Python

Yesterday I posted about a small utility that parses and explains ARINC 424 records. I have updated it.

For each field, the tool now cites the specific tables in the ARINC 424 specification that describe that field. The field-content explanations are also more detailed, so you can spend less time flipping between the parsed output and the printed standard. Here is an example for an Airport SID/STAR/Approach primary record:

This is still a work in progress. Not all record types are supported yet; I am filling those in as I need them.

N.B. An enhanced version is now available, with specification table references and more detailed field explanations. 2026-08-03.

The ARINC 424 specification is the universally accepted standard for encoding aeronautical navigation data. Databases that follow it store that data in fixed-length, 132-character records. I created a utility that parses a single ARINC 424 record and prints the fields along with column numbers and plain-language explanations. The Python arinc424 module does the heavy lifting; I provide a wrapper around it, plus the logic that maps fields to the column numbers used in the official specification.

• Indigo macOS home automation software actions that are assigned to a Trigger, Scheduled or web UI element are not executed sequentially. Design accordingly to avoid race conditions.

This is a proof-of-concept for scripting Shelly relay devices in an Indigo Python action.

I’ve used the Indigo macOS home automation software for many years. It’s a deep, extensible and reliable piece of software. Among the extensible features of the application is its suite of community-supported plugins. There is a plugin for Shelly devices, but it supports only earlier devices and not the current units. As I understand it, the author does not intend to update the plugin. In this post, I’ll show a method for controlling these devices without plugins. The shortcoming here is that the Shelly device doesn’t have a corresponding Indigo device, and everything is handled through action groups and variables.

vim: Jump to specific character on a line

In Vim, to jump to a specific character on a line, you can use the following commands:

• f{char} - Jump to the next occurrence of {char} on the current line
• F{char} - Jump to the previous occurrence of {char} on the current line
• t{char} - Jump until (one position before) the next occurrence of {char}
• T{char} - Jump until (one position after) the previous occurrence of {char}

For your specific example of “go to first #”:

I use ArchiveBox extensively to save web content that might change or disappear. While a REST API is apparently coming eventually, it doesn’t appear to have been merged into the main fork. So I cobbled together a little application to archive links via a POST request. It takes advantage of the archivebox command line interface. If you are impatient, you can skip to the full source code. Otherwise I’ll describe my setup to provide some context.

Most language learners are familiar with Forvo, a site that allows users to download and contribute pronunciations for words and phrases. For my Russian studies, I make daily use of the site. In fact, to facilitate my Anki card-making workflow, I am a paid user of the Forvo API. But that’s where the trouble started.

When the Forvo API works, it works OK, often extremely slow. But lately, it has been down more than up. In an effort to patch my workflow and continue to download Russian word pronunciations, I wrote this little scraper. I’d prefer to use the API, but experience has shown now that the API is slow and unreliable. I’ll keep paying for the API access, because I support what the company does. And as often as not when a company offers a free service, it’s likely to be involved in surveillance capitalism. So I’d rather companies offer a reliable product at a reasonable price.

In my perpetual attempt to make my language learning process using Anki more efficient, I’ve written a tool to extract English-language definitions from Russian words from Wiktionary. I wrote about the idea previously in Scraping Russian word definitions from Wikitionary: utility for Anki but it relied on the WiktionaryParser module which is good but misses some important edge cases. So I rolled up my sleeves and crafted my own solution. As with WiktionaryParser the heavy-lifting is done by the Beautiful Soup parser. Much of the logic of this tool is around detecting the edge cases that I mentioned. For example, the underlying HTML format changes when we’re dealing with a word that has multiple etymologies versus those with a single etymology. Whenever you’re doing web scraping you have to account for those sorts of variations.

I have written previously about stripping syllabic stress marks from Russian text using a Perl-based regex tool. But I needed a means of doing in solely in Python, so this just extends that idea.

#!/usr/bin/env python3

def strip_stress_marks(text: str) -> str:
   b = text.encode('utf-8')
   # correct error where latin accented ó is used
   b = b.replace(b'\xc3\xb3', b'\xd0\xbe')
   # correct error where latin accented á is used
   b = b.replace(b'\xc3\xa1', b'\xd0\xb0')
   # correct error where latin accented é is used
   b = b.replace(b'\xc3\xa0', b'\xd0\xb5')
   # correct error where latin accented ý is used
   b = b.replace(b'\xc3\xbd', b'\xd1\x83')
   # remove combining diacritical mark
   b = b.replace(b'\xcc\x81',b'').decode()
   return b

text = "Том столкну́л Мэри с трампли́на для прыжко́в в во́ду."

print(strip_stress_marks(text))
# prints "Том столкнул Мэри с трамплина для прыжков в воду."

The approach is similar to the Perl-based tool we constructed before, but this time we are working working on the bytes object after encoding as utf-8. Since the bytes object has a replace method, we can use that to do all of the work. The first 4 replacements all deal with edge cases where accented Latin characters are use to show the placement of syllabic stress instead of the Cyrillic character plus the combining diacritical mark. In these cases, we just need to substitute the proper Cyrillic character. Then we just strip out the “combining acute accent” U+301 → \xcc\x81 in UTF-8. After these replacements, we just decode the bytes object back to a str.