pydarn 4.1

Data visualization library for SuperDARN data

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

Changelog

Version 4.1 - Minor Release!

This minor release includes:

• NEW MAG projection
• NEW 'Zooming-in' on GEO and MAG projections
• NEW NSSC Radars Included
• NEW Calculation of Potential at Lat/Lon Position from Map Files
• NEW Map Potential Time-Series Plots at Lat/Lon Position
• NEW User Input Fan Plots
• Cartopy now a full dependency
• Updates to fan plots for usability including scan_time and scan_time_tolerance keywords
• Embargo warning for -CPID data that is less than a year old
• Coordinates converted to magnetic coordinates more efficiently
• Bug fix Map plots lowlat default discrepancy fixed

Documentation

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)
plt.show()

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.