def register(new_acc, db_query, pers_inf, param):
""" Add new user to database. (internal)
Args:
- new_acc (object): User object to be added to database of type ``db_query``
- db_query (class): User class, e.g. ``User``
- pers_inf (object): Object of type personal_information.PersonalInformation
- param (dict): Contains the user id, e.g. `` {'user_id': user_id} ``
Returns:
- success (bool)
- error (dict):
- code (int): one of
- 6 -- Account already existing
- 99 -- Undefined
- message (str): error code meaning
- response (empty dict)
(returns error only if success == False and response otherwise)
"""
session = db.get_db_session()
result = {}
result['success'] = False
try:
# account already registered
if session.query(db_query).filter_by(**param).count():
session.close()
Configuration.print_config()
error = {}
error['code'] = enums.Error.USER_ALREADY_EXISTS
error['message'] = "account already exists"
result['error'] = error
return result
else:
session.add(new_acc)
if pers_inf is not None:
session.add(pers_inf)
for code in UserDataType:
session.add(
PrivacyDefault(code.value, new_acc.user_id, 0, True))
session.commit()
session.close()
except Exception:
if Configuration.test_mode:
logging.exception("An error occured:")
session.rollback()
session.close()
error = {}
error['code'] = enums.Error.UNDEFINED
error['message'] = "undefined"
result['error'] = error
return result
result['success'] = True
result['response'] = {}
return result
def load_config():
try:
return Configuration("config.toml")
except FileNotFoundError:
from lib.config import generate_config, write_config
generated = generate_config()
towrite = write_config(generated, "config.toml")
if towrite:
return Configuration("config.toml")
else:
sys.exit("Couldn't load the configuration")
def _setup_tag_invalid_meta_data_file(confirm_nuke):
exif = ExifEditor(Configuration())
index = Index(TEST_INDEX_LOCATION, exif)
core = TieCoreImpl(exif, index)
cli.run_cmd(["cp", READ_FILE, WRITE_FILE])
frontend = FrontendTest(confirm_nuke, [])
tie_main.run(core, RunOptions(["tag", "foo", WRITE_FILE]), frontend)
def make_celery(flaskapp, test_mode=False):
""" Launch Celery. """
cel = celery.Celery(flaskapp.import_name)
cel.conf.update(flaskapp.config)
if test_mode:
Configuration.enable_test_mode()
cel.config_from_object(Configuration)
taskbase = cel.Task
class ContextTask(taskbase):
""" Create a flask context task. """
abstract = True
def __call__(self, *args, **kwargs):
with flaskapp.app_context():
return taskbase.__call__(self, *args, **kwargs)
cel.Task = ContextTask
return cel
def main(testmode=False, workdir=lib_folder):
if MynedataBackendCtl.backend_process is not None:
return
if not Configuration.initialized:
Configuration.initialize()
mynedata_backend_command = 'celery worker {tasks} {workdir} {celerybeat} {testmode} {logfile} {loglevel} {autoscale}'.format(
tasks='-A lib.backend.tasks.app -B',
workdir='--workdir {}'.format(workdir),
celerybeat='--schedule=/tmp/celerybeat-schedule',
testmode='--testmode' if testmode else '',
logfile='--logfile=/opt/mynedata/log/backend.log',
loglevel='--loglevel=debug',
autoscale='--autoscale %s,%s' %
(Configuration.concurrency_max, Configuration.concurrency_min),
)
if testmode:
Configuration.enable_test_mode()
Configuration.print_config()
MynedataBackendCtl.backend_process = subprocess.Popen(
mynedata_backend_command, shell=True)
with open(PID_FILE, 'w') as f_pid:
f_pid.write(str(MynedataBackendCtl.backend_process.pid) + '\n')
def save_game(self):
self.has_highscore = True
if self.name_input.text:
Configuration.name = self.name_input.text
if Configuration.is_highscore(self.score):
Configuration.register_highscore(self.name_input.text, self.score)
if Configuration.is_highest_score(self.score):
self.highscorer, self.highscore = Configuration.highest_score
