#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# This file graph.py is referred and derived from project NetworkX,
#
# https://github.com/networkx/networkx/blob/master/networkx/classes/graph.py
#
# which has the following license:
#
# Copyright (C) 2004-2020, NetworkX Developers
# Aric Hagberg <[email protected]>
# Dan Schult <[email protected]>
# Pieter Swart <[email protected]>
# All rights reserved.
#
# This file is part of NetworkX.
#
# NetworkX is distributed under a BSD license; see LICENSE.txt for more
# information.
#
import copy
import orjson as json
from networkx import freeze
from networkx.classes.graph import Graph as RefGraph
from networkx.classes.reportviews import DegreeView
from graphscope import nx
from graphscope.client.session import get_default_session
from graphscope.client.session import get_session_by_id
from graphscope.framework import dag_utils
from graphscope.framework import utils
from graphscope.framework.graph_schema import GraphSchema
from graphscope.nx import NetworkXError
from graphscope.nx.classes.cache import Cache
from graphscope.nx.classes.coreviews import AdjacencyView
from graphscope.nx.classes.dict_factory import AdjListDict
from graphscope.nx.classes.dict_factory import NeighborAttrDict
from graphscope.nx.classes.dict_factory import NeighborDict
from graphscope.nx.classes.dict_factory import NodeAttrDict
from graphscope.nx.classes.dict_factory import NodeDict
from graphscope.nx.classes.graphviews import generic_graph_view
from graphscope.nx.classes.reportviews import EdgeView
from graphscope.nx.classes.reportviews import NodeView
from graphscope.nx.convert import to_networkx_graph
from graphscope.nx.utils.compat import patch_docstring
from graphscope.nx.utils.misc import clear_mutation_cache
from graphscope.nx.utils.misc import init_empty_graph_in_engine
from graphscope.proto import graph_def_pb2
from graphscope.proto import types_pb2
__all__ = ["Graph"]
class _GraphBase(object):
"""
Base class for networkx module.
This is an empty class use to classify networkx graph.
"""
pass
[docs]class Graph(_GraphBase):
"""
Base class for undirected graphs.
A Graph that holds the metadata of a graph, and provides NetworkX-like Graph APIs.
It is worth noticing that the graph is actually stored by the Analytical Engine backend.
In other words, the Graph object holds nothing but metadata of a graph.
Graph support nodes and edges with optional data, or attributes.
Graphs support undirected edges. Self loops are allowed but multiple
(parallel) edges are not.
Nodes can be arbitrary int/str/float/bool objects with optional
key/value attributes.
Edges are represented as links between nodes with optional
key/value attributes.
Graph support node label if it's created from a GraphScope graph object.
nodes are identified by `(label, id)` tuple.
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 a GraphScope graph object.
default_label : default node label (optional, default: None)
if incoming_graph_data is a GraphScope graph object, default label means
the nodes of the label can be identified by id directly, other label nodes
need to use `(label, id)` to identify.
attr : keyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See Also
--------
DiGraph
Examples
--------
Create an empty graph structure (a "null graph") with no nodes and
no edges.
>>> G = nx.Graph()
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 integers, strings/floats/bool can represent a node too.
>>> G.add_node('a node')
>>> G.add_node(3.14)
>>> G.add_node(True)
**Edges:**
G can also be grown by adding edges.
Add one edge,
>>> G.add_edge(1, 2)
a list of edges,
>>> G.add_edges_from([(1, 2), (1, 3)])
or a collection of edges,
>>> G.add_edges_from(H.edges)
If some edges connect nodes not yet in the graph, the nodes
are added automatically. There are no errors when adding
nodes or edges that already exist.
**Attributes:**
Each graph, node, and edge can hold key/value attribute pairs
in an associated attribute dictionary (the keys must be string).
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.Graph(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 # node must exist already to use G.nodes
>>> 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.
>>> G.add_edge(1, 2, weight=4.7 )
>>> G.add_edges_from([(3, 4), (4, 5)], color='red')
>>> G.add_edges_from([(1, 2, {'color': 'blue'}), (2, 3, {'weight': 8})])
>>> G[1][2]['weight'] = 4.7
>>> G.edges[1, 2]['weight'] = 4
Warning: we protect the graph data structure by making `G.edges` a
read-only dict-like structure. However, you can assign to attributes
in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
data attributes: `G.edges[1, 2]['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
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, eattr in nbrsdict.items():
... if 'weight' in eattr:
... # Do something useful with the edges
... pass
But the edges() method is often more convenient:
>>> for u, v, weight in G.edges.data('weight'):
... if weight is not None:
... # Do something useful with the edges
... pass
**Transformation**
Create a graph with GraphScope graph object. First we init a GraphScope graph
with two node labels: person and comment`
>>> g = graphscope.g(directed=False).add_vertice("person.csv", label="person").add_vertice("comment.csv", label="comment")
create a graph with g, set default_label to 'person'
>>> G = nx.Graph(g, default_label="person")
`person` label nodes can be identified by id directly, for `comment` label,
we has to use tuple `("comment", id)` identify. Like, add a person label
node and a comment label node
>>> G.add_node(0, type="person")
>>> G.add_node(("comment", 0), type="comment")
print property of two nodes
>>> G.nodes[0]
{"type", "person"}
>>> G.nodes[("comment", 0)]
{"type", "comment"}
**Reporting:**
Simple graph information is obtained using object-attributes and methods.
Reporting typically 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]`, `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.
