In this final project for our block based course, students were tasked with using starter code to finish the game 2048. Complete spec here: bit.ly/BTEP-block 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In this final project for our block based course, students were tasked with using starter code to finish the game 2048. Complete spec here: bit.ly/BTEP-block forward turn turnLeft setHeading doFaceTowards gotoXY doGotoObject doGlide changeXPosition setXPosition changeYPosition setYPosition bounceOffEdge xPosition yPosition direction doSwitchToCostume doWearNextCostume getCostumeIdx doSayFor bubble doThinkFor doThink reportGetImageAttribute reportNewCostumeStretched reportNewCostume changeEffect setEffect clearEffects getEffect changeScale setScale getScale show hide reportShown goToLayer goBack playSound doPlaySoundUntilDone doStopAllSounds doPlaySoundAtRate reportGetSoundAttribute reportNewSoundFromSamples doRest doPlayNote doSetInstrument doChangeTempo doSetTempo getTempo changeVolume setVolume getVolume changePan setPan getPan playFreq stopFreq clear down up getPenDown setColor changePenColorDimension setPenColorDimension getPenAttribute changeSize setSize doStamp floodFill write reportPenTrailsAsCostume doPasteOn doCutFrom receiveGo receiveKey receiveInteraction receiveCondition receiveMessage doBroadcast doBroadcastAndWait doCallCC reportCallCC receiveOnClone createClone newClone removeClone doSwitchToScene reportTouchingObject reportTouchingColor reportColorIsTouchingColor doAsk getLastAnswer reportMouseX reportMouseY reportMouseDown reportKeyPressed reportRelationTo reportAspect doResetTimer getTimer reportAttributeOf reportGet reportObject reportURL reportAudio reportVideo doSetVideoTransparency reportGlobalFlag doSetGlobalFlag reportDate reportBlockAttribute reportIsA reportIsIdentical

For you to fill out! The following is simply starter code.

This is a game board which is expected to be square.

This creates a new empty board of n by n tiles

This 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.

This returns a new distinct copy of the input board which will help prevent you from accidentally reseting list items.

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].

LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

de:drucke _ in Größe _ ca:etiqueta _ de mida _ es:etiqueta _ de tamaño _ fr:étiquette _ d'une taille de _ Hello!12

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 _ _ ca:en cascada fins _ _ _ _

Returns the function x↦f(g(x)) where f and g are the two inputs.

pt:a composição de _ com _ ca:composa _ _

The 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 _ ca:canalitza _ $arrowRight _

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 _

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These tests are for the helper functions we made.16,2,4,02,4,8,064,2,0,02,16,4,04false-147368,8,2,40,2,4,80,0,64,22,0,16,0