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
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Stellar Population Inference with Prospector
TL;DR: In this article, the authors present prospector, a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths, based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces.
Journal ArticleDOI
Python Battery Mathematical Modelling (PyBaMM)
TL;DR: The PyBaMM project as mentioned in this paper provides a flexible platform for implementation and comparison of new models and numerical methods by implementing models as expression trees and processing them in a modular fashion through a pipeline.
Journal ArticleDOI
The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure.
Ahmad A. Cluntun,Rachit Badolia,Sandra Lettlova,K. Mark Parnell,Thirupura S. Shankar,Nikolaos A. Diakos,Kristofor A. Olson,Iosif Taleb,Sean M. Tatum,Jordan A. Berg,Corey N. Cunningham,Tyler Van Ry,Alex J. Bott,Aspasia Thodou Krokidi,Sarah Fogarty,Sophia Skedros,Wojciech I. Swiatek,Xuejing Yu,Bai Luo,Shannon Merx,Sutip Navankasattusas,James E. Cox,Gregory S. Ducker,William L. Holland,Stephen H. McKellar,Stephen H. McKellar,Jared Rutter,Stavros G. Drakos,Stavros G. Drakos +28 more
TL;DR: It is found that alteration of the pyruvate-lactate axis is a fundamental and early feature of cardiac hypertrophy and failure.
Journal ArticleDOI
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
TL;DR: In this paper, the authors investigate how PINNs are biased towards learning functions along the dominant eigen-directions of their limiting NTK and construct novel architectures that employ spatio-temporal and multi-scale random Fourier features, and justify how such coordinate embedding layers can lead to robust and accurate PINN models.
Journal ArticleDOI
A versatile and customizable low-cost 3D-printed open standard for microscopic imaging.
Benedict Diederich,Benedict Diederich,René Lachmann,René Lachmann,Swen Carlstedt,Barbora Marsikova,Barbora Marsikova,Haoran Wang,Xavier Uwurukundo,Alexander S. Mosig,Rainer Heintzmann,Rainer Heintzmann +11 more
TL;DR: This work presents a low-cost, 3D-printed, open-source, modular microscopy toolbox and demonstrates its versatility by realizing a complete microscope development cycle from concept to experimental phase.
References
More filters
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.
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
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.
Related Papers (5)
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
Astropy: A community Python package for astronomy
Thomas P. Robitaille,Erik Tollerud,Perry Greenfield,Michael Droettboom,Erik Bray,Tom Aldcroft,Matt Davis,Adam Ginsburg,Adrian M. Price-Whelan,Wolfgang Kerzendorf,A. Conley,Neil H. M. Crighton,Kyle Barbary,Demitri Muna,Henry C. Ferguson,Frédéric Grollier,Madhura Parikh,Prasanth H. Nair,Hans Moritz Günther,Christoph Deil,Julien Woillez,Simon Conseil,Roban Kramer,J. Turner,Leo Singer,R. T. Fox,Benjamin A. Weaver,Victor Zabalza,Zachary I. Edwards,K. Azalee Bostroem,Douglas Burke,Andrew R. Casey,Steven M. Crawford,Nadia Dencheva,Justin Ely,Tim Jenness,Kathleen Labrie,Pey Lian Lim,Francesco Pierfederici,Andrew Pontzen,Andy Ptak,Brian L. Refsdal,Mathieu Servillat,Ole Streicher +43 more