"""
node_dict_factory = NodeDict
node_attr_dict_factory = NodeAttrDict
adjlist_outer_dict_factory = AdjListDict
adjlist_inner_dict_factory = NeighborDict
edge_attr_dict_factory = NeighborAttrDict
graph_attr_dict_factory = dict
graph_cache_factory = Cache
_graph_type = graph_def_pb2.DYNAMIC_PROPERTY
def to_directed_class(self):
return nx.DiGraph
[docs] @patch_docstring(RefGraph.to_undirected_class)
def to_undirected_class(self):
return Graph
[docs] def __init__(self, incoming_graph_data=None, default_label=None, **attr):
"""Initialize a graph with graph, edges, name, or graph 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 an edge list, any
NetworkX graph object or any GraphScope graph object.
If the corresponding optional Python packages are installed
the data can also be a 2D NumPy array, a SciPy sparse matrix
default_label : default node label (optional, default: "_")
if incoming_graph_data is a GraphScope graph object, default label means
the nodes of the label can be accessed by id directly, other label nodes
need to use `(label, id)` to access.
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
>>> 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'}
Created from a GraphScope graph object
>>> g = graphscope.g(directed=False) # if transform to DiGraph, directed=True
>>> g.add_vertices("person.csv", label="person").add_vertices("comment.csv", label="comment").add_edges(...)
>>> G = nx.Graph(g, default_label="person") # or DiGraph
"""
self.graph_attr_dict_factory = self.graph_attr_dict_factory
self.node_dict_factory = self.node_dict_factory
self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
self.cache = self.graph_cache_factory(self)
# init node and adj (must be after cache)
self.graph = self.graph_attr_dict_factory()
self._node = self.node_dict_factory(self)
self._adj = self.adjlist_outer_dict_factory(self)
self._key = None
self._op = None
self._schema = GraphSchema()
# buffer caches for add_node and add_edge
self._add_node_cache = []
self._add_edge_cache = []
self._remove_node_cache = []
self._remove_edge_cache = []
create_empty_in_engine = attr.pop(
"create_empty_in_engine", True
) # a hidden parameter
self._distributed = attr.pop("dist", False)
if incoming_graph_data is not None and self._is_gs_graph(incoming_graph_data):
# convert from gs graph always use distributed mode
if self._session is None:
self._session = get_session_by_id(incoming_graph_data.session_id)
self._default_label = default_label
self._default_label_id = -1
if self._session is None:
self._session = get_default_session()
if not self._is_gs_graph(incoming_graph_data) and create_empty_in_engine:
graph_def = init_empty_graph_in_engine(
self, self.is_directed(), self._distributed
)
self._key = graph_def.key
# attempt to load graph with data
if incoming_graph_data is not None:
to_networkx_graph(incoming_graph_data, create_using=self)
self.cache.warmup()
# load graph attributes (must be after to_networkx_graph)
self.graph.update(attr)
self._saved_signature = self.signature
self._is_client_view = False
# statically create the unload op
#
# networkx operations update the op, but keep the key as same, thus
# the unload op don't need to be refreshed.
if self.op is None:
self._unload_op = None
else:
self._unload_op = dag_utils.unload_graph(self)
def _is_gs_graph(self, incoming_graph_data):
return (
hasattr(incoming_graph_data, "graph_type")
and incoming_graph_data.graph_type == graph_def_pb2.ARROW_PROPERTY
)
def __del__(self):
if self._key is None or self._session.disconnected:
return
if self.cache.enable_iter_cache:
try:
self.cache.shutdown()
except: # noqa: E722, pylint: disable=bare-except
pass
self.cache.shutdown_executor()
if not self._is_client_view and self._unload_op is not None:
self._unload_op.eval()
self._key = None
@property
def op(self):
"""The DAG op of this graph."""
return self._op
@property
def session(self):
"""Get the session of graph.
Returns:
Return session that the graph belongs to.
"""
if hasattr(self, "_graph") and self._is_client_view:
return (
self._graph.session
) # this graph is a client side graph view, use host graph session
return self._session
@property
def session_id(self):
"""Get session's id of graph.
Returns:
str: Return session id that the graph belongs to.
"""
if hasattr(self, "_graph") and self._is_client_view:
return (
self._graph.session_id
) # this graph is a client side graph view, use host graph session_id
return self._session.session_id
@property
def key(self):
"""Key of the coresponding engine graph."""
if hasattr(self, "_graph") and self._is_client_view:
return (
self._graph.key
) # this graph is a client side graph view, use host graph key
return self._key
@property
def signature(self):
"""Generate a signature of the current graph"""
return self._key
@property
def schema(self):
"""Schema of the graph.
Returns:
:class:`GraphSchema`: the schema of the graph
"""
return self._schema
@property
def template_str(self):
if self._key is None:
raise RuntimeError("graph should be registered in remote.")
s = ""
if self._graph_type == graph_def_pb2.DYNAMIC_PROPERTY:
s = "gs::DynamicFragment"
elif self._graph_type == graph_def_pb2.DYNAMIC_PROJECTED:
vdata_type = utils.data_type_to_cpp(self._schema.vdata_type)
edata_type = utils.data_type_to_cpp(self._schema.edata_type)
s = f"gs::DynamicProjectedFragment<{vdata_type},{edata_type}>"
elif self._graph_type == graph_def_pb2.ARROW_PROPERTY:
oid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.oid_type)
)
vid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.vid_type)
)
s = f"vineyard::ArrowFragment<{oid_type},{vid_type}>"
elif self._graph_type == graph_def_pb2.ARROW_FLATTENED:
oid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.oid_type)
)
vid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.vid_type)
)
vdata_type = utils.data_type_to_cpp(self._schema.vdata_type)
edata_type = utils.data_type_to_cpp(self._schema.edata_type)
s = f"gs::ArrowFlattenedFragment<{oid_type},{vid_type},{vdata_type},{edata_type}>"
elif self._graph_type == graph_def_pb2.ARROW_PROJECTED:
oid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.oid_type)
)
vid_type = utils.normalize_data_type_str(
utils.data_type_to_cpp(self._schema.vid_type)
)
vdata_type = utils.data_type_to_cpp(self._schema.vdata_type)
edata_type = utils.data_type_to_cpp(self._schema.edata_type)
s = f"gs::ArrowProjectedFragment<{oid_type},{vid_type},{vdata_type},{edata_type}>"
else:
raise ValueError(f"Unsupported graph type: {self._graph_type}")
return s
@property
def graph_type(self):
"""The type of the graph object.
