Package: seg1d
seg1dAutomated segmentation of one-dimensional (1D) dataOverviewseg1d is an open-source Python package for the automated segmentation of one-dimensional data using one or more reference segments. The segmentation process allows users to apply various methods and parameters for the process through weighted features (i.e., additional data attributed to the same set) in a rolling correlation size-varying window of a reference(s) to a target. Correlations can be averaged across the references and a peak detection algorithm finds the most prominent segments. Non-overlapping segments are identified and a clustering algorithm groups the most similar segments within the target. The package was developed for movement sciences but should be useful to anyone interested in extracting correlated subsequences from a dataset.Example of the segmentation algorithm using a portion of a sine wave. The initial reference segment is found, along with any additional segments.DocumentationFull documentation available online:https://cadop.github.io/seg1d/AlternativesThere are existing libraries that provide clustering and similarity measures for querying subseries data.The UCR Suite(Ultrafast subsequence search under both Dynamic Time Warping (DTW) and Euclidean Distance (ED))tslearn(Machine learning tools for the analysis of time series)QuickstartDependenciesCurrently tested onPython 3.8on Ubuntu 18.04 and Windows 10. (Should work onPython 3.6and above)Required Packages:numpy>=1.18,scipy>=1.4.1,sklearn>=0.22,numba>=0.48For documentation:sphinx>=2Installationpip install seg1dExample usageThe documentation contains examples using data of both generated data (e.g., sine wave) and real-world examples (i.e., motion capture data).To quickly get started, try importing the seg1d module and using the provided sample data.importseg1d#retrieve the sample reference, target, and weight datar,t,w=seg1d.sampleData()# define scaling percentage and rolling step sizeminW,maxW,step=70,150,1#call the segmentation algorithma=seg1d.segmentData(r,t,w,minW,maxW,step)print(a)# Should output an array equal to:# array([[207.       , 240.       ,   0.9124224],#        [342.       , 381.       ,   0.8801901],#        [ 72.       , 112.       ,   0.8776795]])# where each array is of form [start index, end index, correlation]For more examples, please refer to the full documention.Project InfoThis project was used for the following paper:(Accepted) Schwartz, Mathew; Pataky, Todd; Sui Geok Karen, CHUA; Wei Tech, ANG; and Donnelly, Cyril (2020) "AUTOMATED MULTI-FEATURE SEGMENTATION OF TREADMILL RUNNING," ISBS Proceedings Archive: Vol. 37 : Iss. 2 , Article 1.CommunityIssues and feature requests should be submitted ongithub.Please check the Community Guidelines for further details on contributing.CreditsCode written and developed by Mathew Schwartz (New Jersey Institute of Technology).Data used in sample provided by Precision Rehab, Rehabilitation Research Institute of Singapore.Project was funded in part by the Agency for Science, Technology and Research (A*STAR), Nanyang Technological University (NTU) and the National Health Group (NHG) (RRG3: 2019/19002).LicensePlease refer to the fullLICENSE.