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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Testing new-physics models with global comparisons to collider measurements: the Contur toolkit
Andrew Buckley,Jonathan Butterworth,Louie Corpe,M. H. Habedank,Danping Huang,David Paul Yallup,M. Altakach,G. Bassman,Ishaan Lagwankar,Juan Rocamonde,H. Saunders,Ben Waugh,G. Zilgalvis +12 more
TL;DR: This manual describes the design choices that have been made in the Contur tool, as well as detailing pitfalls and common issues to avoid, and hopes that with the help of this documentation, external groups will be able to run their own Contur studies, for example when proposing a new model, or pitching a new search.
Journal ArticleDOI
pyaneti – II. A multidimensional Gaussian process approach to analysing spectroscopic time-series
TL;DR: Pyaneti as mentioned in this paper uses a built-in multi-dimensional Gaussian process approach to model radial velocity and activity indicator time-series with different underlying covariance functions and allows multi-band and single transit modelling; it runs on Python 3 and features overall improvements in performance.
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GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia
TL;DR: Chong et al. as discussed by the authors developed a method for array-based mtDNA-CN estimation suitable for biobank-scale studies, called AutoMitoC, which applied to 395,781 UKBiobank study participants and performed genome-and exome-wide association studies, identifying novel common and rare genetic determinants.
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Revealing the impact of global heating on North Atlantic circulation using transparent machine learning
TL;DR: The North Atlantic Ocean is key to climate through its role in heat transport and storage as mentioned in this paper, but the physical drivers of this change are poor. But the North Atlantic ocean is also key to heat storage and storage.
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Perfect density models cannot guarantee anomaly detection
Charline Le Lan,Laurent Dinh +1 more
TL;DR: A closer look at the behavior of distribution densities through the lens of reparametrization is taken and it is shown that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality.
References
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Scikit-learn: Machine Learning in Python
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Scikit-learn: Machine Learning in Python
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