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| author | Ross Barnowski <rossbar@berkeley.edu> | 2020-12-12 13:01:43 -0800 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2020-12-12 13:01:43 -0800 |
| commit | 1462350ebb1467a46af4f7b774bb093ce15eb6c6 (patch) | |
| tree | 126ec15cd3a58da324fee9b419e7e73c7e2df8e1 | |
| parent | 812f115e9b98bcefc8fdb8a3479e3cd32468ac5e (diff) | |
| download | networkx-1462350ebb1467a46af4f7b774bb093ce15eb6c6.tar.gz | |
DOC: Switch from napoleon to numpydoc sphinx extension (#4447)
* Setup for numpydoc.
* Add to doc requirements.
* Replace napoleon with numpydoc in conf.py.
* DOC: Fixups from numpydoc.
43 files changed, 222 insertions, 224 deletions
diff --git a/doc/conf.py b/doc/conf.py index 3081148f..cf3468bc 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -19,12 +19,12 @@ extensions = [ "sphinx.ext.doctest", "sphinx.ext.intersphinx", "sphinx.ext.mathjax", - "sphinx.ext.napoleon", "sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx_gallery.gen_gallery", "nb2plots", "texext", + "numpydoc", ] # https://github.com/sphinx-gallery/sphinx-gallery @@ -113,9 +113,6 @@ modindex_common_prefix = ["networkx."] doctest_global_setup = "import networkx as nx" -# treat ``x, y : type`` as vars x and y instead of default ``y(x,) : type`` -napoleon_use_param = False - # Options for HTML output # ----------------------- diff --git a/networkx/algorithms/approximation/clique.py b/networkx/algorithms/approximation/clique.py index af2ed74f..9ba7454e 100644 --- a/networkx/algorithms/approximation/clique.py +++ b/networkx/algorithms/approximation/clique.py @@ -23,7 +23,7 @@ def max_clique(G): The apx-maximum clique of the graph Notes - ------ + ----- A clique in an undirected graph G = (V, E) is a subset of the vertex set `C \subseteq V` such that for every two vertices in C there exists an edge connecting the two. This is equivalent to saying that the subgraph diff --git a/networkx/algorithms/bipartite/edgelist.py b/networkx/algorithms/bipartite/edgelist.py index d480bb54..cdad6869 100644 --- a/networkx/algorithms/bipartite/edgelist.py +++ b/networkx/algorithms/bipartite/edgelist.py @@ -70,8 +70,8 @@ def write_edgelist(G, path, comments="#", delimiter=" ", data=True, encoding="ut See Also -------- - write_edgelist() - generate_edgelist() + write_edgelist + generate_edgelist """ for line in generate_edgelist(G, delimiter, data): line += "\n" diff --git a/networkx/algorithms/centrality/load.py b/networkx/algorithms/centrality/load.py index 6c50c68e..40c21aa0 100644 --- a/networkx/algorithms/centrality/load.py +++ b/networkx/algorithms/centrality/load.py @@ -35,7 +35,7 @@ def newman_betweenness_centrality(G, v=None, cutoff=None, normalized=True, weigh See Also -------- - betweenness_centrality() + betweenness_centrality Notes ----- diff --git a/networkx/algorithms/chordal.py b/networkx/algorithms/chordal.py index 4424f8bc..aa89ca5b 100644 --- a/networkx/algorithms/chordal.py +++ b/networkx/algorithms/chordal.py @@ -438,7 +438,7 @@ def complete_to_chordal_graph(G): The elimination ordering of nodes of G Notes - ------ + ----- There are different approaches to calculate the chordal enhancement of a graph. The algorithm used here is called MCS-M and gives at least minimal (local) triangulation of graph. Note diff --git a/networkx/algorithms/community/kernighan_lin.py b/networkx/algorithms/community/kernighan_lin.py index 33226634..3cbbbf70 100644 --- a/networkx/algorithms/community/kernighan_lin.py +++ b/networkx/algorithms/community/kernighan_lin.py @@ -78,7 +78,7 @@ def kernighan_lin_bisection(G, partition=None, max_iter=10, weight="weight", see A pair of sets of nodes representing the bipartition. Raises - ------- + ------ NetworkXError If partition is not a valid partition of the nodes of the graph. diff --git a/networkx/algorithms/community/label_propagation.py b/networkx/algorithms/community/label_propagation.py index 0fd1c1f3..21138522 100644 --- a/networkx/algorithms/community/label_propagation.py +++ b/networkx/algorithms/community/label_propagation.py @@ -52,7 +52,7 @@ def asyn_lpa_communities(G, weight=None, seed=None): Iterable of communities given as sets of nodes. Notes - ------ + ----- Edge weight attributes must be numerical. References diff --git a/networkx/algorithms/community/lukes.py b/networkx/algorithms/community/lukes.py index ea4c12f8..29ade800 100644 --- a/networkx/algorithms/community/lukes.py +++ b/networkx/algorithms/community/lukes.py @@ -58,7 +58,7 @@ def lukes_partitioning(G, max_size: int, node_weight=None, edge_weight=None) -> partition. Raises - ------- + ------ NotATree If G is not a tree. TypeError diff --git a/networkx/algorithms/components/biconnected.py b/networkx/algorithms/components/biconnected.py index bbd085cb..4aa86cf5 100644 --- a/networkx/algorithms/components/biconnected.py +++ b/networkx/algorithms/components/biconnected.py @@ -186,13 +186,6 @@ def biconnected_components(G): NetworkXNotImplemented If the input graph is not undirected. - See Also - -------- - k_components : this function is a special case where k=2 - bridge_components : similar to this function, but is defined using - 2-edge-connectivity instead of 2-node-connectivity. - - Examples -------- >>> G = nx.lollipop_graph(5, 1) @@ -228,6 +221,9 @@ def biconnected_components(G): is_biconnected articulation_points biconnected_component_edges + k_components : this function is a special case where k=2 + bridge_components : similar to this