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30,152 | networkx.readwrite.graphml | add_node | Add a node to the graph. | def add_node(self, G, node_xml, graphml_keys, defaults):
"""Add a node to the graph."""
# warn on finding unsupported ports tag
ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
if ports is not None:
warnings.warn("GraphML port tag not supported.")
# find the node by id and cast it to the appropriate type
node_id = self.node_type(node_xml.get("id"))
# get data/attributes for node
data = self.decode_data_elements(graphml_keys, node_xml)
G.add_node(node_id, **data)
# get child nodes
if node_xml.attrib.get("yfiles.foldertype") == "group":
graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
self.make_graph(graph_xml, graphml_keys, defaults, G)
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|
30,153 | networkx.readwrite.graphml | construct_types | null | def construct_types(self):
types = [
(int, "integer"), # for Gephi GraphML bug
(str, "yfiles"),
(str, "string"),
(int, "int"),
(int, "long"),
(float, "float"),
(float, "double"),
(bool, "boolean"),
]
# These additions to types allow writing numpy types
try:
import numpy as np
except:
pass
else:
# prepend so that python types are created upon read (last entry wins)
types = [
(np.float64, "float"),
(np.float32, "float"),
(np.float16, "float"),
(np.int_, "int"),
(np.int8, "int"),
(np.int16, "int"),
(np.int32, "int"),
(np.int64, "int"),
(np.uint8, "int"),
(np.uint16, "int"),
(np.uint32, "int"),
(np.uint64, "int"),
(np.int_, "int"),
(np.intc, "int"),
(np.intp, "int"),
] + types
self.xml_type = dict(types)
self.python_type = dict(reversed(a) for a in types)
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|
30,154 | networkx.readwrite.graphml | decode_data_elements | Use the key information to decode the data XML if present. | def decode_data_elements(self, graphml_keys, obj_xml):
"""Use the key information to decode the data XML if present."""
data = {}
for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
key = data_element.get("key")
try:
data_name = graphml_keys[key]["name"]
data_type = graphml_keys[key]["type"]
except KeyError as err:
raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
text = data_element.text
# assume anything with subelements is a yfiles extension
if text is not None and len(list(data_element)) == 0:
if data_type == bool:
# Ignore cases.
# http://docs.oracle.com/javase/6/docs/api/java/lang/
# Boolean.html#parseBoolean%28java.lang.String%29
data[data_name] = self.convert_bool[text.lower()]
else:
data[data_name] = data_type(text)
elif len(list(data_element)) > 0:
# Assume yfiles as subelements, try to extract node_label
node_label = None
# set GenericNode's configuration as shape type
gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
if gn is not None:
data["shape_type"] = gn.get("configuration")
for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
geometry = data_element.find(f"{pref}Geometry")
if geometry is not None:
data["x"] = geometry.get("x")
data["y"] = geometry.get("y")
if node_label is None:
node_label = data_element.find(f"{pref}NodeLabel")
shape = data_element.find(f"{pref}Shape")
if shape is not None:
data["shape_type"] = shape.get("type")
if node_label is not None:
data["label"] = node_label.text
# check all the different types of edges available in yEd.
for edge_type in [
"PolyLineEdge",
"SplineEdge",
"QuadCurveEdge",
"BezierEdge",
"ArcEdge",
]:
pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
edge_label = data_element.find(f"{pref}EdgeLabel")
if edge_label is not None:
break
if edge_label is not None:
data["label"] = edge_label.text
elif text is None:
data[data_name] = ""
return data
| (self, graphml_keys, obj_xml) | [
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|
30,155 | networkx.readwrite.graphml | find_graphml_keys | Extracts all the keys and key defaults from the xml. | def find_graphml_keys(self, graph_element):
"""Extracts all the keys and key defaults from the xml."""
graphml_keys = {}
graphml_key_defaults = {}
for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
attr_id = k.get("id")
attr_type = k.get("attr.type")
attr_name = k.get("attr.name")
yfiles_type = k.get("yfiles.type")
if yfiles_type is not None:
attr_name = yfiles_type
attr_type = "yfiles"
if attr_type is None:
attr_type = "string"
warnings.warn(f"No key type for id {attr_id}. Using string")
if attr_name is None:
raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
graphml_keys[attr_id] = {
"name": attr_name,
"type": self.python_type[attr_type],
"for": k.get("for"),
}
# check for "default" sub-element of key element
default = k.find(f"{{{self.NS_GRAPHML}}}default")
if default is not None:
# Handle default values identically to data element values
python_type = graphml_keys[attr_id]["type"]
if python_type == bool:
graphml_key_defaults[attr_id] = self.convert_bool[
default.text.lower()
]
else:
graphml_key_defaults[attr_id] = python_type(default.text)
return graphml_keys, graphml_key_defaults
| (self, graph_element) | [
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|
30,156 | networkx.readwrite.graphml | get_xml_type | Wrapper around the xml_type dict that raises a more informative
exception message when a user attempts to use data of a type not
supported by GraphML. | def get_xml_type(self, key):
"""Wrapper around the xml_type dict that raises a more informative
exception message when a user attempts to use data of a type not
supported by GraphML."""
try:
return self.xml_type[key]
except KeyError as err:
raise TypeError(
f"GraphML does not support type {key} as data values."
) from err
| (self, key) | [
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|
30,157 | networkx.readwrite.graphml | make_graph | null | def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
# set default graph type
edgedefault = graph_xml.get("edgedefault", None)
if G is None:
if edgedefault == "directed":
G = nx.MultiDiGraph()
else:
G = nx.MultiGraph()
# set defaults for graph attributes
G.graph["node_default"] = {}
G.graph["edge_default"] = {}
for key_id, value in defaults.items():
key_for = graphml_keys[key_id]["for"]
name = graphml_keys[key_id]["name"]
python_type = graphml_keys[key_id]["type"]
if key_for == "node":
G.graph["node_default"].update({name: python_type(value)})
if key_for == "edge":
G.graph["edge_default"].update({name: python_type(value)})
# hyperedges are not supported
hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
if hyperedge is not None:
raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
# add nodes
for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
self.add_node(G, node_xml, graphml_keys, defaults)
# add edges
for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
self.add_edge(G, edge_xml, graphml_keys)
# add graph data
data = self.decode_data_elements(graphml_keys, graph_xml)
G.graph.update(data)
# switch to Graph or DiGraph if no parallel edges were found
if self.multigraph:
return G
G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
# add explicit edge "id" from file as attribute in NX graph.
nx.set_edge_attributes(G, values=self.edge_ids, name="id")
return G
| (self, graph_xml, graphml_keys, defaults, G=None) | [
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|
30,158 | networkx.readwrite.graphml | GraphMLWriter | null | class GraphMLWriter(GraphML):
def __init__(
self,
graph=None,
encoding="utf-8",
prettyprint=True,
infer_numeric_types=False,
named_key_ids=False,
edge_id_from_attribute=None,
):
self.construct_types()
from xml.etree.ElementTree import Element
self.myElement = Element
self.infer_numeric_types = infer_numeric_types
self.prettyprint = prettyprint
self.named_key_ids = named_key_ids
self.edge_id_from_attribute = edge_id_from_attribute
self.encoding = encoding
self.xml = self.myElement(
"graphml",
{
"xmlns": self.NS_GRAPHML,
"xmlns:xsi": self.NS_XSI,
"xsi:schemaLocation": self.SCHEMALOCATION,
},
)
self.keys = {}
self.attributes = defaultdict(list)
self.attribute_types = defaultdict(set)
if graph is not None:
self.add_graph_element(graph)
def __str__(self):
from xml.etree.ElementTree import tostring
if self.prettyprint:
self.indent(self.xml)
s = tostring(self.xml).decode(self.encoding)
return s
def attr_type(self, name, scope, value):
"""Infer the attribute type of data named name. Currently this only
supports inference of numeric types.
If self.infer_numeric_types is false, type is used. Otherwise, pick the
most general of types found across all values with name and scope. This
means edges with data named 'weight' are treated separately from nodes
with data named 'weight'.
"""
if self.infer_numeric_types:
types = self.attribute_types[(name, scope)]
if len(types) > 1:
types = {self.get_xml_type(t) for t in types}
if "string" in types:
return str
elif "float" in types or "double" in types:
return float
else:
return int
else:
return list(types)[0]
else:
return type(value)
def get_key(self, name, attr_type, scope, default):
keys_key = (name, attr_type, scope)
try:
return self.keys[keys_key]
except KeyError:
if self.named_key_ids:
new_id = name
else:
new_id = f"d{len(list(self.keys))}"
self.keys[keys_key] = new_id
key_kwargs = {
"id": new_id,
"for": scope,
"attr.name": name,
"attr.type": attr_type,
}
key_element = self.myElement("key", **key_kwargs)
# add subelement for data default value if present
if default is not None:
default_element = self.myElement("default")
default_element.text = str(default)
key_element.append(default_element)
self.xml.insert(0, key_element)
return new_id
def add_data(self, name, element_type, value, scope="all", default=None):
"""
Make a data element for an edge or a node. Keep a log of the
type in the keys table.
"""
if element_type not in self.xml_type:
raise nx.NetworkXError(
f"GraphML writer does not support {element_type} as data values."
)
keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
data_element = self.myElement("data", key=keyid)
data_element.text = str(value)
return data_element
def add_attributes(self, scope, xml_obj, data, default):
"""Appends attribute data to edges or nodes, and stores type information
to be added later. See add_graph_element.
"""
for k, v in data.items():
self.attribute_types[(str(k), scope)].add(type(v))
self.attributes[xml_obj].append([k, v, scope, default.get(k)])
def add_nodes(self, G, graph_element):
default = G.graph.get("node_default", {})
for node, data in G.nodes(data=True):
node_element = self.myElement("node", id=str(node))
self.add_attributes("node", node_element, data, default)
graph_element.append(node_element)
def add_edges(self, G, graph_element):
if G.is_multigraph():
for u, v, key, data in G.edges(data=True, keys=True):
edge_element = self.myElement(
"edge",
source=str(u),
target=str(v),
id=str(data.get(self.edge_id_from_attribute))
if self.edge_id_from_attribute
and self.edge_id_from_attribute in data
else str(key),
)
default = G.graph.get("edge_default", {})
self.add_attributes("edge", edge_element, data, default)
graph_element.append(edge_element)
else:
for u, v, data in G.edges(data=True):
if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
# select attribute to be edge id
edge_element = self.myElement(
"edge",
source=str(u),
target=str(v),
id=str(data.get(self.edge_id_from_attribute)),
)
else:
# default: no edge id
edge_element = self.myElement("edge", source=str(u), target=str(v))
default = G.graph.get("edge_default", {})
self.add_attributes("edge", edge_element, data, default)
graph_element.append(edge_element)
def add_graph_element(self, G):
"""
Serialize graph G in GraphML to the stream.
"""
if G.is_directed():
default_edge_type = "directed"
else:
default_edge_type = "undirected"
graphid = G.graph.pop("id", None)
if graphid is None:
graph_element = self.myElement("graph", edgedefault=default_edge_type)
else:
graph_element = self.myElement(
"graph", edgedefault=default_edge_type, id=graphid
)
default = {}
data = {
k: v
for (k, v) in G.graph.items()
if k not in ["node_default", "edge_default"]
}
self.add_attributes("graph", graph_element, data, default)
self.add_nodes(G, graph_element)
self.add_edges(G, graph_element)
# self.attributes contains a mapping from XML Objects to a list of
# data that needs to be added to them.
# We postpone processing in order to do type inference/generalization.
# See self.attr_type
for xml_obj, data in self.attributes.items():
for k, v, scope, default in data:
xml_obj.append(
self.add_data(
str(k), self.attr_type(k, scope, v), str(v), scope, default
)
)
self.xml.append(graph_element)
def add_graphs(self, graph_list):
"""Add many graphs to this GraphML document."""
for G in graph_list:
self.add_graph_element(G)
def dump(self, stream):
from xml.etree.ElementTree import ElementTree
if self.prettyprint:
self.indent(self.xml)
document = ElementTree(self.xml)
document.write(stream, encoding=self.encoding, xml_declaration=True)
def indent(self, elem, level=0):
# in-place prettyprint formatter
i = "\n" + level * " "
if len(elem):
if not elem.text or not elem.text.strip():
elem.text = i + " "
if not elem.tail or not elem.tail.strip():
elem.tail = i
for elem in elem:
self.indent(elem, level + 1)
if not elem.tail or not elem.tail.strip():
elem.tail = i
| (graph=None, encoding='utf-8', prettyprint=True, infer_numeric_types=False, named_key_ids=False, edge_id_from_attribute=None) | [
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|
30,159 | networkx.readwrite.graphml | __init__ | null | def __init__(
self,
graph=None,
encoding="utf-8",
prettyprint=True,
infer_numeric_types=False,
named_key_ids=False,
edge_id_from_attribute=None,
):
self.construct_types()
from xml.etree.ElementTree import Element
self.myElement = Element
self.infer_numeric_types = infer_numeric_types
self.prettyprint = prettyprint
self.named_key_ids = named_key_ids
self.edge_id_from_attribute = edge_id_from_attribute
self.encoding = encoding
self.xml = self.myElement(
"graphml",
{
"xmlns": self.NS_GRAPHML,
"xmlns:xsi": self.NS_XSI,
"xsi:schemaLocation": self.SCHEMALOCATION,
},
)
self.keys = {}
self.attributes = defaultdict(list)
self.attribute_types = defaultdict(set)
if graph is not None:
self.add_graph_element(graph)
| (self, graph=None, encoding='utf-8', prettyprint=True, infer_numeric_types=False, named_key_ids=False, edge_id_from_attribute=None) | [
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|
30,160 | networkx.readwrite.graphml | __str__ | null | def __str__(self):
from xml.etree.ElementTree import tostring
if self.prettyprint:
self.indent(self.xml)
s = tostring(self.xml).decode(self.encoding)
return s
| (self) | [
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|
30,161 | networkx.readwrite.graphml | add_attributes | Appends attribute data to edges or nodes, and stores type information
to be added later. See add_graph_element.
| def add_attributes(self, scope, xml_obj, data, default):
"""Appends attribute data to edges or nodes, and stores type information
to be added later. See add_graph_element.
"""
for k, v in data.items():
self.attribute_types[(str(k), scope)].add(type(v))
self.attributes[xml_obj].append([k, v, scope, default.get(k)])
| (self, scope, xml_obj, data, default) | [
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|
30,162 | networkx.readwrite.graphml | add_data |
Make a data element for an edge or a node. Keep a log of the
type in the keys table.
| def add_data(self, name, element_type, value, scope="all", default=None):
"""
Make a data element for an edge or a node. Keep a log of the
type in the keys table.
"""
if element_type not in self.xml_type:
raise nx.NetworkXError(
f"GraphML writer does not support {element_type} as data values."
)
keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
data_element = self.myElement("data", key=keyid)
data_element.text = str(value)
return data_element
| (self, name, element_type, value, scope='all', default=None) | [
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|
30,163 | networkx.readwrite.graphml | add_edges | null | def add_edges(self, G, graph_element):
if G.is_multigraph():
for u, v, key, data in G.edges(data=True, keys=True):
edge_element = self.myElement(
"edge",
source=str(u),
target=str(v),
id=str(data.get(self.edge_id_from_attribute))
if self.edge_id_from_attribute
and self.edge_id_from_attribute in data
else str(key),
)
default = G.graph.get("edge_default", {})
self.add_attributes("edge", edge_element, data, default)
graph_element.append(edge_element)
else:
for u, v, data in G.edges(data=True):
if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
# select attribute to be edge id
edge_element = self.myElement(
"edge",
source=str(u),
target=str(v),
id=str(data.get(self.edge_id_from_attribute)),
)
else:
# default: no edge id
edge_element = self.myElement("edge", source=str(u), target=str(v))
default = G.graph.get("edge_default", {})
self.add_attributes("edge", edge_element, data, default)
graph_element.append(edge_element)
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|
30,164 | networkx.readwrite.graphml | add_graph_element |
Serialize graph G in GraphML to the stream.
| def add_graph_element(self, G):
"""
Serialize graph G in GraphML to the stream.
"""
if G.is_directed():
default_edge_type = "directed"
else:
default_edge_type = "undirected"
graphid = G.graph.pop("id", None)
if graphid is None:
graph_element = self.myElement("graph", edgedefault=default_edge_type)
else:
graph_element = self.myElement(
"graph", edgedefault=default_edge_type, id=graphid
)
default = {}
data = {
k: v
for (k, v) in G.graph.items()
if k not in ["node_default", "edge_default"]
}
self.add_attributes("graph", graph_element, data, default)
self.add_nodes(G, graph_element)
self.add_edges(G, graph_element)
# self.attributes contains a mapping from XML Objects to a list of
# data that needs to be added to them.
# We postpone processing in order to do type inference/generalization.
# See self.attr_type
for xml_obj, data in self.attributes.items():
for k, v, scope, default in data:
xml_obj.append(
self.add_data(
str(k), self.attr_type(k, scope, v), str(v), scope, default
)
)
self.xml.append(graph_element)
| (self, G) | [
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|
30,165 | networkx.readwrite.graphml | add_graphs | Add many graphs to this GraphML document. | def add_graphs(self, graph_list):
"""Add many graphs to this GraphML document."""
for G in graph_list:
self.add_graph_element(G)
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|
30,166 | networkx.readwrite.graphml | add_nodes | null | def add_nodes(self, G, graph_element):
default = G.graph.get("node_default", {})
for node, data in G.nodes(data=True):
node_element = self.myElement("node", id=str(node))
self.add_attributes("node", node_element, data, default)
graph_element.append(node_element)
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|
30,167 | networkx.readwrite.graphml | attr_type | Infer the attribute type of data named name. Currently this only
supports inference of numeric types.
If self.infer_numeric_types is false, type is used. Otherwise, pick the
most general of types found across all values with name and scope. This
means edges with data named 'weight' are treated separately from nodes
with data named 'weight'.
| def attr_type(self, name, scope, value):
"""Infer the attribute type of data named name. Currently this only
supports inference of numeric types.
If self.infer_numeric_types is false, type is used. Otherwise, pick the
most general of types found across all values with name and scope. This
means edges with data named 'weight' are treated separately from nodes
with data named 'weight'.
"""
if self.infer_numeric_types:
types = self.attribute_types[(name, scope)]
if len(types) > 1:
types = {self.get_xml_type(t) for t in types}
if "string" in types:
return str
elif "float" in types or "double" in types:
return float
else:
return int
else:
return list(types)[0]
else:
return type(value)
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|
30,169 | networkx.readwrite.graphml | dump | null | def dump(self, stream):
from xml.etree.ElementTree import ElementTree
if self.prettyprint:
self.indent(self.xml)
document = ElementTree(self.xml)
document.write(stream, encoding=self.encoding, xml_declaration=True)
| (self, stream) | [
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|
30,170 | networkx.readwrite.graphml | get_key | null | def get_key(self, name, attr_type, scope, default):
keys_key = (name, attr_type, scope)
try:
return self.keys[keys_key]
except KeyError:
if self.named_key_ids:
new_id = name
else:
new_id = f"d{len(list(self.keys))}"
self.keys[keys_key] = new_id
key_kwargs = {
"id": new_id,
"for": scope,
"attr.name": name,
"attr.type": attr_type,
}
key_element = self.myElement("key", **key_kwargs)
# add subelement for data default value if present
if default is not None:
default_element = self.myElement("default")
default_element.text = str(default)
key_element.append(default_element)
self.xml.insert(0, key_element)
return new_id
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|
30,172 | networkx.readwrite.graphml | indent | null | def indent(self, elem, level=0):
# in-place prettyprint formatter
i = "\n" + level * " "
if len(elem):
if not elem.text or not elem.text.strip():
elem.text = i + " "
if not elem.tail or not elem.tail.strip():
elem.tail = i
for elem in elem:
self.indent(elem, level + 1)
if not elem.tail or not elem.tail.strip():
elem.tail = i
else:
if level and (not elem.tail or not elem.tail.strip()):
elem.tail = i
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]
|
30,173 | networkx.exception | HasACycle | Raised if a graph has a cycle when an algorithm expects that it
will have no cycles.
| class HasACycle(NetworkXException):
"""Raised if a graph has a cycle when an algorithm expects that it
will have no cycles.
"""
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|
30,174 | networkx.generators.small | LCF_graph |
Return the cubic graph specified in LCF notation.
LCF (Lederberg-Coxeter-Fruchte) notation[1]_ is a compressed
notation used in the generation of various cubic Hamiltonian
graphs of high symmetry. See, for example, `dodecahedral_graph`,
`desargues_graph`, `heawood_graph` and `pappus_graph`.
Nodes are drawn from ``range(n)``. Each node ``n_i`` is connected with
node ``n_i + shift % n`` where ``shift`` is given by cycling through
the input `shift_list` `repeat` s times.
Parameters
----------
n : int
The starting graph is the `n`-cycle with nodes ``0, ..., n-1``.
The null graph is returned if `n` < 1.
shift_list : list
A list of integer shifts mod `n`, ``[s1, s2, .., sk]``
repeats : int
Integer specifying the number of times that shifts in `shift_list`
are successively applied to each current node in the n-cycle
to generate an edge between ``n_current`` and ``n_current + shift mod n``.
Returns
-------
G : Graph
A graph instance created from the specified LCF notation.
Examples
--------
The utility graph $K_{3,3}$
>>> G = nx.LCF_graph(6, [3, -3], 3)
>>> G.edges()
EdgeView([(0, 1), (0, 5), (0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4), (4, 5)])
The Heawood graph:
>>> G = nx.LCF_graph(14, [5, -5], 7)
>>> nx.is_isomorphic(G, nx.heawood_graph())
True
References
----------
.. [1] https://en.wikipedia.org/wiki/LCF_notation
| def sedgewick_maze_graph(create_using=None):
"""
Return a small maze with a cycle.
This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph
Algorithms, Chapter 18, e.g. Figure 18.2 and following [1]_.
Nodes are numbered 0,..,7
Parameters
----------
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
Returns
-------
G : networkx Graph
Small maze with a cycle
References
----------
.. [1] Figure 18.2, Chapter 18, Graph Algorithms (3rd Ed), Sedgewick
"""
G = empty_graph(0, create_using)
G.add_nodes_from(range(8))
G.add_edges_from([[0, 2], [0, 7], [0, 5]])
G.add_edges_from([[1, 7], [2, 6]])
G.add_edges_from([[3, 4], [3, 5]])
G.add_edges_from([[4, 5], [4, 7], [4, 6]])
G.name = "Sedgewick Maze"
return G
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|
30,175 | networkx.generators.community | LFR_benchmark_graph | Returns the LFR benchmark graph.
This algorithm proceeds as follows:
1) Find a degree sequence with a power law distribution, and minimum
value ``min_degree``, which has approximate average degree
``average_degree``. This is accomplished by either
a) specifying ``min_degree`` and not ``average_degree``,
b) specifying ``average_degree`` and not ``min_degree``, in which
case a suitable minimum degree will be found.
