1432 lines
44 KiB
Python
1432 lines
44 KiB
Python
"""
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View Classes provide node, edge and degree "views" of a graph.
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Views for nodes, edges and degree are provided for all base graph classes.
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A view means a read-only object that is quick to create, automatically
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updated when the graph changes, and provides basic access like `n in V`,
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`for n in V`, `V[n]` and sometimes set operations.
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The views are read-only iterable containers that are updated as the
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graph is updated. As with dicts, the graph should not be updated
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while iterating through the view. Views can be iterated multiple times.
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Edge and Node views also allow data attribute lookup.
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The resulting attribute dict is writable as `G.edges[3, 4]['color']='red'`
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Degree views allow lookup of degree values for single nodes.
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Weighted degree is supported with the `weight` argument.
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NodeView
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========
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`V = G.nodes` (or `V = G.nodes()`) allows `len(V)`, `n in V`, set
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operations e.g. "G.nodes & H.nodes", and `dd = G.nodes[n]`, where
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`dd` is the node data dict. Iteration is over the nodes by default.
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NodeDataView
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============
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To iterate over (node, data) pairs, use arguments to `G.nodes()`
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to create a DataView e.g. `DV = G.nodes(data='color', default='red')`.
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The DataView iterates as `for n, color in DV` and allows
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`(n, 'red') in DV`. Using `DV = G.nodes(data=True)`, the DataViews
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use the full datadict in writeable form also allowing contain testing as
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`(n, {'color': 'red'}) in VD`. DataViews allow set operations when
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data attributes are hashable.
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DegreeView
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==========
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`V = G.degree` allows iteration over (node, degree) pairs as well
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as lookup: `deg=V[n]`. There are many flavors of DegreeView
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for In/Out/Directed/Multi. For Directed Graphs, `G.degree`
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counts both in and out going edges. `G.out_degree` and
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`G.in_degree` count only specific directions.
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Weighted degree using edge data attributes is provide via
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`V = G.degree(weight='attr_name')` where any string with the
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attribute name can be used. `weight=None` is the default.
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No set operations are implemented for degrees, use NodeView.
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The argument `nbunch` restricts iteration to nodes in nbunch.
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The DegreeView can still lookup any node even if nbunch is specified.
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EdgeView
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========
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`V = G.edges` or `V = G.edges()` allows iteration over edges as well as
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`e in V`, set operations and edge data lookup `dd = G.edges[2, 3]`.
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Iteration is over 2-tuples `(u, v)` for Graph/DiGraph. For multigraphs
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edges 3-tuples `(u, v, key)` are the default but 2-tuples can be obtained
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via `V = G.edges(keys=False)`.
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Set operations for directed graphs treat the edges as a set of 2-tuples.
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For undirected graphs, 2-tuples are not a unique representation of edges.
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So long as the set being compared to contains unique representations
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of its edges, the set operations will act as expected. If the other
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set contains both `(0, 1)` and `(1, 0)` however, the result of set
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operations may contain both representations of the same edge.
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EdgeDataView
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============
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Edge data can be reported using an EdgeDataView typically created
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by calling an EdgeView: `DV = G.edges(data='weight', default=1)`.
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The EdgeDataView allows iteration over edge tuples, membership checking
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but no set operations.
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Iteration depends on `data` and `default` and for multigraph `keys`
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If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
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If `data is True` iterate over 3-tuples `(u, v, datadict)`.
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Otherwise iterate over `(u, v, datadict.get(data, default))`.
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For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key`
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to create 3-tuples and 4-tuples.
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The argument `nbunch` restricts edges to those incident to nodes in nbunch.
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"""
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from collections.abc import Mapping, Set
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import networkx as nx
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__all__ = [
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"NodeView",
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"NodeDataView",
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"EdgeView",
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"OutEdgeView",
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"InEdgeView",
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"EdgeDataView",
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"OutEdgeDataView",
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"InEdgeDataView",
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"MultiEdgeView",
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"OutMultiEdgeView",
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"InMultiEdgeView",
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"MultiEdgeDataView",
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"OutMultiEdgeDataView",
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"InMultiEdgeDataView",
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"DegreeView",
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"DiDegreeView",
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"InDegreeView",
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"OutDegreeView",
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"MultiDegreeView",
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"DiMultiDegreeView",
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"InMultiDegreeView",
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"OutMultiDegreeView",
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]
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# NodeViews
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class NodeView(Mapping, Set):
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"""A NodeView class to act as G.nodes for a NetworkX Graph
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Set operations act on the nodes without considering data.
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Iteration is over nodes. Node data can be looked up like a dict.
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Use NodeDataView to iterate over node data or to specify a data
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attribute for lookup. NodeDataView is created by calling the NodeView.
