def _create_new_op(self, *args, **kwargs):
"""
Handles an operation call to the current node and returns the new node
built using the operation call.
"""
# Create a new `Operation` object for the
# incoming operation call
op = Operation.create_op(self.proxied_node._new_op_name, *args,
**kwargs)
# Generate next node id
headnode: HeadNode = self.proxied_node.get_head()
headnode.node_counter += 1
# Create a new `Node` object to house the operation
newnode = Node(get_head=self.proxied_node.get_head,
node_id=headnode.node_counter,
operation=op,
parent=self.proxied_node)
self.proxied_node.nchildren += 1
# Append to the list of nodes for this computation graph
headnode.graph_nodes.appendleft(newnode)
# Logger debug statements
logger.debug("Created new {} node".format(op.name))
return get_proxy_for(op, newnode)
def _create_new_op(self, *args, **kwargs):
"""
Handles an operation call to the current node and returns the new node
built using the operation call.
"""
op = Operation.create_op(self.proxied_node._new_op_name, *args,
**kwargs)
newnode = _create_new_node(self.proxied_node, op)
return get_proxy_for(op, newnode)
def create_variations(self) -> VariationsProxy:
"""
Creates a node responsible to signal the creation of variations in the
distributed computation graph, returning a specialized proxy to that
node. This function is usually called from DistRDF.VariationsFor.
"""
return VariationsProxy(
_create_new_node(self.proxied_node,
Operation.create_op("VariationsFor")))
def __setstate__(self, state):
"""
Retrieves the state dictionary of the current
node and sets the instance variables.
Args:
state (dict): This is the state dictionary that needs to be
converted to a `Node` object.
"""
self.children = state['children']
if state.get('operation_name'):
self.operation = Operation.create_op(state['operation_name'],
*state['operation_args'],
**state["operation_kwargs"])
else:
self.operation = None
def _create_new_op(self, *args, **kwargs):
"""
Handles an operation call to the current node and returns the new node
built using the operation call.
"""
# Create a new `Operation` object for the
# incoming operation call
op = Operation.create_op(self.proxied_node._new_op_name, *args,
**kwargs)
# Create a new `Node` object to house the operation
newnode = Node(operation=op, get_head=self.proxied_node.get_head)
# Logger debug statements
logger.debug("Created new {} node".format(op.name))
# Add the new node as a child of the current node
self.proxied_node.children.append(newnode)
return get_proxy_for(op, newnode)
def create_variations(self) -> VariationsProxy:
"""
Creates a node responsible to signal the creation of variations in the
distributed computation graph, returning a specialized proxy to that
node. This function is usually called from DistRDF.VariationsFor.
"""
# Generate next node id
headnode: HeadNode = self.proxied_node.get_head()
headnode.node_counter += 1
newnode = VariationsNode(
get_head=self.proxied_node.get_head,
node_id=headnode.node_counter,
operation=Operation.create_op("VariationsFor"),
parent=self.proxied_node)
self.proxied_node.nchildren += 1
# Append to the list of nodes for this computation graph
headnode.graph_nodes.appendleft(newnode)
return VariationsProxy(newnode)
def test_action(self):
"""Action nodes are classified accurately."""
op = Operation.create_op("Count")
self.assertIsInstance(op, Operation.Action)
def test_histo1d_with_tuple(self):
"""A tuple can be used as a histogram model."""
op = Operation.create_op("Histo1D", ("name", "title", 10, 0, 10), "x")
self.assertIsInstance(op, Operation.Histo)
self.assertEqual(op.name, "Histo1D")
self.assertEqual(op.args, [("name", "title", 10, 0, 10), "x"])
def test_without_args_and_kwargs(self):
"""Check Operation constructor without arguments."""
op = Operation.create_op("Define")
self.assertEqual(op.args, [])
self.assertEqual(op.kwargs, {})
def test_with_args_and_kwargs(self):
"""Check that named and unnamed arguments are properly set."""
op = Operation.create_op("Define", 2, "p", a=1, b="b")
self.assertEqual(op.args, [2, "p"])
self.assertEqual(op.kwargs, {"a": 1, "b": "b"})
def test_without_kwargs(self):
"""Check that unnamed arguments are properly set."""
op = Operation.create_op("Define", 1, "b")
self.assertEqual(op.args, [1, "b"])
self.assertEqual(op.kwargs, {})
def test_none(self):
"""Incorrect operations raise an Exception."""
with self.assertRaises(ValueError):
Operation.create_op("random")
def test_instant_action(self):
"""Instant actions are classified accurately."""
op = Operation.create_op("Snapshot")
self.assertIsInstance(op, Operation.InstantAction)
def test_histond_without_model(self):
"""Creating a histogram without model raises ValueError."""
with self.assertRaises(ValueError):
_ = Operation.create_op("HistoND", ["a", "b", "c", "d"])
def test_histond_with_thndmodel(self):
"""THnDModel"""
op = Operation.create_op("HistoND", ROOT.RDF.THnDModel(),
["a", "b", "c", "d"])
self.assertIsInstance(op, Operation.Histo)
self.assertEqual(op.name, "HistoND")
def test_histo3d_with_th3dmodel(self):
"""TH3DModel"""
op = Operation.create_op("Histo3D", ROOT.RDF.TH3DModel(), "x", "y",
"z")
self.assertIsInstance(op, Operation.Histo)
self.assertEqual(op.name, "Histo3D")
def test_histo3d_without_model(self):
"""Creating a histogram without model raises ValueError."""
with self.assertRaises(ValueError):
_ = Operation.create_op("Histo3D", "x", "y", "z")
def test_transformation(self):
"""Transformation nodes are classified accurately."""
op = Operation.create_op("Define")
self.assertIsInstance(op, Operation.Transformation)
