"""
NetworkX utilizes a plugin-dispatch architecture, which means we can plug in and
out of backends with minimal code changes. A valid NetworkX backend specifies
`entry points <https://packaging.python.org/en/latest/specifications/entry-points>`_,
named ``networkx.backends`` and an optional ``networkx.backend_info`` when it is
installed (not imported). This allows NetworkX to dispatch (redirect) function calls
to the backend so the execution flows to the designated backend
implementation, similar to how plugging a charger into a socket redirects the
electricity to your phone. This design enhances flexibility and integration, making
NetworkX more adaptable and efficient.
There are three main ways to use a backend after the package is installed.
You can set environment variables and run the exact same code you run for
NetworkX. You can use a keyword argument ``backend=...`` with the NetworkX
function. Or, you can convert the NetworkX Graph to a backend graph type and
call a NetworkX function supported by that backend. Environment variables
and backend keywords automatically convert your NetworkX Graph to the
backend type. Manually converting it yourself allows you to use that same
backend graph for more than one function call, reducing conversion time.
For example, you can set an environment variable before starting python to request
all dispatchable functions automatically dispatch to the given backend::
bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py
or you can specify the backend as a kwarg::
nx.betweenness_centrality(G, k=10, backend="parallel")
or you can convert the NetworkX Graph object ``G`` into a Graph-like
object specific to the backend and then pass that in the NetworkX function::
H = nx_parallel.ParallelGraph(G)
nx.betweenness_centrality(H, k=10)
How it works: You might have seen the ``@nx._dispatchable`` decorator on
many of the NetworkX functions in the codebase. It decorates the function
with code that redirects execution to the function's backend implementation.
The code also manages any ``backend_kwargs`` you provide to the backend
version of the function. The code looks for the environment variable or
a ``backend`` keyword argument and if found, converts the input NetworkX
graph to the backend format before calling the backend's version of the
function. If no environment variable or backend keyword are found, the
dispatching code checks the input graph object for an attribute
called ``__networkx_backend__`` which tells it which backend provides this
graph type. That backend's version of the function is then called.
The backend system relies on Python ``entry_point`` system to signal
NetworkX that a backend is installed (even if not imported yet). Thus no
code needs to be changed between running with NetworkX and running with
a backend to NetworkX. The attribute ``__networkx_backend__`` holds a
string with the name of the ``entry_point``. If none of these options
are being used, the decorator code simply calls the NetworkX function
on the NetworkX graph as usual.
The NetworkX library does not need to know that a backend exists for it
to work. So long as the backend package creates the entry_point, and
provides the correct interface, it will be called when the user requests
it using one of the three approaches described above. Some backends have
been working with the NetworkX developers to ensure smooth operation.
They are the following::
- `graphblas <https://github.com/python-graphblas/graphblas-algorithms>`_
- `cugraph <https://github.com/rapidsai/cugraph/tree/branch-24.04/python/nx-cugraph>`_
- `parallel <https://github.com/networkx/nx-parallel>`_
- ``loopback`` is for testing purposes only and is not a real backend.
Note that the ``backend_name`` is e.g. ``parallel``, the package installed
is ``nx-parallel``, and we use ``nx_parallel`` while importing the package.
Creating a Custom backend
-------------------------
1. To be a valid backend that is discoverable by NetworkX, your package must
register an `entry-point <https://packaging.python.org/en/latest/specifications/entry-points/#entry-points>`_
``networkx.backends`` in the package's metadata, with a `key pointing to your
dispatch object <https://packaging.python.org/en/latest/guides/creating-and-discovering-plugins/#using-package-metadata>`_ .
For example, if you are using ``setuptools`` to manage your backend package,
you can `add the following to your pyproject.toml file <https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_::
[project.entry-points."networkx.backends"]
backend_name = "your_dispatcher_class"
You can also add the ``backend_info`` entry-point. It points towards the ``get_info``
function that returns all the backend information, which is then used to build the
"Additional Backend Implementation" box at the end of algorithm's documentation
page (e.g. `nx-cugraph's get_info function <https://github.com/rapidsai/cugraph/blob/branch-24.04/python/nx-cugraph/_nx_cugraph/__init__.py>`_)::
[project.entry-points."networkx.backend_info"]
backend_name = "your_get_info_function"
Note that this would only work if your backend is a trusted backend of NetworkX,
and is present in the `.circleci/config.yml` and
`.github/workflows/deploy-docs.yml` files in the NetworkX repository.
2. The backend must create an ``nx.Graph``-like object which contains an attribute
``__networkx_backend__`` with a value of the entry point name::
class BackendGraph:
__networkx_backend__ = "backend_name"
...
