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IMAP (Interstellar Mapping and Acceleration Probe)

All Contributors

readthedocs

DOI

The Interstellar Mapping and Acceleration Probe (IMAP) is an exciting project aimed at studying the interstellar medium and investigating the acceleration mechanisms of particles within our galaxy. IMAP utilizes cutting-edge technology and advanced instrumentation to gather valuable data and expand our understanding of space.

The IMAP project's Git repository serves as a centralized hub for collaboration among researchers, scientists, and developers involved in the project. It provides a platform for version control, code sharing, issue tracking, and documentation, fostering a collaborative environment for advancing the goals of the IMAP mission.

Through the Git repository, contributors can access project resources, explore the codebase, propose enhancements or bug fixes, and participate in discussions with other team members. This collaborative approach promotes efficient development and fosters innovation in the pursuit of interstellar mapping and particle acceleration research.

Join the IMAP Git repository to be part of an exciting scientific endeavor, contribute your expertise, and help unlock the mysteries of the interstellar medium and particle acceleration in our galaxy. Together, we can advance our knowledge of the cosmos and pave the way for future discoveries in space exploration.

IMAP Website

IMAP Processing Documentation

Getting started

Credits

LASP (Laboratory of Atmospheric and Space Physics)

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Greg Lucas
Greg Lucas

Tenzin Choedon
Tenzin Choedon

Laura Sandoval
Laura Sandoval

Sean Hoyt
Sean Hoyt

Gabriel Moraga
Gabriel Moraga

Matthew Bourque
Matthew Bourque

Maxine Hartnett
Maxine Hartnett

Bryan Harter
Bryan Harter

Marek Strumik
Marek Strumik

Veronica Martinez
Veronica Martinez

Tim Plummer
Tim Plummer

Daralynn Rhode
Daralynn Rhode

Ana Manica
Ana Manica

This project follows the all-contributors specification. Contributions of any kind welcome!

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Languages

  • Python 84.2%
  • HCL 15.7%
  • Other 0.1%