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      The Salient360! toolbox: Handling gaze data in 3D made easy

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      Computers & Graphics
      Elsevier BV

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Array programming with NumPy

            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 waves 1 and in the first imaging of a black hole 2 . 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.
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              scikit-image: image processing in Python

              scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.
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                Author and article information

                Contributors
                Journal
                Computers & Graphics
                Computers & Graphics
                Elsevier BV
                00978493
                April 2024
                April 2024
                : 119
                : 103890
                Article
                10.1016/j.cag.2024.103890
                4ec2c6aa-d499-4bd9-a94f-dd45675028d9
                © 2024

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://www.elsevier.com/legal/tdmrep-license

                http://creativecommons.org/licenses/by-nc/4.0/

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