data:image/png;base64,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

de: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 _ 501

de: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 } elasticsincos

de: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 } elasticdeltasincosy positiondirectionsizetempovolumebalance1 reporter12

de: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 } elasticquadratic-outquadratic-inquadratic-in-outcubic-outcubic-incubic-in-outquart-outquart-inquart-in-outsine-outsine-insine-in-outelasticunknown 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 } elasticreporterAccepts any number of scripts as inputs. Launches a separate thread for each of them, so they are done in parallel, then waits for them all to complete. This doesn't change how the Snap! scheduler works; the threads are not truly asynchronous. And there is no increase in speed. Rather, the point of this block is to allow starting synchronized but independent scripts.

pt:executa _ em paralelo e espera ca:executa en paral·lel _ i espera each scripttestAccepts any number of scripts as inputs. Launches a separate thread for each of them, so they are done in parallel, then continues with the current script while they all run. This doesn't change how the Snap! scheduler works; the threads are not truly asynchronous. And there is no increase in speed. Rather, the point of this block is to allow starting synchronized but independent scripts.

pt:executa _ em paralelo ca:executa en paral·lel _

true

de:Kostüm aus Text _ Größe _ pt:um traje com o texto _ de tamanho _ ca:vestit amb el text _ i mida _ A72

§_costumesMenu2002Use this block to make "clickable" buttons:This block allows you to set the pen's color, transparency (on a scale from 0=opaque to 100=invisible), or size (in pixels). The color can be set in many ways, including RGB values, HSV values, HSL values, fair HSV or HSL (based on a modified spectrum that gives each spectral color equal space and promotes brown to a color), color number, crayon, or X11/W3C color name. See Appendix A in the Snap! Reference Manual for details.

ca:$brush fixa el _ del llapis a _ _ color color number crayon fair hue ~1 size transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB(A) vector RGB(A) hex } }§_ext_clr_numbersNote to myself: When the user sets a fair dimension, say fair HSL, variable HSL contains the corresponding "unfair" values, and "raw HSL" contains the numbers the user actually gave. It has to be this way because in the case of HSV, there is no "HSV" variable; rather, we use the pen's actual remembered-by-Javascript color's HSV coordinates. So our HSL variable has to be analogous to that. In both cases "raw" means the numbers the user gave us.

1

HSV✐ raw HSL100HSV100

The identity function reports its input.

This block reports the pen size, color, or transparency, in all the same ways that the SET PEN block allows you to set those pen properties. (It will also report the current crayon number, if the pen color was most recently set with SET PEN TO CRAYON.) See Appendix A of the Snap! Reference Manual for details.

ca:$brush _ del llapis color color number crayon fair hue ~1 size transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector RGB(A) hex } }huesaturation (HSV)value (brightness)transparencyRGB vectorRGBA vectorredgreenblueRGB(A) hexfloor1616HSV vectorHSL vectorsaturation (HSL)lightnesscolor numbercolorfair huenot setfair saturation (HSL)not setfair lightnessnot setfair HSL vectornot setfair saturation (HSV)2not setfair value (brightness)3not setfair HSV vectornot setcrayonX11/W3C nameChanges any pen property by the specified amount. Some color scales can't be adjusted this way unless the color was first SET using that scale. Vectors (e.g., RGB color as a list of three numbers) can be incremented by a vector as the amount. X11/W3C names can't be incremented.

ca:$brush augmenta _ del llapis en _ color number crayon fair hue ~1 size transparency ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector } }crayonRGB hexnumberlistI don't know how to handle

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont3

de:wirf _ ca:llança _ es:lanzar _ fr:lance _ pt:lança _ catchtag

de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _ pt:ignora _

combines any number of colors, reporting the result of mixing them, either additively (like colored light beams) or subtractively (like paints). If the result is too bright (additive) or too dark (subtractive), try the "averaged" versions, which will make the brightness more like the brightnesses of the input colors.

