def _flatten_graph(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
subgraph_map = {}
# Flatten all nodes
for n in flow.graph.nodes_iter():
subgraph = _flatten(n, flattened)
subgraph_map[n] = subgraph
graph = gu.merge_graphs([graph, subgraph])
# Reconnect all nodes to there corresponding subgraphs
for (u, v) in flow.graph.edges_iter():
# Retain and update the original edge attributes.
u_v_attrs = gu.get_edge_attrs(flow.graph, u, v)
if not u_v_attrs:
u_v_attrs = FLATTEN_EDGE_DATA.copy()
else:
u_v_attrs.update(FLATTEN_EDGE_DATA)
u_no_succ = list(gu.get_no_successors(subgraph_map[u]))
# Connect the ones with no predecessors in v to the ones with no
# successors in u (thus maintaining the edge dependency).
for n in gu.get_no_predecessors(subgraph_map[v]):
# NOTE(harlowja): give each edge its own copy so that if its later
# modified that the same copy isn't modified.
graph.add_edges_from(((n2, n, copy.deepcopy(u_v_attrs))
for n2 in u_no_succ
if not graph.has_edge(n2, n)))
return graph
def _flatten_graph(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
subgraph_map = {}
# Flatten all nodes
for n in flow.graph.nodes_iter():
subgraph = _flatten(n, flattened)
subgraph_map[n] = subgraph
graph = gu.merge_graphs([graph, subgraph])
# Reconnect all nodes to there corresponding subgraphs
for (u, v) in flow.graph.edges_iter():
# Retain and update the original edge attributes.
u_v_attrs = gu.get_edge_attrs(flow.graph, u, v)
if not u_v_attrs:
u_v_attrs = FLATTEN_EDGE_DATA.copy()
else:
u_v_attrs.update(FLATTEN_EDGE_DATA)
u_no_succ = list(gu.get_no_successors(subgraph_map[u]))
# Connect the ones with no predecessors in v to the ones with no
# successors in u (thus maintaining the edge dependency).
for n in gu.get_no_predecessors(subgraph_map[v]):
# NOTE(harlowja): give each edge its own copy so that if its later
# modified that the same copy isn't modified.
graph.add_edges_from(((n2, n, copy.deepcopy(u_v_attrs))
for n2 in u_no_succ
if not graph.has_edge(n2, n)))
return graph
def _flatten_unordered(self, flow):
"""Flattens a unordered flow."""
graph = nx.DiGraph(name=flow.name)
for item in flow:
# NOTE(harlowja): we do *not* connect the graphs together, this
# retains that each item (translated to subgraph) is disconnected
# from each other which will result in unordered execution while
# running.
graph = gu.merge_graphs([graph, self._flatten(item)])
return graph
def _flatten_unordered(self, flow):
"""Flattens a unordered flow."""
graph = nx.DiGraph(name=flow.name)
for item in flow:
# NOTE(harlowja): we do *not* connect the graphs together, this
# retains that each item (translated to subgraph) is disconnected
# from each other which will result in unordered execution while
# running.
graph = gu.merge_graphs([graph, self._flatten(item)])
return graph
def _flatten_linear(self, flow):
"""Flattens a linear flow."""
graph = nx.DiGraph(name=flow.name)
previous_nodes = []
for item in flow:
subgraph = self._flatten(item)
graph = gu.merge_graphs([graph, subgraph])
# Find nodes that have no predecessor, make them have a predecessor
# of the previous nodes so that the linearity ordering is
# maintained. Find the ones with no successors and use this list
# to connect the next subgraph (if any).
self._add_new_edges(graph, previous_nodes,
list(gu.get_no_predecessors(subgraph)))
# There should always be someone without successors, otherwise we
# have a cycle A -> B -> A situation, which should not be possible.
previous_nodes = list(gu.get_no_successors(subgraph))
return graph
def _flatten_linear(self, flow):
"""Flattens a linear flow."""
