def __init__(self, config):
"""
Constructor for a new hypothesis by using the student t test. It includes the value,
the deviation and the number of samples.
:param config: Configuration from yaml file
"""
self._true_mean = Config.get_param(config, 'true_mean')
if Config.has_key(config, 'std_deviation'):
self._std_deviation = Config.get_param(config, 'std_deviation')
else:
self._std_deviation = None
self._significance_level = Config.get_param(config,
'significance_level')
def __init__(self, config):
"""
Constructor for a new nominal value hypothesis. It includes the value and the deviation(s)
:param config: Configuration from yaml file
"""
SingleValueHypothesisCheck.__init__(self)
self._value_check = NominalValueFactory.create_nominal_value(config)
self._deviation_checks = []
if not Config.has_key(config, 'deviation'):
return
for deviation_config in Config.get_param(config, 'deviation'):
self._deviation_checks.append(
NominalValueFactory.create_nominal_value(deviation_config))
def __init__(self, config):
"""
Constructor to create StdDeviationFilter object. It reads and setup
the buffer (defined or not defined buffer size)
:param config: Configuration from yaml file
"""
DeviationFilter.__init__(self)
from math import sqrt
self.sqrt = sqrt
if Config.has_key(config, 'window_size'):
from collections import deque
self.window_size = Config.get_param(config, 'window_size')
self._buffer = deque(maxlen=self.window_size)
self.reset = self.reset_buffered
else:
self._buffer = []
self.reset = self.reset_unbuffered
def __init__(self, config):
"""
Constructor for exponentially weighted moving average (EWMA) filter object.
Applies new values weighted to history.
:param config: Configuration from yaml file
"""
ValueFilter.__init__(self)
self._decay_rate = Config.get_param(config, 'decay_rate')
if Config.has_key(config, 'window_size'):
from collections import deque
self.window_size = Config.get_param(config, 'window_size')
self._ring_buffer = deque(maxlen=self.window_size)
self.update = self.update_buffered
self.get_value = self.get_value_buffered
self.reset = self.reset_buffered
else:
self._current_value = None
self.update = self.update_unbuffered
self.get_value = self.get_value_unbuffered
self.reset = self.reset_unbuffered
def __init__(self, config):
"""
Constructor to create min-max deviation object. It reads and setup
the buffer (defined or not defined buffer size)
:param config: Configuration from yaml file
"""
DeviationFilter.__init__(self)
if Config.has_key(config, 'window_size'):
from collections import deque
self.window_size = Config.get_param(config, 'window_size')
self._ring_buffer = deque(maxlen=self.window_size)
self.update = self.update_buffered
self.get_deviation = self.get_deviation_buffered
self.reset = self.reset_buffered
else:
self._min = float("nan")
self._max = float("nan")
self.update = self.update_unbuffered
self.get_deviation = self.get_deviation_unbuffered
self.reset = self.reset_unbuffered
for node in YamlHelper.get_param(configs, 'nodes'):
name = YamlHelper.get_param(node, 'name')
sub_topic = YamlHelper.get_param(node, 'sub_topic', [])
pub_topic = YamlHelper.get_param(node, 'pub_topic', [])
model_configuration.nodes.append(
node_configuration(name=str(name),
pub_topic=list(pub_topic),
sub_topic=list(sub_topic)))
rospy.loginfo("tug_diagnosis_initialization: load observers")
for observation in YamlHelper.get_param(configs, 'observations'):
type = YamlHelper.get_param(observation, 'type')
resource = []
if YamlHelper.has_key(observation, 'nodes'):
resource = YamlHelper.get_param(observation, 'nodes', [])
elif YamlHelper.has_key(observation, 'topics'):
resource = YamlHelper.get_param(observation, 'topics', [])
model_configuration.observers.append(
observer_configuration(type=str(type), resource=list(resource)))
rospy.loginfo(
str(len(model_configuration.nodes)) + " nodes and " +
str(len(model_configuration.observers)) + " observers loaded")
rospy.loginfo("tug_diagnosis_initialization: waiting for service")
rospy.wait_for_service('diagnosis_configuration_change')
try:
diagnosis_changer_srv = rospy.ServiceProxy(