Configuration.save()
def __init__(self, worker, test_mode, **options):
Configuration.initialize()
if test_mode:
Configuration.enable_test_mode()
Configuration.print_config()
worker.app.config_from_object(Configuration)
DatabaseConnector.initialize(target=Configuration.database_uri)
# Initialize payments
if Configuration.payment_mode in [
PaymentMethod.BITCOIN_DIRECT,
PaymentMethod.BITCOIN_QUERY_BASED,
PaymentMethod.BITCOIN_CENTRAL
]:
BitcoinConnector.initialize()
def main(argv):
parser = OptionParser()
options, args = parser.parse_args_dict()
cfg = Configuration(options)
log_level = cfg.yaml['main']['log_level']
numeric_level = getattr(logging, log_level.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % log_level)
logging.basicConfig(
filename=(cfg.yaml['main']['log_dir']+"/mailer-"+datetime.now().strftime('%Y%m%d')+".log"),
level=numeric_level,
format='%(asctime)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
logging.info('Started')
cmds = getMail(cfg)
for cmd in cmds:
os.system(cmd)
logging.info('Successfully finished')
def parse_args(args_list: List[str]) -> Configuration:
"""Parse arguments"""
parser = ArgumentParser(description="Minesweeper")
parser.add_argument(
"--size",
"-s",
help=
f"Board side length, e.g. input 10 for a 10x10 board. (default {DEFAULT_BOARD_SIZE})",
type=int,
default=DEFAULT_BOARD_SIZE,
dest="board_size",
)
parser.add_argument(
"--bombs",
"-b",
help=
f"Number of bombs to place on the board. (default {DEFAULT_NUM_BOMBS})",
type=int,
default=DEFAULT_NUM_BOMBS,
dest="num_bombs",
)
parser.add_argument(
"--guide",
help="Show column and row numbers around the board.",
action="store_true",
dest="show_guide",
)
args = parser.parse_args(args_list)
return Configuration(board_size=args.board_size,
num_bombs=args.num_bombs,
show_guide=args.show_guide)
cel = celery.Celery(flaskapp.import_name)
cel.conf.update(flaskapp.config)
if test_mode:
Configuration.enable_test_mode()
cel.config_from_object(Configuration)
taskbase = cel.Task
class ContextTask(taskbase):
""" Create a flask context task. """
abstract = True
def __call__(self, *args, **kwargs):
with flaskapp.app_context():
return taskbase.__call__(self, *args, **kwargs)
cel.Task = ContextTask
return cel
Configuration.initialize()
logging.basicConfig(level=logging.DEBUG)
app = connexion.App(__name__)
app.add_api('api.yaml')
CORS(app.app, origins=[Configuration.frontend_url])
app.app.config.update(
celery_broker_url=Configuration.broker_url,
celery_result_backend=Configuration.result_backend,
)
make_celery(app.app, test_mode=Configuration.test_mode)
application = app.app
def main(argv):
parser = OptionParser()
options, args = parser.parse_args_dict()
cfg = Configuration(options)
logging.basicConfig(
filename=(cfg.yaml['main']['log_dir'] + "/preprocessor-" +
datetime.now().strftime('%Y%m%d') + ".log"),
level=cfg.log_level,
format='%(asctime)s %(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
logging.info('Started')
data_dir = cfg.yaml['main']['learn_dir']
picture_dir = data_dir + "/pictures"
tf_dirs = [
name for name in os.listdir(picture_dir)
if os.path.isdir(os.path.join(picture_dir, name))
]
total_pp_files = 0
new_pp_files = 0
for d in sorted(tf_dirs):
out_dir = data_dir + "/diff-%s/%s" % (image_size_x, d)
cmd = "mkdir -p %s" % (out_dir)
p = subprocess.Popen([
cmd,
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
(out, err) = p.communicate()
in_dir = picture_dir + "/" + d
files = [
name for name in os.listdir(in_dir)
if os.path.isfile(os.path.join(in_dir, name))
]
it = iter(sorted(files))
for f1, f2 in zip(it, it):
logging.debug("Processing files %s %s", f1, f2)
df1 = in_dir + "/" + f1
df2 = in_dir + "/" + f2
# cernobily 1 kanalovy diff
diff_file = out_dir + "/" + ("%s-diff2.png" % f2[:-4])
if os.path.exists(diff_file):
