Esempio n. 1
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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
Esempio n. 2
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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
Esempio n. 3
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 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
Esempio n. 4
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 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
Esempio n. 5
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 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
Esempio n. 6
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 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
Esempio n. 7
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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
Esempio n. 8
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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
Esempio n. 9
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 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
Esempio n. 10
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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
Esempio n. 11
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 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
Esempio n. 12
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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
Esempio n. 13
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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