Difference between revisions of "2019 Workshop:Software Engineering for Heliophysics"

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* Modern / efficient coding practices (for any language)
* Modern / efficient coding practices (for any language)
** What should be object-oriented vs. functionalized
** What should be object-oriented vs. functionalized
** use of linters / type checkers (Python: flake8, mypy; C++: clang-tidy)
* Distributing code more easily via:
* Distributing code more easily via:
** code sharing sites (GitHub)
** code sharing sites (GitHub)

Revision as of 11:08, 15 March 2019

Software Engineering for Heliophysics

Location, Date/Time and Duration

2 hours


Michael Hirsch
Matthew Zettergren
Guy Grubbs

Workshop Categories

  • Altitudes: all
  • Latitudes: global

Format of the Workshop


Estimated attendance


Requested Specific Days

non-conflicting time with Python for Space Science and "Hackathon"

Special technology requests

tables so attendees can use laptops. WiFi.


ST #5: Fuse the Knowledge Base across Disciplines

  1. Good software engineering practices expedite reliable, repeatable science results and encourage diverse outside collaborator participation. Repeatable, traceable science analyses are better trusted and solidify community and stakeholder confidence in published results.
  2. Software sharing / collaboration sites like GitHub have matured and are widely used by the heliophysics community. We address minor changes in practice that reduce time to science closure.
  3. Progress is readily measured by semi-automated metrics such as:
    • quantity and diversity (institutional, geographical, participant) of code contributions and issues opened
    • an increase in the use of continuous integration facilities such as Travis-CI
    • increased use of public data sharing such as Zenodo

ST #6: Manage, Mine, Manipulate Geoscience Data and Models

  1. Increased scientific computing efficiencies are essential for computer-aided discovery of the growing petabytes of data collected. Extracting value from the decades of diverse existing data sources can be greatly aided by more efficient software engineering practices
  2. Effective use of software toolchains can be a significant force multiplier in avoiding mistakes and repeated or manual work.
  3. Progress might be measured by mining papers for citations / keywords used such as links to software repos used, which can themselves be mined for use of continuous integration tools, build system type and specific software libraries


Would you like to be more effective at developing software and getting science closure with less time spent debugging and supporting users? This workshop is for you!

The intended audience is as broad as possible: students to senior career--we will discuss intermediate to advanced geospace software engineering at a level accessible and useful to all.

Scope includes coding languages commonly used in heliophysics, including: C++, Fortran, Matlab, Python

Use cases we address include:

  • scripting languages to analyze large data sets quickly
  • model developers reduce the time spent tutoring new users in building / modifying / using the model
  • reduce debugging effort by adding automated self-tests
  • ensure code will be usable on most current computing platforms and easily adaptable to future systems

We intend that most participants will be able to apply industry best-practices to their own work tonight, if not in the workshop itself.

Tutorial topic areas

Please let the organizers know if you have any additions or interest in these areas.

  • Modern / efficient coding practices (for any language)
    • What should be object-oriented vs. functionalized
    • use of linters / type checkers (Python: flake8, mypy; C++: clang-tidy)
  • Distributing code more easily via:
    • code sharing sites (GitHub)
    • Build systems (Meson)
    • Package managers (Julia, Python)
    • proprietary (IDL) or less common languages
  • Version control (Git): sharing and developing code effectively across diverse teams
  • Build systems (CMake, Meson, Pip)
    • make it easy for users to get prereqs and build your code on any computer
    • Deploying a complex application anywhere, from Raspberry Pi to Windows/Mac laptop to CentOS HPC
  • Continuous test / integration (Travis-CI)
    • automatically run tests "in the cloud" on Linux, Mac, Windows for each code change
    • examples with Meson and CMake + compiled languages (C++, Fortran)
    • Seamless integration and testing of scripted languages (Matlab, Python) with compiled (C++, Fortran)
    • how to create tests in your preferred language
  • Asynchronous architectures: parallel and concurrent processing
    • Gentle introduction to asynchronous and parallel programming in:
      • Python asyncio
      • Fortran coarray
      • C++ coroutines


Please let the organizers know if you have something to present.

Workshop Summary

This is where the final summary workshop report will be.

Presentation Resources

Upload presentation and link to it here. We will also try to archive talks in Zenodo.

Upload Files Here

  • Add links to your presentations here, including agendas, that are uploaded above. Please add bullets to separate talks. See further information on how to upload a file and link to it.