Returns:
type (`types_pb2.GraphType`): the type of the graph.
"""
return self._graph_type
@property
@patch_docstring(RefGraph.name)
def name(self):
return self.graph.get("name", "")
@name.setter
def name(self, s):
self.graph["name"] = s
def loaded(self):
return self.key is not None
@clear_mutation_cache
@patch_docstring(RefGraph.__str__)
def __str__(self):
if self.graph_type in (
graph_def_pb2.ARROW_PROPERTY,
graph_def_pb2.DYNAMIC_PROPERTY,
):
return "".join(
[
type(self).__name__,
f" named {self.name!r}" if self.name else "",
f" with {self.number_of_nodes()} nodes and {self.number_of_edges()} edges",
]
)
return f"graphscope.nx.Graph\n{graph_def_pb2.GraphTypePb.Name(self.graph_type)}"
def __copy__(self):
"""override default __copy__"""
raise NetworkXError("graphscope.nx not support shallow copy.")
def __deepcopy__(self, memo):
"""override default __deepcopy__"""
return self.copy()
[docs] @clear_mutation_cache
def __iter__(self):
"""Iterate over the nodes. Use: 'for n in G'.
Returns
-------
niter : iterator
An iterator over all nodes in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> [n for n in G]
[0, 1, 2, 3]
>>> list(G)
[0, 1, 2, 3]
"""
return iter(self._node)
[docs] @clear_mutation_cache
def __contains__(self, n):
"""Returns True if n is a node, False otherwise. Use: 'n in G'.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> 1 in G
True
"""
try:
if self.graph_type == graph_def_pb2.ARROW_PROPERTY:
n = self._convert_to_label_id_tuple(n)
op = dag_utils.report_graph(self, types_pb2.HAS_NODE, node=json.dumps(n))
archive = op.eval()
return archive.get_bool()
except (TypeError, NetworkXError, KeyError):
return False
[docs] def __len__(self):
"""Returns the number of nodes in the graph. Use: 'len(G)'.
Returns
-------
nnodes : int
The number of nodes in the graph.
See Also
--------
number_of_nodes, order which are identical
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> len(G)
4
"""
return self.number_of_nodes()
[docs] def __getitem__(self, n):
"""Returns a dict of neighbors of node n. Use: 'G[n]'.
Parameters
----------
n : node
A node in the graph.
Returns
-------
adj_dict : dictionary
The adjacency dictionary for nodes connected to n.
Notes
-----
G[n] is the same as G.adj[n] and similar to G.neighbors(n)
(which is an iterator over G.adj[n])
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G[0]
AtlasView({1: {}})
"""
return self.adj[n]
[docs] @clear_mutation_cache
def add_node(self, node_for_adding, **attr):
"""Add a single node `node_for_adding` and update node attributes.
Parameters
----------
node_for_adding : node
A node can be int, float, str, tuple or bool object.
attr : keyword arguments, optional
Set or change node attributes using key=value.
See Also
--------
add_nodes_from
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> G.add_node(1)
>>> G.add_node(2)
>>> G.number_of_nodes()
2
Use keywords set/change node attributes:
>>> G.add_node(1, size=10)
>>> G.add_node(3, weight=0.4, type='apple')
Notes
-----
nx.Graph support int, float, str, tuple or bool object of nodes.
"""
self._convert_arrow_to_dynamic()
self._add_node_cache.append(
(node_for_adding, attr) if attr else node_for_adding
)
[docs] @clear_mutation_cache
def add_nodes_from(self, nodes_for_adding, **attr):
"""Add multiple nodes.
Parameters
----------
nodes_for_adding : iterable container
A container of nodes (list, dict, set, etc.).
OR
A container of (node, attribute dict) tuples.
Node attributes are updated using the attribute dict.
attr : keyword arguments, optional (default= no attributes)
Update attributes for all nodes in nodes.
Node attributes specified in nodes as a tuple take
precedence over attributes specified via keyword arguments.
See Also
--------
add_node
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> G.add_nodes_from("Hello")
>>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
>>> G.add_nodes_from(K3)
>>> sorted(G.nodes(), key=str)
[0, 1, 2, 'H', 'e', 'l', 'o']
Use keywords to update specific node attributes for every node.
>>> G.add_nodes_from([1, 2], size=10)
>>> G.add_nodes_from([3, 4], weight=0.4)
Use (node, attrdict) tuples to update attributes for specific nodes.
>>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
>>> G.nodes[1]["size"]
11
>>> H = nx.Graph()
>>> H.add_nodes_from(G.nodes(data=True))
>>> H.nodes[1]["size"]
11
"""
for n in nodes_for_adding:
data = dict(attr)
try:
nn, dd = n
data.update(dd)
self.add_node(nn, **data)
except (TypeError, ValueError):
self.add_node(n, **data)
[docs] @clear_mutation_cache
def remove_node(self, n):
"""Remove node n.
Removes the node n and all adjacent edges.
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
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> list(G.edges)
[(0, 1), (1, 2)]
>>> G.remove_node(1)
>>> list(G.edges)
[]
"""
self._convert_arrow_to_dynamic()
self._remove_node_cache.append(n)
[docs] @clear_mutation_cache
def remove_nodes_from(self, nodes_for_removing):
"""Remove multiple nodes.
