Restore 0.1.5 version from stash

wolai-backend/.venv312/Lib/site-packages/networkx/drawing/tests/test_agraph.py

@@ -0,0 +1,237 @@
+"""Unit tests for PyGraphviz interface."""
+
+import warnings
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+pygraphviz = pytest.importorskip("pygraphviz")
+
+
+class TestAGraph:
+    def build_graph(self, G):
+        edges = [("A", "B"), ("A", "C"), ("A", "C"), ("B", "C"), ("A", "D")]
+        G.add_edges_from(edges)
+        G.add_node("E")
+        G.graph["metal"] = "bronze"
+        return G
+
+    def assert_equal(self, G1, G2):
+        assert nodes_equal(G1.nodes(), G2.nodes())
+        assert edges_equal(G1.edges(), G2.edges(), directed=G1.is_directed())
+        assert G1.graph["metal"] == G2.graph["metal"]
+
+    @pytest.mark.parametrize(
+        "G", (nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph())
+    )
+    def test_agraph_roundtripping(self, G, tmp_path):
+        G = self.build_graph(G)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        self.assert_equal(G, H)
+
+        fname = tmp_path / "test.dot"
+        nx.drawing.nx_agraph.write_dot(H, fname)
+        Hin = nx.nx_agraph.read_dot(fname)
+        self.assert_equal(H, Hin)
+
+        fname = tmp_path / "fh_test.dot"
+        with open(fname, "w") as fh:
+            nx.drawing.nx_agraph.write_dot(H, fh)
+
+        with open(fname) as fh:
+            Hin = nx.nx_agraph.read_dot(fh)
+        self.assert_equal(H, Hin)
+
+    def test_from_agraph_name(self):
+        G = nx.Graph(name="test")
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert G.name == "test"
+
+    @pytest.mark.parametrize(
+        "graph_class", (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+    )
+    def test_from_agraph_create_using(self, graph_class):
+        G = nx.path_graph(3)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A, create_using=graph_class)
+        assert isinstance(H, graph_class)
+
+    def test_from_agraph_named_edges(self):
+        # Create an AGraph from an existing (non-multi) Graph
+        G = nx.Graph()
+        G.add_nodes_from([0, 1])
+        A = nx.nx_agraph.to_agraph(G)
+        # Add edge (+ name, given by key) to the AGraph
+        A.add_edge(0, 1, key="foo")
+        # Verify a.name roundtrips out to 'key' in from_agraph
+        H = nx.nx_agraph.from_agraph(A)
+        assert isinstance(H, nx.Graph)
+        assert ("0", "1", {"key": "foo"}) in H.edges(data=True)
+
+    def test_to_agraph_with_nodedata(self):
+        G = nx.Graph()
+        G.add_node(1, color="red")
+        A = nx.nx_agraph.to_agraph(G)
+        assert dict(A.nodes()[0].attr) == {"color": "red"}
+
+    @pytest.mark.parametrize("graph_class", (nx.Graph, nx.MultiGraph))
+    def test_to_agraph_with_edgedata(self, graph_class):
+        G = graph_class()
+        G.add_nodes_from([0, 1])
+        G.add_edge(0, 1, color="yellow")
+        A = nx.nx_agraph.to_agraph(G)
+        assert dict(A.edges()[0].attr) == {"color": "yellow"}
+
+    def test_view_pygraphviz_path(self, tmp_path):
+        G = nx.complete_graph(3)
+        input_path = str(tmp_path / "graph.png")
+        out_path, A = nx.nx_agraph.view_pygraphviz(G, path=input_path, show=False)
+        assert out_path == input_path
+        # Ensure file is not empty
+        with open(input_path, "rb") as fh:
+            data = fh.read()
+        assert len(data) > 0
+
+    def test_view_pygraphviz_file_suffix(self, tmp_path):
+        G = nx.complete_graph(3)
+        path, A = nx.nx_agraph.view_pygraphviz(G, suffix=1, show=False)
+        assert path[-6:] == "_1.png"
+
+    def test_view_pygraphviz(self):
+        G = nx.Graph()  # "An empty graph cannot be drawn."
+        pytest.raises(nx.NetworkXException, nx.nx_agraph.view_pygraphviz, G)
+        G = nx.barbell_graph(4, 6)
+        nx.nx_agraph.view_pygraphviz(G, show=False)
+
+    def test_view_pygraphviz_edgelabel(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=7)
+        G.add_edge(2, 3, weight=8)
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel="weight", show=False)
+        for edge in A.edges():
+            assert edge.attr["weight"] in ("7", "8")
+
+    def test_view_pygraphviz_callable_edgelabel(self):
+        G = nx.complete_graph(3)
+
+        def foo_label(data):
+            return "foo"
+
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel=foo_label, show=False)
+        for edge in A.edges():
+            assert edge.attr["label"] == "foo"
+
+    def test_view_pygraphviz_multigraph_edgelabels(self):
+        G = nx.MultiGraph()
+        G.add_edge(0, 1, key=0, name="left_fork")
+        G.add_edge(0, 1, key=1, name="right_fork")
+        path, A = nx.nx_agraph.view_pygraphviz(G, edgelabel="name", show=False)
+        edges = A.edges()
+        assert len(edges) == 2
+        for edge in edges:
+            assert edge.attr["label"].strip() in ("left_fork", "right_fork")
+
+    def test_graph_with_reserved_keywords(self):
+        # test attribute/keyword clash case for #1582
+        # node: n
+        # edges: u,v
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.nodes["E"]["n"] = "keyword"
+        G.edges[("A", "B")]["u"] = "keyword"
+        G.edges[("A", "B")]["v"] = "keyword"
+        A = nx.nx_agraph.to_agraph(G)
+
+    def test_view_pygraphviz_no_added_attrs_to_input(self):
+        G = nx.complete_graph(2)
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        assert G.graph == {}
+
+    @pytest.mark.xfail(reason="known bug in clean_attrs")
+    def test_view_pygraphviz_leaves_input_graph_unmodified(self):
+        G = nx.complete_graph(2)
+        # Add entries to graph dict that to_agraph handles specially
+        G.graph["node"] = {"width": "0.80"}
+        G.graph["edge"] = {"fontsize": "14"}
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        assert G.graph == {"node": {"width": "0.80"}, "edge": {"fontsize": "14"}}
+
+    def test_graph_with_AGraph_attrs(self):
+        G = nx.complete_graph(2)
+        # Add entries to graph dict that to_agraph handles specially