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Parameters
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----------
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graph : NetworkX graph-like class
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Examples
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--------
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>>> G = nx.path_graph(3)
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>>> NV = G.nodes()
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>>> 2 in NV
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True
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>>> for n in NV:
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... print(n)
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0
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1
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2
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>>> assert NV & {1, 2, 3} == {1, 2}
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>>> G.add_node(2, color="blue")
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>>> NV[2]
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{'color': 'blue'}
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>>> G.add_node(8, color="red")
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>>> NDV = G.nodes(data=True)
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>>> (2, NV[2]) in NDV
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True
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>>> for n, dd in NDV:
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... print((n, dd.get("color", "aqua")))
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(0, 'aqua')
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(1, 'aqua')
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(2, 'blue')
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(8, 'red')
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>>> NDV[2] == NV[2]
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True
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>>> NVdata = G.nodes(data="color", default="aqua")
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>>> (2, NVdata[2]) in NVdata
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True
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>>> for n, dd in NVdata:
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... print((n, dd))
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(0, 'aqua')
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(1, 'aqua')
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(2, 'blue')
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(8, 'red')
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>>> NVdata[2] == NV[2] # NVdata gets 'color', NV gets datadict
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False
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"""
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__slots__ = ("_nodes",)
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def __getstate__(self):
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return {"_nodes": self._nodes}
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def __setstate__(self, state):
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self._nodes = state["_nodes"]
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def __init__(self, graph):
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self._nodes = graph._node
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# Mapping methods
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def __len__(self):
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return len(self._nodes)
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def __iter__(self):
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return iter(self._nodes)
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def __getitem__(self, n):
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if isinstance(n, slice):
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raise nx.NetworkXError(
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f"{type(self).__name__} does not support slicing, "
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f"try list(G.nodes)[{n.start}:{n.stop}:{n.step}]"
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)
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return self._nodes[n]
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# Set methods
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def __contains__(self, n):
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return n in self._nodes
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@classmethod
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def _from_iterable(cls, it):
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return set(it)
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# DataView method
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def __call__(self, data=False, default=None):
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if data is False:
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return self
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return NodeDataView(self._nodes, data, default)
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def data(self, data=True, default=None):
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"""
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Return a read-only view of node data.
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Parameters
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----------
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data : bool or node data key, default=True
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If ``data=True`` (the default), return a `NodeDataView` object that
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maps each node to *all* of its attributes. `data` may also be an
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arbitrary key, in which case the `NodeDataView` maps each node to
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the value for the keyed attribute. In this case, if a node does
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not have the `data` attribute, the `default` value is used.
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default : object, default=None
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The value used when a node does not have a specific attribute.
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Returns
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-------
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NodeDataView
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The layout of the returned NodeDataView depends on the value of the
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`data` parameter.
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Notes
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-----
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If ``data=False``, returns a `NodeView` object without data.
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See Also
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--------
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NodeDataView
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Examples
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--------
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>>> G = nx.Graph()
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>>> G.add_nodes_from([
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... (0, {"color": "red", "weight": 10}),
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... (1, {"color": "blue"}),
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... (2, {"color": "yellow", "weight": 2})
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... ])
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Accessing node data with ``data=True`` (the default) returns a
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NodeDataView mapping each node to all of its attributes:
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>>> G.nodes.data()
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NodeDataView({0: {'color': 'red', 'weight': 10}, 1: {'color': 'blue'}, 2: {'color': 'yellow', 'weight': 2}})
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If `data` represents a key in the node attribute dict, a NodeDataView mapping
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the nodes to the value for that specific key is returned:
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>>> G.nodes.data("color")
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NodeDataView({0: 'red', 1: 'blue', 2: 'yellow'}, data='color')
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If a specific key is not found in an attribute dict, the value specified
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by `default` is returned:
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>>> G.nodes.data("weight", default=-999)
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NodeDataView({0: 10, 1: -999, 2: 2}, data='weight')
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Note that there is no check that the `data` key is in any of the
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node attribute dictionaries:
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>>> G.nodes.data("height")
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NodeDataView({0: None, 1: None, 2: None}, data='height')
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"""
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if data is False:
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return self
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return NodeDataView(self._nodes, data, default)
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def __str__(self):
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return str(list(self))
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def __repr__(self):
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return f"{self.__class__.__name__}({tuple(self)})"
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class NodeDataView(Set):
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"""A DataView class for nodes of a NetworkX Graph
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The main use for this class is to iterate through node-data pairs.
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The data can be the entire data-dictionary for each node, or it
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can be a specific attribute (with default) for each node.
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Set operations are enabled with NodeDataView, but don't work in
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cases where the data is not hashable. Use with caution.
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Typically, set operations on nodes use NodeView, not NodeDataView.
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That is, they use `G.nodes` instead of `G.nodes(data='foo')`.
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Parameters
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==========
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graph : NetworkX graph-like class
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data : bool or string (default=False)
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default : object (default=None)
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"""
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__slots__ = ("_nodes", "_data", "_default")
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def __getstate__(self):
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return {"_nodes": self._nodes, "_data": self._data, "_default": self._default}
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def __setstate__(self, state):
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self._nodes = state["_nodes"]
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self._data = state["_data"]
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self._default = state["_default"]
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def __init__(self, nodedict, data=False, default=None):
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self._nodes = nodedict
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self._data = data
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self._default = default
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@classmethod
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def _from_iterable(cls, it):
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try:
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return set(it)
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except TypeError as err:
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if "unhashable" in str(err):
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msg = " : Could be b/c data=True or your values are unhashable"
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raise TypeError(str(err) + msg) from err
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raise
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def __len__(self):
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return len(self._nodes)
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def __iter__(self):
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data = self._data
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if data is False:
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return iter(self._nodes)
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if data is True:
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return iter(self._nodes.items())
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return (
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(n, dd[data] if data in dd else self._default)
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for n, dd in self._nodes.items()
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)
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def __contains__(self, n):
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try:
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node_in = n in self._nodes
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except TypeError:
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n, d = n
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return n in self._nodes and self[n] == d
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if node_in is True:
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return node_in
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try:
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n, d = n
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except (TypeError, ValueError):
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return False
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return n in self._nodes and self[n] == d
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def __getitem__(self, n):
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if isinstance(n, slice):
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raise nx.NetworkXError(
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f"{type(self).__name__} does not support slicing, "
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f"try list(G.nodes.data())[{n.start}:{n.stop}:{n.step}]"
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)
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ddict = self._nodes[n]
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data = self._data
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if data is False or data is True:
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return ddict
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return ddict[data] if data in ddict else self._default
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def __str__(self):
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return str(list(self))
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def __repr__(self):
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name = self.__class__.__name__
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if self._data is False:
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return f"{name}({tuple(self)})"
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if self._data is True:
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return f"{name}({dict(self)})"
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return f"{name}({dict(self)}, data={self._data!r})"
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# DegreeViews
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class DiDegreeView:
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"""A View class for degree of nodes in a NetworkX Graph
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The functionality is like dict.items() with (node, degree) pairs.