ca:$brush barreja colors _ amb criteri _ additive (light) additive (averaged) subtractive (ideal) subtractive (averaged) simulated paint paint (sRGB corrected)waveformssum of weightscombined waveformweighted geometric mean of waveformssum of weightscombined waveformTback to RGBTword5.47813E-05 0.000184722 0.000935514 0.003096265 0.009507714 0.017351596 0.022073595 0.016353161 0.002002407 -0.016177731 -0.033929391 -0.046158952 -0.06381706 -0.083911194 -0.091832385 -0.08258148 -0.052950086 -0.012727224 0.037413037 0.091701812 0.147964686 0.181542886 0.210684154 0.210058081 0.181312094 0.132064724 0.093723787 0.057159281 0.033469657 0.018235464 0.009298756 0.004023687 0.002068643 0.00109484 0.000454231 0.000255925-4.65552E-05 -0.000157894 -0.000806935 -0.002707449 -0.008477628 -0.016058258 -0.02200529 -0.020027434 -0.011137726 0.003784809 0.022138944 0.038965605 0.063361718 0.095981626 0.126280277 0.148575844 0.149044804 0.14239936 0.122084916 0.09544734 0.067421931 0.035691251 0.01313278 -0.002384996 -0.009409573 -0.009888983 -0.008379513 -0.005606153 -0.003444663 -0.001921041 -0.000995333 -0.000435322 -0.000224537 -0.000118838 -4.93038E-05 -2.77789E-050.00032594 0.001107914 0.005677477 0.01918448 0.060978641 0.121348231 0.184875618 0.208804428 0.197318551 0.147233899 0.091819086 0.046485543 0.022982618 0.00665036 -0.005816014 -0.012450334 -0.015524259 -0.016712927 -0.01570093 -0.013647887 -0.011317812 -0.008077223 -0.005863171 -0.003943485 -0.002490472 -0.001440876 -0.000852895 -0.000458929 -0.000248389 -0.000129773 -6.41985E-05 -2.71982E-05 -1.38913E-05 -7.35203E-06 -3.05024E-06 -1.71858E-06newsRGBRGB vector255addmax255RGB vectorThis block reports a color. The color can be set in many ways, including RGB values, HSV values, HSL values, fair HSV or HSL (based on a modified spectrum that gives each spectral color equal space and promotes brown to a color), color number, crayon, or X11/W3C color name. See Appendix A in the Snap! Reference Manual for details.

ca:$brush color amb _ _ _ color color number crayon fair hue X11/W3C name ~ fair HSL=fair HSL vector fair HSV=fair HSV vector HSL=HSL vector HSV=HSV vector RGB (0-255)=RGB vector RGB hex§_ext_clr_numberscolor numbercolor300indexfloor15scale1515255fromto122crayon132X11/W3C nameRGB hexcolorRGB vectorHSV vectorHSL vectorfair HSV vectorindex-1scale1index1color100fair HSL vectorindex-1scale1index1color110050

ca:$brush _ del color _ nearest color number crayon number fair hue ~1 transparency X11/W3C name ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness fair HSL vector } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) fair HSV vector } HSL={ hue saturation (HSL) lightness HSL vector } HSV={ hue saturation (HSV) value (brightness) HSV vector } RGB (0-255)={ red green blue RGB vector RGBA vector RGB hex } }X11/W3C namenearest color numberrgbhslhsvrgbtransparencytransparencyRGB vectorRGBA vectorRGB hexredgreenbluehsvfalseHSV vectorhuesaturation (HSV)value (brightness)hsltrueHSL vectorsaturation (HSL)lightnessHSLlightnessfair lightness310030.9833.333333330.986.9444444516.944444451.96100fair lightnessfair saturation (HSL)210075.9493610075.949366.9444444516.944444451.96100fair saturation (HSL)fair HSL vectorfair lightness310030.9833.333333330.986.9444444516.944444458.823529100fair lightnessfair saturation (HSL)210075.9493610075.949366.9444444516.944444458.823529100fair saturation (HSL)fair HSL vectorfair hue11111111fair huefair saturation (HSL)fair lightnessfair HSL vectorbrownfair value (brightness)310054.5166.66666666754.516.9444444516.944444451.96100fair value (brightness)fair saturation (HSV)210086.3310086.336.9444444516.944444451.96100fair saturation (HSV)fair HSV vectorfair value (brightness)310054.5166.66666666754.516.9444444516.944444451.96100fair value (brightness)fair saturation (HSV)210086.3310086.336.9444444516.944444451.96100fair saturation (HSV)fair HSV vectorfair hue11111111fair huefair saturation (HSV)fair value (brightness)fair HSV vectorbrownfair somethingCan't get here -- please post project and screenshot to forum.This block reports the pen color (as a color, not as a list of numbers).