+        G.graph["node"] = {"width": "0.80"}
+        G.graph["edge"] = {"fontsize": "14"}
+        path, A = nx.nx_agraph.view_pygraphviz(G, show=False)
+        # Ensure user-specified values are not lost
+        assert dict(A.node_attr)["width"] == "0.80"
+        assert dict(A.edge_attr)["fontsize"] == "14"
+
+    def test_round_trip_empty_graph(self):
+        G = nx.Graph()
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert graphs_equal(G, H)
+        AA = nx.nx_agraph.to_agraph(H)
+        HH = nx.nx_agraph.from_agraph(AA)
+        assert graphs_equal(H, HH)
+        assert graphs_equal(G, HH)
+
+    @pytest.mark.xfail(reason="integer->string node conversion in round trip")
+    def test_round_trip_integer_nodes(self):
+        G = nx.complete_graph(3)
+        A = nx.nx_agraph.to_agraph(G)
+        H = nx.nx_agraph.from_agraph(A)
+        assert graphs_equal(G, H)
+
+    def test_graphviz_alias(self):
+        G = self.build_graph(nx.Graph())
+        pos_graphviz = nx.nx_agraph.graphviz_layout(G)
+        pos_pygraphviz = nx.nx_agraph.pygraphviz_layout(G)
+        assert pos_graphviz == pos_pygraphviz
+
+    @pytest.mark.parametrize("root", range(5))
+    def test_pygraphviz_layout_root(self, root):
+        # NOTE: test depends on layout prog being deterministic
+        G = nx.complete_graph(5)
+        A = nx.nx_agraph.to_agraph(G)
+        # Get layout with root arg is not None
+        pygv_layout = nx.nx_agraph.pygraphviz_layout(G, prog="circo", root=root)
+        # Equivalent layout directly on AGraph
+        A.layout(args=f"-Groot={root}", prog="circo")
+        # Parse AGraph layout
+        a1_pos = tuple(float(v) for v in dict(A.get_node("1").attr)["pos"].split(","))
+        assert pygv_layout[1] == a1_pos
+
+    def test_2d_layout(self):
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.graph["dimen"] = 2
+        pos = nx.nx_agraph.pygraphviz_layout(G, prog="neato")
+        pos = list(pos.values())
+        assert len(pos) == 5
+        assert len(pos[0]) == 2
+
+    def test_3d_layout(self):
+        G = nx.Graph()
+        G = self.build_graph(G)
+        G.graph["dimen"] = 3
+        pos = nx.nx_agraph.pygraphviz_layout(G, prog="neato")
+        pos = list(pos.values())
+        assert len(pos) == 5
+        assert len(pos[0]) == 3
+
+    def test_no_warnings_raised(self):
+        # Test that no warnings are raised when Networkx graph
+        # is converted to Pygraphviz graph and 'pos'
+        # attribute is given
+        G = nx.Graph()
+        G.add_node(0, pos=(0, 0))
+        G.add_node(1, pos=(1, 1))
+        A = nx.nx_agraph.to_agraph(G)
+        with warnings.catch_warnings(record=True) as record:
+            A.layout()
+        assert len(record) == 0

wolai-backend/.venv312/Lib/site-packages/networkx/drawing/tests/test_image_comparison_pylab_mpl.py

@@ -0,0 +1,229 @@
+"""Unit tests for explicit image comparison with pytest-mpl."""
+
+import pytest
+
+import networkx as nx
+
+pytest.importorskip("pytest_mpl")
+
+mpl = pytest.importorskip("matplotlib")
+mpl.use("PS")
+plt = pytest.importorskip("matplotlib.pyplot")
+plt.rcParams["text.usetex"] = False
+np = pytest.importorskip("numpy")
+
+
+@pytest.mark.mpl_image_compare
+def test_display_house_with_colors():
+    """
+    Originally, I wanted to use the exact samge image as test_house_with_colors.
+    But I can't seem to find the correct value for the margins to get the figures
+    to line up perfectly. To the human eye, these visualizations are basically the
+    same.
+    """
+    G = nx.house_graph()
+    fig, ax = plt.subplots()
+    nx.set_node_attributes(
+        G, {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)}, "pos"
+    )
+    nx.set_node_attributes(
+        G,
+        {
+            n: {
+                "size": 3000 if n != 4 else 2000,
+                "color": "tab:blue" if n != 4 else "tab:orange",
+            }
+            for n in G.nodes()
+        },
+    )
+    nx.display(
+        G,
+        node_pos="pos",
+        edge_alpha=0.5,
+        edge_width=6,
+        node_label=None,
+        node_border_color="k",
+    )
+    ax.margins(0.17)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_labels_and_colors():
+    """See 'Labels and Colors' gallery example"""
+    fig, ax = plt.subplots()
+    G = nx.cubical_graph()
+    pos = nx.spring_layout(G, seed=3113794652)  # positions for all nodes
+    nx.set_node_attributes(G, pos, "pos")  # Will not be needed after PR 7571
+    labels = iter(
+        [
+            r"$a$",
+            r"$b$",
+            r"$c$",
+            r"$d$",
+            r"$\alpha$",
+            r"$\beta$",
+            r"$\gamma$",
+            r"$\delta$",
+        ]
+    )
+    nx.set_node_attributes(
+        G,
+        {
+            n: {
+                "size": 800,
+                "alpha": 0.9,
+                "color": "tab:red" if n < 4 else "tab:blue",
+                "label": {"label": next(labels), "size": 22, "color": "whitesmoke"},
+            }
+            for n in G.nodes()
+        },
+    )
+
+    nx.display(G, node_pos="pos", edge_color="tab:grey")
+
+    # The tricky bit is the highlighted colors for the edges
+    edgelist = [(0, 1), (1, 2), (2, 3), (0, 3)]
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "width": 8,
+                "alpha": 0.5,
+                "color": "tab:red",
+                "visible": (u, v) in edgelist,
+            }
+            for u, v in G.edges()
+        },
+    )
+    nx.display(G, node_pos="pos", node_visible=False)
+    edgelist = [(4, 5), (5, 6), (6, 7), (4, 7)]
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "color": "tab:blue",
+                "visible": (u, v) in edgelist,
+            }
+            for u, v in G.edges()
+        },
+    )
+    nx.display(G, node_pos="pos", node_visible=False)
+
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_complex():
+    import itertools as it
+
+    fig, ax = plt.subplots()
+    G = nx.MultiDiGraph()