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Additional functionality includes read-only lookup of node degree,
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and calling with optional features nbunch (for only a subset of nodes)
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and weight (use edge weights to compute degree).
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Parameters
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==========
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graph : NetworkX graph-like class
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nbunch : node, container of nodes, or None meaning all nodes (default=None)
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weight : bool or string (default=None)
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Notes
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-----
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DegreeView can still lookup any node even if nbunch is specified.
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Examples
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--------
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>>> G = nx.path_graph(3)
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>>> DV = G.degree()
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>>> assert DV[2] == 1
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>>> assert sum(deg for n, deg in DV) == 4
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>>> DVweight = G.degree(weight="span")
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>>> G.add_edge(1, 2, span=34)
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>>> DVweight[2]
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34
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>>> DVweight[0] # default edge weight is 1
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1
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>>> sum(span for n, span in DVweight) # sum weighted degrees
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70
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>>> DVnbunch = G.degree(nbunch=(1, 2))
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>>> assert len(list(DVnbunch)) == 2 # iteration over nbunch only
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"""
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def __init__(self, G, nbunch=None, weight=None):
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self._graph = G
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self._succ = G._succ if hasattr(G, "_succ") else G._adj
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self._pred = G._pred if hasattr(G, "_pred") else G._adj
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self._nodes = self._succ if nbunch is None else list(G.nbunch_iter(nbunch))
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self._weight = weight
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def __call__(self, nbunch=None, weight=None):
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if nbunch is None:
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if weight == self._weight:
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return self
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return self.__class__(self._graph, None, weight)
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try:
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if nbunch in self._nodes:
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if weight == self._weight:
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return self[nbunch]
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return self.__class__(self._graph, None, weight)[nbunch]
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except TypeError:
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pass
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return self.__class__(self._graph, nbunch, weight)
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def __getitem__(self, n):
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weight = self._weight
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succs = self._succ[n]
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preds = self._pred[n]
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if weight is None:
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return len(succs) + len(preds)
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return sum(dd.get(weight, 1) for dd in succs.values()) + sum(
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dd.get(weight, 1) for dd in preds.values()
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)
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def __iter__(self):
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weight = self._weight
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if weight is None:
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for n in self._nodes:
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succs = self._succ[n]
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preds = self._pred[n]
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yield (n, len(succs) + len(preds))
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else:
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for n in self._nodes:
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succs = self._succ[n]
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preds = self._pred[n]
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deg = sum(dd.get(weight, 1) for dd in succs.values()) + sum(
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dd.get(weight, 1) for dd in preds.values()
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)
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yield (n, deg)
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def __len__(self):
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return len(self._nodes)
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def __str__(self):
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return str(list(self))
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def __repr__(self):
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return f"{self.__class__.__name__}({dict(self)})"
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|
|
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class DegreeView(DiDegreeView):