function, but is defined using + 2-edge-connectivity instead of 2-node-connectivity. Notes ----- diff --git a/networkx/algorithms/connectivity/edge_augmentation.py b/networkx/algorithms/connectivity/edge_augmentation.py index 0a564d9b..ff2c44c9 100644 --- a/networkx/algorithms/connectivity/edge_augmentation.py +++ b/networkx/algorithms/connectivity/edge_augmentation.py @@ -46,8 +46,8 @@ def is_k_edge_connected(G, k): -------- :func:`is_locally_k_edge_connected` - Example - ------- + Examples + -------- >>> G = nx.barbell_graph(10, 0) >>> nx.is_k_edge_connected(G, k=1) True @@ -102,8 +102,8 @@ def is_locally_k_edge_connected(G, s, t, k): -------- :func:`is_k_edge_connected` - Example - ------- + Examples + -------- >>> from networkx.algorithms.connectivity import is_locally_k_edge_connected >>> G = nx.barbell_graph(10, 0) >>> is_locally_k_edge_connected(G, 5, 15, k=1) @@ -204,8 +204,8 @@ def k_edge_augmentation(G, k, avail=None, weight=None, partial=False): produces a feasible solution, but provides no guarantees on the solution weight. - Example - ------- + Examples + -------- >>> # Unweighted cases >>> G = nx.path_graph((1, 2, 3, 4)) >>> G.add_node(5) @@ -220,8 +220,6 @@ def k_edge_augmentation(G, k, avail=None, weight=None, partial=False): >>> nx.edge_connectivity(G) 4 - Example - ------- >>> # Weighted cases >>> G = nx.path_graph((1, 2, 3, 4)) >>> G.add_node(5) @@ -326,8 +324,8 @@ def partial_k_edge_augmentation(G, k, avail, weight=None): -------- :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.path_graph((1, 2, 3, 4, 5, 6, 7)) >>> G.add_node(8) >>> avail = [(1, 3), (1, 4), (1, 5), (2, 4), (2, 5), (3, 5), (1, 8)] @@ -547,8 +545,8 @@ def _lightest_meta_edges(mapping, avail_uv, avail_w): minimum weight edge bridging each k-edge-connected component so, we group the edges by meta-edge and take the lightest in each group. - Example - ------- + Examples + -------- >>> # Each group represents a meta-node >>> groups = ([1, 2, 3], [4, 5], [6]) >>> mapping = {n: meta_n for meta_n, ns in enumerate(groups) for n in ns} @@ -594,8 +592,8 @@ def unconstrained_one_edge_augmentation(G): :func:`one_edge_augmentation` :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)]) >>> G.add_nodes_from([6, 7, 8]) >>> sorted(unconstrained_one_edge_augmentation(G)) @@ -646,8 +644,8 @@ def weighted_one_edge_augmentation(G, avail, weight=None, partial=False): :func:`one_edge_augmentation` :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)]) >>> G.add_nodes_from([6, 7, 8]) >>> # any edge not in avail has an implicit weight of infinity @@ -746,8 +744,8 @@ def unconstrained_bridge_augmentation(G): :func:`bridge_augmentation` :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.path_graph((1, 2, 3, 4, 5, 6, 7)) >>> sorted(unconstrained_bridge_augmentation(G)) [(1, 7)] @@ -880,8 +878,8 @@ def weighted_bridge_augmentation(G, avail, weight=None): :func:`bridge_augmentation` :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.path_graph((1, 2, 3, 4)) >>> # When the weights are equal, (1, 4) is the best >>> avail = [(1, 4, 1), (1, 3, 1), (2, 4, 1)] @@ -1114,8 +1112,8 @@ def complement_edges(G): edge : tuple Edges in the complement of G - Example - ------- + Examples + -------- >>> G = nx.path_graph((1, 2, 3, 4)) >>> sorted(complement_edges(G)) [(1, 3), (1, 4), (2, 4)] @@ -1187,8 +1185,8 @@ def greedy_k_edge_augmentation(G, k, avail=None, weight=None, seed=None): -------- :func:`k_edge_augmentation` - Example - ------- + Examples + -------- >>> G = nx.path_graph((1, 2, 3, 4, 5, 6, 7)) >>> sorted(greedy_k_edge_augmentation(G, k=2)) [(1, 7)] diff --git a/networkx/algorithms/connectivity/edge_kcomponents.py b/networkx/algorithms/connectivity/edge_kcomponents.py index 723cbc1a..f76dff26 100644 --- a/networkx/algorithms/connectivity/edge_kcomponents.py +++ b/networkx/algorithms/connectivity/edge_kcomponents.py @@ -40,7 +40,7 @@ def k_edge_components(G, k): will have k-edge-connectivity in the graph G. See Also - ------- + -------- :func:`local_edge_connectivity` :func:`k_edge_subgraphs` : similar to this function, but the subgraph defined by the nodes must also have k-edge-connectivity. @@ -64,8 +64,8 @@ def k_edge_components(G, k): run based on the chain decomposition. Otherwise, the algorithm from _[2] is used. - Example - ------- + Examples + -------- >>> import itertools as it >>> from networkx.utils import pairwise >>> paths = [ @@ -124,7 +124,7 @@ def k_edge_subgraphs(G, k): of G that is k-edge-connected. See Also - ------- + -------- :func:`edge_connectivity` :func:`k_edge_components` : similar to this function, but nodes only need to have k-edge-connctivity within the graph G and the subgraphs @@ -144,8 +144,8 @@ def k_edge_subgraphs(G, k): If k=1, or k=2 and the graph is undirected, then this simply calls `k_edge_components`. Otherwise the algorithm from _[1] is used. - Example - ------- + Examples + -------- >>> import itertools as it >>> from networkx.utils import pairwise >>> paths = [ @@ -223,8 +223,8 @@ def bridge_components(G): ----- Bridge-connected components are also known as 2-edge-connected components. - Example - ------- + Examples + -------- >>> # The barbell graph with parameter zero has a single bridge >>> G = nx.barbell_graph(5, 0) >>> from networkx.algorithms.connectivity.edge_kcomponents import bridge_components @@ -261,8 +261,8 @@ class EdgeComponentAuxGraph: k-edge-connected components. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264 - Example - ------- + Examples + -------- >>> import itertools as it >>> from networkx.utils import pairwise >>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph @@ -288,11 +288,10 @@ class EdgeComponentAuxGraph: >>> sorted(map(sorted, aux_graph.k_edge_components(k=4))) [[0], [1], [2], [3], [4], [5], [6], [7]] - Example - ------- - >>> # The auxiliary graph is primarilly used for k-edge-ccs but it - >>> # can also speed up the queries of k-edge-subgraphs by refining the - >>> # search space. + The auxiliary graph is primarilly used for k-edge-ccs but it + can also speed up the queries of k-edge-subgraphs by refining the + search space. + >>> import itertools as it >>> from networkx.utils import pairwise >>> from networkx.algorithms.connectivity import EdgeComponentAuxGraph @@ -532,8 +531,8 @@ def general_k_edge_subgraphs(G, k): Technology 2012 480-–491. https://openproceedings.org/2012/conf/edbt/ZhouLYLCL12.pdf - Example - ------- + Examples + -------- >>> from networkx.utils import pairwise >>> paths = [ ... (11, 12, 13, 14, 11, 13, 14, 12), # a 4-clique diff --git a/networkx/algorithms/d_separation.py b/networkx/algorithms/d_separation.py index 964a1684..9d41e2d8 100644 --- a/networkx/algorithms/d_separation.py +++ b/networkx/algorithms/d_separation.py @@ -40,13 +40,20 @@ True References ---------- -.. [1] Pearl, J. (2009). Causality. Cambridge: Cambridge University Press. +.. [1] Pearl, J. (2009). Causality. Cambridge: Cambridge University Press. -.. [2] Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge: Cambridge University Press. +.. [2] Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. + Cambridge: Cambridge University Press. -.. [3] Shachter, R. D. (1998). Bayes-ball: rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In , Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 480–487). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. +.. [3] Shachter, R. D. (1998). + Bayes-ball: rational pastime (for determining irrelevance and requisite + information in belief networks and influence diagrams). + In , Proceedings of the Fourteenth Conference on Uncertainty in Artificial + Intelligence (pp. 480–487). + San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. -.. [4] Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. The MIT Press. +.. [4] Koller, D., & Friedman, N. (2009). + Probabilistic graphical models: principles and techniques. The MIT Press. """ diff --git a/networkx/algorithms/euler.py b/networkx/algorithms/euler.py index c2978442..5fd8dce4 100644 --- a/networkx/algorithms/euler.py +++ b/networkx/algorithms/euler.py @@ -377,7 +377,7 @@ def eulerize(G): .. [1] J. Edmonds, E. L. Johnson. Matching, Euler tours and the Chinese postman. Mathematical programming, Volume 5, Issue 1 (1973), 111-114. - [2] https://en.wikipedia.org/wiki/Eulerian_path + .. [2] https://en.wikipedia.org/wiki/Eulerian_path .. [3] http://web.math.princeton.edu/math_alive/5/Notes1.pdf Examples diff --git a/networkx/algorithms/graph_hashing.py b/networkx/algorithms/graph_hashing.py index a03553a2..febabf25 100644 --- a/networkx/algorithms/graph_hashing.py +++ b/networkx/algorithms/graph_hashing.py @@ -91,7 +91,7 @@ def weisfeiler_lehman_graph_hash( 'f9e9cb01c6d2f3b17f83ffeaa24e5986' References - ------- + ---------- .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman Graph Kernels. Journal of Machine Learning Research. 2011. diff --git a/networkx/algorithms/hybrid.py b/networkx/algorithms/hybrid.py index 58868e89..0a1f7a3e 100644 --- a/networkx/algorithms/hybrid.py +++ b/networkx/algorithms/hybrid.py @@ -54,9 +54,9 @@ def kl_connected_subgraph(G, k, l, low_memory=False, same_as_graph=False): References ---------- - .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid - Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, - 2004. 89--104. + .. [1] Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid + Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, + 2004. 89--104. """ H = copy.deepcopy(G) # subgraph we construct by removing from G @@ -148,9 +148,9 @@ def is_kl_connected(G, k, l, low_memory=False): References ---------- - .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid - Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, - 2004. 89--104. + .. [1] Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid + Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, + 2004. 89--104. """ graphOK = True diff --git a/networkx/algorithms/lowest_common_ancestors.py b/networkx/algorithms/lowest_common_ancestors.py index 7961cedb..4e7910b5 100644 --- a/networkx/algorithms/lowest_common_ancestors.py +++ b/networkx/algorithms/lowest_common_ancestors.py @@ -53,8 +53,8 @@ def tree_all_pairs_lowest_common_ancestor(G, root=None, pairs=None): See Also -------- - all_pairs_lowest_common_ancestor (similar routine for general DAGs) - lowest_common_ancestor (just a single pair for general DAGs) + all_pairs_lowest_common_ancestor: similar routine for general DAGs + lowest_common_ancestor: just a single pair for general DAGs """ if len(G) == 0: raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.") diff --git a/networkx/algorithms/moral.py b/networkx/algorithms/moral.py index 96bbf651..74461aae 100644 --- a/networkx/algorithms/moral.py +++ b/networkx/algorithms/moral.py @@ -23,7 +23,7 @@ def moral_graph(G): The undirected moralized graph of G Notes - ------ + ----- A moral graph is an undirected graph H = (V, E) generated from a directed Graph, where if a node has more than