K. Jarrod Millman

University of California, Berkeley

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Matthew Brett

University of Cambridge

Stéfan van der Walt

University of California, Berkeley

Robert Kern

Enthought (United States)

Ralf Gommers

Quansight (United States)

Tyler Reddy

Los Alamos National Laboratory

Warren Weckesser

University of California, Berkeley

Pauli Virtanen

University of Jyväskylä

Pearu Peterson

Tallinn University of Technology

Bruno A. Olshausen

Center for Theoretical Biological Physics

C. R. Harris

Utah State University Space Dynamics Laboratory

Cooperative Institutions

University of California, Berkeley

Centre National de la Recherche Scientifique

Berkeley College

Quansight (United States)

University of Chicago

California Institute of Technology

The University of Texas at Austin

Massachusetts Institute of Technology

University of Colorado Boulder

Imaging Center

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Array programming with NumPy

Nature (2020)

C. R. Harris K. Jarrod Millman Stéfan van der Walt Ralf Gommers Pauli Virtanen

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 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.

10.1038/s41586-020-2649-2

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Citations (16,307)

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python

Nature Methods (2020)

Pauli Virtanen Ralf Gommers Travis E. Oliphant Matt Haberland Tyler Reddy

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

Python

10.1038/s41592-020-0772-5

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Citations (1,103)

NIPY: an open library and development framework for FMRI data analysis

NeuroImage (2009)

Matthew Brett James Taylor Chris Burns K. Jarrod Millman F. Pérez

10.1016/s1053-8119(09)72223-2

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Citations (9)

Changing computational research. The challenges ahead

Source Code for Biology and Medicine (2012)

Cameron Neylon Jan Aerts C. Titus Brown Simon J. Coles Les Hatton

The past year has been an interesting one for those interested in reproducible research.There have been great examples of replicability [1,2] in research communication, and examples of horrifying failure of reproducibility (as described in [3]) with serious questions being raised on the ability of our current system of research communication to guarantee, or even encourage, that published research be reproducible or replicable.When we launched the call for papers for Open Research Computation in late 2010 we saw a clear need for higher standards.Computational research should stand out as an exemplar of just how reproducible research can be, yet it falls short more often than not.With modern computational tools it is entirely possible to provide packages which allow direct replication of results.It is possible to provide data and code in the form of a functional virtual machine image along with automated tests to ensure everything is working as expected.But alongside this we can support the reader's ability to modify and re-purpose tools, to run them against new data, indeed to support efforts to deliberately break the system to identify its limitations.In short, to do what we are supposed to do as scientistsreplicate, reproduce, and test the limits of our models and understanding.We deliberately set the bar high, because we felt it should be high, and because we felt that current standards were, in general, not high enough.Over the past year commentaries [4][5][6] have supported these principles, recognizing that there are serious problemsbut few have actually backed up those words with actions.As with data, so with code, journal statements requiring that it be available often lack substancehow is it to be made availableand policies generally lack teeth.

Replication

Code (set theory)

Open research

Open Science

10.1186/1751-0473-7-2

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Citations (14)

Scientific Python Ecosystem Coordination

K. Jarrod Millman Stéfan van der Walt

Python

10.25080/majora-1b6fd038-028

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Citations (0)

Learning from open source software projects to improve scientific review

Frontiers in Computational Neuroscience (2012)

Satrajit Ghosh Arno Klein Brian Avants K. Jarrod Millman

Peer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. We highlight five salient problems: (1) reviewers are expected to have comprehensive expertise; (2) reviewers do not have sufficient access to methods and materials to evaluate a study; (3) reviewers are neither identified nor acknowledged; (4) there is no measure of the quality of a review; and (5) reviews take a lot of time, and once submitted cannot evolve. We propose that these problems can be resolved by making the following changes to the review process. Distributing reviews to many reviewers would allow each reviewer to focus on portions of the article that reflect the reviewer's specialty or area of interest and place less of a burden on any one reviewer. Providing reviewers materials and methods to perform comprehensive evaluation would facilitate transparency, greater scrutiny, and replication of results. Acknowledging reviewers makes it possible to quantitatively assess reviewer contributions, which could be used to establish the impact of the reviewer in the scientific community. Quantifying review quality could help establish the importance of individual reviews and reviewers as well as the submitted article. Finally, we recommend expediting post-publication reviews and allowing for the dialog to continue and flourish in a dynamic and interactive manner. We argue that these solutions can be implemented by adapting existing features from open-source software management and social networking technologies. We propose a model of an open, interactive review system that quantifies the significance of articles, the quality of reviews, and the reputation of reviewers.

Scrutiny

Pace

10.3389/fncom.2012.00018

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Citations (25)

Scientific Python: By maintainers, for maintainers

Pamphile T. Roy Stéfan van der Walt K. Jarrod Millman Melissa Mendonça

Python

10.25080/majora-212e5952-040

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Citations (0)

Scientific Python: From GitHub to TikTok

Juanita Romero Stéfan van der Walt K. Jarrod Millman Melissa Mendonça Inessa Pawson

The Scientific Python project aims to better coordinate the ecosystem and grow the community. This talk focuses on our efforts to expand our community by generating a welcoming and friendly environment where people collaborate, build, and improve together.

Python

10.25080/majora-212e5952-03f

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Citations (0)

Learning sparse overcomplete image representations

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2000)

Bruno A. Olshausen K. Jarrod Millman

We describe a method for learning an over complete set of basis functions for the purpose of modeling data with sparse structure. Such data re characterized by the fact that they require a relatively small number of non-zero coefficients on the basis functions to describe each data point. The sparsity of the basis function coefficients is modeled with a mixture-of-Gaussians distribution. One Gaussian captures non-active coefficients with a large-variance distribution centered at zero, while one or more other Gaussians capture active coefficients with a large-variance distribution. We show that when the prior is in such a form, there exist efficient methods for learning the basis functions as well as the parameters of the prior. The performance of the algorithm is demonstrated on a number of test cases and also on natural images. The basis functions learned on natural images are similar to those obtained with other methods, but the sparse from of the coefficient distribution is much better described. Also, since the parameters of the prior are adapted to the data, no assumption about sparse structure in the images need be made a priori, rather it is learned from the data.

Basis (linear algebra)

Basis function

10.1117/12.408632

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Citations (1)

Learning Sparse Codes with a Mixture-of-Gaussians Prior

Neural Information Processing Systems (1999)

Bruno A. Olshausen K. Jarrod Millman

We describe a method for learning an overcomplete set of basis functions for the purpose of modeling sparse structure in images. The sparsity of the basis function coefficients is modeled with a mixture-of-Gaussians distribution. One Gaussian captures nonactive coefficients with a small-variance distribution centered at zero, while one or more other Gaussians capture active coefficients with a large-variance distribution. We show that when the prior is in such a form, there exist efficient methods for learning the basis functions as well as the parameters of the prior. The performance of the algorithm is demonstrated on a number of test cases and also on natural images. The basis functions learned on natural images are similar to those obtained with other methods, but the sparse form of the coefficient distribution is much better described. Also, since the parameters of the prior are adapted to the data, no assumption about sparse structure in the images need be made a priori, rather it is learned from the data.

Basis (linear algebra)

Basis function

Source

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Citations (86)