Merge remote-tracking branch 'origin' into forceatlas2-networkx

.github/workflows/circleci.yml

@@ -7,7 +7,7 @@ jobs:
     name: Run CircleCI image artifact redirector
     steps:
       - name: GitHub Action step
-        uses: larsoner/circleci-artifacts-redirector-action@master
+        uses: scientific-python/circleci-artifacts-redirector-action@4e13a10d89177f4bfc8007a7064bdbeda848d8d1 # v1.0.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_ARTIFACT_REDIRECTOR_TOKEN }}
@@ -19,7 +19,7 @@ jobs:
     name: Run CircleCI documentation artifact redirector
     steps:
       - name: GitHub Action step
-        uses: larsoner/circleci-artifacts-redirector-action@master
+        uses: scientific-python/circleci-artifacts-redirector-action@4e13a10d89177f4bfc8007a7064bdbeda848d8d1 # v1.0.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_ARTIFACT_REDIRECTOR_TOKEN }}

.github/workflows/release.yml

@@ -14,6 +14,8 @@ jobs:
     permissions:
       # IMPORTANT: this permission is mandatory for trusted publishing
       id-token: write
+      attestations: write
+      contents: read
     steps:
       - uses: actions/checkout@v4
         with:
@@ -30,5 +32,13 @@ jobs:
           pip install -U build twine wheel
           python -m build --sdist --wheel
 
+      - name: Verify the distribution
+        run: twine check --strict dist/*
+
+      - name: Generate artifact attestation for sdist and wheel
+        uses: actions/attest-build-provenance@173725a1209d09b31f9d30a3890cf2757ebbff0d # v1.1.2
+        with:
+          subject-path: "dist/networkx-*"
+
       - name: Publish package distributions to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1

.gitignore

@@ -55,4 +55,8 @@ networkx.egg-info/
 .pytest_cache
 
 # Virtual environment directory
-networkx-dev/
+networkx-dev/
+
+# Benchmark products
+benchmarks/env
+benchmarks/results

doc/conf.py

@@ -1,6 +1,7 @@
 import os
 from datetime import date
 from sphinx_gallery.sorting import ExplicitOrder, FileNameSortKey
+from intersphinx_registry import get_intersphinx_mapping
 from warnings import filterwarnings
 
 filterwarnings(
@@ -228,19 +229,22 @@
 latex_appendices = ["tutorial"]
 
 # Intersphinx mapping
-intersphinx_mapping = {
-    "python": ("https://docs.python.org/3/", None),
-    "numpy": ("https://numpy.org/doc/stable/", None),
-    "neps": ("https://numpy.org/neps/", None),
-    "matplotlib": ("https://matplotlib.org/stable/", None),
-    "scipy": ("https://docs.scipy.org/doc/scipy/", None),
-    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
-    "geopandas": ("https://geopandas.org/en/stable/", None),
-    "pygraphviz": ("https://pygraphviz.github.io/documentation/stable/", None),
-    "sphinx-gallery": ("https://sphinx-gallery.github.io/stable/", None),
-    "nx-guides": ("https://networkx.org/nx-guides/", None),
-    "sympy": ("https://docs.sympy.org/latest/", None),
-}
+
+intersphinx_mapping = get_intersphinx_mapping(
+    packages={
+        "python",
+        "numpy",
+        "neps",
+        "matplotlib",
+        "scipy",
+        "pandas",
+        "geopandas",
+        "pygraphviz",
+        "sphinx-gallery",
+        "sympy",
+        "nx-guides",
+    }
+)
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.

doc/index.rst

@@ -34,17 +34,17 @@ To cite NetworkX please use the following publication:
 
 Aric A. Hagberg, Daniel A. Schult and Pieter J. Swart,
 `"Exploring network structure, dynamics, and function using NetworkX"
-<https://conference.scipy.org/proceedings/SciPy2008/paper_2/>`_,
+<http://conference.scipy.org.s3-website-us-east-1.amazonaws.com/proceedings/scipy2008/paper_2/>`_,
 in
 `Proceedings of the 7th Python in Science Conference (SciPy2008)
-<https://conference.scipy.org/proceedings/SciPy2008/index.html>`_, Gäel
-Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA
-USA), pp. 11--15, Aug 2008
+<http://conference.scipy.org.s3-website-us-east-1.amazonaws.com/proceedings/scipy2008/index.html>`_,
+Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds),
+(Pasadena, CA USA), pp. 11--15, Aug 2008
 