one parent node, edges between these parent nodes are inserted and all directed edges become diff --git a/networkx/algorithms/node_classification/hmn.py b/networkx/algorithms/node_classification/hmn.py index a4e1ace3..9cdb56ff 100644 --- a/networkx/algorithms/node_classification/hmn.py +++ b/networkx/algorithms/node_classification/hmn.py @@ -32,12 +32,12 @@ def harmonic_function(G, max_iter=30, label_name="label"): name of target labels to predict Returns - ---------- + ------- predicted : array, shape = [n_samples] Array of predicted labels Raises - ---------- + ------ NetworkXError If no nodes on `G` has `label_name`. @@ -76,7 +76,7 @@ def harmonic_function(G, max_iter=30, label_name="label"): Array of pairs of node id and label id Returns - ---------- + ------- P : scipy sparse matrix, shape = [n_samples, n_samples] Propagation matrix @@ -101,7 +101,7 @@ def harmonic_function(G, max_iter=30, label_name="label"): The number of classes (distinct labels) on the input graph Returns - ---------- + ------- B : array, shape = [n_samples, n_classes] Base matrix """ diff --git a/networkx/algorithms/node_classification/lgc.py b/networkx/algorithms/node_classification/lgc.py index dda73502..f873c2b2 100644 --- a/networkx/algorithms/node_classification/lgc.py +++ b/networkx/algorithms/node_classification/lgc.py @@ -34,7 +34,7 @@ def local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name="label") Name of target labels to predict Returns - ---------- + ------- predicted : array, shape = [n_samples] Array of predicted labels @@ -81,7 +81,7 @@ def local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name="label") Clamping factor Returns - ---------- + ------- S : scipy sparse matrix, shape = [n_samples, n_samples] Propagation matrix @@ -107,7 +107,7 @@ def local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name="label") The number of classes (distinct labels) on the input graph Returns - ---------- + ------- B : array, shape = [n_samples, n_classes] Base matrix """ diff --git a/networkx/algorithms/non_randomness.py b/networkx/algorithms/non_randomness.py index ca61c584..e4e57bd8 100644 --- a/networkx/algorithms/non_randomness.py +++ b/networkx/algorithms/non_randomness.py @@ -50,9 +50,9 @@ def non_randomness(G, k=None): References ---------- - .. [1] Xiaowei Ying and Xintao Wu, - On Randomness Measures for Social Networks, - SIAM International Conference on Data Mining. 2009 + .. [1] Xiaowei Ying and Xintao Wu, + On Randomness Measures for Social Networks, + SIAM International Conference on Data Mining. 2009 """ import numpy as np diff --git a/networkx/algorithms/operators/unary.py b/networkx/algorithms/operators/unary.py index a24bbc60..324a7715 100644 --- a/networkx/algorithms/operators/unary.py +++ b/networkx/algorithms/operators/unary.py @@ -17,8 +17,8 @@ def complement(G): GC : A new graph. Notes - ------ - Note that complement() does not create self-loops and also + ----- + Note that `complement` does not create self-loops and also does not produce parallel edges for MultiGraphs. Graph, node, and edge data are not propagated to the new graph. diff --git a/networkx/algorithms/shortest_paths/dense.py b/networkx/algorithms/shortest_paths/dense.py index c8d0df00..f2adc540 100644 --- a/networkx/algorithms/shortest_paths/dense.py +++ b/networkx/algorithms/shortest_paths/dense.py @@ -32,7 +32,7 @@ def floyd_warshall_numpy(G, nodelist=None, weight="weight"): will be Inf. Notes - ------ + ----- Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative @@ -91,7 +91,7 @@ def floyd_warshall_predecessor_and_distance(G, weight="weight"): ['s', 'x', 'u', 'v'] Notes - ------ + ----- Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative cycles. @@ -159,7 +159,7 @@ def reconstruct_path(source, target, predecessors): If source and target are the same, an empty list is returned Notes - ------ + ----- This function is meant to give more applicability to the floyd_warshall_predecessor_and_distance function @@ -196,7 +196,7 @@ def floyd_warshall(G, weight="weight"): between nodes. Notes - ------ + ----- Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative cycles. diff --git a/networkx/algorithms/shortest_paths/generic.py b/networkx/algorithms/shortest_paths/generic.py index 5a277e36..e5a691e2 100644 --- a/networkx/algorithms/shortest_paths/generic.py +++ b/networkx/algorithms/shortest_paths/generic.py @@ -112,12 +112,12 @@ def shortest_path(G, source=None, target=None, weight=None, method="dijkstra"): See Also -------- - all_pairs_shortest_path() - all_pairs_dijkstra_path() - all_pairs_bellman_ford_path() - single_source_shortest_path() - single_source_dijkstra_path() - single_source_bellman_ford_path() + all_pairs_shortest_path + all_pairs_dijkstra_path + all_pairs_bellman_ford_path + single_source_shortest_path + single_source_dijkstra_path + single_source_bellman_ford_path """ if method not in ("dijkstra", "bellman-ford"): # so we don't need to check in each branch later @@ -247,12 +247,12 @@ def shortest_path_length(G, source=None, target=None, weight=None, method="dijks See Also -------- - all_pairs_shortest_path_length() - all_pairs_dijkstra_path_length() - all_pairs_bellman_ford_path_length() - single_source_shortest_path_length() - single_source_dijkstra_path_length() - single_source_bellman_ford_path_length() + all_pairs_shortest_path_length + all_pairs_dijkstra_path_length + all_pairs_bellman_ford_path_length + single_source_shortest_path_length + single_source_dijkstra_path_length + single_source_bellman_ford_path_length """ if method not in ("dijkstra", "bellman-ford"): # so we don't need to check in each branch later @@ -464,9 +464,9 @@ def all_shortest_paths(G, source, target, weight=None, method="dijkstra"): See Also -------- - shortest_path() - single_source_shortest_path() - all_pairs_shortest_path() + shortest_path + single_source_shortest_path + all_pairs_shortest_path """ method = "unweighted" if weight is None else method if method == "unweighted": @@ -515,11 +515,11 @@ def _build_paths_from_predecessors(sources, target, pred): See Also -------- - shortest_path() - single_source_shortest_path() - all_pairs_shortest_path() - all_shortest_paths() - bellman_ford_path() + shortest_path + single_source_shortest_path + all_pairs_shortest_path + all_shortest_paths + bellman_ford_path """ if target not in pred: raise nx.NetworkXNoPath( diff --git a/networkx/algorithms/shortest_paths/unweighted.py b/networkx/algorithms/shortest_paths/unweighted.py index dd0e337f..5363b24d 100644 --- a/networkx/algorithms/shortest_paths/unweighted.py +++ b/networkx/algorithms/shortest_paths/unweighted.py @@ -451,7 +451,7 @@ def all_pairs_shortest_path(G, cutoff=None): See Also -------- - floyd_warshall() + floyd_warshall """ # TODO This can be trivially parallelized. diff --git a/networkx/algorithms/shortest_paths/weighted.py b/networkx/algorithms/shortest_paths/weighted.py index b155dbba..84149f09 100644 --- a/networkx/algorithms/shortest_paths/weighted.py +++ b/networkx/algorithms/shortest_paths/weighted.py @@ -151,8 +151,9 @@ def dijkstra_path(G, source, target, weight="weight"): See Also -------- - bidirectional_dijkstra(), bellman_ford_path() - single_source_dijkstra() + bidirectional_dijkstra + bellman_ford_path + single_source_dijkstra """ (length, path) = single_source_dijkstra(G, source, target=target, weight=weight) return path @@ -220,8 +221,9 @@ def dijkstra_path_length(G, source, target, weight="weight"): See Also -------- - bidirectional_dijkstra(), bellman_ford_path_length() - single_source_dijkstra() + bidirectional_dijkstra + bellman_ford_path_length + single_source_dijkstra """ if source == target: @@ -291,7 +293,7 @@ def single_source_dijkstra_path(G, source, cutoff=None, weight="weight"): See Also -------- - single_source_dijkstra(), single_source_bellman_ford() + single_source_dijkstra, single_source_bellman_ford """ return multi_source_dijkstra_path(G, {source}, cutoff=cutoff, weight=weight) @@ -361,7 +363,7 @@ def single_source_dijkstra_path_length(G, source, cutoff=None, weight="weight"): See Also -------- - single_source_dijkstra(), single_source_bellman_ford_path_length() + single_source_dijkstra, single_source_bellman_ford_path_length """ return multi_source_dijkstra_path_length(G, {source}, cutoff=cutoff, weight=weight) @@ -457,9 +459,9 @@ def single_source_dijkstra(G, source, target=None, cutoff=None, weight="weight") See Also -------- - single_source_dijkstra_path() - single_source_dijkstra_path_length() - single_source_bellman_ford() + single_source_dijkstra_path + single_source_dijkstra_path_length + single_source_bellman_ford """ return multi_source_dijkstra( G, {source}, cutoff=cutoff, target=target, weight=weight @@ -531,7 +533,7 @@ def multi_source_dijkstra_path(G, sources, cutoff=None, weight="weight"): See Also -------- - multi_source_dijkstra(), multi_source_bellman_ford() + multi_source_dijkstra, multi_source_bellman_ford """ length, path = multi_source_dijkstra(G, sources, cutoff=cutoff, weight=weight) @@ -606,7 +608,7 @@ def multi_source_dijkstra_path_length(G, sources, cutoff=None, weight="weight"): See Also -------- - multi_source_dijkstra() + multi_source_dijkstra """ if not sources: @@ -709,8 +711,8 @@ def multi_source_dijkstra(G, sources, target=None, cutoff=None, weight="weight") See Also -------- - multi_source_dijkstra_path() - multi_source_dijkstra_path_length() + multi_source_dijkstra_path + multi_source_dijkstra_path_length """ if not sources: @@ -1085,7 +1087,7 @@ def all_pairs_dijkstra_path(G, cutoff=None, weight="weight"): See Also -------- - floyd_warshall(), all_pairs_bellman_ford_path() + floyd_warshall, all_pairs_bellman_ford_path """ path = single_source_dijkstra_path @@ -1380,7 +1382,7 @@ def bellman_ford_path(G, source, target, weight="weight"): See Also -------- - dijkstra_path(), bellman_ford_path_length() + dijkstra_path, bellman_ford_path_length """ length, path = single_source_bellman_ford(G, source, target=target, weight=weight) return path @@ -1429,7 +1431,7 @@ def bellman_ford_path_length(G, source, target, weight="weight"): See Also -------- - dijkstra_path_length(), bellman_ford_path() + dijkstra_path_length, bellman_ford_path """ if source == target: return 0 @@ -1482,7 +1484,7 @@ def single_source_bellman_ford_path(G, source, weight="weight"): See Also -------- - single_source_dijkstra(), single_source_bellman_ford() + single_source_dijkstra, single_source_bellman_ford """ (length, path) = single_source_bellman_ford(G, source, weight=weight) @@ -1534,7 +1536,7 @@ def single_source_bellman_ford_path_length(G, source, weight="weight"): See Also -------- - single_source_dijkstra(), single_source_bellman_ford() + single_source_dijkstra, single_source_bellman_ford """ weight = _weight_function(G, weight) @@ -1599,9 +1601,9 @@ def single_source_bellman_ford(G, source, target=None, weight="weight"): See Also -------- - single_source_dijkstra() - single_source_bellman_ford_path() - single_source_bellman_ford_path_length() + single_source_dijkstra + single_source_bellman_ford_path + single_source_bellman_ford_path_length """ if source == target: return (0, [source]) @@ -1692,7 +1694,7 @@ def all_pairs_bellman_ford_path(G, weight="weight"): See Also -------- - floyd_warshall(), all_pairs_dijkstra_path() + floyd_warshall, all_pairs_dijkstra_path """ path = single_source_bellman_ford_path diff --git a/networkx/algorithms/similarity.py b/networkx/algorithms/similarity.py index 607cd7f5..0c512281 100644 --- a/networkx/algorithms/similarity.py +++ b/networkx/algorithms/similarity.py @@ -173,7 +173,7 @@ def graph_edit_distance( -------- optimal_edit_paths, optimize_graph_edit_distance, - is_isomorphic (test for graph edit distance of 0) + is_isomorphic: test for graph edit distance of 0 References ---------- diff --git a/networkx/classes/coreviews.py b/networkx/classes/coreviews.py index 61a0a768..6c5b8a48 100644 --- a/networkx/classes/coreviews.py +++ b/networkx/classes/coreviews.py @@ -29,8 +29,8 @@ class AtlasView(Mapping): See Also ======== - AdjacencyView - View into dict-of-dict-of-dict - MultiAdjacencyView - View into dict-of-dict-of-dict-of-dict + AdjacencyView: View into dict-of-dict-of-dict + MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict """ __slots__ = ("_atlas",) @@ -72,8 +72,8 @@ class AdjacencyView(AtlasView): See Also ======== - AtlasView - View into dict-of-dict - MultiAdjacencyView - View into dict-of-dict-of-dict-of-dict + AtlasView: View into dict-of-dict + MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict """ __slots__ = () # Still uses AtlasView slots names _atlas @@ -94,8 +94,8 @@ class MultiAdjacencyView(AdjacencyView): See Also ======== - AtlasView - View into dict-of-dict - AdjacencyView - View into dict-of-dict-of-dict + AtlasView: View into dict-of-dict + AdjacencyView: View into dict-of-dict-of-dict """ __slots__ = () # Still uses AtlasView slots names _atlas @@ -117,8 +117,8 @@ class UnionAtlas(Mapping): See Also ======== - UnionAdjacency - View into dict-of-dict-of-dict - UnionMultiAdjacency - View into dict-of-dict-of-dict-of-dict + UnionAdjacency: View into dict-of-dict-of-dict + UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict """ __slots__ = ("_succ", "_pred") @@ -176,8 +176,8 @@ class UnionAdjacency(Mapping): See Also ======== - UnionAtlas - View into dict-of-dict - UnionMultiAdjacency - View into dict-of-dict-of-dict-of-dict + UnionAtlas: View into dict-of-dict + UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict """ __slots__ = ("_succ", "_pred") @@ -224,9 +224,9 @@ class UnionMultiInner(UnionAtlas): See Also ======== - UnionAtlas - View into dict-of-dict - UnionAdjacency - View into dict-of-dict-of-dict - UnionMultiAdjacency - View into dict-of-dict-of-dict-of-dict + UnionAtlas: View into dict-of-dict + UnionAdjacency: View into dict-of-dict-of-dict + UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict """ __slots__ = () # Still uses UnionAtlas slots names _succ, _pred @@ -254,8 +254,8 @@ class UnionMultiAdjacency(UnionAdjacency): See Also ======== - UnionAtlas - View into dict-of-dict - UnionMultiInner - View into dict-of-dict-of-dict + UnionAtlas: View into dict-of-dict + UnionMultiInner: View into dict-of-dict-of-dict """ __slots__ = () # Still uses UnionAdjacency slots names _succ, _pred diff --git a/networkx/classes/digraph.py b/networkx/classes/digraph.py index e1ba6ec0..6e2de3f6 100644 --- a/networkx/classes/digraph.py +++ b/networkx/classes/digraph.py @@ -236,8 +236,7 @@ class DiGraph(Graph): Class to create a new graph structure in the `to_undirected` method. If `None`, a NetworkX class (Graph or MultiGraph) is used. - Examples - -------- + **Subclassing Example** Create a low memory graph class that effectively disallows edge attributes by using a single attribute dict for all edges. @@ -493,7 +492,7 @@ class DiGraph(Graph): A node in the graph Raises - ------- + ------ NetworkXError If n is not in the graph. @@ -778,7 +777,7 @@ class DiGraph(Graph): A node in the graph Raises - ------- + ------ NetworkXError If n is not in the graph. @@ -810,7 +809,7 @@ class DiGraph(Graph): A node in the graph Raises - ------- + ------ NetworkXError If n is not in the graph. diff --git a/networkx/classes/function.py b/networkx/classes/function.py index 104ab217..b789776b 100644 --- a/networkx/classes/function.py +++ b/networkx/classes/function.py @@ -540,7 +540,7 @@ def create_empty_copy(G, with_data=True): Propagate Graph and Nodes data to the new graph. See Also - ----- + -------- empty_graph """ diff --git a/networkx/classes/graph.py b/networkx/classes/graph.py index 6d48ff2f..8ad9a32e 100644 --- a/networkx/classes/graph.py +++ b/networkx/classes/graph.py @@ -237,8 +237,7 @@ class Graph: Class to create a new graph structure in the `to_undirected` method. If `None`, a NetworkX class (Graph or MultiGraph) is used. - Examples - -------- + **Subclassing Example** Create a low memory graph class that effectively disallows edge attributes by using a single attribute dict for all edges. @@ -438,7 +437,8 @@ class Graph: See Also -------- - number_of_nodes, order which are identical + number_of_nodes: identical method + order: identical method Examples -------- @@ -591,7 +591,7 @@ class Graph: A node in the graph Raises - ------- + ------ NetworkXError If n is not in the graph. @@ -762,7 +762,8 @@ class Graph: See Also -------- - order, __len__ which are identical + order: identical method + __len__: identical method Examples -------- @@ -782,7 +783,8 @@ class Graph: See Also -------- - number_of_nodes, __len__ which are identical + number_of_nodes: identical method + __len__: identical method Examples -------- diff --git a/networkx/classes/multidigraph.py b/networkx/classes/multidigraph.py index 89bcb485..2fe7a460 100644 --- a/networkx/classes/multidigraph.py +++ b/networkx/classes/multidigraph.py @@ -252,9 +252,6 @@ class MultiDiGraph(MultiGraph, DiGraph): Class to create a new graph structure in the `to_undirected` method. If `None`, a NetworkX class (Graph or MultiGraph) is used. - Examples - -------- - Please see :mod:`~networkx.classes.ordered` for examples of creating graph subclasses by overwriting the base class `dict` with a dictionary-like object. diff --git a/networkx/classes/multigraph.py b/networkx/classes/multigraph.py index f6d80938..1779a616 100644 --- a/networkx/classes/multigraph.py +++ b/networkx/classes/multigraph.py @@ -245,9 +245,6 @@ class MultiGraph(Graph): Class to create a new graph structure in the `to_undirected` method. If `None`, a NetworkX class (Graph or MultiGraph) is used. - Examples - -------- - Please see :mod:`~networkx.classes.ordered` for examples of creating graph subclasses by overwriting the base class `dict` with a dictionary-like object. diff --git a/networkx/drawing/layout.py b/networkx/drawing/layout.py index 7bab044a..9ad5948f 100644 --- a/networkx/drawing/layout.py +++ b/networkx/drawing/layout.py @@ -132,7 +132,7 @@ def circular_layout(G, scale=1, center=None, dim=2): A dictionary of positions keyed by node Raises - ------- + ------ ValueError If dim < 2 @@ -205,7 +205,7 @@ def shell_layout(G, nlist=None, rotate=None, scale=1, center=None, dim=2): A dictionary of positions keyed by node Raises - ------- + ------ ValueError If dim != 2 @@ -977,12 +977,14 @@ def spiral_layout(G, scale=1, center=None, dim=2, resolution=0.35, equidistant=F Lower values result in more compressed spiral layouts. equidistant : bool If True, nodes will be plotted equidistant from each other. + Returns ------- pos : dict A dictionary of positions keyed by node + Raises - ------- + ------ ValueError If dim != 2 diff --git a/networkx/drawing/nx_pydot.py b/networkx/drawing/nx_pydot.py index 1591e786..094a62b5 100644 --- a/networkx/drawing/nx_pydot.py +++ b/networkx/drawing/nx_pydot.py @@ -261,7 +261,7 @@ def pydot_layout(G, prog="neato", root=None): """Create node positions using :mod:`pydot` and Graphviz. Parameters - -------- + ---------- G : Graph NetworkX graph to be laid out. prog : string (default: 'neato') @@ -272,7 +272,7 @@ def pydot_layout(G, prog="neato", root=None): The node of G from which to start some layout algorithms. Returns - -------- + ------- dict Dictionary of positions keyed by node. @@ -288,7 +288,7 @@ def pydot_layout(G, prog="neato", root=None): representation and GraphViz could treat them as the same node. The layout may assign both nodes a single location. See Issue #1568 If this occurs in your case, consider relabeling the nodes just - for the layout computation using something similar to: + for the layout computation using something similar to:: H = nx.convert_node_labels_to_integers(G, label_attribute='node_label') H_layout = nx.nx_pydot.pydot_layout(G, prog='dot') diff --git a/networkx/drawing/nx_pylab.py b/networkx/drawing/nx_pylab.py index 5dc84157..e42f1b1d 100644 --- a/networkx/drawing/nx_pylab.py +++ b/networkx/drawing/nx_pylab.py @@ -77,11 +77,11 @@ def draw(G, pos=None, ax=None, **kwds): See Also -------- - draw_networkx() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_labels() - draw_networkx_edge_labels() + draw_networkx + draw_networkx_nodes + draw_networkx_edges + draw_networkx_labels + draw_networkx_edge_labels Notes ----- @@ -144,7 +144,7 @@ def draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds): For directed graphs, if True draw arrowheads. Note: Arrows will be the same color as edges. - arrowstyle : str, optional (default='-|>') + arrowstyle : str, optional (default='-\|>') For directed graphs, choose the style of the arrowsheads. See `matplotlib.patches.ArrowStyle` for more options. @@ -251,11 +251,11 @@ def draw_networkx(G, pos=None, arrows=True, with_labels=True, **kwds): See Also -------- - draw() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_labels() - draw_networkx_edge_labels() + draw + draw_networkx_nodes + draw_networkx_edges + draw_networkx_labels + draw_networkx_edge_labels """ import matplotlib.pyplot as plt @@ -414,11 +414,11 @@ def draw_networkx_nodes( See Also -------- - draw() - draw_networkx() - draw_networkx_edges() - draw_networkx_labels() - draw_networkx_edge_labels() + draw + draw_networkx + draw_networkx_edges + draw_networkx_labels + draw_networkx_edge_labels """ from collections.abc import Iterable import numpy as np @@ -610,11 +610,11 @@ def draw_networkx_edges( See Also -------- - draw() - draw_networkx() - draw_networkx_nodes() - draw_networkx_labels() - draw_networkx_edge_labels() + draw + draw_networkx + draw_networkx_nodes + draw_networkx_labels + draw_networkx_edge_labels """ import numpy as np @@ -880,11 +880,11 @@ def draw_networkx_labels( See Also -------- - draw() - draw_networkx() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_edge_labels() + draw + draw_networkx + draw_networkx_nodes + draw_networkx_edges + draw_networkx_edge_labels """ import matplotlib.pyplot as plt @@ -1014,11 +1014,11 @@ def