+    nodes = "ABC"
+    prod = list(it.product(nodes, repeat=2)) * 4
+    G = nx.MultiDiGraph()
+    for i, (u, v) in enumerate(prod):
+        G.add_edge(u, v, w=round(i / 3, 2))
+    nx.set_node_attributes(G, nx.spring_layout(G, seed=3113794652), "pos")
+    csi = it.cycle([f"arc3,rad={r}" for r in it.accumulate([0.15] * 4)])
+    nx.set_edge_attributes(G, {e: next(csi) for e in G.edges(keys=True)}, "curvature")
+    nx.set_edge_attributes(
+        G,
+        {
+            tuple(e): {"label": w, "bbox": {"alpha": 0}}
+            for *e, w in G.edges(keys=True, data="w")
+        },
+        "label",
+    )
+    nx.apply_matplotlib_colors(G, "w", "color", mpl.colormaps["inferno"], nodes=False)
+    nx.display(G, canvas=ax, node_pos="pos")
+
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_shortest_path():
+    fig, ax = plt.subplots()
+    G = nx.Graph()
+    G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+    G.add_edge("A", "B", weight=4)
+    G.add_edge("A", "H", weight=8)
+    G.add_edge("B", "C", weight=8)
+    G.add_edge("B", "H", weight=11)
+    G.add_edge("C", "D", weight=7)
+    G.add_edge("C", "F", weight=4)
+    G.add_edge("C", "I", weight=2)
+    G.add_edge("D", "E", weight=9)
+    G.add_edge("D", "F", weight=14)
+    G.add_edge("E", "F", weight=10)
+    G.add_edge("F", "G", weight=2)
+    G.add_edge("G", "H", weight=1)
+    G.add_edge("G", "I", weight=6)
+    G.add_edge("H", "I", weight=7)
+
+    # Find the shortest path from node A to node E
+    path = nx.shortest_path(G, "A", "E", weight="weight")
+
+    # Create a list of edges in the shortest path
+    path_edges = list(zip(path, path[1:]))
+    nx.set_node_attributes(G, nx.spring_layout(G, seed=37), "pos")
+    nx.set_edge_attributes(
+        G,
+        {
+            (u, v): {
+                "color": (
+                    "red"
+                    if (u, v) in path_edges or tuple(reversed((u, v))) in path_edges
+                    else "black"
+                ),
+                "label": {"label": d["weight"], "rotate": False},
+            }
+            for u, v, d in G.edges(data=True)
+        },
+    )
+    nx.display(G, canvas=ax)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_display_empty_graph():
+    G = nx.empty_graph()
+    fig, ax = plt.subplots()
+    nx.display(G, canvas=ax)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_house_with_colors():
+    G = nx.house_graph()
+    # explicitly set positions
+    fig, ax = plt.subplots()
+    pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)}
+
+    # Plot nodes with different properties for the "wall" and "roof" nodes
+    nx.draw_networkx_nodes(
+        G,
+        pos,
+        node_size=3000,
+        nodelist=[0, 1, 2, 3],
+        node_color="tab:blue",
+    )
+    nx.draw_networkx_nodes(
+        G, pos, node_size=2000, nodelist=[4], node_color="tab:orange"
+    )
+    nx.draw_networkx_edges(G, pos, alpha=0.5, width=6)
+    # Customize axes
+    ax.margins(0.11)
+    plt.tight_layout()
+    plt.axis("off")
+    return fig

wolai-backend/.venv312/Lib/site-packages/networkx/drawing/tests/test_latex.py

@@ -0,0 +1,285 @@
+import pytest
+
+import networkx as nx
+
+
+def test_tikz_attributes():
+    G = nx.path_graph(4, create_using=nx.DiGraph)
+    pos = {n: (n, n) for n in G}
+
+    G.add_edge(0, 0)
+    G.edges[(0, 0)]["label"] = "Loop"
+    G.edges[(0, 0)]["label_options"] = "midway"
+
+    G.nodes[0]["style"] = "blue"
+    G.nodes[1]["style"] = "line width=3,draw"
+    G.nodes[2]["style"] = "circle,draw,blue!50"
+    G.nodes[3]["label"] = "Stop"
+    G.edges[(0, 1)]["label"] = "1st Step"
+    G.edges[(0, 1)]["label_options"] = "near end"
+    G.edges[(2, 3)]["label"] = "3rd Step"
+    G.edges[(2, 3)]["label_options"] = "near start"
+    G.edges[(2, 3)]["style"] = "bend left,green"
+    G.edges[(1, 2)]["label"] = "2nd"
+    G.edges[(1, 2)]["label_options"] = "pos=0.5"
+    G.edges[(1, 2)]["style"] = ">->,bend right,line width=3,green!90"
+
+    output_tex = nx.to_latex(
+        G,
+        pos=pos,
+        as_document=False,
+        tikz_options="[scale=3]",
+        node_options="style",
+        edge_options="style",
+        node_label="label",
+        edge_label="label",
+        edge_label_options="label_options",
+    )
+    expected_tex = r"""\begin{figure}
+  \begin{tikzpicture}[scale=3]
+      \draw
+        (0, 0) node[blue] (0){0}
+        (1, 1) node[line width=3,draw] (1){1}
+        (2, 2) node[circle,draw,blue!50] (2){2}
+        (3, 3) node (3){Stop};
+      \begin{scope}[->]
+        \draw (0) to node[near end] {1st Step} (1);
+        \draw[loop,] (0) to node[midway] {Loop} (0);
+        \draw[>->,bend right,line width=3,green!90] (1) to node[pos=0.5] {2nd} (2);
+        \draw[bend left,green] (2) to node[near start] {3rd Step} (3);
+      \end{scope}
+    \end{tikzpicture}
+\end{figure}"""
+
+    # First, check for consistency line-by-line - if this fails, the mismatched
+    # line will be shown explicitly in the failure summary
+    for expected, actual in zip(expected_tex.split("\n"), output_tex.split("\n")):
+        assert expected == actual
+
+    assert output_tex == expected_tex
+
+
+def test_basic_multiple_graphs():
+    H1 = nx.path_graph(4)
+    H2 = nx.complete_graph(4)
+    H3 = nx.path_graph(8)
+    H4 = nx.complete_graph(8)
+    captions = [
+        "Path on 4 nodes",
+        "Complete graph on 4 nodes",
+        "Path on 8 nodes",
+        "Complete graph on 8 nodes",
+    ]
+    labels = ["fig2a", "fig2b", "fig2c", "fig2d"]
+    latex_code = nx.to_latex(
+        [H1, H2, H3, H4],
+        n_rows=2,
+        sub_captions=captions,
+        sub_labels=labels,
+    )
+    assert "begin{document}" in latex_code
+    assert "begin{figure}" in latex_code
+    assert latex_code.count("begin{subfigure}") == 4
+    assert latex_code.count("tikzpicture") == 8
+    assert latex_code.count("[-]") == 4
+
+
+def test_basic_tikz():
+    expected_tex = r"""\documentclass{report}
+\usepackage{tikz}
+\usepackage{subcaption}
+
+\begin{document}