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"""A DegreeView class to act as G.degree for a NetworkX Graph
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Typical usage focuses on iteration over `(node, degree)` pairs.
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The degree is by default the number of edges incident to the node.
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Optional argument `weight` enables weighted degree using the edge
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attribute named in the `weight` argument. Reporting and iteration
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can also be restricted to a subset of nodes using `nbunch`.
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Additional functionality include node lookup so that `G.degree[n]`
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reported the (possibly weighted) degree of node `n`. Calling the
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view creates a view with different arguments `nbunch` or `weight`.
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Parameters
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==========
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graph : NetworkX graph-like class
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nbunch : node, container of nodes, or None meaning all nodes (default=None)
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weight : string or None (default=None)
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Notes
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-----
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DegreeView can still lookup any node even if nbunch is specified.
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Examples
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--------
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>>> G = nx.path_graph(3)
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>>> DV = G.degree()
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>>> assert DV[2] == 1
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>>> assert G.degree[2] == 1
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>>> assert sum(deg for n, deg in DV) == 4
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>>> DVweight = G.degree(weight="span")
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>>> G.add_edge(1, 2, span=34)
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>>> DVweight[2]
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34
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>>> DVweight[0] # default edge weight is 1
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1
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>>> sum(span for n, span in DVweight) # sum weighted degrees
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70
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>>> DVnbunch = G.degree(nbunch=(1, 2))
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>>> assert len(list(DVnbunch)) == 2 # iteration over nbunch only
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"""
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def __getitem__(self, n):
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weight = self._weight
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nbrs = self._succ[n]
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if weight is None:
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return len(nbrs) + (n in nbrs)
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return sum(dd.get(weight, 1) for dd in nbrs.values()) + (
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n in nbrs and nbrs[n].get(weight, 1)
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)
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
yield (n, len(nbrs) + (n in nbrs))
|
|
else:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
deg = sum(dd.get(weight, 1) for dd in nbrs.values()) + (
|
|
n in nbrs and nbrs[n].get(weight, 1)
|
|
)
|
|
yield (n, deg)
|
|
|
|
|
|
class OutDegreeView(DiDegreeView):
|
|
"""A DegreeView class to report out_degree for a DiGraph; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
nbrs = self._succ[n]
|
|
if self._weight is None:
|
|
return len(nbrs)
|
|
return sum(dd.get(self._weight, 1) for dd in nbrs.values())
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
succs = self._succ[n]
|
|
yield (n, len(succs))
|
|
else:
|
|
for n in self._nodes:
|
|
succs = self._succ[n]
|
|
deg = sum(dd.get(weight, 1) for dd in succs.values())
|
|
yield (n, deg)
|
|
|
|
|
|
class InDegreeView(DiDegreeView):
|
|
"""A DegreeView class to report in_degree for a DiGraph; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
nbrs = self._pred[n]
|
|
if weight is None:
|
|
return len(nbrs)
|
|
return sum(dd.get(weight, 1) for dd in nbrs.values())
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
preds = self._pred[n]
|
|
yield (n, len(preds))
|
|
else:
|
|
for n in self._nodes:
|
|
preds = self._pred[n]
|
|
deg = sum(dd.get(weight, 1) for dd in preds.values())
|
|
yield (n, deg)
|
|
|
|
|
|
class MultiDegreeView(DiDegreeView):
|
|
"""A DegreeView class for undirected multigraphs; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
nbrs = self._succ[n]
|
|
if weight is None:
|
|
return sum(len(keys) for keys in nbrs.values()) + (
|
|
n in nbrs and len(nbrs[n])
|
|
)
|
|
# edge weighted graph - degree is sum of nbr edge weights
|
|
deg = sum(
|
|
d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
|
|
)
|
|
if n in nbrs:
|
|
deg += sum(d.get(weight, 1) for d in nbrs[n].values())
|
|
return deg
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
deg = sum(len(keys) for keys in nbrs.values()) + (
|
|
n in nbrs and len(nbrs[n])
|
|
)
|
|
yield (n, deg)
|
|
else:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
deg = sum(
|
|
d.get(weight, 1)
|
|
for key_dict in nbrs.values()
|
|
for d in key_dict.values()
|
|
)
|
|
if n in nbrs:
|
|
deg += sum(d.get(weight, 1) for d in nbrs[n].values())
|
|
yield (n, deg)
|
|
|
|
|
|
class DiMultiDegreeView(DiDegreeView):
|
|
"""A DegreeView class for MultiDiGraph; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
succs = self._succ[n]
|
|
preds = self._pred[n]
|
|
if weight is None:
|
|
return sum(len(keys) for keys in succs.values()) + sum(
|
|
len(keys) for keys in preds.values()
|
|
)
|
|
# edge weighted graph - degree is sum of nbr edge weights
|
|
deg = sum(