Parameters
----------
nodes_for_removing : 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
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> e = list(G.nodes)
>>> e
[0, 1, 2]
>>> G.remove_nodes_from(e)
>>> list(G.nodes)
[]
"""
for n in nodes_for_removing:
self.remove_node(n)
@property
@clear_mutation_cache
@patch_docstring(RefGraph.nodes)
def nodes(self):
nodes = NodeView(self)
self.__dict__["nodes"] = nodes
return nodes
[docs] @clear_mutation_cache
def number_of_nodes(self):
"""Returns the number of nodes in the graph.
Returns
-------
nnodes : int
The number of nodes in the graph.
See Also
--------
order, __len__ which are identical
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> G.number_of_nodes()
3
"""
op = dag_utils.report_graph(self, types_pb2.NODE_NUM)
archive = op.eval()
return archive.get_size()
[docs] def order(self):
"""Returns the number of nodes in the graph.
Returns
-------
nnodes : int
The number of nodes in the graph.
See Also
--------
number_of_nodes, __len__ which are identical
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> G.order()
3
"""
return self.number_of_nodes()
[docs] def has_node(self, n):
"""Returns True if the graph contains the node n.
Identical to `n in G`
Parameters
----------
n : node
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> G.has_node(0)
True
It is more readable and simpler to use
>>> 0 in G
True
"""
return n in self
[docs] @clear_mutation_cache
def add_edge(self, u_of_edge, v_of_edge, **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, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be int/string/float/tuple/bool hashable Python objects.
attr : keyword arguments, optional
Edge data can be assigned using
keyword arguments.
See Also
--------
add_edges_from : add a collection of edges
Notes
-----
Adding an edge that already exists updates the edge data.
Many networkx algorithms designed for weighted graphs use
an edge attribute (by default `weight`) to hold a numerical value.
Examples
--------
The following all add the edge e=(1, 2) to graph G:
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> e = (1, 2)
>>> G.add_edge(1, 2) # explicit two-node form
>>> G.add_edge(*e) # single edge as tuple of two nodes
>>> G.add_edges_from([(1, 2)]) # add edges from iterable container
Associate data to edges using keywords:
>>> G.add_edge(1, 2, weight=3)
>>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
For non-string attribute keys, use subscript notation.
>>> G.add_edge(1, 2)
>>> G[1][2].update({0: 5})
>>> G.edges[1, 2].update({0: 5})
"""
self._convert_arrow_to_dynamic()
self._add_edge_cache.append(
(u_of_edge, v_of_edge, attr) if attr else (u_of_edge, v_of_edge)
)
[docs] @clear_mutation_cache
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 must be given as as 2-tuples (u, v) or
3-tuples (u, v, d) where d is a dictionary containing edge data.
attr : keyword arguments, optional
Edge data can be assigned using
keyword arguments.
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.
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> 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")
"""
for e in ebunch_to_add:
ne = len(e)
data = dict(attr)
if ne == 3:
u, v, dd = e
# make attributes specified in ebunch take precedence to attr
data.update(dd)
elif ne == 2:
u, v = e
else:
raise NetworkXError(
"Edge tuple %s must be a 2-tuple or 3-tuple." % (e,)
)
self.add_edge(u, v, **data)
[docs] @clear_mutation_cache
def add_weighted_edges_from(self, ebunch_to_add, weight="weight", **attr):
"""Add weighted edges in `ebunch_to_add` with specified weight attr
Parameters
----------
ebunch_to_add : container of edges
Each edge given in the list or container will be added
to the graph. The edges must be given as 3-tuples (u, v, w)
where w is a number.
weight : string, optional (default= 'weight')
The attribute name for the edge weights to be added.
attr : keyword arguments, optional (default= no attributes)
Edge attributes to add/update for all edges.
See Also
--------
add_edge : add a single edge
add_edges_from : add multiple edges
Notes
-----
Adding the same edge twice for Graph/DiGraph simply updates
the edge data.
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
"""
for u, v, d in ebunch_to_add:
# make attributes specified in ebunch take precedence to attr
attr[weight] = d
self.add_edge(u, v, **attr)
[docs] @clear_mutation_cache
@patch_docstring(RefGraph.remove_edge)
def remove_edge(self, u, v):
self._convert_arrow_to_dynamic()
self._remove_edge_cache.append((u, v))
[docs] @clear_mutation_cache
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) edge between u and v.
- 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
>>> ebunch = [(1, 2), (2, 3)]
>>> G.remove_edges_from(ebunch)
"""
for e in ebunch:
ne = len(e)
if ne < 2:
raise ValueError("Edge tuple %s must be a 2-tuple or 3-tuple." % (e,))
self.remove_edge(e[0], e[1])
[docs] @clear_mutation_cache
def set_edge_data(self, u, v, data):
"""Set edge data of edge (u, v).
Parameters
----------
u, v : nodes
Nodes can be int, str, float, tuple, bool hashable Python objects.
data: dict
Edge data to set to edge (u, v)
See Also
--------
set_node_data : set node data of node
Notes
-----
the method is called when to set_items in AdjEdgeAttr
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> G.add_edge(1, 2)
>>> dd = {'foo': 'bar'}
>>> G[1][2] = dd # call G.set_edge_data(1, 2, dd)
>>> G[1][2]
{'foo': 'bar'}
"""
self._convert_arrow_to_dynamic()
edge = json.dumps(((u, v, data),), option=json.OPT_SERIALIZE_NUMPY)
self._op = dag_utils.modify_edges(self, types_pb2.NX_UPDATE_EDGES, edge)
self._op.eval(leaf=False)
self.cache.clear_neighbor_attr_cache()
[docs] @clear_mutation_cache
def set_node_data(self, n, data):
"""Set data of node.
