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:

const textIntake = 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:

const textIntake = transformCase('camelCasedInput')
textIntake.humanSentence()  // ==> 'Camel cased input'

const textIntake2 = 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 {
        (see above)
    }
    _phrase (delimited string),
    words (string[]),
    ...the transformation functions
}

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