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data:image/png;base64,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07.8falseCamera YFrame

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 strreturn str.slice(1);

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

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 strreturn str.slice(0,-1);

All but last 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

arrreturn new List(arr.asArray().slice(0,-1));

All but last of empty sentence.

1 11 1

pt:a primeira palavra de _

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

pt:o último caractere de _ 0

pt:a última palavra de _

Last of empty sentence.

1 1

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 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

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.

pt:lança o erro _

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

(

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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 _

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