Parameters
----------
n : node
node can be int, str, float, tuple, bool hashable Python object which is existed in graph.
data : dict
data to set to n
See Also
--------
set_edge_data : set data of edge
Notes
-----
the method is called when to set_items in NodeAttr
Examples
--------
>>> G = nx.Graph() # or DiGraph
>>> G.add_node(1)
>>> dd = {'weight': 3}
>>> G.nodes[1] = dd # call G.set_node_data(1, dd)
>>> G.nodes[1]
{'weight': 3}
"""
self._convert_arrow_to_dynamic()
node = json.dumps(((n, data),), option=json.OPT_SERIALIZE_NUMPY)
self._op = dag_utils.modify_vertices(self, types_pb2.NX_UPDATE_NODES, node)
self._op.eval(leaf=False)
self.cache.clear_node_attr_cache()
[docs] @clear_mutation_cache
def update(self, edges=None, nodes=None):
"""Update the graph using nodes/edges/graphs as input.
Like dict.update, this method takes a graph as input, adding the
graph's nodes and edges to this graph. It can also take two inputs:
edges and nodes. Finally it can take either edges or nodes.
To specify only nodes the keyword `nodes` must be used.
The collections of edges and nodes are treated similarly to
the add_edges_from/add_nodes_from methods. When iterated, they
should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
Parameters
----------
edges : Graph object, collection of edges, or None
The first parameter can be a graph or some edges. If it has
attributes `nodes` and `edges`, then it is taken to be a
Graph-like object and those attributes are used as collections
of nodes and edges to be added to the graph.
If the first parameter does not have those attributes, it is
treated as a collection of edges and added to the graph.
If the first argument is None, no edges are added.
nodes : collection of nodes, or None
The second parameter is treated as a collection of nodes
to be added to the graph unless it is None.
If `edges is None` and `nodes is None` an exception is raised.
If the first parameter is a Graph, then `nodes` is ignored.
Examples
--------
>>> G = nx.path_graph(5)
>>> G.update(nx.complete_graph(range(4, 10)))
>>> from itertools import combinations
>>> edges = (
... (u, v, {"power": u * v})
... for u, v in combinations(range(10, 20), 2)
... if u * v < 225
... )
>>> nodes = [1000] # for singleton, use a container
>>> G.update(edges, nodes)
See Also
--------
add_edges_from: add multiple edges to a graph
add_nodes_from: add multiple nodes to a graph
"""
if edges is not None:
if nodes is not None:
self.add_nodes_from(nodes)
self.add_edges_from(edges)
else:
try:
graph_nodes = edges.nodes
graph_edges = edges.edges
except AttributeError:
self.add_edges_from(edges)
else: # edges is Graph-like
self.add_nodes_from(graph_nodes.data())
self.add_edges_from(graph_edges.data())
self.graph.update(edges.graph)
elif nodes is not None:
self.add_nodes_from(nodes)
else:
raise NetworkXError("update needs nodes or edges input")
[docs] @clear_mutation_cache
def size(self, weight=None):
"""Returns the number of edges or total of all edge weights.
Parameters
----------
weight : string or None, optional (default=None)
The edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
Returns
-------
size : numeric
The number of edges or
(if weight keyword is provided) the total weight sum.
If weight is None, returns an int. Otherwise a float
(or more general numeric if the weights are more general).
See Also
--------
number_of_edges
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G.size()
3
>>> G = nx.Graph() # or DiGraph
>>> G.add_edge("a", "b", weight=2)
>>> G.add_edge("b", "c", weight=4)
>>> G.size()
2
>>> G.size(weight="weight")
6.0
"""
if weight:
return sum(d for v, d in self.degree(weight=weight)) / 2
op = dag_utils.report_graph(self, types_pb2.EDGE_NUM)
archive = op.eval()
return archive.get_size() // 2
[docs] @clear_mutation_cache
@patch_docstring(RefGraph.number_of_edges)
def number_of_edges(self, u=None, v=None):
edges_num = 0
if u is None:
edges_num = self.size()
elif self.has_edge(u, v):
edges_num = 1
return edges_num
@clear_mutation_cache
def number_of_selfloops(self):
op = dag_utils.report_graph(self, types_pb2.SELFLOOPS_NUM)
archive = op.eval()
return archive.get_size()
[docs] @clear_mutation_cache
def has_edge(self, u, v):
"""Returns True if the edge (u, v) is in the graph.
This is the same as `v in G[u]` without KeyError exceptions.
Parameters
----------
u, v : nodes
Nodes can be, for example, strings or numbers.
Nodes must be int, str, float, tuple, bool hashable Python objects.
Returns
-------
edge_ind : bool
True if edge is in the graph, False otherwise.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G.has_edge(0, 1) # using two nodes
True
>>> e = (0, 1)
>>> G.has_edge(*e) # e is a 2-tuple (u, v)
True
>>> e = (0, 1, {"weight": 7})
>>> G.has_edge(*e[:2]) # e is a 3-tuple (u, v, data_dictionary)
True
The following syntax are equivalent:
>>> G.has_edge(0, 1)
True
>>> 1 in G[0] # though this gives KeyError if 0 not in G
True
"""
try:
return v in self._adj[u]
except KeyError:
return False
[docs] @clear_mutation_cache
def neighbors(self, n):
"""Returns an iterator over all neighbors of node n.
