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NumPy

About: NumPy is a(n) research topic. Over the lifetime, 566 publication(s) have been published within this topic receiving 50318 citation(s). The topic is also known as: Numpy.

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Papers
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Journal ArticleDOI: 10.1109/MCSE.2007.55
Abstract: 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

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Topics: 2D computer graphics (56%), Computer graphics (55%), Python (programming language) (54%) ...read more

16,056 Citations


Open accessJournal ArticleDOI: 10.1109/MCSE.2011.37
Abstract: In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.

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Topics: NumPy (71%), Python (programming language) (53%)

7,607 Citations


Open accessJournal ArticleDOI: 10.1109/MCSE.2011.37
Abstract: In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.

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4,799 Citations


Open accessJournal ArticleDOI: 10.1038/S41586-020-2649-2
16 Sep 2020-Nature
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.

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Topics: NumPy (70%), Array programming (60%), Python (programming language) (59%) ...read more

2,681 Citations


Journal ArticleDOI: 10.1107/S0021889813003531
Brian H. Toby1, Robert B. Von Dreele1Institutions (1)
Abstract: The newly developed GSAS-II software is a general purpose package for data reduction, structure solution and structure refinement that can be used with both single-crystal and powder diffraction data from both neutron and X-ray sources, including laboratory and synchrotron sources, collected on both two- and one-dimensional detectors. It is intended that GSAS-II will eventually replace both the GSAS and the EXPGUI packages, as well as many other utilities. GSAS-II is open source and is written largely in object-oriented Python but offers speeds comparable to compiled code because of its reliance on the Python NumPy and SciPy packages for computation. It runs on all common computer platforms and offers highly integrated graphics, both for a user interface and for interpretation of parameters. The package can be applied to all stages of crystallographic analysis for constant-wavelength X-ray and neutron data. Plans for considerable additional development are discussed.

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Topics: NumPy (59%), Python (programming language) (55%)

1,780 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
202192
202071
201955
201871
201740

Top Attributes

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Topic's top 5 most impactful authors

Brian Vinter

12 papers, 63 citations

Sylwester Arabas

6 papers, 9 citations

Mads Ruben Burgdorff Kristensen

5 papers, 49 citations

T. Megies

4 papers, 1.5K citations

William F. Spotz

4 papers, 30 citations

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