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Title: Recent developments in histogram libraries
Boost.Histogram, a header-only C++14 library that provides multidimensional histograms and profiles, became available in Boost 1.70. It is extensible, fast, and uses modern C++ features. Using template metaprogramming, the most efficient code path for any given configuration is automatically selected. The library includes key features designed for the particle physics community, such as optional under- and overflow bins, weighted increments, reductions, growing axes, thread-safe filling, and memory-efficient counters with high-dynamic range. Python bindings for Boost.Histogram are being developed in the Scikit-HEP project to provide a fast, easy-to-install package as a backend for other Python libraries and for advanced users to manipulate histograms. Versatile and efficient histogram filling, effective manipulation, multithreading support, and other features make this a powerful tool. This library has also driven package distribution efforts in Scikit-HEP, allowing binary packages hosted on PyPI to be available for a very wide variety of platforms. Two other libraries fill out the remainder of the Scikit-HEP Python histogramming effort. Aghast is a library designed to provide conversions between different forms of histograms, enabling interaction between histogram libraries, often without an extra copy in memory. This enables a user to make a histogram in one library and then save it in another more » form, such as saving a Boost.Histogram in ROOT. And Hist is a library providing friendly, analyst-targeted syntax and shortcuts for quick manipulations and fast plotting using these two libraries. « less
Authors:
; ;
Editors:
Doglioni, C.; Kim, D.; Stewart, G.A.; Silvestris, L.; Jackson, P.; Kamleh, W.
Award ID(s):
1836650
Publication Date:
NSF-PAR ID:
10256978
Journal Name:
EPJ Web of Conferences
Volume:
245
Page Range or eLocation-ID:
05014
ISSN:
2100-014X
Sponsoring Org:
National Science Foundation
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