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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TLDR
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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netrd: A library for network reconstruction and graph distances
Stefan McCabe,Leo Torres,Timothy LaRock,Syed Arefinul Haque,Chia-Hung Yang,Harrison Hartle,Brennan Klein +6 more
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A comparison of experimental assays and analytical methods for genome-wide identification of active enhancers
TL;DR: In this article , the authors compared 13 genome-wide RNA sequencing (RNA-seq) assays in K562 cells and showed that nuclear run-on followed by cap-selection assay (GRO/PRO-cap) has advantages in enhancer RNA detection and active enhancer identification.
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MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity.
Franz Paul Spitzner,Jonas Dehning,Jens Wilting,Annika Hagemann,Joao Pinheiro Neto,Johannes Zierenberg,Viola Priesemann +6 more
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Spatiotemporal control of cell cycle acceleration during axolotl spinal cord regeneration
Emanuel Cura Costa,Leo Otsuki,Aida Rodrigo Albors,Elly M. Tanaka,Osvaldo Chara,Osvaldo Chara +5 more
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Andromeda XXI - a dwarf galaxy in a low-density dark matter halo
Michelle L. M. Collins,Justin I. Read,Rodrigo A. Ibata,R. Michael Rich,Nicolas F. Martin,Jorge Peñarrubia,Scott Chapman,Erik Tollerud,Daniel R. Weisz +8 more
TL;DR: Andromeda XXI (And XXI) has been proposed as a dwarf spheroidal galaxy with a central dark matter density that is lower than expected in the Standard Lambda$ Cold Dark Matter ($Lambda$CDM) cosmology as discussed by the authors.
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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Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
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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