ver alpha 1.0.0
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1000selectedFileTypeaudioaudio/*STORE THEME IN HEREsite new- https://codepen.io/1082893/full/qBajEjg
site new- https://codepen.io/1082893/full/KKgqKBzReports the value previously stored under
the input key in the browser's local storage.
Reports False if the key is not found.Sii Pointer01Opens the file select dialog. When the user has submitted at least one file, it will run the ring with the file list as the input.
To make it accept only a certain file extension, write the file extension, including the dot, or the MIME type (e.g. ".txt", "text/plain") in the text field. You can make it accept multiple file extension types by adding more text fields.
Remove all input fields to make it accept all files. Reads a file as a certain type.
The binary string type is the file's actual contents in binary (I think). So can someone please make a MIDI player with my library.texttext
binary string
costume
vector costume
soundtextimage
audio
video
text
SVG
MIDI
anyanyMIDISVG1type/Gets an attribute of a file.
The last modified attribute reports the time the file was last modified in milliseconds since the Unix epoch (Jan 1, 1970, 12:00 AM)namename
size
type
last modifiedsizetypelast modifiedReports 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 first one is kept in the result.ca:elimina els duplicats de _
1Reports 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#2The 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 _ _
11This 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.()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.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.1The 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.)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:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
pt:captura _ _
contde:wirf _
ca:llança _
es:lanzar _
fr:lance _
pt:lança _
catchtagde:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
pt:captura _ _
contde:wirf _ _
ca:llança _ _
es:lanzar _ _
fr:lance _ _
pt:lança _ _
catchtagThis is a breakpoint block, to be used in debugging.
The Boolean input specifies the condition under which
the program should pause. (Use True for an unconditional breakpoint.) The intent is that the script should have
SHOW VARIABLE blocks for the process's local variables.
After the pause, this block automatically hides all temporary variables.de:falls _ dann _ und pause $pause-1-255-220-0
ca:si _ fes _ i atura-ho tot $pause-1-255-220-0
es:si _ haz _ y páralo todo $pause-1-255-220-0
fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0
pt:se _ , então _ e faz pausa em tudo $pause-1-255-220-0
This block does nothing.
Use it when you are calling a reporter
for its side effect and don't care about
its return value.de:ignoriere _
ca:ignora _
es:ignorar _
fr:ignore _
pt:ignora _
Call f(f(f(...(f(x))))) n times where the three input slots are n, f, and x from left to right. The # variable can be used inside f to represent how many times f has been called.pt:o resultado de _ invocações em cascata de _ com argumento inicial _ _
Call f(f(f(...(f(x))))) until condition is true, where the three input slots are condition, f, and x from left to right. The # variable can be used inside f or condition to indicate how many times f has been called.pt:o resultado da invocação em cascata até que _ de _ com argumento inicial _ _
Returns the function x↦f(g(x)) where f and g are the two inputs.pt:a composição de _ com _
Like the built-in REPEAT UNTIL block, except that the ending condition is not tested until the script has been run the first time. So the script is run at least once.pt:repete _ até que _ $loop-0.7
Run the script repeatedly, as long as the given condition is true. Runs the script at least once before testing the condition.pt:repete _ enquanto _ $loop-0.7
Runs the script repeatedly, as long as the condition is true. Tests the condition before the first time the script is run. Like the built in REPEAT UNTIL except that in this block the condition must be true, not false.pt:enquanto _ , repete _ $loop-0.7
Runs the script the specified number of times, like the built-in REPEAT block, but this one provides the # variable that can be used inside the script. Try
REPEAT (200)
MOVE (#) STEPS
RIGHT 92
with the pen down.pt:repete _ vezes _ _ $loop-0.7
The primitive FOR block uses an implicit step of ±1 depending on which of the starting and ending values is larger. This version allows you to provide an explicit step value. If the sign of the step input is incompatible with the ordering of the starting and ending values, the script will not be run at all.pt:para _ de _ com passo _ a _ _ $loop-0.7
1110Provides LOOP as a function of one input that runs
the body of the LET with A set to the function input,
so the body can run itself recursively.
See COPY block in Variables for an example of use.pt:tendo _ o valor inicial _ , executa _ definido como _
new valueThe inputs are a starting value and one or more monadic functions.
The first function is called with the starting value as its input.
The second function is called with the value reported by the first function as its input, and so on. PIPE reports the value reported by the last function. PIPE provides an alternative notation for function composition instead of the usual nesting.
nested acronym:
report (combine (keep items (capital-letter? ( )) from
(map (letter (1) of ( )) over
(sentence→list (phrase)))) using (join ( ) ( )))
piped acronym:
report (pipe (phrase) →
(sentence→list ( ))
(map (letter (1) of ( )) over ( ))
(keep items (capital-letter? ( )) from ( ))
(combine ( ) using (join ( ) ( )))
Some people find PIPE easier to read because the functions are called in the same order in which you read them, whereas in nested composition the innermost function is called first. pt:a aplicação a _ de _
101000noyes0audio000F