logging.debug("%s already exists", diff_file)
else:
img1 = Image.open(df1)
img2 = Image.open(df2)
img1 = img1.resize((image_size_x, image_size_y),
Image.ANTIALIAS)
img2 = img2.resize((image_size_x, image_size_y),
Image.ANTIALIAS)
img1 = img1.convert('L')
img2 = img2.convert('L')
np_img1 = np.array(img1.getdata(), dtype=np.uint8).reshape(
(image_size_y, image_size_x))
np_img2 = np.array(img2.getdata(), dtype=np.uint8).reshape(
(image_size_y, image_size_x))
np_img_diff = difference_image(np_img1, np_img2)
img_diff = Image.fromarray(np_img_diff, mode='L')
img_diff.save(diff_file)
logging.info("%s written", diff_file)
#sleep(0.5)
# # barevny 3 kanalovy diff
# diff_file_3 = out_dir + "/" + ("%s-diffc.png" % f2[:-4])
# if os.path.exists(diff_file_3):
# logging.debug("%s already exists", diff_file_3)
# else:
# img1 = Image.open(df1)
# img2 = Image.open(df2)
# img1 = img1.resize((image_size_x, image_size_y), Image.ANTIALIAS)
# img2 = img2.resize((image_size_x, image_size_y), Image.ANTIALIAS)
# np_img1 = np.array(img1.getdata(),dtype=np.uint8).reshape((image_size_y, image_size_x, 3))
# np_img2 = np.array(img2.getdata(),dtype=np.uint8).reshape((image_size_y, image_size_x, 3))
# np_img_diff = difference_image(np_img1, np_img2)
# img_diff = Image.fromarray(np_img_diff)
# img_diff.save(diff_file_3)
# logging.info("%s written", diff_file_3)
pp_file = out_dir + "/" + ("%s-c%s.pp" % (f2[:-4], cluster_size))
if os.path.exists(pp_file):
logging.debug("%s already exists", pp_file)
else:
npi = read_preprocess_image(diff_file, cluster_size)
f = open(pp_file, "w")
npi.astype(np.uint16).tofile(f)
f.close()
logging.info("%s written", pp_file)
new_pp_files += 1
# kontrolni zobrazeni preprocesovaneho souboru
# normalizaci co rozsahu 0-255 a vykreslenim jako png
if False:
npi_max = np.amax(npi)
npi_png = npi.astype(np.float64) / npi_max
npi_png = npi_png * 255
npi_png = npi_png.astype(np.uint8)
png = Image.fromarray(npi_png, mode='L')
png.save(pp_file + ".png")
#sleep(0.5)
total_pp_files += 1
logging.info("Found %s new diff files", new_pp_files)
logging.info("Found %s total diff files", total_pp_files)
if not new_pp_files:
logging.info("No now diff files found")
sys.exit(1)
def main(argv):
parser = OptionParser()
options, args = parser.parse_args_dict()
cfg = Configuration(options)
learn_dir = cfg.yaml['main']['learn_dir']
model_dir = cfg.yaml['main']['model_dir']
data_dir = "%s/diff-%s" % (learn_dir, image_size_x)
model_name = "model-d%s-c%s-1h%s-2h%s" % (image_div, cluster_size,
n_hidden_1, n_hidden_2)
print model_name
# Construct model
model = MultilayerPerceptron(n_input, n_hidden_1, n_hidden_2, n_classes)
# Define loss function
cost = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=model.out_layer,
labels=y)) #label je index spravneho vystupu
# Define optimizer
global_step = tf.Variable(0, name='global_step', trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
cost, global_step=global_step)
#TODO vyzkouset jine optimalizace
#optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost, global_step=global_step)
# Initializing the variables
init = tf.global_variables_initializer()
# Test model
correct_prediction = tf.equal(tf.argmax(model.out_layer, 1), y)
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
train_files, test_files = get_files(data_dir)
n_train = len(train_files)
print "reading testing data"
start = time.time()
test_images, test_labels = get_images_labels(test_files)
end = time.time()
print "testing data complete (%s s)" % (end - start)
print "reading training data"
start = time.time()
train_images, train_labels = get_images_labels(train_files)
end = time.time()
print "training data complete (%s s)" % (end - start)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
index_in_epoch = 0
total_batches = int(n_train / batch_size)