|
|
d.get(weight, 1) for key_dict in succs.values() for d in key_dict.values()
|
|
) + sum(
|
|
d.get(weight, 1) for key_dict in preds.values() for d in key_dict.values()
|
|
)
|
|
return deg
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
succs = self._succ[n]
|
|
preds = self._pred[n]
|
|
deg = sum(len(keys) for keys in succs.values()) + sum(
|
|
len(keys) for keys in preds.values()
|
|
)
|
|
yield (n, deg)
|
|
else:
|
|
for n in self._nodes:
|
|
succs = self._succ[n]
|
|
preds = self._pred[n]
|
|
deg = sum(
|
|
d.get(weight, 1)
|
|
for key_dict in succs.values()
|
|
for d in key_dict.values()
|
|
) + sum(
|
|
d.get(weight, 1)
|
|
for key_dict in preds.values()
|
|
for d in key_dict.values()
|
|
)
|
|
yield (n, deg)
|
|
|
|
|
|
class InMultiDegreeView(DiDegreeView):
|
|
"""A DegreeView class for inward degree of MultiDiGraph; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
nbrs = self._pred[n]
|
|
if weight is None:
|
|
return sum(len(data) for data in nbrs.values())
|
|
# edge weighted graph - degree is sum of nbr edge weights
|
|
return sum(
|
|
d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
|
|
)
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
nbrs = self._pred[n]
|
|
deg = sum(len(data) for data in nbrs.values())
|
|
yield (n, deg)
|
|
else:
|
|
for n in self._nodes:
|
|
nbrs = self._pred[n]
|
|
deg = sum(
|
|
d.get(weight, 1)
|
|
for key_dict in nbrs.values()
|
|
for d in key_dict.values()
|
|
)
|
|
yield (n, deg)
|
|
|
|
|
|
class OutMultiDegreeView(DiDegreeView):
|
|
"""A DegreeView class for outward degree of MultiDiGraph; See DegreeView"""
|
|
|
|
def __getitem__(self, n):
|
|
weight = self._weight
|
|
nbrs = self._succ[n]
|
|
if weight is None:
|
|
return sum(len(data) for data in nbrs.values())
|
|
# edge weighted graph - degree is sum of nbr edge weights
|
|
return sum(
|
|
d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
|
|
)
|
|
|
|
def __iter__(self):
|
|
weight = self._weight
|
|
if weight is None:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
deg = sum(len(data) for data in nbrs.values())
|
|
yield (n, deg)
|
|
else:
|
|
for n in self._nodes:
|
|
nbrs = self._succ[n]
|
|
deg = sum(
|
|
d.get(weight, 1)
|
|
for key_dict in nbrs.values()
|
|
for d in key_dict.values()
|
|
)
|
|
yield (n, deg)
|
|
|
|
|
|
# EdgeDataViews
|
|
class OutEdgeDataView:
|
|
"""EdgeDataView for outward edges of DiGraph; See EdgeDataView"""
|
|
|
|
__slots__ = (
|
|
"_viewer",
|
|
"_nbunch",
|
|
"_data",
|
|
"_default",
|
|
"_adjdict",
|
|
"_nodes_nbrs",
|
|
"_report",
|
|
)
|
|
|
|
def __getstate__(self):
|
|
return {
|
|
"viewer": self._viewer,
|
|
"nbunch": self._nbunch,
|
|
"data": self._data,
|
|
"default": self._default,
|
|
}
|
|
|
|
def __setstate__(self, state):
|
|
self.__init__(**state)
|
|
|
|
def __init__(self, viewer, nbunch=None, data=False, *, default=None):
|
|
self._viewer = viewer
|
|
adjdict = self._adjdict = viewer._adjdict
|
|
if nbunch is None:
|
|
self._nodes_nbrs = adjdict.items
|
|
else:
|
|
# dict retains order of nodes but acts like a set
|
|
nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
|
|
self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
|
|
self._nbunch = nbunch
|
|
self._data = data
|
|
self._default = default
|
|
# Set _report based on data and default
|
|
if data is True:
|
|
self._report = lambda n, nbr, dd: (n, nbr, dd)
|
|
elif data is False:
|
|
self._report = lambda n, nbr, dd: (n, nbr)
|
|
else: # data is attribute name
|
|
self._report = (
|
|
lambda n, nbr, dd: (n, nbr, dd[data])
|
|
if data in dd
|
|
else (n, nbr, default)
|
|
)
|
|
|
|
def __len__(self):
|
|
return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
|
|
|
|
def __iter__(self):
|
|
return (
|
|
self._report(n, nbr, dd)
|
|
for n, nbrs in self._nodes_nbrs()
|
|
for nbr, dd in nbrs.items()
|
|
)
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and u not in self._nbunch:
|
|
return False # this edge doesn't start in nbunch
|
|
try:
|
|
ddict = self._adjdict[u][v]
|
|
except KeyError:
|
|
return False
|
|
return e == self._report(u, v, ddict)
|
|
|
|
def __str__(self):
|
|
return str(list(self))
|
|
|
|
def __repr__(self):
|
|
return f"{self.__class__.__name__}({list(self)})"
|
|
|
|
|
|
class EdgeDataView(OutEdgeDataView):
|
|
"""A EdgeDataView class for edges of Graph
|
|
|
|
This view is primarily used to iterate over the edges reporting
|
|
edges as node-tuples with edge data optionally reported. The
|
|
argument `nbunch` allows restriction to edges incident to nodes
|
|
in that container/singleton. The default (nbunch=None)
|
|
reports all edges. The arguments `data` and `default` control
|
|
what edge data is reported. The default `data is False` reports
|
|
only node-tuples for each edge. If `data is True` the entire edge
|
|
data dict is returned. Otherwise `data` is assumed to hold the name
|
|
of the edge attribute to report with default `default` if that
|
|
edge attribute is not present.
|
|
|
|
Parameters
|
|
----------
|
|
nbunch : container of nodes, node or None (default None)
|
|
data : False, True or string (default False)
|
|
default : default value (default None)
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.path_graph(3)
|
|
>>> G.add_edge(1, 2, foo="bar")
|
|
>>> list(G.edges(data="foo", default="biz"))
|
|
[(0, 1, 'biz'), (1, 2, 'bar')]
|
|
>>> assert (0, 1, "biz") in G.edges(data="foo", default="biz")
|
|
"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __len__(self):
|
|
return sum(1 for e in self)
|
|
|
|
def __iter__(self):
|
|
seen = {}
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr, dd in nbrs.items():
|
|
if nbr not in seen:
|
|
yield self._report(n, nbr, dd)
|
|
seen[n] = 1
|
|
del seen
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
|
|
return False # this edge doesn't start and it doesn't end in nbunch
|
|
try:
|
|
ddict = self._adjdict[u][v]
|
|
except KeyError:
|
|
return False
|
|
return e == self._report(u, v, ddict)
|
|
|
|
|
|
class InEdgeDataView(OutEdgeDataView):
|
|
"""An EdgeDataView class for outward edges of DiGraph; See EdgeDataView"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __iter__(self):
|
|
return (
|
|
self._report(nbr, n, dd)
|
|
for n, nbrs in self._nodes_nbrs()
|
|
for nbr, dd in nbrs.items()
|
|
)
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and v not in self._nbunch:
|
|
return False # this edge doesn't end in nbunch
|
|
try:
|
|
ddict = self._adjdict[v][u]
|
|
except KeyError:
|
|
return False
|
|