``max_degree`` can also be specified, otherwise it will be set to
``n``. Each node *u* will have $\mu \mathrm{deg}(u)$ edges
joining it to nodes in communities other than its own and $(1 -
\mu) \mathrm{deg}(u)$ edges joining it to nodes in its own
community.
2) Generate community sizes according to a power law distribution
with exponent ``tau2``. If ``min_community`` and
``max_community`` are not specified they will be selected to be
``min_degree`` and ``max_degree``, respectively. Community sizes
are generated until the sum of their sizes equals ``n``.
3) Each node will be randomly assigned a community with the
condition that the community is large enough for the node's
intra-community degree, $(1 - \mu) \mathrm{deg}(u)$ as
described in step 2. If a community grows too large, a random node
will be selected for reassignment to a new community, until all
nodes have been assigned a community.
4) Each node *u* then adds $(1 - \mu) \mathrm{deg}(u)$
intra-community edges and $\mu \mathrm{deg}(u)$ inter-community
edges.
Parameters
----------
n : int
Number of nodes in the created graph.
tau1 : float
Power law exponent for the degree distribution of the created
graph. This value must be strictly greater than one.
tau2 : float
Power law exponent for the community size distribution in the
created graph. This value must be strictly greater than one.
mu : float
Fraction of inter-community edges incident to each node. This
value must be in the interval [0, 1].
average_degree : float
Desired average degree of nodes in the created graph. This value
must be in the interval [0, *n*]. Exactly one of this and
``min_degree`` must be specified, otherwise a
:exc:`NetworkXError` is raised.
min_degree : int
Minimum degree of nodes in the created graph. This value must be
in the interval [0, *n*]. Exactly one of this and
``average_degree`` must be specified, otherwise a
:exc:`NetworkXError` is raised.
max_degree : int
Maximum degree of nodes in the created graph. If not specified,
this is set to ``n``, the total number of nodes in the graph.
min_community : int
Minimum size of communities in the graph. If not specified, this
is set to ``min_degree``.
max_community : int
Maximum size of communities in the graph. If not specified, this
is set to ``n``, the total number of nodes in the graph.
tol : float
Tolerance when comparing floats, specifically when comparing
average degree values.
max_iters : int
Maximum number of iterations to try to create the community sizes,
degree distribution, and community affiliations.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Returns
-------
G : NetworkX graph
The LFR benchmark graph generated according to the specified
parameters.
Each node in the graph has a node attribute ``'community'`` that
stores the community (that is, the set of nodes) that includes
it.
Raises
------
NetworkXError
If any of the parameters do not meet their upper and lower bounds:
- ``tau1`` and ``tau2`` must be strictly greater than 1.
- ``mu`` must be in [0, 1].
- ``max_degree`` must be in {1, ..., *n*}.
- ``min_community`` and ``max_community`` must be in {0, ...,
*n*}.
If not exactly one of ``average_degree`` and ``min_degree`` is
specified.
If ``min_degree`` is not specified and a suitable ``min_degree``
cannot be found.
ExceededMaxIterations
If a valid degree sequence cannot be created within
``max_iters`` number of iterations.
If a valid set of community sizes cannot be created within
``max_iters`` number of iterations.
If a valid community assignment cannot be created within ``10 *
n * max_iters`` number of iterations.
Examples
--------
Basic usage::
>>> from networkx.generators.community import LFR_benchmark_graph
>>> n = 250
>>> tau1 = 3
>>> tau2 = 1.5
>>> mu = 0.1
>>> G = LFR_benchmark_graph(
... n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
... )
Continuing the example above, you can get the communities from the
node attributes of the graph::
>>> communities = {frozenset(G.nodes[v]["community"]) for v in G}
Notes
-----
This algorithm differs slightly from the original way it was
presented in [1].
1) Rather than connecting the graph via a configuration model then
rewiring to match the intra-community and inter-community
degrees, we do this wiring explicitly at the end, which should be
equivalent.
2) The code posted on the author's website [2] calculates the random
power law distributed variables and their average using
continuous approximations, whereas we use the discrete
distributions here as both degree and community size are
discrete.
Though the authors describe the algorithm as quite robust, testing
during development indicates that a somewhat narrower parameter set
is likely to successfully produce a graph. Some suggestions have
been provided in the event of exceptions.
References
----------
.. [1] "Benchmark graphs for testing community detection algorithms",
Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi,
Phys. Rev. E 78, 046110 2008
.. [2] https://www.santofortunato.net/resources
| def _generate_communities(degree_seq, community_sizes, mu, max_iters, seed):
"""Returns a list of sets, each of which represents a community.
``degree_seq`` is the degree sequence that must be met by the
graph.
``community_sizes`` is the community size distribution that must be
met by the generated list of sets.
``mu`` is a float in the interval [0, 1] indicating the fraction of
intra-community edges incident to each node.
``max_iters`` is the number of times to try to add a node to a
community. This must be greater than the length of
``degree_seq``, otherwise this function will always fail. If
the number of iterations exceeds this value,
:exc:`~networkx.exception.ExceededMaxIterations` is raised.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
The communities returned by this are sets of integers in the set {0,
..., *n* - 1}, where *n* is the length of ``degree_seq``.
"""
# This assumes the nodes in the graph will be natural numbers.
result = [set() for _ in community_sizes]
n = len(degree_seq)
free = list(range(n))
for i in range(max_iters):
v = free.pop()
c = seed.choice(range(len(community_sizes)))
# s = int(degree_seq[v] * (1 - mu) + 0.5)
s = round(degree_seq[v] * (1 - mu))
# If the community is large enough, add the node to the chosen
# community. Otherwise, return it to the list of unaffiliated
# nodes.
if s < community_sizes[c]:
result[c].add(v)
else:
free.append(v)
# If the community is too big, remove a node from it.
if len(result[c]) > community_sizes[c]:
free.append(result[c].pop())
if not free:
return result
msg = "Could not assign communities; try increasing min_community"
raise nx.ExceededMaxIterations(msg)
| (n, tau1, tau2, mu, average_degree=None, min_degree=None, max_degree=None, min_community=None, max_community=None, tol=1e-07, max_iters=500, seed=None, *, backend=None, **backend_kwargs) | [
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|
30,176 | networkx.classes.multidigraph | MultiDiGraph | A directed graph class that can store multiedges.
Multiedges are multiple edges between two nodes. Each edge
can hold optional data or attributes.
A MultiDiGraph holds directed edges. Self loops are allowed.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes. By convention `None` is not used as a node.
Edges are represented as links between nodes with optional
key/value attributes.
Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If None (default) an empty
graph is created. The data can be any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
sparse matrix, or PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
DiGraph
MultiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.MultiDiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
In addition to strings and integers any hashable Python object
(except None) can represent a node, e.g. a customized node object,
or even another Graph.
>>> G.add_node(H)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> key = G.add_edge(1, 2)
a list of edges,
>>> keys = G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> keys = G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. If an edge already exists, an additional
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.
>>> keys = G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])
>>> G[4]
AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.MultiDiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time="5pm")
>>> G.add_nodes_from([3], time="2pm")
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]["room"] = 714
>>> del G.nodes[1]["room"] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> key = G.add_edge(1, 2, weight=4.7)
>>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
>>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
>>> G[1][2][0]["weight"] = 4.7
>>> G.edges[1, 2, 0]["weight"] = 4
Warning: we protect the graph data structure by making `G.edges[1,
2, 0]` a read-only dict-like structure. However, you can assign to
attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`
(for multigraphs the edge key is required: `MG.edges[u, v,
key][name] = value`).
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n < 3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
>>> G[1] # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are available as an adjacency-view `G.adj` object or via
the method `G.adjacency()`.
>>> for n, nbrsdict in G.adjacency():
... for nbr, keydict in nbrsdict.items():
... for key, eattr in keydict.items():
... if "weight" in eattr:
... # Do something useful with the edges
... pass
But the edges() method is often more convenient:
>>> for u, v, keys, weight in G.edges(data="weight", keys=True):
... if weight is not None:
... # Do something useful with the edges
... pass
**Reporting:**
Simple graph information is obtained using methods and object-attributes.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes`, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information
and holds edge_key dicts keyed by neighbor. The edge_key dict holds
each edge_attr dict keyed by edge key. The inner dict
(edge_attr_dict) represents the edge data and holds edge attribute
values keyed by attribute names.
Each of these four dicts in the dict-of-dict-of-dict-of-dict
structure can be replaced by a user defined dict-like object.
In general, the dict-like features should be maintained but
extra features can be added. To replace one of the dicts create
a new graph class by changing the class(!) variable holding the
factory for that dict-like structure. The variable names are
node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
and graph_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the dict containing node
attributes, keyed by node id.
It should require no arguments and return a dict-like object
node_attr_dict_factory: function, (default: dict)
Factory function to be used to create the node attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
in the data structure that holds adjacency info keyed by node.
It should require no arguments and return a dict-like object.
adjlist_inner_dict_factory : function, (default: dict)
Factory function to be used to create the adjacency list
dict which holds multiedge key dicts keyed by neighbor.
It should require no arguments and return a dict-like object.
edge_key_dict_factory : function, (default: dict)
Factory function to be used to create the edge key dict
which holds edge data keyed by edge key.
It should require no arguments and return a dict-like object.
edge_attr_dict_factory : function, (default: dict)
Factory function to be used to create the edge attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.
graph_attr_dict_factory : function, (default: dict)
Factory function to be used to create the graph attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.
Typically, if your extension doesn't impact the data structure all
methods will inherited without issue except: `to_directed/to_undirected`.
By default these methods create a DiGraph/Graph class and you probably
want them to create your extension of a DiGraph/Graph. To facilitate
this we define two class variables that you can set in your subclass.
to_directed_class : callable, (default: DiGraph or MultiDiGraph)
Class to create a new graph structure in the `to_directed` method.
If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
to_undirected_class : callable, (default: Graph or MultiGraph)
Class to create a new graph structure in the `to_undirected` method.
If `None`, a NetworkX class (Graph or MultiGraph) is used.
**Subclassing Example**
Create a low memory graph class that effectively disallows edge
attributes by using a single attribute dict for all edges.
This reduces the memory used, but you lose edge attributes.
>>> class ThinGraph(nx.Graph):
... all_edge_dict = {"weight": 1}
...
... def single_edge_dict(self):
... return self.all_edge_dict
...
... edge_attr_dict_factory = single_edge_dict
>>> G = ThinGraph()
>>> G.add_edge(2, 1)
>>> G[2][1]
{'weight': 1}
>>> G.add_edge(2, 2)
>>> G[2][1] is G[2][2]
True
| class MultiDiGraph(MultiGraph, DiGraph):
"""A directed graph class that can store multiedges.
Multiedges are multiple edges between two nodes. Each edge
can hold optional data or attributes.
A MultiDiGraph holds directed edges. Self loops are allowed.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes. By convention `None` is not used as a node.
Edges are represented as links between nodes with optional
key/value attributes.
Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If None (default) an empty
graph is created. The data can be any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
sparse matrix, or PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
DiGraph
MultiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.MultiDiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
In addition to strings and integers any hashable Python object
(except None) can represent a node, e.g. a customized node object,
or even another Graph.
>>> G.add_node(H)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> key = G.add_edge(1, 2)
a list of edges,
>>> keys = G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> keys = G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. If an edge already exists, an additional
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.
>>> keys = G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])
>>> G[4]
AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.MultiDiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time="5pm")
>>> G.add_nodes_from([3], time="2pm")
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]["room"] = 714
>>> del G.nodes[1]["room"] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> key = G.add_edge(1, 2, weight=4.7)
>>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
>>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
>>> G[1][2][0]["weight"] = 4.7
>>> G.edges[1, 2, 0]["weight"] = 4
Warning: we protect the graph data structure by making `G.edges[1,
2, 0]` a read-only dict-like structure. However, you can assign to
attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`
(for multigraphs the edge key is required: `MG.edges[u, v,
key][name] = value`).
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n < 3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
>>> G[1] # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are available as an adjacency-view `G.adj` object or via
the method `G.adjacency()`.
>>> for n, nbrsdict in G.adjacency():
... for nbr, keydict in nbrsdict.items():
... for key, eattr in keydict.items():
... if "weight" in eattr:
... # Do something useful with the edges
... pass
But the edges() method is often more convenient:
>>> for u, v, keys, weight in G.edges(data="weight", keys=True):
... if weight is not None:
... # Do something useful with the edges
... pass
**Reporting:**
Simple graph information is obtained using methods and object-attributes.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes`, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information
and holds edge_key dicts keyed by neighbor. The edge_key dict holds
each edge_attr dict keyed by edge key. The inner dict
(edge_attr_dict) represents the edge data and holds edge attribute
values keyed by attribute names.
Each of these four dicts in the dict-of-dict-of-dict-of-dict
structure can be replaced by a user defined dict-like object.
In general, the dict-like features should be maintained but
extra features can be added. To replace one of the dicts create
a new graph class by changing the class(!) variable holding the
factory for that dict-like structure. The variable names are
node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
and graph_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the dict containing node
attributes, keyed by node id.
It should require no arguments and return a dict-like object
node_attr_dict_factory: function, (default: dict)
Factory function to be used to create the node attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
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|
30,179 | networkx.classes.multidigraph | __init__ | Initialize a graph with edges, name, or graph attributes.
Parameters
----------
incoming_graph_data : input graph
Data to initialize graph. If incoming_graph_data=None (default)
an empty graph is created. The data can be an edge list, or any
NetworkX graph object. If the corresponding optional Python
packages are installed the data can also be a 2D NumPy array, a
SciPy sparse array, or a PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
convert
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name="my graph")
>>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
>>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
| def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
"""Initialize a graph with edges, name, or graph attributes.
Parameters
----------
incoming_graph_data : input graph
Data to initialize graph. If incoming_graph_data=None (default)
an empty graph is created. The data can be an edge list, or any
NetworkX graph object. If the corresponding optional Python
packages are installed the data can also be a 2D NumPy array, a
SciPy sparse array, or a PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
convert
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name="my graph")
>>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
>>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
"""
# multigraph_input can be None/True/False. So check "is not False"
if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
DiGraph.__init__(self)
try:
convert.from_dict_of_dicts(
incoming_graph_data, create_using=self, multigraph_input=True
)
self.graph.update(attr)
except Exception as err:
if multigraph_input is True:
raise nx.NetworkXError(
f"converting multigraph_input raised:\n{type(err)}: {err}"
)
DiGraph.__init__(self, incoming_graph_data, **attr)
else:
DiGraph.__init__(self, incoming_graph_data, **attr)
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|
30,183 | networkx.classes.multidigraph | add_edge | Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph.
Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u_for_edge, v_for_edge : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer)
Used to distinguish multiedges between a pair of nodes.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
The edge key assigned to the edge.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
To replace/update edge data, use the optional key argument
to identify a unique edge. Otherwise a new edge will be created.
NetworkX algorithms designed for weighted graphs cannot use
multigraphs directly because it is not clear how to handle
multiedge weights. Convert to Graph using edge attribute
'weight' to enable weighted graph algorithms.
Default keys are generated using the method `new_edge_key()`.
This method can be overridden by subclassing the base class and
providing a custom `new_edge_key()` method.
Examples
--------
The following all add the edge e=(1, 2) to graph G:
>>> G = nx.MultiDiGraph()
>>> e = (1, 2)
>>> key = G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
1
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
[2]
Associate data to edges using keywords:
>>> key = G.add_edge(1, 2, weight=3)
>>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
>>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> ekey = G.add_edge(1, 2)
>>> G[1][2][0].update({0: 5})
>>> G.edges[1, 2, 0].update({0: 5})
| def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
"""Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph.
Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u_for_edge, v_for_edge : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer)
Used to distinguish multiedges between a pair of nodes.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
The edge key assigned to the edge.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
To replace/update edge data, use the optional key argument
to identify a unique edge. Otherwise a new edge will be created.
NetworkX algorithms designed for weighted graphs cannot use
multigraphs directly because it is not clear how to handle
multiedge weights. Convert to Graph using edge attribute
'weight' to enable weighted graph algorithms.
Default keys are generated using the method `new_edge_key()`.
This method can be overridden by subclassing the base class and
providing a custom `new_edge_key()` method.
Examples
--------
The following all add the edge e=(1, 2) to graph G:
>>> G = nx.MultiDiGraph()
>>> e = (1, 2)
>>> key = G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
1
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
[2]
Associate data to edges using keywords:
>>> key = G.add_edge(1, 2, weight=3)
>>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
>>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> ekey = G.add_edge(1, 2)
>>> G[1][2][0].update({0: 5})
>>> G.edges[1, 2, 0].update({0: 5})
"""
u, v = u_for_edge, v_for_edge
# add nodes
if u not in self._succ:
if u is None:
raise ValueError("None cannot be a node")
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = self.node_attr_dict_factory()
if v not in self._succ:
if v is None:
raise ValueError("None cannot be a node")
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.adjlist_inner_dict_factory()
self._node[v] = self.node_attr_dict_factory()
if key is None:
key = self.new_edge_key(u, v)
if v in self._succ[u]:
keydict = self._adj[u][v]
datadict = keydict.get(key, self.edge_attr_dict_factory())
datadict.update(attr)
keydict[key] = datadict
else:
# selfloops work this way without special treatment
datadict = self.edge_attr_dict_factory()
datadict.update(attr)
keydict = self.edge_key_dict_factory()
keydict[key] = datadict
self._succ[u][v] = keydict
self._pred[v][u] = keydict
nx._clear_cache(self)
return key
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|
30,184 | networkx.classes.multigraph | add_edges_from | Add all the edges in ebunch_to_add.
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the container will be added to the
graph. The edges can be:
- 2-tuples (u, v) or
- 3-tuples (u, v, d) for an edge data dict d, or
- 3-tuples (u, v, k) for not iterable key k, or
- 4-tuples (u, v, k, d) for an edge with data and key k
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
A list of edge keys assigned to the edges in `ebunch`.
See Also
--------
add_edge : add a single edge
add_weighted_edges_from : convenient way to add weighted edges
Notes
-----
Adding the same edge twice has no effect but any edge data
will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over
attributes specified via keyword arguments.
Default keys are generated using the method ``new_edge_key()``.
This method can be overridden by subclassing the base class and
providing a custom ``new_edge_key()`` method.
When adding edges from an iterator over the graph you are changing,
a `RuntimeError` can be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_edges)`, and pass this
object to `G.add_edges_from`.
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
>>> e = zip(range(0, 3), range(1, 4))
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
Evaluate an iterator over a graph if using it to modify the same graph
>>> G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
>>> # Grow graph by one new node, adding edges to all existing nodes.
>>> # wrong way - will raise RuntimeError
>>> # G.add_edges_from(((5, n) for n in G.nodes))
>>> # right way - note that there will be no self-edge for node 5
>>> assigned_keys = G.add_edges_from(list((5, n) for n in G.nodes))
| def add_edges_from(self, ebunch_to_add, **attr):
"""Add all the edges in ebunch_to_add.
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the container will be added to the
graph. The edges can be:
- 2-tuples (u, v) or
- 3-tuples (u, v, d) for an edge data dict d, or
- 3-tuples (u, v, k) for not iterable key k, or
- 4-tuples (u, v, k, d) for an edge with data and key k
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
A list of edge keys assigned to the edges in `ebunch`.
See Also
--------
add_edge : add a single edge
add_weighted_edges_from : convenient way to add weighted edges
Notes
-----
Adding the same edge twice has no effect but any edge data
will be updated when each duplicate edge is added.
Edge attributes specified in an ebunch take precedence over
attributes specified via keyword arguments.
Default keys are generated using the method ``new_edge_key()``.
This method can be overridden by subclassing the base class and
providing a custom ``new_edge_key()`` method.
When adding edges from an iterator over the graph you are changing,
a `RuntimeError` can be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_edges)`, and pass this
object to `G.add_edges_from`.
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
>>> e = zip(range(0, 3), range(1, 4))
>>> G.add_edges_from(e) # Add the path graph 0-1-2-3
Associate data to edges
>>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
>>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
Evaluate an iterator over a graph if using it to modify the same graph
>>> G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
>>> # Grow graph by one new node, adding edges to all existing nodes.
>>> # wrong way - will raise RuntimeError
>>> # G.add_edges_from(((5, n) for n in G.nodes))
>>> # right way - note that there will be no self-edge for node 5
>>> assigned_keys = G.add_edges_from(list((5, n) for n in G.nodes))
"""
keylist = []
for e in ebunch_to_add:
ne = len(e)
if ne == 4:
u, v, key, dd = e
elif ne == 3:
u, v, dd = e
key = None
elif ne == 2:
u, v = e
dd = {}
key = None
else:
msg = f"Edge tuple {e} must be a 2-tuple, 3-tuple or 4-tuple."
raise NetworkXError(msg)
ddd = {}
ddd.update(attr)
try:
ddd.update(dd)
except (TypeError, ValueError):
if ne != 3:
raise
key = dd # ne == 3 with 3rd value not dict, must be a key
key = self.add_edge(u, v, key)
self[u][v][key].update(ddd)
keylist.append(key)
nx._clear_cache(self)
return keylist
| (self, ebunch_to_add, **attr) | [
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|
30,191 | networkx.classes.multigraph | copy | Returns a copy of the graph.
The copy method by default returns an independent shallow copy
of the graph and attributes. That is, if an attribute is a
container, that container is shared by the original an the copy.
Use Python's `copy.deepcopy` for new containers.
If `as_view` is True then a view is returned instead of a copy.
Notes
-----
All copies reproduce the graph structure, but data attributes
may be handled in different ways. There are four types of copies
of a graph that people might want.
Deepcopy -- A "deepcopy" copies the graph structure as well as
all data attributes and any objects they might contain.
The entire graph object is new so that changes in the copy
do not affect the original object. (see Python's copy.deepcopy)
Data Reference (Shallow) -- For a shallow copy the graph structure
is copied but the edge, node and graph attribute dicts are
references to those in the original graph. This saves
time and memory but could cause confusion if you change an attribute
in one graph and it changes the attribute in the other.
NetworkX does not provide this level of shallow copy.
Independent Shallow -- This copy creates new independent attribute
dicts and then does a shallow copy of the attributes. That is, any
attributes that are containers are shared between the new graph
and the original. This is exactly what `dict.copy()` provides.
You can obtain this style copy using:
>>> G = nx.path_graph(5)
>>> H = G.copy()
>>> H = G.copy(as_view=False)
>>> H = nx.Graph(G)
>>> H = G.__class__(G)
Fresh Data -- For fresh data, the graph structure is copied while
new empty data attribute dicts are created. The resulting graph
is independent of the original and it has no edge, node or graph
attributes. Fresh copies are not enabled. Instead use:
>>> H = G.__class__()
>>> H.add_nodes_from(G)
>>> H.add_edges_from(G.edges)
View -- Inspired by dict-views, graph-views act like read-only
versions of the original graph, providing a copy of the original
structure without requiring any memory for copying the information.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Parameters
----------
as_view : bool, optional (default=False)
If True, the returned graph-view provides a read-only view
of the original graph without actually copying any data.