This is identical to `iter(G[n])`
Parameters
----------
n : node
A node in the graph
Returns
-------
neighbors : iterator
An iterator over all neighbors of node n
Raises
------
NetworkXError
If the node n is not in the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> [n for n in G.neighbors(0)]
[1]
Notes
-----
Alternate ways to access the neighbors are ``G.adj[n]`` or ``G[n]``:
>>> G = nx.Graph() # or DiGraph
>>> G.add_edge("a", "b", weight=7)
>>> G["a"]
AtlasView({'b': {'weight': 7}})
>>> G = nx.path_graph(4)
>>> [n for n in G[0]]
[1]
"""
try:
return iter(self._adj[n])
except KeyError:
raise NetworkXError("The node %s is not in the graph." % (n,))
@property
@clear_mutation_cache
def edges(self):
"""An EdgeView of the Graph as G.edges or G.edges().
edges(self, nbunch=None, data=False, default=None)
The EdgeView provides set-like operations on the edge-tuples
as well as edge attribute lookup. When called, it also provides
an EdgeDataView object which allows control of access to edge
attributes (but does not provide set-like operations).
Hence, `G.edges[u, v]['color']` provides the value of the color
attribute for edge `(u, v)` while
`for (u, v, c) in G.edges.data('color', default='red'):`
iterates through all the edges yielding the color attribute
with default `'red'` if no color attribute exists.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
data : string or bool, optional (default=False)
The edge attribute returned in 3-tuple (u, v, ddict[data]).
If True, return edge attribute dict in 3-tuple (u, v, ddict).
If False, return 2-tuple (u, v).
default : value, optional (default=None)
Value used for edges that don't have the requested attribute.
Only relevant if data is not True or False.
Returns
-------
edges : EdgeView
A view of edge attributes, usually it iterates over (u, v)
or (u, v, d) tuples of edges, but can also be used for
attribute lookup as `edges[u, v]['foo']`.
Notes
-----
Nodes in nbunch that are not in the graph will be (quietly) ignored.
For directed graphs this returns the out-edges.
Examples
--------
>>> G = nx.path_graph(3) # or DiGraph
>>> G.add_edge(2, 3, weight=5)
>>> [e for e in G.edges]
[(0, 1), (1, 2), (2, 3)]
>>> G.edges.data() # default data is {} (empty dict)
EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
>>> G.edges.data("weight", default=1)
EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
>>> G.edges([0, 3]) # only edges incident to these nodes
EdgeDataView([(0, 1), (3, 2)])
>>> G.edges(0) # only edges incident to a single node (use G.adj[0]?)
EdgeDataView([(0, 1)])
"""
return EdgeView(self)
[docs] @clear_mutation_cache
def get_edge_data(self, u, v, default=None):
"""Returns the attribute dictionary associated with edge (u, v).
This is identical to `G[u][v]` except the default is returned
instead of an exception if the edge doesn't exist.
Parameters
----------
u, v : nodes
default: any Python object (default=None)
Value to return if the edge (u, v) is not found.
Returns
-------
edge_dict : dictionary
The edge attribute dictionary.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G[0][1]
{}
Warning: Assigning to `G[u][v]` is not permitted.
But it is safe to assign attributes `G[u][v]['foo']`
>>> G[0][1]["weight"] = 7
>>> G[0][1]["weight"]
7
>>> G[1][0]["weight"]
7
>>> G = nx.path_graph(4) # or DiGraph
>>> G.get_edge_data(0, 1) # default edge data is {}
{}
>>> e = (0, 1)
>>> G.get_edge_data(*e) # tuple form
{}
>>> G.get_edge_data("a", "b", default=0) # edge not in graph, return 0
0
"""
if self.has_edge(u, v):
if self.graph_type == graph_def_pb2.ARROW_PROPERTY:
u = self._convert_to_label_id_tuple(u)
v = self._convert_to_label_id_tuple(v)
op = dag_utils.report_graph(
self,
types_pb2.EDGE_DATA,
edge=json.dumps((u, v)),
key="",
)
archive = op.eval()
return json.loads(archive.get_bytes())
else:
return default
@property
@clear_mutation_cache
@patch_docstring(RefGraph.adj)
def adj(self):
return AdjacencyView(self._adj)
[docs] @clear_mutation_cache
def adjacency(self):
"""Returns an iterator over (node, adjacency dict) tuples for all nodes.
For directed graphs, only outgoing neighbors/adjacencies are included.
Returns
-------
adj_iter : iterator
An iterator over (node, adjacency dictionary) for all nodes in
the graph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> [(n, nbrdict) for n, nbrdict in G.adjacency()]
[(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
"""
return iter(self._adj.items())
@property
@clear_mutation_cache
def degree(self):
"""A DegreeView for the Graph as G.degree or G.degree().
The node degree is the number of edges adjacent to the node.
The weighted node degree is the sum of the edge weights for
edges incident to that node.
This object provides an iterator for (node, degree) as well as
lookup for the degree for a single node.
Parameters
----------
nbunch : single node, container, or all nodes (default= all nodes)
The view will only report edges incident to these nodes.
weight : string or None, optional (default=None)
The name of an edge attribute that holds the numerical value used
as a weight. If None, then each edge has weight 1.
The degree is the sum of the edge weights adjacent to the node.
Returns
-------
If a single node is requested
deg : int
Degree of the node
OR if multiple nodes are requested
nd_view : A DegreeView object capable of iterating (node, degree) pairs
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G.degree[0] # node 0 has degree 1
1
>>> list(G.degree([0, 1, 2]))
[(0, 1), (1, 2), (2, 2)]
"""
return DegreeView(self)
[docs] def clear(self):
"""Remove all nodes and edges from the graph.
