"""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
"""
# Copyright (C) 2006-2013 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
import warnings
import networkx as nx
__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
'Pieter Swart (swart@lanl.gov)',
'Dan Schult(dschult@colgate.edu)'])
__all__ = ['to_networkx_graph',
'from_dict_of_dicts', 'to_dict_of_dicts',
'from_dict_of_lists', 'to_dict_of_lists',
'from_edgelist', 'to_edgelist']
[docs]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
list of edges
Pandas DataFrame (row per edge)
numpy matrix
numpy ndarray
scipy sparse matrix
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())
if hasattr(data, 'graph'): # data.graph should be dict-like
result.graph.update(data.graph)
if hasattr(data, 'nodes'): # data.nodes should be dict-like
result._node.update((n, dd.copy()) for n, dd in data.nodes.items())
return result
except:
raise nx.NetworkXError("Input is not a correct NetworkX graph.")
# pygraphviz agraph
if hasattr(data, "is_strict"):
try:
return nx.nx_agraph.from_agraph(data, create_using=create_using)
except:
raise nx.NetworkXError("Input is not a correct pygraphviz graph.")
# dict of dicts/lists
if isinstance(data, dict):
try:
return from_dict_of_dicts(data, create_using=create_using,
multigraph_input=multigraph_input)
except:
try:
return from_dict_of_lists(data, create_using=create_using)
except:
raise TypeError("Input is not known type.")
# list or generator of edges
if (isinstance(data, (list, tuple)) or
any(hasattr(data, attr) for attr in ['_adjdict', 'next', '__next__'])):
try:
return from_edgelist(data, create_using=create_using)
except:
raise nx.NetworkXError("Input is not a valid edge list")
# 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:
msg = "Input is not a correct Pandas DataFrame adjacency matrix."
raise nx.NetworkXError(msg)
else:
try:
return nx.from_pandas_edgelist(data, edge_attr=True, create_using=create_using)
except:
msg = "Input is not a correct Pandas DataFrame edge-list."
raise nx.NetworkXError(msg)
except ImportError:
msg = 'pandas not found, skipping conversion test.'
warnings.warn(msg, ImportWarning)
# numpy matrix or ndarray
try:
import numpy
if isinstance(data, (numpy.matrix, numpy.ndarray)):
try:
return nx.from_numpy_matrix(data, create_using=create_using)
except:
raise nx.NetworkXError(
"Input is not a correct numpy matrix or array.")
except ImportError:
warnings.warn('numpy not found, skipping conversion test.',
ImportWarning)
# scipy sparse matrix - any format
try:
import scipy
if hasattr(data, "format"):
try:
return nx.from_scipy_sparse_matrix(data, create_using=create_using)
except:
raise nx.NetworkXError(
"Input is not a correct scipy sparse matrix type.")
except ImportError:
warnings.warn('scipy not found, skipping conversion test.',
ImportWarning)
raise nx.NetworkXError(
"Input is not a known data type for conversion.")
[docs]def to_dict_of_lists(G, nodelist=None):
"""Return 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
[docs]def from_dict_of_lists(d, create_using=None):
"""Return 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
[docs]def to_dict_of_dicts(G, nodelist=None, edge_data=None):
"""Return 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 : list, optional
If provided, the value of the dictionary will be
set to edge_data for all edges. This is useful to make
an adjacency matrix type representation with 1 as the edge data.
If edgedata is None, the edgedata in G is used to fill the values.
If G is a multigraph, the edgedata is a dict for each pair (u,v).
"""
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
[docs]def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
"""Return 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 values of the inner dict are assumed
to be containers of edge data for multiple edges.
Otherwise this routine assumes the edge data are singletons.
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)
# is dict a MultiGraph or MultiDiGraph?
if multigraph_input:
# make a copy of the list of edge data (but not the edge data)
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
[docs]def to_edgelist(G, nodelist=None):
"""Return 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)
[docs]def from_edgelist(edgelist, create_using=None):
"""Return 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