def test_graph(self):
g = nx.cycle_graph(10)
G = nx.Graph()
G.add_nodes_from(g)
G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
# Dict of dicts
dod = to_dict_of_dicts(G)
GG = from_dict_of_dicts(dod, create_using=nx.Graph)
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod, create_using=nx.Graph)
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.Graph(dod)
assert sorted(G.nodes()) == sorted(GI.nodes())
assert sorted(G.edges()) == sorted(GI.edges())
# Dict of lists
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.Graph)
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph)
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert_nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_edges_equal(enone, sorted(GI.edges(data=True)))
def test_graph(self):
g = nx.cycle_graph(10)
G = nx.Graph()
G.add_nodes_from(g)
G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
# Dict of dicts
dod = to_dict_of_dicts(G)
GG = from_dict_of_dicts(dod, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.Graph(dod)
assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_equal(sorted(G.edges()), sorted(GI.edges()))
# Dict of lists
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.Graph())
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert_nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_edges_equal(enone, sorted(GI.edges(data=True)))
def test_with_multiedges_self_loops(self):
G = cycle_graph(10)
XG = nx.Graph()
XG.add_nodes_from(G)
XG.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGM = nx.MultiGraph()
XGM.add_nodes_from(G)
XGM.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGM.add_edge(0, 1, weight=2) # multiedge
XGS = nx.Graph()
XGS.add_nodes_from(G)
XGS.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGS.add_edge(0, 0, weight=100) # self loop
# Dict of dicts
# with self loops, OK
dod = to_dict_of_dicts(XGS)
GG = from_dict_of_dicts(dod, create_using=nx.Graph)
assert_nodes_equal(XGS.nodes(), GG.nodes())
assert_edges_equal(XGS.edges(), GG.edges())
GW = to_networkx_graph(dod, create_using=nx.Graph)
assert_nodes_equal(XGS.nodes(), GW.nodes())
assert_edges_equal(XGS.edges(), GW.edges())
GI = nx.Graph(dod)
assert_nodes_equal(XGS.nodes(), GI.nodes())
assert_edges_equal(XGS.edges(), GI.edges())
# Dict of lists
# with self loops, OK
dol = to_dict_of_lists(XGS)
GG = from_dict_of_lists(dol, create_using=nx.Graph)
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in XGS.edges(data=True)]
assert_nodes_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
assert_edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph)
assert_nodes_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
assert_edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert_nodes_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
assert_edges_equal(enone, sorted(GI.edges(data=True)))
# Dict of dicts
# with multiedges, OK
dod = to_dict_of_dicts(XGM)
GG = from_dict_of_dicts(dod,
create_using=nx.MultiGraph,
multigraph_input=True)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod,
create_using=nx.MultiGraph,
multigraph_input=True)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GW.edges()))
GI = nx.MultiGraph(
dod) # convert can't tell whether to duplicate edges!
