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Streamlined data analysis in Python

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arxiv 2308.06652 v3 pith:NFI4XBLN submitted 2023-08-12 hep-lat

classification hep-lat
keywords dataanalysispythoncarryfeatureslatticelibrariesmodules
verification ladder T0 review T1 audit T2 compute T3 formal
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Python is a particularly appealing language to carry out data analysis, owing in part to its user-friendly character as well as its access to well maintained and powerful libraries like NumPy and SciPy. Still, for the purpose of analyzing data in a lattice QCD context, some desirable functionality is missing from these libraries. Moreover, scripting languages tend to be slower than compiled ones. To help address these points we present the AnalysisToolbox, a collection of Python modules to facilitate lattice QCD data analysis. Some highlighted features include general-purpose jackknife and bootstrap routines; modules for reading in and storing gauge configurations; a module to carry out hadron resonance gas model calculations; and convenience wrappers for SciPy integration, curve fitting, and splines. These features are sped up behind the scenes using parallelization and just-in-time compilation.

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Cited by 2 Pith papers

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  1. Quark number susceptibility and conserved charge fluctuation for (2+1)-flavor QCD with M\"obius domain wall fermions

    hep-lat 2025-01 conditional novelty 6.0 of 10

    New lattice QCD results for second- and fourth-order quark number susceptibilities and charge fluctuation ratios using Möbius domain wall fermions show a possible discrepancy with staggered fermions in the electric ch...

  2. Thermodynamics of charmed hadrons across chiral crossover from lattice QCD

    hep-lat 2025-01 conditional novelty 5.0 of 10

    Lattice QCD charm fluctuations show that charmed baryon pressure is about twice the known-state expectation and that low-lying charmed hadrons survive above the chiral crossover before charm quark-like degrees of free...

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