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diff --git a/.venv/lib/python3.12/site-packages/networkx/generators/time_series.py b/.venv/lib/python3.12/site-packages/networkx/generators/time_series.py
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+"""
+Time Series Graphs
+"""
+
+import itertools
+
+import networkx as nx
+
+__all__ = ["visibility_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def visibility_graph(series):
+    """
+    Return a Visibility Graph of an input Time Series.
+
+    A visibility graph converts a time series into a graph. The constructed graph
+    uses integer nodes to indicate which event in the series the node represents.
+    Edges are formed as follows: consider a bar plot of the series and view that
+    as a side view of a landscape with a node at the top of each bar. An edge
+    means that the nodes can be connected by a straight "line-of-sight" without
+    being obscured by any bars between the nodes.
+
+    The resulting graph inherits several properties of the series in its structure.
+    Thereby, periodic series convert into regular graphs, random series convert
+    into random graphs, and fractal series convert into scale-free networks [1]_.
+
+    Parameters
+    ----------
+    series : Sequence[Number]
+       A Time Series sequence (iterable and sliceable) of numeric values
+       representing times.
+
+    Returns
+    -------
+    NetworkX Graph
+        The Visibility Graph of the input series
+
+    Examples
+    --------
+    >>> series_list = [range(10), [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3]]
+    >>> for s in series_list:
+    ...     g = nx.visibility_graph(s)
+    ...     print(g)
+    Graph with 10 nodes and 9 edges
+    Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] Lacasa, Lucas, Bartolo Luque, Fernando Ballesteros, Jordi Luque, and Juan Carlos Nuno.
+           "From time series to complex networks: The visibility graph." Proceedings of the
+           National Academy of Sciences 105, no. 13 (2008): 4972-4975.
+           https://www.pnas.org/doi/10.1073/pnas.0709247105
+    """
+
+    # Sequential values are always connected
+    G = nx.path_graph(len(series))
+    nx.set_node_attributes(G, dict(enumerate(series)), "value")
+
+    # Check all combinations of nodes n series
+    for (n1, t1), (n2, t2) in itertools.combinations(enumerate(series), 2):
+        # check if any value between obstructs line of sight
+        slope = (t2 - t1) / (n2 - n1)
+        offset = t2 - slope * n2
+
+        obstructed = any(
+            t >= slope * n + offset
+            for n, t in enumerate(series[n1 + 1 : n2], start=n1 + 1)
+        )
+
+        if not obstructed:
+            G.add_edge(n1, n2)
+
+    return G