We heard from seasoned developers as well as end-users who had the simplest use case: using DavitPy to load/read SuperDARN data. Topics aired included:

• DavitPy hosting: issue where someone leaves, data inaccessible for weeks. Don’t want that to happen with code. Data should be in archive like Zenodo.
  • consider countries with filtered internet. Code mirrored across multiple sites should help in general.
• Breaking up DavitPy into a lean core that strictly handles SuperDARN data and makes basic plots. All else should be in a DavitPy-extras module.
• Some users just want to load the SuperDARN data without using IDL. I noted that a lean DavitPy is amenable for loading transparently into Matlab. That is, Matlab can use Python user code, so that it’s transparent to Matlab users how to load the SuperDARN data into Matlab.

Previously, I had made an overly large pull request that incorporated fixes for the issues below. I will make separate, small pull requests for these issues.

A general issue for any Python program is that pyproject.toml should be used to configure the package as much as possible. A working example pyproject.toml using these features is illustrative.

Avoid Bash scripts as these don’t work on Windows unless using Windows Subsystem for Linux.

For my own programs, I only use Python 3 for several years already.

Example of rich Python 3 exception handling with backward compatibility:

import six

if six.PY2:
    ConnectionError = OSError

# ...

try:
    # download/upload from server or device
except ConnectionError:
    print('could not connect to device')
    return

Best practice for forward compatibility: do not use if six.PY3 or if not six.PY3, rather use if six.PY2.

Require “new enough” Python version

To avoid lots of needless GitHub Issues and emails from users with obsolete Python versions, set pyproject.toml to limit to Python required by the program.