait_ip':
tend = time.time() + opts.wait_time
for vmname in opts.vmnames:
while True:
try:
ips = list(cloud.get_vm_ips(vmname))
except socket.error as err:
if err.errno != errno.EPERM:
raise
print "Not enought permissions for arp-scan"
return 1
if len(ips) != 0:
print "{0:<15} => {1}".format(
vmname, " ".join(ips))
break
if time.time() >= tend:
print "VM {0} don't get ip in time".format(vmname)
return 1
time.sleep(0.01)
elif opts.cmd == 'wait_ssh':
tend = time.time() + opts.wait_time
for vmname in opts.vmnames:
while True:
try:
ip = cloud.get_vm_ssh_ip(vmname)
except socket.error as err:
if err.errno != errno.EPERM:
raise
print "Not enought permissions for arp-scan"
return 1
if ip is not None:
print "{0:<15} => {1}".format(vmname, ip)
break
if time.time() >= tend:
templ = "VM {0} don't start ssh server in time"
print templ.format(vmname)
return 1
time.sleep(0.01)
else:
print >> sys.stderr, "Error : Unknown cmd {0}".format(opts.cmd)
except CloudError as err:
print >> sys.stderr, err
return 1
return 0
def model_test(X, Y, method):
amount = 0
times = 0
for cv_data, cv_target, test_data, test_target in SAMPLE.iter_sample_data(
X, Y, method):
times += 1
model = LogisticRegression(delta=0.01, alpha=0.01)
model.fit(cv_data, cv_target)
predict_y = model.predict(test_data)
amount += FORMULA.cal(predict_y, test_target)
return float(amount) / times
if __name__ == '__main__':
filename = "../data/iris.csv"
X, Y = FU.load_iris_data(filename)
X = StandardScaler().fit_transform(X)
X = FORMULA.plus_one(X)
LOGGER.setLevel(LOGGER.LEVEL_NORMAL)
print u"10折交叉法:", model_test(X, Y, 10)
print u"留一法:", model_test(X, Y, 1)
# model.draw_data(X, Y)
# model.draw_line(X)
# model.draw_loss()
# plt.show()
ret[band] = [hpx,maglim]
return ret
if __name__ == "__main__":
import argparse
description = __doc__
parser = argparse.ArgumentParser(description=description)
parser.add_argument('config',nargs='?')
parser.add_argument('-n','--nside',default=NSIDE,type=int)
parser.add_argument('-v','--verbose',action='store_true')
parser.add_argument('-s','--survey',default='des')
args = parser.parse_args()
if args.verbose:
logger.setLevel(logger.INFO)
if args.config:
config = yaml.load(open(args.config))
BANDS = config['bands']
NSIDE = args.nside
outdir = mkdir('release/depth')
infiles = sorted(glob.glob('cat/cat_hpx_*.fits'))
p = Pool(maxtasksperchild=1,processes=20)
out = p.map(depth,infiles)
skymaps = dict()
import os
import logging
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn_cell, rnn
from tensorflow.python.platform import gfile
from utils import r2_score, mean_squared_error
from utils import logger, Print, PrintMess
logger.setLevel("CRITICAL")
logging.disable("INFO")
tf.app.flags.DEFINE_boolean("print_twice", False, "Work interactively or not?")
tf.app.flags.DEFINE_float("learning_rate", 0.001, "Learning Rate")
tf.app.flags.DEFINE_float("max_grad_norm", 1., "Clipping gradient norm")
tf.app.flags.DEFINE_float("init_scale", .1, "Norm of initial weights")
tf.app.flags.DEFINE_integer("hidden_dim", 100, "hidden size of Neural net")
tf.app.flags.DEFINE_integer("num_layers", 2, "Number of layers in neural net")
tf.app.flags.DEFINE_integer("input_dim", 1, "Dimension of the target")
tf.app.flags.DEFINE_integer("output_dim", 1, "Dimension of the target")
tf.app.flags.DEFINE_integer("batch_size", 128, "Batch Size for SGD")
tf.app.flags.DEFINE_integer("n_iter", 100000, "Number of Iteration")
tf.app.flags.DEFINE_integer("n_valid", 5000, "Number of obs for valid set")
tf.app.flags.DEFINE_integer("freq_mess", 100, "Print a message every ... iter")
tf.app.flags.DEFINE_integer("seed", 2, "Random Number Seed")
tf.app.flags.DEFINE_string("task", "poly_eval", """Choose the task from
poly_eval, poly_der_eval, and poly_div,
newton_eval""")
tf.app.flags.DEFINE_string("train_degrees", "5,10,15", "The degrees to train")
tf.app.flags.DEFINE_string("valid_degrees", "20", "The single degree to valid")
def main(argv=None):
opts = create_parser().parse_args(argv)
