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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Array programming with NumPy

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.

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Array Programming with NumPy

Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition)

to be done in this area. Face recognition is a problem that scarcely clamoured for attention before the computer age but, having surfaced, has involved a wide range of techniques and has attracted the attention of some fine minds (David Mumford was a Fields Medallist in 1974). This singular application of mathematical modelling to a messy applied problem of obvious utility and importance but with no unique solution is a pretty one to share with students: perhaps, returning to the source of our opening quotation, we may invert Duncan's earlier observation, 'There is an art to find the mind's construction in the face!'.

Linear and nonlinear waves, by G. B. Whitham. Pp.636. £50. 1999. ISBN 0 471 35942 4 (Wiley).

This book is a tutorial written by researchers and developers behind the FEniCS Project and explores an advanced, expressive approach to the development of mathematical software. The presentation spans mathematical background, software design and the use of FEniCS in applications. Theoretical aspects are complemented with computer code which is available as free/open source software. The book begins with a special introductory tutorial for beginners. Followingare chapters in Part I addressing fundamental aspects of the approach to automating the creation of finite element solvers. Chapters in Part II address the design and implementation of the FEnicS software. Chapters in Part III present the application of FEniCS to a wide range of applications, including fluid flow, solid mechanics, electromagnetics and geophysics.

Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book

X. Cai, E. Acklam, H. P. Langtangen, A. Tveito: Parallel Computing.- X. Cai: Overlapping Domain Decomposition Methods.- K.-A. Mardal, G. W. Zumbusch, H. P. Langtangen: Software Tools for Multigrid Methods.- K.-A. Mardal, H. P. Langtangen: Mixed Finite Elements.- K.-A. Mardal, J. Sundnes, H. P. Langtangen, A. Tveito: Systems of PDEs and Block Preconditioning.- A. Odegard, H. P. Langtangen, A. Tveito: Object-Oriented Implementation of Fully Implicit Methods for Systems of PDEs.- H. P. Langtangen, H. Osnes: Stochastic Partial Differential Equations.- H. P. Langtangen and K.-A. Mardal: Using Diffpack from Python Scripts.- X. Cai, A. M. Bruaset, H. P. Langtangen, G. T. Lines, K. Samuelsson, W. Shen, A. Tveito, G. Zumbusch: Performance Modeling of PDE Solvers.- J. Sundnes, G.T. Lines, P. Grottum, A. Tveito: Numerical Methods and Software for Modeling the Electrical Activity in the Human Heart.- O. Skavhaug, B. F. Nielsen, A. Tveito: Mathematical Models of Financial Derivatives.- O. Skavhaug, B. F. Nielsen, A. Tveito: Numerical Methods for Financial Derivatives.- T. Thorvaldsen, H. P. Langtangen, H. Osnes: Finite Element Modeling of Elastic Structures.- K. M. Okstad, T. Kvamsdal: Simulation of Aluminum Extrusion.- A. Kjeldstad, H. P. Langtangen, J. Skogseid, K. Bjorlykke: Simulation of Deformations, Fluid Flow and HeatTransfer in Sedimentary Basins

Advanced topics in computational partial differential equations : numerical methods and Diffpack programming

From the Publisher:
The target audience of this book is students and researchers in computational sciences who need to develop computer codes for solving partial differential equations. The exposition is focused on numerics and software related to mathematical models in solid and fluid mechanics. The book teaches finite element methods and basic finite difference methods from a computational point of view. The main emphasis regards development of flexible computer programs, using the numerical library Diffpack. The application of Diffpack is explained in detail for problems including model equations in applied mathematics, heat transfer, elasticity, and viscous fluid flow. Diffpack is a modern software development environment based on C++ and object-oriented programming.

Computational Partial Differential Equations: Numerical Methods and Diffpack Programming

New knowledge in science and engineering relies increasingly on results produced by scientific software. Therefore, knowing how scientists develop and use software in their research is critical to assessing the necessity for improving current development practices and to making decisions about the future allocation of resources. To that end, this paper presents the results of a survey conducted online in October-December 2008 which received almost 2000 responses. Our main conclusions are that (1) the knowledge required to develop and use scientific software is primarily acquired from peers and through self-study, rather than from formal education and training; (2) the number of scientists using supercomputers is small compared to the number using desktop or intermediate computers; (3) most scientists rely primarily on software with a large user base; (4) while many scientists believe that software testing is important, a smaller number believe they have sufficient understanding about testing concepts; and (5) that there is a tendency for scientists to rank standard software engineering concepts higher if they work in large software development projects and teams, but that there is no uniform trend of association between rank of importance of software engineering concepts and project/team size.

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How do scientists develop and use scientific software

Chaospy: An open source tool for designing methods of uncertainty quantification

The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.

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Hans Petter Langtangen

Papers

Advanced topics in computational partial differential equations : numerical methods and Diffpack programming

Computational Partial Differential Equations: Numerical Methods and Diffpack Programming

How do scientists develop and use scientific software

Chaospy: An open source tool for designing methods of uncertainty quantification

Python scripting for computational science