Returns
-------
G : Graph
A copy of the graph.
See Also
--------
to_directed: return a directed copy of the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.copy()
| def copy(self, as_view=False):
"""Returns a copy of the graph.
The copy method by default returns an independent shallow copy
of the graph and attributes. That is, if an attribute is a
container, that container is shared by the original an the copy.
Use Python's `copy.deepcopy` for new containers.
If `as_view` is True then a view is returned instead of a copy.
Notes
-----
All copies reproduce the graph structure, but data attributes
may be handled in different ways. There are four types of copies
of a graph that people might want.
Deepcopy -- A "deepcopy" copies the graph structure as well as
all data attributes and any objects they might contain.
The entire graph object is new so that changes in the copy
do not affect the original object. (see Python's copy.deepcopy)
Data Reference (Shallow) -- For a shallow copy the graph structure
is copied but the edge, node and graph attribute dicts are
references to those in the original graph. This saves
time and memory but could cause confusion if you change an attribute
in one graph and it changes the attribute in the other.
NetworkX does not provide this level of shallow copy.
Independent Shallow -- This copy creates new independent attribute
dicts and then does a shallow copy of the attributes. That is, any
attributes that are containers are shared between the new graph
and the original. This is exactly what `dict.copy()` provides.
You can obtain this style copy using:
>>> G = nx.path_graph(5)
>>> H = G.copy()
>>> H = G.copy(as_view=False)
>>> H = nx.Graph(G)
>>> H = G.__class__(G)
Fresh Data -- For fresh data, the graph structure is copied while
new empty data attribute dicts are created. The resulting graph
is independent of the original and it has no edge, node or graph
attributes. Fresh copies are not enabled. Instead use:
>>> H = G.__class__()
>>> H.add_nodes_from(G)
>>> H.add_edges_from(G.edges)
View -- Inspired by dict-views, graph-views act like read-only
versions of the original graph, providing a copy of the original
structure without requiring any memory for copying the information.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Parameters
----------
as_view : bool, optional (default=False)
If True, the returned graph-view provides a read-only view
of the original graph without actually copying any data.
Returns
-------
G : Graph
A copy of the graph.
See Also
--------
to_directed: return a directed copy of the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> H = G.copy()
"""
if as_view is True:
return nx.graphviews.generic_graph_view(self)
G = self.__class__()
G.graph.update(self.graph)
G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
G.add_edges_from(
(u, v, key, datadict.copy())
for u, nbrs in self._adj.items()
for v, keydict in nbrs.items()
for key, datadict in keydict.items()
)
return G
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|
30,193 | networkx.classes.multigraph | get_edge_data | Returns the attribute dictionary associated with edge (u, v,
key).
If a key is not provided, returns a dictionary mapping edge keys
to attribute dictionaries for each edge between u and v.
This is identical to `G[u][v][key]` except the default is returned
instead of an exception is the edge doesn't exist.
Parameters
----------
u, v : nodes
default : any Python object (default=None)
Value to return if the specific edge (u, v, key) is not
found, OR if there are no edges between u and v and no key
is specified.
key : hashable identifier, optional (default=None)
Return data only for the edge with specified key, as an
attribute dictionary (rather than a dictionary mapping keys
to attribute dictionaries).
Returns
-------
edge_dict : dictionary
The edge attribute dictionary, OR a dictionary mapping edge
keys to attribute dictionaries for each of those edges if no
specific key is provided (even if there's only one edge
between u and v).
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> key = G.add_edge(0, 1, key="a", weight=7)
>>> G[0][1]["a"] # key='a'
{'weight': 7}
>>> G.edges[0, 1, "a"] # key='a'
{'weight': 7}
Warning: we protect the graph data structure by making
`G.edges` and `G[1][2]` read-only dict-like structures.
However, you can assign values to attributes in e.g.
`G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional
bracket as shown next. You need to specify all edge info
to assign to the edge data associated with an edge.
>>> G[0][1]["a"]["weight"] = 10
>>> G.edges[0, 1, "a"]["weight"] = 10
>>> G[0][1]["a"]["weight"]
10
>>> G.edges[1, 0, "a"]["weight"]
10
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.edges[0, 1, 0]["weight"] = 5
>>> G.get_edge_data(0, 1)
{0: {'weight': 5}}
>>> e = (0, 1)
>>> G.get_edge_data(*e) # tuple form
{0: {'weight': 5}}
>>> G.get_edge_data(3, 0) # edge not in graph, returns None
>>> G.get_edge_data(3, 0, default=0) # edge not in graph, return default
0
>>> G.get_edge_data(1, 0, 0) # specific key gives back
{'weight': 5}
| def get_edge_data(self, u, v, key=None, default=None):
"""Returns the attribute dictionary associated with edge (u, v,
key).
If a key is not provided, returns a dictionary mapping edge keys
to attribute dictionaries for each edge between u and v.
This is identical to `G[u][v][key]` except the default is returned
instead of an exception is the edge doesn't exist.
Parameters
----------
u, v : nodes
default : any Python object (default=None)
Value to return if the specific edge (u, v, key) is not
found, OR if there are no edges between u and v and no key
is specified.
key : hashable identifier, optional (default=None)
Return data only for the edge with specified key, as an
attribute dictionary (rather than a dictionary mapping keys
to attribute dictionaries).
Returns
-------
edge_dict : dictionary
The edge attribute dictionary, OR a dictionary mapping edge
keys to attribute dictionaries for each of those edges if no
specific key is provided (even if there's only one edge
between u and v).
Examples
--------
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> key = G.add_edge(0, 1, key="a", weight=7)
>>> G[0][1]["a"] # key='a'
{'weight': 7}
>>> G.edges[0, 1, "a"] # key='a'
{'weight': 7}
Warning: we protect the graph data structure by making
`G.edges` and `G[1][2]` read-only dict-like structures.
However, you can assign values to attributes in e.g.
`G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional
bracket as shown next. You need to specify all edge info
to assign to the edge data associated with an edge.
>>> G[0][1]["a"]["weight"] = 10
>>> G.edges[0, 1, "a"]["weight"] = 10
>>> G[0][1]["a"]["weight"]
10
>>> G.edges[1, 0, "a"]["weight"]
10
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.edges[0, 1, 0]["weight"] = 5
>>> G.get_edge_data(0, 1)
{0: {'weight': 5}}
>>> e = (0, 1)
>>> G.get_edge_data(*e) # tuple form
{0: {'weight': 5}}
>>> G.get_edge_data(3, 0) # edge not in graph, returns None
>>> G.get_edge_data(3, 0, default=0) # edge not in graph, return default
0
>>> G.get_edge_data(1, 0, 0) # specific key gives back
{'weight': 5}
"""
try:
if key is None:
return self._adj[u][v]
else:
return self._adj[u][v][key]
except KeyError:
return default
| (self, u, v, key=None, default=None) | [
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|
30,194 | networkx.classes.multigraph | has_edge | Returns True if the graph has an edge between nodes u and v.
This is the same as `v in G[u] or key in G[u][v]`
without KeyError exceptions.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
key : hashable identifier, optional (default=None)
If specified return True only if the edge with
key is found.
Returns
-------
edge_ind : bool
True if edge is in the graph, False otherwise.
Examples
--------
Can be called either using two nodes u, v, an edge tuple (u, v),
or an edge tuple (u, v, key).
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.has_edge(0, 1) # using two nodes
True
>>> e = (0, 1)
>>> G.has_edge(*e) # e is a 2-tuple (u, v)
True
>>> G.add_edge(0, 1, key="a")
'a'
>>> G.has_edge(0, 1, key="a") # specify key
True
>>> G.has_edge(1, 0, key="a") # edges aren't directed
True
>>> e = (0, 1, "a")
>>> G.has_edge(*e) # e is a 3-tuple (u, v, 'a')
True
The following syntax are equivalent:
>>> G.has_edge(0, 1)
True
>>> 1 in G[0] # though this gives :exc:`KeyError` if 0 not in G
True
>>> 0 in G[1] # other order; also gives :exc:`KeyError` if 0 not in G
True
| def has_edge(self, u, v, key=None):
"""Returns True if the graph has an edge between nodes u and v.
This is the same as `v in G[u] or key in G[u][v]`
without KeyError exceptions.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
key : hashable identifier, optional (default=None)
If specified return True only if the edge with
key is found.
Returns
-------
edge_ind : bool
True if edge is in the graph, False otherwise.
Examples
--------
Can be called either using two nodes u, v, an edge tuple (u, v),
or an edge tuple (u, v, key).
>>> G = nx.MultiGraph() # or MultiDiGraph
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.has_edge(0, 1) # using two nodes
True
>>> e = (0, 1)
>>> G.has_edge(*e) # e is a 2-tuple (u, v)
True
>>> G.add_edge(0, 1, key="a")
'a'
>>> G.has_edge(0, 1, key="a") # specify key
True
>>> G.has_edge(1, 0, key="a") # edges aren't directed
True
>>> e = (0, 1, "a")
>>> G.has_edge(*e) # e is a 3-tuple (u, v, 'a')
True
The following syntax are equivalent:
>>> G.has_edge(0, 1)
True
>>> 1 in G[0] # though this gives :exc:`KeyError` if 0 not in G
True
>>> 0 in G[1] # other order; also gives :exc:`KeyError` if 0 not in G
True
"""
try:
if key is None:
return v in self._adj[u]
else:
return key in self._adj[u][v]
except KeyError:
return False
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|
30,199 | networkx.classes.multidigraph | is_multigraph | Returns True if graph is a multigraph, False otherwise. | def is_multigraph(self):
"""Returns True if graph is a multigraph, False otherwise."""
return True
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|
30,202 | networkx.classes.multigraph | new_edge_key | Returns an unused key for edges between nodes `u` and `v`.
The nodes `u` and `v` do not need to be already in the graph.
Notes
-----
In the standard MultiGraph class the new key is the number of existing
edges between `u` and `v` (increased if necessary to ensure unused).
The first edge will have key 0, then 1, etc. If an edge is removed
further new_edge_keys may not be in this order.
Parameters
----------
u, v : nodes
Returns
-------
key : int
| def new_edge_key(self, u, v):
"""Returns an unused key for edges between nodes `u` and `v`.
The nodes `u` and `v` do not need to be already in the graph.
Notes
-----
In the standard MultiGraph class the new key is the number of existing
edges between `u` and `v` (increased if necessary to ensure unused).
The first edge will have key 0, then 1, etc. If an edge is removed
further new_edge_keys may not be in this order.
Parameters
----------
u, v : nodes
Returns
-------
key : int
"""
try:
keydict = self._adj[u][v]
except KeyError:
return 0
key = len(keydict)
while key in keydict:
key += 1
return key
| (self, u, v) | [
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|
30,203 | networkx.classes.multigraph | number_of_edges | Returns the number of edges between two nodes.
Parameters
----------
u, v : nodes, optional (Default=all edges)
If u and v are specified, return the number of edges between
u and v. Otherwise return the total number of all edges.
Returns
-------
nedges : int
The number of edges in the graph. If nodes `u` and `v` are
specified return the number of edges between those nodes. If
the graph is directed, this only returns the number of edges
from `u` to `v`.
See Also
--------
size
Examples
--------
For undirected multigraphs, this method counts the total number
of edges in the graph::
>>> G = nx.MultiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])
[0, 1, 0]
>>> G.number_of_edges()
3
If you specify two nodes, this counts the total number of edges
joining the two nodes::
>>> G.number_of_edges(0, 1)
2
For directed multigraphs, this method can count the total number
of directed edges from `u` to `v`::
>>> G = nx.MultiDiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])
[0, 1, 0]
>>> G.number_of_edges(0, 1)
2
>>> G.number_of_edges(1, 0)
1
| def number_of_edges(self, u=None, v=None):
"""Returns the number of edges between two nodes.
Parameters
----------
u, v : nodes, optional (Default=all edges)
If u and v are specified, return the number of edges between
u and v. Otherwise return the total number of all edges.
Returns
-------
nedges : int
The number of edges in the graph. If nodes `u` and `v` are
specified return the number of edges between those nodes. If
the graph is directed, this only returns the number of edges
from `u` to `v`.
See Also
--------
size
Examples
--------
For undirected multigraphs, this method counts the total number
of edges in the graph::
>>> G = nx.MultiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])
[0, 1, 0]
>>> G.number_of_edges()
3
If you specify two nodes, this counts the total number of edges
joining the two nodes::
>>> G.number_of_edges(0, 1)
2
For directed multigraphs, this method can count the total number
of directed edges from `u` to `v`::
>>> G = nx.MultiDiGraph()
>>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])
[0, 1, 0]
>>> G.number_of_edges(0, 1)
2
>>> G.number_of_edges(1, 0)
1
"""
if u is None:
return self.size()
try:
edgedata = self._adj[u][v]
except KeyError:
return 0 # no such edge
return len(edgedata)
| (self, u=None, v=None) | [
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|
30,207 | networkx.classes.multidigraph | remove_edge | Remove an edge between u and v.
Parameters
----------
u, v : nodes
Remove an edge between nodes u and v.
key : hashable identifier, optional (default=None)
Used to distinguish multiple edges between a pair of nodes.
If None, remove a single edge between u and v. If there are
multiple edges, removes the last edge added in terms of
insertion order.
Raises
------
NetworkXError
If there is not an edge between u and v, or
if there is no edge with the specified key.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.MultiDiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.remove_edge(0, 1)
>>> e = (1, 2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
For multiple edges
>>> G = nx.MultiDiGraph()
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
[0, 1, 2]
When ``key=None`` (the default), edges are removed in the opposite
order that they were added:
>>> G.remove_edge(1, 2)
>>> G.edges(keys=True)
OutMultiEdgeView([(1, 2, 0), (1, 2, 1)])
For edges with keys
>>> G = nx.MultiDiGraph()
>>> G.add_edge(1, 2, key="first")
'first'
>>> G.add_edge(1, 2, key="second")
'second'
>>> G.remove_edge(1, 2, key="first")
>>> G.edges(keys=True)
OutMultiEdgeView([(1, 2, 'second')])
| def remove_edge(self, u, v, key=None):
"""Remove an edge between u and v.
Parameters
----------
u, v : nodes
Remove an edge between nodes u and v.
key : hashable identifier, optional (default=None)
Used to distinguish multiple edges between a pair of nodes.
If None, remove a single edge between u and v. If there are
multiple edges, removes the last edge added in terms of
insertion order.
Raises
------
NetworkXError
If there is not an edge between u and v, or
if there is no edge with the specified key.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.MultiDiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.remove_edge(0, 1)
>>> e = (1, 2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
For multiple edges
>>> G = nx.MultiDiGraph()
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
[0, 1, 2]
When ``key=None`` (the default), edges are removed in the opposite
order that they were added:
>>> G.remove_edge(1, 2)
>>> G.edges(keys=True)
OutMultiEdgeView([(1, 2, 0), (1, 2, 1)])
For edges with keys
>>> G = nx.MultiDiGraph()
>>> G.add_edge(1, 2, key="first")
'first'
>>> G.add_edge(1, 2, key="second")
'second'
>>> G.remove_edge(1, 2, key="first")
>>> G.edges(keys=True)
OutMultiEdgeView([(1, 2, 'second')])
"""
try:
d = self._adj[u][v]
except KeyError as err:
raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
# remove the edge with specified data
if key is None:
d.popitem()
else:
try:
del d[key]
except KeyError as err:
msg = f"The edge {u}-{v} with key {key} is not in the graph."
raise NetworkXError(msg) from err
if len(d) == 0:
# remove the key entries if last edge
del self._succ[u][v]
del self._pred[v][u]
nx._clear_cache(self)
| (self, u, v, key=None) | [
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|
30,208 | networkx.classes.multigraph | remove_edges_from | Remove all edges specified in ebunch.
Parameters
----------
ebunch: list or container of edge tuples
Each edge given in the list or container will be removed
from the graph. The edges can be:
- 2-tuples (u, v) A single edge between u and v is removed.
- 3-tuples (u, v, key) The edge identified by key is removed.
- 4-tuples (u, v, key, data) where data is ignored.
See Also
--------
remove_edge : remove a single edge
Notes
-----
Will fail silently if an edge in ebunch is not in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
Removing multiple copies of edges
>>> G = nx.MultiGraph()
>>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])
>>> G.remove_edges_from([(1, 2), (2, 1)]) # edges aren't directed
>>> list(G.edges())
[(1, 2)]
>>> G.remove_edges_from([(1, 2), (1, 2)]) # silently ignore extra copy
>>> list(G.edges) # now empty graph
[]
When the edge is a 2-tuple ``(u, v)`` but there are multiple edges between
u and v in the graph, the most recent edge (in terms of insertion
order) is removed.
>>> G = nx.MultiGraph()
>>> for key in ("x", "y", "a"):
... k = G.add_edge(0, 1, key=key)
>>> G.edges(keys=True)
MultiEdgeView([(0, 1, 'x'), (0, 1, 'y'), (0, 1, 'a')])
>>> G.remove_edges_from([(0, 1)])
>>> G.edges(keys=True)
MultiEdgeView([(0, 1, 'x'), (0, 1, 'y')])
| def remove_edges_from(self, ebunch):
"""Remove all edges specified in ebunch.
Parameters
----------
ebunch: list or container of edge tuples
Each edge given in the list or container will be removed
from the graph. The edges can be:
- 2-tuples (u, v) A single edge between u and v is removed.
- 3-tuples (u, v, key) The edge identified by key is removed.
- 4-tuples (u, v, key, data) where data is ignored.
See Also
--------
remove_edge : remove a single edge
Notes
-----
Will fail silently if an edge in ebunch is not in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
Removing multiple copies of edges
>>> G = nx.MultiGraph()
>>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])
>>> G.remove_edges_from([(1, 2), (2, 1)]) # edges aren't directed
>>> list(G.edges())
[(1, 2)]
>>> G.remove_edges_from([(1, 2), (1, 2)]) # silently ignore extra copy
>>> list(G.edges) # now empty graph
[]
When the edge is a 2-tuple ``(u, v)`` but there are multiple edges between
u and v in the graph, the most recent edge (in terms of insertion
order) is removed.
>>> G = nx.MultiGraph()
>>> for key in ("x", "y", "a"):
... k = G.add_edge(0, 1, key=key)
>>> G.edges(keys=True)
MultiEdgeView([(0, 1, 'x'), (0, 1, 'y'), (0, 1, 'a')])
>>> G.remove_edges_from([(0, 1)])
>>> G.edges(keys=True)
MultiEdgeView([(0, 1, 'x'), (0, 1, 'y')])
"""
for e in ebunch:
try:
self.remove_edge(*e[:3])
except NetworkXError:
pass
nx._clear_cache(self)
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|
30,211 | networkx.classes.multidigraph | reverse | Returns the reverse of the graph.
The reverse is a graph with the same nodes and edges
but with the directions of the edges reversed.
Parameters
----------
copy : bool optional (default=True)
If True, return a new DiGraph holding the reversed edges.
If False, the reverse graph is created using a view of
the original graph.
| def reverse(self, copy=True):
"""Returns the reverse of the graph.
The reverse is a graph with the same nodes and edges
but with the directions of the edges reversed.
Parameters
----------
copy : bool optional (default=True)
If True, return a new DiGraph holding the reversed edges.
If False, the reverse graph is created using a view of
the original graph.
"""
if copy:
H = self.__class__()
H.graph.update(deepcopy(self.graph))
H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
H.add_edges_from(
(v, u, k, deepcopy(d))
for u, v, k, d in self.edges(keys=True, data=True)
)
return H
return nx.reverse_view(self)
| (self, copy=True) | [
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|
30,215 | networkx.classes.multigraph | to_directed | Returns a directed representation of the graph.
Returns
-------
G : MultiDiGraph
A directed graph with the same name, same nodes, and with
each edge (u, v, k, data) replaced by two directed edges
(u, v, k, data) and (v, u, k, data).
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar D=MultiDiGraph(G) which
returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiGraph to use dict-like objects
in the data structure, those changes do not transfer to the
MultiDiGraph created by this method.
Examples
--------
>>> G = nx.MultiGraph()
>>> G.add_edge(0, 1)
0
>>> G.add_edge(0, 1)
1
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1)]
If already directed, return a (deep) copy
>>> G = nx.MultiDiGraph()
>>> G.add_edge(0, 1)
0
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0)]
| def to_directed(self, as_view=False):
"""Returns a directed representation of the graph.
Returns
-------
G : MultiDiGraph
A directed graph with the same name, same nodes, and with
each edge (u, v, k, data) replaced by two directed edges
(u, v, k, data) and (v, u, k, data).
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar D=MultiDiGraph(G) which
returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiGraph to use dict-like objects
in the data structure, those changes do not transfer to the
MultiDiGraph created by this method.
Examples
--------
>>> G = nx.MultiGraph()
>>> G.add_edge(0, 1)
0
>>> G.add_edge(0, 1)
1
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1)]
If already directed, return a (deep) copy
>>> G = nx.MultiDiGraph()
>>> G.add_edge(0, 1)
0
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0)]
"""
graph_class = self.to_directed_class()
if as_view is True:
return nx.graphviews.generic_graph_view(self, graph_class)
# deepcopy when not a view
G = graph_class()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
G.add_edges_from(
(u, v, key, deepcopy(datadict))
for u, nbrs in self.adj.items()
for v, keydict in nbrs.items()
for key, datadict in keydict.items()
)
return G
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|
30,216 | networkx.classes.multigraph | to_directed_class | Returns the class to use for empty directed copies.
If you subclass the base classes, use this to designate
what directed class to use for `to_directed()` copies.
| def to_directed_class(self):
"""Returns the class to use for empty directed copies.
If you subclass the base classes, use this to designate
what directed class to use for `to_directed()` copies.
"""
return nx.MultiDiGraph
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|
30,217 | networkx.classes.multidigraph | to_undirected | Returns an undirected representation of the digraph.
Parameters
----------
reciprocal : bool (optional)
If True only keep edges that appear in both directions
in the original digraph.
as_view : bool (optional, default=False)
If True return an undirected view of the original directed graph.
Returns
-------
G : MultiGraph
An undirected graph with the same name and nodes and
with edge (u, v, data) if either (u, v, data) or (v, u, data)
is in the digraph. If both edges exist in digraph and
their edge data is different, only one edge is created
with an arbitrary choice of which edge data to use.
You must check and correct for this manually if desired.
See Also
--------
MultiGraph, copy, add_edge, add_edges_from
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar D=MultiDiGraph(G) which
returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiDiGraph to use dict-like
objects in the data structure, those changes do not transfer
to the MultiGraph created by this method.
Examples
--------
>>> G = nx.path_graph(2) # or MultiGraph, etc
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1)]
| def to_undirected(self, reciprocal=False, as_view=False):
"""Returns an undirected representation of the digraph.
Parameters
----------
reciprocal : bool (optional)
If True only keep edges that appear in both directions
in the original digraph.
as_view : bool (optional, default=False)
If True return an undirected view of the original directed graph.
