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 credit: Add better visual confirmation here!==============================================
These are your graded test cases!
For four out of five of the required blocks you must have 2 different sets of input and expected output.
Additionally, each function should have a brief comment explaining why your test cases
are good / useful.
You are more than welcome to add as many tests as you like, for additional blocks, as long as you meet the requirements above.
We strongly recommend that you write tests BEFORE each block!! If you're doing the testing extra credit, (see spec), your extra credit tests should go in this sprite.EXAMPLES:
These tester blocks take in a reporter -- something that you want to test.
The inputs are what you would want to pass into the reporter as a test.
The output is what you expect the reporter to output when passed in the input(s).
The block will report a Boolean based on whether your test passed.
Note: You must leave the inputs to the block you are testing empty.From lab: fill in two of your own tests for the "merge column up" block. Refer to our tests of "item of" and "+" above to remember how to deal with "merge column up" needing multiple inputs.From lab: Fill in what the output should be for the following test case for "merge up". In HW3, you'll add more of your own "merge up" test cases.This is a game board which is expected to be square.This creates a new empty board of n by n tiles4This is just like the ITEM OF block, but for our game board. You don't need to know how it works.This is just like the REPLACE block, but for our game board. You don't need to know how it works.For you to fill out!This returns a new distinct copy of the input board which will help prevent you from accidentally reseting list items. For you to fill out!The block reports the score of an individual tile.Returns a list of locations in board which are empty. The locations are length 2 lists as [row, col].1datamapmany1data lists11110i1SpriteSpriteSpriteSpriteFor you to fill out!Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of adding them all, so
(SUM (4) (100) (8))
reports 112.pt:a soma de _
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of multiplying them all, so
(PRODUCT (4) (100) (8))
reports 3200.pt:o produto de _
1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE only if all of the inputs are TRUE, otherwise FALSE. Like AND but for multiple inputs.pt:todas as afirmações _ são verdadeiras
1Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE if at least one input is TRUE, otherwise FALSE.pt:alguma das afirmações _ é verdadeira
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the smallest of them, so
(MINIMUM (4) (100) (8))
reports 4.pt:o produto de _
1Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the largest of them, so
(MAXIMUM (4) (100) (8))
reports 100.pt:o produto de _
1de: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 _
1This is just like the ITEM OF block, but for our game board. You don't need to know how it works.valueindexlist0,0,0,20,0,0,40,8,4,20,512,32,84false-12633362,0,0,04,0,0,08,4,2,0512,32,8,0a,b,c,d