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-0,0 +1,429 @@ + + + + 20240420T094806-2a217ec0ca4234839f41d85c73a60f90ed6b4aff + 20240420094806 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 04 + 2024 + + + 9 + + 96 + + + + Paicos: A Python package for analysis of (cosmological) +simulations performed with Arepo + + + + Thomas + Berlok + https://orcid.org/0000-0003-0466-603X + + + Léna + Jlassi + https://orcid.org/0009-0007-9039-294X + + + Ewald + Puchwein + https://orcid.org/0000-0001-8778-7587 + + + Troels + Haugbølle + https://orcid.org/0000-0002-9422-8684 + + + + 04 + 20 + 2024 + + + 6296 + + + 10.21105/joss.06296 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10994256 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6296 + + + + 10.21105/joss.06296 + https://joss.theoj.org/papers/10.21105/joss.06296 + + + https://joss.theoj.org/papers/10.21105/joss.06296.pdf + + + + + + Astropy: A community Python package for +astronomy + Astropy Collaboration + Astronomy and Astrophysics + 558 + 10.1051/0004-6361/201322068 + 2013 + Astropy Collaboration. 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(2004). +Pytest 7.4. https://github.com/pytest-dev/pytest + + + Jupyter notebooks – a publishing format for +reproducible computational workflows + Kluyver + Positioning and power in academic publishing: +Players, agents and agendas + 10.3233/978-1-61499-649-1-87 + 2016 + Kluyver, T., Ragan-Kelley, B., Pérez, +F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., +Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., +& team, J. development. (2016). Jupyter notebooks – a publishing +format for reproducible computational workflows. In F. Loizides & B. +Scmidt (Eds.), Positioning and power in academic publishing: Players, +agents and agendas (pp. 87–90). IOS Press. +https://doi.org/10.3233/978-1-61499-649-1-87 + + + IPython: A System for Interactive Scientific +Computing + Perez + Computing in Science and +Engineering + 3 + 9 + 10.1109/MCSE.2007.53 + 2007 + Perez, F., & Granger, B. E. +(2007). IPython: A System for Interactive Scientific Computing. +Computing in Science and Engineering, 9(3), 21–29. +https://doi.org/10.1109/MCSE.2007.53 + + + Cosmological simulations of galaxy +formation + Vogelsberger + Nature Reviews Physics + 1 + 2 + 10.1038/s42254-019-0127-2 + 2020 + Vogelsberger, M., Marinacci, F., +Torrey, P., & Puchwein, E. (2020). Cosmological simulations of +galaxy formation. Nature Reviews Physics, 2(1), 42–66. +https://doi.org/10.1038/s42254-019-0127-2 + + + Hydromagnetic waves in an expanding universe +- cosmological MHD code tests using analytic solutions + Berlok + Monthly Notices of the RAS + 3 + 515 + 10.1093/mnras/stac1882 + 2022 + Berlok, T. (2022). Hydromagnetic +waves in an expanding universe - cosmological MHD code tests using +analytic solutions. Monthly Notices of the RAS, 515(3), 3492–3511. +https://doi.org/10.1093/mnras/stac1882 + + + + + + From 5d867574acf501378525faeb0f4206deb2cf0807 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Sat, 20 Apr 2024 10:48:32 +0100 Subject: [PATCH 3/3] Creating 10.21105.joss.06296.jats --- joss.06296/10.21105.joss.06296.jats | 787 ++++++++++++++++++++++++++++ 1 file changed, 787 insertions(+) create mode 100644 joss.06296/10.21105.joss.06296.jats diff --git a/joss.06296/10.21105.joss.06296.jats b/joss.06296/10.21105.joss.06296.jats new file mode 100644 index 0000000000..79dec074d8 --- /dev/null +++ b/joss.06296/10.21105.joss.06296.jats @@ -0,0 +1,787 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6296 +10.21105/joss.06296 + +Paicos: A Python package for analysis of (cosmological) +simulations performed with Arepo + + + +https://orcid.org/0000-0003-0466-603X + +Berlok +Thomas + + + +* + + +https://orcid.org/0009-0007-9039-294X + +Jlassi +Léna + + + + +https://orcid.org/0000-0001-8778-7587 + +Puchwein +Ewald + + + + +https://orcid.org/0000-0002-9422-8684 + +Haugbølle +Troels + + + + + +Niels Bohr Institute, University of Copenhagen, +Denmark + + + + +Leibniz-Institut für Astrophysik Potsdam, +Germany + + + + +* E-mail: + + +31 +1 +2024 + +9 +96 +6296 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +astronomy + + + + + + Summary +