 .. only:: html
 
-   `PDF <https://conference.scipy.org/proceedings/SciPy2008/paper_2/full_text.pdf>`_
-   `BibTeX <https://conference.scipy.org/proceedings/SciPy2008/paper_2/reference.bib>`_
+   `PDF <http://conference.scipy.org.s3-website-us-east-1.amazonaws.com/proceedings/scipy2008/paper_2/full_text.pdf>`_
+   `BibTeX <http://conference.scipy.org.s3-website-us-east-1.amazonaws.com/proceedings/scipy2008/paper_2/reference.bib>`_
 
 Audience
 --------

networkx/algorithms/assortativity/pairs.py

@@ -1,26 +1,30 @@
-"""Generators of  x-y pairs of node data."""
+"""Generators of x-y pairs of node data."""
 import networkx as nx
 
 __all__ = ["node_attribute_xy", "node_degree_xy"]
 
 
 @nx._dispatchable(node_attrs="attribute")
 def node_attribute_xy(G, attribute, nodes=None):
-    """Returns iterator of node-attribute pairs for all edges in G.
+    """Yields 2-tuples of node attribute values for all edges in `G`.
+
+    This generator yields, for each edge in `G` incident to a node in `nodes`,
+    a 2-tuple of form ``(attribute value,  attribute value)`` for the parameter
+    specified node-attribute.
 
     Parameters
     ----------
     G: NetworkX graph
 
     attribute: key
-       The node attribute key.
+        The node attribute key.
 
     nodes: list or iterable (optional)
         Use only edges that are incident to specified nodes.
         The default is all nodes.
 
-    Returns
-    -------
+    Yields
+    ------
     (x, y): 2-tuple
         Generates 2-tuple of (attribute, attribute) values.
 
@@ -29,13 +33,14 @@ def node_attribute_xy(G, attribute, nodes=None):
     >>> G = nx.DiGraph()
     >>> G.add_node(1, color="red")
     >>> G.add_node(2, color="blue")
+    >>> G.add_node(3, color="green")
     >>> G.add_edge(1, 2)
     >>> list(nx.node_attribute_xy(G, "color"))
     [('red', 'blue')]
 
     Notes
     -----
-    For undirected graphs each edge is produced twice, once for each edge
+    For undirected graphs, each edge is produced twice, once for each edge
     representation (u, v) and (v, u), with the exception of self-loop edges
     which only appear once.
     """
@@ -61,7 +66,11 @@ def node_attribute_xy(G, attribute, nodes=None):
 
 @nx._dispatchable(edge_attrs="weight")
 def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
-    """Generate node degree-degree pairs for edges in G.
+    """Yields 2-tuples of ``(degree, degree)`` values for edges in `G`.
+
+    This generator yields, for each edge in `G` incident to a node in `nodes`,
+    a 2-tuple of form ``(degree, degree)``. The node degrees are weighted
+    when a `weight` attribute is specified.
 
     Parameters
     ----------
@@ -82,12 +91,11 @@ def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
         Use only edges that are adjacency to specified nodes.
         The default is all nodes.
 
-    Returns
-    -------
+    Yields
+    ------
     (x, y): 2-tuple
         Generates 2-tuple of (degree, degree) values.
 
