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License: LGPL v3 Python 3.6 GitHub release (latest by date) DOI

Python data visualization library for the Super Dual Auroral Radar Network (SuperDARN).


Version 4.0 - Major Release!

This major release includes:

  • NEW IQ level data plotting
  • NEW Latitude and longitude y-axis in RTP
  • NEW Ball and stick plots
  • NEW Map file variable time series plotting
  • NEW Terminator plotting
  • Coastlines available in magnetic coordinate spatial plots without Cartopy
  • More flexibility in fan plots - single beams/ range gate range options
  • TDiff correction for elevation data available
  • Boxcar filtering available for data before plotting
  • Corrections to geolocation algorithms
  • Standardized plot return values across all plots
  • Bug fix Radar position labels no longer overlap
  • Bug fix Warning use refactored
  • Bug fix Multiple bug fixes for data handling and plotting in grid plots
  • Bug fix Multiple bug fixes for the aesthetics of convection maps


pyDARN's documentation can be found here

Getting Started

pip install pydarn

Or read the installation guide.

If wish to get access to SuperDARN data please read the SuperDARN data access documentation. Please make sure to also read the documentation on citing superDARN and pydarn.

As a quick tutorial on using pydarn to read a non-compressed file:

import matplotlib.pyplot as plt

import pydarn

# read a non-compressed file
fitacf_file = '20190831.C0.cly.fitacf'

# pyDARN functions to read a fitacf file
fitacf_data = pydarn.SuperDARNRead(fitacf_file).read_fitacf()

pydarn.RTP.plot_summary(fitacf_data, beam_num=2)

summary plot

For more information and tutorials on pyDARN please see the tutorial section.

We also have a Jupyter notebook with many examples to support our recent publication.

Getting involved

pyDARN is always looking for testers and developers keen on learning python, github, and/or SuperDARN data visualizations! Here are some ways to get started:

  • Testing Pull Request: to determine which pull requests need to be tested right away, filter them by their milestones (v3.0 is currently highest priority).
  • Getting involved in projects: if you are looking to help in a specific area, look at pyDARN's projects tab. The project you are interested in will give you information on what is needed to reach completion. This includes things currently in progress, and those awaiting reviews.
  • Answer questions: if you want to try your hand at answering some pyDARN questions, or adding to the discussion, look at pyDARN's issues and filter by labels.
  • Become a developer: if you want to practice those coding skills and add to the library, look at pyDARN issues and filter by milestone's to see what needs to get done right away.

Please read pyDARN team on how to join the pyDARN team.