Gist 641fc44c27c66ee9ef09

Refactored autoscan_greek.py for CLTK

This visualization demonstrates the prosimetric scansion of Ancient Greek texts by tokenizing cleaned input into sentences and syllables, then applying quantitative metrical rules to classify each syllable as long or short. The Python program uses the CLTK-inspired `ScansionGreek` class, which handles accent stripping, punctuation removal, and diphthong/vowel-length detection through custom character lists and replacement dictionaries. It relies on standard Python string operations and list comprehensions to process the text, with the final output showing the scansion patterns for each sentence. The code also includes a unittest framework for testing the scansion logic.

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Gist d117b3d6bcfdf1722bd5

This example analyzes the prosody of Latin prose by counting occurrences of specific metrical patterns, called clausulae, in a string of syllable stress marks. The Clausulae class uses Python’s `str.count` method to tally matches for eighteen named rhythm patterns, such as “cretic + trochee” and “double spondee,” against a concatenated prosody input. It returns a dictionary mapping each pattern name to its frequency, providing a quantitative breakdown of rhythmic endings. The code is plain Python with no external visualization libraries, focusing solely on the text-analysis computation.

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Gist 4e556c8ccbb579e82ba9

This visualization scans Greek prose and poetry to produce a prosimetric scansion, showing syllable lengths (long or short) across sentences. It first cleans and tokenizes the text using the Classical Language Toolkit’s `TokenizeSentence` and NLTK’s `PunktLanguageVars`, then syllabifies and applies Greek metrical rules such as diphthongs, long vowels, and consonant clusters. The code defines a `Scansion` class with methods like `_long_by_nature` to classify syllables, ultimately outputting the metrical pattern for each line of the input text.

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XQuery Working Group Challenge

This example transforms the PROIEL treebank’s Latin New Testament XML into a sequence of verse elements, each tagged with its citation and concatenated token forms. It fetches the remote XML via `fn:doc`, extracts distinct citation parts, and filters tokens by matching `@citation-part` to build structured verses. The code uses XQuery 3.1’s element constructors and `fn:data` to output text, demonstrating a direct pipeline from hierarchical treebank data to flat verse-like text.

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For XQuery4Humanists

This example visualizes the frequency and distribution of words extracted from a TEI-encoded poem, processing the text to remove punctuation, normalize case, and tokenize it into a list. The visualization shows the resulting word list as a simple textual display, highlighting the raw output of the XQuery processing pipeline. The code uses XQuery 3.0 functions like `fn:tokenize`, `fn:lower-case`, and `fn:replace` to clean the data, pulling the source directly from a remote XML file hosted on GitHub. The rendering relies solely on the XQuery return, with no additional JavaScript or charting libraries involved.

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A simple JAVA program for practicing regex.

This Java program provides an interactive way to practice writing regular expressions, with a focus on Latin texts. It offers three practice modes: finding a pattern's index in a user-supplied string, searching a text file for a pattern, and counting the frequency of a pattern in Cicero's First Catilinarian. The program uses Java's Pattern and Matcher classes to handle regex operations, and reads from included Latin text files to provide realistic NLP-style exercises.

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XQuery for Pig Latin

The example shows a progression of XQuery 3.0 scripts that implement Pig Latin translation, starting from a single hardcoded word and evolving to handle full sentences with punctuation. It demonstrates functional recursion and string manipulation using core XQuery functions like `fn:substring`, `fn:replace`, `fn:string-to-codepoints`, `fn:tokenize`, and `fn:string-join`. The final version declares custom functions `local:move-letters`, `local:parse-word`, and `local:parse-sentence` to process the phrase "Will you adjust the thermometer?" into its Pig Latin equivalent, preserving sentence structure. The code highlights XQuery's ability to handle text transformation without external libraries, relying solely on its built-in string and sequence operations.

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Pig Latin Converter

This visualization demonstrates how an English sentence is transformed into Pig Latin, showing the word-by-word conversion process. It displays the original sentence "I speak Pig Latin" alongside its Pig Latin equivalent by applying phonetic rules to each word. The code uses the XQuery 3.0 string-join function and fn:tokenize to split the input, then iterates through each word with a for loop. It checks the first letter against a list of vowels using fn:lower-case and fn:substring, then concatenates the word parts with the "||" operator to form the translated output. The rendering approach is purely text-based, with no graphical or interactive elements.