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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Detecting stochastic gravitational waves with binary resonance
Diego Blas,Alexander C. Jenkins +1 more
Abstract: LIGO and Virgo have initiated the era of gravitational-wave (GW) astronomy; but in order to fully explore GW frequency spectrum, we must turn our attention to innovative techniques for GW detection. One such approach is to use binary systems as dynamical GW detectors by studying the subtle perturbations to their orbits caused by impinging GWs. We present a powerful new formalism for calculating the orbital evolution of a generic binary coupled to a stochastic background of GWs, deriving from first principles a secularly-averaged Fokker-Planck equation which fully characterises the statistical evolution of all six of the binary's orbital elements. We also develop practical tools for numerically integrating this equation, and derive the necessary statistical formalism to search for GWs in observational data from binary pulsars and laser-ranging experiments.
Journal ArticleDOI
Plastics, (bio)polymers and their apparent biogeochemical cycle: An infrared spectroscopy study on foraminifera.
Giovanni Birarda,Carla Buosi,Francesca Caridi,Maria Antonietta Casu,Giovanni De Giudici,Letizia Di Bella,Daniela Medas,Carlo Meneghini,Martina Pierdomenico,Anna Sabbatini,Artur Dawid Surowka,Lisa Vaccari +11 more
TL;DR: In this article, the incorporation of bio-polymers and microplastics in selected benthic foraminiferal species by applying FTIR (Fourier Transform Infrared) microscopy was investigated.
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Determining the Dielectric Tensor of Microtextured Organic Thin Films by Imaging Mueller Matrix Ellipsometry.
TL;DR: Polycrystalline textured thin films with distinct pleochroism and birefringence comprising oriented rotational domains of the orthorhombic polymorph of an anilino squaraine with isobutyl side chains are analyzed to obtain the biaxial dielectric tensor.
Journal ArticleDOI
SIGLEC1 (CD169): a marker of active neuroinflammation in the brain but not in the blood of multiple sclerosis patients
Lennard Ostendorf,Philipp Dittert,Philipp Dittert,Robert Biesen,Ankelien Duchow,Victoria Stiglbauer,Klemens Ruprecht,Klemens Ruprecht,Judith Bellmann-Strobl,Dominik Seelow,Werner Stenzel,Raluca A. Niesner,Raluca A. Niesner,Anja E. Hauser,Anja E. Hauser,Friedemann Paul,Helena Radbruch +16 more
TL;DR: In this article, the authors evaluated SIGLEC1 (CD169) as a biomarker in multiple sclerosis (MS) and Neuromyelitis optica spectrum disorder (NMOSD) and evaluated the presence of SIGleC1+ myeloid cells in demyelinating diseases.
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Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics
TL;DR: Results show a correlation between the variance of the discharge capacity difference and the end-of-life for cells aged under a wide range of charge/discharge C-rates and operating temperatures, but the correlation weakens considerably when the voltage data window for feature extraction is reduced, or when features from the charge voltage curve instead of discharge are used.
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Journal Article
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
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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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
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Matplotlib: A 2D Graphics Environment
TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
Journal ArticleDOI
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
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
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