+\begin{figure}
+  \begin{subfigure}{0.5\textwidth}
+  \begin{tikzpicture}[scale=2]
+      \draw[gray!90]
+        (0.749, 0.702) node[red!90] (0){0}
+        (1.0, -0.014) node[red!90] (1){1}
+        (-0.777, -0.705) node (2){2}
+        (-0.984, 0.042) node (3){3}
+        (-0.028, 0.375) node[cyan!90] (4){4}
+        (-0.412, 0.888) node (5){5}
+        (0.448, -0.856) node (6){6}
+        (0.003, -0.431) node[cyan!90] (7){7};
+      \begin{scope}[->,gray!90]
+        \draw (0) to (4);
+        \draw (0) to (5);
+        \draw (0) to (6);
+        \draw (0) to (7);
+        \draw (1) to (4);
+        \draw (1) to (5);
+        \draw (1) to (6);
+        \draw (1) to (7);
+        \draw (2) to (4);
+        \draw (2) to (5);
+        \draw (2) to (6);
+        \draw (2) to (7);
+        \draw (3) to (4);
+        \draw (3) to (5);
+        \draw (3) to (6);
+        \draw (3) to (7);
+      \end{scope}
+    \end{tikzpicture}
+    \caption{My tikz number 1 of 2}\label{tikz_1_2}
+  \end{subfigure}
+  \begin{subfigure}{0.5\textwidth}
+  \begin{tikzpicture}[scale=2]
+      \draw[gray!90]
+        (0.749, 0.702) node[green!90] (0){0}
+        (1.0, -0.014) node[green!90] (1){1}
+        (-0.777, -0.705) node (2){2}
+        (-0.984, 0.042) node (3){3}
+        (-0.028, 0.375) node[purple!90] (4){4}
+        (-0.412, 0.888) node (5){5}
+        (0.448, -0.856) node (6){6}
+        (0.003, -0.431) node[purple!90] (7){7};
+      \begin{scope}[->,gray!90]
+        \draw (0) to (4);
+        \draw (0) to (5);
+        \draw (0) to (6);
+        \draw (0) to (7);
+        \draw (1) to (4);
+        \draw (1) to (5);
+        \draw (1) to (6);
+        \draw (1) to (7);
+        \draw (2) to (4);
+        \draw (2) to (5);
+        \draw (2) to (6);
+        \draw (2) to (7);
+        \draw (3) to (4);
+        \draw (3) to (5);
+        \draw (3) to (6);
+        \draw (3) to (7);
+      \end{scope}
+    \end{tikzpicture}
+    \caption{My tikz number 2 of 2}\label{tikz_2_2}
+  \end{subfigure}
+  \caption{A graph generated with python and latex.}
+\end{figure}
+\end{document}"""
+
+    edges = [
+        (0, 4),
+        (0, 5),
+        (0, 6),
+        (0, 7),
+        (1, 4),
+        (1, 5),
+        (1, 6),
+        (1, 7),
+        (2, 4),
+        (2, 5),
+        (2, 6),
+        (2, 7),
+        (3, 4),
+        (3, 5),
+        (3, 6),
+        (3, 7),
+    ]
+    G = nx.DiGraph()
+    G.add_nodes_from(range(8))
+    G.add_edges_from(edges)
+    pos = {
+        0: (0.7490296171687696, 0.702353520257394),
+        1: (1.0, -0.014221357723796535),
+        2: (-0.7765783344161441, -0.7054170966808919),
+        3: (-0.9842690223417624, 0.04177547602465483),
+        4: (-0.02768523817180917, 0.3745724439551441),
+        5: (-0.41154855146767433, 0.8880106515525136),
+        6: (0.44780153389148264, -0.8561492709269164),
+        7: (0.0032499953371383505, -0.43092436645809945),
+    }
+
+    rc_node_color = {0: "red!90", 1: "red!90", 4: "cyan!90", 7: "cyan!90"}
+    gp_node_color = {0: "green!90", 1: "green!90", 4: "purple!90", 7: "purple!90"}
+
+    H = G.copy()
+    nx.set_node_attributes(G, rc_node_color, "color")
+    nx.set_node_attributes(H, gp_node_color, "color")
+
+    sub_captions = ["My tikz number 1 of 2", "My tikz number 2 of 2"]
+    sub_labels = ["tikz_1_2", "tikz_2_2"]
+
+    output_tex = nx.to_latex(
+        [G, H],
+        [pos, pos],
+        tikz_options="[scale=2]",
+        default_node_options="gray!90",
+        default_edge_options="gray!90",
+        node_options="color",
+        sub_captions=sub_captions,
+        sub_labels=sub_labels,
+        caption="A graph generated with python and latex.",
+        n_rows=2,
+        as_document=True,
+    )
+
+    # First, check for consistency line-by-line - if this fails, the mismatched
+    # line will be shown explicitly in the failure summary
+    for expected, actual in zip(expected_tex.split("\n"), output_tex.split("\n")):
+        assert expected == actual
+    # Double-check for document-level consistency
+    assert output_tex == expected_tex
+
+
+def test_exception_pos_single_graph(to_latex=nx.to_latex):
+    # smoke test that pos can be a string
+    G = nx.path_graph(4)
+    to_latex(G, pos="pos")
+
+    # must include all nodes
+    pos = {0: (1, 2), 1: (0, 1), 2: (2, 1)}
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+
+    # must have 2 values
+    pos[3] = (1, 2, 3)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+    pos[3] = 2
+    with pytest.raises(nx.NetworkXError):
+        to_latex(G, pos)
+
+    # check that passes with 2 values
+    pos[3] = (3, 2)
+    to_latex(G, pos)
+
+
+def test_exception_multiple_graphs(to_latex=nx.to_latex):
+    G = nx.path_graph(3)
+    pos_bad = {0: (1, 2), 1: (0, 1)}
+    pos_OK = {0: (1, 2), 1: (0, 1), 2: (2, 1)}
+    fourG = [G, G, G, G]
+    fourpos = [pos_OK, pos_OK, pos_OK, pos_OK]
+
+    # input single dict to use for all graphs
+    to_latex(fourG, pos_OK)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, pos_bad)
+
+    # input list of dicts to use for all graphs
+    to_latex(fourG, fourpos)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, [pos_bad, pos_bad, pos_bad, pos_bad])
+
+    # every pos dict must include all nodes
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, [pos_OK, pos_OK, pos_bad, pos_OK])
+
+    # test sub_captions and sub_labels (len must match Gbunch)
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, fourpos, sub_captions=["hi", "hi"])
+
+    with pytest.raises(nx.NetworkXError):
+        to_latex(fourG, fourpos, sub_labels=["hi", "hi"])
+
+    # all pass
+    to_latex(fourG, fourpos, sub_captions=["hi"] * 4, sub_labels=["lbl"] * 4)
+
+
+def test_exception_multigraph():
+    G = nx.path_graph(4, create_using=nx.MultiGraph)
+    G.add_edge(1, 2)
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.to_latex(G)

wolai-backend/.venv312/Lib/site-packages/networkx/drawing/tests/test_layout.py

@@ -0,0 +1,631 @@
+"""Unit tests for layout functions."""
