495 lines
16 KiB
Python
495 lines
16 KiB
Python
"""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
|
|
"""
|
|
import warnings
|
|
from collections.abc import Collection, Generator, Iterator
|
|
|
|
import networkx as nx
|
|
|
|
__all__ = [
|
|
"to_networkx_graph",
|
|
"from_dict_of_dicts",
|
|
"to_dict_of_dicts",
|
|
"from_dict_of_lists",
|
|
"to_dict_of_lists",
|
|
"from_edgelist",
|
|
"to_edgelist",
|
|
]
|
|
|
|
|
|
def to_networkx_graph(data, create_using=None, multigraph_input=False):
|
|
"""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.
|
|
|
|
"""
|
|
# NX graph
|
|
if hasattr(data, "adj"):
|
|
try:
|
|
result = from_dict_of_dicts(
|
|
data.adj,
|
|
create_using=create_using,
|
|
multigraph_input=data.is_multigraph(),
|
|
)
|
|
# data.graph should be dict-like
|
|
result.graph.update(data.graph)
|
|
# data.nodes should be dict-like
|
|
# result.add_node_from(data.nodes.items()) possible but
|
|
# for custom node_attr_dict_factory which may be hashable
|
|
# will be unexpected behavior
|
|
for n, dd in data.nodes.items():
|
|
result._node[n].update(dd)
|
|
return result
|
|
except Exception as err:
|
|
raise nx.NetworkXError("Input is not a correct NetworkX graph.") from err
|
|
|
|
# pygraphviz agraph
|
|
if hasattr(data, "is_strict"):
|
|
try:
|
|
return nx.nx_agraph.from_agraph(data, create_using=create_using)
|
|
except Exception as err:
|
|
raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from err
|
|
|
|
# dict of dicts/lists
|
|
if isinstance(data, dict):
|
|
try:
|
|
return from_dict_of_dicts(
|
|
data, create_using=create_using, multigraph_input=multigraph_input
|
|
)
|
|
except Exception as err1:
|
|
if multigraph_input is True:
|
|
raise nx.NetworkXError(
|
|
f"converting multigraph_input raised:\n{type(err1)}: {err1}"
|
|
)
|
|
try:
|
|
return from_dict_of_lists(data, create_using=create_using)
|
|
except Exception as err2:
|
|
raise TypeError("Input is not known type.") from err2
|
|
|
|
# Pandas DataFrame
|
|
try:
|
|
import pandas as pd
|
|
|
|
if isinstance(data, pd.DataFrame):
|
|
if data.shape[0] == data.shape[1]:
|
|
try:
|
|
return nx.from_pandas_adjacency(data, create_using=create_using)
|
|
except Exception as err:
|
|
msg = "Input is not a correct Pandas DataFrame adjacency matrix."
|
|
raise nx.NetworkXError(msg) from err
|
|
else:
|
|
try:
|
|
return nx.from_pandas_edgelist(
|
|
data, edge_attr=True, create_using=create_using
|
|
)
|
|
except Exception as err:
|
|
msg = "Input is not a correct Pandas DataFrame edge-list."
|
|
raise nx.NetworkXError(msg) from err
|
|
except ImportError:
|
|
warnings.warn("pandas not found, skipping conversion test.", ImportWarning)
|
|
|
|
# numpy array
|
|
try:
|
|
import numpy as np
|
|
|
|
if isinstance(data, np.ndarray):
|
|
try:
|
|
return nx.from_numpy_array(data, create_using=create_using)
|
|
except Exception as err:
|
|
raise nx.NetworkXError(
|
|
f"Failed to interpret array as an adjacency matrix."
|
|
) from err
|
|
except ImportError:
|
|
warnings.warn("numpy not found, skipping conversion test.", ImportWarning)
|
|
|
|
# scipy sparse array - any format
|
|
try:
|
|
import scipy
|
|
|
|
if hasattr(data, "format"):
|
|
try:
|
|
return nx.from_scipy_sparse_array(data, create_using=create_using)
|
|
except Exception as err:
|
|
raise nx.NetworkXError(
|
|
"Input is not a correct scipy sparse array type."
|
|
) from err
|
|
except ImportError:
|
|
warnings.warn("scipy not found, skipping conversion test.", ImportWarning)
|
|
|
|
# Note: most general check - should remain last in order of execution
|
|
# Includes containers (e.g. list, set, dict, etc.), generators, and
|
|
# iterators (e.g. itertools.chain) of edges
|
|
|
|
if isinstance(data, Collection | Generator | Iterator):
|
|
try:
|
|
return from_edgelist(data, create_using=create_using)
|
|
except Exception as err:
|
|
raise nx.NetworkXError("Input is not a valid edge list") from err
|
|
|
|
raise nx.NetworkXError("Input is not a known data type for conversion.")
|
|
|
|
|
|
@nx._dispatchable
|
|
def to_dict_of_lists(G, nodelist=None):
|
|
"""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.
