data:image/png;base64,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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 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 _

Multi-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 _ _ cont

pt:lança _ catchtag

For 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, _

Reports a new list containing the items of the input list, but in the opposite order.

ca:capgira _ 1inputresult

Reports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _ 1

Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ copy of datasplitmerge11#1#2

The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ 11

This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

position11

The identity function reports its input.

The stream version of IN FRONT OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is here in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:a prefixação de _ ao canal _

The stream version of ITEM 1 OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:o primeiro item do canal _

The stream version of ALL BUT FIRST OF. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:um canal com todos os itens de _ menos o primeiro 423true4

The stream version of variadic MAP (any number of input lists, like MULTIMAP in the list utilities library). Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:a aplicação de _ aos itens dos canais _

The stream version of KEEP. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:os itens tais que _ do canal _

The inputs are a stream and a positive integer. SHOW STREAM reports a regular finite list of length less than (for short finite streams) or equal to the second input. It's useful because an infinite stream can't be displayed in a finite amount of time.

pt:uma lista com os itens do canal _ até ao _ º 100

Make a stream from a finite collection of items, like the LIST primitive. Since this block is typically used for small amounts of data, it does not delay computation of its inputs.

pt:um canal com _

Call this block with STREAM WITH NUMBERS FROM 2 as its input to get the infinite stream of all the prime numbers. It's called SIEVE because the algorithm it uses is the Sieve of Eratosthenes (clickable link): https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes Look inside; it's a beautifully elegant algorithm. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:a crivagem do canal _

The stream version of NUMBERS FROM 1 TO, with no ending number because it reports the infinite stream of all the integers greater than or equal to its input. Read the code! It's deceptively simple. Streams, also called lazy lists, are like lists except that items are not computed until they're needed. This allows for more efficient handling of large computed lists, and even infinite lists. The only piece of magic is in IN FRONT OF STREAM, whose second input is of type Unevaluated, which means that it's a procedure, with a sort of invisible gray ring. So HEAD OF STREAM is just ITEM 1 OF, but TAIL OF STREAM has a CALL block to make the computation happen. For a tutorial introduction to streams, read SICP 3.5: https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book-Z-H-24.html#%_sec_3.5 or Brian's lecture notes (start on page 74): https://people.eecs.berkeley.edu/~bh/61a-pages/Volume2/notes.pdf The above are clickable links!

pt:um canal com os números a partir de _ 1

Read the code while running it,

huge,enormous,extremely00