Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-13T05:01:32.004Z Has data issue: false hasContentIssue false
  • English
  • Français

BEANS – distributed data analysis for numerical simulations

Published online by Cambridge University Press:  11 March 2020

Arkadiusz Hypki Open the ORCID record for Arkadiusz Hypki [Opens in a new window] ,
Mirek Giersz Open the ORCID record for Mirek Giersz [Opens in a new window] ,
Abbas Askar Open the ORCID record for Abbas Askar [Opens in a new window] ,
Diogo Belloni Open the ORCID record for Diogo Belloni [Opens in a new window]  and
Agostino Leveque Open the ORCID record for Agostino Leveque [Opens in a new window]
Arkadiusz Hypki
Affiliation:
Astronomical Observatory Institute, Faculty of Physics, A. Mickiewicz University, Słoneczna 36, 60-286Poznań, Poland email: ahypki@amu.edu.pl
Mirek Giersz
Affiliation:
Nicolaus Copernicus Astronomical Centre, Polish Academy of Sciences, Warsaw, Poland email: mig@camk.edu.pl
Abbas Askar
Affiliation:
Lund Observatory, Department of Astronomy, and Theoretical Physics, Lund University, Box 43, SE-221 00Lund, Sweden email: askar@astro.lu.se
Diogo Belloni
Affiliation:
National Institute for Space Research, São José dos Campos, Brazil email: diogo.belloni@inpe.br Instituto de Física y Astronomía, Universidad de Valparaíso, Valparaíso, Chile
Agostino Leveque
Affiliation:
Nicolaus Copernicus Astronomical Centre, Polish Academy of Sciences, Warsaw, Poland email: mig@camk.edu.pl
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

beans is a tool for distributed data analysis. It provides web and command line interface for data analysis and plotting for huge datasets. beans is written in a general form and can be used in any field of research to analyze the data. The main purpose of beans is to provide to the community a versatile tool to store, analyze and then visualize any amount of scientific data (e.g. numerical simulation, observations).

Keywords

methods: data analysisnumericalstatistical – astronomical data bases: miscellaneous
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 14 , Symposium S351: Star Clusters: From the Milky Way to the Early Universe , May 2019 , pp. 460 - 463
Copyright
© International Astronomical Union 2020

References

Giersz, M., Heggie, D. C., Hurley, J. R., & Hypki, A. 2013, MNRAS 431, 2184CrossRefGoogle Scholar
Hypki, A. & Giersz, M., 2013, MNRAS, 429, 1221CrossRefGoogle Scholar
Hypki, A. 2018, MNRAS, 477, 3076CrossRefGoogle Scholar
Dean, J. & Ghemawat, S. 2008, Commun. ACM, 51, 107CrossRefGoogle Scholar