Mercurial > repos > guerler > springsuite
diff planemo/lib/python3.7/site-packages/networkx/classes/reportviews.py @ 1:56ad4e20f292 draft
"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
| author | guerler |
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| date | Fri, 31 Jul 2020 00:32:28 -0400 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/planemo/lib/python3.7/site-packages/networkx/classes/reportviews.py Fri Jul 31 00:32:28 2020 -0400 @@ -0,0 +1,1178 @@ +# Copyright (C) 2004-2019 by +# Aric Hagberg <hagberg@lanl.gov> +# Dan Schult <dschult@colgate.edu> +# Pieter Swart <swart@lanl.gov> +# All rights reserved. +# BSD license. +# +# Authors: Aric Hagberg (hagberg@lanl.gov), +# Pieter Swart (swart@lanl.gov), +# Dan Schult(dschult@colgate.edu) +""" +View Classes provide node, edge and degree "views" of a graph. + +Views for nodes, edges and degree are provided for all base graph classes. +A view means a read-only object that is quick to create, automatically +updated when the graph changes, and provides basic access like `n in V`, +`for n in V`, `V[n]` and sometimes set operations. + +The views are read-only iterable containers that are updated as the +graph is updated. As with dicts, the graph should not be updated +while iterating through the view. Views can be iterated multiple times. + +Edge and Node views also allow data attribute lookup. +The resulting attribute dict is writable as `G.edges[3, 4]['color']='red'` +Degree views allow lookup of degree values for single nodes. +Weighted degree is supported with the `weight` argument. + +NodeView +======== + + `V = G.nodes` (or `V = G.nodes()`) allows `len(V)`, `n in V`, set + operations e.g. "G.nodes & H.nodes", and `dd = G.nodes[n]`, where + `dd` is the node data dict. Iteration is over the nodes by default. + +NodeDataView +============ + + To iterate over (node, data) pairs, use arguments to `G.nodes()` + to create a DataView e.g. `DV = G.nodes(data='color', default='red')`. + The DataView iterates as `for n, color in DV` and allows + `(n, 'red') in DV`. Using `DV = G.nodes(data=True)`, the DataViews + use the full datadict in writeable form also allowing contain testing as + `(n, {'color': 'red'}) in VD`. DataViews allow set operations when + data attributes are hashable. + +DegreeView +========== + + `V = G.degree` allows iteration over (node, degree) pairs as well + as lookup: `deg=V[n]`. There are many flavors of DegreeView + for In/Out/Directed/Multi. For Directed Graphs, `G.degree` + counts both in and out going edges. `G.out_degree` and + `G.in_degree` count only specific directions. + Weighted degree using edge data attributes is provide via + `V = G.degree(weight='attr_name')` where any string with the + attribute name can be used. `weight=None` is the default. + No set operations are implemented for degrees, use NodeView. + + The argument `nbunch` restricts iteration to nodes in nbunch. + The DegreeView can still lookup any node even if nbunch is specified. + +EdgeView +======== + + `V = G.edges` or `V = G.edges()` allows iteration over edges as well as + `e in V`, set operations and edge data lookup `dd = G.edges[2, 3]`. + Iteration is over 2-tuples `(u, v)` for Graph/DiGraph. For multigraphs + edges 3-tuples `(u, v, key)` are the default but 2-tuples can be obtained + via `V = G.edges(keys=False)`. + + Set operations for directed graphs treat the edges as a set of 2-tuples. + For undirected graphs, 2-tuples are not a unique representation of edges. + So long as the set being compared to contains unique representations + of its edges, the set operations will act as expected. If the other + set contains both `(0, 1)` and `(1, 0)` however, the result of set + operations may contain both representations of the same edge. + +EdgeDataView +============ + + Edge data can be reported using an EdgeDataView typically created + by calling an EdgeView: `DV = G.edges(data='weight', default=1)`. + The EdgeDataView allows iteration over edge tuples, membership checking + but no set operations. + + Iteration depends on `data` and `default` and for multigraph `keys` + 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` + to create 3-tuples and 