# nahodne zamichame obrazky i labely ve stejnem poradi
# random.random nefunguje spravne na vicerozmerne np.array
# TODO je to k necemu?
r = np.arange(len(train_images))
np.random.shuffle(r)
train_images = train_images[r]
train_labels = train_labels[r]
# pokud data nejsou delitelna davkou, chcem pouzit i zbytek
if n_train > total_batches * batch_size:
total_batches += 1
# Loop over all batches
for i in range(total_batches):
_, c = sess.run(
[optimizer, cost],
feed_dict={
model.input_ph:
train_images[index_in_epoch:index_in_epoch +
batch_size],
y:
train_labels[index_in_epoch:index_in_epoch +
batch_size]
})
# Compute average loss
avg_cost += c / total_batches
index_in_epoch += batch_size
# Display logs per epoch step
print("Epoch:", '%04d' % (epoch), "cost=", \
"{:.9f}".format(avg_cost))
if epoch % eval_step == 0:
train_acc = accuracy.eval({
model.input_ph: train_images,
y: train_labels
})
print("Accuracy on train images:", train_acc)
if n_test_pct:
test_acc = accuracy.eval({
model.input_ph: test_images,
y: test_labels
})
print("Accuracy on test images:", test_acc)
if test_acc == 1.0 or avg_cost < 1.0:
break
elif train_acc == 1.0:
break
if epoch and save_step and epoch % save_step == 0:
saver.save(sess,
"%s/%s" % (learn_dir, model_name),
global_step=epoch)
# toto je tu zamerne, kvuli snizeni vytizeni procesoru
#sleep(0.5)
print("Optimization Finished!")
saver.save(sess, "%s/%s" % (model_dir, model_name))
# TODO presunout do testeru
print "prediction mismatches in test data:"
dt = 0
df = 0
mt = 0
mf = 0
for n, test_image in enumerate(test_images):
cl = sess.run(tf.argmax(model.out_layer, 1),
feed_dict={model.input_ph: [test_image]})
label = test_labels[n]
if label:
dt = dt + 1
else:
df = df + 1
if cl != label:
print "Prediction: %s, label: %s, test file: %s" % (
cl, label, test_files[n])
if label:
mt = mt + 1
else:
mf = mf + 1
print "Model %s" % model_name
train_acc = accuracy.eval({
model.input_ph: train_images,
y: train_labels
})
test_acc = accuracy.eval({model.input_ph: test_images, y: test_labels})
print("Accuracy on train images:", train_acc)
print("Accuracy on test images:", test_acc)
print "%s/%s true and %s/%s false mismatches on test images" % (mt, dt,
mf, df)
def main():
parser = OptionParser()
parser.add_option('--once',
action='store_true',
help='start once, only to process batch')
parser.add_option(
'--noftp',
action='store_true',
help='prevent loading files from FTP server (overrides config)')
parser.add_option('--nomail',
action='store_true',
help='prevent sending mail (overrides config)')
options, args = parser.parse_args_dict()
cfg = Configuration(options)
logger = Logger(cfg.yaml)
logger.log("Starting camera filter")
# Construct model
model_path = cfg.yaml['main']['model_dir'] + '/' + cfg.yaml['classifier'][
'model_name']
model = MultilayerPerceptron(cfg.n_input,
cfg.yaml['classifier']['n_hidden_1'],
cfg.yaml['classifier']['n_hidden_2'],
cfg.yaml['classifier']['n_classes'])
# Initializing the variables
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
saver = tf.train.Saver()
logger.log("Opening model %s" % model_path)
saver.restore(sess, model_path)
prev_hour = get_hour()
stats_true = 0
stats_false = 0
if cfg.ftp_opt:
ftp = connect_ftp(cfg.yaml)
err_count = 0
while (err_count < 2):
try:
hour = get_hour()
if prev_hour != hour:
# logrotate
if hour == 0:
logger.rotate()
# monitoring + hodinove statistiky
p = subprocess.Popen([
"du -sh /www/camera-filter/",
],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
(out, err) = p.communicate()
status = p.wait()