Returns
-------
G : MultiGraph
An undirected graph with the same name and nodes and
with edge (u, v, data) if either (u, v, data) or (v, u, data)
is in the digraph. If both edges exist in digraph and
their edge data is different, only one edge is created
with an arbitrary choice of which edge data to use.
You must check and correct for this manually if desired.
See Also
--------
MultiGraph, copy, add_edge, add_edges_from
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar D=MultiDiGraph(G) which
returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiDiGraph to use dict-like
objects in the data structure, those changes do not transfer
to the MultiGraph created by this method.
Examples
--------
>>> G = nx.path_graph(2) # or MultiGraph, etc
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1)]
"""
graph_class = self.to_undirected_class()
if as_view is True:
return nx.graphviews.generic_graph_view(self, graph_class)
# deepcopy when not a view
G = graph_class()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
if reciprocal is True:
G.add_edges_from(
(u, v, key, deepcopy(data))
for u, nbrs in self._adj.items()
for v, keydict in nbrs.items()
for key, data in keydict.items()
if v in self._pred[u] and key in self._pred[u][v]
)
else:
G.add_edges_from(
(u, v, key, deepcopy(data))
for u, nbrs in self._adj.items()
for v, keydict in nbrs.items()
for key, data in keydict.items()
)
return G
| (self, reciprocal=False, as_view=False) | [
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|
30,218 | networkx.classes.multigraph | to_undirected_class | Returns the class to use for empty undirected copies.
If you subclass the base classes, use this to designate
what directed class to use for `to_directed()` copies.
| def to_undirected_class(self):
"""Returns the class to use for empty undirected copies.
If you subclass the base classes, use this to designate
what directed class to use for `to_directed()` copies.
"""
return MultiGraph
| (self) | [
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|
30,220 | networkx.classes.multigraph | MultiGraph |
An undirected graph class that can store multiedges.
Multiedges are multiple edges between two nodes. Each edge
can hold optional data or attributes.
A MultiGraph holds undirected edges. Self loops are allowed.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes. By convention `None` is not used as a node.
Edges are represented as links between nodes with optional
key/value attributes, in a MultiGraph each edge has a key to
distinguish between multiple edges that have the same source and
destination nodes.
Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If None (default) an empty
graph is created. The data can be any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, 2D NumPy array,
SciPy sparse array, or PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
DiGraph
MultiDiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.MultiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
In addition to strings and integers any hashable Python object
(except None) can represent a node, e.g. a customized node object,
or even another Graph.
>>> G.add_node(H)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> key = G.add_edge(1, 2)
a list of edges,
>>> keys = G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> keys = G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. If an edge already exists, an additional
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.
>>> keys = G.add_edges_from([(4, 5, {"route": 28}), (4, 5, {"route": 37})])
>>> G[4]
AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}})
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.MultiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time="5pm")
>>> G.add_nodes_from([3], time="2pm")
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]["room"] = 714
>>> del G.nodes[1]["room"] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> key = G.add_edge(1, 2, weight=4.7)
>>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
>>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
>>> G[1][2][0]["weight"] = 4.7
>>> G.edges[1, 2, 0]["weight"] = 4
Warning: we protect the graph data structure by making `G.edges[1,
2, 0]` a read-only dict-like structure. However, you can assign to
attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`.
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n < 3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
>>> G[1] # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`.
>>> for n, nbrsdict in G.adjacency():
... for nbr, keydict in nbrsdict.items():
... for key, eattr in keydict.items():
... if "weight" in eattr:
... # Do something useful with the edges
... pass
But the edges() method is often more convenient:
>>> for u, v, keys, weight in G.edges(data="weight", keys=True):
... if weight is not None:
... # Do something useful with the edges
... pass
**Reporting:**
Simple graph information is obtained using methods and object-attributes.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes`, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information
and holds edge_key dicts keyed by neighbor. The edge_key dict holds
each edge_attr dict keyed by edge key. The inner dict
(edge_attr_dict) represents the edge data and holds edge attribute
values keyed by attribute names.
Each of these four dicts in the dict-of-dict-of-dict-of-dict
structure can be replaced by a user defined dict-like object.
In general, the dict-like features should be maintained but
extra features can be added. To replace one of the dicts create
a new graph class by changing the class(!) variable holding the
factory for that dict-like structure. The variable names are
node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
and graph_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the dict containing node
attributes, keyed by node id.
It should require no arguments and return a dict-like object
node_attr_dict_factory: function, (default: dict)
Factory function to be used to create the node attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
in the data structure that holds adjacency info keyed by node.
It should require no arguments and return a dict-like object.
adjlist_inner_dict_factory : function, (default: dict)
Factory function to be used to create the adjacency list
dict which holds multiedge key dicts keyed by neighbor.
It should require no arguments and return a dict-like object.
edge_key_dict_factory : function, (default: dict)
Factory function to be used to create the edge key dict
which holds edge data keyed by edge key.
It should require no arguments and return a dict-like object.
edge_attr_dict_factory : function, (default: dict)
Factory function to be used to create the edge attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.
graph_attr_dict_factory : function, (default: dict)
Factory function to be used to create the graph attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object.
Typically, if your extension doesn't impact the data structure all
methods will inherited without issue except: `to_directed/to_undirected`.
By default these methods create a DiGraph/Graph class and you probably
want them to create your extension of a DiGraph/Graph. To facilitate
this we define two class variables that you can set in your subclass.
to_directed_class : callable, (default: DiGraph or MultiDiGraph)
Class to create a new graph structure in the `to_directed` method.
If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
to_undirected_class : callable, (default: Graph or MultiGraph)
Class to create a new graph structure in the `to_undirected` method.
If `None`, a NetworkX class (Graph or MultiGraph) is used.
**Subclassing Example**
Create a low memory graph class that effectively disallows edge
attributes by using a single attribute dict for all edges.
This reduces the memory used, but you lose edge attributes.
>>> class ThinGraph(nx.Graph):
... all_edge_dict = {"weight": 1}
...
... def single_edge_dict(self):
... return self.all_edge_dict
...
... edge_attr_dict_factory = single_edge_dict
>>> G = ThinGraph()
>>> G.add_edge(2, 1)
>>> G[2][1]
{'weight': 1}
>>> G.add_edge(2, 2)
>>> G[2][1] is G[2][2]
True
| class MultiGraph(Graph):
"""
An undirected graph class that can store multiedges.
Multiedges are multiple edges between two nodes. Each edge
can hold optional data or attributes.
A MultiGraph holds undirected edges. Self loops are allowed.
Nodes can be arbitrary (hashable) Python objects with optional
key/value attributes. By convention `None` is not used as a node.
Edges are represented as links between nodes with optional
key/value attributes, in a MultiGraph each edge has a key to
distinguish between multiple edges that have the same source and
destination nodes.
Parameters
----------
incoming_graph_data : input graph (optional, default: None)
Data to initialize graph. If None (default) an empty
graph is created. The data can be any format that is supported
by the to_networkx_graph() function, currently including edge list,
dict of dicts, dict of lists, NetworkX graph, 2D NumPy array,
SciPy sparse array, or PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
Graph
DiGraph
MultiDiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.MultiGraph()
G can be grown in several ways.
**Nodes:**
Add one node at a time:
>>> G.add_node(1)
Add the nodes from any container (a list, dict, set or
even the lines from a file or the nodes from another graph).
>>> G.add_nodes_from([2, 3])
>>> G.add_nodes_from(range(100, 110))
>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)
In addition to strings and integers any hashable Python object
(except None) can represent a node, e.g. a customized node object,
or even another Graph.
>>> G.add_node(H)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> key = G.add_edge(1, 2)
a list of edges,
>>> keys = G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> keys = G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. If an edge already exists, an additional
edge is created and stored using a key to identify the edge.
By default the key is the lowest unused integer.
>>> keys = G.add_edges_from([(4, 5, {"route": 28}), (4, 5, {"route": 37})])
>>> G[4]
AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}})
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be hashable).
By default these are empty, but can be added or changed using
add_edge, add_node or direct manipulation of the attribute
dictionaries named graph, node and edge respectively.
>>> G = nx.MultiGraph(day="Friday")
>>> G.graph
{'day': 'Friday'}
Add node attributes using add_node(), add_nodes_from() or G.nodes
>>> G.add_node(1, time="5pm")
>>> G.add_nodes_from([3], time="2pm")
>>> G.nodes[1]
{'time': '5pm'}
>>> G.nodes[1]["room"] = 714
>>> del G.nodes[1]["room"] # remove attribute
>>> list(G.nodes(data=True))
[(1, {'time': '5pm'}), (3, {'time': '2pm'})]
Add edge attributes using add_edge(), add_edges_from(), subscript
notation, or G.edges.
>>> key = G.add_edge(1, 2, weight=4.7)
>>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
>>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
>>> G[1][2][0]["weight"] = 4.7
>>> G.edges[1, 2, 0]["weight"] = 4
Warning: we protect the graph data structure by making `G.edges[1,
2, 0]` a read-only dict-like structure. However, you can assign to
attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`.
**Shortcuts:**
Many common graph features allow python syntax to speed reporting.
>>> 1 in G # check if node in graph
True
>>> [n for n in G if n < 3] # iterate through nodes
[1, 2]
>>> len(G) # number of nodes in graph
5
>>> G[1] # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
Often the best way to traverse all edges of a graph is via the neighbors.
The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`.
>>> for n, nbrsdict in G.adjacency():
... for nbr, keydict in nbrsdict.items():
... for key, eattr in keydict.items():
... if "weight" in eattr:
... # Do something useful with the edges
... pass
But the edges() method is often more convenient:
>>> for u, v, keys, weight in G.edges(data="weight", keys=True):
... if weight is not None:
... # Do something useful with the edges
... pass
**Reporting:**
Simple graph information is obtained using methods and object-attributes.
Reporting usually provides views instead of containers to reduce memory
usage. The views update as the graph is updated similarly to dict-views.
The objects `nodes`, `edges` and `adj` provide access to data attributes
via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
(e.g. `nodes.items()`, `nodes.data('color')`,
`nodes.data('color', default='blue')` and similarly for `edges`)
Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
For details on these and other miscellaneous methods, see below.
**Subclasses (Advanced):**
The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
The outer dict (node_dict) holds adjacency information keyed by node.
The next dict (adjlist_dict) represents the adjacency information
and holds edge_key dicts keyed by neighbor. The edge_key dict holds
each edge_attr dict keyed by edge key. The inner dict
(edge_attr_dict) represents the edge data and holds edge attribute
values keyed by attribute names.
Each of these four dicts in the dict-of-dict-of-dict-of-dict
structure can be replaced by a user defined dict-like object.
In general, the dict-like features should be maintained but
extra features can be added. To replace one of the dicts create
a new graph class by changing the class(!) variable holding the
factory for that dict-like structure. The variable names are
node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
and graph_attr_dict_factory.
node_dict_factory : function, (default: dict)
Factory function to be used to create the dict containing node
attributes, keyed by node id.
It should require no arguments and return a dict-like object
node_attr_dict_factory: function, (default: dict)
Factory function to be used to create the node attribute
dict which holds attribute values keyed by attribute name.
It should require no arguments and return a dict-like object
adjlist_outer_dict_factory : function, (default: dict)
Factory function to be used to create the outer-most dict
| (incoming_graph_data=None, multigraph_input=None, **attr) | [
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|
30,223 | networkx.classes.multigraph | __init__ | Initialize a graph with edges, name, or graph attributes.
Parameters
----------
incoming_graph_data : input graph
Data to initialize graph. If incoming_graph_data=None (default)
an empty graph is created. The data can be an edge list, or any
NetworkX graph object. If the corresponding optional Python
packages are installed the data can also be a 2D NumPy array, a
SciPy sparse array, or a PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
convert
Examples
--------
>>> G = nx.MultiGraph()
>>> G = nx.MultiGraph(name="my graph")
>>> e = [(1, 2), (1, 2), (2, 3), (3, 4)] # list of edges
>>> G = nx.MultiGraph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.MultiGraph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
| def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
"""Initialize a graph with edges, name, or graph attributes.
Parameters
----------
incoming_graph_data : input graph
Data to initialize graph. If incoming_graph_data=None (default)
an empty graph is created. The data can be an edge list, or any
NetworkX graph object. If the corresponding optional Python
packages are installed the data can also be a 2D NumPy array, a
SciPy sparse array, or a PyGraphviz graph.
multigraph_input : bool or None (default None)
Note: Only used when `incoming_graph_data` is a dict.
If True, `incoming_graph_data` 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.
A NetworkXError is raised if this is not the case.
If False, :func:`to_networkx_graph` is used to try to determine
the dict's graph data structure as either a dict-of-dict-of-dict
keyed by node to neighbor to edge data, or a dict-of-iterable
keyed by node to neighbors.
If None, the treatment for True is tried, but if it fails,
the treatment for False is tried.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
convert
Examples
--------
>>> G = nx.MultiGraph()
>>> G = nx.MultiGraph(name="my graph")
>>> e = [(1, 2), (1, 2), (2, 3), (3, 4)] # list of edges
>>> G = nx.MultiGraph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.MultiGraph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}
"""
# multigraph_input can be None/True/False. So check "is not False"
if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
Graph.__init__(self)
try:
convert.from_dict_of_dicts(
incoming_graph_data, create_using=self, multigraph_input=True
)
self.graph.update(attr)
except Exception as err:
if multigraph_input is True:
raise nx.NetworkXError(
f"converting multigraph_input raised:\n{type(err)}: {err}"
)
Graph.__init__(self, incoming_graph_data, **attr)
else:
Graph.__init__(self, incoming_graph_data, **attr)
| (self, incoming_graph_data=None, multigraph_input=None, **attr) | [
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|
30,227 | networkx.classes.multigraph | add_edge | Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph.
Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u_for_edge, v_for_edge : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer)
Used to distinguish multiedges between a pair of nodes.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
The edge key assigned to the edge.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
To replace/update edge data, use the optional key argument
to identify a unique edge. Otherwise a new edge will be created.
NetworkX algorithms designed for weighted graphs cannot use
multigraphs directly because it is not clear how to handle
multiedge weights. Convert to Graph using edge attribute
'weight' to enable weighted graph algorithms.
Default keys are generated using the method `new_edge_key()`.
This method can be overridden by subclassing the base class and
providing a custom `new_edge_key()` method.
Examples
--------
The following each add an additional edge e=(1, 2) to graph G:
>>> G = nx.MultiGraph()
>>> e = (1, 2)
>>> ekey = G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
1
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
[2]
Associate data to edges using keywords:
>>> ekey = G.add_edge(1, 2, weight=3)
>>> ekey = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
>>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> ekey = G.add_edge(1, 2)
>>> G[1][2][0].update({0: 5})
>>> G.edges[1, 2, 0].update({0: 5})
| def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
"""Add an edge between u and v.
The nodes u and v will be automatically added if they are
not already in the graph.
Edge attributes can be specified with keywords or by directly
accessing the edge's attribute dictionary. See examples below.
Parameters
----------
u_for_edge, v_for_edge : nodes
Nodes can be, for example, strings or numbers.
Nodes must be hashable (and not None) Python objects.
key : hashable identifier, optional (default=lowest unused integer)
Used to distinguish multiedges between a pair of nodes.
attr : keyword arguments, optional
Edge data (or labels or objects) can be assigned using
keyword arguments.
Returns
-------
The edge key assigned to the edge.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
To replace/update edge data, use the optional key argument
to identify a unique edge. Otherwise a new edge will be created.
NetworkX algorithms designed for weighted graphs cannot use
multigraphs directly because it is not clear how to handle
multiedge weights. Convert to Graph using edge attribute
'weight' to enable weighted graph algorithms.
Default keys are generated using the method `new_edge_key()`.
This method can be overridden by subclassing the base class and
providing a custom `new_edge_key()` method.
Examples
--------
The following each add an additional edge e=(1, 2) to graph G:
>>> G = nx.MultiGraph()
>>> e = (1, 2)
>>> ekey = G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
1
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
[2]
Associate data to edges using keywords:
>>> ekey = G.add_edge(1, 2, weight=3)
>>> ekey = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
>>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> ekey = G.add_edge(1, 2)
>>> G[1][2][0].update({0: 5})
>>> G.edges[1, 2, 0].update({0: 5})
"""
u, v = u_for_edge, v_for_edge
# add nodes
if u not in self._adj:
if u is None:
raise ValueError("None cannot be a node")
self._adj[u] = self.adjlist_inner_dict_factory()
self._node[u] = self.node_attr_dict_factory()
if v not in self._adj:
if v is None:
raise ValueError("None cannot be a node")
self._adj[v] = self.adjlist_inner_dict_factory()
self._node[v] = self.node_attr_dict_factory()
if key is None:
key = self.new_edge_key(u, v)
if v in self._adj[u]:
keydict = self._adj[u][v]
datadict = keydict.get(key, self.edge_attr_dict_factory())
datadict.update(attr)
keydict[key] = datadict
else:
# selfloops work this way without special treatment
datadict = self.edge_attr_dict_factory()
datadict.update(attr)
keydict = self.edge_key_dict_factory()
keydict[key] = datadict
self._adj[u][v] = keydict
self._adj[v][u] = keydict
nx._clear_cache(self)
return key
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|
30,248 | networkx.classes.multigraph | remove_edge | Remove an edge between u and v.
Parameters
----------
u, v : nodes
Remove an edge between nodes u and v.
key : hashable identifier, optional (default=None)
Used to distinguish multiple edges between a pair of nodes.
If None, remove a single edge between u and v. If there are
multiple edges, removes the last edge added in terms of
insertion order.
Raises
------
NetworkXError
If there is not an edge between u and v, or
if there is no edge with the specified key.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.MultiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.remove_edge(0, 1)
>>> e = (1, 2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
For multiple edges
>>> G = nx.MultiGraph() # or MultiDiGraph, etc
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
[0, 1, 2]
When ``key=None`` (the default), edges are removed in the opposite
order that they were added:
>>> G.remove_edge(1, 2)
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 0), (1, 2, 1)])
>>> G.remove_edge(2, 1) # edges are not directed
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 0)])
For edges with keys
>>> G = nx.MultiGraph()
>>> G.add_edge(1, 2, key="first")
'first'
>>> G.add_edge(1, 2, key="second")
'second'
>>> G.remove_edge(1, 2, key="first")
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 'second')])
| def remove_edge(self, u, v, key=None):
"""Remove an edge between u and v.
Parameters
----------
u, v : nodes
Remove an edge between nodes u and v.
key : hashable identifier, optional (default=None)
Used to distinguish multiple edges between a pair of nodes.
If None, remove a single edge between u and v. If there are
multiple edges, removes the last edge added in terms of
insertion order.
Raises
------
NetworkXError
If there is not an edge between u and v, or
if there is no edge with the specified key.
See Also
--------
remove_edges_from : remove a collection of edges
Examples
--------
>>> G = nx.MultiGraph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> G.remove_edge(0, 1)
>>> e = (1, 2)
>>> G.remove_edge(*e) # unpacks e from an edge tuple
For multiple edges
>>> G = nx.MultiGraph() # or MultiDiGraph, etc
>>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
[0, 1, 2]
When ``key=None`` (the default), edges are removed in the opposite
order that they were added:
>>> G.remove_edge(1, 2)
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 0), (1, 2, 1)])
>>> G.remove_edge(2, 1) # edges are not directed
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 0)])
For edges with keys
>>> G = nx.MultiGraph()
>>> G.add_edge(1, 2, key="first")
'first'
>>> G.add_edge(1, 2, key="second")
'second'
>>> G.remove_edge(1, 2, key="first")
>>> G.edges(keys=True)
MultiEdgeView([(1, 2, 'second')])
"""
try:
d = self._adj[u][v]
except KeyError as err:
raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
# remove the edge with specified data
if key is None:
d.popitem()
else:
try:
del d[key]
except KeyError as err:
msg = f"The edge {u}-{v} with key {key} is not in the graph."
raise NetworkXError(msg) from err
if len(d) == 0:
# remove the key entries if last edge
del self._adj[u][v]
if u != v: # check for selfloop
del self._adj[v][u]
nx._clear_cache(self)
| (self, u, v, key=None) | [
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|
30,256 | networkx.classes.multigraph | to_undirected | Returns an undirected copy of the graph.
Returns
-------
G : Graph/MultiGraph
A deepcopy of the graph.
See Also
--------
copy, add_edge, add_edges_from
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar `G = nx.MultiGraph(D)`
which returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiGraph to use dict-like
objects in the data structure, those changes do not transfer
to the MultiGraph created by this method.
Examples
--------
>>> G = nx.MultiGraph([(0, 1), (0, 1), (1, 2)])
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 2, 0), (2, 1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1, 0), (0, 1, 1), (1, 2, 0)]
| def to_undirected(self, as_view=False):
"""Returns an undirected copy of the graph.
Returns
-------
G : Graph/MultiGraph
A deepcopy of the graph.
See Also
--------
copy, add_edge, add_edges_from
Notes
-----
This returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
This is in contrast to the similar `G = nx.MultiGraph(D)`
which returns a shallow copy of the data.
See the Python copy module for more information on shallow
and deep copies, https://docs.python.org/3/library/copy.html.
Warning: If you have subclassed MultiGraph to use dict-like
objects in the data structure, those changes do not transfer
to the MultiGraph created by this method.
Examples
--------
>>> G = nx.MultiGraph([(0, 1), (0, 1), (1, 2)])
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 2, 0), (2, 1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1, 0), (0, 1, 1), (1, 2, 0)]
"""
graph_class = self.to_undirected_class()
if as_view is True:
return nx.graphviews.generic_graph_view(self, graph_class)
# deepcopy when not a view
G = graph_class()
G.graph.update(deepcopy(self.graph))
G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
G.add_edges_from(
(u, v, key, deepcopy(datadict))
for u, nbrs in self._adj.items()
for v, keydict in nbrs.items()
for key, datadict in keydict.items()
)
return G
| (self, as_view=False) | [
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|
30,259 | networkx.exception | NetworkXAlgorithmError | Exception for unexpected termination of algorithms. | class NetworkXAlgorithmError(NetworkXException):
"""Exception for unexpected termination of algorithms."""
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|
30,260 | networkx.exception | NetworkXError | Exception for a serious error in NetworkX | class NetworkXError(NetworkXException):
"""Exception for a serious error in NetworkX"""
| null | [
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|
30,261 | networkx.exception | NetworkXException | Base class for exceptions in NetworkX. | class NetworkXException(Exception):
"""Base class for exceptions in NetworkX."""
| null | [
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|
30,262 | networkx.exception | NetworkXNoCycle | Exception for algorithms that should return a cycle when running
on graphs where such a cycle does not exist. | class NetworkXNoCycle(NetworkXUnfeasible):
"""Exception for algorithms that should return a cycle when running
on graphs where such a cycle does not exist."""
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|
30,263 | networkx.exception | NetworkXNoPath | Exception for algorithms that should return a path when running
on graphs where such a path does not exist. | class NetworkXNoPath(NetworkXUnfeasible):
"""Exception for algorithms that should return a path when running
on graphs where such a path does not exist."""
