4Ysh-dZddlZejeZdZeefdZdfdZddfd Zdd fd Z e d ge d gd ddfdZ y)aConvert (to and) from rdflib graphs to other well known graph libraries. Currently the following libraries are supported: - networkx: MultiDiGraph, DiGraph, Graph - graph_tool: Graph Doctests in this file are all skipped, as we can't run them conditionally if networkx or graph_tool are available and they would err otherwise. see ../../test/test_extras_external_graph_libs.py for conditional tests Nc|S)N)xs /var/www/sten-cake5-migrate2.hellocrow.space/lexinfo-master/env/lib/python3.12/site-packages/rdflib/extras/external_graph_libs.py _identityrs Hct|sJt|sJt|sJddl}|D]\}}} |||| } } |j| | } | t||jr&|||| } |rd| d<|j | | fi| g|r | dxxdz cc<d| vs{|||| } | dj | dy)aHelper method for multidigraph, digraph and graph. Modifies nxgraph in-place! Arguments: graph: an rdflib.Graph. nxgraph: a networkx.Graph/DiGraph/MultiDigraph. calc_weights: If True adds a 'weight' attribute to each edge according to the count of s,p,o triples between s and o, which is meaningful for Graph/DiGraph. edge_attrs: Callable to construct edge data from s, p, o. 'triples' attribute is handled specially to be merged. 'weight' should not be generated if calc_weights==True. (see invokers below!) transform_s: Callable to transform node generated from s. transform_o: Callable to transform node generated from o. rNweighttriples)callablenetworkx get_edge_data isinstance MultiDiGraphadd_edgeextend)graphnxgraph calc_weights edge_attrs transform_s transform_onxspotstodatads r_rdflib_to_networkx_graphr"s2 J   K  K  51aQQB$$R, <:gr?aA&D!"X G  R ,t ,X!#D q!Q'Y&&q|45rc d|iS)Nkeyrrrrs rr&Is uajrc Lddl}|j}t||d|fi||S)aConverts the given graph into a networkx.MultiDiGraph. The subjects and objects are the later nodes of the MultiDiGraph. The predicates are used as edge keys (to identify multi-edges). :Parameters: - graph: a rdflib.Graph. - edge_attrs: Callable to construct later edge_attributes. It receives 3 variables (s, p, o) and should construct a dictionary that is passed to networkx's add_edge(s, o, \*\*attrs) function. By default this will include setting the MultiDiGraph key=p here. If you don't want to be able to re-identify the edge later on, you can set this to `lambda s, p, o: {}`. In this case MultiDiGraph's default (increasing ints) will be used. Returns: networkx.MultiDiGraph >>> from rdflib import Graph, URIRef, Literal >>> g = Graph() >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') >>> p, q = URIRef('p'), URIRef('q') >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] >>> for t in edges: ... g.add(t) ... >>> mdg = rdflib_to_networkx_multidigraph(g) >>> len(mdg.edges()) 4 >>> mdg.has_edge(a, b) True >>> mdg.has_edge(a, b, key=p) True >>> mdg.has_edge(a, b, key=q) True >>> mdg = rdflib_to_networkx_multidigraph(g, edge_attrs=lambda s,p,o: {}) >>> mdg.has_edge(a, b, key=0) True >>> mdg.has_edge(a, b, key=1) True rNF)rrr")rrkwdsrmdgs rrdflib_to_networkx_multidigraphr*Hs,^ // CeS%DtD JrTcd|||fgiSNr rr%s rr&r& Q1I;7rc Lddl}|j}t||||fi||S)aConverts the given graph into a networkx.DiGraph. As an rdflib.Graph() can contain multiple edges between nodes, by default adds the a 'triples' attribute to the single DiGraph edge with a list of all triples between s and o. Also by default calculates the edge weight as the length of triples. :Parameters: - `graph`: a rdflib.Graph. - `calc_weights`: If true calculate multi-graph edge-count as edge 'weight' - `edge_attrs`: Callable to construct later edge_attributes. It receives 3 variables (s, p, o) and should construct a dictionary that is passed to networkx's add_edge(s, o, \*\*attrs) function. By default this will include setting the 'triples' attribute here, which is treated specially by us to be merged. Other attributes of multi-edges will only contain the attributes of the first edge. If you don't want the 'triples' attribute for tracking, set this to `lambda s, p, o: {}`. Returns: networkx.DiGraph >>> from rdflib import Graph, URIRef, Literal >>> g = Graph() >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') >>> p, q = URIRef('p'), URIRef('q') >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] >>> for t in edges: ... g.add(t) ... >>> dg = rdflib_to_networkx_digraph(g) >>> dg[a][b]['weight'] 2 >>> sorted(dg[a][b]['triples']) == [(a, p, b), (a, q, b)] True >>> len(dg.edges()) 3 >>> dg.size() 3 >>> dg.size(weight='weight') 4.0 >>> dg = rdflib_to_networkx_graph(g, False, edge_attrs=lambda s,p,o:{}) >>> 