+
+import pytest
+
+import networkx as nx
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+
+class TestLayout:
+    @classmethod
+    def setup_class(cls):
+        cls.Gi = nx.grid_2d_graph(5, 5)
+        cls.Gs = nx.Graph()
+        nx.add_path(cls.Gs, "abcdef")
+        cls.bigG = nx.grid_2d_graph(25, 25)  # > 500 nodes for sparse
+
+    def test_spring_fixed_without_pos(self):
+        G = nx.path_graph(4)
+        # No pos dict at all
+        with pytest.raises(ValueError, match="nodes are fixed without positions"):
+            nx.spring_layout(G, fixed=[0])
+
+        pos = {0: (1, 1), 2: (0, 0)}
+        # Node 1 not in pos dict
+        with pytest.raises(ValueError, match="nodes are fixed without positions"):
+            nx.spring_layout(G, fixed=[0, 1], pos=pos)
+
+        # All fixed nodes in pos dict
+        out = nx.spring_layout(G, fixed=[0, 2], pos=pos)  # No ValueError
+        assert all(np.array_equal(out[n], pos[n]) for n in (0, 2))
+
+    def test_spring_init_pos(self):
+        # Tests GH #2448
+        import math
+
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3)])
+
+        init_pos = {0: (0.0, 0.0)}
+        fixed_pos = [0]
+        pos = nx.fruchterman_reingold_layout(G, pos=init_pos, fixed=fixed_pos)
+        has_nan = any(math.isnan(c) for coords in pos.values() for c in coords)
+        assert not has_nan, "values should not be nan"
+
+    def test_smoke_empty_graph(self):
+        G = []
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.spectral_layout(G)
+        nx.shell_layout(G)
+        nx.bipartite_layout(G, G)
+        nx.spiral_layout(G)
+        nx.multipartite_layout(G)
+        nx.kamada_kawai_layout(G)
+
+    def test_smoke_int(self):
+        G = self.Gi
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.forceatlas2_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.fruchterman_reingold_layout(self.bigG)
+        nx.spectral_layout(G)
+        nx.spectral_layout(G.to_directed())
+        nx.spectral_layout(self.bigG)
+        nx.spectral_layout(self.bigG.to_directed())
+        nx.shell_layout(G)
+        nx.spiral_layout(G)
+        nx.kamada_kawai_layout(G)
+        nx.kamada_kawai_layout(G, dim=1)
+        nx.kamada_kawai_layout(G, dim=3)
+        nx.arf_layout(G)
+
+    def test_smoke_string(self):
+        G = self.Gs
+        nx.random_layout(G)
+        nx.circular_layout(G)
+        nx.planar_layout(G)
+        nx.spring_layout(G)
+        nx.forceatlas2_layout(G)
+        nx.fruchterman_reingold_layout(G)
+        nx.spectral_layout(G)
+        nx.shell_layout(G)
+        nx.spiral_layout(G)
+        nx.kamada_kawai_layout(G)
+        nx.kamada_kawai_layout(G, dim=1)
+        nx.kamada_kawai_layout(G, dim=3)
+        nx.arf_layout(G)
+
+    def check_scale_and_center(self, pos, scale, center):
+        center = np.array(center)
+        low = center - scale
+        hi = center + scale
+        vpos = np.array(list(pos.values()))
+        length = vpos.max(0) - vpos.min(0)
+        assert (length <= 2 * scale).all()
+        assert (vpos >= low).all()
+        assert (vpos <= hi).all()
+
+    def test_scale_and_center_arg(self):
+        sc = self.check_scale_and_center
+        c = (4, 5)
+        G = nx.complete_graph(9)
+        G.add_node(9)
+        sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
+        # rest can have 2*scale length: [-scale, scale]
+        sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c)
+        sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c)
+
+        c = (2, 3, 5)
+        sc(nx.kamada_kawai_layout(G, dim=3, scale=2, center=c), scale=2, center=c)
+
+    def test_planar_layout_non_planar_input(self):
+        G = nx.complete_graph(9)
+        pytest.raises(nx.NetworkXException, nx.planar_layout, G)
+
+    def test_smoke_planar_layout_embedding_input(self):
+        embedding = nx.PlanarEmbedding()
+        embedding.set_data({0: [1, 2], 1: [0, 2], 2: [0, 1]})
+        nx.planar_layout(embedding)
+
+    def test_default_scale_and_center(self):
+        sc = self.check_scale_and_center
+        c = (0, 0)
+        G = nx.complete_graph(9)
+        G.add_node(9)
+        sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
+        sc(nx.spring_layout(G), scale=1, center=c)
+        sc(nx.spectral_layout(G), scale=1, center=c)
+        sc(nx.circular_layout(G), scale=1, center=c)
+        sc(nx.shell_layout(G), scale=1, center=c)
+        sc(nx.spiral_layout(G), scale=1, center=c)
+        sc(nx.kamada_kawai_layout(G), scale=1, center=c)
+
+        c = (0, 0, 0)
+        sc(nx.kamada_kawai_layout(G, dim=3), scale=1, center=c)
+
+    def test_circular_planar_and_shell_dim_error(self):
+        G = nx.path_graph(4)
+        pytest.raises(ValueError, nx.circular_layout, G, dim=1)
+        pytest.raises(ValueError, nx.shell_layout, G, dim=1)
+        pytest.raises(ValueError, nx.shell_layout, G, dim=3)
+        pytest.raises(ValueError, nx.planar_layout, G, dim=1)
+        pytest.raises(ValueError, nx.planar_layout, G, dim=3)
+
+    def test_adjacency_interface_numpy(self):
+        A = nx.to_numpy_array(self.Gs)
+        pos = nx.drawing.layout._fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._fruchterman_reingold(A, dim=3)
+        assert pos.shape == (6, 3)
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+
+    def test_adjacency_interface_scipy(self):
+        A = nx.to_scipy_sparse_array(self.Gs, dtype="d")
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._sparse_spectral(A)
+        assert pos.shape == (6, 2)
+        pos = nx.drawing.layout._sparse_fruchterman_reingold(A, dim=3)
+        assert pos.shape == (6, 3)
+
+    def test_single_nodes(self):
+        G = nx.path_graph(1)
+        vpos = nx.shell_layout(G)
+        assert not vpos[0].any()
+        G = nx.path_graph(4)
+        vpos = nx.shell_layout(G, [[0], [1, 2], [3]])
+        assert not vpos[0].any()
+        assert vpos[3].any()  # ensure node 3 not at origin (#3188)
+        assert np.linalg.norm(vpos[3]) <= 1  # ensure node 3 fits (#3753)
+        vpos = nx.shell_layout(G, [[0], [1, 2], [3]], rotate=0)
+        assert np.linalg.norm(vpos[3]) <= 1  # ensure node 3 fits (#3753)
+
+    def test_smoke_initial_pos_forceatlas2(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.forceatlas2_layout(self.Gi, pos=pos)
+
+    def test_smoke_initial_pos_fruchterman_reingold(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.fruchterman_reingold_layout(self.Gi, pos=pos)
+
+    def test_smoke_initial_pos_arf(self):
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.arf_layout(self.Gi, pos=pos)
+
+    def test_fixed_node_fruchterman_reingold(self):
+        # Dense version (numpy based)
+        pos = nx.circular_layout(self.Gi)
+        npos = nx.spring_layout(self.Gi, pos=pos, fixed=[(0, 0)])
+        assert tuple(pos[(0, 0)]) == tuple(npos[(0, 0)])
+        # Sparse version (scipy based)
+        pos = nx.circular_layout(self.bigG)
+        npos = nx.spring_layout(self.bigG, pos=pos, fixed=[(0, 0)])
+        for axis in range(2):
+            assert pos[(0, 0)][axis] == pytest.approx(npos[(0, 0)][axis], abs=1e-7)
+
+    def test_center_parameter(self):
+        G = nx.path_graph(1)
+        nx.random_layout(G, center=(1, 1))
+        vpos = nx.circular_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.planar_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spring_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spectral_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.shell_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+        vpos = nx.spiral_layout(G, center=(1, 1))
+        assert tuple(vpos[0]) == (1, 1)
+
+    def test_center_wrong_dimensions(self):
+        G = nx.path_graph(1)
+        assert id(nx.spring_layout) == id(nx.fruchterman_reingold_layout)
+        pytest.raises(ValueError, nx.random_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.circular_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.planar_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spring_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spring_layout, G, dim=3, center=(1, 1))
