Würfelkörper im Schrägbild und in der isometrischen Darstellungen drehen und kippen.data:image/png;base64,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Würfelkörper im Schrägbild und in der isometrischen Darstellungen drehen und kippen.

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 _

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 _

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 _ _

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 a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

ca:notació textual de _ (

)

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 _

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.

The identity function reports its input.

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.)

ca:multi-mapeja _ sobre _

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 _

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