Vibes extracts cores in simulations using the virial theorem to define boundaries, yielding more stable and physically motivated structures than density-threshold methods like hop and dendrogram.
Powerlaw: a Python package for analysis of heavy-tailed distributions
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.
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UNVERDICTED 2representative citing papers
Wideband observations show M28A giant pulses differ from FRB 20200120E bursts in duration, luminosity, timing statistics, and spectral structure, yielding no strong evidence for a direct link.
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Virial-based extraction of structures in numerical simulations: The vibes tool
Vibes extracts cores in simulations using the virial theorem to define boundaries, yielding more stable and physically motivated structures than density-threshold methods like hop and dendrogram.
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Searching for links between energetic millisecond pulsars and repeating fast radio bursts
Wideband observations show M28A giant pulses differ from FRB 20200120E bursts in duration, luminosity, timing statistics, and spectral structure, yielding no strong evidence for a direct link.