Testing the Custom backend
--------------------------
To test your custom backend, you can run the NetworkX test suite with your backend.
This also ensures that the custom backend is compatible with NetworkX's API.
Testing Environment Setup
~~~~~~~~~~~~~~~~~~~~~~~~~
To enable automatic testing with your custom backend, follow these steps:
1. Set Backend Environment Variables:
- ``NETWORKX_TEST_BACKEND`` : Setting this to your registered backend key will let
the NetworkX's dispatch machinery automatically convert a regular NetworkX
``Graph``, ``DiGraph``, ``MultiGraph``, etc. to their backend equivalents, using
``your_dispatcher_class.convert_from_nx(G, ...)`` function.
- ``NETWORKX_FALLBACK_TO_NX`` (default=False) : Setting this variable to `True` will
instruct tests to use a NetworkX ``Graph`` for algorithms not implemented by your
custom backend. Setting this to `False` will only run the tests for algorithms
implemented by your custom backend and tests for other algorithms will ``xfail``.
2. Defining ``convert_from_nx`` and ``convert_to_nx`` methods:
The arguments to ``convert_from_nx`` are:
- ``G`` : NetworkX Graph
- ``edge_attrs`` : dict, optional
Dictionary mapping edge attributes to default values if missing in ``G``.
If None, then no edge attributes will be converted and default may be 1.
- ``node_attrs``: dict, optional
Dictionary mapping node attributes to default values if missing in ``G``.
If None, then no node attributes will be converted.
- ``preserve_edge_attrs`` : bool
Whether to preserve all edge attributes.
- ``preserve_node_attrs`` : bool
Whether to preserve all node attributes.
- ``preserve_graph_attrs`` : bool
Whether to preserve all graph attributes.
- ``preserve_all_attrs`` : bool
Whether to preserve all graph, node, and edge attributes.
- ``name`` : str
The name of the algorithm.
- ``graph_name`` : str
The name of the graph argument being converted.
Running Tests
~~~~~~~~~~~~~
You can invoke NetworkX tests for your custom backend with the following commands::
NETWORKX_TEST_BACKEND=<backend_name>
NETWORKX_FALLBACK_TO_NX=True # or False
pytest --pyargs networkx
Conversions while running tests :
- Convert NetworkX graphs using ``<your_dispatcher_class>.convert_from_nx(G, ...)`` into
the backend graph.
- Pass the backend graph objects to the backend implementation of the algorithm.
- Convert the result back to a form expected by NetworkX tests using
``<your_dispatcher_class>.convert_to_nx(result, ...)``.
Notes
~~~~~
- Dispatchable algorithms that are not implemented by the backend
will cause a ``pytest.xfail``, giving some indication that not all
tests are running, while avoiding causing an explicit failure.
- If a backend only partially implements some algorithms, it can define
a ``can_run(name, args, kwargs)`` function that returns True or False
indicating whether it can run the algorithm with the given arguments.
It may also return a string indicating why the algorithm can't be run;
this string may be used in the future to give helpful info to the user.
- A backend may also define ``should_run(name, args, kwargs)`` that is similar
to ``can_run``, but answers whether the backend *should* be run (converting
if necessary). Like ``can_run``, it receives the original arguments so it
can decide whether it should be run by inspecting the arguments. ``can_run``
runs before ``should_run``, so ``should_run`` may assume ``can_run`` is True.
If not implemented by the backend, ``can_run`` and ``should_run`` are
assumed to always return True if the backend implements the algorithm.
- A special ``on_start_tests(items)`` function may be defined by the backend.
It will be called with the list of NetworkX tests discovered. Each item
is a test object that can be marked as xfail if the backend does not support
the test using ``item.add_marker(pytest.mark.xfail(reason=...))``.
- A backend graph instance may have a ``G.__networkx_cache__`` dict to enable
caching, and care should be taken to clear the cache when appropriate.
"""
import inspect
import itertools
import os
import warnings
from functools import partial
from importlib.metadata import entry_points
import networkx as nx
from .decorators import argmap
__all__ = ["_dispatchable"]
def _do_nothing():
"""This does nothing at all, yet it helps turn `_dispatchable` into functions."""
def _get_backends(group, *, load_and_call=False):
"""
Retrieve NetworkX ``backends`` and ``backend_info`` from the entry points.
Parameters
-----------
group : str
The entry_point to be retrieved.
load_and_call : bool, optional
If True, load and call the backend. Defaults to False.
Returns
--------
dict
A dictionary mapping backend names to their respective backend objects.