opts.users = None
logger.setLevel(getattr(logging, opts.loglevel))
logger_handler.setLevel(getattr(logging, opts.loglevel))
try:
cloud = cloud_connect(opts.config)
if opts.cmd == 'vms':
print "\n".join(sorted(cloud))
else:
if opts.cmd == 'start':
for name in opts.vmnames:
cloud.start_vm(name, opts.users, opts.prepare)
elif opts.cmd == 'stop':
for name in opts.vmnames:
cloud.stop_vm(name, timeout1=opts.wait_time)
elif opts.cmd == 'login':
assert len(opts.vmnames) == 1
cloud.login_to_vm(opts.vmnames[0], opts.users)
elif opts.cmd == 'list':
for domain in cloud.list_vms():
try:
all_ips = ", ".join(cloud.get_vm_ips(domain.name()))
except socket.error as err:
if err.errno != errno.EPERM:
raise
all_ips = "Not enought permissions for arp-scan"
print "{0:>5} {1:<15} => {2}".format(domain.ID(),
domain.name(),
all_ips)
elif opts.cmd == 'wait_ip':
tend = time.time() + opts.wait_time
for vmname in opts.vmnames:
while True:
try:
ips = list(cloud.get_vm_ips(vmname))
except socket.error as err:
if err.errno != errno.EPERM:
raise
print "Not enought permissions for arp-scan"
return 1
if len(ips) != 0:
print "{0:<15} => {1}".format(vmname,
" ".join(ips))
break
if time.time() >= tend:
print "VM {0} don't get ip in time".format(vmname)
return 1
time.sleep(0.01)
elif opts.cmd == 'wait_ssh':
tend = time.time() + opts.wait_time
for vmname in opts.vmnames:
while True:
try:
ip = cloud.get_vm_ssh_ip(vmname)
except socket.error as err:
if err.errno != errno.EPERM:
raise
print "Not enought permissions for arp-scan"
return 1
if ip is not None:
print "{0:<15} => {1}".format(vmname, ip)
break
if time.time() >= tend:
templ = "VM {0} don't start ssh server in time"
print templ.format(vmname)
return 1
time.sleep(0.01)
else:
print >>sys.stderr, "Error : Unknown cmd {0}".format(opts.cmd)
except CloudError as err:
print >>sys.stderr, err
return 1
return 0
import os
import logging
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn_cell, rnn
from tensorflow.python.platform import gfile
from utils import r2_score, mean_squared_error
from utils import logger, Print, PrintMess
logger.setLevel("CRITICAL")
logging.disable("INFO")
tf.app.flags.DEFINE_boolean("print_twice", False, "Work interactively or not?")
tf.app.flags.DEFINE_float("learning_rate", 0.001, "Learning Rate")
tf.app.flags.DEFINE_float("max_grad_norm", 1., "Clipping gradient norm")
tf.app.flags.DEFINE_float("init_scale", .1, "Norm of initial weights")
tf.app.flags.DEFINE_integer("hidden_dim", 100, "hidden size of Neural net")
tf.app.flags.DEFINE_integer("num_layers", 2, "Number of layers in neural net")
tf.app.flags.DEFINE_integer("input_dim", 1, "Dimension of the target")
tf.app.flags.DEFINE_integer("output_dim", 1, "Dimension of the target")
tf.app.flags.DEFINE_integer("batch_size", 128, "Batch Size for SGD")
tf.app.flags.DEFINE_integer("n_iter", 100000, "Number of Iteration")
tf.app.flags.DEFINE_integer("n_valid", 5000, "Number of obs for valid set")
tf.app.flags.DEFINE_integer("freq_mess", 100, "Print a message every ... iter")
tf.app.flags.DEFINE_integer("seed", 2, "Random Number Seed")
tf.app.flags.DEFINE_string(
"task", "poly_eval", """Choose the task from
poly_eval, poly_der_eval, and poly_div,
newton_eval""")
tf.app.flags.DEFINE_string("train_degrees", "5,10,15", "The degrees to train")
'--verbose',
action='count',
help='Increase verbosity')
parser.add_argument('-l',
'--limit',
type=int,
default=10,
help='Number of results')
parser.add_argument('-u',
'--url',
default='https://news.ycombinator.com/rss',
help='Starting page')
args = parser.parse_args()
if args.verbose:
logger.setLevel('DEBUG' if args.verbose > 1 else 'INFO')
db = DB('db')
action, *extra = args.action
if action == 'complete':
for item in extra:
for suggestion in db.complete(item):
print(item, suggestion)
elif action == 'search':
for doc in db.search(extra):
print(doc)
elif action == 'crawl':
crawl(db, args.url)
elif action == 'insert':
fragments = TextParser.get_text(args.url)
db.insert(args.url, list(fragments))
