Array programming with NumPy
Charles R. Harris,K. Jarrod Millman,Stefan van der Walt,Stefan van der Walt,Ralf Gommers,Pauli Virtanen,David Cournapeau,Eric Wieser,Julian Taylor,Sebastian Berg,Nathaniel J. Smith,Robert Kern,Matti Picus,Stephan Hoyer,Marten H. van Kerkwijk,Matthew Brett,Matthew Brett,Allan Haldane,Jaime Fernández del Río,Mark Wiebe,Mark Wiebe,Pearu Peterson,Pierre Gérard-Marchant,Kevin Sheppard,Tyler Reddy,Warren Weckesser,Hameer Abbasi,Christoph Gohlke,Travis E. Oliphant +28 more
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In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.Abstract:
Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.read more
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Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries
Kathrin Blagec,Jesse J. Swen,Rudolf Koopmann,Ka-Chun Cheung,Mandy H Crommentuijn-van Rhenen,Inge G. A. Holsappel,Lidija Konta,Simon Ott,Daniela Steinberger,Hong Xu,Erika Cecchin,Vita Dolzan,Cristina Lucía Dávila-Fajardo,George P. Patrinos,Gere Sunder-Plassmann,Richard M. Turner,Munir Pirmohamed,Henk-Jan Guchelaar,Matthias Samwald +18 more
TL;DR: The feasibility of implementing a standardized PGx decision support solution in a multinational, multi-language and multi-center setting was demonstrated and remaining challenges for future wide-scale roll-out were identified.
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Flybys, orbits, splashback: subhalos and the importance of the halo boundary.
TL;DR: In this paper, the authors investigate the dependence of the subhalo fraction on the radius definition and show that it can vary by factors of unity between different spherical overdensity definitions.
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OUP accepted manuscript
TL;DR: In this paper , a tomographic model was applied to constrain the phase-dependent illumination profile of the disc, which is consistent with the model of a truncated disc with an inner precessing hot flow.
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Dark energy survey year 3 results: cosmological constraints from the analysis of cosmic shear in harmonic space
C. Doux,Bhuvnesh Jain,Duane Zeurcher,J. Lee,Xiao Fang,Rogerio Rosenfeld,Alexandra Amon,H. Camacho,Ami Choi,L. F. Secco,Jonathan Blazek,C. L. Chang,M. Gatti,Enrique Gaztanaga,Niall Jeffrey,Marco Roveri,S. Samuroff,A. Alarcon,O. Alves,F. Andrade-Oliveira,Eric J. Baxter,Keith Bechtol,Matthew R. Becker,Gary Bernstein,A. Campos,A. Carnero Rosell,M. Carrasco Kind,R. Cawthon,Ru Chen,J. P. Cordero,Martin Crocce,Christopher J. Davis,Joseph DeRose,Scott Dodelson,Alex Drlica-Wagner,Kathleen D. Eckert,Tim Eifler,F. Elsner,Jack Elvin-Poole,S. Everett,A. Fert'e,Pablo Fosalba,Oliver Friedrich,G. Giannini,Daniel Gruen,Robert A. Gruendl,Ian Harrison,W. G. Hartley,K. Herner,E. M. Huff,D. Huterer,Mike Jarvis,Elisabeth Krause,Nikolay Kuropatkin,P.-F. Leget,P. Lemos,Andrew R. Liddle,Niall MacCrann,J. F. McCullough,J. Muir,Justin Myles,A. Navarro-Alsina,S. B. Pandey,Y.C. Park,A. Porredon,J. Prat,Mario Rodríguez-Monroy,R. P. Rollins,A. Roodman,Ashley J. Ross,Eli S. Rykoff,C. S'anchez,E. Sanchez,I. Sevilla-Noarbe,Erin Sheldon,T. Shin,A. Troja,M. Troxel,Isaac Tutusaus,Tamas Varga,N. Weaverdyck,Risa H. Wechsler,Brian Yanny,B. Yin,Yue Zhang,Joe Zuntz,T. M. C. Abbott,Michel Aguena,S. Allam,James Annis,David Bacon,E. Bertin,Sebastian Bocquet,David J. Brooks,D. L. Burke,J. Carretero,M. Costanzi,Luiz N. da Costa,Maria E. S. Pereira,J. De Vicente,Shantanu Desai,H. T. Diehl,Peter Doel,I. Ferrero,B. Flaugher,Joshua A. Frieman,Juan Garcia-Bellido,D. W. Gerdes,Tommaso Giannantonio,J. Gschwend,G. Gutierrez,Samuel Hinton,D. L. Hollowood,K. Honscheid,David J. James,A. G. Kim,Kyler Kuehn,Ofer Lahav,Jennifer L. Marshall,Felipe Menanteau,Ramon Miquel,Robert Morgan,Ricardo L. C. Ogando,Antonella Palmese,F. Paz-Chinch'on,Adriano Pieres,Kevin Reil,E. Sánchez,V. Scarpine,S. Serrano,M. Smith,E. Suchyta,Molly E. C. Swanson,Gregory Tarle,D. Thomas,Chun-Hao To,Jochen Weller +136 more
TL;DR: In this article , cosmological constraints from the analysis of angular power spectra of cosmic shear maps based on data from the first three years of observations by the Dark Energy Survey (DES Y3) were presented.
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How merger-driven gas motions in galaxy clusters can turn AGN bubbles into radio relics
TL;DR: In this paper, the shape of the underlying distribution of seed relativistic electrons, lit up by a recent shock passage, is explored, which suggests that the electrons are accelerated or re-accelerated by merger shock fronts propagating outward in the intracluster plasma.
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Scikit-learn: Machine Learning in Python
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Posted Content
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