+        pytest.raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1))
+        pytest.raises(ValueError, nx.shell_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.spiral_layout, G, center=(1, 1, 1))
+        pytest.raises(ValueError, nx.kamada_kawai_layout, G, center=(1, 1, 1))
+
+    def test_empty_graph(self):
+        G = nx.empty_graph()
+        vpos = nx.random_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.circular_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.planar_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.bipartite_layout(G, G)
+        assert vpos == {}
+        vpos = nx.spring_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.spectral_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.shell_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.spiral_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.multipartite_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.kamada_kawai_layout(G, center=(1, 1))
+        assert vpos == {}
+        vpos = nx.forceatlas2_layout(G)
+        assert vpos == {}
+        vpos = nx.arf_layout(G)
+        assert vpos == {}
+
+    def test_bipartite_layout(self):
+        G = nx.complete_bipartite_graph(3, 5)
+        top, bottom = nx.bipartite.sets(G)
+
+        vpos = nx.bipartite_layout(G, top)
+        assert len(vpos) == len(G)
+
+        top_x = vpos[list(top)[0]][0]
+        bottom_x = vpos[list(bottom)[0]][0]
+        for node in top:
+            assert vpos[node][0] == top_x
+        for node in bottom:
+            assert vpos[node][0] == bottom_x
+
+        vpos = nx.bipartite_layout(
+            G, top, align="horizontal", center=(2, 2), scale=2, aspect_ratio=1
+        )
+        assert len(vpos) == len(G)
+
+        top_y = vpos[list(top)[0]][1]
+        bottom_y = vpos[list(bottom)[0]][1]
+        for node in top:
+            assert vpos[node][1] == top_y
+        for node in bottom:
+            assert vpos[node][1] == bottom_y
+
+        pytest.raises(ValueError, nx.bipartite_layout, G, top, align="foo")
+
+    def test_multipartite_layout(self):
+        sizes = (0, 5, 7, 2, 8)
+        G = nx.complete_multipartite_graph(*sizes)
+
+        vpos = nx.multipartite_layout(G)
+        assert len(vpos) == len(G)
+
+        start = 0
+        for n in sizes:
+            end = start + n
+            assert all(vpos[start][0] == vpos[i][0] for i in range(start + 1, end))
+            start += n
+
+        vpos = nx.multipartite_layout(G, align="horizontal", scale=2, center=(2, 2))
+        assert len(vpos) == len(G)
+
+        start = 0
+        for n in sizes:
+            end = start + n
+            assert all(vpos[start][1] == vpos[i][1] for i in range(start + 1, end))
+            start += n
+
+        pytest.raises(ValueError, nx.multipartite_layout, G, align="foo")
+
+    def test_kamada_kawai_costfn_1d(self):
+        costfn = nx.drawing.layout._kamada_kawai_costfn
+
+        pos = np.array([4.0, 7.0])
+        invdist = 1 / np.array([[0.1, 2.0], [2.0, 0.3]])
+
+        cost, grad = costfn(pos, np, invdist, meanweight=0, dim=1)
+
+        assert cost == pytest.approx(((3 / 2.0 - 1) ** 2), abs=1e-7)
+        assert grad[0] == pytest.approx((-0.5), abs=1e-7)
+        assert grad[1] == pytest.approx(0.5, abs=1e-7)
+
+    def check_kamada_kawai_costfn(self, pos, invdist, meanwt, dim):
+        costfn = nx.drawing.layout._kamada_kawai_costfn
+
+        cost, grad = costfn(pos.ravel(), np, invdist, meanweight=meanwt, dim=dim)
+
+        expected_cost = 0.5 * meanwt * np.sum(np.sum(pos, axis=0) ** 2)
+        for i in range(pos.shape[0]):
+            for j in range(i + 1, pos.shape[0]):
+                diff = np.linalg.norm(pos[i] - pos[j])
+                expected_cost += (diff * invdist[i][j] - 1.0) ** 2
+
+        assert cost == pytest.approx(expected_cost, abs=1e-7)
+
+        dx = 1e-4
+        for nd in range(pos.shape[0]):
+            for dm in range(pos.shape[1]):
+                idx = nd * pos.shape[1] + dm
+                ps = pos.flatten()
+
+                ps[idx] += dx
+                cplus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
+
+                ps[idx] -= 2 * dx
+                cminus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
+
+                assert grad[idx] == pytest.approx((cplus - cminus) / (2 * dx), abs=1e-5)
+
+    def test_kamada_kawai_costfn(self):
+        invdist = 1 / np.array([[0.1, 2.1, 1.7], [2.1, 0.2, 0.6], [1.7, 0.6, 0.3]])
+        meanwt = 0.3
+
+        # 2d
+        pos = np.array([[1.3, -3.2], [2.7, -0.3], [5.1, 2.5]])
+
+        self.check_kamada_kawai_costfn(pos, invdist, meanwt, 2)
+
+        # 3d
+        pos = np.array([[0.9, 8.6, -8.7], [-10, -0.5, -7.1], [9.1, -8.1, 1.6]])
+
+        self.check_kamada_kawai_costfn(pos, invdist, meanwt, 3)
+
+    def test_spiral_layout(self):
+        G = self.Gs
+
+        # a lower value of resolution should result in a more compact layout
+        # intuitively, the total distance from the start and end nodes
+        # via each node in between (transiting through each) will be less,
+        # assuming rescaling does not occur on the computed node positions
+        pos_standard = np.array(list(nx.spiral_layout(G, resolution=0.35).values()))
+        pos_tighter = np.array(list(nx.spiral_layout(G, resolution=0.34).values()))
+        distances = np.linalg.norm(pos_standard[:-1] - pos_standard[1:], axis=1)
+        distances_tighter = np.linalg.norm(pos_tighter[:-1] - pos_tighter[1:], axis=1)
+        assert sum(distances) > sum(distances_tighter)
+
+        # return near-equidistant points after the first value if set to true
+        pos_equidistant = np.array(list(nx.spiral_layout(G, equidistant=True).values()))
+        distances_equidistant = np.linalg.norm(
+            pos_equidistant[:-1] - pos_equidistant[1:], axis=1
+        )
+        assert np.allclose(
+            distances_equidistant[1:], distances_equidistant[-1], atol=0.01
+        )
+
+    def test_spiral_layout_equidistant(self):
+        G = nx.path_graph(10)
+        nx.spiral_layout(G, equidistant=True, store_pos_as="pos")
+        pos = nx.get_node_attributes(G, "pos")
+        # Extract individual node positions as an array
+        p = np.array(list(pos.values()))
+        # Elementwise-distance between node positions
+        dist = np.linalg.norm(p[1:] - p[:-1], axis=1)
+        assert np.allclose(np.diff(dist), 0, atol=1e-3)
+
+    def test_forceatlas2_layout_partial_input_test(self):
+        # check whether partial pos input still returns a full proper position
+        G = self.Gs
+        node = nx.utils.arbitrary_element(G)
+        pos = nx.circular_layout(G)
+        del pos[node]
+        pos = nx.forceatlas2_layout(G, pos=pos)
+        assert len(pos) == len(G)
+
+    def test_rescale_layout_dict(self):
+        G = nx.empty_graph()
+        vpos = nx.random_layout(G, center=(1, 1))
+        assert nx.rescale_layout_dict(vpos) == {}
+
+        G = nx.empty_graph(2)
+        vpos = {0: (0.0, 0.0), 1: (1.0, 1.0)}
+        s_vpos = nx.rescale_layout_dict(vpos)
+        assert np.linalg.norm([sum(x) for x in zip(*s_vpos.values())]) < 1e-6
+
+        G = nx.empty_graph(3)
+        vpos = {0: (0, 0), 1: (1, 1), 2: (0.5, 0.5)}
+        s_vpos = nx.rescale_layout_dict(vpos)
+
+        expectation = {
+            0: np.array((-1, -1)),
+            1: np.array((1, 1)),
+            2: np.array((0, 0)),
+        }
+        for k, v in expectation.items():
+            assert (s_vpos[k] == v).all()
+        s_vpos = nx.rescale_layout_dict(vpos, scale=2)
+        expectation = {
+            0: np.array((-2, -2)),
+            1: np.array((2, 2)),
+            2: np.array((0, 0)),
+        }
+        for k, v in expectation.items():
+            assert (s_vpos[k] == v).all()
+
+    def test_arf_layout_partial_input_test(self):
+        # Checks whether partial pos input still returns a proper position.