return e == self._report(u, v, ddict)
|
|
|
|
|
|
class OutMultiEdgeDataView(OutEdgeDataView):
|
|
"""An EdgeDataView for outward edges of MultiDiGraph; See EdgeDataView"""
|
|
|
|
__slots__ = ("keys",)
|
|
|
|
def __getstate__(self):
|
|
return {
|
|
"viewer": self._viewer,
|
|
"nbunch": self._nbunch,
|
|
"keys": self.keys,
|
|
"data": self._data,
|
|
"default": self._default,
|
|
}
|
|
|
|
def __setstate__(self, state):
|
|
self.__init__(**state)
|
|
|
|
def __init__(self, viewer, nbunch=None, data=False, *, default=None, keys=False):
|
|
self._viewer = viewer
|
|
adjdict = self._adjdict = viewer._adjdict
|
|
self.keys = keys
|
|
if nbunch is None:
|
|
self._nodes_nbrs = adjdict.items
|
|
else:
|
|
# dict retains order of nodes but acts like a set
|
|
nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
|
|
self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
|
|
self._nbunch = nbunch
|
|
self._data = data
|
|
self._default = default
|
|
# Set _report based on data and default
|
|
if data is True:
|
|
if keys is True:
|
|
self._report = lambda n, nbr, k, dd: (n, nbr, k, dd)
|
|
else:
|
|
self._report = lambda n, nbr, k, dd: (n, nbr, dd)
|
|
elif data is False:
|
|
if keys is True:
|
|
self._report = lambda n, nbr, k, dd: (n, nbr, k)
|
|
else:
|
|
self._report = lambda n, nbr, k, dd: (n, nbr)
|
|
else: # data is attribute name
|
|
if keys is True:
|
|
self._report = (
|
|
lambda n, nbr, k, dd: (n, nbr, k, dd[data])
|
|
if data in dd
|
|
else (n, nbr, k, default)
|
|
)
|
|
else:
|
|
self._report = (
|
|
lambda n, nbr, k, dd: (n, nbr, dd[data])
|
|
if data in dd
|
|
else (n, nbr, default)
|
|
)
|
|
|
|
def __len__(self):
|
|
return sum(1 for e in self)
|
|
|
|
def __iter__(self):
|
|
return (
|
|
self._report(n, nbr, k, dd)
|
|
for n, nbrs in self._nodes_nbrs()
|
|
for nbr, kd in nbrs.items()
|
|
for k, dd in kd.items()
|
|
)
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and u not in self._nbunch:
|
|
return False # this edge doesn't start in nbunch
|
|
try:
|
|
kdict = self._adjdict[u][v]
|
|
except KeyError:
|
|
return False
|
|
if self.keys is True:
|
|
k = e[2]
|
|
try:
|
|
dd = kdict[k]
|
|
except KeyError:
|
|
return False
|
|
return e == self._report(u, v, k, dd)
|
|
return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
|
|
|
|
|
|
class MultiEdgeDataView(OutMultiEdgeDataView):
|
|
"""An EdgeDataView class for edges of MultiGraph; See EdgeDataView"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __iter__(self):
|
|
seen = {}
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr, kd in nbrs.items():
|
|
if nbr not in seen:
|
|
for k, dd in kd.items():
|
|
yield self._report(n, nbr, k, dd)
|
|
seen[n] = 1
|
|
del seen
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
|
|
return False # this edge doesn't start and doesn't end in nbunch
|
|
try:
|
|
kdict = self._adjdict[u][v]
|
|
except KeyError:
|
|
try:
|
|
kdict = self._adjdict[v][u]
|
|
except KeyError:
|
|
return False
|
|
if self.keys is True:
|
|
k = e[2]
|
|
try:
|
|
dd = kdict[k]
|
|
except KeyError:
|
|
return False
|
|
return e == self._report(u, v, k, dd)
|
|
return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
|
|
|
|
|
|
class InMultiEdgeDataView(OutMultiEdgeDataView):
|
|
"""An EdgeDataView for inward edges of MultiDiGraph; See EdgeDataView"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __iter__(self):
|
|
return (
|
|
self._report(nbr, n, k, dd)
|
|
for n, nbrs in self._nodes_nbrs()
|
|
for nbr, kd in nbrs.items()
|
|
for k, dd in kd.items()
|
|
)
|
|
|
|
def __contains__(self, e):
|
|
u, v = e[:2]
|
|
if self._nbunch is not None and v not in self._nbunch:
|
|
return False # this edge doesn't end in nbunch
|
|
try:
|
|
kdict = self._adjdict[v][u]
|
|
except KeyError:
|
|
return False
|
|
if self.keys is True:
|
|
k = e[2]
|
|
dd = kdict[k]
|
|
return e == self._report(u, v, k, dd)
|
|
return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
|
|
|
|
|
|
# EdgeViews have set operations and no data reported
|
|
class OutEdgeView(Set, Mapping):
|
|
"""A EdgeView class for outward edges of a DiGraph"""
|
|
|
|
__slots__ = ("_adjdict", "_graph", "_nodes_nbrs")
|
|
|
|
def __getstate__(self):
|
|
return {"_graph": self._graph, "_adjdict": self._adjdict}
|
|
|
|
def __setstate__(self, state):
|
|
self._graph = state["_graph"]
|
|
self._adjdict = state["_adjdict"]
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
@classmethod
|
|
def _from_iterable(cls, it):
|
|
return set(it)
|
|
|
|
dataview = OutEdgeDataView
|
|
|
|
def __init__(self, G):
|
|
self._graph = G
|
|
self._adjdict = G._succ if hasattr(G, "succ") else G._adj
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
# Set methods
|
|
def __len__(self):
|
|
return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
|
|
|
|
def __iter__(self):
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr in nbrs:
|
|
yield (n, nbr)
|
|
|
|
def __contains__(self, e):
|
|
try:
|
|
u, v = e
|
|
return v in self._adjdict[u]
|
|
except KeyError:
|
|
return False
|
|
|
|
# Mapping Methods
|
|
def __getitem__(self, e):
|
|
if isinstance(e, slice):
|
|
raise nx.NetworkXError(
|
|
f"{type(self).__name__} does not support slicing, "
|
|
f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
|
|
)
|
|
u, v = e
|
|
return self._adjdict[u][v]
|
|
|
|
# EdgeDataView methods
|
|
def __call__(self, nbunch=None, data=False, *, default=None):
|
|
if nbunch is None and data is False:
|
|
return self
|
|
return self.dataview(self, nbunch, data, default=default)
|
|
|
|
def data(self, data=True, default=None, nbunch=None):
|
|
"""
|
|
Return a read-only view of edge data.
|
|
|
|
Parameters
|
|
----------
|
|
data : bool or edge attribute key
|
|
If ``data=True``, then the data view maps each edge to a dictionary
|
|
containing all of its attributes. If `data` is a key in the edge
|
|
dictionary, then the data view maps each edge to its value for
|
|
the keyed attribute. In this case, if the edge doesn't have the
|
|
attribute, the `default` value is returned.
|
|
default : object, default=None
|
|
The value used when an edge does not have a specific attribute
|
|
nbunch : container of nodes, optional (default=None)
|
|
Allows restriction to edges only involving certain nodes. All edges
|
|
are considered by default.
|
|
|
|
Returns
|
|
-------
|
|
dataview
|
|
Returns an `EdgeDataView` for undirected Graphs, `OutEdgeDataView`
|
|
for DiGraphs, `MultiEdgeDataView` for MultiGraphs and
|
|
`OutMultiEdgeDataView` for MultiDiGraphs.
