h LSrSSKrSSKrS/r\R "SSS9S5rg)z Time Series Graphs Nvisibility_graphT)graphs returns_graphc ^^[R"[U55n[R"U[ [ U55S5 [ R"[ U5S5H[uup#upEXS- XB- - mUTU-- m[UU4Sj[ XS-UUS-S955nU(aMJURX$5 M] U$)a 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 valuec3>># UHupUTU-T-:v M g7f)N).0ntoffsetslopes Q/var/www/html/env/lib/python3.13/site-packages/networkx/generators/time_series.py #visibility_graph..Bs( D V# #Ds)start) nx path_graphlenset_node_attributesdict enumerate itertools combinationsanyadd_edge) seriesGn1t1n2t2 obstructedrrs @@rrr sZ c&k"A1d9V#45w?(44Yv5FJ(2RW%ebj !&a""5R!VD  z JJr K H)__doc__rnetworkxr__all__ _dispatchablerr r&rr+s;  T2= 3= r&