Spaces:
Running
Running
File size: 15,977 Bytes
b200bda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 |
"""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._dispatch
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._dispatch(graphs=None)
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._dispatch(graphs=None)
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._dispatch(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._dispatch(graphs=None)
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
|