draw_networkx_edge_labels( See Also -------- - draw() - draw_networkx() - draw_networkx_nodes() - draw_networkx_edges() - draw_networkx_labels() + draw + draw_networkx + draw_networkx_nodes + draw_networkx_edges + draw_networkx_labels """ import matplotlib.pyplot as plt import numpy as np diff --git a/networkx/generators/geometric.py b/networkx/generators/geometric.py index ed72cafc..4b767951 100644 --- a/networkx/generators/geometric.py +++ b/networkx/generators/geometric.py @@ -247,7 +247,7 @@ def soft_random_geometric_graph( ---------- .. [1] Penrose, Mathew D. "Connectivity of soft random geometric graphs." The Annals of Applied Probability 26.2 (2016): 986-1028. - [2] scipy.stats - + .. [2] scipy.stats - https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html """ diff --git a/networkx/generators/internet_as_graphs.py b/networkx/generators/internet_as_graphs.py index 28b5b986..2c93ab0e 100644 --- a/networkx/generators/internet_as_graphs.py +++ b/networkx/generators/internet_as_graphs.py @@ -417,21 +417,22 @@ def random_internet_as_graph(n, seed=None): ----- This algorithm returns an undirected graph resembling the Internet Autonomous System (AS) network, it uses the approach by Elmokashfi et al. - [1] and it grants the properties described in the related paper [1]. + [1]_ and it grants the properties described in the related paper [1]_. Each node models an autonomous system, with an attribute 'type' specifying its kind; tier-1 (T), mid-level (M), customer (C) or content-provider (CP). Each edge models an ADV communication link (hence, bidirectional) with attributes: - - type: transit|peer, the kind of commercial agreement between nodes; - - customer: <node id>, the identifier of the node acting as customer - ('none' if type is peer). + + - type: transit|peer, the kind of commercial agreement between nodes; + - customer: <node id>, the identifier of the node acting as customer + ('none' if type is peer). References ---------- - [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of - BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas - in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010. + .. [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of + BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas + in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010. """ GG = AS_graph_generator(n, seed) diff --git a/networkx/generators/interval_graph.py b/networkx/generators/interval_graph.py index faf46715..86888233 100644 --- a/networkx/generators/interval_graph.py +++ b/networkx/generators/interval_graph.py @@ -35,7 +35,7 @@ def interval_graph(intervals): [((-2, 3), (1, 4)), ((-2, 3), (2, 3)), ((1, 4), (2, 3)), ((1, 4), (4, 6))] Raises - -------- + ------ :exc:`TypeError` if `intervals` contains None or an element which is not collections.abc.Sequence or not a length of 2. diff --git a/networkx/generators/mycielski.py b/networkx/generators/mycielski.py index e5e7e57e..1b343686 100644 --- a/networkx/generators/mycielski.py +++ b/networkx/generators/mycielski.py @@ -48,7 +48,7 @@ def mycielskian(G, iterations=1): The Mycielskian of G after the specified number of iterations. Notes - ------ + ----- Graph, node, and edge data are not necessarily propagated to the new graph. """ diff --git a/networkx/generators/random_graphs.py b/networkx/generators/random_graphs.py index 25871224..3dd35924 100644 --- a/networkx/generators/random_graphs.py +++ b/networkx/generators/random_graphs.py @@ -196,7 +196,7 @@ def dense_gnm_random_graph(n, m, seed=None): See Also -------- - gnm_random_graph() + gnm_random_graph Notes ----- @@ -319,7 +319,7 @@ def newman_watts_strogatz_graph(n, k, p, seed=None): See Also -------- - watts_strogatz_graph() + watts_strogatz_graph References ---------- @@ -379,8 +379,8 @@ def watts_strogatz_graph(n, k, p, seed=None): See Also -------- - newman_watts_strogatz_graph() - connected_watts_strogatz_graph() + newman_watts_strogatz_graph + connected_watts_strogatz_graph Notes ----- @@ -469,8 +469,8 @@ def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None): See Also -------- - newman_watts_strogatz_graph() - watts_strogatz_graph() + newman_watts_strogatz_graph + watts_strogatz_graph References ---------- diff --git a/networkx/readwrite/json_graph/cytoscape.py b/networkx/readwrite/json_graph/cytoscape.py index 8f3acfbb..977fd9d2 100644 --- a/networkx/readwrite/json_graph/cytoscape.py +++ b/networkx/readwrite/json_graph/cytoscape.py @@ -36,7 +36,7 @@ def cytoscape_data(G, attrs=None): See Also -------- - cytoscape_graph - convert a dictionary in cyjs format to a graph + cytoscape_graph: convert a dictionary in cyjs format to a graph References ---------- @@ -144,7 +144,7 @@ def cytoscape_graph(data, attrs=None): See Also -------- - cytoscape_data - convert a NetworkX graph to a dict in cyjs format + cytoscape_data: convert a NetworkX graph to a dict in cyjs format References ---------- diff --git a/networkx/readwrite/pajek.py b/networkx/readwrite/pajek.py index 762ad642..25b1a852 100644 --- a/networkx/readwrite/pajek.py +++ b/networkx/readwrite/pajek.py @@ -176,7 +176,7 @@ def parse_pajek(lines): See Also -------- - read_pajek() + read_pajek """ import shlex diff --git a/requirements/doc.txt b/requirements/doc.txt index 3fc3093f..2d2245cd 100644 --- a/requirements/doc.txt +++ b/requirements/doc.txt @@ -1,6 +1,7 @@ sphinx==3.3.1 sphinx_rtd_theme==0.5.0 sphinx-gallery==0.8.1 +numpydoc>=1.1 pillow>=8.0 nb2plots>=0.6 texext>=0.6.6 |