Cosmological simulations evolve dark matter and baryons subject to + gravitational and hydrodynamic forces + (Vogelsberger + et al., 2020). The simulations start at high redshift and + capture hierarchical structure formation where small structures form + first and later assemble to larger structures + (Springel + et al., 2005). The Arepo code is a versatile finite-volume code + which can solve the magnetohydrodynamic equations on an unstructured + Voronoi mesh in a cosmologically comoving frame + (Springel, + 2010; + Weinberger + et al., 2020).

+

Here we present Paicos, a new object-oriented Python package for + analyzing simulations performed with Arepo. Paicos strives to reduce + the learning curve for students and researchers getting started with + Arepo simulations. As such, Paicos includes many examples in the form + of Python scripts and Jupyter notebooks + (Kluyver + et al., 2016) as well as an online documentation describing the + installation procedure and recommended first steps.

+

Paicos’ main features are automatic handling of cosmological and + physical units, computation of derived variables, 2D visualization + (slices and projections), 1D and 2D histograms, and easy saving and + loading of derived data including units and all the relevant + metadata.

+

Paicos relies heavily on well-established open source software + libraries such as NumPy + (Harris + et al., 2020; + van + der Walt et al., 2011), Scipy + (Virtanen + et al., 2020), h5py + (Collette, + 2013), Cython + (Behnel + et al., 2011) and astropy + (Astropy + Collaboration, 2013) and contains features for interactive data + analysis inside Ipython terminals + (Perez + & Granger, 2007) and Jupyter notebooks + (Kluyver + et al., 2016), e.g., tab completion of data keywords and Latex + rendering of data units. Paicos also contains a number of tests that + are automated using pytest + (Krekel + et al., 2004), CircleCi and GitHub workflows.

+
+ + Statement of need +

The Arepo code stores its data output as HDF5 files, which can + easily be loaded as NumPy arrays using h5py. However, data + visualization of the unstructured mesh used in Arepo is non-trivial + and keeping track of the units used in the data outputs can also be a + tedious task. The general purpose visualization and data analysis + software package yt1 + (Turk + et al., 2011) and the visualization package py-sphviewer + (Benitez-Llambay, + 2015) are both able to perform visualizations of Arepo + simulations. Paicos provides an alternative, which is specifically + tailored to Arepo simulations. It is in this regard similar to + swiftsimio + (Borrow + & Borrisov, 2020), which was developed specifically for + SWIFT simulations + (Schaller + & others, 2023).

+

We have developed Paicos because we identified a need for an + analysis code that simultaneously fulfills the following requirements: + 1) is specifically written for analysis of Arepo simulations + discretized on a Voronoi mesh 2) provides safeguards against errors + related to conversions of cosmological and physical units 3) + facilitates working with large data sets by supporting the saving and + loading of reduced/derived data including units and metadata 4) + contains enough functionality to be useful for practical research + tasks while still being light-weight and well-documented enough that + it can be installed, used and understood by a junior researcher with + little or no assistance.

+
+ + Overview of key Paicos features +

The key Paicos features, which were implemented to fulfill the + above requirements, are as follows:

+ + +

Functionality for reading cell/particle and group/subhalo + catalog data as saved by Arepo in the HDF5 format.

