data:image/png;base64,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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 _ _
1Takes 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 _
de:Bogen $turnRight Radius: _ Grad: _
503090de:Bogen $turnLeft Radius: _ Grad: _
503014102880.540item1814102880.520item916de:animiere Setter _ Getter _ Delta _ über _ Sek. beschleunigt _
ca:modifica setter _ i getter _ amb delta _ en _ segons de forma _
pt:anima com modificador _ e inspector _ de _ em _ s de forma _
501de:Grad _
ca:graus de _ radians
pt:_ radianos em graus
0.5de:Fehler _
pt:assinala o erro _
de:gleite _ in _ Sek. beschleunigt: _
ca:llisca _ en _ segons de forma _
fr:glisser _ en _ sec. accélérer _
pt:desliza _ em _ s de forma _
501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
sigmoid
elasticde:animiere _ um _ in _ Sek. beschleunigt: _
ca:modifica _ amb _ en _ segons de forma _
pt:anima _ de _ em _ s de forma _
x positionsteps
x position
y position
direction
size
ghost effect
color effect
saturation effect
brightness effect
fisheye effect
whirl effect
pixelate effect
mosaic effect
negative effect
tempo
volume
balance501linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
sigmoid
elasticdeltay positiondirectionsizetempovolumebalance112de:Beschleunigung _
ca:de forma _
pt:a forma _
linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
sigmoid
elasticquadratic-outquadratic-inquadratic-in-outcubic-outcubic-incubic-in-outquart-outquart-inquart-in-outsine-outsine-insine-in-outsigmoidelasticunknown easing function ""de:für _ = _ bis _ _ in _ Sek. beschleunigt: _ _
ca:per _ = _ fins _ _ en _ segons de forma _ _
pt:para _ de _ a _ _ em _ s de forma _ _
01001linearlinear
quadratic={
quadratic-in
quadratic-out
quadratic-in-out
}
cubic={
cubic-in
cubic-out
cubic-in-out
}
quart={
quart-in
quart-out
quart-in-out
}
sinusoidal={
sine-in
sine-out
sine-in-out
}
sigmoid
elasticdata:image/png;base64,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