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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The OpenGATE ecosystem for Monte Carlo simulation in medical physics
David Sarrut,Nicolas Arbor,Thomas Baudier,Damian Borys,A. Etxebeste,Hermann Fuchs,J. Gajewski,L. Grevillot,Sébastien Jan,George C. Kagadis,Han Gyu Kang,Assen S. Kirov,O. Kochebina,W. Krzemień,Antony J. Lomax,Panagiotis Papadimitroulas,Christian Pommranz,Emilie Roncali,Antoni Rucinski,Carla Winterhalter,Lydia Maigne +20 more
TL;DR: GATE as mentioned in this paper is an open-source Monte Carlo toolkit for medical physics, based on Geant4, with a focus on geometry, physics, and scorers, with brief descriptions of key concepts (Volume, Actors, Digitizer).
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A Bayesian neural network predicts the dissolution of compact planetary systems.
Miles Cranmer,Daniel Tamayo,Hanno Rein,Peter W. Battaglia,Samuel Hadden,Philip J. Armitage,Shirley Ho,Shirley Ho,David N. Spergel +8 more
TL;DR: Cranmer et al. as mentioned in this paper introduced a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable.
Journal ArticleDOI
Warm Jupiters in TESS Full-Frame Images: A Catalog and Observed Eccentricity Distribution for Year 1
Jiayin Dong,Chelsea X. Huang,Rebekah I. Dawson,Daniel Foreman-Mackey,Karen A. Collins,Samuel N. Quinn,Jack J. Lissauer,Thomas G. Beatty,Billy Quarles,Lizhou Sha,Avi Shporer,Zhao Guo,Stephen R. Kane,Lyu Abe,Khalid Barkaoui,Zouhair Benkhaldoun,Rafael Brahm,François Bouchy,Theron W. Carmichael,Kevin I. Collins,Dennis M. Conti,Nicolas Crouzet,Georgina Dransfield,Phil Evans,Tianjun Gan,Mourad Ghachoui,Michaël Gillon,Nolan Grieves,Tristan Guillot,Coel Hellier,Emmanuel Jehin,Eric L. N. Jensen,Andrés Jordán,Jacob Kamler,John F. Kielkopf,Djamel Mékarnia,Louise D. Nielsen,Francisco J. Pozuelos,Don J. Radford,François-Xavier Schmider,Richard P. Schwarz,Chris Stockdale,Thiam-Guan Tan,Mathilde Timmermans,Amaury H. M. J. Triaud,Gavin Wang,George R. Ricker,Roland Vanderspek,David W. Latham,Sara Seager,Joshua N. Winn,Jon M. Jenkins,Ismael Mireles,Daniel A. Yahalomi,Edward H. Morgan,Michael Vezie,Elisa V. Quintana,Mark E. Rose,Jeffrey C. Smith,Bernie Shiao +59 more
TL;DR: In this paper, a catalog of 55 warm Jupiter candidates, including 19 candidates that were not originally released as \TESS Objects of Interest (TOIs) by the NASA TESS team, is presented.
Journal ArticleDOI
Volumetric emission tomography for combustion processes
TL;DR: A comprehensive, critical, and pedagogical review of volumetric emission tomography for combustion processes can be found in this article , where four emission modalities are considered: chemiluminescence, laser-induced fluorescence, passive incandescence, and laser-induced fluorescence.
Posted ContentDOI
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
Casey M Schneider-Mizell,Ágnes L. Bodor,Derrick Brittain,JoAnn Buchanan,Daniel J. Bumbarger,Leila Elabbady,D.J. Kapner,Sam Kinn,Gayathri Mahalingam,Sharmishtaa Seshamani,Shelby K. Suckow,Marc Takeno,Russel Torres,Wenjing Yin,Sven Dorkenwald,J. Alexander Bae,Manuel Castro,Paul G. Fahey,Emmanouil Froudakis,Akhilesh Halageri,Zhen Jia,Chris S. Jordan,Nico Kemnitz,Kisuk Lee,Kai Li,Ran Lu,Thomas Macrina,Eric Mitchell,Shanka Subhra Mondal,Shang Mu,Barak Nehoran,Stelios Papadopoulos,Saumil S. Patel,Xaq Pitkow,Sergiy Popovych,William Silversmith,Nicholas L. Turner,William Wong,Jingpeng Wu,Szi-chieh Yu,Jake Reimer,Andreas S. Tolias,H. Sebastian Seung,R. Clay Reid,Forrest Collman,Nuno Maçarico da Costa +45 more
TL;DR: In this article , the authors used millimeter-scale volumetric electron microscopy to investigate the connectivity of inhibitory neurons across a dense neuronal population spanning all layers of mouse visual cortex with synaptic resolution.
References
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Scikit-learn: Machine Learning in Python
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