ca:✐ color de llapis actual

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ pt:captura _ _ cont

de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ pt:lança _ _ catchtagSets the pen color to one of 100 preselected colors, like a box of 100 crayons. The colors have names that are meant to be evocative. They are organized in families, more or less corresponding to spectral (rainbow) colors; the input slot has a two-level menu in which you can hover over a family name to see the colors of that family, each including a crayon number and an RGB value. See Appendix A of the Snap! Reference Manual for more details.

ca:✐ fixa el llapis de color _ _ grays={ 0 black #000000=0 1 gray7 #121212=1 2 gray14 #242424=2 3 gray21 #363636=3 4 gray28 #484848=4 5 gray36 #5c5c5c=5 6 gray43 #6d6d6d=6 7 gray50 #7f7f7f=7 8 gray57 #919191=8 9 gray64 #a3a3a3=9 10 gray71 #b5b5b5=10 11 gray78 #c8c8c8=11 12 gray85 #dadada=12 13 gray92 #ececec=13 14 white #ffffff=14 } pinks={ 15 deep pink #ff1493=15 16 hot pink #ff69b4=16 17 bright pink #ff007f=17 18 raspberry #e30b5d=18 19 amaranth #e52b50=19 } reds={ 20 red #ff0000=20 21 burgundy #900020=21 22 cherry #990000=22 23 dark candy apple red #a40000=23 24 sanguine #c00000=24 25 maroon #800000=25 26 crimson #c90016=26 27 Lists #d94d11=27 28 candy apple red #ff0800=28 29 coquelicot #ff3800=29 } browns={ 30 saddle brown #8b4513=30 31 chocolate #7b3f00=31 32 kobicha #6b4423=32 33 sepia #704214=33 34 chestnut #954535=34 35 dark brown #654321=35 36 brown #964b00=36 37 golden brown #996515=37 38 cinnamon #b87333=38 39 copper #d2691e=39 } oranges={ 40 orange #ff7f00=40 41 Pantone orange #ff5800=41 42 pumpkin #ff7518=42 43 Variables #f3761d=43 44 Spanish orange #e86100=44 45 burnt orange #cc5500=45 46 sinopia #cb410b=46 47 ochre #cc7722=47 48 carrot #ed9121=48 49 tangerine #f28500=49 } yellows={ 50 yellow #ffff00=50 51 Control #e6a822=51 52 dark goldenrod #b8860b=52 53 goldenrod #daa520=53 54 saffron #f4c430=54 55 sandstorm #ecd540=55 56 mustard #ffdb58=56 57 gold #ffd700=57 58 egg yolk #fee33e=58 59 rubber duck #fbe108=59 } greens={ 60 lime #00ff00=60 61 apple green #8db600=61 62 Operators #62c213=62 63 forest green #228b22=63 64 green #008000=64 65 dark green #006400=65 66 dark pastel green #03c03c=66 67 emerald #50c878=67 68 mint #3eb489=68 69 Pen #00a178=69 } cyans={ 70 aqua (cyan) #00ffff=70 71 dark cyan #008b8b=71 72 cerulean #007ba7=72 73 iceberg #71a6d2=73 74 Sensing #0494dc=74 75 teal #008080=75 76 light sky blue #87cefa=76 77 deep sky blue #00bfff=77 78 dodger blue #1e90ff=78 79 azure #007fff=79 } blues={ 80 blue #0000ff=80 81 midnight blue #191970=81 82 dark powder blue #003399=82 83 cobalt #0047ab=83 84 denim #1560bd=84 85 navy blue #000080=85 86 steel blue #4682b4=86 87 Motion #4a6cd4=87 88 cornflower #6495ed=88 89 slate blue #6a5acd=89 } purples={ 90 violet #8000ff=90 91 Looks #8f56e3=91 92 grape #6f2da8=92 93 indigo #4b0082=93 94 x11 purple #a020f0=94 95 magenta (fuchia) #ff00ff=95 96 dark orchid #9932cc=96 97 Sound #cf4ad9=97 98 purple #7f007f=98 99 dark magenta #8b008b=99 }✐ last set ascrayon✐ fair?false1231numberTakes three inputs for red, green, and blue values, each between 0 and 255. 0,0,0 is black; 255,255,255 is white. 255,255,0 is yellow, and so on. The SET PEN block in this library lets you set individual red, green, or blue without changing the others, lets you provide a list of three RGB color components, and lets you provide a six-digit hexadecimal number, the form in which RGB color values are usually found online. See Appendix A of the Snap! Reference Manual for details.

