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.

/pdf/array-programming-with-numpy-31s3jl24ra.pdf

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.

/pdf/array-programming-with-numpy-4gbytt9sw2.pdf

Array Programming with NumPy

Aims. Solar Orbiter, the first mission of ESA’s Cosmic Vision 2015–2025 programme and a mission of international collaboration between ESA and NASA, will explore the Sun and heliosphere from close up and out of the ecliptic plane. It was launched on 10 February 2020 04:03 UTC from Cape Canaveral and aims to address key questions of solar and heliospheric physics pertaining to how the Sun creates and controls the Heliosphere, and why solar activity changes with time. To answer these, the mission carries six remote-sensing instruments to observe the Sun and the solar corona, and four in-situ instruments to measure the solar wind, energetic particles, and electromagnetic fields. In this paper, we describe the science objectives of the mission, and how these will be addressed by the joint observations of the instruments onboard.Methods. The paper first summarises the mission-level science objectives, followed by an overview of the spacecraft and payload. We report the observables and performance figures of each instrument, as well as the trajectory design. This is followed by a summary of the science operations concept. The paper concludes with a more detailed description of the science objectives.Results. Solar Orbiter will combine in-situ measurements in the heliosphere with high-resolution remote-sensing observations of the Sun to address fundamental questions of solar and heliospheric physics. The performance of the Solar Orbiter payload meets the requirements derived from the mission’s science objectives. Its science return will be augmented further by coordinated observations with other space missions and ground-based observatories.

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The Solar Orbiter mission. Science overview

NSFNational Science Foundation (NSF) [AST-1715122]; DIRAC Institute in the Department of Astronomy at the University of Washington; STFC studentshipScience & Technology Facilities Council (STFC) [ST/N504336/1]; STFC grantScience & Technology Facilities Council (STFC) [ST/N000692/1]; Google; NumFocus; Solar Physics Division of the American Astronomical Society; Space program

The SunPy Project: Open Source Development and Status of the Version 1.0 Core Package

Ensemble modeling of coronal mass ejections (CMEs) provides a probabilistic forecast of CME arrival time that includes an estimation of arrival-time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the Wang–Sheeley–Arge (WSA)–ENLIL+Cone model installed at the Community Coordinated Modeling Center (CCMC) and executed in real-time at the CCMC/Space Weather Research Center. The current implementation of this ensemble-modeling method evaluates the sensitivity of WSA–ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events that occurred between January 2013 – July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival-time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth, the correct-rejection rate is 62 %, the false-alarm rate is 38 %, the correct-alarm ratio is 77 %, and the false-alarm ratio is 23 %. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. The Brier Score for CME arrival-predictions is 0.15 (where a score of 0 on a range of 0 to 1 is a perfect forecast), which indicates that on average, the predicted probability, or likelihood, of CME arrival is fairly accurate. The reliability of ensemble CME-arrival predictions is heavily dependent on the initial distribution of CME input parameters (e.g. speed, direction, and width), particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these ensembles do not sample a wide-enough spread in CME input parameters. Prediction errors can also arise from ambient-model parameters, the accuracy of the solar-wind background derived from coronal maps, or other model limitations. Finally, predictions of the K
 P geomagnetic index differ from observed values by less than one for 11 out of 17 of the ensembles and K
 P prediction errors computed from the mean predicted K
 P show a mean absolute error of 1.3.

Ensemble Modeling of CMEs Using the WSA–ENLIL+Cone Model

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualization and plotting (matplotlib). SunPy is a data-analysis environment specializing in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPyʼs functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

/pdf/sunpy-python-for-solar-physics-4e8qusnvrr.pdf

SunPy—Python for solar physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

/pdf/sunpy-python-for-solar-physics-4x1ew91q2b.pdf

SunPy - Python for Solar Physics

Across all disciplines that work with image data - from astrophysics to medical research and historic preservation - there is a growing need for efficient ways to browse and inspect large sets of high-resolution images. We present the development of a visualization software for solar physics data based on the JPEG 2000 image compression standard. Our implementation consists of the JHelioviewer client application that enables users to browse petabyte-scale image archives and the JHelioviewer server, which integrates a JPIP server, metadata catalog and an event server. JPEG 2000 offers many useful new features and has the potential to revolutionize the way high-resolution image data are disseminated and analyzed. This is especially relevant for solar physics, a research field in which upcoming space missions will provide more than a terabyte of image data per day. Providing efficient access to such large data volumes at both high spatial and high time resolution is of paramount importance to support scientific discovery.

Keith Hughitt

Papers

SunPy—Python for solar physics

SunPy - Python for Solar Physics

JHelioviewer - Visualizing large sets of solar images using JPEG 2000

The Helioviewer Project: Discovery For Everyone Everywhere