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1. Array Programming with NumPy

Author

Harris, Charles R., Millman, K. Jarrod, van der Walt, Stéfan J., Gommers, Ralf, Virtanen, Pauli, Cournapeau, David, Wieser, Eric, Taylor, Julian, Berg, Sebastian, Smith, Nathaniel J., Kern, Robert, Picus, Matti, Hoyer, Stephan, van Kerkwijk, Marten H., Brett, Matthew, Haldane, Allan, del Río, Jaime Fernández, Wiebe, Mark, Peterson, Pearu, Gérard-Marchant, Pierre, Sheppard, Kevin, Reddy, Tyler, Weckesser, Warren, Abbasi, Hameer, Gohlke, Christoph, and Oliphant, Travis E.

Subjects
Computer Science - Mathematical Software, Statistics - Computation
Abstract

Array programming provides a powerful, compact, 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 plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material 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 and the first imaging of a black hole. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.

Published
2020
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2. SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Author

Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, CJ, Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R, Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul, and Contributors, SciPy 1. 0

Subjects
Computer Science - Mathematical Software, Computer Science - Data Structures and Algorithms, Computer Science - Software Engineering, Physics - Computational Physics
Abstract

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library 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. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

Published
2019
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6. Improving NumPy for Better Data Science: Progress Report (III)

Author

Van der Walt, Stéfan, Berg, Sebastian, Weckesser, Warren, Barnowski, Ross, Millman, Jarrod, and Pérez, Fernando

Subjects
Open Source, NumPy
Abstract

2020-10 grant report by the NumPy team at the Berkeley Institute for Data Science (BIDS) for grants from the Moore and Sloan foundations.

Published
2020
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8. SciPy 1.0 : fundamental algorithms for scientific computing in Python

Author

Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C. J., Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul, and SciPy 1.0 Contributors

Subjects
avoin lähdekoodi, algoritmit, laskennallinen tiede, SciPy, Python
Abstract

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. peerReviewed

Published
2020

9. Array programming with NumPy

Author

Harris, Charles R., primary, Millman, K. Jarrod, additional, van der Walt, Stéfan J., additional, Gommers, Ralf, additional, Virtanen, Pauli, additional, Cournapeau, David, additional, Wieser, Eric, additional, Taylor, Julian, additional, Berg, Sebastian, additional, Smith, Nathaniel J., additional, Kern, Robert, additional, Picus, Matti, additional, Hoyer, Stephan, additional, van Kerkwijk, Marten H., additional, Brett, Matthew, additional, Haldane, Allan, additional, del Río, Jaime Fernández, additional, Wiebe, Mark, additional, Peterson, Pearu, additional, Gérard-Marchant, Pierre, additional, Sheppard, Kevin, additional, Reddy, Tyler, additional, Weckesser, Warren, additional, Abbasi, Hameer, additional, Gohlke, Christoph, additional, and Oliphant, Travis E., additional

Published
2020
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10. Improving NumPy for Better Data Science: Progress Report (II)

Author

Van der Walt, Stéfan Johann, Berg, Sebastian, Millman, Jarrod, Pérez, Fernando, Picus, Matti, Reddy, Tyler, and Weckesser, Warren

Subjects
Open Source, NumPy
Abstract

2019-10 grant report by the NumPy team at the Berkeley Institute for Data Science (BIDS) for grants from the Moore and Sloan foundations.

Published
2019
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12. A ball rolling on a freely spinning turntable

Author

Weckesser, Warren

Subjects
Inertia (Mechanics) -- Research, Rotational motion -- Research, Angular momentum -- Research, Physics
Abstract

We consider a ball rolling on a freely spinning turntable. We show that the path of the ball (in an inertial frame) is a conic section. This generalizes the well-known problem of a ball rolling on a turntable that is spinning with constant angular velocity, for which the path is a circle.

Published
1997

13. The Forced van der Pol Equation II: Canards in the reduced system

Author

Bold, Katherine, Edwards, Chantal, Guckenheimer, John, Guharay, Sabyasachi, Hoffman, Kathleen, Hubbard, Judith, Oliva, Ricardo, Weckesser, Warren, Bold, Katherine, Edwards, Chantal, Guckenheimer, John, Guharay, Sabyasachi, Hoffman, Kathleen, Hubbard, Judith, Oliva, Ricardo, and Weckesser, Warren

Abstract

This is the second in a series of papers about the dynamics of the forced van der Pol oscillator [J. Guckenheimer, K. Hoffman, and W. Weckesser, SIAM J. Appl. Dyn. Syst., 2 (2003), pp. 1–35]. The first paper described the reduced system, a two dimensional flow with jumps that reflect fast trajectory segments in this vector field with two time scales. This paper extends the reduced system to account for canards, trajectory segments that follow the unstable portion of the slow manifold in the forced van der Pol oscillator. This extension of the reduced system serves as a template for approximating the full nonwandering set of the forced van der Pol oscillator for large sets of parameter values, including parameters for which the system is chaotic. We analyze some bifurcations in the extension of the reduced system, building upon our previous work in [J. Guckenheimer, K. Hoffman, and W. Weckesser, SIAM J. Appl. Dyn. Syst., 2 (2003), pp. 1–35]. We conclude with computations of return maps and periodic orbits in the full three dimensional flow that are compared with the computations and analysis of the reduced system. These comparisons demonstrate numerically the validity of results we derive from the study of canards in the reduced system.

Published
2003

21. The Forced van der Po Equation I:The Slow Flow and Its Bifurcations.

Author

Guckenheimer, John, Hoffman, Kathleen, and Weckesser, Warren

Subjects
VAN der Pol oscillators (Physics), BIFURCATION theory, ASTRODYNAMICS, MANIFOLDS (Mathematics), COMBINATORIAL dynamics
Abstract

The forced van der Pol oscillator has been the focus of scientific scrutiny for almost a century, yet its global bifurcation structure is still poorly understood. In this paper, we present a hybrid system consisting of the dynamics of the trajectories on the slow manifold coupled with "jumps" at the folds in the critical manifold to approximate the fast subsystem. The global bifurcations of the fixed points and periodic points of this hybrid system lead to an understanding of the bifurcations in the periodic orbits (without canards) of the forced van der Pol system. [ABSTRACT FROM AUTHOR]

Published
2003
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22. Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

Author

Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C J, Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, and van Mulbregt, Paul

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020
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23. SciPy 1.0: fundamental algorithms for scientific computing in Python.

Author

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, and van Mulbregt P

Subjects
Computational Biology history, Computer Simulation, History, 20th Century, History, 21st Century, Linear Models, Models, Biological, Nonlinear Dynamics, Signal Processing, Computer-Assisted, Algorithms, Computational Biology methods, Programming Languages, Software
Abstract

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.

Published
2020
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