+        G = self.Gs
+        node = nx.utils.arbitrary_element(G)
+        pos = nx.circular_layout(G)
+        del pos[node]
+        pos = nx.arf_layout(G, pos=pos)
+        assert len(pos) == len(G)
+
+    def test_arf_layout_negative_a_check(self):
+        """
+        Checks input parameters correctly raises errors. For example,  `a` should be larger than 1
+        """
+        G = self.Gs
+        pytest.raises(ValueError, nx.arf_layout, G=G, a=-1)
+
+    def test_smoke_seed_input(self):
+        G = self.Gs
+        nx.random_layout(G, seed=42)
+        nx.spring_layout(G, seed=42)
+        nx.arf_layout(G, seed=42)
+        nx.forceatlas2_layout(G, seed=42)
+
+    def test_node_at_center(self):
+        # see gh-7791 avoid divide by zero
+        G = nx.path_graph(3)
+        orig_pos = {i: [i - 1, 0.0] for i in range(3)}
+        new_pos = nx.forceatlas2_layout(G, pos=orig_pos)
+
+    def test_initial_only_some_pos(self):
+        G = nx.path_graph(3)
+        orig_pos = {i: [i - 1, 0.0] for i in range(2)}
+        new_pos = nx.forceatlas2_layout(G, pos=orig_pos, seed=42)
+
+
+def test_multipartite_layout_nonnumeric_partition_labels():
+    """See gh-5123."""
+    G = nx.Graph()
+    G.add_node(0, subset="s0")
+    G.add_node(1, subset="s0")
+    G.add_node(2, subset="s1")
+    G.add_node(3, subset="s1")
+    G.add_edges_from([(0, 2), (0, 3), (1, 2)])
+    pos = nx.multipartite_layout(G)
+    assert len(pos) == len(G)
+
+
+def test_multipartite_layout_layer_order():
+    """Return the layers in sorted order if the layers of the multipartite
+    graph are sortable. See gh-5691"""
+    G = nx.Graph()
+    node_group = dict(zip(("a", "b", "c", "d", "e"), (2, 3, 1, 2, 4)))
+    for node, layer in node_group.items():
+        G.add_node(node, subset=layer)
+
+    # Horizontal alignment, therefore y-coord determines layers
+    pos = nx.multipartite_layout(G, align="horizontal")
+
+    layers = nx.utils.groups(node_group)
+    pos_from_layers = nx.multipartite_layout(G, align="horizontal", subset_key=layers)
+    for (n1, p1), (n2, p2) in zip(pos.items(), pos_from_layers.items()):
+        assert n1 == n2 and (p1 == p2).all()
+
+    # Nodes "a" and "d" are in the same layer
+    assert pos["a"][-1] == pos["d"][-1]
+    # positions should be sorted according to layer
+    assert pos["c"][-1] < pos["a"][-1] < pos["b"][-1] < pos["e"][-1]
+
+    # Make sure that multipartite_layout still works when layers are not sortable
+    G.nodes["a"]["subset"] = "layer_0"  # Can't sort mixed strs/ints
+    pos_nosort = nx.multipartite_layout(G)  # smoke test: this should not raise
+    assert pos_nosort.keys() == pos.keys()
+
+
+def _num_nodes_per_bfs_layer(pos):
+    """Helper function to extract the number of nodes in each layer of bfs_layout"""
+    x = np.array(list(pos.values()))[:, 0]  # node positions in layered dimension
+    _, layer_count = np.unique(x, return_counts=True)
+    return layer_count
+
+
+@pytest.mark.parametrize("n", range(2, 7))
+def test_bfs_layout_complete_graph(n):
+    """The complete graph should result in two layers: the starting node and
+    a second layer containing all neighbors."""
+    G = nx.complete_graph(n)
+    nx.bfs_layout(G, start=0, store_pos_as="pos")
+    pos = nx.get_node_attributes(G, "pos")
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), [1, n - 1])
+
+
+def test_bfs_layout_barbell():
+    G = nx.barbell_graph(5, 3)
+    # Start in one of the "bells"
+    pos = nx.bfs_layout(G, start=0)
+    # start, bell-1, [1] * len(bar)+1, bell-1
+    expected_nodes_per_layer = [1, 4, 1, 1, 1, 1, 4]
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+    # Start in the other "bell" - expect same layer pattern
+    pos = nx.bfs_layout(G, start=12)
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+    # Starting in the center of the bar, expect layers to be symmetric
+    pos = nx.bfs_layout(G, start=6)
+    # Expected layers: {6 (start)}, {5, 7}, {4, 8}, {8 nodes from remainder of bells}
+    expected_nodes_per_layer = [1, 2, 2, 8]
+    assert np.array_equal(_num_nodes_per_bfs_layer(pos), expected_nodes_per_layer)
+
+
+def test_bfs_layout_disconnected():
+    G = nx.complete_graph(5)
+    G.add_edges_from([(10, 11), (11, 12)])
+    with pytest.raises(nx.NetworkXError, match="bfs_layout didn't include all nodes"):
+        nx.bfs_layout(G, start=0)
+
+
+def test_bipartite_layout_default_nodes_raises_non_bipartite_input():
+    G = nx.complete_graph(5)
+    with pytest.raises(nx.NetworkXError, match="Graph is not bipartite"):
+        nx.bipartite_layout(G)
+    # No exception if nodes are explicitly specified
+    pos = nx.bipartite_layout(G, nodes=[2, 3])
+
+
+def test_bipartite_layout_default_nodes():
+    G = nx.complete_bipartite_graph(3, 3)
+    pos = nx.bipartite_layout(G)  # no nodes specified
+    # X coords of nodes should be the same within the bipartite sets
+    for nodeset in nx.bipartite.sets(G):
+        xs = [pos[k][0] for k in nodeset]
+        assert all(x == pytest.approx(xs[0]) for x in xs)
+
+
+@pytest.mark.parametrize(
+    "layout",
+    [
+        nx.random_layout,
+        nx.circular_layout,
+        nx.shell_layout,
+        nx.spring_layout,
+        nx.kamada_kawai_layout,
+        nx.spectral_layout,
+        nx.planar_layout,
+        nx.spiral_layout,
+        nx.forceatlas2_layout,
+    ],
+)
+def test_layouts_negative_dim(layout):
+    """Test all layouts that support dim kwarg handle invalid inputs."""
