pydarnio 1.2.1

Python library for reading and writing SuperDARN data

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

Changelog

Version 1.2.1 - Patch Release!

This patch release includes a fix for the depreciation of a Numpy method. Below is more information for the most recent minor release.

The most recent minor release included:

• Handling of extra fields in fitacf files coming in the next RST release, including the name of the ACF fitting algorithm used (algorithm), the value of TDIFF used to calculate the elevation angles (tdiff), and more descriptive field names for the elevation angle fields in FitACF 3.0 (elv_error & elv_fitted)
• More flexibility around optional fields for all file types
• Updates and speed improvements for Borealis file io

Documentation

pyDARNio's documentation can found here

Getting Started

pip install pydarnio

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 pyDARNio to read a non-compressed file:

import pydarnio

# read a non-compressed file
fitacf_file = '20180220.C0.rkn.stream.fitacf'

# pyDARNio functions to read a fitacf file
reader = pydarnio.SDarnRead(fitacf_file)
records = reader.read_fitacf()

or to read a compressed file:

import bz2
import pydarnio
# read in compressed file
fitacf_file = '20180220.C0.rkn.stream.fitacf.bz2'
with bz2.open(fitacf_file) as fp: 
      fitacf_stream = fp.read()

# pyDARNio functions to read a fitacf file stream
reader = pydarnio.SDarnRead(fitacf_stream, True)
records = reader.read_fitacf()

For more information and tutorials on pyDARNio please see the tutorial section

Getting involved

pyDARNio 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 (v1.2.0 is currently highest priority).
• Getting involved in projects: if you are looking to help in a specific area, look at pyDARNio'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 pyDARNio questions, or adding to the discussion, look at pyDARNio's issues and filter by labels.
• Become a developer: if you want to practice those coding skills and add to the library, look at pyDARNio issues and filter by milestone's to see what needs to get done right away.

Please contact the Data Visualization Working Group, if you would like to become a member of the team!