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|
30,264 | networkx.exception | NetworkXNotImplemented | Exception raised by algorithms not implemented for a type of graph. | class NetworkXNotImplemented(NetworkXException):
"""Exception raised by algorithms not implemented for a type of graph."""
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|
30,265 | networkx.exception | NetworkXPointlessConcept | Raised when a null graph is provided as input to an algorithm
that cannot use it.
The null graph is sometimes considered a pointless concept [1]_,
thus the name of the exception.
References
----------
.. [1] Harary, F. and Read, R. "Is the Null Graph a Pointless
Concept?" In Graphs and Combinatorics Conference, George
Washington University. New York: Springer-Verlag, 1973.
| class NetworkXPointlessConcept(NetworkXException):
"""Raised when a null graph is provided as input to an algorithm
that cannot use it.
The null graph is sometimes considered a pointless concept [1]_,
thus the name of the exception.
References
----------
.. [1] Harary, F. and Read, R. "Is the Null Graph a Pointless
Concept?" In Graphs and Combinatorics Conference, George
Washington University. New York: Springer-Verlag, 1973.
"""
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|
30,266 | networkx.algorithms.chordal | NetworkXTreewidthBoundExceeded | Exception raised when a treewidth bound has been provided and it has
been exceeded | class NetworkXTreewidthBoundExceeded(nx.NetworkXException):
"""Exception raised when a treewidth bound has been provided and it has
been exceeded"""
| null | [
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|
30,267 | networkx.exception | NetworkXUnbounded | Exception raised by algorithms trying to solve a maximization
or a minimization problem instance that is unbounded. | class NetworkXUnbounded(NetworkXAlgorithmError):
"""Exception raised by algorithms trying to solve a maximization
or a minimization problem instance that is unbounded."""
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|
30,268 | networkx.exception | NetworkXUnfeasible | Exception raised by algorithms trying to solve a problem
instance that has no feasible solution. | class NetworkXUnfeasible(NetworkXAlgorithmError):
"""Exception raised by algorithms trying to solve a problem
instance that has no feasible solution."""
| null | [
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|
30,269 | networkx.exception | NodeNotFound | Exception raised if requested node is not present in the graph | class NodeNotFound(NetworkXException):
"""Exception raised if requested node is not present in the graph"""
| null | [
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]
|
30,270 | networkx.algorithms.tree.coding | NotATree | Raised when a function expects a tree (that is, a connected
undirected graph with no cycles) but gets a non-tree graph as input
instead.
| class NotATree(nx.NetworkXException):
"""Raised when a function expects a tree (that is, a connected
undirected graph with no cycles) but gets a non-tree graph as input
instead.
"""
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|
30,271 | networkx.algorithms.planarity | PlanarEmbedding | Represents a planar graph with its planar embedding.
The planar embedding is given by a `combinatorial embedding
<https://en.wikipedia.org/wiki/Graph_embedding#Combinatorial_embedding>`_.
.. note:: `check_planarity` is the preferred way to check if a graph is planar.
**Neighbor ordering:**
In comparison to a usual graph structure, the embedding also stores the
order of all neighbors for every vertex.
The order of the neighbors can be given in clockwise (cw) direction or
counterclockwise (ccw) direction. This order is stored as edge attributes
in the underlying directed graph. For the edge (u, v) the edge attribute
'cw' is set to the neighbor of u that follows immediately after v in
clockwise direction.
In order for a PlanarEmbedding to be valid it must fulfill multiple
conditions. It is possible to check if these conditions are fulfilled with
the method :meth:`check_structure`.
The conditions are:
* Edges must go in both directions (because the edge attributes differ)
* Every edge must have a 'cw' and 'ccw' attribute which corresponds to a
correct planar embedding.
As long as a PlanarEmbedding is invalid only the following methods should
be called:
* :meth:`add_half_edge`
* :meth:`connect_components`
Even though the graph is a subclass of nx.DiGraph, it can still be used
for algorithms that require undirected graphs, because the method
:meth:`is_directed` is overridden. This is possible, because a valid
PlanarGraph must have edges in both directions.
**Half edges:**
In methods like `add_half_edge` the term "half-edge" is used, which is
a term that is used in `doubly connected edge lists
<https://en.wikipedia.org/wiki/Doubly_connected_edge_list>`_. It is used
to emphasize that the edge is only in one direction and there exists
another half-edge in the opposite direction.
While conventional edges always have two faces (including outer face) next
to them, it is possible to assign each half-edge *exactly one* face.
For a half-edge (u, v) that is oriented such that u is below v then the
face that belongs to (u, v) is to the right of this half-edge.
See Also
--------
is_planar :
Preferred way to check if an existing graph is planar.
check_planarity :
A convenient way to create a `PlanarEmbedding`. If not planar,
it returns a subgraph that shows this.
Examples
--------
Create an embedding of a star graph (compare `nx.star_graph(3)`):
>>> G = nx.PlanarEmbedding()
>>> G.add_half_edge(0, 1)
>>> G.add_half_edge(0, 2, ccw=1)
>>> G.add_half_edge(0, 3, ccw=2)
>>> G.add_half_edge(1, 0)
>>> G.add_half_edge(2, 0)
>>> G.add_half_edge(3, 0)
Alternatively the same embedding can also be defined in counterclockwise
orientation. The following results in exactly the same PlanarEmbedding:
>>> G = nx.PlanarEmbedding()
>>> G.add_half_edge(0, 1)
>>> G.add_half_edge(0, 3, cw=1)
>>> G.add_half_edge(0, 2, cw=3)
>>> G.add_half_edge(1, 0)
>>> G.add_half_edge(2, 0)
>>> G.add_half_edge(3, 0)
After creating a graph, it is possible to validate that the PlanarEmbedding
object is correct:
>>> G.check_structure()
| class PlanarEmbedding(nx.DiGraph):
"""Represents a planar graph with its planar embedding.
The planar embedding is given by a `combinatorial embedding
<https://en.wikipedia.org/wiki/Graph_embedding#Combinatorial_embedding>`_.
.. note:: `check_planarity` is the preferred way to check if a graph is planar.
**Neighbor ordering:**
In comparison to a usual graph structure, the embedding also stores the
order of all neighbors for every vertex.
The order of the neighbors can be given in clockwise (cw) direction or
counterclockwise (ccw) direction. This order is stored as edge attributes
in the underlying directed graph. For the edge (u, v) the edge attribute
'cw' is set to the neighbor of u that follows immediately after v in
clockwise direction.
In order for a PlanarEmbedding to be valid it must fulfill multiple
conditions. It is possible to check if these conditions are fulfilled with
the method :meth:`check_structure`.
The conditions are:
* Edges must go in both directions (because the edge attributes differ)
* Every edge must have a 'cw' and 'ccw' attribute which corresponds to a
correct planar embedding.
As long as a PlanarEmbedding is invalid only the following methods should
be called:
* :meth:`add_half_edge`
* :meth:`connect_components`
Even though the graph is a subclass of nx.DiGraph, it can still be used
for algorithms that require undirected graphs, because the method
:meth:`is_directed` is overridden. This is possible, because a valid
PlanarGraph must have edges in both directions.
**Half edges:**
In methods like `add_half_edge` the term "half-edge" is used, which is
a term that is used in `doubly connected edge lists
<https://en.wikipedia.org/wiki/Doubly_connected_edge_list>`_. It is used
to emphasize that the edge is only in one direction and there exists
another half-edge in the opposite direction.
While conventional edges always have two faces (including outer face) next
to them, it is possible to assign each half-edge *exactly one* face.
For a half-edge (u, v) that is oriented such that u is below v then the
face that belongs to (u, v) is to the right of this half-edge.
See Also
--------
is_planar :
Preferred way to check if an existing graph is planar.
check_planarity :
A convenient way to create a `PlanarEmbedding`. If not planar,
it returns a subgraph that shows this.
Examples
--------
Create an embedding of a star graph (compare `nx.star_graph(3)`):
>>> G = nx.PlanarEmbedding()
>>> G.add_half_edge(0, 1)
>>> G.add_half_edge(0, 2, ccw=1)
>>> G.add_half_edge(0, 3, ccw=2)
>>> G.add_half_edge(1, 0)
>>> G.add_half_edge(2, 0)
>>> G.add_half_edge(3, 0)
Alternatively the same embedding can also be defined in counterclockwise
orientation. The following results in exactly the same PlanarEmbedding:
>>> G = nx.PlanarEmbedding()
>>> G.add_half_edge(0, 1)
>>> G.add_half_edge(0, 3, cw=1)
>>> G.add_half_edge(0, 2, cw=3)
>>> G.add_half_edge(1, 0)
>>> G.add_half_edge(2, 0)
>>> G.add_half_edge(3, 0)
After creating a graph, it is possible to validate that the PlanarEmbedding
object is correct:
>>> G.check_structure()
"""
def __init__(self, incoming_graph_data=None, **attr):
super().__init__(incoming_graph_data=incoming_graph_data, **attr)
self.add_edge = self.__forbidden
self.add_edges_from = self.__forbidden
self.add_weighted_edges_from = self.__forbidden
def __forbidden(self, *args, **kwargs):
"""Forbidden operation
Any edge additions to a PlanarEmbedding should be done using
method `add_half_edge`.
"""
raise NotImplementedError(
"Use `add_half_edge` method to add edges to a PlanarEmbedding."
)
def get_data(self):
"""Converts the adjacency structure into a better readable structure.
Returns
-------
embedding : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
set_data
"""
embedding = {}
for v in self:
embedding[v] = list(self.neighbors_cw_order(v))
return embedding
def set_data(self, data):
"""Inserts edges according to given sorted neighbor list.
The input format is the same as the output format of get_data().
Parameters
----------
data : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
get_data
"""
for v in data:
ref = None
for w in reversed(data[v]):
self.add_half_edge(v, w, cw=ref)
ref = w
def remove_node(self, n):
"""Remove node n.
Removes the node n and all adjacent edges, updating the
PlanarEmbedding to account for any resulting edge removal.
Attempting to remove a non-existent node will raise an exception.
Parameters
----------
n : node
A node in the graph
Raises
------
NetworkXError
If n is not in the graph.
See Also
--------
remove_nodes_from
"""
try:
for u in self._pred[n]:
succs_u = self._succ[u]
un_cw = succs_u[n]["cw"]
un_ccw = succs_u[n]["ccw"]
del succs_u[n]
del self._pred[u][n]
if n != un_cw:
succs_u[un_cw]["ccw"] = un_ccw
succs_u[un_ccw]["cw"] = un_cw
del self._node[n]
del self._succ[n]
del self._pred[n]
except KeyError as err: # NetworkXError if n not in self
raise nx.NetworkXError(
f"The node {n} is not in the planar embedding."
) from err
nx._clear_cache(self)
def remove_nodes_from(self, nodes):
"""Remove multiple nodes.
Parameters
----------
nodes : iterable container
A container of nodes (list, dict, set, etc.). If a node
in the container is not in the graph it is silently ignored.
See Also
--------
remove_node
Notes
-----
When removing nodes from an iterator over the graph you are changing,
a `RuntimeError` will be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_nodes)`, and pass this
object to `G.remove_nodes_from`.
"""
for n in nodes:
if n in self._node:
self.remove_node(n)
# silently skip non-existing nodes
def neighbors_cw_order(self, v):
"""Generator for the neighbors of v in clockwise order.
Parameters
----------
v : node
Yields
------
node
"""
succs = self._succ[v]
if not succs:
# v has no neighbors
return
start_node = next(reversed(succs))
yield start_node
current_node = succs[start_node]["cw"]
while start_node != current_node:
yield current_node
current_node = succs[current_node]["cw"]
def add_half_edge(self, start_node, end_node, *, cw=None, ccw=None):
"""Adds a half-edge from `start_node` to `end_node`.
If the half-edge is not the first one out of `start_node`, a reference
node must be provided either in the clockwise (parameter `cw`) or in
the counterclockwise (parameter `ccw`) direction. Only one of `cw`/`ccw`
can be specified (or neither in the case of the first edge).
Note that specifying | (incoming_graph_data=None, **attr) | [
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|
30,272 | networkx.algorithms.planarity | __forbidden | Forbidden operation
Any edge additions to a PlanarEmbedding should be done using
method `add_half_edge`.
| def __forbidden(self, *args, **kwargs):
"""Forbidden operation
Any edge additions to a PlanarEmbedding should be done using
method `add_half_edge`.
"""
raise NotImplementedError(
"Use `add_half_edge` method to add edges to a PlanarEmbedding."
)
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|
30,275 | networkx.algorithms.planarity | __init__ | null | def __init__(self, incoming_graph_data=None, **attr):
super().__init__(incoming_graph_data=incoming_graph_data, **attr)
self.add_edge = self.__forbidden
self.add_edges_from = self.__forbidden
self.add_weighted_edges_from = self.__forbidden
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|
30,281 | networkx.algorithms.planarity | add_half_edge | Adds a half-edge from `start_node` to `end_node`.
If the half-edge is not the first one out of `start_node`, a reference
node must be provided either in the clockwise (parameter `cw`) or in
the counterclockwise (parameter `ccw`) direction. Only one of `cw`/`ccw`
can be specified (or neither in the case of the first edge).
Note that specifying a reference in the clockwise (`cw`) direction means
inserting the new edge in the first counterclockwise position with
respect to the reference (and vice-versa).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
cw, ccw: node
End node of reference edge.
Omit or pass `None` if adding the first out-half-edge of `start_node`.
Raises
------
NetworkXException
If the `cw` or `ccw` node is not a successor of `start_node`.
If `start_node` has successors, but neither `cw` or `ccw` is provided.
If both `cw` and `ccw` are specified.
See Also
--------
connect_components
| def add_half_edge(self, start_node, end_node, *, cw=None, ccw=None):
"""Adds a half-edge from `start_node` to `end_node`.
If the half-edge is not the first one out of `start_node`, a reference
node must be provided either in the clockwise (parameter `cw`) or in
the counterclockwise (parameter `ccw`) direction. Only one of `cw`/`ccw`
can be specified (or neither in the case of the first edge).
Note that specifying a reference in the clockwise (`cw`) direction means
inserting the new edge in the first counterclockwise position with
respect to the reference (and vice-versa).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
cw, ccw: node
End node of reference edge.
Omit or pass `None` if adding the first out-half-edge of `start_node`.
Raises
------
NetworkXException
If the `cw` or `ccw` node is not a successor of `start_node`.
If `start_node` has successors, but neither `cw` or `ccw` is provided.
If both `cw` and `ccw` are specified.
See Also
--------
connect_components
"""
succs = self._succ.get(start_node)
if succs:
# there is already some edge out of start_node
leftmost_nbr = next(reversed(self._succ[start_node]))
if cw is not None:
if cw not in succs:
raise nx.NetworkXError("Invalid clockwise reference node.")
if ccw is not None:
raise nx.NetworkXError("Only one of cw/ccw can be specified.")
ref_ccw = succs[cw]["ccw"]
super().add_edge(start_node, end_node, cw=cw, ccw=ref_ccw)
succs[ref_ccw]["cw"] = end_node
succs[cw]["ccw"] = end_node
# when (cw == leftmost_nbr), the newly added neighbor is
# already at the end of dict self._succ[start_node] and
# takes the place of the former leftmost_nbr
move_leftmost_nbr_to_end = cw != leftmost_nbr
elif ccw is not None:
if ccw not in succs:
raise nx.NetworkXError("Invalid counterclockwise reference node.")
ref_cw = succs[ccw]["cw"]
super().add_edge(start_node, end_node, cw=ref_cw, ccw=ccw)
succs[ref_cw]["ccw"] = end_node
succs[ccw]["cw"] = end_node
move_leftmost_nbr_to_end = True
else:
raise nx.NetworkXError(
"Node already has out-half-edge(s), either cw or ccw reference node required."
)
if move_leftmost_nbr_to_end:
# LRPlanarity (via self.add_half_edge_first()) requires that
# we keep track of the leftmost neighbor, which we accomplish
# by keeping it as the last key in dict self._succ[start_node]
succs[leftmost_nbr] = succs.pop(leftmost_nbr)
else:
if cw is not None or ccw is not None:
raise nx.NetworkXError("Invalid reference node.")
# adding the first edge out of start_node
super().add_edge(start_node, end_node, ccw=end_node, cw=end_node)
| (self, start_node, end_node, *, cw=None, ccw=None) | [
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|
30,282 | networkx.algorithms.planarity | add_half_edge_ccw | Adds a half-edge from start_node to end_node.
The half-edge is added counter clockwise next to the existing half-edge
(start_node, reference_neighbor).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
reference_neighbor: node
End node of reference edge.
Raises
------
NetworkXException
If the reference_neighbor does not exist.
See Also
--------
add_half_edge
add_half_edge_cw
connect_components
| def add_half_edge_ccw(self, start_node, end_node, reference_neighbor):
"""Adds a half-edge from start_node to end_node.
The half-edge is added counter clockwise next to the existing half-edge
(start_node, reference_neighbor).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
reference_neighbor: node
End node of reference edge.
Raises
------
NetworkXException
If the reference_neighbor does not exist.
See Also
--------
add_half_edge
add_half_edge_cw
connect_components
"""
self.add_half_edge(start_node, end_node, cw=reference_neighbor)
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|
30,283 | networkx.algorithms.planarity | add_half_edge_cw | Adds a half-edge from start_node to end_node.
The half-edge is added clockwise next to the existing half-edge
(start_node, reference_neighbor).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
reference_neighbor: node
End node of reference edge.
Raises
------
NetworkXException
If the reference_neighbor does not exist.
See Also
--------
add_half_edge
add_half_edge_ccw
connect_components
| def add_half_edge_cw(self, start_node, end_node, reference_neighbor):
"""Adds a half-edge from start_node to end_node.
The half-edge is added clockwise next to the existing half-edge
(start_node, reference_neighbor).
Parameters
----------
start_node : node
Start node of inserted edge.
end_node : node
End node of inserted edge.
reference_neighbor: node
End node of reference edge.
Raises
------
NetworkXException
If the reference_neighbor does not exist.
See Also
--------
add_half_edge
add_half_edge_ccw
connect_components
"""
self.add_half_edge(start_node, end_node, ccw=reference_neighbor)
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|
30,284 | networkx.algorithms.planarity | add_half_edge_first | Add a half-edge and set end_node as start_node's leftmost neighbor.
The new edge is inserted counterclockwise with respect to the current
leftmost neighbor, if there is one.
Parameters
----------
start_node : node
end_node : node
See Also
--------
add_half_edge
connect_components
| def add_half_edge_first(self, start_node, end_node):
"""Add a half-edge and set end_node as start_node's leftmost neighbor.
The new edge is inserted counterclockwise with respect to the current
leftmost neighbor, if there is one.
Parameters
----------
start_node : node
end_node : node
See Also
--------
add_half_edge
connect_components
"""
succs = self._succ.get(start_node)
# the leftmost neighbor is the last entry in the
# self._succ[start_node] dict
leftmost_nbr = next(reversed(succs)) if succs else None
self.add_half_edge(start_node, end_node, cw=leftmost_nbr)
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|
30,289 | networkx.algorithms.planarity | check_structure | Runs without exceptions if this object is valid.
Checks that the following properties are fulfilled:
* Edges go in both directions (because the edge attributes differ).
* Every edge has a 'cw' and 'ccw' attribute which corresponds to a
correct planar embedding.
Running this method verifies that the underlying Graph must be planar.
Raises
------
NetworkXException
This exception is raised with a short explanation if the
PlanarEmbedding is invalid.
| def check_structure(self):
"""Runs without exceptions if this object is valid.
Checks that the following properties are fulfilled:
* Edges go in both directions (because the edge attributes differ).
* Every edge has a 'cw' and 'ccw' attribute which corresponds to a
correct planar embedding.
Running this method verifies that the underlying Graph must be planar.
Raises
------
NetworkXException
This exception is raised with a short explanation if the
PlanarEmbedding is invalid.
"""
# Check fundamental structure
for v in self:
try:
sorted_nbrs = set(self.neighbors_cw_order(v))
except KeyError as err:
msg = f"Bad embedding. Missing orientation for a neighbor of {v}"
raise nx.NetworkXException(msg) from err
unsorted_nbrs = set(self[v])
if sorted_nbrs != unsorted_nbrs:
msg = "Bad embedding. Edge orientations not set correctly."
raise nx.NetworkXException(msg)
for w in self[v]:
# Check if opposite half-edge exists
if not self.has_edge(w, v):
msg = "Bad embedding. Opposite half-edge is missing."
raise nx.NetworkXException(msg)
# Check planarity
counted_half_edges = set()
for component in nx.connected_components(self):
if len(component) == 1:
# Don't need to check single node component
continue
num_nodes = len(component)
num_half_edges = 0
num_faces = 0
for v in component:
for w in self.neighbors_cw_order(v):
num_half_edges += 1
if (v, w) not in counted_half_edges:
# We encountered a new face
num_faces += 1
# Mark all half-edges belonging to this face
self.traverse_face(v, w, counted_half_edges)
num_edges = num_half_edges // 2 # num_half_edges is even
if num_nodes - num_edges + num_faces != 2:
# The result does not match Euler's formula
msg = "Bad embedding. The graph does not match Euler's formula"
raise nx.NetworkXException(msg)
| (self) | [
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|
30,292 | networkx.algorithms.planarity | connect_components | Adds half-edges for (v, w) and (w, v) at some position.
This method should only be called if v and w are in different
components, or it might break the embedding.
This especially means that if `connect_components(v, w)`
is called it is not allowed to call `connect_components(w, v)`
afterwards. The neighbor orientations in both directions are
all set correctly after the first call.
Parameters
----------
v : node
w : node
See Also
--------
add_half_edge
| def connect_components(self, v, w):
"""Adds half-edges for (v, w) and (w, v) at some position.
This method should only be called if v and w are in different
components, or it might break the embedding.
This especially means that if `connect_components(v, w)`
is called it is not allowed to call `connect_components(w, v)`
afterwards. The neighbor orientations in both directions are
all set correctly after the first call.
Parameters
----------
v : node
w : node
See Also
--------
add_half_edge
"""
if v in self._succ and self._succ[v]:
ref = next(reversed(self._succ[v]))
else:
ref = None
self.add_half_edge(v, w, cw=ref)
if w in self._succ and self._succ[w]:
ref = next(reversed(self._succ[w]))
else:
ref = None
self.add_half_edge(w, v, cw=ref)
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]
|
30,293 | networkx.algorithms.planarity | copy | null | def copy(self, as_view=False):
if as_view is True:
return nx.graphviews.generic_graph_view(self)
G = self.__class__()
G.graph.update(self.graph)
G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
super(self.__class__, G).add_edges_from(
(u, v, datadict.copy())
for u, nbrs in self._adj.items()
for v, datadict in nbrs.items()
)
return G
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|
30,295 | networkx.algorithms.planarity | get_data | Converts the adjacency structure into a better readable structure.
Returns
-------
embedding : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
set_data
| def get_data(self):
"""Converts the adjacency structure into a better readable structure.