This also removes the name, and all graph, node, and edge attributes.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G.clear()
>>> list(G.nodes)
[]
>>> list(G.edges)
[]
"""
if self._graph_type == graph_def_pb2.ARROW_PROPERTY:
# create an empty graph, no need to convert arrow to dynamic
self._graph_type = graph_def_pb2.DYNAMIC_PROPERTY
graph_def = init_empty_graph_in_engine(
self, self.is_directed(), self._distributed
)
self._key = graph_def.key
else:
op = dag_utils.clear_graph(self)
op.eval()
self.graph.clear()
self.schema.clear()
self._add_node_cache.clear()
self._add_edge_cache.clear()
self._remove_node_cache.clear()
self._remove_edge_cache.clear()
self.cache.clear()
[docs] @clear_mutation_cache
def clear_edges(self):
"""Remove all edges from the graph without altering nodes.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> G.clear_edges()
>>> list(G.nodes)
[0, 1, 2, 3]
>>> list(G.edges)
[]
"""
self._convert_arrow_to_dynamic()
self._op = dag_utils.clear_edges(self)
self._op.eval(leaf=False)
self.cache.clear()
[docs] @patch_docstring(RefGraph.is_directed)
def is_directed(self):
return False
[docs] @patch_docstring(RefGraph.is_multigraph)
def is_multigraph(self):
return False
[docs] @clear_mutation_cache
@patch_docstring(RefGraph.nbunch_iter)
def nbunch_iter(self, nbunch=None):
if nbunch is None: # include all nodes via iterator
bunch = iter(self.nodes)
elif nbunch in self: # if nbunch is a single node
bunch = iter([nbunch])
else: # if nbunch is a sequence of nodes
def bunch_iter(nlist, adj):
try:
for n in nlist:
if n in adj:
yield n
except TypeError as e:
message = e.args[0]
# capture error for non-sequence/iterator nbunch.
if "iter" in message:
msg = "nbunch is not a node or a sequence of nodes."
raise NetworkXError(msg) from e
# capture error for invalid node.
elif "hashable" in message:
msg = "Node {} in sequence nbunch is not a valid node."
raise NetworkXError(msg) from e
else:
raise
bunch = bunch_iter(nbunch, self._adj)
return bunch
[docs] @clear_mutation_cache
def copy(self, as_view=False):
"""Returns a copy of the graph.
The copy method by default returns an independent deep copy
of the graph and attributes.
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 three 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 in Engine backend.
The entire graph object is new so that changes in the copy
do not affect the original object.
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.
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
>>> H = G.copy()
"""
if as_view:
g = generic_graph_view(self)
g._is_client_view = True
g._op = self._op
g.cache = self.cache
else:
self._convert_arrow_to_dynamic()
g = self.__class__(create_empty_in_engine=False)
g.graph = copy.deepcopy(self.graph)
op = dag_utils.copy_graph(self, "identical")
graph_def = op.eval(leaf=False)
g._op = op
g._key = graph_def.key
g.cache.warmup()
g._session = self._session
return g
[docs] @clear_mutation_cache
def to_undirected(self, as_view=False):
"""Returns an undirected copy of the graph.
Parameters
----------
as_view : bool (optional, default=False)
If True return a view of the original undirected graph.
Returns
-------
G : Graph
A deepcopy of the graph.
See Also
--------
Graph, 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.
Examples
--------
>>> G = nx.path_graph(2)
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
>>> G2 = H.to_undirected()
>>> list(G2.edges)
[(0, 1)]
"""
self._convert_arrow_to_dynamic()
if self.is_directed():
graph_class = self.to_undirected_class()
g = graph_class(create_empty_in_engine=False)
if as_view:
g.graph.update(self.graph)
g._graph = self
g._is_client_view = False
g = freeze(g)
else:
g.graph = copy.deepcopy(self.graph)
op = dag_utils.to_undirected(self)
graph_def = op.eval(leaf=False)
g._op = op
g._key = graph_def.key
g._session = self._session
g.cache.warmup()
return g
return self.copy(as_view=as_view)
[docs] @clear_mutation_cache
def to_directed(self, as_view=False):
"""Returns a directed representation of the graph.
Parameters
----------
as_view : bool, optional (default=False)
If True return a view of the original directed graph.
Returns
-------
G : DiGraph
A directed graph with the same name, same nodes, and with
each edge (u, v, data) replaced by two directed edges
(u, v, data) and (v, u, data).
Notes
-----
This by default returns a "deepcopy" of the edge, node, and
graph attributes which attempts to completely copy
all of the data and references.
Examples
--------
>>> G = nx.Graph()
>>> G.add_edge(0, 1)
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1), (1, 0)]
If already directed, return a (deep) copy
>>> G = nx.DiGraph()
>>> G.add_edge(0, 1)
>>> H = G.to_directed()
>>> list(H.edges)
[(0, 1)]
"""
self._convert_arrow_to_dynamic()
if self.is_directed():
return self.copy(as_view=as_view)
graph_class = self.to_directed_class()
if as_view:
g = generic_graph_view(self, graph_class)
g._op = self._op
g._key = self._key
g._session = self._session
g._is_client_view = True
return g
g = graph_class(create_empty_in_engine=False)
g.graph = copy.deepcopy(self.graph)
op = dag_utils.to_directed(self)
graph_def = op.eval(leaf=False)
g._key = graph_def.key
g._session = self._session
g._op = op
g.cache.warmup()
return g
[docs] @clear_mutation_cache
def subgraph(self, nodes):
"""Returns a independent deep copy subgraph induced on `nodes`.
The induced subgraph of the graph contains the nodes in `nodes`
and the edges between those nodes.
Parameters
----------
nodes : list, iterable
A container of nodes which will be iterated through once.
Returns
-------
G : Graph
A subgraph of the graph.
Notes
-----
Unlike NetowrkX return a view, here return a independent deep copy subgraph.
Examples
--------
>>> G = nx.path_graph(4) # or DiGraph
>>> H = G.subgraph([0, 1, 2])
>>> list(H.edges)
[(0, 1), (1, 2)]
"""
self._convert_arrow_to_dynamic()
induced_nodes = json.dumps(list(nodes))
g = self.__class__(create_empty_in_engine=False)
g.graph.update(self.graph)
op = dag_utils.create_subgraph(self, nodes=induced_nodes)
graph_def = op.eval(leaf=False)
g._key = graph_def.key
g._session = self._session
g._op = op
g.cache.warmup()
return g
[docs] @clear_mutation_cache
def edge_subgraph(self, edges):
"""Returns a independent deep copy subgraph induced by the specified edges.
