K i? 8 d Z ddlmZmZmZ ddlZg dZddZej dd Z
ej dd dd Z dd
Z ej dd dd Z
ej d dd
Z ej dd dd Zy)a Functions 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
)
Collection GeneratorIteratorN)to_networkx_graphfrom_dict_of_dictsto_dict_of_dictsfrom_dict_of_liststo_dict_of_lists
from_edgelist to_edgelistc t | d r t | j || j }|j j | j | j j D ]# \ }}|j | j | % |S t | t r t | || S t | t" t$ z t j& j( z t* z r t- | | S t | d
r" t j. j1 | | S d dl} t | | j4 rP| j6 d | j6 d
k( r t j8 | | S t j: | d| S d dl } t | | j@ r t jB | | S d dl"} t | d r t jF | | S t | tH tJ z t* z r t- | | S t j d # t $ r}t j d |d}~ww xY w# t $ r]}|du r$t j dt | d| t | | cY d}~S # t $ r}t! d |d}~ww xY wd}~ww xY w# Y xY w# t $ r}t j d |d}~ww xY w# t $ r }d}
t j |
|d}~ww xY w# t $ r }d}
t j |
|d}~ww xY w# t< $ r Y w xY w# t $ r}t j d |d}~ww xY w# t< $ r Y w xY w# t $ r}t j d |d}~ww xY w# t< $ r Y w xY w# t $ r}t j d |d}~ww xY w)a Make 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: )r zInput is not known type. is_strictz(Input is not a correct pygraphviz graph.r z9Input is not a correct Pandas DataFrame adjacency matrix.) edge_attrr z2Input 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.z Input is not a valid edge listz.Input is not a known data type for conversion.)&hasattrr r
is_multigraphgraphupdatenodesitems_node Exceptionnx
NetworkXError
isinstancedicttyper TypeErrorlisttuple reportviews EdgeViewABCr r nx_agraph from_agraphpandas DataFrameshapefrom_pandas_adjacencyfrom_pandas_edgelist ImportErrornumpyndarrayfrom_numpy_arrayscipyfrom_scipy_sparse_arrayr r )
datar r resultndderrerr1err2pdmsgnpsps
V/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/networkx/convert.pyr r " s P tU V' )!%!3!3!5F
LL
+
))+
+2 Q&&r*
+M
$ F%
B H: ,
I! 4 K
K 09M *K9 L" N 7M N9 !N O : I II ! K*(K J% K% J?. J::J??KK
K K6K11K69 L LL M " M+MMM MM M?$M::M??N NN N6N11N66N9 9 OO O-O((O-c x || }i }|D ]) }| j | D cg c] }||v s| c}||< + |S c c}w )a& Returns adjacency representation of graph as a dictionary of lists.
Parameters
----------
G : graph
A NetworkX graph
nodelist : list
Use only nodes specified in nodelist
Notes
-----
Completely ignores edge data for MultiGraph and MultiDiGraph.
) neighbors)Gnodelistdr6 nbrs r? r
r
sR "
A
B {{1~A A!B H Bs 77T)graphs
returns_graphc ^ t j d| }|j | |j rO|j s?i }| j D ]( \ }}|D ] }||vs|j
|| d||< * |S |j d | j D |S )a Returns a graph from a dictionary of lists.
Parameters
----------
d : dictionary of lists
A dictionary of lists adjacency representation.
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
Examples
--------
>>> dol = {0: [1]} # single edge (0,1)
>>> G = nx.from_dict_of_lists(dol)
or
>>> G = nx.Graph(dol) # use Graph constructor
r r c 3 8 K | ] \ }}|D ] }||f
y wN ).0nodenbrlistrE s r? z%from_dict_of_lists.. s$ K]T77 KCdC[ K[ Ks )r empty_graphadd_nodes_fromr is_directedr add_edgeadd_edges_from)rD r rB seenrM rN rE s r? r r s , q,'AQ WWY MD'
*d?JJtS)
* DJ
H
Kaggi K
H c i }^|-| j D ] \ }}|j ||< |S | j D ] \ }}|j || ||< |S |:D ]3 }i ||< fd| | j D D ]
\ }}||| |< 5 |S D ]" }i ||< fd| | D D ]
}||| |< $ |S )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. If None, all nodes in G.
edge_data : scalar, optional (default: the G edgedatadict for each edge)
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 as it will
be the same container for all the edges.
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}}
c 3 6 K | ] \ }}|v s ||f y wrJ rK )rL vr4 rC s r? rO z#to_dict_of_dicts..k s Wgah D Ws c 3 , K | ] }|v s|
y wrJ rK )rL rY rC s r? rO z#to_dict_of_dicts..p s ;Q(]!;s ) adjacencycopyfromkeysr )rB rC edge_datadodunbrdictrY r4 s ` r? r r s D
C kkm
(
7 A
( J kkm
:
7 gy9A
: J
%A W1 W %GAt $CF1I%
% J
*A;QqT; *A )CF1I*
* JrV c t j d| }|j | |r~|j r`|j r(|j d | j
D |S |j d | j
D |S |j rt }| j
D ]` \ }|j
D ]H \ } f|vs
|j fd|j
D |j f J b |S t }| j
D ]` \ }|j
D ]H \ } f|vs
|j fd|j
D |j f J b |S |j r|j st }| j
D ]d \ }|j
D ]L \ } f|vr.|j d | d j | |j f N f |S |j d | j
D |S )aO Returns 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
r c 3 K | ]= \ }}|j D ]% \ }}|j D ]
\ }}||||f ' ? y wrJ r rL r` nbrsrY datadictkeyr4 s r? rO z%from_dict_of_dicts.. sd ! 4'+zz|! $8%-^^%5 ! "T 3%!%!%!s AAc 3 K | ]< \ }}|j D ]$ \ }}|j D ] \ }}|||f & > y wrJ rd re s r? rO z%from_dict_of_dicts.. sa ! 4'+zz|! $8%-^^%5 ! "T 4L! ! !s AAc 3 0 K | ]
\ }}||f y wrJ rK rL rh r4 r` rY s r? rO z%from_dict_of_dicts.. s# - 6?c4AsD 1- s c 3 . K | ] \ }}|f y wrJ rK rk s r? rO z%from_dict_of_dicts.. s - 1:dAt - s )rh c 3 \ K | ]$ \ }}|j D ] \ }}|||f & y wrJ rd )rL r` rf rY r4 s r? rO z%from_dict_of_dicts.. s0 S'!TdjjlS71d!Q S Ss *,) r rP rQ rR r rT r setaddrS r )
rD r r rB rU rf rg r4 r` rY s
@@r? r r u sa : q,'AQ ==? !#$779! b
HU !#$779! T
HG u wwy -GAt'+zz| - 8 q6- , , - CK>>CS- !HHaV , --B
H3 u wwy -GAt'+zz| - 8 q6- , , - >Fnn>N- !HHaV , --0
H
?? Q]]_ 5D 779
%4 #zz| %GAt1vT)
1aQ
/!Q
))$/HHaV$ %
%
H
SQWWYS
HrV )preserve_edge_attrsc P || j d S | j |d S )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)rB rC s r? r r s. wwDw!! 778$7 ''rV c T t j d| }|j | |S )a Returns 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 )r rP rT )edgelistr rB s r? r r s' , q,'AX HrV )NFrJ )NN)__doc__collections.abcr r r networkxr __all__r
_dispatchabler
r r r r r rK rV r? rz s $ < ; UMp
2 T2%
3%
Pup T2S
3S
l d+( ,(" T2
3
rV