data:image/png;base64,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

Digitsi1

+-x/+-x/outClone

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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KBgAAH6CYJECBAgIAA+wECBAgQIBAICHCAbpIAAQIECByxcQFpoRMBzwAAAABJRU5ErkJggg===C

00789C/456ex123+0.=-4515=-2000false3472912510