h>6SrSSKrSSKJrJrJr SSKr/SQrSSjr \RSSj5r \R"SSS9SS j5r SS jr \R"SSS9SS j5r\R"SS 9SS j5r\R"SSS9SSj5rg)aFunctions to convert NetworkX graphs to and from other formats. The preferred way of converting data to a NetworkX graph is through the graph constructor. The constructor calls the to_networkx_graph() function which attempts to guess the input type and convert it automatically. Examples -------- Create a graph with a single edge from a dictionary of dictionaries >>> d = {0: {1: 1}} # dict-of-dicts single edge (0,1) >>> G = nx.Graph(d) See Also -------- nx_agraph, nx_pydot N) Collection GeneratorIterator)to_networkx_graphfrom_dict_of_dictsto_dict_of_dictsfrom_dict_of_liststo_dict_of_lists from_edgelist to_edgelistc[US5(a[URUUR5S9nURR UR5 UR R5H#upEURUR U5 M% U$[U[5(a [XUS9$[U["[$-[R&R(-[*-5(a [-XS9$[US 5(a[R.R1XS9$S SKn [X R45(aLUR6S UR6S :Xa[R8"XS9$[R:"USUS9$S SKn [X R@5(a[RB"XS9$S SK"n [US5(a[RF"XS9$[U[H[J-[*-5(a [-XS9$[R"S5e![an[R"S5UeSnAff=f![a\nUSLa%[R"S[U5SU35e[XS9sSnA$![an[!S 5UeSnAff=fSnAff=f! GN=f![an[R"S 5UeSnAff=f![anSn [R"U 5UeSnAff=f![anSn [R"U 5UeSnAff=f![<a GNf=f![an[R"S5UeSnAff=f![<a GNf=f![an[R"S5UeSnAff=f![<a GNf=f![an[R"S5UeSnAff=f)aMake a NetworkX graph from a known data structure. The preferred way to call this is automatically from the class constructor >>> d = {0: {1: {"weight": 1}}} # dict-of-dicts single edge (0,1) >>> G = nx.Graph(d) instead of the equivalent >>> G = nx.from_dict_of_dicts(d) Parameters ---------- data : object to be converted Current known types are: any NetworkX graph dict-of-dicts dict-of-lists container (e.g. set, list, tuple) of edges iterator (e.g. itertools.chain) that produces edges generator of edges Pandas DataFrame (row per edge) 2D numpy array scipy sparse array pygraphviz agraph create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. multigraph_input : bool (default False) If True and data is a dict_of_dicts, try to create a multigraph assuming dict_of_dict_of_lists. If data and create_using are both multigraphs then create a multigraph from a multigraph. adj) create_usingmultigraph_inputz&Input is not a correct NetworkX graph.NTz$converting multigraph_input raised: z: )rzInput is not known type. is_strictz(Input is not a correct pygraphviz graph.rz9Input is not a correct Pandas DataFrame adjacency matrix.) edge_attrrz2Input is not a correct Pandas DataFrame edge-list.z1Failed to interpret array as an adjacency matrix.formatz/Input is not a correct scipy sparse array type.zInput is not a valid edge listz.Input is not a known data type for conversion.)&hasattrrr is_multigraphgraphupdatenodesitems_node Exceptionnx NetworkXError isinstancedicttyper TypeErrorlisttuple reportviews EdgeViewABCrr nx_agraph from_agraphpandas DataFrameshapefrom_pandas_adjacencyfrom_pandas_edgelist ImportErrornumpyndarrayfrom_numpy_arrayscipyfrom_scipy_sparse_arrayrr) datarrresultndderrerr1err2pdmsgnpr2s B/var/www/html/env/lib/python3.13/site-packages/networkx/convert.pyrr#sPtU V')!%!3!3!5F LL   + ))+ Q&&r*,M $ F%BR $u r~~'A'AAHLMM  A A t[!! X<<++D+L L   dLL ) )zz!} 1 -933DTT 922< *&   dJJ ' ' **4KK (   4 " " 11$RR #$ Y.9:: N A A  K LL{ V""#KLRU U V F4'&&;DJB I4 I.