data:image/png;base64,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: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 _
1SENTENCE 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.err_resetSearches a list to see if each item shares letters with "key." Returns a list of these items.
Inclusive search -- IF something has X thing. 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change1, 11, 125Kai,32 Forge Street,555-2322Jay,55 Ninjago Avenue,555-6774Zane,"66.5° N, 136°W",555-3636Cole,"111 Rock Blvd, Apt 3",555-4316Wu,899 Temple Avenue,555-1111Lloyd,22 Gold Street,555-9212Benny,55 Aroyo Lane,555-5555Kai