Nascent Political Miscreant

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


    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.


    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)



    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


    npm i transform-case

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