text = (
"Hourly statistics:\nALARMS count: %s\nFalse alarms count: %s\n\nError count: %s\n\nTotal size of dir: %s"
% (stats_true, stats_false, err_count, out))
logger.log(text)
if cfg.mail_opt:
send_mail(logger, cfg.yaml, "Camera stats", text)
stats_true = 0
stats_false = 0
err_count = 0
prev_hour = hour
if cfg.ftp_opt:
fetch_files(ftp, cfg.yaml, logger)
# TODO lepsi prace se soubory, pres pythoni libky
# TODO lepe zpracovat davku (nenacitat znovu porad dokola)
# TODO co se souborama s nulovym timestampem? (rozhodi nam parovani fotek)
p = subprocess.Popen(
"find %s -maxdepth 1 -type f | grep ARC | grep -v diff | sort -r | head -n%s"
% (cfg.yaml["main"]["input_dir"], cfg.input_batch_size),
stdout=subprocess.PIPE,
shell=True)
(output, err) = p.communicate()
p_status = p.wait()
files = output.split()
if not files:
if cfg.once_opt:
# pokud nechcem sluzbu a mame zpracovano, koncime
break
logger.log("no files to compare, sleeping for %s seconds.." %
cfg.yaml['main']['sleep_sec'])
sleep(cfg.yaml['main']['sleep_sec'])
continue
if len(files) < cfg.input_batch_size:
logger.log("less than %s files to compare, trying again.." %
cfg.input_batch_size)
sleep(1)
continue
logger.log("%s files to compare: %s" % (len(files), files))
if process_mp(cfg, logger, sess, model, files):
stats_true += 1
else:
stats_false += 1
sleep(1)
except Exception as ex:
print ex
traceback.print_exc(file=stdout)
logger.log("%s" % ex, level="ERROR")
traceback.print_exc(file=logger.log_file)
if cfg.mail_opt:
try:
send_mail(logger,
cfg.yaml,
'Camera ALARM Error',
"%s" % ex,
notify=True)
except Exception as ex:
logger.log("sending error mail failed")
logger.log("%s" % ex, level="ERROR")
err_count += 1
logger.log("error counter %s" % err_count)
sleep(err_count)
logger.log("Too many errors in one hour, giving up...")
if cfg.ftp_opt:
ftp.quit()
logger.close()
def setUp(self):
_remove_index()
self.exif = ee.ExifEditor(Configuration())
self.files_base_path = _path_to_linkname(os.path.abspath("../res"))
self.index = Index(TEST_INDEX_LOCATION, self.exif)
self.tie_core = TieCoreImpl(self.exif, self.index)
#!/usr/bin/env python3
""" This script starts the myneData API. """
import sys
import os
from gevent import monkey
lib_folder = '/'.join(os.path.split(os.path.dirname(sys.path[0]))[:-1])
sys.path.insert(0, lib_folder)
from lib.config import Configuration
from lib.api import endpoint
monkey.patch_all()
if __name__ == '__main__':
Configuration.initialize()
Configuration.print_config()
endpoint.app.debug = True
endpoint.app.run(port=int(Configuration.api_port), server='gevent')
class OpenDictApp(wx.App):
"""Top-level class of wxWidgets application"""
locale = wx.Locale()
def OnInit(self):
_ = wx.GetTranslation
_start = time.time()
wx.Version = []
try:
wx.Version = wx.__version__
except Exception, e:
try:
wx.Version = wx.Python.__version__
except:
pass
if wx.Version.split('.') < ['2', '8']:
from lib.gui import errorwin
title = _("wxPython Version Error")
msg = _("wxPython %s is installed on this system.\n\n"
"OpenDict %s requires wxPython 2.8 or newer to run smoothly.\n\n"
"You can find wxPython at "
"http://www.wxpython.org or you can "
"install it using your system package manager.") \
% (wx.Version, info.VERSION)
errorwin.showErrorMessage(title, msg)
return False
util.makeDirectories()
systemLog(DEBUG, "Unicode version: %s" % wx.USE_UNICODE)
# Init gettext support
wx.Locale_AddCatalogLookupPathPrefix(
os.path.join(info.GLOBAL_HOME, 'po'))
self.locale.Init(wx.LANGUAGE_DEFAULT)
self.locale.AddCatalog('opendict')