|
|
|
|
Notes
|
|
-----
|
|
If ``data=False``, returns an `EdgeView` without any edge data.
|
|
|
|
See Also
|
|
--------
|
|
EdgeDataView
|
|
OutEdgeDataView
|
|
MultiEdgeDataView
|
|
OutMultiEdgeDataView
|
|
|
|
Examples
|
|
--------
|
|
>>> G = nx.Graph()
|
|
>>> G.add_edges_from([
|
|
... (0, 1, {"dist": 3, "capacity": 20}),
|
|
... (1, 2, {"dist": 4}),
|
|
... (2, 0, {"dist": 5})
|
|
... ])
|
|
|
|
Accessing edge data with ``data=True`` (the default) returns an
|
|
edge data view object listing each edge with all of its attributes:
|
|
|
|
>>> G.edges.data()
|
|
EdgeDataView([(0, 1, {'dist': 3, 'capacity': 20}), (0, 2, {'dist': 5}), (1, 2, {'dist': 4})])
|
|
|
|
If `data` represents a key in the edge attribute dict, a dataview listing
|
|
each edge with its value for that specific key is returned:
|
|
|
|
>>> G.edges.data("dist")
|
|
EdgeDataView([(0, 1, 3), (0, 2, 5), (1, 2, 4)])
|
|
|
|
`nbunch` can be used to limit the edges:
|
|
|
|
>>> G.edges.data("dist", nbunch=[0])
|
|
EdgeDataView([(0, 1, 3), (0, 2, 5)])
|
|
|
|
If a specific key is not found in an edge attribute dict, the value
|
|
specified by `default` is used:
|
|
|
|
>>> G.edges.data("capacity")
|
|
EdgeDataView([(0, 1, 20), (0, 2, None), (1, 2, None)])
|
|
|
|
Note that there is no check that the `data` key is present in any of
|
|
the edge attribute dictionaries:
|
|
|
|
>>> G.edges.data("speed")
|
|
EdgeDataView([(0, 1, None), (0, 2, None), (1, 2, None)])
|
|
"""
|
|
if nbunch is None and data is False:
|
|
return self
|
|
return self.dataview(self, nbunch, data, default=default)
|
|
|
|
# String Methods
|
|
def __str__(self):
|
|
return str(list(self))
|
|
|
|
def __repr__(self):
|
|
return f"{self.__class__.__name__}({list(self)})"
|
|
|
|
|
|
class EdgeView(OutEdgeView):
|
|
"""A EdgeView class for edges of a Graph
|
|
|
|
This densely packed View allows iteration over edges, data lookup
|
|
like a dict and set operations on edges represented by node-tuples.
|
|
In addition, edge data can be controlled by calling this object
|
|
possibly creating an EdgeDataView. Typically edges are iterated over
|
|
and reported as `(u, v)` node tuples or `(u, v, key)` node/key tuples
|
|
for multigraphs. Those edge representations can also be using to
|
|
lookup the data dict for any edge. Set operations also are available
|
|
where those tuples are the elements of the set.
|
|
Calling this object with optional arguments `data`, `default` and `keys`
|
|
controls the form of the tuple (see EdgeDataView). Optional argument
|
|
`nbunch` allows restriction to edges only involving certain nodes.
|
|
|
|
If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
|
|
If `data is True` iterate over 3-tuples `(u, v, datadict)`.
|
|
Otherwise iterate over `(u, v, datadict.get(data, default))`.
|
|
For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key` above.
|
|
|
|
Parameters
|
|
==========
|
|
graph : NetworkX graph-like class
|
|
nbunch : (default= all nodes in graph) only report edges with these nodes
|
|
keys : (only for MultiGraph. default=False) report edge key in tuple
|
|
data : bool or string (default=False) see above
|
|
default : object (default=None)
|
|
|
|
Examples
|
|
========
|
|
>>> G = nx.path_graph(4)
|
|
>>> EV = G.edges()
|
|
>>> (2, 3) in EV
|
|
True
|
|
>>> for u, v in EV:
|
|
... print((u, v))
|
|
(0, 1)
|
|
(1, 2)
|
|
(2, 3)
|
|
>>> assert EV & {(1, 2), (3, 4)} == {(1, 2)}
|
|
|
|
>>> EVdata = G.edges(data="color", default="aqua")
|
|
>>> G.add_edge(2, 3, color="blue")
|
|
>>> assert (2, 3, "blue") in EVdata
|
|
>>> for u, v, c in EVdata:
|
|
... print(f"({u}, {v}) has color: {c}")
|
|
(0, 1) has color: aqua
|
|
(1, 2) has color: aqua
|
|
(2, 3) has color: blue
|
|
|
|
>>> EVnbunch = G.edges(nbunch=2)
|
|
>>> assert (2, 3) in EVnbunch
|
|
>>> assert (0, 1) not in EVnbunch
|
|
>>> for u, v in EVnbunch:
|
|
... assert u == 2 or v == 2
|
|
|
|
>>> MG = nx.path_graph(4, create_using=nx.MultiGraph)
|
|
>>> EVmulti = MG.edges(keys=True)
|
|
>>> (2, 3, 0) in EVmulti
|
|
True
|
|
>>> (2, 3) in EVmulti # 2-tuples work even when keys is True
|
|
True
|
|
>>> key = MG.add_edge(2, 3)
|
|
>>> for u, v, k in EVmulti:
|
|
... print((u, v, k))
|
|
(0, 1, 0)
|
|
(1, 2, 0)
|
|
(2, 3, 0)
|
|
(2, 3, 1)
|
|
"""
|
|
|
|
__slots__ = ()
|
|
|
|
dataview = EdgeDataView
|
|
|
|
def __len__(self):
|
|
num_nbrs = (len(nbrs) + (n in nbrs) for n, nbrs in self._nodes_nbrs())
|
|
