TransformCase

Here is the case and separation transformer that transliterates diacriticals and ligatures when your texts are in any Latin script.

Install

Install the package as npm package. Provided are a umd-formatted file in the dist folder to require or just read and an es-module in the module folder to import.

Usage

A human text can be transformed to a systematic phrase like this:

transformCase('A sentence, text for humans.').camelCase()

These will render:

consttextIntake=transformCase('A sentence, text for humans.')textIntake.camelCase()// ==> 'aSentenceTextForHumans'textIntake.pascalCase()// ==> 'ASentenceTextForHumans'textIntake.dotCase()// ==> 'a.sentence.text.for.humans'textIntake.paramCase()// ==> 'a-sentence-text-for-humans'textIntake.pathCase()// ==> 'a/sentence/text/for/humans'textIntake.searchCase()// ==> 'a+sentence+text+for+humans'textIntake.snakeCase()// ==> 'a_sentence_text_for_humans'textIntake.spaceCase()// ==> 'a sentence text for humans'textIntake.constantCase()// ==> 'THIS_SENTENCE_TEXT_FOR_HUMANS'textIntake.headerCase()// ==> 'This-Sentence-Text-For-Humans'

A systematic text can be transformed to a human phrase like this:

consttextIntake=transformCase('camelCasedInput')textIntake.humanSentence()// ==> 'Camel cased input'consttextIntake2=transformCase('snake_cased_input',{delimitInput: '_'})textIntake2.humanTitle()// ==> 'Snake Cased Input'

With a second argument, an options object can be passed:

{
delimit: [word-or-regex1, word-or-regex2, ...],
preserve: [word-or-regex1, word-or-regex2, ...],
}
delimit: {Array}
keeps a letter-combination or a regular expression match
as a delimited word,
the word will be processed according to the pattern
preserve: {Array}
keeps a letter-combination or a regular expression match
as a delimited word and protects the case

Options for pure alphanumeric input

delimitLetterNumber: {Boolean}
delimit when a letter is followed by a number (default: true)
delimitLowerUpper: {Boolean}
delimit when a lowercase is followed by a uppercase (default: true)
delimitNumberLetter: {Boolean}
delimit when a number is followed by a letter (default: true)
delimitUpperLower: {Boolean}
delimit when a uppercase is followed by a lowercase (default: false)
delimitUpperUpperLower: {Boolean}
delimit when a uppercase is followed by a uppercase plus lowercase (default: true)

Demo

.../transform-case/demo/demo.html
.../transform-case/demo/play.html

Transformation process

This module has two steps, an intake and a render step.

The intake step deduplicates whitespace in a space character, removes control characters, finds a delimiter, isolates delimit and preserve options and ends with an array of words.

We then have an object with collected data and a number of transform patterns to choose from.

{_orgin{input(string),isAlphaNumeric(boolean),normalised(string),revised(string)}options{(seeabove)}_phrase(delimitedstring),words(string[]),
...thetransformationfunctions}

The render step is merely choosing a pattern to apply to the array of words. There are three groups of similar patterns:

• Cap-marked words (camelCase, pascalCase)
• Human, linguistic (humanSentence, humanTitle)
• Delimited lowercase (dotCase, paramCase, etcetera)

Apart from the human group, in all patterns punctuation is stripped, diacritics are stripped, ligatures are decomposed