nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure
By Nick Hand, Yu Feng, F. Beutler, Yin Li, Chirag Modi, Uros Seljak, Zachary Slepian. 2017
204 citations recorded by the source corpus.
We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.
Subfields: Computational Mathematics, Materials Chemistry, Molecular Biology
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Citations in the corpus
- Learning to predict the cosmological structure formation (2018, methodology): “The open source software toolkit nbodykit (73) is employed for the clustering analysis.”
- The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from the anisotropic power spectrum of the quasar sample between redshift 0.8 and 2.2 (2020, methodology): “The fibre assignment algorithms (based on nbodykit; Hand et al. 2018) is run on this set of targets using the plate geometry of the DR16 data.”
- Redshift-space distortions in Lagrangian perturbation theory (2020, methodology): “[89] and employ NbodyKit [90] to compute redshift-space power spectrum wedges, multipoles and correlation function multipoles at z = 0.”
- The Completed SDSS-IV Extended Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations with Lyα Forests (2020, methodology): “28 QL sm 2 2 2 2 peak ⎡ ⎣ ⎢⎢ ⎤ ⎦ ⎥⎥( ) ( ) ( ) ( ) The smooth component, Psm, is derived from the linear power spectrum, P k z,L ( ), via the sideband technique (Kirkby et al. 2013), which is implemented in picca, with the help of nbodykit31 (Hand et al. 2018).”
- Consistent modeling of velocity statistics and redshift-space distortions in one-loop perturbation theory (2020, methodology): “he last row, labeled ‘Galaxies’, refers to the mock galaxy sample drawn from the halo occupation distribution described in the text. were all computed using the publically available nbodykit software [56]. The number densities and rough estimates for the linear biases of the halo samples we consider”
- The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from anisotropic clustering analysis of the quasar sample in configuration space between redshift 0.8 and 2.2 (2020, methodology): “The fibre collision effect is added to the EZmocks using the FiberCollision module from Nbodykit (Hand et al. 2018).”