return sum(num_nbrs) // 2
|
|
|
|
def __iter__(self):
|
|
seen = {}
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr in list(nbrs):
|
|
if nbr not in seen:
|
|
yield (n, nbr)
|
|
seen[n] = 1
|
|
del seen
|
|
|
|
def __contains__(self, e):
|
|
try:
|
|
u, v = e[:2]
|
|
return v in self._adjdict[u] or u in self._adjdict[v]
|
|
except (KeyError, ValueError):
|
|
return False
|
|
|
|
|
|
class InEdgeView(OutEdgeView):
|
|
"""A EdgeView class for inward edges of a DiGraph"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __setstate__(self, state):
|
|
self._graph = state["_graph"]
|
|
self._adjdict = state["_adjdict"]
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
dataview = InEdgeDataView
|
|
|
|
def __init__(self, G):
|
|
self._graph = G
|
|
self._adjdict = G._pred if hasattr(G, "pred") else G._adj
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
def __iter__(self):
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr in nbrs:
|
|
yield (nbr, n)
|
|
|
|
def __contains__(self, e):
|
|
try:
|
|
u, v = e
|
|
return u in self._adjdict[v]
|
|
except KeyError:
|
|
return False
|
|
|
|
def __getitem__(self, e):
|
|
if isinstance(e, slice):
|
|
raise nx.NetworkXError(
|
|
f"{type(self).__name__} does not support slicing, "
|
|
f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
|
|
)
|
|
u, v = e
|
|
return self._adjdict[v][u]
|
|
|
|
|
|
class OutMultiEdgeView(OutEdgeView):
|
|
"""A EdgeView class for outward edges of a MultiDiGraph"""
|
|
|
|
__slots__ = ()
|
|
|
|
dataview = OutMultiEdgeDataView
|
|
|
|
def __len__(self):
|
|
return sum(
|
|
len(kdict) for n, nbrs in self._nodes_nbrs() for nbr, kdict in nbrs.items()
|
|
)
|
|
|
|
def __iter__(self):
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr, kdict in nbrs.items():
|
|
for key in kdict:
|
|
yield (n, nbr, key)
|
|
|
|
def __contains__(self, e):
|
|
N = len(e)
|
|
if N == 3:
|
|
u, v, k = e
|
|
elif N == 2:
|
|
u, v = e
|
|
k = 0
|
|
else:
|
|
raise ValueError("MultiEdge must have length 2 or 3")
|
|
try:
|
|
return k in self._adjdict[u][v]
|
|
except KeyError:
|
|
return False
|
|
|
|
def __getitem__(self, e):
|
|
if isinstance(e, slice):
|
|
raise nx.NetworkXError(
|
|
f"{type(self).__name__} does not support slicing, "
|
|
f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
|
|
)
|
|
u, v, k = e
|
|
return self._adjdict[u][v][k]
|
|
|
|
def __call__(self, nbunch=None, data=False, *, default=None, keys=False):
|
|
if nbunch is None and data is False and keys is True:
|
|
return self
|
|
return self.dataview(self, nbunch, data, default=default, keys=keys)
|
|
|
|
def data(self, data=True, default=None, nbunch=None, keys=False):
|
|
if nbunch is None and data is False and keys is True:
|
|
return self
|
|
return self.dataview(self, nbunch, data, default=default, keys=keys)
|
|
|
|
|
|
class MultiEdgeView(OutMultiEdgeView):
|
|
"""A EdgeView class for edges of a MultiGraph"""
|
|
|
|
__slots__ = ()
|
|
|
|
dataview = MultiEdgeDataView
|
|
|
|
def __len__(self):
|
|
return sum(1 for e in self)
|
|
|
|
def __iter__(self):
|
|
seen = {}
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr, kd in nbrs.items():
|
|
if nbr not in seen:
|
|
for k, dd in kd.items():
|
|
yield (n, nbr, k)
|
|
seen[n] = 1
|
|
del seen
|
|
|
|
|
|
class InMultiEdgeView(OutMultiEdgeView):
|
|
"""A EdgeView class for inward edges of a MultiDiGraph"""
|
|
|
|
__slots__ = ()
|
|
|
|
def __setstate__(self, state):
|
|
self._graph = state["_graph"]
|
|
self._adjdict = state["_adjdict"]
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
dataview = InMultiEdgeDataView
|
|
|
|
def __init__(self, G):
|
|
self._graph = G
|
|
self._adjdict = G._pred if hasattr(G, "pred") else G._adj
|
|
self._nodes_nbrs = self._adjdict.items
|
|
|
|
def __iter__(self):
|
|
for n, nbrs in self._nodes_nbrs():
|
|
for nbr, kdict in nbrs.items():
|
|
for key in kdict:
|
|
yield (nbr, n, key)
|
|
|
|
def __contains__(self, e):
|
|
N = len(e)
|
|
if N == 3:
|
|
u, v, k = e
|
|
elif N == 2:
|
|
u, v = e
|
|
k = 0
|
|
else:
|
|
raise ValueError("MultiEdge must have length 2 or 3")
|
|
try:
|
|
return k in self._adjdict[v][u]
|
|
except KeyError:
|
|
return False
|
|
|
|
def __getitem__(self, e):
|
|
if isinstance(e, slice):
|
|
raise nx.NetworkXError(
|
|
f"{type(self).__name__} does not support slicing, "
|
|
f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
|
|
)
|
|
u, v, k = e
|
|
return self._adjdict[v][u][k]
|