nicholasjguerra / pydarn Goto Github PK

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

This patch release includes:

• Bug fix hdw repository installation issues resolved
• Inclusion of ICE and ICW in hdw repository and superdarn_radars module

Most recent minor release (3.1.0) changes listed below:

• Full Cartopy coastline plotting options for all spatial plots
  • NEW coastline keyword in method calls
• Full Cartopy integration for plotting in geographic coordinates for grid and fan plots
• Completed polar coordinate convection maps including reference vector and many customization options
• Improved ACF plotting
• New HALF_SLANT range estimation for RTP
• Bug fix Multiple fan plots now available on one axis
• Bug fix lowlat keyword now available for geographic coordinate plots
• Bug fix Colorbars now extend/don't extend as required along with many other minor improvements and bug fixes!

pyDARN's documentation can be found here

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.

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.