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Table 6 Guidelines for which implementation is best-suited for which data set, based on our benchmarking

From: A roadmap for the computation of persistent homology

Data type Complex Suggested software
networks WRCF jHoles
image data cubical Gudhi or DIPHA (st)
distance matrix VR Ripser
distance matrix W javaPlex
points in Euclidean space VR Gudhi
points in Euclidean space Č Dionysus
points in Euclidean space α (only in dim 2 and 3) Dionysus ((st) in dim 2, (d) in dim 3) or Gudhi
  1. Recall that we indicate the implementation of the dual algorithm using the abbreviation ‘d’ following the name of a package, and similarly we indicate the implementation of the standard algorithm by ‘st’. Note that for smaller data sets one can also use javaPlex to compute PH with VR complexes from points in Euclidean space, and Perseus to compute PH with cubical complexes for image data, and with VR complexes for distance matrices. The library jHoles can only handle networks with density much less than 1.