graph = nx.DiGraph(name=flow.name)
previous_nodes = []
for item in flow:
subgraph = self._flatten(item)
graph = gu.merge_graphs([graph, subgraph])
# Find nodes that have no predecessor, make them have a predecessor
# of the previous nodes so that the linearity ordering is
# maintained. Find the ones with no successors and use this list
# to connect the next subgraph (if any).
self._add_new_edges(graph,
previous_nodes,
list(gu.get_no_predecessors(subgraph)))
# There should always be someone without successors, otherwise we
# have a cycle A -> B -> A situation, which should not be possible.
previous_nodes = list(gu.get_no_successors(subgraph))
return graph
def _flatten_graph(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
subgraph_map = {}
# Flatten all nodes
for n in flow.graph.nodes_iter():
subgraph = _flatten(n, flattened)
subgraph_map[n] = subgraph
graph = gu.merge_graphs([graph, subgraph])
# Reconnect all nodes to there corresponding subgraphs
for (u, v) in flow.graph.edges_iter():
u_no_succ = list(gu.get_no_successors(subgraph_map[u]))
# Connect the ones with no predecessors in v to the ones with no
# successors in u (thus maintaining the edge dependency).
for n in gu.get_no_predecessors(subgraph_map[v]):
graph.add_edges_from(((n2, n, FLATTEN_EDGE_DATA)
for n2 in u_no_succ
if not graph.has_edge(n2, n)))
return graph
def _flatten_linear(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
previous_nodes = []
for f in flow:
subgraph = _flatten(f, flattened)
graph = gu.merge_graphs([graph, subgraph])
# Find nodes that have no predecessor, make them have a predecessor of
# the previous nodes so that the linearity ordering is maintained. Find
# the ones with no successors and use this list to connect the next
# subgraph (if any).
for n in gu.get_no_predecessors(subgraph):
graph.add_edges_from(((n2, n, FLATTEN_EDGE_DATA)
for n2 in previous_nodes
if not graph.has_edge(n2, n)))
# There should always be someone without successors, otherwise we have
# a cycle A -> B -> A situation, which should not be possible.
previous_nodes = list(gu.get_no_successors(subgraph))
return graph
def _flatten_graph(self, flow):
"""Flattens a graph flow."""
graph = nx.DiGraph(name=flow.name)
# Flatten all nodes into a single subgraph per node.
subgraph_map = {}
for item in flow:
subgraph = self._flatten(item)
subgraph_map[item] = subgraph
graph = gu.merge_graphs([graph, subgraph])
# Reconnect all node edges to there corresponding subgraphs.
for (u, v) in flow.graph.edges_iter():
# Retain and update the original edge attributes.
u_v_attrs = gu.get_edge_attrs(flow.graph, u, v)
# Connect the ones with no predecessors in v to the ones with no
# successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph,
list(gu.get_no_successors(subgraph_map[u])),
list(gu.get_no_predecessors(subgraph_map[v])),
edge_attrs=u_v_attrs)
return graph
def _flatten_linear(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
previous_nodes = []
for f in flow:
subgraph = _flatten(f, flattened)
graph = gu.merge_graphs([graph, subgraph])
# Find nodes that have no predecessor, make them have a predecessor of
# the previous nodes so that the linearity ordering is maintained. Find
# the ones with no successors and use this list to connect the next
# subgraph (if any).
for n in gu.get_no_predecessors(subgraph):
# NOTE(harlowja): give each edge its own copy so that if its later
# modified that the same copy isn't modified.
graph.add_edges_from(((n2, n, FLATTEN_EDGE_DATA.copy())
for n2 in previous_nodes
if not graph.has_edge(n2, n)))
# There should always be someone without successors, otherwise we have
# a cycle A -> B -> A situation, which should not be possible.
previous_nodes = list(gu.get_no_successors(subgraph))
return graph
def _flatten_graph(self, flow):
"""Flattens a graph flow."""
graph = nx.DiGraph(name=flow.name)
# Flatten all nodes into a single subgraph per node.
subgraph_map = {}
for item in flow:
subgraph = self._flatten(item)
subgraph_map[item] = subgraph
graph = gu.merge_graphs([graph, subgraph])
# Reconnect all node edges to there corresponding subgraphs.
for (u, v) in flow.graph.edges_iter():
# Retain and update the original edge attributes.
u_v_attrs = gu.get_edge_attrs(flow.graph, u, v)
# Connect the ones with no predecessors in v to the ones with no
# successors in u (thus maintaining the edge dependency).
self._add_new_edges(graph,
list(gu.get_no_successors(subgraph_map[u])),
list(gu.get_no_predecessors(subgraph_map[v])),
edge_attrs=u_v_attrs)
return graph
def _flatten_unordered(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
for f in flow:
graph = gu.merge_graphs([graph, _flatten(f, flattened)])
return graph
def _flatten_unordered(flow, flattened):
graph = nx.DiGraph(name=_graph_name(flow))
for f in flow:
graph = gu.merge_graphs([graph, _flatten(f, flattened)])
return graph