Notes
------
If a backend is defined more than once, a warning is issued.
The `nx-loopback` backend is removed if it exists, as it is only available during testing.
A warning is displayed if an error occurs while loading a backend.
"""
items = entry_points(group=group)
rv = {}
for ep in items:
if ep.name in rv:
warnings.warn(
f"networkx backend defined more than once: {ep.name}",
RuntimeWarning,
stacklevel=2,
)
elif load_and_call:
try:
rv[ep.name] = ep.load()()
except Exception as exc:
warnings.warn(
f"Error encountered when loading info for backend {ep.name}: {exc}",
RuntimeWarning,
stacklevel=2,
)
else:
rv[ep.name] = ep
rv.pop("nx-loopback", None)
return rv
backends = _get_backends("networkx.backends")
backend_info = _get_backends("networkx.backend_info", load_and_call=True)
# We must import from config after defining `backends` above
from .configs import Config, config
# Get default configuration from environment variables at import time
config.backend_priority = [
x.strip()
for x in os.environ.get(
"NETWORKX_BACKEND_PRIORITY",
os.environ.get("NETWORKX_AUTOMATIC_BACKENDS", ""),
).split(",")
if x.strip()
]
# Initialize default configuration for backends
config.backends = Config(
**{
backend: (
cfg if isinstance(cfg := info["default_config"], Config) else Config(**cfg)
)
if "default_config" in info
else Config()
for backend, info in backend_info.items()
}
)
type(config.backends).__doc__ = "All installed NetworkX backends and their configs."
# Load and cache backends on-demand
_loaded_backends = {} # type: ignore[var-annotated]
def _always_run(name, args, kwargs):
return True
def _load_backend(backend_name):
if backend_name in _loaded_backends:
return _loaded_backends[backend_name]
rv = _loaded_backends[backend_name] = backends[backend_name].load()
if not hasattr(rv, "can_run"):
rv.can_run = _always_run
if not hasattr(rv, "should_run"):
rv.should_run = _always_run
return rv
_registered_algorithms = {}
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
# 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(self.mutates_input, bool | dict):
raise TypeError(
f"Bad type for mutates_input: {type(self.mutates_input)}."
" Expected bool or dict."
) from None
if not isinstance(self._returns_graph, bool):
raise TypeError(
f"Bad type for returns_graph: {type(self._returns_graph)}."
" Expected bool."
) from None
if isinstance(graphs, str):
graphs = {graphs: 0}
elif graphs is None:
pass
elif not isinstance(graphs, dict):
raise TypeError(
f"Bad type for graphs: {type(graphs)}. Expected str or dict."
) from None
elif len(graphs) == 0:
raise KeyError("'graphs' must contain at least one variable name") from None
# This dict comprehension is complicated for better performance; equivalent shown below.
self.optional_graphs = set()
self.list_graphs = set()
if graphs is None:
self.graphs = {}
else:
self.graphs = {
self.optional_graphs.add(val := k[:-1]) or val
if (last := k[-1]) == "?"
else self.list_graphs.add(val := k[1:-1]) or val
if last == "]"
else k: v
for k, v in graphs.items()
}
# The above is equivalent to:
# self.optional_graphs = {k[:-1] for k in graphs if k[-1] == "?"}
# self.list_graphs = {k[1:-1] for k in graphs if k[-1] == "]"}
# self.graphs = {k[:-1] if k[-1] == "?" else k: v for k, v in graphs.items()}
# Compute and cache the signature on-demand
self._sig = None
# Which backends implement this function?
self.backends = {
backend
for backend, info in backend_info.items()
if "functions" in info and name in info["functions"]
}
if name in _registered_algorithms:
raise KeyError(
f"Algorithm already exists in dispatch registry: {name}"
) from None
# Use the magic of `argmap` to turn `self` into a function. This does result
# in small additional overhead compared to calling `_dispatchable` directly,
# but `argmap` has the magical property that it can stack with other `argmap`
# decorators "for free". Being a function is better for REPRs and type-checkers.
self = argmap(_do_nothing)(self)
_registered_algorithms[name] = self
return self
@property
def __doc__(self):
"""If the cached documentation exists, it is returned.