pt:altera a cor da tua caneta para vermelho _ , verde _ e azul _ (0 a 255) ca:fixa el llapis al color r: _ g: _ b: _ 25500Takes three inputs for hue, saturation, and value ("brightness") values, each between 0 and 1. 0,0,0 is black; 0,0,1 is white. 0.15,1,1 is yellow, and so on. The SET PEN block in this library lets you set individual hue, saturation, or value without changing the others, lets you provide a list of three HSV color components, and lets you use the very much superior HSL (hue, saturation, lightness) color space. See Appendix A of the Snap! Reference Manual for details.

pt:altera a cor da caneta para matiz _ , saturação _ e brilho _ (0 a 1) ca:fixa el llapis al color h: _ s: _ v: _ 0.30.70.6Reports the current pen color as a list of three RGB values in the range 0-255. The PEN block in this library provides a large set of ways to examine the color. See Appendix A in the Snap! Reference Manual for details.

pt:a cor da caneta em RGB (vermelho, verde e azul; 0 a 255) Reports the current pen color as a list of three HSV values in the range 0-1. See Appendix A in the Snap! Reference Manual for details.

pt:a cor da caneta em HSV (matiz, saturação e brilho, 0 a 1) Takes a value from 0 to 15 and reports the corresponding hexadecimal digit. For internal use of the color library.

Don't ask. Only for internal use by the SET PEN block.

truetrueCreates a bunch of global variables. For internal use by Color library.

global✐ last set as✐ fair?✐ color scale✐ HSL✐ last crayon✐ last X11✐ raw HSL✐ raw HSV✐ X11✐ crayon colors✐ fair hue table✐ colors✐ brights✐ pivots✐ HSV brights✐ HSL brights✐ HSV colors✐ HSL colors✐ X11 catch✐ dimension names✐ dimension dispatch✐ last set asRGB✐ fair?false✐ raw HSV0100100✐ HSL010050✐ raw HSL010050listlistlistlistlistlistlistlistlistlistlistlist

not a crayon

not an X11 color

1last1000huescolumns1lengthcolumnsfair hues00hue10110002last1000✐ fair hue table10

11

1number

smallest componentrgb255

smallest component2rgb255

2Red family8.7Brown/orange family17yellow501427.7522.334green52cyan67blue83.33333333purple9550505left of black

falsehue603.61001003.6100100

hue mod 100, other components cut off at [0, 100]

value100color3.6index1index-1scale1255fromto1newHSVHSL✐ fair?truetransparency

color300indexfloor15scale15grayscale15255fromto121number✐ last set ascolor number✐ fair?true✐ raw HSLhue10050✐ raw HSVhue100100

015numberlength8length66length161216341656✐ last set asRGB✐ fair?false1number

44partial matchestrue4matchhead matchestrueSUBSET throws here if it handles a unique match1number

44partial matchesfalse4matchhead matchesfalseSUBSET throws here if it handles a unique match2

015numberlength8length66length161216341656

fair hue transparency ~2 (3D color spaces)={ fair HSL={ fair hue fair saturation (HSL) fair lightness } fair HSV={ fair hue fair saturation (HSV) fair value (brightness) } HSL={ hue saturation (HSL) lightness } HSV={ hue saturation (HSV) value (brightness) } RGB (0-255)={ red green blue } }255100We have to do this last test to rule out the vector options, which aren't numbers; their code makes three recursive calls and we catch range issues then.huesaturation (HSV)value (brightness)saturation (HSL)lightnesstransparencyredgreenbluefair huefair saturation (HSV)fair value (brightness)fair saturation (HSL)fair lightness

✐ last set asHSL33✐ fair?false1number

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

✐ last set asRGB✐ fair?false

0

4numbervalue0255131number✐ last set asRGB✐ fair?false

✐ last set asHSL22✐ fair?false1number

✐ last set asHSL✐ HSL11000100✐ raw HSL✐ fair?false1number

✐ last set asHSV✐ fair?false✐ raw HSV13100length31number

hue100if USED TO BE fairHSL✐ fair?false1number

✐ last set asHSL2222✐ fair?true1number

✐ last set asHSL3333✐ fair?true1number

231length3

saturationsaturation22✐ last set asHSV✐ fair?true1number

brightnessbrightness33✐ last set asHSV✐ fair?true1number

231length31number

pt:o texto multilinha _ Reports the part of the first string up to the first instance of the second string inside it. If the second string isn't found, reports the entire first string.