+    G = nx.path_graph(4)
+    valid_err_msgs = "|".join(
+        [
+            "negative dimensions.*not allowed",
+            "can only handle 2",
+            "cannot handle.*2",
+        ]
+    )
+    with pytest.raises(ValueError, match=valid_err_msgs):
+        layout(G, dim=-1)
+
+
+@pytest.mark.parametrize(
+    ("num_nodes", "expected_method"), [(100, "force"), (501, "energy")]
+)
+@pytest.mark.parametrize(
+    "extra_layout_kwargs",
+    [
+        {},  # No extra kwargs
+        {"pos": {0: (0, 0)}, "fixed": [0]},  # Fixed node position
+        {"dim": 3},  # 3D layout
+    ],
+)
+def test_spring_layout_graph_size_heuristic(
+    num_nodes, expected_method, extra_layout_kwargs
+):
+    """Expect 'force' layout for n < 500 and 'energy' for n >= 500"""
+    G = nx.cycle_graph(num_nodes)
+    # Seeded layout to compare explicit method to one determined by "auto"
+    seed = 163674319
+
+    # Compare explicit method to auto method
+    expected = nx.spring_layout(
+        G, method=expected_method, seed=seed, **extra_layout_kwargs
+    )
+    actual = nx.spring_layout(G, method="auto", seed=seed, **extra_layout_kwargs)
+    assert np.allclose(list(expected.values()), list(actual.values()), atol=1e-5)

wolai-backend/.venv312/Lib/site-packages/networkx/drawing/tests/test_pydot.py

@@ -0,0 +1,146 @@
+"""Unit tests for pydot drawing functions."""
+
+from io import StringIO
+
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+pydot = pytest.importorskip("pydot")
+
+
+class TestPydot:
+    @pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+    @pytest.mark.parametrize("prog", ("neato", "dot"))
+    def test_pydot(self, G, prog, tmp_path):
+        """
+        Validate :mod:`pydot`-based usage of the passed NetworkX graph with the
+        passed basename of an external GraphViz command (e.g., `dot`, `neato`).
+        """
+
+        # Set the name of this graph to... "G". Failing to do so will
+        # subsequently trip an assertion expecting this name.
+        G.graph["name"] = "G"
+
+        # Add arbitrary nodes and edges to the passed empty graph.
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("A", "D")])
+        G.add_node("E")
+
+        # Validate layout of this graph with the passed GraphViz command.
+        graph_layout = nx.nx_pydot.pydot_layout(G, prog=prog)
+        assert isinstance(graph_layout, dict)
+
+        # Convert this graph into a "pydot.Dot" instance.
+        P = nx.nx_pydot.to_pydot(G)
+
+        # Convert this "pydot.Dot" instance back into a graph of the same type.
+        G2 = G.__class__(nx.nx_pydot.from_pydot(P))
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, G2)
+
+        fname = tmp_path / "out.dot"
+
+        # Serialize this "pydot.Dot" instance to a temporary file in dot format
+        P.write_raw(fname)
+
+        # Deserialize a list of new "pydot.Dot" instances back from this file.
+        Pin_list = pydot.graph_from_dot_file(path=fname, encoding="utf-8")
+
+        # Validate this file to contain only one graph.
+        assert len(Pin_list) == 1
+
+        # The single "pydot.Dot" instance deserialized from this file.
+        Pin = Pin_list[0]
+
+        # Sorted list of all nodes in the original "pydot.Dot" instance.
+        n1 = sorted(p.get_name() for p in P.get_node_list())
+
+        # Sorted list of all nodes in the deserialized "pydot.Dot" instance.
+        n2 = sorted(p.get_name() for p in Pin.get_node_list())
+
+        # Validate these instances to contain the same nodes.
+        assert n1 == n2
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e1 = sorted((e.get_source(), e.get_destination()) for e in P.get_edge_list())
+
+        # Sorted list of all edges in the original "pydot.Dot" instance.
+        e2 = sorted((e.get_source(), e.get_destination()) for e in Pin.get_edge_list())
+
+        # Validate these instances to contain the same edges.
+        assert e1 == e2
+
+        # Deserialize a new graph of the same type back from this file.
+        Hin = nx.nx_pydot.read_dot(fname)
+        Hin = G.__class__(Hin)
+
+        # Validate the original and resulting graphs to be the same.
+        assert graphs_equal(G, Hin)
+
+    def test_read_write(self):
+        G = nx.MultiGraph()
+        G.graph["name"] = "G"
+        G.add_edge("1", "2", key="0")  # read assumes strings
+        fh = StringIO()
+        nx.nx_pydot.write_dot(G, fh)
+        fh.seek(0)
+        H = nx.nx_pydot.read_dot(fh)
+        assert graphs_equal(G, H)
+
+
+def test_pydot_issue_7581(tmp_path):
+    """Validate that `nx_pydot.pydot_layout` handles nodes
+    with characters like "\n", " ".
+
+    Those characters cause `pydot` to escape and quote them on output,
+    which caused #7581.
+    """
+    G = nx.Graph()
+    G.add_edges_from([("A\nbig test", "B"), ("A\nbig test", "C"), ("B", "C")])
+
+    graph_layout = nx.nx_pydot.pydot_layout(G, prog="dot")
+    assert isinstance(graph_layout, dict)
+
+    # Convert the graph to pydot and back into a graph. There should be no difference.
+    P = nx.nx_pydot.to_pydot(G)
+    G2 = nx.Graph(nx.nx_pydot.from_pydot(P))
+    assert graphs_equal(G, G2)
+
+
+@pytest.mark.parametrize(
+    "graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+)
+def test_hashable_pydot(graph_type):
+    # gh-5790
+    G = graph_type()
+    G.add_edge("5", frozenset([1]), t='"Example:A"', l=False)
+    G.add_edge("1", 2, w=True, t=("node1",), l=frozenset(["node1"]))
+    G.add_edge("node", (3, 3), w="string")
+
+    assert [
+        {"t": '"Example:A"', "l": "False"},
+        {"w": "True", "t": "('node1',)", "l": "frozenset({'node1'})"},
+        {"w": "string"},
+    ] == [
+        attr
+        for _, _, attr in nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).edges.data()
+    ]
+
+    assert {str(i) for i in G.nodes()} == set(
+        nx.nx_pydot.from_pydot(nx.nx_pydot.to_pydot(G)).nodes
+    )
+
+
+def test_pydot_numerical_name():
+    G = nx.Graph()
+    G.add_edges_from([("A", "B"), (0, 1)])
+    graph_layout = nx.nx_pydot.pydot_layout(G, prog="dot")
+    assert isinstance(graph_layout, dict)
+    assert "0" not in graph_layout
+    assert 0 in graph_layout
+    assert "1" not in graph_layout
+    assert 1 in graph_layout
+    assert "A" in graph_layout
+    assert "B" in graph_layout