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
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
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
All but last of empty sentence.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 only the first word, with no spaces before or after it.pt:a primeira palavra de _
Takes a text string as input, and reports the last character in the string.pt:o último caractere de _
0Takes 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 only the last word of the input, with no spaces before or after it.pt:a última palavra 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
0Takes 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 _
firstbffirst111bfTakes 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 wordde: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 itde: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).()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 _
Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of adding them all, so
(SUM (4) (100) (8))
reports 112.pt:a soma de _
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of multiplying them all, so
(PRODUCT (4) (100) (8))
reports 3200.pt:o produto de _
1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE only if all of the inputs are TRUE, otherwise FALSE. Like AND but for multiple inputs.pt:todas as afirmações _ são verdadeiras
1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE if at least one input is TRUE, otherwise FALSE.pt:alguma das afirmações _ é verdadeira
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the smallest of them, so
(MINIMUM (4) (100) (8))
reports 4.pt:o produto de _
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the largest of them, so
(MAXIMUM (4) (100) (8))
reports 100.pt:o produto de _
1Multi-branched conditional.
If the first (Boolean) input is True, then the script in
the second (C-slot) input is run, and this block stops.
Additional tests can be added by putting one or more
ELSE IF blocks in the third (variadic) Boolean slot.
Use the arrowheads to get the number of Boolean
slots you need. Each ELSE IF block provides a Boolean
slot for a condition to test and a script slot for what to do
if the condition is True. You can put an ELSE block in
the last Boolean input to CASES for a script to run if
all the tests turn out False.
As soon as a condition is True, no further tests are done
and no further scripts are run.pt:se _ , então _ _
pt:captura _ _
contpt:lança _
catchtagFor use with the CASES block.
See its help message.pt:senão, se _ , então _
For use with the CASES block.
See its help message.pt:senão, _
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