def matches(self, msg):
total, pages, query = self.construct_query(msg)
if self._d['operation'] == 'count':
result = total
elif isinstance(self._d['operation'], dict):
expression = self._d['operation']['lambda']
result = single_argument_lambda_factory(
expression=expression, argument=query, name='query')
else:
operation = getattr(query, self._d['operation'])
result = operation()
return self.condition(result)
def matches(self, msg):
# Check if we should just aggregate the results of our children.
# Otherwise, we are a leaf-node doing a straightforward comparison.
if self.children:
return operator_lookup[self.attribute](
(child.matches(msg) for child in self.children))
elif self.attribute == 'lambda':
return single_argument_lambda_factory(
expression=self.expected_value, argument=msg, name='msg')
elif self.attribute == 'category':
# TODO -- use fedmsg.meta.msg2processor(msg).__name__.lower()
return msg['topic'].split('.')[3] == self.expected_value
else:
if hasattr(msg[self.attribute], 'endswith'):
return msg[self.attribute].endswith(self.expected_value)
else:
return msg[self.attribute] == self.expected_value
def matches(self, msg):
# Check if we should just aggregate the results of our children.
# Otherwise, we are a leaf-node doing a straightforward comparison.
if self.children:
return operator_lookup[self.attribute]((
child.matches(msg) for child in self.children
))
elif self.attribute == 'lambda':
return single_argument_lambda_factory(
expression=self.expected_value, argument=msg, name='msg')
elif self.attribute == 'category':
# TODO -- use fedmsg.meta.msg2processor(msg).__name__.lower()
return msg['topic'].split('.')[3] == self.expected_value
else:
if hasattr(msg[self.attribute], 'endswith'):
return msg[self.attribute].endswith(self.expected_value)
else:
return msg[self.attribute] == self.expected_value
def matches(self, msg):
""" A datanommer criteria check is composed of three steps.
- A datanommer query is constructed by combining our yaml definition
with the incoming fedmsg message that triggered us.
- An operation in python is constructed by comining our yaml definition
with the incoming fedmsg message that triggered us. That operation
is then executed against the datanommer query object.
- A condition, derived from our yaml definition, is evaluated with the
result of the operation from the previous step and is returned.
"""
total, pages, query = self._construct_query(msg)
if self._d['operation'] == 'count':
result = total
elif isinstance(self._d['operation'], dict):
expression = self._format_lambda_operation(msg)
result = single_argument_lambda_factory(
expression=expression, argument=query, name='query')
else:
operation = getattr(query, self._d['operation'])
result = operation()
return self.condition(result)
def matches(self, msg):
""" A datanommer criteria check is composed of three steps.
- A datanommer query is constructed by combining our yaml definition
with the incoming fedmsg message that triggered us.
- An operation in python is constructed by comining our yaml definition
with the incoming fedmsg message that triggered us. That operation
is then executed against the datanommer query object.
- A condition, derived from our yaml definition, is evaluated with the
result of the operation from the previous step and is returned.
"""
total, pages, query = self._construct_query(msg)
if self._d['operation'] == 'count':
result = total
elif isinstance(self._d['operation'], dict):
expression = self._format_lambda_operation(msg)
result = single_argument_lambda_factory(expression=expression,
argument=query,
name='query')
else:
operation = getattr(query, self._d['operation'])
result = operation()
return self.condition(result)
def test_basic(self):
expression = "value + 2"
target = 4
actual = single_argument_lambda_factory(expression, 2)
eq_(actual, target)