pt:o texto de _ antes de _ Reports the portion of the first input string starting after the first occurrence of the second string. If the second string isn't found in the first string, reports an empty string.

pt:o texto de _ depois de _ Reports the character position (starting from 1) of the beginning of the first input string inside the second input string. If it's not found, reports 0.

pt:a posição de _ em _ If input is TRUE, comparisons made by functions in the string library will be case-independent (so "FOO" = "foo"). This is the default. If input is FALSE, comparisons will be exact.

pt:altera comparações ignorando distinção minúsculas/maiúsculas para _ Reports the portion of the first input (string) starting at the position given by the second input (counting from 1, like LETTER n OF) and ending at the position given by the third input (also counting from 1). If the third input is empty, reports the portion from the first position to the end of the string. If a position number is negative, counts from the end backward, so -1 is the last character, -2 is the next to last, etc.

pt:o texto de _ entre as posições _ e _ , inclusive Reports True if the first input string contains the second input string, otherwise false. Comparison is case-independent by default; use USE CASE-INDEPENDENT COMPARISONS to change that.

pt:o texto de _ antes de _ Reports the input text with lower case letters instead of capital letters in the input. Uses the user's locale's rules for case conversion.

pt:em minúsculas _

err_resetThis block creates new variables on the selected scope: global (for all sprites), sprite (for this sprite only) or script (only for that blocks stack) with the names given (in 'names' list). If there is already a variable with that name in that scope, it does nothing: no errors and no overwrites.

pt:cria as variáveis _ _ ca:crea les _ variables _ es:crear las _ variables _ de:erstellen _ var _ globalglobal sprite scriptThis block reports "true" if there is a variable with this given name (input slot) in that context. It can be a global, sprite or script variable. Otherwise it reports "false".

pt:a variável _ existe ca:existeix la variable _ ? es:existe la variable _ ? de:existiert var _ ? err_reset

pt:executa no modo turbo _

pt:as amostras de som com _ Hz de _ s amostrado a _ Hz ca:_ Hz durant _ segons amb mostreig de _ Hz 440a 55=55 a 110=110 a 220=220 a 440=440 a 880=880 a 1760=1760 a 3520=352014410022.05 kHz=22050 44.1 kHz=44100 88.2 kHz=88200 96 kHz=96000

pt:π

pt:_ radianos em graus ca:graus de _ radiants 0.5

pt:o som _ ca:so de nom _ §_soundsMenuquickly plot the samples of a sound, a list of samples (single channel), or a list of lists (multiple channels) to the stage at a lower resolution.

pt:desenha o gráfico do som _ ca:dibuixa el so _ §_soundsMenu1listoffsetwidthstage-2records an audio snippet and reports it as a new sound, or zero if the user cancels

pt:$circleSolid-1-255-0-0 uma nova gravação ca:$circleSolid-1-255-0-0 enregistra

pt:a nota com _ Hz ca:nota de _ Hz 440

pt:a frequência da nota _ ca:freqüència (hz) de la nota _ 69

pt:o nome da nota _ ca:nom de la nota _ 69

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra es:todos menos la primera letra de _ All but first of empty word.letterTakes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula es:todas menos la primera palabra de _ All but first of empty sentence.Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra es:todos menos la última letra de _ reversereverseAll but first of empty word.letterTakes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula es:todos menos la última palabra de _ reversereverseAll but last of empty sentence.Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the first word, with no spaces before or after it.

pt:a primeira palavra de _ ca:primera paraula de _ es:primera palabra de _ Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ es:última letra de _ length0lengthTakes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing only the last word of the input, with no spaces before or after it.

pt:a última palavra de _ ca:darrera paraula de _ es:última palabra de _ lastLast of empty sentence.Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ es:¿palabra vacía? _ length0Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _ es:¿oración vacía? _ Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1list11bf Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _ 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 _ report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _ letterjoin all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _ listlistSENTENCE 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 _ es:oración _

pt:lança o erro _ Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ es:imprimible _ ( )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.

es:requerir no vacío _ _ Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _ es:primera letra de _ 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absedgeCPUP1Press space to restart.999CPUscore1Score_Reset00P1score1Score_ResetMAXVEL200040Score_Reset551200