4-tuples. + + The argument `nbunch` restricts edges to those incident to nodes in nbunch. +""" +from collections.abc import Mapping, Set, Iterable +import networkx as nx + +__all__ = ['NodeView', 'NodeDataView', + 'EdgeView', 'OutEdgeView', 'InEdgeView', + 'EdgeDataView', 'OutEdgeDataView', 'InEdgeDataView', + 'MultiEdgeView', 'OutMultiEdgeView', 'InMultiEdgeView', + 'MultiEdgeDataView', 'OutMultiEdgeDataView', 'InMultiEdgeDataView', + 'DegreeView', 'DiDegreeView', 'InDegreeView', 'OutDegreeView', + 'MultiDegreeView', 'DiMultiDegreeView', + 'InMultiDegreeView', 'OutMultiDegreeView'] + + +# NodeViews +class NodeView(Mapping, Set): + """A NodeView class to act as G.nodes for a NetworkX Graph + + Set operations act on the nodes without considering data. + Iteration is over nodes. Node data can be looked up like a dict. + Use NodeDataView to iterate over node data or to specify a data + attribute for lookup. NodeDataView is created by calling the NodeView. + + Parameters + ---------- + graph : NetworkX graph-like class + + Examples + -------- + >>> G = nx.path_graph(3) + >>> NV = G.nodes() + >>> 2 in NV + True + >>> for n in NV: print(n) + 0 + 1 + 2 + >>> assert(NV & {1, 2, 3} == {1, 2}) + + >>> G.add_node(2, color='blue') + >>> NV[2] + {'color': 'blue'} + >>> G.add_node(8, color='red') + >>> NDV = G.nodes(data=True) + >>> (2, NV[2]) in NDV + True + >>> for n, dd in NDV: print((n, dd.get('color', 'aqua'))) + (0, 'aqua') + (1, 'aqua') + (2, 'blue') + (8, 'red') + >>> NDV[2] == NV[2] + True + + >>> NVdata = G.nodes(data='color', default='aqua') + >>> (2, NVdata[2]) in NVdata + True + >>> for n, dd in NVdata: print((n, dd)) + (0, 'aqua') + (1, 'aqua') + (2, 'blue') + (8, 'red') + >>> NVdata[2] == NV[2] # NVdata gets 'color', NV gets datadict + False + """ + __slots__ = '_nodes', + + def __getstate__(self): + return {'_nodes': self._nodes} + + def __setstate__(self, state): + self._nodes = state['_nodes'] + + def __init__(self, graph): + self._nodes = graph._node + + # Mapping methods + def __len__(self): + return len(self._nodes) + + def __iter__(self): + return iter(self._nodes) + + def __getitem__(self, n): + return self._nodes[n] + + # Set methods + def __contains__(self, n): + return n in self._nodes + + @classmethod + def _from_iterable(cls, it): + return set(it) + + # DataView method + def __call__(self, data=False, default=None): + if data is False: + return self + return NodeDataView(self._nodes, data, default) + + def data(self, data=True, default=None): + if data is False: + return self + return NodeDataView(self._nodes, data, default) + + def __str__(self): + return str(list(self)) + + def __repr__(self): + return '%s(%r)' % (self.__class__.__name__, tuple(self)) + + +class NodeDataView(Set): + """A DataView class for nodes of a NetworkX Graph + + The main use for this class is to iterate through node-data pairs. + The data can be the entire data-dictionary for each node, or it + can be a specific attribute (with default) for each node. + Set operations are enabled with NodeDataView, but don't work in + cases where the data is not hashable. Use with caution. + Typically, set operations on nodes use NodeView, not NodeDataView. + That is, they use `G.nodes` instead of `G.nodes(data='foo')`. + + Parameters + ========== + graph : NetworkX graph-like class + data : bool or string (default=False) + default : object (default=None) + """ + __slots__ = ('_nodes', '_data', '_default') + + def __getstate__(self): + return {'_nodes': self._nodes, + '_data': self._data, + '_default': self._default} + + def __setstate__(self, state): + self._nodes = state['_nodes'] + self._data = state['_data'] + self._default = state['_default'] + + def __init__(self, nodedict, data=False, default=None): + self._nodes = nodedict + self._data = data + self._default = default + + @classmethod + def _from_iterable(cls, it): + try: + return set(it) + except TypeError as err: + if "unhashable" in str(err): + msg = " : Could be b/c data=True or your values are unhashable" + raise TypeError(str(err) + msg) + raise + + def __len__(self): + return len(self._nodes) + + def __iter__(self): + data = self._data + if data is False: + return iter(self._nodes) + if