=KK6>M5L L3"NM/O-N)&O# I+I&&I+. K8*K#J1+K1 K ; KK  KKK L)LL L0L++L00M3 M=MMM M,+M,/ N9NNN N&%N&) O3O  OO O O # P -PP cUcUn0nUH.nURU5Vs/sH oDU;dM UPM snX#'M0 U$s snf)zReturns adjacency representation of graph as a dictionary of lists. Parameters ---------- G : graph A NetworkX graph nodelist : list Use only nodes specified in nodelist Notes ----- Completely ignores edge data for MultiGraph and MultiDiGraph. ) neighbors)Gnodelistdr6nbrs r>r r sK" A  {{1~A~~A HBs >>T)graphs returns_graphcz[R"SU5nURU5 UR5(aWUR 5(dB0nUR 5H*upEUHnXc;dM UR XF5 M SX4'M, U$URSUR 555 U$)aReturns a graph from a dictionary of lists. Parameters ---------- d : dictionary of lists A dictionary of lists adjacency representation. create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. Examples -------- >>> dol = {0: [1]} # single edge (0,1) >>> G = nx.from_dict_of_lists(dol) or >>> G = nx.Graph(dol) # use Graph constructor rrc3@# UHupUHo1U4v M M g7fN).0nodenbrlistrDs r> %from_dict_of_lists..s Ki]T7CC[7[is)r empty_graphadd_nodes_fromr is_directedradd_edgeadd_edges_from)rCrrAseenrLrMrDs r>r r s, q,'AQWWYMD?JJt)DJ ' H  Kaggi K  Hc^0nTc_Uc-UR5HupEUR5X4'M U$UR5HupEURXR5X4'M U$Uc;TH3n0X4'U4SjXR55H upgXsUU'M M5 U$TH#n0X4'U4SjX5H nX#UU'M M% U$)a Returns adjacency representation of graph as a dictionary of dictionaries. Parameters ---------- G : graph A NetworkX graph nodelist : list Use only nodes specified in nodelist edge_data : scalar, optional If provided, the value of the dictionary will be set to `edge_data` for all edges. Usual values could be `1` or `True`. If `edge_data` is `None` (the default), the edgedata in `G` is used, resulting in a dict-of-dict-of-dicts. If `G` is a MultiGraph, the result will be a dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize handling edge data. `edge_data` should *not* be a container. Returns ------- dod : dict A nested dictionary representation of `G`. Note that the level of nesting depends on the type of `G` and the value of `edge_data` (see Examples). See Also -------- from_dict_of_dicts, to_dict_of_lists Notes ----- For a more custom approach to handling edge data, try:: dod = { n: {nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()} for n, nbrdict in G.adj.items() } where `custom` returns the desired edge data for each edge between `n` and `nbr`, given existing edge data `dd`. Examples -------- >>> G = nx.path_graph(3) >>> nx.to_dict_of_dicts(G) {0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}} Edge data is preserved by default (``edge_data=None``), resulting in dict-of-dict-of-dicts where the innermost dictionary contains the edge data: >>> G = nx.Graph() >>> G.add_edges_from( ... [ ... (0, 1, {"weight": 1.0}), ... (1, 2, {"weight": 2.0}), ... (2, 0, {"weight": 1.0}), ... ] ... ) >>> d = nx.to_dict_of_dicts(G) >>> d # doctest: +SKIP {0: {1: {'weight': 1.0}, 2: {'weight': 1.0}}, 1: {0: {'weight': 1.0}, 2: {'weight': 2.0}}, 2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}} >>> d[1][2]["weight"] 2.0 If `edge_data` is not `None`, edge data in the original graph (if any) is replaced: >>> d = nx.to_dict_of_dicts(G, edge_data=1) >>> d {0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}} >>> d[1][2] 1 This also applies to MultiGraphs: edge data is preserved by default: >>> G = nx.MultiGraph() >>> G.add_edge(0, 1, key="a", weight=1.0) 'a' >>> G.add_edge(0, 1, key="b", weight=5.0) 'b' >>> d = nx.to_dict_of_dicts(G) >>> d # doctest: +SKIP {0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}, 1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}} >>> d[0][1]["b"]["weight"] 5.0 But multi edge data is lost if `edge_data` is not `None`: >>> d = nx.to_dict_of_dicts(G, edge_data=10) >>> d {0: {1: 10}, 1: {0: 10}} c3>># UHupUT;dM X4v M g7frIrJ)rKvr4rBs r>rN#to_dict_of_dicts..ksWgah  s  c36># UHoT;dM Uv M g7frIrJ)rKrYrBs r>rNrZps;T(]!!Ts  ) adjacencycopyfromkeysr)rArB edge_datadodunbrdictrYr4s ` r>rrsB C  kkm  , J kkm g9, J  WWGA $F1I X J ;QT;A )F1I< JrVcT^^ [R"SU5nURU5 U(GaUR5(afUR 5(a(UR SUR 555 U$UR SUR 555 U$UR 5(a[5nUR 5HgumnUR 5HMum nTT 4U;dMUR UU 4SjUR 555 URT T45 MO Mi U$[5nUR 5HgumnUR 5HMum nTT 4U;dMUR UU 4SjUR 555 URT T45 MO Mi U$UR 5(aUR5(d[5nUR 5HfumnUR 5HLum nTT 4U;a+URTT SS9 UTT SRU5 URT T45 MN Mh U$UR SUR 555 U$)aReturns a graph from a dictionary of dictionaries. Parameters ---------- d : dictionary of dictionaries A dictionary of dictionaries adjacency representation. create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. multigraph_input : bool (default False) When True, the dict `d` is assumed to be a dict-of-dict-of-dict-of-dict structure keyed by node to neighbor to edge keys to edge data for multi-edges. Otherwise this routine assumes dict-of-dict-of-dict keyed by node to neighbor to edge data. Examples -------- >>> dod = {0: {1: {"weight": 1}}} # single edge (0,1) >>> G = nx.from_dict_of_dicts(dod) or >>> G = nx.Graph(dod) # use Graph constructor rc3# UH?upUR5H%up4UR5H upVXXV4v M M' MA g7frIrrKranbrsrYdatadictkeyr4s r>rN%from_dict_of_dicts..sK!#,'+zz| %-^^%5 3%&6&'3&#,AA c3# UH?upUR5H%up4UR5H upVXU4v M M' MA g7frIrerfs r>rNrjsJ!#,'+zz| %-^^%5 4L&6!'3!#,rkc32># UH upTTX4v M g7frIrJrKrir4rarYs r>rNrjs-CSicAs 1CSc32># UH upTTU4v M g7frIrJrns r>rNrjs->NAt >Nro)ric3b# UH%upUR5H up4XU4v M M' g7frIre)rKrargrYr4s r>rNrjs&SY'!djjl71!lYs-/) rrPrQrRrrTrsetaddrSr) rCrrrArUrgrhr4rarYs @@r>rrusP: q,'AQ ==??    !#$779!b HU  !#$779!T HG  u wwyGAt'+zz| 8q6-,,-CK>>CS-!HHaV, (4 )B H3u wwyGAt'+zz| 8q6-,,->Fnn>N-!HHaV, (4 )0 H ??  Q]]__5D7794#zz|GAt1vT) 1aQ /!Q ))$/HHaV$ ,% H   SQWWYS  HrV)preserve_edge_attrscFUcURSS9$URUSS9$)zReturns a list of edges in the graph. Parameters ---------- G : graph A NetworkX graph nodelist : list Use only nodes specified in nodelist T)r4)edges)rArBs r>r r s.wwDw!! 778$7 ''rVcV[R"SU5nURU5 U$)aReturns a graph from a list of edges. Parameters ---------- edgelist : list or iterator Edge tuples create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. Examples -------- >>> edgelist = [(0, 1)] # single edge (0,1) >>> G = nx.from_edgelist(edgelist) or >>> G = nx.Graph(edgelist) # use Graph constructor r)rrPrT)edgelistrrAs r>r r s', q,'AX HrV)NFrI)NN)__doc__warningscollections.abcrrrnetworkxr__all__r _dispatchabler r rrr r rJrVr>rs$;; UMp  2T2% 3% PtnT2S 3S ld+(,("T2 3 rV