'weight' in dg[a][b] False >>> 'triples' in dg[a][b] False rN)rDiGraphr")rrrr(rdgs rrdflib_to_networkx_digraphr1~s+p BeRzJTJ Ircd|||fgiSr,rr%s rr&r&r-rc Lddl}|j}t||||fi||S)aConverts the given graph into a networkx.Graph. As an rdflib.Graph() can contain multiple directed edges between nodes, by default adds the a 'triples' attribute to the single DiGraph edge with a list of triples between s and o in graph. Also by default calculates the edge weight as the len(triples). :Parameters: - graph: a rdflib.Graph. - calc_weights: If true calculate multi-graph edge-count as edge 'weight' - edge_attrs: Callable to construct later edge_attributes. It receives 3 variables (s, p, o) and should construct a dictionary that is passed to networkx's add_edge(s, o, \*\*attrs) function. By default this will include setting the 'triples' attribute here, which is treated specially by us to be merged. Other attributes of multi-edges will only contain the attributes of the first edge. If you don't want the 'triples' attribute for tracking, set this to `lambda s, p, o: {}`. Returns: networkx.Graph >>> from rdflib import Graph, URIRef, Literal >>> g = Graph() >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') >>> p, q = URIRef('p'), URIRef('q') >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] >>> for t in edges: ... g.add(t) ... >>> ug = rdflib_to_networkx_graph(g) >>> ug[a][b]['weight'] 3 >>> sorted(ug[a][b]['triples']) == [(a, p, b), (a, q, b), (b, p, a)] True >>> len(ug.edges()) 2 >>> ug.size() 2 >>> ug.size(weight='weight') 4.0 >>> ug = rdflib_to_networkx_graph(g, False, edge_attrs=lambda s,p,o:{}) >>> 'weight' in ug[a][b] False >>> 'triples' in ug[a][b] False rN)rGraphr")rrrr(rgs rrdflib_to_networkx_graphr6s+p  AeQ jIDI Hrtermctd|iSNr7strr%s rr&r&Va 0rctd|iSr9r:r%s rr&r&r<rctd|iSr9r:r%s rr&r&r<rcddl}|j}|Dcgc]}||jdf} }| D]\}} | |j|<|D cgc]} | |j df} } | D]\} } | |j | <i}|D]\}}}|j |}|3|j}|||<||||}| D] \}} ||| |<|}|j |}|3|j}|||<||||}| D] \}} ||| |<|}|j||}||||}| D] \} } || | |<|Scc}wcc} w)aConverts the given graph into a graph_tool.Graph(). The subjects and objects are the later vertices of the Graph. The predicates become edges. :Parameters: - graph: a rdflib.Graph. - v_prop_names: a list of names for the vertex properties. The default is set to ['term'] (see transform_s, transform_o below). - e_prop_names: a list of names for the edge properties. - transform_s: callable with s, p, o input. Should return a dictionary containing a value for each name in v_prop_names. By default is set to {'term': s} which in combination with v_prop_names = ['term'] adds s as 'term' property to the generated vertex for s. - transform_p: similar to transform_s, but wrt. e_prop_names. By default returns {'term': p} which adds p as a property to the generated edge between the vertex for s and the vertex for o. - transform_o: similar to transform_s. Returns: graph_tool.Graph() >>> from rdflib import Graph, URIRef, Literal >>> g = Graph() >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') >>> p, q = URIRef('p'), URIRef('q') >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] >>> for t in edges: ... g.add(t) ... >>> mdg = rdflib_to_graphtool(g) >>> len(list(mdg.edges())) 4 >>> from graph_tool import util as gt_util >>> vpterm = mdg.vertex_properties['term'] >>> va = gt_util.find_vertex(mdg, vpterm, a)[0] >>> vb = gt_util.find_vertex(mdg, vpterm, b)[0] >>> vl = gt_util.find_vertex(mdg, vpterm, l)[0] >>> (va, vb) in [(e.source(), e.target()) for e in list(mdg.edges())] True >>> epterm = mdg.edge_properties['term'] >>> len(list(gt_util.find_edge(mdg, epterm, p))) == 3 True >>> len(list(gt_util.find_edge(mdg, epterm, q))) == 1 True >>> mdg = rdflib_to_graphtool( ... g, ... e_prop_names=[str('name')], ... transform_p=lambda s, p, o: {str('name'): unicode(p)}) >>> epterm = mdg.edge_properties['name'] >>> len(list(gt_util.find_edge(mdg, epterm, unicode(p)))) == 3 True >>> len(list(gt_util.find_edge(mdg, epterm, unicode(q)))) == 1 True rNobject) graph_toolr4new_vertex_propertyvertex_propertiesnew_edge_propertyedge_propertiesget add_vertexr)r v_prop_names e_prop_namesr transform_prgtr5vpnvpropsvpropepnepropsepropnode_to_vertexrrrsvv tmp_propsoves rrdflib_to_graphtoolrXs@  A@L MsA))(34 MF M) U#(C )>J KssA''12 KF K' U!&#'N&1a    " : A !N1 #Aq!,I$ * U$S>a *B    " : A !N1 #Aq!,I$ * U$S>a *B JJr2 1a(   &JC ~E!H &+&. H=NLs EE) __doc__logging getLogger__name__loggerrr"r*r1r6r;rXrrrr^s~    8 $  -5b13p7<B7< Bf+f+000 b r