+
+ +

Unit-handling via astropy with additional support for the + + + a + and + + h + factors that are used in cosmological + simulations.2

+
+ +

Functionality for automatically obtaining derived variables + from the variables present in the Arepo snapshot (e.g. the cell + volume, temperature, or magnetic field strength). Within Jupyter + notebooks or an Ipython terminal: Tab completion to show which + variables are available for a given data group (either by directly + loading from the HDF5-file or by automatically calculating a + derived variable).

+
+ +

Functionality for saving and loading data including + cosmological and physical units in HDF5-files that automatically + include all metadata from the original Arepo snapshot (i.e. the + Header, Config and Param groups).

+
+ +

Functionality for creating slices and projections and saving + them in a format that additionally includes information about the + size, center and orientation of the image.

+
+ +

Functionality for creating 1D and 2D histograms (e.g. radial + profiles and + + ρ- + + T + phase-space plots) of large data sets (using OpenMP-enabled + Cython).

+
+ +

Functionality for user customization, e.g. adding new units or + functions for computing derived variables.

+
+ +

Functionality for selecting only a part of a snapshot for + analysis and for storing this selection as a new reduced + snapshot.

+
+
+

Finally, we also make public GPU implementations of projection + functionalities, which are much faster than the OpenMP-parallel CPU + implementations described above. The GPU implementations include a + mass-conserving SPH-like projection and a ray tracing implementation + using a bounding volume hierarchy (BVH) in the form of a binary radix + tree for nearest neighbor searches. These GPU implementations use + Numba + (Lam + et al., 2015) and CuPy + (Okuta + et al., 2017) and require that the user has a CUDA-enabled GPU. + Our binary tree implementation follows the the GPU-optimized + tree-construction algorithm described in + (Karras, + 2012) and the implementation of it found in the publicly + available Cornerstone Octree GPU-library + (Keller + et al., 2023). Using our SPH-like implementation on an + Nvidia + A100 GPU, it takes less than a second to project 100 mio. + particles onto an image plane with + + 40962 + pixels. This speed enables interactive data exploration. We provide an + example IPython widget illustrating this feature. Finally, we note + that the returned images include physical units and can be used for + scientific analysis.

+

Paicos is hosted on GitHub at https://github.com/tberlok/paicos. We + strongly encourage contributions to Paicos by opening issues and + submitting pull requests.

+
+ + Acknowledgements +

We thank the JOSS referees, Kyle Oman and Terrence Tricco, and the + editor, Josh Borrow, for constructive feedback that greatly helped + improve Paicos. We are grateful to Christoph Pfrommer for support and + advice. We thank Rüdiger Pakmor, Rosie Talbot and Timon Thomas for + useful discussions as well as Matthias Weber, Lorenzo Maria Perrone, + Arne Trabert and Joseph Whittingham for beta-testing Paicos. We are + grateful to Volker Springel for making the Arepo code available and to + the main developers of arepo-snap-util (Federico Marinacci and Rüdiger + Pakmor), a non-public code which we have used for comparison of + projections and slices. TB gratefully acknowledges funding from the + European Union’s Horizon Europe research and innovation programme + under the Marie Skłodowska-Curie grant agreement No 101106080. LJ + acknowledges support by the German Science Foundation (DFG) under + grant “DFG Research Unit FOR 5195 – Relativistic Jets in Active + Galaxies”. LJ and TB acknowledge support by the European Research + Council under ERC-AdG grant PICOGAL-101019746. The authors gratefully + acknowledge the Gauss Centre for Supercomputing e.V. + (www.gauss-centre.eu) for funding this project by providing computing + time on the GCS Supercomputer SuperMUC at Leibniz Supercomputing + Centre (www.lrz.de). The Tycho supercomputer hosted at the SCIENCE HPC + center at the University of Copenhagen was used for supporting this + work.

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See http://yt-project.org/.

+
+ +

See e.g. Pakmor & Springel + (2013) + and Berlok + (2022) + for detailed discussions and a derivation of the comoving + magnetohydrodynamic equations used in cosmological simulations + including magnetic fields.

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