assert_nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
# assert_not_equal(sorted(XGM.edges()), sorted(GI.edges()))
assert not sorted(XGM.edges()) == sorted(GI.edges())
GE = from_dict_of_dicts(dod,
create_using=nx.MultiGraph,
multigraph_input=False)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
assert sorted(XGM.edges()) != sorted(GE.edges())
GI = nx.MultiGraph(XGM)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GI.edges()))
GM = nx.MultiGraph(G)
assert_nodes_equal(sorted(GM.nodes()), sorted(G.nodes()))
assert_edges_equal(sorted(GM.edges()), sorted(G.edges()))
# Dict of lists
# with multiedges, OK, but better write as DiGraph else you'll
# get double edges
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.MultiGraph)
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dol, create_using=nx.MultiGraph)
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.MultiGraph(dol)
assert_nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GI.edges()))
def test_with_multiedges_self_loops(self):
G = cycle_graph(10)
XG = nx.Graph()
XG.add_nodes_from(G)
XG.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGM = nx.MultiGraph()
XGM.add_nodes_from(G)
XGM.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGM.add_edge(0, 1, weight=2) # multiedge
XGS = nx.Graph()
XGS.add_nodes_from(G)
XGS.add_weighted_edges_from((u, v, u) for u, v in G.edges())
XGS.add_edge(0, 0, weight=100) # self loop
# Dict of dicts
# with self loops, OK
dod = to_dict_of_dicts(XGS)
GG = from_dict_of_dicts(dod, create_using=nx.Graph())
assert_nodes_equal(XGS.nodes(), GG.nodes())
assert_edges_equal(XGS.edges(), GG.edges())
GW = to_networkx_graph(dod, create_using=nx.Graph())
assert_nodes_equal(XGS.nodes(), GW.nodes())
assert_edges_equal(XGS.edges(), GW.edges())
GI = nx.Graph(dod)
assert_nodes_equal(XGS.nodes(), GI.nodes())
assert_edges_equal(XGS.edges(), GI.edges())
# Dict of lists
# with self loops, OK
dol = to_dict_of_lists(XGS)
GG = from_dict_of_lists(dol, create_using=nx.Graph())
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in XGS.edges(data=True)]
assert_nodes_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
assert_edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph())
assert_nodes_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
assert_edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert_nodes_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
assert_edges_equal(enone, sorted(GI.edges(data=True)))
# Dict of dicts
# with multiedges, OK
dod = to_dict_of_dicts(XGM)
GG = from_dict_of_dicts(dod, create_using=nx.MultiGraph(),
multigraph_input=True)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod, create_using=nx.MultiGraph(), multigraph_input=True)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GW.edges()))
GI = nx.MultiGraph(dod) # convert can't tell whether to duplicate edges!
assert_nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
#assert_not_equal(sorted(XGM.edges()), sorted(GI.edges()))
assert_false(sorted(XGM.edges()) == sorted(GI.edges()))
GE = from_dict_of_dicts(dod, create_using=nx.MultiGraph(),
multigraph_input=False)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
assert_not_equal(sorted(XGM.edges()), sorted(GE.edges()))
GI = nx.MultiGraph(XGM)
assert_nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
assert_edges_equal(sorted(XGM.edges()), sorted(GI.edges()))
GM = nx.MultiGraph(G)
assert_nodes_equal(sorted(GM.nodes()), sorted(G.nodes()))
assert_edges_equal(sorted(GM.edges()), sorted(G.edges()))
# Dict of lists
# with multiedges, OK, but better write as DiGraph else you'll
# get double edges
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.MultiGraph())
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dol, create_using=nx.MultiGraph())
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.MultiGraph(dol)
assert_nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GI.edges()))
def to_nx_graph(data, create_using=None, multigraph_input=False): # noqa: C901
"""Make a 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 (ie set, list, tuple, iterator) of edges
Pandas DataFrame (row per edge)
numpy matrix
numpy ndarray
scipy sparse matrix
create_using : nx 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.
"""
# networkx graph or graphscope.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.add_nodes_from(data.nodes.items())
return result
except Exception as e:
raise nx.NetworkXError(
"Input is not a correct NetworkX-like graph.") from e
# 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:
try:
return from_dict_of_lists(data, create_using=create_using)
except Exception as e:
raise TypeError("Input is not known type.") from e
# 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 Exception as e:
raise nx.NetworkXError("Input is not a valid edge list") from e
# 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 e:
msg = "Input is not a correct Pandas DataFrame adjacency matrix."
raise nx.NetworkXError(msg) from e
else:
try:
return nx.from_pandas_edgelist(data,
edge_attr=True,
create_using=create_using)
except Exception as e:
msg = "Input is not a correct Pandas DataFrame edge-list."
raise nx.NetworkXError(msg) from e
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 Exception as e:
raise nx.NetworkXError(
"Input is not a correct numpy matrix or array.") from e
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 Exception as e:
raise nx.NetworkXError(
"Input is not a correct scipy sparse matrix type.") from e
except ImportError:
warnings.warn("scipy not found, skipping conversion test.",
ImportWarning)
raise nx.NetworkXError("Input is not a known data type for conversion.")
def get_networkx_graph(self) -> nx.DiGraph:
"""make a nx graph from processed vertices/edges."""
return convert.from_dict_of_lists(self.adjacency)
