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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TIC 168789840: A Sextuply-Eclipsing Sextuple Star System
Brian P. Powell,Veselin B. Kostov,Saul Rappaport,Tamás Borkovits,Petr Zasche,Andrei Tokovinin,Ethan Kruse,David W. Latham,Benjamin T. Montet,Eric L. N. Jensen,Rahul Jayaraman,Karen A. Collins,Martin Mašek,Coel Hellier,Phil Evans,Thiam-Guan Tan,Joshua E. Schlieder,Guillermo Torres,Alan P. Smale,Adam H. Friedman,Thomas Barclay,Robert Gagliano,Elisa V. Quintana,Tom Jacobs,Emily A. Gilbert,Martti H. Kristiansen,Knicole D. Colón,Daryll LaCourse,Greg Olmschenk,Mark Omohundro,Jeremy D. Schnittman,Hans Martin Schwengeler,Richard Barry,Ivan Terentev,Patricia T. Boyd,Allan R. Schmitt,Samuel N. Quinn,Andrew Vanderburg,Enric Palle,James D. Armstrong,George R. Ricker,Roland Vanderspek,Sara Seager,Joshua N. Winn,Jon M. Jenkins,Douglas A. Caldwell,Bill Wohler,Bernie Shiao,Christopher J. Burke,Tansu Daylan,J. Villasenor +50 more
TL;DR: The first known sextuple system consisting of three eclipsing binaries is TIC 168789840, also known as TYC 7037-89-1 as mentioned in this paper, which was observed in Sectors 4 and 5 during Cycle 1, with lightcurves extracted from TESS Full Frame Image data.
Journal ArticleDOI
EntropyHub: An open-source toolkit for entropic time series analysis.
Matthew W. Flood,Bernd Grimm +1 more
TL;DR: EntropyHub as discussed by the authors is an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia, which provides an extensive range of more than forty functions for estimating cross-, multi-scale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allow the user to specify multiple parameters in the entropy calculation.
Journal ArticleDOI
Milky Way Satellite Census. IV. Constraints on Decaying Dark Matter from Observations of Milky Way Satellite Galaxies
S. Mau,Ethan O. Nadler,Risa H. Wechsler,Alex Drlica-Wagner,Keith Bechtol,Gerald R. Green,D. Huterer,Y. Mao,C. Martínez-Vázquez,Mitch McNanna,Burçin Mutlu-Pakdil,A. E. Pace,Annika H. G. Peter,A. H. Riley,Louis E. Strigari,M.Y. Wang,Michel Aguena,S. Allam,James Annis,David Bacon,E. Bertin,Sebastian Bocquet,David J. Brooks,D. L. Burke,A. Carnero Rosell,M. Carrasco Kind,J. Carretero,M. Costanzi,Martin Crocce,Maria E. S. Pereira,Tamara M. Davis,J. De Vicente,Shantanu Desai,Peter Doel,I. Ferrero,B. Flaugher,Joshua A. Frieman,J. Garc'ia-Bellido,M. Gatti,G. Giannini,Daniel Gruen,Robert A. Gruendl,J. Gschwend,G. Gutierrez,Samuel Hinton,D. L. Hollowood,K. Honscheid,David J. James,Kyler Kuehn,Ofer Lahav,Marcio A. G. Maia,Jennifer L. Marshall,Ramon Miquel,Joseph J. Mohr,Robert Morgan,Ricardo L. C. Ogando,F. Paz-Chinch'on,Adriano Pieres,Mario Rodríguez-Monroy,E. Sánchez,V. Scarpine,S. Serrano,I. Sevilla-Noarbe,E. Suchyta,Gregory Tarle,Chun-Hao To,Douglas L. Tucker,Jochen Weller +67 more
TL;DR: In this article , the authors used a recent census of the Milky Way satellite galaxy population population to constrain the lifetime of particle dark matter (DM) particles, and fit the suppression of the present-day DDM subhalo mass function (SHMF) as a function of τ and V kick using a suite of high-resolution zoom-in simulations of MW-mass halos.
Journal ArticleDOI
Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites
Natasha MacBean,Russell L. Scott,Joel A. Biederman,Catherine Ottlé,N. Vuichard,Agnès Ducharne,Thomas Kolb,Sabina Dore,Marcy E. Litvak,David J. P. Moore +9 more
TL;DR: In this article, a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion was proposed to improve the performance of a simple bucket-model hydrology model for semi-arid ecosystems.
Journal ArticleDOI
Comparison of seven modelling algorithms for γ‐aminobutyric acid–edited proton magnetic resonance spectroscopy
Alexander R. Craven,Pallab K. Bhattacharyya,William T. Clarke,Ulrike Dydak,Richard A.E. Edden,Lars Ersland,Pravat K. Mandal,Mark Mikkelsen,James B. Murdoch,Jamie Near,Reuben Rideaux,Deepika Shukla,Min Wang,Martin Wilson,Helge J. Zöllner,Kenneth Hugdahl,Georg Oeltzschner +16 more
TL;DR: The findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.
References
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Scikit-learn: Machine Learning in Python
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