|
|
|
|
"""
|
|
if nodelist is None:
|
|
nodelist = G
|
|
|
|
d = {}
|
|
for n in nodelist:
|
|
d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
|
|
return d
|
|
|
|
|
|
@nx._dispatchable(graphs=None, returns_graph=True)
|
|
def from_dict_of_lists(d, create_using=None):
|
|
"""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
|
|
|
|
"""
|
|
G = nx.empty_graph(0, create_using)
|
|
G.add_nodes_from(d)
|
|
if G.is_multigraph() and not G.is_directed():
|
|
# a dict_of_lists can't show multiedges. BUT for undirected graphs,
|
|
# each edge shows up twice in the dict_of_lists.
|
|
# So we need to treat this case separately.
|
|
seen = {}
|
|
for node, nbrlist in d.items():
|
|
for nbr in nbrlist:
|
|
if nbr not in seen:
|
|
G.add_edge(node, nbr)
|
|
seen[node] = 1 # don't allow reverse edge to show up
|
|
else:
|
|
G.add_edges_from(
|
|
((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
|
|
)
|
|
return G
|
|
|
|
|
|
def to_dict_of_dicts(G, nodelist=None, edge_data=None):
|
|
"""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}}
|
|
"""
|
|
dod = {}
|
|
if nodelist is None:
|
|
if edge_data is None:
|
|
for u, nbrdict in G.adjacency():
|
|
dod[u] = nbrdict.copy()
|
|
else: # edge_data is not None
|
|
for u, nbrdict in G.adjacency():
|
|
dod[u] = dod.fromkeys(nbrdict, edge_data)
|
|
else: # nodelist is not None
|
|
if edge_data is None:
|
|
for u in nodelist:
|
|
dod[u] = {}
|
|
for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
|
|
dod[u][v] = data
|
|
else: # nodelist and edge_data are not None
|
|
for u in nodelist:
|
|
dod[u] = {}
|
|
for v in (v for v in G[u] if v in nodelist):
|
|
dod[u][v] = edge_data
|
|
return dod
|
|
|
|
|
|
@nx._dispatchable(graphs=None, returns_graph=True)
|
|
def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
|
|
"""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
|
|
|
|
"""
|
|
G = nx.empty_graph(0, create_using)
|
|
G.add_nodes_from(d)
|
|
# does dict d represent a MultiGraph or MultiDiGraph?
|
|
if multigraph_input:
|
|
if G.is_directed():
|
|
if G.is_multigraph():
|
|
G.add_edges_from(
|
|
(u, v, key, data)
|
|
for u, nbrs in d.items()
|
|
for v, datadict in nbrs.items()
|
|
for key, data in datadict.items()
|
|
)
|
|
else:
|
|
G.add_edges_from(
|
|
(u, v, data)
|
|
for u, nbrs in d.items()
|
|
for v, datadict in nbrs.items()
|
|
for key, data in datadict.items()
|
|
)
|
|
else: # Undirected
|
|
if G.is_multigraph():
|
|
seen = set() # don't add both directions of undirected graph
|
|
for u, nbrs in d.items():
|
|
for v, datadict in nbrs.items():
|
|
if (u, v) not in seen:
|
|
G.add_edges_from(
|
|
(u, v, key, data) for key, data in datadict.items()
|
|
)
|
|
seen.add((v, u))
|
|
else:
|
|
seen = set() # don't add both directions of undirected graph
|
|
for u, nbrs in d.items():
|
|
for v, datadict in nbrs.items():
|
|
if (u, v) not in seen:
|
|
G.add_edges_from(
|
|
(u, v, data) for key, data in datadict.items()
|
|
)
|
|
seen.add((v, u))
|
|
|
|
else: # not a multigraph to multigraph transfer
|
|
if G.is_multigraph() and not G.is_directed():
|
|
# d can have both representations u-v, v-u in dict. Only add one.
|
|
# We don't need this check for digraphs since we add both directions,
|
|
# or for Graph() since it is done implicitly (parallel edges not allowed)
|
|
seen = set()
|
|
for u, nbrs in d.items():
|
|
for v, data in nbrs.items():
|
|
if (u, v) not in seen:
|
|
G.add_edge(u, v, key=0)
|
|
G[u][v][0].update(data)
|
|
seen.add((v, u))
|
|
else:
|
|
G.add_edges_from(
|
|
((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
|
|
)
|
|
return G
|
|
|
|
|
|
@nx._dispatchable(preserve_edge_attrs=True)
|
|
def to_edgelist(G, nodelist=None):
|
|
"""Returns a list of edges in the graph.
|
|
|
|
Parameters
|
|
----------
|
|
G : graph
|
|
A NetworkX graph
|
|
|
|
nodelist : list
|
|
Use only nodes specified in nodelist
|
|
|
|
"""
|
|
if nodelist is None:
|
|
return G.edges(data=True)
|
|
return G.edges(nodelist, data=True)
|
|
|
|
|
|
@nx._dispatchable(graphs=None, returns_graph=True)
|
|
def from_edgelist(edgelist, create_using=None):
|
|
"""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
|
|
|
|
"""
|
|
G = nx.empty_graph(0, create_using)
|
|
G.add_edges_from(edgelist)
|
|
return G
|