-
     Examples
     --------
     >>> G = nx.DiGraph()
@@ -99,7 +107,7 @@ def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
 
     Notes
     -----
-    For undirected graphs each edge is produced twice, once for each edge
+    For undirected graphs, each edge is produced twice, once for each edge
     representation (u, v) and (v, u), with the exception of self-loop edges
     which only appear once.
     """

networkx/algorithms/bridges.py

@@ -70,7 +70,6 @@ def bridges(G, root=None):
     H = nx.Graph(G) if multigraph else G
     chains = nx.chain_decomposition(H, root=root)
     chain_edges = set(chain.from_iterable(chains))
-    H_copy = H.copy()
     if root is not None:
         H = H.subgraph(nx.node_connected_component(H, root)).copy()
     for u, v in H.edges():

networkx/algorithms/link_prediction.py

@@ -300,7 +300,7 @@ def common_neighbor_centrality(G, ebunch=None, alpha=0.8):
         If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
 
     NetworkXAlgorithmError
-        If self loops exsists in `ebunch` or in `G` (if `ebunch` is `None`).
+        If self loops exist in `ebunch` or in `G` (if `ebunch` is `None`).
 
     NodeNotFound
         If `ebunch` has a node that is not in `G`.

networkx/algorithms/polynomials.py

@@ -12,8 +12,8 @@
 >>> import sympy
 >>> x = sympy.Symbol("x")
 >>> G = nx.complete_graph(4)
->>> A = nx.adjacency_matrix(G)
->>> M = sympy.SparseMatrix(A.todense())
+>>> A = nx.to_numpy_array(G, dtype=int)
+>>> M = sympy.SparseMatrix(A)
 >>> M.charpoly(x).as_expr()
 x**4 - 6*x**2 - 8*x - 3
 

networkx/algorithms/shortest_paths/generic.py

@@ -224,7 +224,7 @@ def shortest_path_length(G, source=None, target=None, weight=None, method="dijks
 
     Returns
     -------
-    length: int or iterator
+    length: number or iterator
         If the source and target are both specified, return the length of
         the shortest path from the source to the target.
 

networkx/classes/digraph.py

@@ -25,6 +25,9 @@ class _CachedPropertyResetterAdjAndSucc:
     are set to new objects. In addition, the attributes `_succ` and `_adj`
     are synced so these two names point to the same object.
 
+    Warning: most of the time, when ``G._adj`` is set, ``G._pred`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
     This object sits on a class and ensures that any instance of that
     class clears its cached properties "succ" and "adj" whenever the
     underlying instance attributes "_succ" or "_adj" are set to a new object.
@@ -39,10 +42,18 @@ def __set__(self, obj, value):
         od["_adj"] = value
         od["_succ"] = value
         # reset cached properties
-        if "adj" in od:
-            del od["adj"]
-        if "succ" in od:
-            del od["succ"]
+        props = [
+            "adj",
+            "succ",
+            "edges",
+            "out_edges",
+            "degree",
+            "out_degree",
+            "in_degree",
+        ]
+        for prop in props:
+            if prop in od:
+                del od[prop]
 
 
 class _CachedPropertyResetterPred:
@@ -51,6 +62,9 @@ class _CachedPropertyResetterPred:
     This assumes that the ``cached_property`` ``G.pred`` should be reset whenever
     ``G._pred`` is set to a new value.
 
+    Warning: most of the time, when ``G._pred`` is set, ``G._adj`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
     This object sits on a class and ensures that any instance of that
     class clears its cached property "pred" whenever the underlying
     instance attribute "_pred" is set to a new object. It only affects
@@ -63,8 +77,11 @@ class clears its cached property "pred" whenever the underlying
     def __set__(self, obj, value):
         od = obj.__dict__
         od["_pred"] = value
-        if "pred" in od:
-            del od["pred"]
+        # reset cached properties
+        props = ["pred", "in_edges", "degree", "out_degree", "in_degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
 
 
 class DiGraph(Graph):

networkx/classes/graph.py

@@ -37,8 +37,11 @@ class clears its cached property "adj" whenever the underlying
     def __set__(self, obj, value):
         od = obj.__dict__
         od["_adj"] = value
-        if "adj" in od:
-            del od["adj"]
+        # reset cached properties
+        props = ["adj", "edges", "degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
 
 
 class _CachedPropertyResetterNode:
@@ -59,6 +62,7 @@ class clears its cached property "nodes" whenever the underlying
     def __set__(self, obj, value):
         od = obj.__dict__
         od["_node"] = value
+        # reset cached properties
         if "nodes" in od:
             del od["nodes"]