One thing I’ve noticed is that over time, the designers of these languages are steadily adding more of the features that one would expect to find in a general-purpose programming language.” “I’ve used almost all of the available numerical languages at one time or another over the past 8 years. In 1995, Jim Hugunin, a graduate student at the Massachusetts Institute of Technology, wrote the first message in a new Python Matrix Special Interest Group (Matrix-SIG) mailing list 14: Scientists could thereby gain access to a wide variety of existing computational libraries without concerning themselves with low-level programming concepts such as memory management. Yet scientists soon discovered the language’s virtues, such as its ability to wrap C and Fortran libraries, and to then drive those libraries interactively. As a general-purpose programming language, it had no special support for scientific data structures or algorithms, unlike many of the other established computation platforms of the time. Python is an interpreted, high-level, general-purpose computer programming language, designed by Guido van Rossum in the late 1980s, with a dynamic type system and an emphasis on readability and rapid prototyping 13 ( ). Despite what we highlight here, it is important to understand that a project like SciPy is only possible because of the contributions of very many contributors-too many to mention individually, but each bringing an important piece to the puzzle. Here we capture a selective history of some milestones and important events in the growth of SciPy. The packages in the SciPy ecosystem share high standards of implementation, documentation and testing, and a culture eager to learn and adopt better practices-both for community management and software development. They led not only to the library described in this paper, but also to an entire ecosystem of related packages ( ) and a variety of social activities centered around them ( ). Yet the philosophical motivations behind a fully open tool stack, combined with an excited, friendly community with a singular focus, have proven auspicious in the long run. To even imagine that a small group of ‘rogue’ student programmers could upend the already well-established ecosystem of research software-backed by millions in funding and many hundreds of highly qualified engineers 10, 11, 12-was preposterous. When started in 2001, the library had little funding and was written mainly by graduate students-many of them without a computer science education and often without the blessing of their advisors. SciPy’s arrival at this point is surprising and somewhat anomalous. This version numbering convention, however, belies the history of a project that has become the standard others follow and has seen extensive adoption in research and industry. Recently, SciPy released version 1.0, a milestone that traditionally signals a library’s API (application programming interface) being mature enough to be trusted in production pipelines. For example, published scripts 5, 6 used in the analysis of gravitational waves 7, 8 import several subpackages of SciPy, and the M87 black hole imaging project cites SciPy 9. Scientists, engineers and others around the world rely on SciPy. SciPy is built on top of NumPy 1, 2, which provides array data structures and related fast numerical routines, and SciPy is itself the foundation upon which higher level scientific libraries, including scikit-learn 3 and scikit-image 4, are built. SciPy includes algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations and many other classes of problems it also provides specialized data structures, such as sparse matrices and k-dimensional trees. SciPy is a library of numerical routines for the Python programming language that provides fundamental building blocks for modeling and solving scientific problems. Nature Methods volume 17, pages 261–272 ( 2020) Cite this article SciPy 1.0: fundamental algorithms for scientific computing in Python
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