Returns
-------
embedding : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
set_data
"""
embedding = {}
for v in self:
embedding[v] = list(self.neighbors_cw_order(v))
return embedding
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|
30,301 | networkx.algorithms.planarity | is_directed | A valid PlanarEmbedding is undirected.
All reverse edges are contained, i.e. for every existing
half-edge (v, w) the half-edge in the opposite direction (w, v) is also
contained.
| def is_directed(self):
"""A valid PlanarEmbedding is undirected.
All reverse edges are contained, i.e. for every existing
half-edge (v, w) the half-edge in the opposite direction (w, v) is also
contained.
"""
return False
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|
30,305 | networkx.algorithms.planarity | neighbors_cw_order | Generator for the neighbors of v in clockwise order.
Parameters
----------
v : node
Yields
------
node
| def neighbors_cw_order(self, v):
"""Generator for the neighbors of v in clockwise order.
Parameters
----------
v : node
Yields
------
node
"""
succs = self._succ[v]
if not succs:
# v has no neighbors
return
start_node = next(reversed(succs))
yield start_node
current_node = succs[start_node]["cw"]
while start_node != current_node:
yield current_node
current_node = succs[current_node]["cw"]
| (self, v) | [
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|
30,306 | networkx.algorithms.planarity | next_face_half_edge | Returns the following half-edge left of a face.
Parameters
----------
v : node
w : node
Returns
-------
half-edge : tuple
| def next_face_half_edge(self, v, w):
"""Returns the following half-edge left of a face.
Parameters
----------
v : node
w : node
Returns
-------
half-edge : tuple
"""
new_node = self[w][v]["ccw"]
return w, new_node
| (self, v, w) | [
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|
30,311 | networkx.algorithms.planarity | remove_edge | Remove the edge between u and v.
Parameters
----------
u, v : nodes
Remove the half-edges (u, v) and (v, u) and update the
edge ordering around the removed edge.
Raises
------
NetworkXError
If there is not an edge between u and v.
See Also
--------
remove_edges_from : remove a collection of edges
| def remove_edge(self, u, v):
"""Remove the edge between u and v.
Parameters
----------
u, v : nodes
Remove the half-edges (u, v) and (v, u) and update the
edge ordering around the removed edge.
Raises
------
NetworkXError
If there is not an edge between u and v.
See Also
--------
remove_edges_from : remove a collection of edges
"""
try:
succs_u = self._succ[u]
succs_v = self._succ[v]
uv_cw = succs_u[v]["cw"]
uv_ccw = succs_u[v]["ccw"]
vu_cw = succs_v[u]["cw"]
vu_ccw = succs_v[u]["ccw"]
del succs_u[v]
del self._pred[v][u]
del succs_v[u]
del self._pred[u][v]
if v != uv_cw:
succs_u[uv_cw]["ccw"] = uv_ccw
succs_u[uv_ccw]["cw"] = uv_cw
if u != vu_cw:
succs_v[vu_cw]["ccw"] = vu_ccw
succs_v[vu_ccw]["cw"] = vu_cw
except KeyError as err:
raise nx.NetworkXError(
f"The edge {u}-{v} is not in the planar embedding."
) from err
nx._clear_cache(self)
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|
30,312 | networkx.algorithms.planarity | remove_edges_from | Remove all edges specified in ebunch.
Parameters
----------
ebunch: list or container of edge tuples
Each pair of half-edges between the nodes given in the tuples
will be removed from the graph. The nodes can be passed as:
- 2-tuples (u, v) half-edges (u, v) and (v, u).
- 3-tuples (u, v, k) where k is ignored.
See Also
--------
remove_edge : remove a single edge
Notes
-----
Will fail silently if an edge in ebunch is not in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
| def remove_edges_from(self, ebunch):
"""Remove all edges specified in ebunch.
Parameters
----------
ebunch: list or container of edge tuples
Each pair of half-edges between the nodes given in the tuples
will be removed from the graph. The nodes can be passed as:
- 2-tuples (u, v) half-edges (u, v) and (v, u).
- 3-tuples (u, v, k) where k is ignored.
See Also
--------
remove_edge : remove a single edge
Notes
-----
Will fail silently if an edge in ebunch is not in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
"""
for e in ebunch:
u, v = e[:2] # ignore edge data
# assuming that the PlanarEmbedding is valid, if the half_edge
# (u, v) is in the graph, then so is half_edge (v, u)
if u in self._succ and v in self._succ[u]:
self.remove_edge(u, v)
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|
30,313 | networkx.algorithms.planarity | remove_node | Remove node n.
Removes the node n and all adjacent edges, updating the
PlanarEmbedding to account for any resulting edge removal.
Attempting to remove a non-existent node will raise an exception.
Parameters
----------
n : node
A node in the graph
Raises
------
NetworkXError
If n is not in the graph.
See Also
--------
remove_nodes_from
| def remove_node(self, n):
"""Remove node n.
Removes the node n and all adjacent edges, updating the
PlanarEmbedding to account for any resulting edge removal.
Attempting to remove a non-existent node will raise an exception.
Parameters
----------
n : node
A node in the graph
Raises
------
NetworkXError
If n is not in the graph.
See Also
--------
remove_nodes_from
"""
try:
for u in self._pred[n]:
succs_u = self._succ[u]
un_cw = succs_u[n]["cw"]
un_ccw = succs_u[n]["ccw"]
del succs_u[n]
del self._pred[u][n]
if n != un_cw:
succs_u[un_cw]["ccw"] = un_ccw
succs_u[un_ccw]["cw"] = un_cw
del self._node[n]
del self._succ[n]
del self._pred[n]
except KeyError as err: # NetworkXError if n not in self
raise nx.NetworkXError(
f"The node {n} is not in the planar embedding."
) from err
nx._clear_cache(self)
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|
30,314 | networkx.algorithms.planarity | remove_nodes_from | Remove multiple nodes.
Parameters
----------
nodes : iterable container
A container of nodes (list, dict, set, etc.). If a node
in the container is not in the graph it is silently ignored.
See Also
--------
remove_node
Notes
-----
When removing nodes from an iterator over the graph you are changing,
a `RuntimeError` will be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_nodes)`, and pass this
object to `G.remove_nodes_from`.
| def remove_nodes_from(self, nodes):
"""Remove multiple nodes.
Parameters
----------
nodes : iterable container
A container of nodes (list, dict, set, etc.). If a node
in the container is not in the graph it is silently ignored.
See Also
--------
remove_node
Notes
-----
When removing nodes from an iterator over the graph you are changing,
a `RuntimeError` will be raised with message:
`RuntimeError: dictionary changed size during iteration`. This
happens when the graph's underlying dictionary is modified during
iteration. To avoid this error, evaluate the iterator into a separate
object, e.g. by using `list(iterator_of_nodes)`, and pass this
object to `G.remove_nodes_from`.
"""
for n in nodes:
if n in self._node:
self.remove_node(n)
# silently skip non-existing nodes
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|
30,316 | networkx.algorithms.planarity | set_data | Inserts edges according to given sorted neighbor list.
The input format is the same as the output format of get_data().
Parameters
----------
data : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
get_data
| def set_data(self, data):
"""Inserts edges according to given sorted neighbor list.
The input format is the same as the output format of get_data().
Parameters
----------
data : dict
A dict mapping all nodes to a list of neighbors sorted in
clockwise order.
See Also
--------
get_data
"""
for v in data:
ref = None
for w in reversed(data[v]):
self.add_half_edge(v, w, cw=ref)
ref = w
| (self, data) | [
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|
30,324 | networkx.algorithms.planarity | traverse_face | Returns nodes on the face that belong to the half-edge (v, w).
The face that is traversed lies to the right of the half-edge (in an
orientation where v is below w).
Optionally it is possible to pass a set to which all encountered half
edges are added. Before calling this method, this set must not include
any half-edges that belong to the face.
Parameters
----------
v : node
Start node of half-edge.
w : node
End node of half-edge.
mark_half_edges: set, optional
Set to which all encountered half-edges are added.
Returns
-------
face : list
A list of nodes that lie on this face.
| def traverse_face(self, v, w, mark_half_edges=None):
"""Returns nodes on the face that belong to the half-edge (v, w).
The face that is traversed lies to the right of the half-edge (in an
orientation where v is below w).
Optionally it is possible to pass a set to which all encountered half
edges are added. Before calling this method, this set must not include
any half-edges that belong to the face.
Parameters
----------
v : node
Start node of half-edge.
w : node
End node of half-edge.
mark_half_edges: set, optional
Set to which all encountered half-edges are added.
Returns
-------
face : list
A list of nodes that lie on this face.
"""
if mark_half_edges is None:
mark_half_edges = set()
face_nodes = [v]
mark_half_edges.add((v, w))
prev_node = v
cur_node = w
# Last half-edge is (incoming_node, v)
incoming_node = self[v][w]["cw"]
while cur_node != v or prev_node != incoming_node:
face_nodes.append(cur_node)
prev_node, cur_node = self.next_face_half_edge(prev_node, cur_node)
if (prev_node, cur_node) in mark_half_edges:
raise nx.NetworkXException("Bad planar embedding. Impossible face.")
mark_half_edges.add((prev_node, cur_node))
return face_nodes
| (self, v, w, mark_half_edges=None) | [
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|
30,326 | networkx.exception | PowerIterationFailedConvergence | Raised when the power iteration method fails to converge within a
specified iteration limit.
`num_iterations` is the number of iterations that have been
completed when this exception was raised.
| class PowerIterationFailedConvergence(ExceededMaxIterations):
"""Raised when the power iteration method fails to converge within a
specified iteration limit.
`num_iterations` is the number of iterations that have been
completed when this exception was raised.
"""
def __init__(self, num_iterations, *args, **kw):
msg = f"power iteration failed to converge within {num_iterations} iterations"
exception_message = msg
superinit = super().__init__
superinit(self, exception_message, *args, **kw)
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|
30,327 | networkx.exception | __init__ | null | def __init__(self, num_iterations, *args, **kw):
msg = f"power iteration failed to converge within {num_iterations} iterations"
exception_message = msg
superinit = super().__init__
superinit(self, exception_message, *args, **kw)
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|
30,328 | networkx.algorithms.tree.mst | SpanningTreeIterator |
Iterate over all spanning trees of a graph in either increasing or
decreasing cost.
Notes
-----
This iterator uses the partition scheme from [1]_ (included edges,
excluded edges and open edges) as well as a modified Kruskal's Algorithm
to generate minimum spanning trees which respect the partition of edges.
For spanning trees with the same weight, ties are broken arbitrarily.
References
----------
.. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning
trees in order of increasing cost, Pesquisa Operacional, 2005-08,
Vol. 25 (2), p. 219-229,
https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en
| class SpanningTreeIterator:
"""
Iterate over all spanning trees of a graph in either increasing or
decreasing cost.
Notes
-----
This iterator uses the partition scheme from [1]_ (included edges,
excluded edges and open edges) as well as a modified Kruskal's Algorithm
to generate minimum spanning trees which respect the partition of edges.
For spanning trees with the same weight, ties are broken arbitrarily.
References
----------
.. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning
trees in order of increasing cost, Pesquisa Operacional, 2005-08,
Vol. 25 (2), p. 219-229,
https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en
"""
@dataclass(order=True)
class Partition:
"""
This dataclass represents a partition and stores a dict with the edge
data and the weight of the minimum spanning tree of the partition dict.
"""
mst_weight: float
partition_dict: dict = field(compare=False)
def __copy__(self):
return SpanningTreeIterator.Partition(
self.mst_weight, self.partition_dict.copy()
)
def __init__(self, G, weight="weight", minimum=True, ignore_nan=False):
"""
Initialize the iterator
Parameters
----------
G : nx.Graph
The directed graph which we need to iterate trees over
weight : String, default = "weight"
The edge attribute used to store the weight of the edge
minimum : bool, default = True
Return the trees in increasing order while true and decreasing order
while false.
ignore_nan : bool, default = False
If a NaN is found as an edge weight normally an exception is raised.
If `ignore_nan is True` then that edge is ignored instead.
"""
self.G = G.copy()
self.G.__networkx_cache__ = None # Disable caching
self.weight = weight
self.minimum = minimum
self.ignore_nan = ignore_nan
# Randomly create a key for an edge attribute to hold the partition data
self.partition_key = (
"SpanningTreeIterators super secret partition attribute name"
)
def __iter__(self):
"""
Returns
-------
SpanningTreeIterator
The iterator object for this graph
"""
self.partition_queue = PriorityQueue()
self._clear_partition(self.G)
mst_weight = partition_spanning_tree(
self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
).size(weight=self.weight)
self.partition_queue.put(
self.Partition(mst_weight if self.minimum else -mst_weight, {})
)
return self
def __next__(self):
"""
Returns
-------
(multi)Graph
The spanning tree of next greatest weight, which ties broken
arbitrarily.
"""
if self.partition_queue.empty():
del self.G, self.partition_queue
raise StopIteration
partition = self.partition_queue.get()
self._write_partition(partition)
next_tree = partition_spanning_tree(
self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
)
self._partition(partition, next_tree)
self._clear_partition(next_tree)
return next_tree
def _partition(self, partition, partition_tree):
"""
Create new partitions based of the minimum spanning tree of the
current minimum partition.
Parameters
----------
partition : Partition
The Partition instance used to generate the current minimum spanning
tree.
partition_tree : nx.Graph
The minimum spanning tree of the input partition.
"""
# create two new partitions with the data from the input partition dict
p1 = self.Partition(0, partition.partition_dict.copy())
p2 = self.Partition(0, partition.partition_dict.copy())
for e in partition_tree.edges:
# determine if the edge was open or included
if e not in partition.partition_dict:
# This is an open edge
p1.partition_dict[e] = EdgePartition.EXCLUDED
p2.partition_dict[e] = EdgePartition.INCLUDED
self._write_partition(p1)
p1_mst = partition_spanning_tree(
self.G,
self.minimum,
self.weight,
self.partition_key,
self.ignore_nan,
)
p1_mst_weight = p1_mst.size(weight=self.weight)
if nx.is_connected(p1_mst):
p1.mst_weight = p1_mst_weight if self.minimum else -p1_mst_weight
self.partition_queue.put(p1.__copy__())
p1.partition_dict = p2.partition_dict.copy()
def _write_partition(self, partition):
"""
Writes the desired partition into the graph to calculate the minimum
spanning tree.
Parameters
----------
partition : Partition
A Partition dataclass describing a partition on the edges of the
graph.
"""
for u, v, d in self.G.edges(data=True):
if (u, v) in partition.partition_dict:
d[self.partition_key] = partition.partition_dict[(u, v)]
else:
d[self.partition_key] = EdgePartition.OPEN
def _clear_partition(self, G):
"""
Removes partition data from the graph
"""
for u, v, d in G.edges(data=True):
if self.partition_key in d:
del d[self.partition_key]
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|
30,329 | networkx.algorithms.tree.mst | __init__ |
Initialize the iterator
Parameters
----------
G : nx.Graph
The directed graph which we need to iterate trees over
weight : String, default = "weight"
The edge attribute used to store the weight of the edge
minimum : bool, default = True
Return the trees in increasing order while true and decreasing order
while false.
ignore_nan : bool, default = False
If a NaN is found as an edge weight normally an exception is raised.
If `ignore_nan is True` then that edge is ignored instead.
| def __init__(self, G, weight="weight", minimum=True, ignore_nan=False):
"""
Initialize the iterator
Parameters
----------
G : nx.Graph
The directed graph which we need to iterate trees over
weight : String, default = "weight"
The edge attribute used to store the weight of the edge
minimum : bool, default = True
Return the trees in increasing order while true and decreasing order
while false.
ignore_nan : bool, default = False
If a NaN is found as an edge weight normally an exception is raised.
If `ignore_nan is True` then that edge is ignored instead.
"""
self.G = G.copy()
self.G.__networkx_cache__ = None # Disable caching
self.weight = weight
self.minimum = minimum
self.ignore_nan = ignore_nan
# Randomly create a key for an edge attribute to hold the partition data
self.partition_key = (
"SpanningTreeIterators super secret partition attribute name"
)
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|
30,330 | networkx.algorithms.tree.mst | __iter__ |
Returns
-------
SpanningTreeIterator
The iterator object for this graph
| def __iter__(self):
"""
Returns
-------
SpanningTreeIterator
The iterator object for this graph
"""
self.partition_queue = PriorityQueue()
self._clear_partition(self.G)
mst_weight = partition_spanning_tree(
self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
).size(weight=self.weight)
self.partition_queue.put(
self.Partition(mst_weight if self.minimum else -mst_weight, {})
)
return self
| (self) | [
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|
30,331 | networkx.algorithms.tree.mst | __next__ |
Returns
-------
(multi)Graph
The spanning tree of next greatest weight, which ties broken
arbitrarily.
| def __next__(self):
"""
Returns
-------
(multi)Graph
The spanning tree of next greatest weight, which ties broken
arbitrarily.
"""
if self.partition_queue.empty():
del self.G, self.partition_queue
raise StopIteration
partition = self.partition_queue.get()
self._write_partition(partition)
next_tree = partition_spanning_tree(
self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan
)
self._partition(partition, next_tree)
self._clear_partition(next_tree)
return next_tree
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|
30,332 | networkx.algorithms.tree.mst | _clear_partition |
Removes partition data from the graph
| def _clear_partition(self, G):
"""
Removes partition data from the graph
"""
for u, v, d in G.edges(data=True):
if self.partition_key in d:
del d[self.partition_key]
| (self, G) | [
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|
30,333 | networkx.algorithms.tree.mst | _partition |
Create new partitions based of the minimum spanning tree of the
current minimum partition.
Parameters
----------
partition : Partition
The Partition instance used to generate the current minimum spanning
tree.
partition_tree : nx.Graph
The minimum spanning tree of the input partition.
| def _partition(self, partition, partition_tree):
"""
Create new partitions based of the minimum spanning tree of the
current minimum partition.
Parameters
----------
partition : Partition
The Partition instance used to generate the current minimum spanning
tree.
partition_tree : nx.Graph
The minimum spanning tree of the input partition.
"""
# create two new partitions with the data from the input partition dict
p1 = self.Partition(0, partition.partition_dict.copy())
p2 = self.Partition(0, partition.partition_dict.copy())
for e in partition_tree.edges:
# determine if the edge was open or included
if e not in partition.partition_dict:
# This is an open edge
p1.partition_dict[e] = EdgePartition.EXCLUDED
p2.partition_dict[e] = EdgePartition.INCLUDED
self._write_partition(p1)
p1_mst = partition_spanning_tree(
self.G,
self.minimum,
self.weight,
self.partition_key,
self.ignore_nan,
)
p1_mst_weight = p1_mst.size(weight=self.weight)
if nx.is_connected(p1_mst):
p1.mst_weight = p1_mst_weight if self.minimum else -p1_mst_weight
self.partition_queue.put(p1.__copy__())
p1.partition_dict = p2.partition_dict.copy()
| (self, partition, partition_tree) | [
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|
30,334 | networkx.algorithms.tree.mst | _write_partition |
Writes the desired partition into the graph to calculate the minimum
spanning tree.
Parameters
----------
partition : Partition
A Partition dataclass describing a partition on the edges of the
graph.
| def _write_partition(self, partition):
"""
Writes the desired partition into the graph to calculate the minimum
spanning tree.
Parameters
----------
partition : Partition
A Partition dataclass describing a partition on the edges of the
graph.
"""
for u, v, d in self.G.edges(data=True):
if (u, v) in partition.partition_dict:
d[self.partition_key] = partition.partition_dict[(u, v)]
else:
d[self.partition_key] = EdgePartition.OPEN
| (self, partition) | [
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|
30,335 | networkx.utils.misc | _clear_cache | Clear the cache of a graph (currently stores converted graphs).
Caching is controlled via ``nx.config.cache_converted_graphs`` configuration.
| def _clear_cache(G):
"""Clear the cache of a graph (currently stores converted graphs).
Caching is controlled via ``nx.config.cache_converted_graphs`` configuration.
"""
if cache := getattr(G, "__networkx_cache__", None):
cache.clear()
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|
30,336 | networkx.utils.backends | _dispatchable | null | class _dispatchable:
"""Allow any of the following decorator forms:
- @_dispatchable
- @_dispatchable()
- @_dispatchable(name="override_name")
- @_dispatchable(graphs="graph")
- @_dispatchable(edge_attrs="weight")
- @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})
These class attributes are currently used to allow backends to run networkx tests.
For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
Future work: add configuration to control these.
"""
_is_testing = False
_fallback_to_nx = (
os.environ.get("NETWORKX_FALLBACK_TO_NX", "true").strip().lower() == "true"
)
def __new__(
cls,
func=None,
*,
name=None,
graphs="G",
edge_attrs=None,
node_attrs=None,
preserve_edge_attrs=False,
preserve_node_attrs=False,
preserve_graph_attrs=False,
preserve_all_attrs=False,
mutates_input=False,
returns_graph=False,
):
"""A decorator that makes certain input graph types dispatch to ``func``'s
backend implementation.
Usage can be any of the following decorator forms:
- @_dispatchable
- @_dispatchable()
- @_dispatchable(name="override_name")
- @_dispatchable(graphs="graph_var_name")
- @_dispatchable(edge_attrs="weight")
- @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})
with 0 and 1 giving the position in the signature function for graph objects.
When edge_attrs is a dict, keys are keyword names and values are defaults.
The class attributes are used to allow backends to run networkx tests.
For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
Future work: add configuration to control these.
Parameters
----------
func : callable, optional
The function to be decorated. If ``func`` is not provided, returns a
partial object that can be used to decorate a function later. If ``func``
is provided, returns a new callable object that dispatches to a backend
algorithm based on input graph types.
name : str, optional
The name of the algorithm to use for dispatching. If not provided,
the name of ``func`` will be used. ``name`` is useful to avoid name
conflicts, as all dispatched algorithms live in a single namespace.
For example, ``tournament.is_strongly_connected`` had a name conflict
with the standard ``nx.is_strongly_connected``, so we used
``@_dispatchable(name="tournament_is_strongly_connected")``.
graphs : str or dict or None, default "G"
If a string, the parameter name of the graph, which must be the first
argument of the wrapped function. If more than one graph is required
for the algorithm (or if the graph is not the first argument), provide
a dict of parameter name to argument position for each graph argument.
For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
indicates the 0th parameter ``G`` of the function is a required graph,
and the 4th parameter ``auxiliary`` is an optional graph.
To indicate an argument is a list of graphs, do e.g. ``"[graphs]"``.
Use ``graphs=None`` if *no* arguments are NetworkX graphs such as for
graph generators, readers, and conversion functions.
edge_attrs : str or dict, optional
``edge_attrs`` holds information about edge attribute arguments
and default values for those edge attributes.
If a string, ``edge_attrs`` holds the function argument name that
indicates a single edge attribute to include in the converted graph.
The default value for this attribute is 1. To indicate that an argument
is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
values that are either the default value or, if a string, the argument
name that indicates the default value.
node_attrs : str or dict, optional
Like ``edge_attrs``, but for node attributes.
preserve_edge_attrs : bool or str or dict, optional
For bool, whether to preserve all edge attributes.
