Metadata-Version: 2.4
Name: sw1pers_l
Version: 0.1.1
Summary: SW1Pers landscape for time series periodicity analysis
Author: Miguel Almeida
Author-email: Miguel Almeida <migpinalm@gmail.com>
License: Copyright (c) 2026 Miguel Almeida
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "sw1pers-l"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND.
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: tqdm
Requires-Dist: ripser
Requires-Dist: plotly
Dynamic: license-file

The algorithm SW1PerS yields a scalar periodicity score for univariate time series.
The aim of this project is to make extend the algorithm in order to correspond an array of periodicity scores to univariate time series:
    The time series is divided into overlapping snippets (sub-time-series), to each of which we apply SW1PerS.
    The size of the snippet and the overlapping size are hyper-parameters (dependent on the data)

This is useful to locate periodic behaviour in general time series.
On the other hand, this is useful to locate aperiodic behaviour in periodc time series.
