TylerKirby

@TylerKirby·11 public vizzes

Gist f6a6da7b5e77722e0b29851712579ccb

A scatterplot displays 80 data points from dataset.csv, mapping x1 along the horizontal axis and x2 along the vertical axis. All points share a uniform blue color with no distinguishing marks, and the y column appears unused in the visualization. The plot uses a simple Cartesian coordinate system with points distributed across the range of x1 (roughly -1 to 2.5) and x2 (roughly -0.5 to 3). No axis labels, titles, or legends are present, giving the chart a minimalist appearance focused purely on the spatial distribution of the data.

Apr 20, 2022

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.vimrc

This example visualizes the structure of a `.vimrc` configuration file as a tree of text lines, using Vim’s built-in `foldmethod=indent` to group nested settings and plugins. It renders the file with syntax highlighting via `syntax on` and the `gruvbox` colorscheme, while NERDTree displays the file system. The code also configures autocompletion with `jedi-vim` and `supertab`, linting with `syntastic` and `vim-flake8`, and folding previews through `SimpylFold`. The data source is the raw gist content, displayed directly in Vim’s buffer.

Oct 6, 2020

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zsh

This example visualizes the configuration of a Zsh shell environment, showing the setup of oh-my-zsh, plugins, themes, and system integrations. The gist file contains plain-text configuration directives, including exported environment variables like `COMET_API_KEY` and `ZSH`, plugin declarations, and conditional blocks for Google Cloud SDK and iTerm2 shell integration. The rendering approach is purely textual, with no graphical libraries or data-processing APIs involved. It relies on shell scripting syntax to define aliases, completion settings, and path updates, illustrating how user preferences and third-party tools are wired into the interactive command-line interface.

Jan 26, 2020

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Vim settings

This example visualizes a Vim configuration file as a static listing, showing the structure of a `.vimrc` setup. It displays the file’s contents in plain text, highlighting plugin declarations, settings, and syntax-enabling commands. The code uses Vundle’s `Plugin` calls to manage dependencies like gruvbox, NERDTree, and jedi-vim, while `filetype`, `syntax`, and `set` directives configure editor behavior. The rendering approach is a straightforward text display, with no interactive or graphical elements, relying solely on the raw source from TylerKirby’s gist.

Aug 31, 2019

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Visual Studio Code Settings Sync Gist

This visualization presents an interactive snapshot of a developer’s Visual Studio Code setup, sourced from a settings-sync gist. Built with React, it renders the list of installed extensions as a filterable grid, grouping them by publisher and showing version numbers. The display also includes the sync metadata (last upload time and extension version), while empty keybindings files are noted. The layout highlights the user’s tooling choices and extension ecosystem at a glance.

Apr 5, 2018

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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.

Mar 8, 2016

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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.

Sep 10, 2015

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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.

Aug 5, 2015

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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.

Aug 4, 2015

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Gist 641fc44c27c66ee9ef09

This example demonstrates a Latin text preprocessing pipeline that converts raw words into lemmata and macronized dictionary entries. It shows how a sample sentence like "quo usque tandem abutere catilina nostra patientia" is tokenized, cleaned of punctuation, and processed word-by-word. The code uses the CLTK `LemmaReplacer` for lemmatization, lxml’s `etree` to parse the Perseus Latin lexicon XML file, and custom string manipulation to transform entries like "a^bu_tor" into "abūtor". The workflow defines classes for text and word-level morphological analysis, though the dictionary lookup for full texts runs too slowly to complete.

Jun 16, 2015

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Python Dictionary of Latin Inflections

This example visualizes the complete inflectional paradigm of first-conjugation Latin verbs as a structured Python dictionary. It shows how verb forms are organized by number, tense, voice, mood, and person, with entries like `singular_pluperfect_active_indicative_first` mapping to forms such as `'eram'`. The code defines each conjugation cell as a dictionary keyed by grammatical person or gender-case, covering active and passive indicative, subjunctive, infinitive, and participle forms. It uses plain Python dict literals and underscores in variable names to encode grammatical categories, making the morphological system directly inspectable in source form.

Jun 2, 2015