Programming

Three-line (though non-standard) interlinear glossing

Still thinking about interlinear glossing for my language learning project. The leizig.js library is great but my use case isn’t really what the author had in mind. I really just need to display a unit consisting of the word as it appears in the text, the lemma for that word form, and (possibly) the part of speech. For academic linguistics purposes, what I have in mind is completely non-standard.

The other issue with leizig.js for my use case is that I need to be able to respond to click events on individual words so that they can be tagged, defined or otherwise worked with. It’s straightforward how I could apply CSS id attributes to word-level elements to support that functionality.

Splitting text into sentences: Russian edition

Splitting text into sentences is one of those tasks that looks simple but on closer inspection is more difficult than you think. A common approach is to use regular expressions to divide up the text on punction marks. But without adding layers of complexity, that method fails on some sentences. This is a method using spaCy.
Read more…

Bash variable scope and pipelines

I alluded to this nuance involving variable scope in my post on automating pdf processing, but I wanted to expand on it a bit.

Consider this little snippet:

i=0
printf "foo:bar:baz:quux" | grep -o '[^:]\+' | while read -r line ; do
   printf "Inner scope: %d - %s\n" $i $line
   ((i++))
   [ $i -eq 3 ] && break;
done
printf "====\nOuter scope\ni = %d\n" $i;

If you run this script - not in interactive mode in the shell - but as a script, what will i be in the outer scope? And why?

Automating the handling of bank and financial statements

In my perpetual effort to get out of work, I’ve developed a suite of automation tools to help file statements that I download from banks, credit cards and others. While my setup described here is tuned to my specific needs, any of the ideas should be adaptable for your particular circumstances. For the purposes of this post, I’m going to assume you already have Hazel. None of what follows will be of much use to you without it. I’ll also emphasize that this is a macOS-specific post. Bear in mind, too, that companies have the nasty habit of tweaking their statement formats. That fact alone makes any approach like this fragile; so be aware that maintaining these rules is just part of the game. With that out of the way, let’s dive in.

Bulk rename tags in DEVONthink 3

In DEVONthink, I tag a lot. It’s an integral part of my strategy for finding things in my paperless environment. As I wrote about previously hierarchical tags are a big part of my organizational system in DEVONthink. For many years, I tagged subject matter with tags that emmanate from a single tag named topic_, but it was really an unnecessary top-level complication. So, the first item on my to-do list was to get rid of the all tags with a topic_ first level.

Stripping Russian syllabic stress marks in Python

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.

Accessing Anki collection models from Python

For one-off projects that target Anki collections, I often use Python in a standalone application rather than an Anki add-on. Since I’m not going to distribute these little creations that are specific to my own needs, there’s no reason to create an add-on. These are just a few notes - nothing comprehensive - on the process.

One thing to be aware of is that there must be a perfect match between the Anki major and minor version numbers for the Python anki module to work. If you are running Anki 2.1.48 on your desktop application but have the Python module built for 2.1.49, it will not work. This is a huge irritation and there’s no backwards compatibility; the versions must match precisely.

Converting Cyrillic UTF-8 text encoded as Latin-1

This may be obvious to some, but visually-recognizing character encoding at a glance is not always obvious.

For example, pronunciation files downloaded form Forvo have the following appearance:

pronunciation_ru_оÑ‚бывание.mp3

How can we extact the actual word from this gibberish? Optimally, the filename should reflect that actual word uttered in the pronunciation file, after all.

Step 1 - Extracting the interesting bits

The gibberish begins after the pronunciation_ru_ and ends before the file extension. Any regex tool can tease that out.