data:image/png;base64,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

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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