# Data cache instance
self.cache = {}
# Dictionaries container
# Mapping: name -> object
self.dictionaries = {}
# Failed dictionaries.
# For error message that may be shown after creating main window
self.invalidDictionaries = []
self.config = Configuration()
self.config.load()
self.agreements = util.AgreementsManager(
os.path.join(info.LOCAL_HOME, 'agreements.txt'))
# Set unique ids
for plugin in newplugin.loadDictionaryPlugins(
self.dictionaries, self.invalidDictionaries):
self.config.ids[wx.NewId()] = plugin.getName()
for plain in plaindict.loadPlainDictionaries(self.dictionaries):
self.config.ids[wx.NewId()] = plain.getName()
for d in self.dictionaries.values():
if not self.config.activedict.init:
if not self.config.activedict.enabled(d.getName()):
d.setActive(active=False)
else:
# Fill up with names if not initialized yet
self.config.activedict.add(d.getName())
windowPos = (int(self.config.get('windowPosX')),
int(self.config.get('windowPosY')))
windowSize = (int(self.config.get('windowWidth')),
int(self.config.get('windowHeight')))
self.window = MainWindow(None,
-1,
"OpenDict",
windowPos,
windowSize,
style=wx.DEFAULT_FRAME_STYLE)
try:
systemLog(INFO, "OpenDict %s" % info.VERSION)
systemLog(INFO, "wxPython %s" % wx.Version)
systemLog(INFO, "Global home: %s:" % info.GLOBAL_HOME)
systemLog(INFO, "Local home: %s" % info.LOCAL_HOME)
systemLog(DEBUG, "Loaded in %f seconds" % (time.time() - _start))
except Exception, e:
print "Logger Error: Unable to write to log (%s)" % e
- timestamp
- nonce
"""
self.config.accounts[self.acc_name]['timestamp'] = str(int(
time.time()))
self.config.accounts[self.acc_name]['nonce'] = self.get_nonce()
def get_nonce(self):
"""Unique token generated for each request"""
n = base64.b64encode(''.join(
[str(random.randint(0, 9)) for i in range(24)]).encode('utf-8'))
return str(n.decode('utf-8'))
if __name__ == '__main__':
folder = '/Users/peter/Workspace/FincLab/settings/'
# Reset Program Settings
Config.restore_to_defaults()
# Load new configurations
Config = Configuration(folder)
yf = YahooOAuth(config=Config)
print(
yf.parse(
'select * from yahoo.finance.historicaldata where symbol = "000058.sz" and startDate = "2015-08-26" and endDate = "2015-08-28"'
))
print('current time is: ', time.time())
print('Done!')
def setUp(self):
_remove_index()
self.index = Index(TEST_INDEX_LOCATION, ee.ExifEditor(Configuration()))
self.files_base_path = _path_to_linkname(os.path.abspath("../res"))
def setUp(self):
self.ee = exif_editor.ExifEditor(Configuration())
import asyncio
from pprint import pformat
from azure.eventhub.aio import EventHubConsumerClient, EventHubSharedKeyCredential
from azure.eventhub.extensions.checkpointstoreblobaio import BlobCheckpointStore
from lib.config import ConsumerConfig, Configuration
from lib.storage import PostgresMessageStorageDelegate, MessageStorageDelegate
from lib.handler import MessageHandler
import logging
logging.basicConfig(
format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
datefmt='%Y-%m-%d:%H:%M:%S',
level=Configuration.log_level(),
)
logger = logging.getLogger(__name__)
async def errored(partition_context, error):
"""
The callback function that will be called when an error is raised during receiving after retry
attempts are exhausted, or during the process of load-balancing.
:param azure.eventhub.PartitionContext partition_context: The EventHub partition context.
:param error: the exception
:return: None
"""
if partition_context:
logger.error("An exception: {} occurred during receiving from Partition: {}.".format(
partition_context.partition_id if partition_context else None, error
))
else:
def setUp(self):
self.frontend = FrontendTest(UserReply.yes, ["foo", "bar"])
self.ee = ExifEditor(Configuration())