For str, the parameter name that may indicate (with ``True`` or a
callable argument) whether all edge attributes should be preserved
when converting.
For dict of ``{graph_name: {attr: default}}``, indicate pre-determined
edge attributes (and defaults) to preserve for input graphs.
preserve_node_attrs : bool or str or dict, optional
Like ``preserve_edge_attrs``, but for node attributes.
preserve_graph_attrs : bool or set
For bool, whether to preserve all graph attributes.
For set, which input graph arguments to preserve graph attributes.
preserve_all_attrs : bool
Whether to preserve all edge, node and graph attributes.
This overrides all the other preserve_*_attrs.
mutates_input : bool or dict, default False
For bool, whether the functions mutates an input graph argument.
For dict of ``{arg_name: arg_pos}``, arguments that indicates whether an
input graph will be mutated, and ``arg_name`` may begin with ``"not "``
to negate the logic (for example, this is used by ``copy=`` arguments).
By default, dispatching doesn't convert input graphs to a different
backend for functions that mutate input graphs.
returns_graph : bool, default False
Whether the function can return or yield a graph object. By default,
dispatching doesn't convert input graphs to a different backend for
functions that return graphs.
"""
if func is None:
return partial(
_dispatchable,
name=name,
graphs=graphs,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
preserve_all_attrs=preserve_all_attrs,
mutates_input=mutates_input,
returns_graph=returns_graph,
)
if isinstance(func, str):
raise TypeError("'name' and 'graphs' must be passed by keyword") from None
# If name not provided, use the name of the function
if name is None:
name = func.__name__
self = object.__new__(cls)
# standard function-wrapping stuff
# __annotations__ not used
self.__name__ = func.__name__
# self.__doc__ = func.__doc__ # __doc__ handled as cached property
self.__defaults__ = func.__defaults__
# We "magically" add `backend=` keyword argument to allow backend to be specified
if func.__kwdefaults__:
self.__kwdefaults__ = {**func.__kwdefaults__, "backend": None}
else:
self.__kwdefaults__ = {"backend": None}
self.__module__ = func.__module__
self.__qualname__ = func.__qualname__
self.__dict__.update(func.__dict__)
self.__wrapped__ = func
# Supplement docstring with backend info; compute and cache when needed
self._orig_doc = func.__doc__
self._cached_doc = None
self.orig_func = func
self.name = name
self.edge_attrs = edge_attrs
self.node_attrs = node_attrs
self.preserve_edge_attrs = preserve_edge_attrs or preserve_all_attrs
self.preserve_node_attrs = preserve_node_attrs or preserve_all_attrs
self.preserve_graph_attrs = preserve_graph_attrs or preserve_all_attrs
self.mutates_input = mutates_input
| null | [
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]
|
30,337 | networkx.utils.backends | __call__ | Returns the result of the original function, or the backend function if
the backend is specified and that backend implements `func`. | def __call__(self, /, *args, backend=None, **kwargs):
"""Returns the result of the original function, or the backend function if
the backend is specified and that backend implements `func`."""
if not backends:
# Fast path if no backends are installed
return self.orig_func(*args, **kwargs)
# Use `backend_name` in this function instead of `backend`
backend_name = backend
if backend_name is not None and backend_name not in backends:
raise ImportError(f"Unable to load backend: {backend_name}")
graphs_resolved = {}
for gname, pos in self.graphs.items():
if pos < len(args):
if gname in kwargs:
raise TypeError(f"{self.name}() got multiple values for {gname!r}")
val = args[pos]
elif gname in kwargs:
val = kwargs[gname]
elif gname not in self.optional_graphs:
raise TypeError(
f"{self.name}() missing required graph argument: {gname}"
)
else:
continue
if val is None:
if gname not in self.optional_graphs:
raise TypeError(
f"{self.name}() required graph argument {gname!r} is None; must be a graph"
)
else:
graphs_resolved[gname] = val
# Alternative to the above that does not check duplicated args or missing required graphs.
# graphs_resolved = {
# val
# for gname, pos in self.graphs.items()
# if (val := args[pos] if pos < len(args) else kwargs.get(gname)) is not None
# }
# Check if any graph comes from a backend
if self.list_graphs:
# Make sure we don't lose values by consuming an iterator
args = list(args)
for gname in self.list_graphs & graphs_resolved.keys():
val = list(graphs_resolved[gname])
graphs_resolved[gname] = val
if gname in kwargs:
kwargs[gname] = val
else:
args[self.graphs[gname]] = val
has_backends = any(
hasattr(g, "__networkx_backend__")
if gname not in self.list_graphs
else any(hasattr(g2, "__networkx_backend__") for g2 in g)
for gname, g in graphs_resolved.items()
)
if has_backends:
graph_backend_names = {
getattr(g, "__networkx_backend__", "networkx")
for gname, g in graphs_resolved.items()
if gname not in self.list_graphs
}
for gname in self.list_graphs & graphs_resolved.keys():
graph_backend_names.update(
getattr(g, "__networkx_backend__", "networkx")
for g in graphs_resolved[gname]
)
else:
has_backends = any(
hasattr(g, "__networkx_backend__") for g in graphs_resolved.values()
)
if has_backends:
graph_backend_names = {
getattr(g, "__networkx_backend__", "networkx")
for g in graphs_resolved.values()
}
backend_priority = config.backend_priority
if self._is_testing and backend_priority and backend_name is None:
# Special path if we are running networkx tests with a backend.
# This even runs for (and handles) functions that mutate input graphs.
return self._convert_and_call_for_tests(
backend_priority[0],
args,
kwargs,
fallback_to_nx=self._fallback_to_nx,
)
if has_backends:
# Dispatchable graphs found! Dispatch to backend function.
# We don't handle calls with different backend graphs yet,
# but we may be able to convert additional networkx graphs.
backend_names = graph_backend_names - {"networkx"}
if len(backend_names) != 1:
# Future work: convert between backends and run if multiple backends found
raise TypeError(
f"{self.name}() graphs must all be from the same backend, found {backend_names}"
)
[graph_backend_name] = backend_names
if backend_name is not None and backend_name != graph_backend_name:
# Future work: convert between backends to `backend_name` backend
raise TypeError(
f"{self.name}() is unable to convert graph from backend {graph_backend_name!r} "
f"to the specified backend {backend_name!r}."
)
if graph_backend_name not in backends:
raise ImportError(f"Unable to load backend: {graph_backend_name}")
if (
"networkx" in graph_backend_names
and graph_backend_name not in backend_priority
):
# Not configured to convert networkx graphs to this backend
raise TypeError(
f"Unable to convert inputs and run {self.name}. "
f"{self.name}() has networkx and {graph_backend_name} graphs, but NetworkX is not "
f"configured to automatically convert graphs from networkx to {graph_backend_name}."
)
backend = _load_backend(graph_backend_name)
if hasattr(backend, self.name):
if "networkx" in graph_backend_names:
# We need to convert networkx graphs to backend graphs.
# There is currently no need to check `self.mutates_input` here.
return self._convert_and_call(
graph_backend_name,
args,
kwargs,
fallback_to_nx=self._fallback_to_nx,
)
# All graphs are backend graphs--no need to convert!
return getattr(backend, self.name)(*args, **kwargs)
# Future work: try to convert and run with other backends in backend_priority
raise nx.NetworkXNotImplemented(
f"'{self.name}' not implemented by {graph_backend_name}"
)
# If backend was explicitly given by the user, so we need to use it no matter what
if backend_name is not None:
return self._convert_and_call(
backend_name, args, kwargs, fallback_to_nx=False
)
# Only networkx graphs; try to convert and run with a backend with automatic
# conversion, but don't do this by default for graph generators or loaders,
# or if the functions mutates an input graph or returns a graph.
# Only convert and run if `backend.should_run(...)` returns True.
if (
not self._returns_graph
and (
not self.mutates_input
or isinstance(self.mutates_input, dict)
# If `mutates_input` begins with "not ", then assume the argument is boolean,
# otherwise treat it as a node or edge attribute if it's not None.
and any(
not (
args[arg_pos]
if len(args) > arg_pos
else kwargs.get(arg_name[4:], True)
)
if arg_name.startswith("not ")
else (
args[arg_pos] if len(args) > arg_pos else kwargs.get(arg_name)
)
is not None
for arg_name, arg_pos in self.mutates_input.items()
)
)
):
# Should we warn or log if we don't convert b/c the input will be mutated?
for backend_name in backend_priority:
if self._should_backend_run(backend_name, *args, **kwargs):
return self._convert_and_call(
backend_name,
args,
kwargs,
fallback_to_nx=self._fallback_to_nx,
)
# Default: run with networkx on networkx inputs
return self.orig_func(*args, **kwargs)
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|
30,338 | networkx.utils.backends | __new__ | A decorator that makes certain input graph types dispatch to ``func``'s
backend implementation.
Usage can be any of the following decorator forms:
- @_dispatchable
- @_dispatchable()
- @_dispatchable(name="override_name")
- @_dispatchable(graphs="graph_var_name")
- @_dispatchable(edge_attrs="weight")
- @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})
with 0 and 1 giving the position in the signature function for graph objects.
When edge_attrs is a dict, keys are keyword names and values are defaults.
The class attributes are used to allow backends to run networkx tests.
For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
Future work: add configuration to control these.
Parameters
----------
func : callable, optional
The function to be decorated. If ``func`` is not provided, returns a
partial object that can be used to decorate a function later. If ``func``
is provided, returns a new callable object that dispatches to a backend
algorithm based on input graph types.
name : str, optional
The name of the algorithm to use for dispatching. If not provided,
the name of ``func`` will be used. ``name`` is useful to avoid name
conflicts, as all dispatched algorithms live in a single namespace.
For example, ``tournament.is_strongly_connected`` had a name conflict
with the standard ``nx.is_strongly_connected``, so we used
``@_dispatchable(name="tournament_is_strongly_connected")``.
graphs : str or dict or None, default "G"
If a string, the parameter name of the graph, which must be the first
argument of the wrapped function. If more than one graph is required
for the algorithm (or if the graph is not the first argument), provide
a dict of parameter name to argument position for each graph argument.
For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
indicates the 0th parameter ``G`` of the function is a required graph,
and the 4th parameter ``auxiliary`` is an optional graph.
To indicate an argument is a list of graphs, do e.g. ``"[graphs]"``.
Use ``graphs=None`` if *no* arguments are NetworkX graphs such as for
graph generators, readers, and conversion functions.
edge_attrs : str or dict, optional
``edge_attrs`` holds information about edge attribute arguments
and default values for those edge attributes.
If a string, ``edge_attrs`` holds the function argument name that
indicates a single edge attribute to include in the converted graph.
The default value for this attribute is 1. To indicate that an argument
is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
values that are either the default value or, if a string, the argument
name that indicates the default value.
node_attrs : str or dict, optional
Like ``edge_attrs``, but for node attributes.
preserve_edge_attrs : bool or str or dict, optional
For bool, whether to preserve all edge attributes.
For str, the parameter name that may indicate (with ``True`` or a
callable argument) whether all edge attributes should be preserved
when converting.
For dict of ``{graph_name: {attr: default}}``, indicate pre-determined
edge attributes (and defaults) to preserve for input graphs.
preserve_node_attrs : bool or str or dict, optional
Like ``preserve_edge_attrs``, but for node attributes.
preserve_graph_attrs : bool or set
For bool, whether to preserve all graph attributes.
For set, which input graph arguments to preserve graph attributes.
preserve_all_attrs : bool
Whether to preserve all edge, node and graph attributes.
This overrides all the other preserve_*_attrs.
mutates_input : bool or dict, default False
For bool, whether the functions mutates an input graph argument.
For dict of ``{arg_name: arg_pos}``, arguments that indicates whether an
input graph will be mutated, and ``arg_name`` may begin with ``"not "``
to negate the logic (for example, this is used by ``copy=`` arguments).
By default, dispatching doesn't convert input graphs to a different
backend for functions that mutate input graphs.
returns_graph : bool, default False
Whether the function can return or yield a graph object. By default,
dispatching doesn't convert input graphs to a different backend for
functions that return graphs.
| def __new__(
cls,
func=None,
*,
name=None,
graphs="G",
edge_attrs=None,
node_attrs=None,
preserve_edge_attrs=False,
preserve_node_attrs=False,
preserve_graph_attrs=False,
preserve_all_attrs=False,
mutates_input=False,
returns_graph=False,
):
"""A decorator that makes certain input graph types dispatch to ``func``'s
backend implementation.
Usage can be any of the following decorator forms:
- @_dispatchable
- @_dispatchable()
- @_dispatchable(name="override_name")
- @_dispatchable(graphs="graph_var_name")
- @_dispatchable(edge_attrs="weight")
- @_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})
with 0 and 1 giving the position in the signature function for graph objects.
When edge_attrs is a dict, keys are keyword names and values are defaults.
The class attributes are used to allow backends to run networkx tests.
For example: `PYTHONPATH=. pytest --backend graphblas --fallback-to-nx`
Future work: add configuration to control these.
Parameters
----------
func : callable, optional
The function to be decorated. If ``func`` is not provided, returns a
partial object that can be used to decorate a function later. If ``func``
is provided, returns a new callable object that dispatches to a backend
algorithm based on input graph types.
name : str, optional
The name of the algorithm to use for dispatching. If not provided,
the name of ``func`` will be used. ``name`` is useful to avoid name
conflicts, as all dispatched algorithms live in a single namespace.
For example, ``tournament.is_strongly_connected`` had a name conflict
with the standard ``nx.is_strongly_connected``, so we used
``@_dispatchable(name="tournament_is_strongly_connected")``.
graphs : str or dict or None, default "G"
If a string, the parameter name of the graph, which must be the first
argument of the wrapped function. If more than one graph is required
for the algorithm (or if the graph is not the first argument), provide
a dict of parameter name to argument position for each graph argument.
For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
indicates the 0th parameter ``G`` of the function is a required graph,
and the 4th parameter ``auxiliary`` is an optional graph.
To indicate an argument is a list of graphs, do e.g. ``"[graphs]"``.
Use ``graphs=None`` if *no* arguments are NetworkX graphs such as for
graph generators, readers, and conversion functions.
edge_attrs : str or dict, optional
``edge_attrs`` holds information about edge attribute arguments
and default values for those edge attributes.
If a string, ``edge_attrs`` holds the function argument name that
indicates a single edge attribute to include in the converted graph.
The default value for this attribute is 1. To indicate that an argument
is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
values that are either the default value or, if a string, the argument
name that indicates the default value.
node_attrs : str or dict, optional
Like ``edge_attrs``, but for node attributes.
preserve_edge_attrs : bool or str or dict, optional
For bool, whether to preserve all edge attributes.
For str, the parameter name that may indicate (with ``True`` or a
callable argument) whether all edge attributes should be preserved
when converting.
For dict of ``{graph_name: {attr: default}}``, indicate pre-determined
edge attributes (and defaults) to preserve for input graphs.
preserve_node_attrs : bool or str or dict, optional
Like ``preserve_edge_attrs``, but for node attributes.
preserve_graph_attrs : bool or set
For bool, whether to preserve all graph attributes.
For set, which input graph arguments to preserve graph attributes.
preserve_all_attrs : bool
Whether to preserve all edge, node and graph attributes.
This overrides all the other preserve_*_attrs.
mutates_input : bool or dict, default False
For bool, whether the functions mutates an input graph argument.
For dict of ``{arg_name: arg_pos}``, arguments that indicates whether an
input graph will be mutated, and ``arg_name`` may begin with ``"not "``
to negate the logic (for example, this is used by ``copy=`` arguments).
By default, dispatching doesn't convert input graphs to a different
backend for functions that mutate input graphs.
returns_graph : bool, default False
Whether the function can return or yield a graph object. By default,
dispatching doesn't convert input graphs to a different backend for
functions that return graphs.
"""
if func is None:
return partial(
_dispatchable,
name=name,
graphs=graphs,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
preserve_all_attrs=preserve_all_attrs,
mutates_input=mutates_input,
returns_graph=returns_graph,
)
if isinstance(func, str):
raise TypeError("'name' and 'graphs' must be passed by keyword") from None
# If name not provided, use the name of the function
if name is None:
name = func.__name__
self = object.__new__(cls)
# standard function-wrapping stuff
# __annotations__ not used
self.__name__ = func.__name__
# self.__doc__ = func.__doc__ # __doc__ handled as cached property
self.__defaults__ = func.__defaults__
# We "magically" add `backend=` keyword argument to allow backend to be specified
if func.__kwdefaults__:
self.__kwdefaults__ = {**func.__kwdefaults__, "backend": None}
else:
self.__kwdefaults__ = {"backend": None}
self.__module__ = func.__module__
self.__qualname__ = func.__qualname__
self.__dict__.update(func.__dict__)
self.__wrapped__ = func
# Supplement docstring with backend info; compute and cache when needed
self._orig_doc = func.__doc__
self._cached_doc = None
self.orig_func = func
self.name = name
self.edge_attrs = edge_attrs
self.node_attrs = node_attrs
self.preserve_edge_attrs = preserve_edge_attrs or preserve_all_attrs
self.preserve_node_attrs = preserve_node_attrs or preserve_all_attrs
self.preserve_graph_attrs = preserve_graph_attrs or preserve_all_attrs
self.mutates_input = mutates_input
# Keep `returns_graph` private for now, b/c we may extend info on return types
self._returns_graph = returns_graph
if edge_attrs is not None and not isinstance(edge_attrs, str | dict):
raise TypeError(
f"Bad type for edge_attrs: {type(edge_attrs)}. Expected str or dict."
) from None
if node_attrs is not None and not isinstance(node_attrs, str | dict):
raise TypeError(
f"Bad type for node_attrs: {type(node_attrs)}. Expected str or dict."
) from None
if not isinstance(self.preserve_edge_attrs, bool | str | dict):
raise TypeError(
f"Bad type for preserve_edge_attrs: {type(self.preserve_edge_attrs)}."
" Expected bool, str, or dict."
) from None
if not isinstance(self.preserve_node_attrs, bool | str | dict):
raise TypeError(
f"Bad type for preserve_node_attrs: {type(self.preserve_node_attrs)}."
" Expected bool, str, or dict."
) from None
if not isinstance(self.preserve_graph_attrs, bool | set):
raise TypeError(
f"Bad type for preserve_graph_attrs: {type(self.preserve_graph_attrs)}."
" Expected bool or set."
) from None
if not isinstance | (cls, func=None, *, name=None, graphs='G', edge_attrs=None, node_attrs=None, preserve_edge_attrs=False, preserve_node_attrs=False, preserve_graph_attrs=False, preserve_all_attrs=False, mutates_input=False, returns_graph=False) | [
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|
30,339 | networkx.utils.backends | __reduce__ | Allow this object to be serialized with pickle.
This uses the global registry `_registered_algorithms` to deserialize.
| def __reduce__(self):
"""Allow this object to be serialized with pickle.
This uses the global registry `_registered_algorithms` to deserialize.
"""
return _restore_dispatchable, (self.name,)
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|
30,340 | networkx.utils.backends | _can_backend_run | Can the specified backend run this algorithm with these arguments? | def _can_backend_run(self, backend_name, /, *args, **kwargs):
"""Can the specified backend run this algorithm with these arguments?"""
backend = _load_backend(backend_name)
# `backend.can_run` and `backend.should_run` may return strings that describe
# why they can't or shouldn't be run. We plan to use the strings in the future.
return (
hasattr(backend, self.name)
and (can_run := backend.can_run(self.name, args, kwargs))
and not isinstance(can_run, str)
)
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|
30,341 | networkx.utils.backends | _convert_and_call | Call this dispatchable function with a backend, converting graphs if necessary. | def _convert_and_call(self, backend_name, args, kwargs, *, fallback_to_nx=False):
"""Call this dispatchable function with a backend, converting graphs if necessary."""
backend = _load_backend(backend_name)
if not self._can_backend_run(backend_name, *args, **kwargs):
if fallback_to_nx:
return self.orig_func(*args, **kwargs)
msg = f"'{self.name}' not implemented by {backend_name}"
if hasattr(backend, self.name):
msg += " with the given arguments"
raise RuntimeError(msg)
try:
converted_args, converted_kwargs = self._convert_arguments(
backend_name, args, kwargs, use_cache=config.cache_converted_graphs
)
result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
except (NotImplementedError, nx.NetworkXNotImplemented) as exc:
if fallback_to_nx:
return self.orig_func(*args, **kwargs)
raise
return result
| (self, backend_name, args, kwargs, *, fallback_to_nx=False) | [
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|
30,342 | networkx.utils.backends | _convert_and_call_for_tests | Call this dispatchable function with a backend; for use with testing. | def _convert_and_call_for_tests(
self, backend_name, args, kwargs, *, fallback_to_nx=False
):
"""Call this dispatchable function with a backend; for use with testing."""
