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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Journal ArticleDOI
Identifying RR Lyrae Variable Stars in Six Years of the Dark Energy Survey
K. M. Stringer,Alex Drlica-Wagner,Lucas M. Macri,C. E. Martínez-Vázquez,A. K. Vivas,P. S. Ferguson,Andrew B. Pace,A. R. Walker,Eric H. Neilsen,K. Tavangar,W. C. Wester,T. M. C. Abbott,Michel Aguena,S. Allam,David Bacon,K. Bechtol,E. Bertin,David J. Brooks,D. L. Burke,A. Carnero Rosell,M. Carrasco Kind,J. Carretero,M. Costanzi,Martin Crocce,L. N. da Costa,Maria E. S. Pereira,J. De Vicente,S. Desai,H. T. Diehl,P. Doel,I. Ferrero,Juan Garcia-Bellido,Enrique Gaztanaga,D. W. Gerdes,Daniel Gruen,Robert A. Gruendl,J. Gschwend,G. Gutierrez,Samuel Hinton,D. L. Hollowood,K. Honscheid,Ben Hoyle,David J. James,K. Kuehn,N. Kuropatkin,Tianjun Li,M. A. G. Maia,Jennifer L. Marshall,Felipe Menanteau,Ramon Miquel,Robert Morgan,R. L. C. Ogando,Antonella Palmese,F. Paz-Chinchón,A. A. Plazas,A. Roodman,E. J. Sanchez,Michael Schubnell,S. Serrano,I. Sevilla-Noarbe,M. N. K. Smith,Marcelle Soares-Santos,E. Suchyta,G. Tarle,Daniel B. Thomas,Chun-Hao To,T. N. Varga,R. D. Wilkinson,Y.-H. Zhang +68 more
TL;DR: In this article, a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey (DES) covering ~5,000 sq. deg.
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Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials
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When MAML Can Adapt Fast and How to Assist When It Cannot
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Fast and accurate distance-based phylogenetic placement using divide and conquer.
TL;DR: APPLES-2 as discussed by the authors uses divide-and-conquer technique to limit distance calculation and phylogenetic placement to parts of the tree most relevant to each query and achieves high accuracy.
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Estimating transient rates from cosmological simulations and BPASS
TL;DR: In this paper , the authors present predicted rates for electromagnetic and gravitational wave transients over the age of the Universe using Binary Population and Spectral Synthesis (BPASS) results combined with four cosmic star formation histories (SFH).
References
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
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Scikit-learn: Machine Learning in Python
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Pauli Virtanen,Ralf Gommers,Travis E. Oliphant,Matt Haberland,Matt Haberland,Tyler Reddy,David Cournapeau,Evgeni Burovski,Pearu Peterson,Warren Weckesser,Jonathan Bright,Stefan van der Walt,Matthew Brett,Joshua Wilson,K. Jarrod Millman,Nikolay Mayorov,Andrew Nelson,Eric Jones,Robert Kern,Eric B. Larson,CJ Carey,Ilhan Polat,Yu Feng,Eric Moore,Jake Vanderplas,Denis Laxalde,Josef Perktold,Robert Cimrman,Ian Henriksen,Ian Henriksen,E. A. Quintero,Charles R. Harris,Anne M. Archibald,Antônio H. Ribeiro,Fabian Pedregosa,Paul van Mulbregt,SciPy . Contributors +36 more
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