The induced subgraph contains each edge in `edges` and each
node incident to any one of those edges.
Parameters
----------
edges : iterable
An iterable of edges in this graph.
Returns
-------
G : Graph
An edge-induced subgraph of this graph with the same edge
attributes.
Notes
-----
Unlike NetworkX return a view, here return a independent deep copy subgraph.
Examples
--------
>>> G = nx.path_graph(5) # or DiGraph
>>> H = G.edge_subgraph([(0, 1), (3, 4)])
>>> list(H.nodes)
[0, 1, 3, 4]
>>> list(H.edges)
[(0, 1), (3, 4)]
"""
self._convert_arrow_to_dynamic()
induced_edges = json.dumps(list(edges))
g = self.__class__(create_empty_in_engine=False)
g.graph.update(self.graph)
op = dag_utils.create_subgraph(self, edges=induced_edges)
graph_def = op.eval(leaf=False)
g._key = graph_def.key
g._session = self._session
g._op = op
g.cache.warmup()
return g
@clear_mutation_cache
def _project_to_simple(self, v_prop=None, e_prop=None):
"""Project nx graph to a simple graph to run builtin algorithms.
A simple graph is a wrapper of property graph that only single edge
attribute and single node attribute are available.
Parameters
----------
v_prop: the node attribute key to project, (optional, default None)
e_prop: the edge attribute key to project, (optional, default None)
Returns
-------
simple_graph: nx.Graph or nx.DiGraph
A nx.Graph object that hold a simple graph projected by host property graph.
Notes
-------
the method is implicit called in builtin apps.
"""
if hasattr(self, "_graph") and self._is_client_view:
# is a graph view, project the original graph(just for copy)
graph = self._graph
while hasattr(graph, "_graph"):
graph = graph._graph
return graph._project_to_simple(v_prop=v_prop, e_prop=e_prop)
graph = self.__class__(create_empty_in_engine=False)
graph = nx.freeze(graph)
op = dag_utils.project_to_simple(self, str(v_prop), str(e_prop))
graph_def = op.eval(leaf=False)
graph._graph_type = graph_def.graph_type
graph._key = graph_def.key
graph._schema.from_graph_def(graph_def)
graph._default_label_id = self._default_label_id
graph._saved_signature = graph._key
graph._op = op
graph._session = self._session
graph._graph = self # projected graph also can report nodes.
graph._is_client_view = False
return graph
def _clear_adding_cache(self):
reset_cache = bool(
len(self._add_node_cache) > 0 or len(self._add_edge_cache) > 0
)
if self._add_node_cache:
nodes_to_modify = json.dumps(
self._add_node_cache, option=json.OPT_SERIALIZE_NUMPY
)
self._op = dag_utils.modify_vertices(
self, types_pb2.NX_ADD_NODES, nodes_to_modify
)
self._op.eval(leaf=False)
self._add_node_cache.clear()
if self._add_edge_cache:
edges_to_modify = json.dumps(
self._add_edge_cache, option=json.OPT_SERIALIZE_NUMPY
)
self._op = dag_utils.modify_edges(
self, types_pb2.NX_ADD_EDGES, edges_to_modify
)
self._op.eval(leaf=False)
self._add_edge_cache.clear()
if reset_cache:
self.cache.clear()
def _clear_removing_cache(self):
reset_cache = bool(
len(self._remove_node_cache) > 0 or len(self._remove_edge_cache) > 0
)
if self._remove_node_cache:
nodes_to_modify = json.dumps(
self._remove_node_cache, option=json.OPT_SERIALIZE_NUMPY
)
self._op = dag_utils.modify_vertices(
self, types_pb2.NX_DEL_NODES, nodes_to_modify
)
self._op.eval(leaf=False)
self._remove_node_cache.clear()
if self._remove_edge_cache:
edges_to_modify = json.dumps(
self._remove_edge_cache, option=json.OPT_SERIALIZE_NUMPY
)
self._op = dag_utils.modify_edges(
self, types_pb2.NX_DEL_EDGES, edges_to_modify
)
self._op.eval(leaf=False)
self._remove_edge_cache.clear()
if reset_cache:
self.cache.clear()
def _convert_arrow_to_dynamic(self):
"""Try to convert the hosted graph from arrow_property to dynamic_property.
Notes
-------
the method is implicit called by modification and graph view methods.
"""
if self.graph_type == graph_def_pb2.ARROW_PROPERTY:
self._op = dag_utils.arrow_to_dynamic(self)
graph_def = self._op.eval(leaf=False)
self._key = graph_def.key
self._graph_type = graph_def.graph_type
def _convert_to_label_id_tuple(self, n):
"""Convert the node to (label_id, id) format.
The input node may be id or (label, id), convert the node
to tuple (label_id, id) format.
Notes
-------
the method is implicit called by report methods and the hosted graph is
arrow_property graph.
"""
if isinstance(n, tuple):
label_id = n[1]
new_n = (self._schema.get_vertex_label_id(n[0]), n[1])
if new_n[0] == self._default_label_id:
raise KeyError("default label's node must be non-tuple format.")
elif self._default_label_id == -1:
# the n is non-tuple, but default id is -1
raise KeyError("default label id is -1.")
else:
label_id = n
new_n = (self._default_label_id, n)
if not isinstance(label_id, utils.data_type_to_python(self._schema.oid_type)):
# id is not oid type
raise KeyError("the node type is not arrow_property oid_type.")
return new_n