backend = _load_backend(backend_name)
if not self._can_backend_run(backend_name, *args, **kwargs):
if fallback_to_nx or not self.graphs:
return self.orig_func(*args, **kwargs)
import pytest
msg = f"'{self.name}' not implemented by {backend_name}"
if hasattr(backend, self.name):
msg += " with the given arguments"
pytest.xfail(msg)
from collections.abc import Iterable, Iterator, Mapping
from copy import copy
from io import BufferedReader, BytesIO, StringIO, TextIOWrapper
from itertools import tee
from random import Random
import numpy as np
from numpy.random import Generator, RandomState
from scipy.sparse import sparray
# We sometimes compare the backend result to the original result,
# so we need two sets of arguments. We tee iterators and copy
# random state so that they may be used twice.
if not args:
args1 = args2 = args
else:
args1, args2 = zip(
*(
(arg, copy(arg))
if isinstance(
arg, BytesIO | StringIO | Random | Generator | RandomState
)
else tee(arg)
if isinstance(arg, Iterator)
and not isinstance(arg, BufferedReader | TextIOWrapper)
else (arg, arg)
for arg in args
)
)
if not kwargs:
kwargs1 = kwargs2 = kwargs
else:
kwargs1, kwargs2 = zip(
*(
((k, v), (k, copy(v)))
if isinstance(
v, BytesIO | StringIO | Random | Generator | RandomState
)
else ((k, (teed := tee(v))[0]), (k, teed[1]))
if isinstance(v, Iterator)
and not isinstance(v, BufferedReader | TextIOWrapper)
else ((k, v), (k, v))
for k, v in kwargs.items()
)
)
kwargs1 = dict(kwargs1)
kwargs2 = dict(kwargs2)
try:
converted_args, converted_kwargs = self._convert_arguments(
backend_name, args1, kwargs1, use_cache=False
)
result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
except (NotImplementedError, nx.NetworkXNotImplemented) as exc:
if fallback_to_nx:
return self.orig_func(*args2, **kwargs2)
import pytest
pytest.xfail(
exc.args[0] if exc.args else f"{self.name} raised {type(exc).__name__}"
)
# Verify that `self._returns_graph` is correct. This compares the return type
# to the type expected from `self._returns_graph`. This handles tuple and list
# return types, but *does not* catch functions that yield graphs.
if (
self._returns_graph
!= (
isinstance(result, nx.Graph)
or hasattr(result, "__networkx_backend__")
or isinstance(result, tuple | list)
and any(
isinstance(x, nx.Graph) or hasattr(x, "__networkx_backend__")
for x in result
)
)
and not (
# May return Graph or None
self.name in {"check_planarity", "check_planarity_recursive"}
and any(x is None for x in result)
)
and not (
# May return Graph or dict
self.name in {"held_karp_ascent"}
and any(isinstance(x, dict) for x in result)
)
and self.name
not in {
# yields graphs
"all_triads",
"general_k_edge_subgraphs",
# yields graphs or arrays
"nonisomorphic_trees",
}
):
raise RuntimeError(f"`returns_graph` is incorrect for {self.name}")
def check_result(val, depth=0):
if isinstance(val, np.number):
raise RuntimeError(
f"{self.name} returned a numpy scalar {val} ({type(val)}, depth={depth})"
)
if isinstance(val, np.ndarray | sparray):
return
if isinstance(val, nx.Graph):
check_result(val._node, depth=depth + 1)
check_result(val._adj, depth=depth + 1)
return
if isinstance(val, Iterator):
raise NotImplementedError
if isinstance(val, Iterable) and not isinstance(val, str):
for x in val:
check_result(x, depth=depth + 1)
if isinstance(val, Mapping):
for x in val.values():
check_result(x, depth=depth + 1)
def check_iterator(it):
for val in it:
try:
check_result(val)
except RuntimeError as exc:
raise RuntimeError(
f"{self.name} returned a numpy scalar {val} ({type(val)})"
) from exc
yield val
if self.name in {"from_edgelist"}:
# numpy scalars are explicitly given as values in some tests
pass
elif isinstance(result, Iterator):
result = check_iterator(result)
else:
try:
check_result(result)
except RuntimeError as exc:
raise RuntimeError(
f"{self.name} returned a numpy scalar {result} ({type(result)})"
) from exc
check_result(result)
if self.name in {
"edmonds_karp",
"barycenter",
"contracted_edge",
"contracted_nodes",
"stochastic_graph",
"relabel_nodes",
"maximum_branching",
"incremental_closeness_centrality",
"minimal_branching",
"minimum_spanning_arborescence",
"recursive_simple_cycles",
"connected_double_edge_swap",
}:
# Special-case algorithms that mutate input graphs
bound = self.__signature__.bind(*converted_args, **converted_kwargs)
bound.apply_defaults()
bound2 = self.__signature__.bind(*args2, **kwargs2)
bound2.apply_defaults()
if self.name in {
"minimal_branching",
"minimum_spanning_arborescence",
"recursive_simple_cycles",
"connected_double_edge_swap",
}:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
G2._adj = G1._adj
nx._clear_cache(G2)
elif self.name == "edmonds_karp":
R1 = backend.convert_to_nx(bound.arguments["residual"])
R2 = bound2.arguments["residual"]
if R1 is not None and R2 is not None:
for k, v in R1.edges.items():
R2.edges[k]["flow"] = v["flow"]
R2.graph.update(R1.graph)
nx._clear_cache(R2)
elif self.name == "barycenter" and bound.arguments["attr"] is not None:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
attr = bound.arguments["attr"]
for k, v in G1.nodes.items():
G2.nodes[k][attr] = v[attr]
nx._clear_cache(G2)
elif (
self.name in {"contracted_nodes", "contracted_edge"}
and not bound.arguments["copy"]
):
# Edges and nodes changed; node "contraction" and edge "weight" attrs
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
G2.__dict__.update(G1.__dict__)
nx._clear_cache(G2)
elif self.name == "stochastic_graph" and not bound.arguments["copy"]:
G1 = backend.convert_to_nx(bound.arguments["G"])
G2 = bound2.arguments["G"]
for k, v in G1.edges.items():
G2.edges[k]["weight"] = v["weight"]
nx._clear_cache(G2)
elif (
self.name == "relabel_nodes"
and not bound.arguments["copy"]
or self.name in {"incremental_closeness_centrality"}
):
G1 = backend.convert_to_nx(bound.arguments["G"])
| (self, backend_name, args, kwargs, *, fallback_to_nx=False) | [
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|
30,343 | networkx.utils.backends | _convert_arguments | Convert graph arguments to the specified backend.
Returns
-------
args tuple and kwargs dict
| def _convert_arguments(self, backend_name, args, kwargs, *, use_cache):
"""Convert graph arguments to the specified backend.
Returns
-------
args tuple and kwargs dict
"""
bound = self.__signature__.bind(*args, **kwargs)
bound.apply_defaults()
if not self.graphs:
bound_kwargs = bound.kwargs
del bound_kwargs["backend"]
return bound.args, bound_kwargs
# Convert graphs into backend graph-like object
# Include the edge and/or node labels if provided to the algorithm
preserve_edge_attrs = self.preserve_edge_attrs
edge_attrs = self.edge_attrs
if preserve_edge_attrs is False:
# e.g. `preserve_edge_attrs=False`
pass
elif preserve_edge_attrs is True:
# e.g. `preserve_edge_attrs=True`
edge_attrs = None
elif isinstance(preserve_edge_attrs, str):
if bound.arguments[preserve_edge_attrs] is True or callable(
bound.arguments[preserve_edge_attrs]
):
# e.g. `preserve_edge_attrs="attr"` and `func(attr=True)`
# e.g. `preserve_edge_attrs="attr"` and `func(attr=myfunc)`
preserve_edge_attrs = True
edge_attrs = None
elif bound.arguments[preserve_edge_attrs] is False and (
isinstance(edge_attrs, str)
and edge_attrs == preserve_edge_attrs
or isinstance(edge_attrs, dict)
and preserve_edge_attrs in edge_attrs
):
# e.g. `preserve_edge_attrs="attr"` and `func(attr=False)`
# Treat `False` argument as meaning "preserve_edge_data=False"
# and not `False` as the edge attribute to use.
preserve_edge_attrs = False
edge_attrs = None
else:
# e.g. `preserve_edge_attrs="attr"` and `func(attr="weight")`
preserve_edge_attrs = False
# Else: e.g. `preserve_edge_attrs={"G": {"weight": 1}}`
if edge_attrs is None:
# May have been set to None above b/c all attributes are preserved
pass
elif isinstance(edge_attrs, str):
if edge_attrs[0] == "[":
# e.g. `edge_attrs="[edge_attributes]"` (argument of list of attributes)
# e.g. `func(edge_attributes=["foo", "bar"])`
edge_attrs = {
edge_attr: 1 for edge_attr in bound.arguments[edge_attrs[1:-1]]
}
elif callable(bound.arguments[edge_attrs]):
# e.g. `edge_attrs="weight"` and `func(weight=myfunc)`
preserve_edge_attrs = True
edge_attrs = None
elif bound.arguments[edge_attrs] is not None:
# e.g. `edge_attrs="weight"` and `func(weight="foo")` (default of 1)
edge_attrs = {bound.arguments[edge_attrs]: 1}
elif self.name == "to_numpy_array" and hasattr(
bound.arguments["dtype"], "names"
):
# Custom handling: attributes may be obtained from `dtype`
edge_attrs = {
edge_attr: 1 for edge_attr in bound.arguments["dtype"].names
}
else:
# e.g. `edge_attrs="weight"` and `func(weight=None)`
edge_attrs = None
else:
# e.g. `edge_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
# e.g. `edge_attrs={"attr": 0}` and `func(attr="foo")`
edge_attrs = {
edge_attr: bound.arguments.get(val, 1) if isinstance(val, str) else val
for key, val in edge_attrs.items()
if (edge_attr := bound.arguments[key]) is not None
}
preserve_node_attrs = self.preserve_node_attrs
node_attrs = self.node_attrs
if preserve_node_attrs is False:
# e.g. `preserve_node_attrs=False`
pass
elif preserve_node_attrs is True:
# e.g. `preserve_node_attrs=True`
node_attrs = None
elif isinstance(preserve_node_attrs, str):
if bound.arguments[preserve_node_attrs] is True or callable(
bound.arguments[preserve_node_attrs]
):
# e.g. `preserve_node_attrs="attr"` and `func(attr=True)`
# e.g. `preserve_node_attrs="attr"` and `func(attr=myfunc)`
preserve_node_attrs = True
node_attrs = None
elif bound.arguments[preserve_node_attrs] is False and (
isinstance(node_attrs, str)
and node_attrs == preserve_node_attrs
or isinstance(node_attrs, dict)
and preserve_node_attrs in node_attrs
):
# e.g. `preserve_node_attrs="attr"` and `func(attr=False)`
# Treat `False` argument as meaning "preserve_node_data=False"
# and not `False` as the node attribute to use. Is this used?
preserve_node_attrs = False
node_attrs = None
else:
# e.g. `preserve_node_attrs="attr"` and `func(attr="weight")`
preserve_node_attrs = False
# Else: e.g. `preserve_node_attrs={"G": {"pos": None}}`
if node_attrs is None:
# May have been set to None above b/c all attributes are preserved
pass
elif isinstance(node_attrs, str):
if node_attrs[0] == "[":
# e.g. `node_attrs="[node_attributes]"` (argument of list of attributes)
# e.g. `func(node_attributes=["foo", "bar"])`
node_attrs = {
node_attr: None for node_attr in bound.arguments[node_attrs[1:-1]]
}
elif callable(bound.arguments[node_attrs]):
# e.g. `node_attrs="weight"` and `func(weight=myfunc)`
preserve_node_attrs = True
node_attrs = None
elif bound.arguments[node_attrs] is not None:
# e.g. `node_attrs="weight"` and `func(weight="foo")`
node_attrs = {bound.arguments[node_attrs]: None}
else:
# e.g. `node_attrs="weight"` and `func(weight=None)`
node_attrs = None
else:
# e.g. `node_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
# e.g. `node_attrs={"attr": 0}` and `func(attr="foo")`
node_attrs = {
node_attr: bound.arguments.get(val) if isinstance(val, str) else val
for key, val in node_attrs.items()
if (node_attr := bound.arguments[key]) is not None
}
preserve_graph_attrs = self.preserve_graph_attrs
# It should be safe to assume that we either have networkx graphs or backend graphs.
# Future work: allow conversions between backends.
for gname in self.graphs:
if gname in self.list_graphs:
bound.arguments[gname] = [
self._convert_graph(
backend_name,
g,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
graph_name=gname,
use_cache=use_cache,
)
if getattr(g, "__networkx_backend__", "networkx") == "networkx"
else g
for g in bound.arguments[gname]
]
else:
graph = bound.arguments[gname]
if graph is None:
if gname in self.optional_graphs:
continue
raise TypeError(
f"Missing required graph argument `{gname}` in {self.name} function"
)
if isinstance(preserve_edge_attrs, dict):
preserve_edges = False
edges = preserve_edge_attrs.get(gname, edge_attrs)
else:
preserve_edges = preserve_edge_attrs
edges = edge_attrs
if isinstance(preserve_node_attrs, dict):
preserve_nodes = False
nodes = preserve_node_attrs.get(gname, node_attrs)
else:
preserve_nodes = preserve_node_attrs
nodes = node_attrs
if isinstance(preserve_graph_attrs, set):
preserve_graph = gname in preserve_graph_attrs
else:
prese | (self, backend_name, args, kwargs, *, use_cache) | [
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|
30,344 | networkx.utils.backends | _convert_graph | null | def _convert_graph(
self,
backend_name,
graph,
*,
edge_attrs,
node_attrs,
preserve_edge_attrs,
preserve_node_attrs,
preserve_graph_attrs,
graph_name,
use_cache,
):
if (
use_cache
and (nx_cache := getattr(graph, "__networkx_cache__", None)) is not None
):
cache = nx_cache.setdefault("backends", {}).setdefault(backend_name, {})
# edge_attrs: dict | None
# node_attrs: dict | None
# preserve_edge_attrs: bool (False if edge_attrs is not None)
# preserve_node_attrs: bool (False if node_attrs is not None)
# preserve_graph_attrs: bool
key = edge_key, node_key, graph_key = (
frozenset(edge_attrs.items())
if edge_attrs is not None
else preserve_edge_attrs,
frozenset(node_attrs.items())
if node_attrs is not None
else preserve_node_attrs,
preserve_graph_attrs,
)
if cache:
warning_message = (
f"Using cached graph for {backend_name!r} backend in "
f"call to {self.name}.\n\nFor the cache to be consistent "
"(i.e., correct), the input graph must not have been "
"manually mutated since the cached graph was created. "
"Examples of manually mutating the graph data structures "
"resulting in an inconsistent cache include:\n\n"
" >>> G[u][v][key] = val\n\n"
"and\n\n"
" >>> for u, v, d in G.edges(data=True):\n"
" ... d[key] = val\n\n"
"Using methods such as `G.add_edge(u, v, weight=val)` "
"will correctly clear the cache to keep it consistent. "
"You may also use `G.__networkx_cache__.clear()` to "
"manually clear the cache, or set `G.__networkx_cache__` "
"to None to disable caching for G. Enable or disable "
"caching via `nx.config.cache_converted_graphs` config."
)
# Do a simple search for a cached graph with compatible data.
# For example, if we need a single attribute, then it's okay
# to use a cached graph that preserved all attributes.
# This looks for an exact match first.
for compat_key in itertools.product(
(edge_key, True) if edge_key is not True else (True,),
(node_key, True) if node_key is not True else (True,),
(graph_key, True) if graph_key is not True else (True,),
):
if (rv := cache.get(compat_key)) is not None:
warnings.warn(warning_message)
return rv
if edge_key is not True and node_key is not True:
# Iterate over the items in `cache` to see if any are compatible.
# For example, if no edge attributes are needed, then a graph
# with any edge attribute will suffice. We use the same logic
# below (but switched) to clear unnecessary items from the cache.
# Use `list(cache.items())` to be thread-safe.
for (ekey, nkey, gkey), val in list(cache.items()):
if edge_key is False or ekey is True:
pass
elif (
edge_key is True
or ekey is False
or not edge_key.issubset(ekey)
):
continue
if node_key is False or nkey is True:
pass
elif (
node_key is True
or nkey is False
or not node_key.issubset(nkey)
):
continue
if graph_key and not gkey:
continue
warnings.warn(warning_message)
return val
backend = _load_backend(backend_name)
rv = backend.convert_from_nx(
graph,
edge_attrs=edge_attrs,
node_attrs=node_attrs,
preserve_edge_attrs=preserve_edge_attrs,
preserve_node_attrs=preserve_node_attrs,
preserve_graph_attrs=preserve_graph_attrs,
name=self.name,
graph_name=graph_name,
)
if use_cache and nx_cache is not None:
# Remove old cached items that are no longer necessary since they
# are dominated/subsumed/outdated by what was just calculated.
# This uses the same logic as above, but with keys switched.
cache[key] = rv # Set at beginning to be thread-safe
for cur_key in list(cache):
if cur_key == key:
continue
ekey, nkey, gkey = cur_key
if ekey is False or edge_key is True:
pass
elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
continue
if nkey is False or node_key is True:
pass
elif nkey is True or node_key is False or not nkey.issubset(node_key):
continue
if gkey and not graph_key:
continue
cache.pop(cur_key, None) # Use pop instead of del to be thread-safe
return rv
| (self, backend_name, graph, *, edge_attrs, node_attrs, preserve_edge_attrs, preserve_node_attrs, preserve_graph_attrs, graph_name, use_cache) | [
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|
30,345 | networkx.utils.backends | _make_doc | Generate the backends section at the end for functions having an alternate
backend implementation(s) using the `backend_info` entry-point. | def _make_doc(self):
"""Generate the backends section at the end for functions having an alternate
backend implementation(s) using the `backend_info` entry-point."""
if not self.backends:
return self._orig_doc
lines = [
"Backends",
"--------",
]
for backend in sorted(self.backends):
info = backend_info[backend]
if "short_summary" in info:
lines.append(f"{backend} : {info['short_summary']}")
else:
lines.append(backend)
if "functions" not in info or self.name not in info["functions"]:
lines.append("")
continue
func_info = info["functions"][self.name]
# Renaming extra_docstring to additional_docs
if func_docs := (
func_info.get("additional_docs") or func_info.get("extra_docstring")
):
lines.extend(
f" {line}" if line else line for line in func_docs.split("\n")
)
add_gap = True
else:
add_gap = False
# Renaming extra_parameters to additional_parameters
if extra_parameters := (
func_info.get("extra_parameters")
or func_info.get("additional_parameters")
):
if add_gap:
lines.append("")
lines.append(" Additional parameters:")
for param in sorted(extra_parameters):
lines.append(f" {param}")
if desc := extra_parameters[param]:
lines.append(f" {desc}")
lines.append("")
else:
lines.append("")
if func_url := func_info.get("url"):
lines.append(f"[`Source <{func_url}>`_]")
lines.append("")
lines.pop() # Remove last empty line
to_add = "\n ".join(lines)
return f"{self._orig_doc.rstrip()}\n\n {to_add}"
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|
30,346 | networkx.utils.backends | _should_backend_run | Can/should the specified backend run this algorithm with these arguments? | def _should_backend_run(self, backend_name, /, *args, **kwargs):
"""Can/should the specified backend run this algorithm with these arguments?"""
backend = _load_backend(backend_name)
# `backend.can_run` and `backend.should_run` may return strings that describe
# why they can't or shouldn't be run. We plan to use the strings in the future.
return (
hasattr(backend, self.name)
and (can_run := backend.can_run(self.name, args, kwargs))
and not isinstance(can_run, str)
and (should_run := backend.should_run(self.name, args, kwargs))
and not isinstance(should_run, str)
)
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|
30,347 | networkx.lazy_imports | _lazy_import | Return a lazily imported proxy for a module or library.
Warning
-------
Importing using this function can currently cause trouble
when the user tries to import from a subpackage of a module before
the package is fully imported. In particular, this idiom may not work:
np = lazy_import("numpy")
from numpy.lib import recfunctions
This is due to a difference in the way Python's LazyLoader handles
subpackage imports compared to the normal import process. Hopefully
we will get Python's LazyLoader to fix this, or find a workaround.
In the meantime, this is a potential problem.
The workaround is to import numpy before importing from the subpackage.
Notes
-----
We often see the following pattern::
def myfunc():
import scipy as sp
sp.argmin(...)
....
This is to prevent a library, in this case `scipy`, from being
imported at function definition time, since that can be slow.
This function provides a proxy module that, upon access, imports
the actual module. So the idiom equivalent to the above example is::
sp = lazy.load("scipy")
def myfunc():
sp.argmin(...)
....
The initial import time is fast because the actual import is delayed
until the first attribute is requested. The overall import time may
decrease as well for users that don't make use of large portions
of the library.
Parameters
----------
fullname : str
The full name of the package or subpackage to import. For example::
sp = lazy.load("scipy") # import scipy as sp
spla = lazy.load("scipy.linalg") # import scipy.linalg as spla
Returns
-------
pm : importlib.util._LazyModule
Proxy module. Can be used like any regularly imported module.
Actual loading of the module occurs upon first attribute request.
| def _lazy_import(fullname):
"""Return a lazily imported proxy for a module or library.
Warning
-------
Importing using this function can currently cause trouble
when the user tries to import from a subpackage of a module before
the package is fully imported. In particular, this idiom may not work:
np = lazy_import("numpy")
from numpy.lib import recfunctions
This is due to a difference in the way Python's LazyLoader handles
subpackage imports compared to the normal import process. Hopefully
we will get Python's LazyLoader to fix this, or find a workaround.
In the meantime, this is a potential problem.
The workaround is to import numpy before importing from the subpackage.
Notes
-----
We often see the following pattern::
def myfunc():
import scipy as sp
sp.argmin(...)
....
This is to prevent a library, in this case `scipy`, from being
imported at function definition time, since that can be slow.
This function provides a proxy module that, upon access, imports
the actual module. So the idiom equivalent to the above example is::
sp = lazy.load("scipy")
def myfunc():
sp.argmin(...)
....
The initial import time is fast because the actual import is delayed
until the first attribute is requested. The overall import time may
decrease as well for users that don't make use of large portions
of the library.
Parameters
----------
fullname : str
The full name of the package or subpackage to import. For example::
sp = lazy.load("scipy") # import scipy as sp
spla = lazy.load("scipy.linalg") # import scipy.linalg as spla
Returns
-------
pm : importlib.util._LazyModule
Proxy module. Can be used like any regularly imported module.
Actual loading of the module occurs upon first attribute request.
"""
try:
return sys.modules[fullname]
except:
pass
# Not previously loaded -- look it up
spec = importlib.util.find_spec(fullname)
if spec is None:
try:
parent = inspect.stack()[1]
frame_data = {
"spec": fullname,
"filename": parent.filename,
"lineno": parent.lineno,
"function": parent.function,
"code_context": parent.code_context,
}
return DelayedImportErrorModule(frame_data, "DelayedImportErrorModule")
finally:
del parent
module = importlib.util.module_from_spec(spec)
sys.modules[fullname] = module
loader = importlib.util.LazyLoader(spec.loader)
loader.exec_module(module)
return module
| (fullname) | [
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|
30,349 | networkx.classes.function | add_cycle | Add a cycle to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_cycle: iterable container
A container of nodes. A cycle will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in cycle.
See Also
--------
add_path, add_star
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> nx.add_cycle(G, [0, 1, 2, 3])
>>> nx.add_cycle(G, [10, 11, 12], weight=7)
| def add_cycle(G_to_add_to, nodes_for_cycle, **attr):
"""Add a cycle to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_cycle: iterable container
A container of nodes. A cycle will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in cycle.
See Also
--------
add_path, add_star
Examples
--------
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> nx.add_cycle(G, [0, 1, 2, 3])
>>> nx.add_cycle(G, [10, 11, 12], weight=7)
"""
nlist = iter(nodes_for_cycle)
try:
first_node = next(nlist)
except StopIteration:
return
G_to_add_to.add_node(first_node)
G_to_add_to.add_edges_from(
pairwise(chain((first_node,), nlist), cyclic=True), **attr
)
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|
30,350 | networkx.classes.function | add_path | Add a path to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_path : iterable container
A container of nodes. A path will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in path.
See Also
--------
add_star, add_cycle
Examples
--------
>>> G = nx.Graph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> nx.add_path(G, [10, 11, 12], weight=7)
| def add_path(G_to_add_to, nodes_for_path, **attr):
"""Add a path to the Graph G_to_add_to.
Parameters
----------
G_to_add_to : graph
A NetworkX graph
nodes_for_path : iterable container
A container of nodes. A path will be constructed from
the nodes (in order) and added to the graph.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to every edge in path.
See Also
--------
add_star, add_cycle
Examples
--------
>>> G = nx.Graph()
>>> nx.add_path(G, [0, 1, 2, 3])
>>> nx.add_path(G, [10, 11, 12], weight=7)
"""
nlist = iter(nodes_for_path)
try:
first_node = next(nlist)
except StopIteration:
return
G_to_add_to.add_node(first_node)
G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist)), **attr)
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|
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