data is True: + return iter(self._nodes.items()) + return ((n, dd[data] if data in dd else self._default) + for n, dd in self._nodes.items()) + + def __contains__(self, n): + try: + node_in = n in self._nodes + except TypeError: + n, d = n + return n in self._nodes and self[n] == d + if node_in is True: + return node_in + try: + n, d = n + except (TypeError, ValueError): + return False + return n in self._nodes and self[n] == d + + def __getitem__(self, n): + ddict = self._nodes[n] + data = self._data + if data is False or data is True: + return ddict + return ddict[data] if data in ddict else self._default + + def __str__(self): + return str(list(self)) + + def __repr__(self): + if self._data is False: + return '%s(%r)' % (self.__class__.__name__, tuple(self)) + if self._data is True: + return '%s(%r)' % (self.__class__.__name__, dict(self)) + return '%s(%r, data=%r)' % \ + (self.__class__.__name__, dict(self), self._data) + + +# DegreeViews +class DiDegreeView(object): + """A View class for degree of nodes in a NetworkX Graph + + The functionality is like dict.items() with (node, degree) pairs. + Additional functionality includes read-only lookup of node degree, + and calling with optional features nbunch (for only a subset of nodes) + and weight (use edge weights to compute degree). + + Parameters + ========== + graph : NetworkX graph-like class + nbunch : node, container of nodes, or None meaning all nodes (default=None) + weight : bool or string (default=None) + + Notes + ----- + DegreeView can still lookup any node even if nbunch is specified. + + Examples + -------- + >>> G = nx.path_graph(3) + >>> DV = G.degree() + >>> assert(DV[2] == 1) + >>> assert(sum(deg for n, deg in DV) == 4) + + >>> DVweight = G.degree(weight="span") + >>> G.add_edge(1, 2, span=34) + >>> DVweight[2] + 34 + >>> DVweight[0] # default edge weight is 1 + 1 + >>> sum(span for n, span in DVweight) # sum weighted degrees + 70 + + >>> DVnbunch = G.degree(nbunch=(1, 2)) + >>> assert(len(list(DVnbunch)) == 2) # iteration over nbunch only + """ + + def __init__(self, G, nbunch=None, weight=None): + self._graph = G + self._succ = G._succ if hasattr(G, "_succ") else G._adj + self._pred = G._pred if hasattr(G, "_pred") else G._adj + self._nodes = self._succ if nbunch is None \ + else list(G.nbunch_iter(nbunch)) + self._weight = weight + + def __call__(self, nbunch=None, weight=None): + if nbunch is None: + if weight == self._weight: + return self + return self.__class__(self._graph, None, weight) + try: + if nbunch in self._nodes: + if weight == self._weight: + return self[nbunch] + return self.__class__(self._graph, None, weight)[nbunch] + except TypeError: + pass + return self.__class__(self._graph, nbunch, weight) + + def __getitem__(self, n): + weight = self._weight + succs = self._succ[n] + preds = self._pred[n] + if weight is None: + return len(succs) + len(preds) + return sum(dd.get(weight, 1) for dd in succs.values()) + \ + sum(dd.get(weight, 1) for dd in preds.values()) + + def __iter__(self): + weight = self._weight + if weight is None: + for n in self._nodes: + succs = self._succ[n] + preds = self._pred[n] + yield (n, len(succs) + len(preds)) + else: + for n in self._nodes: + succs = self._succ[n] + preds = self._pred[n] + deg = sum(dd.get(weight, 1) for dd in succs.values()) \ + + sum(dd.get(weight, 1) for dd in preds.values()) + yield (n, deg) + + def __len__(self): + return len(self._nodes) + + def __str__(self): + return str(list(self)) + + def __repr__(self): + return '%s(%r)' % (self.__class__.__name__, dict(self)) + + +class DegreeView(DiDegreeView): + """A DegreeView class to act as G.degree for a NetworkX Graph + + Typical usage focuses on iteration over `(node, degree)` pairs. + The degree is by default the number of edges incident to the node. + Optional argument `weight` enables weighted degree using the edge + attribute named in the `weight` argument. Reporting and iteration + can also be restricted to a subset of nodes using `nbunch`. + + Additional functionality include node lookup so that `G.degree[n]` + reported the (possibly weighted) degree of node `n`. Calling the + view creates a view with different arguments `nbunch` or `weight`. + + Parameters + ========== + graph : NetworkX graph-like class + nbunch : node, container of nodes, or None meaning all nodes (default=None) + weight : string or None (default=None) + + Notes + ----- + DegreeView can still lookup any node even if nbunch is specified. + + Examples + -------- + >>> G = nx.path_graph(3) + >>> DV = G.degree() + >>> assert(DV[2] == 1) + >>> assert(G.degree[2] == 1) + >>> assert(sum(deg for n, deg in DV) == 4) + + >>> DVweight = G.degree(weight="span") + >>> G.add_edge(1, 2, span=34) + >>> DVweight[2] + 34 + >>> DVweight[0] # default edge weight is 1 + 1 + >>> sum(span for n, span in DVweight) # sum weighted degrees + 70 + + >>> DVnbunch = G.degree(nbunch=(1, 2)) + >>> assert(len(list(DVnbunch)) == 2) # iteration over nbunch only + """ + + def __getitem__(self, n): + weight = self._weight + nbrs = self._succ[n] + if weight is None: + return len(nbrs) + (n in nbrs) + return sum(dd.get(weight, 1) for dd in nbrs.values()) + \ + (n in nbrs and nbrs[n].get(weight, 1)) + + 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(object): + """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: + nbunch = list(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): + try: + u, v = e[:2] + 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 '%s(%r)' % (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): + try: + u, v = e[:2] + ddict = self._adjdict[u][v] + except KeyError: + try: + ddict = self._adjdict[v][u] + 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): + try: + u, v = e[:2] + 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, keys=False, default=None): + self._viewer = viewer + adjdict = self._adjdict = viewer._adjdict + self.keys = keys + if nbunch is None: + self._nodes_nbrs = adjdict.items + else: + nbunch = list(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] + 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) + for k, dd in kdict.items(): + if e == self._report(u, v, k, dd): + return True + return False + + +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] + 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) + for k, dd in kdict.items(): + if e == self._report(u, v, k, dd): + return True + return False + + +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] + 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) + for k, dd in kdict.items(): + if e == self._report(u, v, k, dd): + return True + return False + + +# 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} + + def __setstate__(self, state): + self._graph = G = state['_graph'] + self._adjdict = G._succ if hasattr(G, "succ") else G._adj + 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): + 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) + + def data(self, data=True, default=None, nbunch=None): + if nbunch is None and data is False: + return self + return self.dataview(self, nbunch, data, default) + + # String Methods + def __str__(self): + return str(list(self)) + + def __repr__(self): + return "{0.__class__.__name__}({1!r})".format(self, 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("({}, {}) has color: {}".format(u, v, 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) in EVnbunch) # nbunch is ignored in __contains__ + >>> 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 = G = state['_graph'] + self._adjdict = G._pred if hasattr(G, "pred") else G._adj + 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): + 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): + u, v, k = e + return self._adjdict[u][v][k] + + def __call__(self, nbunch=None, data=False, keys=False, default=None): + if nbunch is None and data is False and keys is True: + return self + return self.dataview(self, nbunch, data, keys, default) + + def data(self, data=True, keys=False, default=None, nbunch=None): + if nbunch is None and data is False and keys is True: + return self + return self.dataview(self, nbunch, data, keys, default) + + +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 = G = state['_graph'] + self._adjdict = G._pred if hasattr(G, "pred") else G._adj + 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): + u, v, k = e + return self._adjdict[v][u][k]