o ¹iý.ã@sÌddlmZ ddlZddlmZmZmZmZerddlm Z e  e ¡Z dd„Z e e fd!d d „Zd d„fd"dd„Zddd„fd!dd„Zddd„fd!dd„Zedƒgedƒgdd„dd„dd„fd#dd „ZdS)$é)Ú annotationsN)Ú TYPE_CHECKINGÚAnyÚDictÚList)ÚGraphcCs|S)N©)Úxrrú\/var/www/edux/Edux_v2/venv/lib/python3.10/site-packages/rdflib/extras/external_graph_libs.pyÚ _identitysr ÚgraphrÚ calc_weightsÚboolcCsÚt|ƒsJ‚t|ƒs J‚t|ƒsJ‚ddl}|D]R\}}} ||ƒ|| ƒ} } | | | ¡} | dus6t||jƒrM|||| ƒ} |rBd| d<|j| | fi| ¤Žq|rW| dd7<d| vrj|||| ƒ} | d | d¡qdS)aèHelper 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. rNéÚweightÚtriples)ÚcallableÚnetworkxÚ get_edge_dataÚ isinstanceÚ MultiDiGraphÚadd_edgeÚextend)r Únxgraphr Ú edge_attrsÚ transform_sÚ transform_oÚnxÚsÚpÚoÚtsÚtoÚdataÚdrrr Ú_rdflib_to_networkx_graphs&      €ñr%cCsd|iS)NÚkeyr©rrr rrr ÚNsr(cKs*ddl}| ¡}t||d|fi|¤Ž|S)a±Converts 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%)r rÚkwdsrÚmdgrrr Úrdflib_to_networkx_multidigraphMs/r+TcCód|||fgiS©Nrrr'rrr r(†ócKó*ddl}| ¡}t||||fi|¤Ž|S)aÕConverts 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)rÚDiGraphr%)r r rr)rÚdgrrr Úrdflib_to_networkx_digraphƒó8r2cCr,r-rr'rrr r(År.cKr/)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)rrr%)r r rr)rÚgrrr Úrdflib_to_networkx_graphÂr3r5ÚtermcCs tdƒ|iS©Nr6©Ústrr'rrr r(ó cCs tdƒ|iSr7r8r'rrr r(r:cCs tdƒ|iSr7r8r'rrr r(r:Ú v_prop_namesú List[str]Ú e_prop_namescsFddl}| ¡‰‡fdd„|Dƒ}|D] \}} | ˆj|<q‡fdd„|Dƒ} | D] \} } | ˆj| <q(i} |D]j\}}}|  |¡}|duraˆ ¡}|| |<||||ƒ}|D] \}} ||| |<qT|}|  |¡}|dur‡ˆ ¡}|| |<||||ƒ}|D] \}} ||| |<qz|}ˆ ||¡}||||ƒ}| D] \} } || | |<q•q6ˆS)a¾Converts 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 rNcóg|] }|ˆ d¡f‘qS©Úobject)Únew_vertex_property)Ú.0Úvpn©r4rr Ú Fóz'rdflib_to_graphtool..cr>r?)Únew_edge_property)rBÚepnrDrr rEIrF)Ú graph_toolrÚvertex_propertiesÚedge_propertiesÚgetÚ add_vertexr)r r;r=rÚ transform_prÚgtÚvpropsrCÚvpropÚepropsrHÚepropÚnode_to_vertexrrr ÚsvÚvÚ tmp_propsÚovÚerrDr Úrdflib_to_graphtools@A             ÿrZ)r rr r)r r)r rr;r<r=r<)Ú __future__rÚloggingÚtypingrrrrÚ rdflib.graphrÚ getLoggerÚ__name__Úloggerr r%r+r2r5r9rZrrrr Ús2      ú1 ÿ8 ýA ýAú