Otherwise, the documentation is generated using _make_doc() method,
cached, and then returned."""
if (rv := self._cached_doc) is not None:
return rv
rv = self._cached_doc = self._make_doc()
return rv
@__doc__.setter
def __doc__(self, val):
"""Sets the original documentation to the given value and resets the
cached documentation."""
self._orig_doc = val
self._cached_doc = None
@property
def __signature__(self):
"""Return the signature of the original function, with the addition of
the `backend` and `backend_kwargs` parameters."""
if self._sig is None:
sig = inspect.signature(self.orig_func)
# `backend` is now a reserved argument used by dispatching.
# assert "backend" not in sig.parameters
if not any(
p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
):
sig = sig.replace(
parameters=[
*sig.parameters.values(),
inspect.Parameter(
"backend", inspect.Parameter.KEYWORD_ONLY, default=None
),
inspect.Parameter(
"backend_kwargs", inspect.Parameter.VAR_KEYWORD
),
]
)
else:
*parameters, var_keyword = sig.parameters.values()
sig = sig.replace(
parameters=[
*parameters,
inspect.Parameter(
"backend", inspect.Parameter.KEYWORD_ONLY, default=None
),
var_keyword,
]
)
self._sig = sig
return self._sig
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)
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)
)
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)
)
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:
preserve_graph = preserve_graph_attrs
if getattr(graph, "__networkx_backend__", "networkx") == "networkx":
bound.arguments[gname] = self._convert_graph(
backend_name,
graph,
edge_attrs=edges,
node_attrs=nodes,
preserve_edge_attrs=preserve_edges,
preserve_node_attrs=preserve_nodes,
preserve_graph_attrs=preserve_graph,
graph_name=gname,
use_cache=use_cache,
)
bound_kwargs = bound.kwargs
del bound_kwargs["backend"]
return bound.args, bound_kwargs
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
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
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"])
G2 = bound2.arguments["G"]
if G1 is G2:
return G2
G2._node.clear()
G2._node.update(G1._node)
G2._adj.clear()
G2._adj.update(G1._adj)
if hasattr(G1, "_pred") and hasattr(G2, "_pred"):
G2._pred.clear()
G2._pred.update(G1._pred)
if hasattr(G1, "_succ") and hasattr(G2, "_succ"):
G2._succ.clear()
G2._succ.update(G1._succ)
nx._clear_cache(G2)
if self.name == "relabel_nodes":
return G2
return backend.convert_to_nx(result)
converted_result = backend.convert_to_nx(result)
if isinstance(converted_result, nx.Graph) and self.name not in {
"boykov_kolmogorov",
"preflow_push",
"quotient_graph",
"shortest_augmenting_path",
"spectral_graph_forge",
# We don't handle tempfile.NamedTemporaryFile arguments
"read_gml",
"read_graph6",
"read_sparse6",
# We don't handle io.BufferedReader or io.TextIOWrapper arguments
"bipartite_read_edgelist",
"read_adjlist",
"read_edgelist",
"read_graphml",
"read_multiline_adjlist",
"read_pajek",
"from_pydot",
"pydot_read_dot",
"agraph_read_dot",
# graph comparison fails b/c of nan values
"read_gexf",
}:
# For graph return types (e.g. generators), we compare that results are
# the same between the backend and networkx, then return the original
# networkx result so the iteration order will be consistent in tests.
G = self.orig_func(*args2, **kwargs2)
if not nx.utils.graphs_equal(G, converted_result):
assert G.number_of_nodes() == converted_result.number_of_nodes()
assert G.number_of_edges() == converted_result.number_of_edges()
assert G.graph == converted_result.graph
assert G.nodes == converted_result.nodes
assert G.adj == converted_result.adj
assert type(G) is type(converted_result)
raise AssertionError("Graphs are not equal")
return G
return converted_result
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}"
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,)
def _restore_dispatchable(name):
return _registered_algorithms[name]
if os.environ.get("_NETWORKX_BUILDING_DOCS_"):
# When building docs with Sphinx, use the original function with the
# dispatched __doc__, b/c Sphinx renders normal Python functions better.
# This doesn't show e.g. `*, backend=None, **backend_kwargs` in the
# signatures, which is probably okay. It does allow the docstring to be
# updated based on the installed backends.
_orig_dispatchable = _dispatchable
[docs]
def _dispatchable(func=None, **kwargs): # type: ignore[no-redef]
if func is None:
return partial(_dispatchable, **kwargs)
dispatched_func = _orig_dispatchable(func, **kwargs)
func.__doc__ = dispatched_func.__doc__
return func
_dispatchable.__doc__ = _orig_dispatchable.__new__.__doc__ # type: ignore[method-assign,assignment]
_sig = inspect.signature(_orig_dispatchable.__new__)
_dispatchable.__signature__ = _sig.replace( # type: ignore[method-assign,assignment]
parameters=[v for k, v in _sig.parameters.items() if k != "cls"]
)
